DETAILED ACTION
Notice of Pre-AIA or AIA Status
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
Response to Amendment
The Amendment filed June 17, 2026 has been entered. Prior to the Amendment, claims 1-30 were pending in the application. By the Amendment, claims 1, 9, 13, 16, 21, 29, and 30 were amended, claim 15 was canceled, and claim 31 was newly added. Accordingly, claims 1-14 and 16-31 remain pending and ready for examination.
The amendments change the scopes of the previously presented claims. New grounds of rejections are applied to the amended claims and the current Office Action is made FINAL as necessitated by the claim amendments.
Withdrawal of Claim 13 under 35 U.S.C. § 112(b)
In view of the amendment to claim 13, the rejection under 35 U.S.C. §112(b) in the previous Office Action is now withdrawn.
Claim Rejections - 35 USC § 103
In the event the determination of the status of the application as subject to AIA 35 U.S.C. §102 and §103 (or as subject to pre-AIA 35 U.S.C. §102 and §103) is incorrect, any correction of the statutory basis (i.e., changing from AIA to pre-AIA ) for the rejection will not be considered a new ground of rejection if the prior art relied upon, and the rationale supporting the rejection, would be the same under either status.
The following is a quotation of 35 U.S.C. §103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made.
Claims 1, 2, 5, 10, 12-14 and 29 are rejected under 35 U.S.C. §103 as being unpatentable over Gan et al. (US Patent Publication No. 2009/0196379) (“Gan”) in view of Landis et al. (US Patent Publication No. 2020/0028617) (“Landis”).
Regarding claim 1, Gan teaches a user equipment (UE) for wireless communication (See, e.g., Fig. 1, #120; Fig. 4, #420; Fig. 7, #710; and ¶[0064], “an example of a remote device for implementing various aspects described herein”.), comprising:
one or more memories (Fig. 7, #730); and
one or more processors (Fig. 7, #720), coupled to the one or more memories (See, e.g., ¶[0066], “Memory 730 can also contain data and/or program modules that are immediately accessible to and/or presently being operated on by processing unit 720.”), which are configured, individually or in any combination, to:
obtain a performance-complexity tradeoff parameter value (“δ,” See, ¶[0035], “where δ with ¼<δ<1 is a factor that can be selected to achieve a good quality-complexity tradeoff.” Alternatively, “ϵ” See, ¶[0043], “where 0<ϵ<1 is a user-defined adjustment factor introduced for further fine-tuning the performance-complexity tradeoff”) associated with a quantity of iterations and channel orthogonality (See, ¶[0036], size reduction is … a process aimed at making basis vectors … closer to orthogonal …two consecutive basis vectors hk-1 and hk are swapped … size reduction and basis vector swapping steps can then iterate until Equation (9) is satisfied for all pairs of hk-1 and hk”. That is, a larger δ would require a larger number of iterations before the Equation (9) is met. See, also, ¶[0005], “Relaxed LLL allows various constraints of the LLL reduction algorithm to be relaxed, which can result in a smaller amount of basis vector swapping and a reduction in the overall complexity and delay of an associated detector.”) for an algorithm for a lattice reduction (See, e.g., ¶[0005], “Various systems and methodologies presented herein can utilize a relaxed form of the Lenstra-Lenstra-Lovasz (LLL) lattice reduction algorithm”) of a first matrix (“H”, See, ¶[0022], “H is the nxm complex channel matrix”) for a downlink communication (See, Fig. 1; and ¶[0019], “terminals 110 and/or 120 in system 100 can be capable of both receiving and transmitting at one or more time intervals.”);
receive the downlink communication that corresponds to the first matrix (See, Fig. 5, #502: and ¶[0059], “one or more signals received”);
perform the lattice reduction to transfer the first matrix to a second matrix based at least in part on the performance-complexity tradeoff parameter value (See, Fig. 5, #504; and ¶s[0038], “the channel matrix H can be processed by a lattice reduction block 310 to transform H into a reduced basis H'=HU, where U is an unimodular matrix;” and [0059], “At 504, relaxed-LLL lattice reduction is performed on the channel matrix”.); and
perform multiple-input-multiple-output detection of the downlink communication using the second matrix (See, Fig. 5, #506; and ¶s[0038], “Following this lattice reduction, a traditional signal detector 320 can be applied on the reduced basis;” and [0059] “At 506, signal detection is performed (e.g., by a signal detection component 220) for one or more receivers based at least in part on the lattice-reduced channel matrix obtained at 504.”).
Gan, however, fails to teach explicitly:
obtain[ing] at least one of a frequency domain granularity for the lattice reduction or a time domain granularity for the lattice reduction; and
Perform[ing] the lattice reduction based at least in part on the at least one of the frequency domain granularity or the time domain granularity.
Landis teaches an analogous field of art, i.e., lattice reduction aided MIMO detection with reduced decoder complexity, see, Title; ¶[0085], “As the number of REs included in a transmission increases …. the computational-cost of performing ML-based demapping procedures, and the associated silicon die size, greatly increases, resulting in high power usage at a UE 115;” ¶[0089], “by applying the same LR across multiple REs, an MMSE-based demapper may spread the computational cost of computing the transformation matrix across multiple REs, thus increasing the computational efficiency”.), and teaches:
Obtain[ing] at least one of a frequency domain granularity for the lattice reduction or a time domain granularity for the lattice reduction (See, ¶[0149], “UE 115-b may identify, based on the channel property, a number of REs associated with the beamformed transmission on which to apply the transformation matrix…..the number of REs corresponds to a number of different sub-carriers in a same symbol period….. In some instances, …. a number of sub-carriers located in a plurality of different symbol periods”.); and
perform the lattice reduction based at least in part on the at least one of the frequency domain granularity or the time domain granularity (See, ¶[0079], “a resource element may consist of one symbol period (e.g., a duration of one modulation symbol) and one subcarrier;” Fig. 4, #s 410 and #415; ¶[0109], “Channel estimator 410 may determine matrix H (e.g., and initial lattice domain 435) once per scheduled RE;” ¶[0110], “LR preprocessing component 415 may perform an LR calculation on matrices Y and H to determine transformation matrix T. LR preprocessing component 415 may calculate transformation T once per sub-carrier; and ¶[0149], “UE 115-b may identify, based on the channel property, a number of REs associated with the beamformed transmission on which to apply the transformation matrix”.).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the MIMO detector taught by Gan to incorporate the above teachings of Landis, i.e., the application of the transform matrix obtained from a lattice reduction operation for multiple resource elements, in order for so modified MIMO detector to have an improved computational efficiency (See, e.g., Landis, Abstract).
Regarding claim 2/1, Gan in view of Landis teach a UE comprising all elements recited in claim 1 as discussed above.
Landis further teaches that the frequency domain granularity indicates one or more subbands (See, e.g., ¶[0149], “a number of REs associated with the beamformed transmission on which to apply the transformation matrix …. the number of REs corresponds to a number of different sub-carriers in a same symbol period”.), and wherein the one or more processors, to perform the lattice reduction based at least in part on the frequency domain granularity, are configured to apply the lattice reduction identically across the one or more subbands (See, e.g., ¶[0041], “the UE may apply the same transformation matrix to multiple resource elements (REs) of the beamformed transmission across multiple symbols in the time domain and/or across multiple sub-carriers in the frequency domain.”).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the MIMO detector taught by Gan to incorporate the above teachings of Landis, i.e., the application of the transform matrix obtained from a lattice reduction operation for multiple resource elements, in order for so modified MIMO detector to have an improved computational efficiency (See, e.g., Landis, Abstract).
Regarding claim 5/1, Gan in view of Landis teach a UE comprising all elements recited in claim 1 as discussed above.
