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 .
Summary
This action is in reply to Applicant’s Amendments and Remarks filed on 06/04/2026.
Claims 1-20 are pending.
Response to Arguments
Applicant’s arguments, with respect to claims 1-20, filed on 05/04/2026 have been considered fully but they are moot as they are not applicable to the combinations of prior arts used in this office action.
Claim Rejections - 35 USC § 103
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.
This application currently names joint inventors. In considering patentability of the claims the examiner presumes that the subject matter of the various claims was commonly owned as of the effective filing date of the claimed invention(s) absent any evidence to the contrary. Applicant is advised of the obligation under 37 CFR 1.56 to point out the inventor and effective filing dates of each claim that was not commonly owned as of the effective filing date of the later invention in order for the examiner to consider the applicability of 35 U.S.C. 102(b)(2)(C) for any potential 35 U.S.C. 102(a)(2) prior art against the later invention.
Claims 1-2 and 8-9 are rejected under 35 U.S.C. 103 being unpatentable over Lee et al. (US 20170117956 A1, of record, hereinafter ‘LEE’) in view of Thangarasa et al. (WO 2017061939 A1, of record, hereinafter ‘THANGARASA’) and with further in view of Xu et al. (EP 3097652 B1, herein after ‘XU’).
Regarding claim 1, LEE teaches one or more non-transitory computer-readable media (Fig. 19, Storage Unit 1930 of a base station) storing one or more computer programs for performing Time Division Duplexing (TDD) coverage enhancement in Frequency Division Duplexing (FDD)-TDD Carrier Aggregation (CA), the one or more computer programs configured to cause at least one processor (
Fig. 3A, Fig. 3B, [0049] FIGS. 3A and 3B depict coverage of an FDD cell and a TDD Cell in a wireless communication system according to an embodiment of the present invention. FIG. 3A depicts a case where a macro base station 320 offers both of the FDD cell and the TDD cell, and FIG. 3B depicts a case where the macro base station 320 offers the FDD cell and a small base station 330 offers the TDD cell. Referring to FIGS. 3A and 3B, TDD coverage including an uplink is smaller than the TDD coverage according to an embodiment of the present invention. That is, a system according to various embodiments of the present invention can expand the TDD cell coverage by performing uplink communication over the FDD cell.
Fig. 19, a storage unit 1930, and a control unit 1940, [0113] FIG. 19 depicts a block diagram of a base station apparatus for performing CA in a wireless communication system according to an embodiment of the present invention.
[0114] Referring to FIG. 19, the apparatus includes a wireless communication unit 1910, a backhaul communication unit 1920, a storage unit 1930, and a control unit 1940.
[0117] The storage unit 1930 stores a basic program for the operations of the base station apparatus for the CA execution, an application program, and data such as configuration information. … The storage unit 1930 provides the stored data according to a request of the control unit 1940.
[0118] The control unit 1940 controls general operations of the base station apparatus for the CA execution. For example, the control unit 1940 transmits a signal to the terminal through the wireless communication unit 1910. The control unit controls the apparatus for the CA execution to perform the procedures of FIG. 8, FIG. 9, FIG. 10, FIG. 11, FIG. 12, and FIG. 13.
See also [0121] programs stored in the computer-readable storage medium can be configured for execution by one or more processors….
[0122] Program … stored to …. a non-volatile memory) to:
collect data measurements from one or more Radio Access Network (RAN) nodes (
Fig. 12 step 1220, [0080] In step 1220, the terminal transmits measurement report information to the first cell of the base station.);
perform a CA coverage and balance check using the collected data measurements (
[0015] According to an embodiment of the present invention, a terminal capable of supporting Time Division Duplex (TDD)-Frequency Division Duplex (FDD) can effectively achieve Carrier Aggregation (CA) in a wireless communication system.
[0016] The wireless communication system can support the CA combining FDD carriers and TDD carriers. In particular, a TDD coverage, particularly, a TDD coverage using a high frequency is more restricted by an uplink coverage than an FDD coverage.
[0080] In step 1220, the terminal transmits measurement report information to the first cell of the base station. The terminal transmits information about whether to add the secondary cell, to the base station.);
detect one or more User Equipment (UE) devices using FDD-TDD CA in a target TDD cell that have insufficient coverage as defined by one or more metrics based on the CA coverage and balance check (
[0016] The wireless communication system can support the CA combining FDD carriers and TDD carriers. In particular, a TDD coverage, particularly, a TDD coverage using a high frequency is more restricted by an uplink coverage than an FDD coverage.