Landis further teaches that the time domain granularity indicates one or more symbols or slots (See, ¶[0079], “a resource element may consist of one symbol period (e.g., a duration of one modulation symbol) and one subcarrier”), and wherein the one or more processors, to perform the lattice reduction based at least in part on the time domain granularity, are configured to apply the lattice reduction identically across the one or more symbols or slots (See, e.g., ¶[0041], “the UE may apply the same transformation matrix to multiple resource elements (REs) of the beamformed transmission across multiple symbols in the time domain and/or across multiple sub-carriers in the frequency domain.”).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the MIMO detector taught by Gan to incorporate the above teachings of Landis, i.e., the application of the transform matrix obtained from a lattice reduction operation for multiple resource elements, in order for so modified MIMO detector to have an improved computational efficiency (See, e.g., Landis, Abstract).
Regarding claim 10/1, Gan in view of Landis teach a UE comprising all elements recited in claim 1 as discussed above.
Landis further teaches that the one or more processors are configured to receive an indication of one or more of a block error rate, a constellation size, a modulation and coding scheme or channel profile information (See, ¶[0009] An apparatus for wireless communication at a wireless device is described. The apparatus may include a processor, memory in electronic communication with the processor, and instructions stored in the memory. The instructions may be executable by the processor to cause the apparatus to receive a configuration message indicating a number of spatial layers and a modulation and coding scheme for a beamformed transmission;” and ¶[0126], “For example, a base station 105 may transmit beamformed transmissions according to a 16 QAM MCS such that initial lattice domain 540 may include sixteen constellation symbols 545.”).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the MIMO detector taught by Gan to incorporate the above teachings of Landis, i.e., the application of the transform matrix obtained from a lattice reduction operation for multiple resource elements, in order for so modified MIMO detector to have an improved computational efficiency (See, e.g., Landis, Abstract).
Regarding claim 12/1, Gan in view of Landis teach a UE comprising all elements recited in claim 1 as discussed above.
Gan further teaches that the performance-complexity tradeoff parameter value is associated with one or more of a constellation size or a quantity of layers (See, ¶[0042] “In fact, it is possible to set δ to vary with factors such as processing time spent or a current working lattice dimension.”).
Regarding claim 13/1, Gan in view of Landis teach a UE comprising all elements recited in claim 1as discussed above.
Gan further teaches the one or more processors are configured to perform machine learning with one or more model inputs to obtain at least one of the performance-complexity tradeoff parameter value, the frequency domain granularity, or the time domain granularity (See, ¶[0058], “Furthermore, as will be appreciated various portions of the disclosed systems above and methods below may include or consist of artificial intelligence or knowledge or rule based components.”).
Regarding claim 14/13, Gan in view of Landis teach a UE comprising all elements recited in claim 13 as discussed above.
Gan further teaches that the one or more model inputs include one or more of:
packet data protocol information, a coarse frequency selectivity level, an actual frequency selectivity level, a coarse channel singularity level, an actual channel singularity level, coarse Doppler information, actual Doppler information, coarse Doppler spread information, actual Doppler spread information, channel coherence time information, a target block error rate, a constellation size, a modulation and coding scheme, a quantity of layers, a quantity of receive antennas, or a history of channel measurements (See, ¶[0022] “communications from the m transmitters to the n receivers in the communication link pass through a communication channel that can be modeled as an uncorrelated Rayleigh fading channel.” The “n receivers” corresponding to the claimed “quantity of receive antennas.”).
Regarding claim 29, Gan teaches a method of wireless communication (See, e.g., Fig. 5) performed by a user equipment (UE) (See, e.g., Fig. 1, #120; Fig. 4, #420; Fig. 7, #710; and ¶[0064], “an example of a remote device for implementing various aspects described herein”.), comprising:
obtaining a performance-complexity tradeoff parameter value (“δ,” See, ¶[0035], “where δ with ¼<δ<1 is a factor that can be selected to achieve a good quality-complexity tradeoff.” Alternatively, “ϵ” See, ¶[0043], “where 0<ϵ<1 is a user-defined adjustment factor introduced for further fine-tuning the performance-complexity tradeoff”) associated with a quantity of iterations and channel orthogonality (See, ¶[0036], size reduction is … a process aimed at making basis vectors … closer to orthogonal …two consecutive basis vectors hk-1 and hk are swapped … size reduction and basis vector swapping steps can then iterate until Equation (9) is satisfied for all pairs of hk-1 and hk”. That is, a larger δ would require a larger number of iterations before the Equation (9) is met. See, also, ¶[0005], “Relaxed LLL allows various constraints of the LLL reduction algorithm to be relaxed, which can result in a smaller amount of basis vector swapping and a reduction in the overall complexity and delay of an associated detector.”) for an algorithm for a lattice reduction (See, e.g., ¶[0005], “Various systems and methodologies presented herein can utilize a relaxed form of the Lenstra-Lenstra-Lovasz (LLL) lattice reduction algorithm”) of a first matrix (“H”, See, ¶[0022], “H is the nxm complex channel matrix”) for a downlink communication (See, Fig. 1; and ¶[0019], “terminals 110 and/or 120 in system 100 can be capable of both receiving and transmitting at one or more time intervals.”);
receiving the downlink communication that corresponds to the first matrix (See, Fig. 5, #502: and ¶[0059], “one or more signals received”);
performing the lattice reduction to transfer the first matrix to a second matrix based at least in part on the performance-complexity tradeoff parameter value (See, Fig. 5, #504; and ¶s[0038], “the channel matrix H can be processed by a lattice reduction block 310 to transform H into a reduced basis H'=HU, where U is an unimodular matrix;” and [0059], “At 504, relaxed-LLL lattice reduction is performed on the channel matrix”.); and
performing multiple-input-multiple-output detection of the downlink communication using the second matrix (See, Fig. 5, #506; and ¶s[0038], “Following this lattice reduction, a traditional signal detector 320 can be applied on the reduced basis;” and [0059] “At 506, signal detection is performed (e.g., by a signal detection component 220) for one or more receivers based at least in part on the lattice-reduced channel matrix obtained at 504.”).
Gan, however, fails to teach explicitly:
obtaining at least one of a frequency domain granularity for the lattice reduction or a time domain granularity for the lattice reduction; and
performing the lattice reduction based at least in part on the at least one of the frequency domain granularity or the time domain granularity.
Landis teaches an analogous field of art, i.e., lattice reduction aided MIMO detection with reduced decoder complexity, see, Title; ¶[0085], “As the number of REs included in a transmission increases …. the computational-cost of performing ML-based demapping procedures, and the associated silicon die size, greatly increases, resulting in high power usage at a UE 115;” ¶[0089], “by applying the same LR across multiple REs, an MMSE-based demapper may spread the computational cost of computing the transformation matrix across multiple REs, thus increasing the computational efficiency”.), and teaches:
obtaining at least one of a frequency domain granularity for the lattice reduction or a time domain granularity for the lattice reduction (See, ¶[0149], “UE 115-b may identify, based on the channel property, a number of REs associated with the beamformed transmission on which to apply the transformation matrix…..the number of REs corresponds to a number of different sub-carriers in a same symbol period….. In some instances, …. a number of sub-carriers located in a plurality of different symbol periods”.); and
performing the lattice reduction based at least in part on at least one of the frequency domain granularity or the time domain granularity (See, ¶[0079], “a resource element may consist of one symbol period (e.g., a duration of one modulation symbol) and one subcarrier;” Fig. 4, #s 410 and #415; ¶[0109], “Channel estimator 410 may determine matrix H (e.g., and initial lattice domain 435) once per scheduled RE;” ¶[0110], “LR preprocessing component 415 may perform an LR calculation on matrices Y and H to determine transformation matrix T. LR preprocessing component 415 may calculate transformation T once per sub-carrier; and ¶[0149], “UE 115-b may identify, based on the channel property, a number of REs associated with the beamformed transmission on which to apply the transformation matrix”.).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the MIMO detector taught by Gan to incorporate the above teachings of Landis, i.e., the application of the transform matrix obtained from a lattice reduction operation for multiple resource elements, in order for so modified MIMO detector to have an improved computational efficiency (See, e.g., Landis, Abstract).
Claims 3, 4, 9 and 31 are rejected under 35 U.S.C. §103 as being unpatentable over Gan in view of Landis and in further view of MolavianJazi et al. (US Published Patent Application No. US 20230058307) (“MolavianJazi”).
Regarding claim 3/1, Gan in view of Landis teach a UE comprising all elements recited in claim 1 as discussed above.
Gan in view of Landis, however, fails to teach explicitly that the frequency domain granularity corresponds to a precoding resource block granularity.
MolavianJazi teaches an analogous field of art, i.e., a lattice reduction aided MIMO detector using an integer-forcing (IF) receiver (detector), see, e.g., title; ¶[0068]; and ¶[0109]), and teaches that the frequency domain granularity corresponds to a precoding resource block granularity (See, ¶[0199], “The channel is assumed to experience semi-static, flat fading, i.e., no or almost no time/frequency variation within a codeblock transmission. This may be achieved by constraining the mapping of the modulation symbols to be within a resource allocation comprising small number (e.g., 1) of RBG (Resource Block Groups, RBG .sub.= 4 RBs, RB .sub.= 12 subcarriers), subband (e.g., 8 RBs), PRG (Precoding Resource Group, such as 2 RBs). The resource allocation may span at least a slot (e.g., comprising 14 OFDM symbols) or multi-slot allocation to support sufficient number of information bits”).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the MIMO detector taught by Gan in view of Landis to incorporate the above teachings of MolavianJazi, i.e., the mapping of the modulation symbols to be within a small group of resources, in order to enhance the “flat fading,” i.e., a stable channel over a period of time, which channel condition is recognized and taught by each of Gan (See, Gan, ¶[0022]), Landis (See, e.g., ¶[0007]) and MolavianJazi (See, e.g., ¶[0210]), and to improve the computational efficiency of the MIMO detector (See, e.g., Landis, Abstract).
Regarding claim 4/1, Gan in view of Landis teach a UE comprising all elements recited in claim 1 as discussed above.
Gan in view of Landis, however, fails to teach explicitly that the frequency domain granularity corresponds to a resource block group associated with a precoding resource block granularity.
MolavianJazi teaches an analogous field of art, i.e., a lattice reduction aided MIMO detector using an integer-forcing (IF) receiver (detector), see, e.g., title; ¶[0068]; and ¶[0109]), and teaches that the frequency domain granularity corresponds to a resource block group associated with a precoding resource block granularity (See, ¶[0199], “The channel is assumed to experience semi-static, flat fading, i.e., no or almost no time/frequency variation within a codeblock transmission. This may be achieved by constraining the mapping of the modulation symbols to be within a resource allocation comprising small number (e.g., 1) of RBG (Resource Block Groups, RBG .sub.= 4 RBs, RB .sub.= 12 subcarriers), subband (e.g., 8 RBs), PRG (Precoding Resource Group, such as 2 RBs). The resource allocation may span at least a slot (e.g., comprising 14 OFDM symbols) or multi-slot allocation to support sufficient number of information bits”).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the MIMO detector taught by Gan in view of Landis to incorporate the above teachings of MolavianJazi, i.e., the mapping of the modulation symbols to be within a small group of resources, in order to enhance the “flat fading,” i.e., a stable channel over a period of time, which channel condition is recognized and taught by each of Gan (See, Gan, ¶[0022]), Landis (See, e.g., ¶[0007]) and MolavianJazi (See, e.g., ¶[0210]), and to improve the computational efficiency of the MIMO detector (See, e.g., Landis, Abstract).
Regarding claim 9/1, Gan in view of Landis teach a UE comprising all elements recited in claim 1 as discussed above.
As discussed above, Gan in view of Landis teach further the one or more processors configured to obtain an indication of the performance- complexity tradeoff parameter value (See, Gan, ¶[0035]; and [0043]. See, above discussion of claim 1), the frequency domain granularity, or the time domain granularity (See, Landis, ¶[0079], “a resource element may consist of one symbol period (e.g., a duration of one modulation symbol) and one subcarrier.” That is, the frequency domain granularity may be one subcarrier while the time domain granularity may be one symbol. Landis further teaches that “[c]hannel estimator 510 may determine matrix H, where matrix H is the matrix representation of the channel on the scheduled RE over which the beamformed transmissions are received, see, Landis, Fig. 5 and ¶[0125], and that “LR preprocessing component 515 may perform an LR calculation on matrices Y and H to determine transformation matrix T,” see, Landis, Fig. 5; and ¶[0128]. While Landis refers the RE for which the lattice reduction is to be performed as the “scheduled RE,” does not explicitly indicate the entity (i.e., the BS or UE) that performs the “scheduling.”).
Gan in view of Landis fails to teach explicitly that the indication is received via downlink control information in a downlink grant, a radio resource control configuration, or a medium access control control element (MAC CE).
MolavianJazi teaches an analogous field of art, i.e., a lattice reduction aided MIMO detector using an integer-forcing (IF) receiver (detector), see, e.g., title; ¶[0068]; and ¶[0109]), and teaches that the indication of the frequency domain granularity, or the time domain granularity is received via downlink control information in a downlink grant, a radio resource control configuration, or a medium access control control element (MAC CE) (See, ¶[0252], “the transmit node may be a network entity, base station, gNB, eNB, relay node, TRP (Transmission/Reception Point) etc. …. The transmit node may indicate in a DCI (Downlink Control Information) on a PDCCH (Physical Downlink Control Channel) scheduling the data transmission”).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the MIMO detector taught by Gan in view of Landis to incorporate the above teachings of MolavianJazi, i.e., the resource allocation via DCI, as such modification would be considered by one of ordinary skill in the art as a necessary part of data transmission in 5G NR. See, also MPEP §2141.III(A); and §2143.I(A), Example 2, the discussion of Ruiz v. A.B. Chance Co., 357 F.3d 1270, 69 USPQ2d 1686 (Fed. Cir. 2004).
Regarding claim 31/1, Gan in view of Landis teach a UE comprising all elements recited in claim 1 as discussed above.
Landis teach further that the one or more processors are further configured to receive, from a network entity, an indication of the performance-complexity tradeoff parameter value1 (See, e.g., ¶[0092], “base station 105-a may transmit a configuration message to UE 115-a, where the configuration message indicates the number of spatial layers 215 and a modulation and coding scheme (MCS) that base station 105-a may utilize for a set of beamformed transmissions.”).
Landis further teaches the frequency domain granularity and the time domain granularity (See, Landis, ¶[0079], “a resource element may consist of one symbol period (e.g., a duration of one modulation symbol) and one subcarrier.” That is, the frequency domain granularity may be one subcarrier while the time domain granularity may be one symbol. Landis further teaches that “[c]hannel estimator 510 may determine matrix H, where matrix H is the matrix representation of the channel on the scheduled RE over which the beamformed transmissions are received, see, Landis, Fig. 5 and ¶[0125], and that “LR preprocessing component 515 may perform an LR calculation on matrices Y and H to determine transformation matrix T,” see, Landis, Fig. 5; and ¶[0128]. While Landis refers the RE for which the lattice reduction is to be performed as the “scheduled RE,” does not explicitly indicate the entity (i.e., the BS or UE) that performs the “scheduling.”).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the MIMO detector taught by Gan to incorporate the above teachings of Landis, i.e., the application of the transform matrix obtained from a lattice reduction operation for multiple resource elements, in order for so modified MIMO detector to have an improved computational efficiency (See, e.g., Landis, Abstract).
Gan in view of Landis, however, fails to teach explicitly that the one or more processors are further configured to receive, from a network entity, an indication of the at least one of the frequency domain granularity or the time domain granularity;
the at least one of the frequency domain granularity or the time domain granularity comprises the frequency domain granularity; and
the lattice reduction is applied identically across all resource blocks within a precoding resource block group corresponding to the frequency domain granularity.
MolavianJazi teaches an analogous field of art, i.e., a lattice reduction aided MIMO detector using an integer-forcing (IF) receiver (detector), see, e.g., title; ¶[0068]; and ¶[0109]), and teaches that the one or more processors are further configured to receive, from a network entity, an indication of the at least one of the frequency domain granularity or the time domain granularity (See, ¶[0252], “the transmit node may be a network entity, base station, gNB, eNB, relay node, TRP (Transmission/Reception Point) etc. …. The transmit node may indicate in a DCI (Downlink Control Information) on a PDCCH (Physical Downlink Control Channel) scheduling the data transmission”. MolavianJazi thus teaches that the “scheduled RE" of Landis is scheduled by a network entity, e.g., a base station, through DCI, which indicates the resource allocation, i.e., the frequency domain resources and the time domain resources for data transmission.);
the at least one of the frequency domain granularity or the time domain granularity comprises the frequency domain granularity (See, ¶[0199], “subband (e.g., 8 RBs)”); and
the lattice reduction is applied identically across all resource blocks within a precoding resource block group corresponding to the frequency domain granularity (See, ¶[0199], “The channel is assumed to experience semi-static, flat fading, i.e., no or almost no time/frequency variation within a codeblock transmission. This may be achieved by constraining the mapping of the modulation symbols to be within a resource allocation comprising small number (e.g., 1) of RBG (Resource Block Groups, RBG .sub.= 4 RBs, RB .sub.= 12 subcarriers), subband (e.g., 8 RBs), PRG (Precoding Resource Group, such as 2 RBs).” MolavianJazi thus teaches that the same channel estimation (thus also the same transform matrix resulting from the lattice reduction) may be used to detect symbols from multiple frequency/time domain resources, and that such multiple resources may comprise a PRG.).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the MIMO detector taught by Gan in view of Landis to incorporate the above teachings of MolavianJazi, i.e., the mapping of the modulation symbols to be within a small group of resources, in order to enhance the “flat fading,” i.e., a stable channel over a period of time, which channel condition is recognized and taught by each of Gan (See, Gan, ¶[0022]), Landis (See, e.g., ¶[0007]) and MolavianJazi (See, e.g., ¶[0210]), and to improve the computational efficiency of the MIMO detector (See, e.g., Landis, Abstract).
Claims 6-8 are rejected under 35 U.S.C. §103 as being unpatentable over Gan in view of Landis and in further view of Bhattad et al. (US Published Patent Application No. US 2021/0051052) (“Bhattad”).
Regarding claim 6/1, Gan in view of Landis teach a UE comprising all elements recited in claim 1, including the one or more processors, to perform the lattice reduction, are configured to perform the lattice reduction, as discussed above.
Gan in view of Landis, however, fails to teach explicitly perform[ing] the lattice reduction based at least in part on a determination that the downlink communication includes a demodulation reference signal.
Bhattad teaches an analogous field of art, i.e., a MIMO detector (see, Fig. 2, #256), and teaches performing the lattice reduction based at least in part on a determination that the downlink communication includes a demodulation reference signal (See, e.g., ¶[0069], “the BS and/or the UE may shift a last DMRS symbol to ensure a DMRS gap of less than or equal to 8 symbols, thereby satisfying an interpolation-reduction criterion and enabling use of a Type-A PDSCH channel estimation interpolation matrix;” and ¶[0066], “Moreover, double symbol DMRS may not be supported for various durations of Type-B PDSCH. It may be advantageous to reuse existing channel estimate”. Thus, according to Bhattad, a channel estimation is performed when PDSCH, i.e., the claimed “downlink communication,” includes a DMRS, and, as taught by Gan in view of Landis, the lattice reduction would be performed on the estimated channel.).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the MIMO detector taught by Gan in view of Landis to incorporate the above teaching of Bhattad, i.e., the detection of DMRS for channel estimation, in order to enable DMRS detection in communication network, i.e., 5G NR (See, e.g., Bhattad. ¶[0064]).
Regarding claim 7/1, Gan in view of Landis teach a UE comprising all elements recited in claim 1, including the one or more processors, to perform the lattice reduction, are configured to perform the lattice reduction, as discussed above.
Gan in view of Landis, however, fails to teach explicitly performing the lattice reduction based at least in part on a determination that the downlink communication does not include multiple demodulation reference signals.
Bhattad teaches an analogous field of art, i.e., a MIMO detector (see, Fig. 2, #256), and teaches performing the lattice reduction based at least in part on a determination that the downlink communication does not include multiple demodulation reference signals (Bhattad teaches that: i) for a type-B, the PDSCH length can be configured to be L=2, 4 or 7; ii) a double symbol DMRS is supported only when L=7 (See, ¶[0065]); and iii) for the cases of L=2 or L=4, not performing the channel estimation, but to rely on a previously obtained channel estimation. See, ¶[0066], “Moreover, double symbol DMRS may not be supported for various durations of Type-B PDSCH. It may be advantageous to reuse existing channel estimate interpolation tables that may correspond to existing time domain resource patterns,” Emphasis added. Accordingly, when a PDSCH is determined not containing multiple DMRS symbols, a channel estimation would be performed. A lattice reduction would be performed on the channel matrix resulting from the channel estimation as taught by Gan et al.).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the MIMO detector taught by Gan in view of Landis to incorporate the above teaching of Bhattad, i.e., the detection of DMRS for channel estimation, in order to enable DMRS detection in communication network, i.e., 5G NR (See, e.g., Bhattad. ¶[0064]).
Regarding claim 8/1, Gan in view of Landis teach a UE comprising all elements recited in claim 1, including the one or more processors, to perform the lattice reduction, are configured to perform the lattice reduction, as discussed above.
Gan in view of Landis, however, fails to teach explicitly disabling a lattice reduction of a next downlink communication based at least in part on a determination that the next downlink communication includes multiple demodulation reference signals.
Bhattad teaches an analogous field of art, i.e., a MIMO detector (see, Fig. 2, #256), and teaches disabling a lattice reduction of a next downlink communication based at least in part on a determination that the next downlink communication includes multiple demodulation reference signals (Bhattad teaches that; i) for a type-B, the PDSCH length can be configured to be L=2, 4 or 7, ii) a double symbol DMRS is supported only when L=7 (See, ¶[0065]); and iii) for the cases of L=2 or L=4, not performing the channel estimation, but to rely on a previously obtained channel estimation. See, ¶[0066], “Moreover, double symbol DMRS may not be supported for various durations of Type-B PDSCH. It may be advantageous to reuse existing channel estimate interpolation tables that may correspond to existing time domain resource patterns,” Emphasis added. Accordingly, when a PDSCH is determined containing multiple DMRS symbols, a channel estimation would not be performed. A lattice reduction also would not be performed because there would be no new channel matrix upon which to perform the lattice reduction, whenever such PDSCH occurs.).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the MIMO detector taught by Gan in view of Landis to incorporate the above teaching of Bhattad, i.e., the detection of DMRS for channel estimation, in order to enable DMRS detection in communication network, i.e., 5G NR (See, e.g., Bhattad. ¶[0064]).
Claim 11is rejected under 35 U.S.C. §103 as being unpatentable over Gan in view of Landis and in further view of Xu et al. (US Published Patent Application No. US 2020/0100286) (“Xu”).
Regarding claim 11/1, Gan in view of Landis teach a UE comprising all elements recited in claim 1as discussed above.
Gan further teaches the performance-complexity tradeoff parameter value may “vary with factors such as processing time spent or a current working lattice dimension.” See, Gan, ¶[0042].
Landis, as discussed above, teaches a BS sending the MCS to a UE. See, e.g., Landis, ¶[0009], and that a lattice reduction is performed for a channel for the transmission of a “scheduled RE. See, e.g., Landis, Fig. 5 and ¶[0125].
Gan in view of Landis, however, fails to teach explicitly that the performance-complexity tradeoff parameter value is specific to a frequency subband, a bandwidth part, or a serving cell.
Xu teaches the performance-complexity tradeoff parameter value is specific to a frequency subband, a bandwidth part, or a serving cell (See, Xu, ¶[0318], “the information in the DCI formats for downlink scheduling may comprise at least one of: identifier of a DCI format; carrier indicator; frequency domain resource assignment; time domain resource assignment; bandwidth part indicator; HARQ process number; one or more MCS; one or more NDI; one or more RV; MIMO related information; Downlink assignment index (DAI); PUCCH resource indicator; PDSCH-to-HARQ_feedback timing indicator; TPC for PUCCH; SRS request; and padding if necessary. In an example, the MIMO related information may comprise at least one of: PMI; precoding information; transport block swap flag; power offset between PDSCH and reference signal; reference-signal scrambling sequence; number of layers; and/or antenna ports for the transmission; and/or Transmission Configuration Indication (TCI).” Accordingly, for every dynamic downlink transmission, at least each of the sub-bands, the BWP, the MCS, i.e., which defines the “current working lattice dimension,” see, above, Gan ¶[0042], are defined, and are included in the DCI sent by the base station. Therefore, the performance-complexity tradeoff parameter value is, according to Gan as discussed above, specific to the MCS, and to the sub-band and BWP as these are all associated with the particular downlink transmission, such as, e.g., the PDCCH. In addition, assuming, the UE is receiving the downlink transmission from a base station that is a part of the serving cell, the performance-complexity tradeoff parameter value is specific to the current serving cell at least for the particular downlink transmission.)
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the above teachings of Gan in view Landis to incorporate the above teaching of Xu, i.e., the content of a downlink control information (DCI), as such teaching would have been recognized by a person having an ordinary skill in the art (PHOSITA) as a technical fact relating to the 5G NR. See, e.g., MPEP §2144.I.
Claims 16-23 and 30 are rejected under 35 U.S.C. §103 as being unpatentable over Nammi et al. (US Published Patent Application No. 2024/0260028) in view of MolavianJazi.
Regarding claim 16, Nammi teaches a network entity for wireless communication (See, e.g., Fig. 1a, “gNB” and Fig. 13a, “Network Node 110”), comprising:
one or more memories (See, Fig. 13a, #1360; and ¶[0225]); and
one or more processors, coupled to the one or more memories (Fig. 13a, #1350; and ¶s[0224]-[0225]), which are configured, individually or in any combination, to:
generate an indication (“DCI’ further discussed below) of a performance-complexity tradeoff parameter value associated with a quantity of iterations and channel orthogonality for an algorithm for a lattice reduction of a first matrix for a downlink communication2 (“MCS, see, Fig. 1a, step #14, “MCS, Power, PRBs, etc.”; and ¶[0015]-[0016]. See, also, ¶[0168]; Equation 1; and ¶[0169], teaching the use of the DMRS for channel estimation.), and at least one of a frequency domain granularity or a time domain granularity (See, e.g., ¶[0022] and Table 2; ¶[0023], “The Physical Downlink Control Channel (PDCCH) carries information about the scheduling grants. Typically, this comprises the number of MIMO layers scheduled, transport block sizes, modulation for each codeword, parameters related to HARQ, sub-band locations etc.” See, also, ¶[0024]-[0047] for typical information included in a DCI.);
transmit the indication (See, ¶[0016], “The network sends 15 the scheduling parameters to the UE in the downlink control channel.”); and
transmit the downlink communication (See, ¶[0016], “After that actual data transfer 16 takes place from network to the UE.”).
Nammi, however, fails to teach explicitly that the at least one of a frequency domain granularity or a time domain granularity is for the lattice reduction.
MolavianJazi teaches an analogous field of art, i.e., a lattice reduction aided MIMO detector using an integer-forcing (IF) receiver (detector), see, e.g., title; ¶[0068]; and ¶[0109]), and teaches that the at least one of a frequency domain granularity or a time domain granularity is for the lattice reduction (See, e.g., ¶[0068], “Lattice Reduction (particularly Lenstra-Lenstra-Lovasz (LLL) lattice reduction algorithm);” and ¶[0210], “The complex channel has (M) transmit antennas and (N) receive antennas. The channel is assumed to experience semi-static, flat fading, i.e., no or almost no time/frequency variation within a codeblock transmission. This may be achieved by constraining the mapping of the modulation symbols to be within a resource allocation comprising small number (e.g., 1) of RBG (Resource Block Groups, RBG _= 4 RBs, RB _= 12 subcarriers), subband (e.g., 8 RBs), PRG (Precoding Resource Group, such as 2 RBs).” MolavianJazi thus teaches the utilization of the same channel estimation (and thus also the lattice reduction) for relatively small frequency domain granularity and time domain granularity. See, also, ¶[0250], “The second encoding/mapping type may support only resource allocation below a first number of RBs (e.g., RBG, PRG, subband) and at least a second number of OFDM symbols (e.g., slot), same channel coding scheme, codeword length and modulation-and-coding scheme (MCS) across all the transmission layers.”).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the DMRS based channel estimation and MIMO detection taught by Nammi to incorporate the above teachings of MolavianJazi, i.e., the resource allocation based on the “flat fading” nature of the channel, as such modification would reduce complexity of and latency in MIMO detection while maintaining a reasonable performance (See, e.g., MolavianJazi, ¶[0210].).
Regarding claim 17/16, Nammi in view of MolavianJazi teach a network entity comprising all elements recited in claim 16 as discussed above.
MolavianJazi teaches further that the frequency domain granularity indicates one or more subcarriers (See, ¶[0210], “the mapping of the modulation symbols to be within a resource allocation comprising small number (e.g., 1) of RBG (Resource Block Groups, RBG .sub.= 4 RBs, RB .sub.= 12 subcarriers), subband (e.g., 8 RBs), PRG (Precoding Resource Group, such as 2 RBs).”).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the DMRS based channel estimation and MIMO detection taught by Nammi to incorporate the above teachings of MolavianJazi, i.e., the resource allocation based on the “flat fading” nature of the channel, as such modification would reduce complexity of and latency in MIMO detection while maintaining a reasonable performance (See, e.g., MolavianJazi, ¶[0210].).
Regarding claim 18/16, Nammi in view of MolavianJazi teach a network entity comprising all elements recited in claim 16 as discussed above.
MolavianJazi teaches further that the frequency domain granularity corresponds to a precoding resource block granularity (See, ¶[0210], “the mapping of the modulation symbols to be within a resource allocation comprising small number (e.g., 1) of RBG (Resource Block Groups, RBG .sub.= 4 RBs, RB .sub.= 12 subcarriers), subband (e.g., 8 RBs), PRG (Precoding Resource Group, such as 2 RBs).”).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the DMRS based channel estimation and MIMO detection taught by Nammi to incorporate the above teachings of MolavianJazi, i.e., the resource allocation based on the “flat fading” nature of the channel, as such modification would reduce complexity of and latency in MIMO detection while maintaining a reasonable performance (See, e.g., MolavianJazi, ¶[0210].).
Regarding claim 19/16, Nammi in view of MolavianJazi teach a network entity comprising all elements recited in claim 16 as discussed above.
MolavianJazi teaches further that the frequency domain granularity corresponds to a resource block group associated with a precoding resource block granularity (See, ¶[0210], “the mapping of the modulation symbols to be within a resource allocation comprising small number (e.g., 1) of RBG (Resource Block Groups, RBG .sub.= 4 RBs, RB .sub.= 12 subcarriers), subband (e.g., 8 RBs), PRG (Precoding Resource Group, such as 2 RBs).”).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the DMRS based channel estimation and MIMO detection taught by Nammi to incorporate the above teachings of MolavianJazi, i.e., the resource allocation based on the “flat fading” nature of the channel, as such modification would reduce complexity of and latency in MIMO detection while maintaining a reasonable performance (See, e.g., MolavianJazi, ¶[0210].).
Regarding claim 20/16, Nammi in view of MolavianJazi teach a network entity comprising all elements recited in claim 16 as discussed above.
MolavianJazi teaches further that the time domain granularity indicates one or more symbols or slots (See, ¶[0250], “The second encoding/mapping type may support only resource allocation below a first number of RBs (e.g., RBG, PRG, subband) and at least a second number of OFDM symbols (e.g., slot)”.).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the DMRS based channel estimation and MIMO detection taught by Nammi to incorporate the above teachings of MolavianJazi, i.e., the resource allocation based on the “flat fading” nature of the channel, as such modification would reduce complexity of and latency in MIMO detection while maintaining a reasonable performance (See, e.g., MolavianJazi, ¶[0210].).
Regarding claim 21/16, Nammi in view of MolavianJazi teach a network entity comprising all elements recited in claim 16 as discussed above.
Nammi teaches further that the one or more processors are configured to transmit the indication via downlink control information in a downlink grant, a radio resource control configuration, or a medium access control control element (MAC CE) (See, ¶[0023], “The Physical Downlink Control Channel (PDCCH) carries information about the scheduling grants. Typically, this comprises the number of MIMO layers scheduled, transport block sizes, modulation for each codeword, parameters related to HARQ, sub-band locations etc;” and ¶[0024]-[0047] for typical information included in a DCI.”).
Regarding claim 22/16, Nammi in view of MolavianJazi teach a network entity comprising all elements recited in claim 16 as discussed above.
Nammi teaches further that the one or more processors, to generate the indication, are configured to generate the indication based at least in part on channel state information or a measurement of a reference signal (See, ¶[0049], “A UE receiver estimates channel quality, typically Signal-to-Interference Ratio (SINR), from channel sounding, and computes a preferred precoding matrix (PMI), rank indicator (RI), and CQI for the next downlink transmission. This information may be referred to as CSI. The UE conveys this information through the feedback channel as mentioned above;” and ¶[0050] For downlink data transmission, the gNode B uses this information and chooses the precoding matrix as suggested by the UE, or it may choose on its own other than the UE recommended PMI), CQI and the transport block size etc.”).
Regarding claim 23/16, Nammi in view of MolavianJazi teach a network entity comprising all elements recited in claim 16 as discussed above.
Nammi teaches further that the indication indicates one or more of a block error rate, a size, a modulation and coding scheme, or channel profile information (See, ¶[0034], “Modulation and coding scheme for each Transport Block (TB))”).
Regarding claim 30, Nammi teaches a method of wireless communication (See, e.g., Fig. 1a.) performed by a network entity (See, e.g., Fig. 1a, “gNB” and Fig. 13a, “Network Node 110”), comprising:
generating an indication (“DCI’ further discussed below) of a performance-complexity tradeoff parameter value associated with a quantity of iterations and channel orthogonality for an algorithm for a lattice reduction of a first matrix for a downlink communication3 (“MCS, see, Fig. 1a, step #14, “MCS, Power, PRBs, etc.”; and ¶[0015]-[0016]. See, also, ¶[0168]; Equation 1; and ¶[0169], teaching the use of the DMRS for channel estimation.), and at least one of a frequency domain granularity or a time domain granularity (See, e.g., ¶[0022] and Table 2; ¶[0023], “The Physical Downlink Control Channel (PDCCH) carries information about the scheduling grants. Typically, this comprises the number of MIMO layers scheduled, transport block sizes, modulation for each codeword, parameters related to HARQ, sub-band locations etc.” See, also, ¶[0024]-[0047] for typical information included in a DCI.);
transmitting the indication (See, ¶[0016], “The network sends 15 the scheduling parameters to the UE in the downlink control channel.”); and
transmitting the downlink communication (See, ¶[0016], “After that actual data transfer 16 takes place from network to the UE.”).
Nammi, however, fails to teach explicitly that the at least one of a frequency domain granularity or a time domain granularity is for the lattice reduction.
MolavianJazi teaches an analogous field of art, i.e., a lattice reduction aided MIMO detector using an integer-forcing (IF) receiver (detector), see, e.g., title; ¶[0068]; and ¶[0109]), and teaches that the at least one of a frequency domain granularity or a time domain granularity is for the lattice reduction (See, e.g., ¶[0068], “Lattice Reduction (particularly Lenstra-Lenstra-Lovasz (LLL) lattice reduction algorithm);” and ¶[0210], “The complex channel has (M) transmit antennas and (N) receive antennas. The channel is assumed to experience semi-static, flat fading, i.e., no or almost no time/frequency variation within a codeblock transmission. This may be achieved by constraining the mapping of the modulation symbols to be within a resource allocation comprising small number (e.g., 1) of RBG (Resource Block Groups, RBG _= 4 RBs, RB _= 12 subcarriers), subband (e.g., 8 RBs), PRG (Precoding Resource Group, such as 2 RBs).” MolavianJazi thus teaches the utilization of the same channel estimation (and thus also the lattice reduction) for relatively small frequency domain granularity and time domain granularity. See, also, ¶[0250], “The second encoding/mapping type may support only resource allocation below a first number of RBs (e.g., RBG, PRG, subband) and at least a second number of OFDM symbols (e.g., slot), same channel coding scheme, codeword length and modulation-and-coding scheme (MCS) across all the transmission layers.”).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the DMRS based channel estimation and MIMO detection taught by Nammi to incorporate the above teachings of MolavianJazi, i.e., the resource allocation based on the “flat fading” nature of the channel, as such modification would reduce complexity of and latency in MIMO detection while maintaining a reasonable performance (See, e.g., MolavianJazi, ¶[0210].).
Claims 24-27 are rejected under 35 U.S.C. §103 as being unpatentable over Nammi in view of MolavianJazi and in further view of Gan.
Regarding claim 24/16, Nammi in view of MolavianJazi teach a network entity comprising all elements recited in claim 16 as discussed above.
As discussed above, Nammi in view of MolavianJazi further teach a BS sending the MCS to a UE (See, Nammi, ¶[0023], “The Physical Downlink Control Channel (PDCCH) carries information about the scheduling grants;” and ¶[0034], “Modulation and coding scheme for each Transport Block (TB))”. See, also, MolavianJazi, ¶[0252], “The transmit node may indicate in a DCI (Downlink Control Information) on a PDCCH (Physical Downlink Control Channel) scheduling the data transmission;” and ¶[0250], “The second encoding/mapping type may support only resource allocation below a first number of RBs (e.g., RBG, PRG, subband) and at least a second number of OFDM symbols (e.g., slot), same channel coding scheme, codeword length and modulation-and-coding scheme (MCS) across all the transmission layers (at least for each modulation level) from the set of transmit antenna ports.”).
Nammi in view of MolavianJazi, however, fails to teach that the performance-complexity tradeoff parameter value is specific to a frequency subband, a bandwidth part, or a serving cell.
Gan teaches an analogous field of art, i.e., a lattice reduction aided MIMO detection (see, e.g., Gan, Title), and teaches that the performance-complexity tradeoff parameter value is specific to a frequency subband, a bandwidth part, or a serving cell (See, Gan, ¶[0042], “In accordance with another aspect, the lattice reduction component 240 can perform relax the conditions of LLL based on two observations. First, it can be observed that the LLL factor δ needs not be fixed throughout the execution of lattice reduction. In fact, it is possible to set δ to vary with factors such as processing time spent or a current working lattice dimension.” As discussed above, for every dynamic downlink transmission, at least each of the sub-bands, the BWP, the MCS, i.e., which defines the “current working lattice dimension,” see, above, Gan ¶[0042], are defined, and are included in the DCI sent by the base station. Therefore, the performance-complexity tradeoff parameter value is, according to Gan as discussed above, specific to the MCS, and to the sub-band and BWP as these are all associated with the particular downlink transmission, such as, e.g., the PDCCH. In addition, assuming, the UE is receiving the downlink transmission from a base station that is a part of the serving cell, the performance-complexity tradeoff parameter value is specific to the current serving cell at least for the particular downlink transmission.).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the MIMO detection taught by Nammi in view of MolavianJazi to incorporate the above teaching of Gan, i.e., the use of performance-complexity tradeoff parameter value for lattice reduction for MIMO detection, as such modification enables a balance between the complexity and the performance of the lattice reduction operation (See, e.g., Gan, ¶[0035], “where δ with ¼<δ<1 is a factor that can be selected to achieve a good quality-complexity tradeoff.”).
Regarding claim 25/16, Nammi in view of MolavianJazi teach a network entity comprising all elements recited in claim 16 as discussed above.
As discussed above, Nammi in view of MolavianJazi further teach a BS sending the MCS to a UE (See, Nammi, ¶[0023], “The Physical Downlink Control Channel (PDCCH) carries information about the scheduling grants;” and ¶[0034], “Modulation and coding scheme for each Transport Block (TB))”. See, also, MolavianJazi, ¶[0252], “The transmit node may indicate in a DCI (Downlink Control Information) on a PDCCH (Physical Downlink Control Channel) scheduling the data transmission;” and ¶[0250], “The second encoding/mapping type may support only resource allocation below a first number of RBs (e.g., RBG, PRG, subband) and at least a second number of OFDM symbols (e.g., slot), same channel coding scheme, codeword length and modulation-and-coding scheme (MCS) across all the transmission layers (at least for each modulation level) from the set of transmit antenna ports.”).
Nammi in view of MolavianJazi, however, fails to teach that the performance-complexity tradeoff parameter value is associated with one or more of a constellation size or a quantity of layers.
Gan teaches an analogous field of art, i.e., a lattice reduction aided MIMO detection (see, e.g., Gan, Title), and teaches that the performance-complexity tradeoff parameter value is associated with one or more of a constellation size or a quantity of layers (See, Gan, ¶[0042], “In accordance with another aspect, the lattice reduction component 240 can perform relax the conditions of LLL based on two observations. First, it can be observed that the LLL factor δ needs not be fixed throughout the execution of lattice reduction. In fact, it is possible to set δ to vary with factors such as processing time spent or a current working lattice dimension.”).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the MIMO detection taught by Nammi in view of MolavianJazi to incorporate the above teaching of Gan, i.e., the use of performance-complexity tradeoff parameter value for lattice reduction for MIMO detection, as such modification enables a balance between the complexity and the performance of the lattice reduction operation (See, e.g., Gan, ¶[0035], “where δ with ¼<δ<1 is a factor that can be selected to achieve a good quality-complexity tradeoff.”).
Regarding claim 26/16, Nammi in view of MolavianJazi teach a network entity comprising all elements recited in claim 16 as discussed above.
Nammi in view of MolavianJazi, however, fail to teach explicitly that the one or more processors, to generate the indication, are configured to perform machine learning with one or more model inputs.
Gan teaches an analogous field of art, i.e., a lattice reduction aided MIMO detection (see, e.g., Gan, Title), and teaches that the one or more processors, to generate the indication, are configured to perform machine learning with one or more model inputs (See, ¶[0058], “Furthermore, as will be appreciated various portions of the disclosed systems above and methods below may include or consist of artificial intelligence or knowledge or rule based components.”).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the MIMO detection taught by Nammi in view of MolavianJazi to incorporate the above teaching of Gan, i.e., the use of performance-complexity tradeoff parameter value for lattice reduction for MIMO detection, as such modification enables a balance between the complexity and the performance of the lattice reduction operation (See, e.g., Gan, ¶[0035], “where δ with ¼<δ<1 is a factor that can be selected to achieve a good quality-complexity tradeoff.”).
Regarding claim 27/26, Nammi in view of MolavianJazi in further view of Gan teach a network entity comprising all elements recited in claim 26 as discussed above.
Gan further teaches that the one or more model inputs include one or more of:
packet data protocol information, a coarse frequency selectivity level, an actual frequency selectivity level, a coarse channel singularity level, an actual channel singularity level, coarse Doppler information, actual Doppler information, coarse Doppler spread information, actual Doppler spread information, channel coherence time information, a target block error rate, a constellation size, a modulation and coding scheme, a quantity of layers, a quantity of receive antennas, or a history of channel measurements (See, ¶[0022] “communications from the m transmitters to the n receivers in the communication link pass through a communication channel that can be modeled as an uncorrelated Rayleigh fading channel.” The “n receivers” corresponding to the claimed “quantity of receive antennas.”).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the MIMO detection taught by Nammi in view of MolavianJazi to incorporate the above teaching of Gan, i.e., the use of performance-complexity tradeoff parameter value for lattice reduction for MIMO detection, as such modification enables a balance between the complexity and the performance of the lattice reduction operation (See, e.g., Gan, ¶[0035], “where δ with ¼<δ<1 is a factor that can be selected to achieve a good quality-complexity tradeoff.”).
Claim 28 is rejected under 35 U.S.C. §103 as being unpatentable over Nammi in view of MolavianJazi in further view of Gan and in even further view of Mu (US Patent Publication No. 2025/0048137).
Regarding claim 28/26, Nammi in view of MolavianJazi in further view of Gan teach a network entity comprising all elements recited in claim 26 as discussed above.
Nammi in view of MolavianJazi in further view of Gan teaches further the one or more machine learning models (See, Gan, ¶[0058], “Furthermore, as will be appreciated various portions of the disclosed systems above and methods below may include or consist of artificial intelligence or knowledge or rule based components.”) to obtain the performance-complexity tradeoff parameter value, the frequency domain granularity, or the time domain granularity (See, Gan, ¶[0035], “where δ with ¼<δ<1 is a factor that can be selected to achieve a good quality-complexity tradeoff.” See, also, Gan, ¶[0043], “where 0<ϵ<1 is a user-defined adjustment factor introduced for further fine-tuning the performance-complexity tradeoff”).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the MIMO detection taught by Nammi in view of MolavianJazi to incorporate the above teaching of Gan, i.e., the use of performance-complexity tradeoff parameter value for lattice reduction for MIMO detection, as such modification enables a balance between the complexity and the performance of the lattice reduction operation (See, e.g., Gan, ¶[0035], “where δ with ¼<δ<1 is a factor that can be selected to achieve a good quality-complexity tradeoff.”).
Nammi in view of MolavianJazi in further view of Gan, however, fails to teach explicitly that the one or more processors are configured to transmit the one or more machine learning models for a user equipment.
Mu teaches that the one or more processors are configured to transmit one or more machine learning models to a user equipment (See, Abstract, “an Artificial Intelligence (AI) model to perform channel estimation;” Fig. 6; and ¶[0137], “In step S61, in response to determining, based on the model deployment information of the UE, that no AI model corresponding to the target number of RBs exists in the UE, model information is sent to the UE. The model information includes: an AI model corresponding to the target number of RBs.”).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the MIMO detection taught by Nammi in view of MolavianJazi in further view of Gan to incorporate the above teaching of Mu in order to improve the availability for the AI model to be used for channel estimation and to improve the UE's ability to perform channel estimation based on the AI model (See, e.g., Mu ¶[0155]).
Response to Arguments
Applicant's arguments filed on June 17, 2026 have been fully considered but they are not persuasive.
Applicant argues first that the prior art references relied upon in support of the rejections in the previous Office Action of claims 1-5, in particular, Gan, does not disclose or teach the newly added limitations of claims 1, which requires as amended, both (i) obtaining the performance-complexity tradeoff parameter value; and (ii) obtaining at least one of the frequency-domain granularity or the time-domain granularity (Amendment at Pp. 12-13); 2). This argument is now moot in light of the new grounds of rejections presented in this Office Action.
Applicant next argues, with respect to rejections in the previous Office Action of claims 16-23 and 30, the prior art references relied upon in support of the rejections, Nammi in particular, does not disclose or teach the limitation recited in claim 16 as amended, i.e., "generat[ing] an indication of a performance-complexity tradeoff parameter value associated with a quantity of iterations and channel orthogonality for an algorithm for a lattice reduction of a first matrix for a downlink communication, and at least one of a frequency domain granularity for the lattice reduction or a time domain granularity for the lattice reduction".
This argument appears to be in two folds. That is, 1) Nammi, because it is “silent regarding lattice reduction,” (or about lattice reduction in general), cannot be relied upon for the disclosure or teaching of the limitation recited in claim 16, namely, “an indication of a performance-complexity tradeoff parameter value associated with a quantity of iterations and channel orthogonality for an algorithm for a lattice reduction of a first matrix for a downlink communication” (Amendment, at Pp. 13-14); and 2) claim 16 as amended now requires, both (i) obtaining the performance-complexity tradeoff parameter value; and (ii) obtaining at least one of the frequency-domain granularity or the time-domain granularity (Amendment, at Pp. 14-15). This prong 2) of the argument is now moot in light of the new grounds of rejection presented in this Office Action.
As for the prong 1) above, the Examiner respectfully disagrees. What the claim requires to be actually generated and transmitted is an “indication,” despite the long label thereof. The plain meaning (See, MPEP §2111.01 (I)) of the word “indication” as provided in an on-line dictionary, See below4, i.e., something that serves to indicate, does not require the entity sending the indication has a knowledge or an intention to so indicate, so long as it “serves to indicate,” e.g., to the receiving entity as a result of receiving the “something.”
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Indeed, as was already indicated in footnote 2, for example, of the previous Office Action, the above plain meaning reading is entirely consistent with Applicant’s own disclosure. See, Applicant’s specification (“Spec,” the pre-grant published application (PGPUB)) at ¶¶[0095] and [01137]. Applicant’s disclosure provides that “[t]he UE 620 may obtain a performance-complexity tradeoff parameter value (e.g., convergence parameter delta in an LLL-algorithm)” (Spec at ¶[0094]); “the network entity 610 may generate an indication of such parameters based at least in part on CSI and/or measurements of a reference signal (e.g., SRS)…..”The UE 620 may prepare to use the parameters for multi-layer PDSCH detection/demapping or multi-layer PUSCH precoding” (Id. at ¶[0095]); and “the indication indicates one or more of a BLER, a size, an MCS, or channel profile information.” (Id. at ¶[0137]). There is nothing in Applicant’s disclosure that is inconsistent with, exclude or disavows the above plain meaning.
Accordingly, under the broadest reasonable interpretation (BRI) principle, the acts of generating/transmitting of, e.g., an MCS, by a network entity, e.g., the network node of Mammi, to a UE meets the claim requirement that the network entity generates and transmits “an indication of a performance-complexity tradeoff parameter value associated with a quantity of iterations and channel orthogonality for an algorithm for a lattice reduction of a first matrix for a downlink communication” so long as the UE receiving such MCS information is able to use the information as something serving to indicate some aspect of the performance-complexity tradeoff parameter value, e.g., the size of the lattice to be subjected to the reduction procedure.
Applicant then argues that, with respect to the rejections of claims 1-5, 13, 14 and 29, the prior references relied upon in support of the rejection, namely, Gan and Lee, alone or in combination, do not disclose or suggest "obtain[ing] at least one of a frequency domain granularity for the lattice reduction or a time domain granularity for the lattice reduction" (See, Amendment at p.15). This argument, too, is moot in light of the new grounds of rejection presented in this Office Action.
Finally, Applicant argues, with respect to the remaining dependent claims, that these claims overcomes the prior art of record relying generally on the various dependencies to the independent claims for similar reasons argued for the independent claims. These arguments for the remaining dependent claims are either addressed above or are moot in light of the new grounds of rejections presented in this Office Action.
Conclusion
Applicant's amendment necessitated the new ground(s) of rejection presented in this Office action. Accordingly, THIS ACTION IS MADE FINAL. See MPEP § 706.07(a). Applicant is reminded of the extension of time policy as set forth in 37 CFR 1.136(a).
A shortened statutory period for reply to this final action is set to expire THREE MONTHS from the mailing date of this action. In the event a first reply is filed within TWO MONTHS of the mailing date of this final action and the advisory action is not mailed until after the end of the THREE-MONTH shortened statutory period, then the shortened statutory period will expire on the date the advisory action is mailed, and any extension fee pursuant to 37 CFR 1.136(a) will be calculated from the mailing date of the advisory action. In no event, however, will the statutory period for reply expire later than SIX MONTHS from the date of this final action.
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/K.S.K./Examiner, Art Unit 2418 August 7, 2026
/Moo Jeong/Supervisory Patent Examiner, Art Unit 2418
1 The recited limitation “an indication of the performance-complexity tradeoff parameter value” is construed, in light of Applicant’s disclosure at ¶¶[0095] and [0137], as an indication for a UE for performing a lattice reduction, and to encompass one or more of a BLER, a [constellation] size, an MCS, or channel profile information
.
2 The recited limitation “an indication of one or more of a performance-complexity tradeoff parameter value associated with a quantity of iterations and channel orthogonality for an algorithm for a lattice reduction of a first matrix for a downlink communication” is construed, in light of Applicant’s disclosure at ¶¶[0095] and [0137], as an indication for a UE for performing a lattice reduction, and to encompass one or more of a BLER, a [constellation] size, an MCS, or channel profile information
.
3 The recited limitation “an indication of one or more of a performance-complexity tradeoff parameter value associated with a quantity of iterations and channel orthogonality for an algorithm for a lattice reduction of a first matrix for a downlink communication” is construed, in light of Applicant’s disclosure at ¶¶[0095] and [0137], as an indication for a UE for performing a lattice reduction, and to encompass one or more of a BLER, a [constellation] size, an MCS, or channel profile information
.
4 “Indication.” Merriam-Webster.com Dictionary, Merriam-Webster, https://www.merriam-webster.com/dictionary/indication. Accessed 5 Aug. 2026.
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