Fig. 3A, Fig. 3B, [0049] Referring to FIGS. 3A and 3B, TDD coverage including an uplink is smaller than the TDD coverage according to an embodiment of the present invention. That is, a system according to various embodiments of the present invention can expand the TDD cell coverage by performing uplink communication over the FDD cell.
(Construed that Base Station detects TDD cell that have insufficient coverage as defined by one or more metrics based on the CA coverage and balance check compared to FDD coverage and modifies TDD operation for TDD coverage enhancement)).
LEE does not explicitly disclose determine modifications to Physical Downlink Control Channel (PDCCH) settings for the one or more UE devices based on a PDCCH-related policy, wherein the PDCCH-related policy determines the modifications by processing the collected data measurements of an FDD cell and the target TDD cell to output an updated aggregation level (AL) or power boosting level for the target TDD cell; and
transmit the PDCCH settings modifications to at least one of the one or more RAN nodes to implement the modifications to the PDCCH settings for the one or more UE devices.
In an analogous art, THANGARASA teaches determine modifications to Physical Downlink Control Channel (PDCCH) settings for the one or more UE devices based on a PDCCH-related policy (
Page 3 Lines 4-7:
advanced techniques in the wireless device and/or in the access node for enhancing the coverage. Some non-limiting examples of such advanced techniques are, but not limited to, transmit power boosting, repetition of transmitted signal….
Page 10 Lines 21-25:
A scenario herein comprises at least one network node such as the first network node 12 serving the first cell 11 , say Primary Cell (PCell) aka serving cell etc. The wireless device 10 may also be configured with one or more additional cells on a need basis e.g. second cell 14, e.g. a Secondary Cell (SCell) in a carrier aggregation (CA)…..
Page 36, Lines 17-18, 28-30:
The embodiments are applicable to single carrier as well as to multicarrier or carrier aggregation (CA) operation of the UE …..
the embodiments are applicable to any RAT or multi-RAT systems, where the UE receives and/or transmit signals (e.g. data) e.g. LTE FDD/TDD…
(It is obvious the UE is operating in multi-RAT FDD-TDD carrier aggregation)
Page 21 lines 32 – Page 22 Line 9:
Table 1 and Table 2 show the SNR to BLER mapping for Additive White Gaussian Noise (AWGN) for Q.sub.out in Table 1 and Qin in Table 2…… the same tables show the achievable BLER for different SNR values as function of aggregation level (AL) and number of repetitions. AL indicates what aggregation level is assumed and bundle indicates the number of repetitions that have been assumed. …. No power boosting is used for PDCCH and M-PDCCH.
Page 25 Lines 11-24:
TDD operation includes different TDD-configurations …. measurements over multiple consecutive subframes are essential to achieve good measurement accuracy in coverage enhancement ….. for TDD the mapping tables may be different compared to those used for FDD. Furthermore one set of mapping tables may be associated with one particular TDD configuration.
Page 26 Lines 8, 17-20:
The first network node 12 can also modify the aggregation level for M-PDCCH….
The transmit power of M-PDCCH may certainly affect the SNR of M-PDCCH. In cases with bad coverage, e.g. cell-edge, basements, it may be necessary to boost the transmit power of M-PDCCH to reach the wireless device 10. The power boosting level and the reception level used may affect the SNR-BLER of M-PDCCH.
Fig. 4, Steps 402-404,
Page 29 Lines 3-28:
Action 402. ..the first network node 12 may assume the wireless device 10 is in the enhanced coverage mode and ….. the first network node 12 may determine number of repetitions based on estimated quality at the first network node 12.
(Base Station receives measurement of one or more cells/RAN nodes, and determine modifications to Physical Downlink Control Channel (PDCCH) settings for the one or more UE devices, modification of M-PDCCH based on mapping Table for repetition/aggregation)
Action 403. The first network node 12 then transmits the DL control channel the determined number of repetitions within the cell 11.
Action 404. The wireless device 10 estimates a DL link quality of the DL control channel.); and
transmit the PDCCH settings modifications to at least one of the one or more RAN nodes to implement the modifications to the PDCCH settings for the one or more UE devices (
Fig. 4, Steps 403-404,
Page 29 Lines 6-28:
Action 403. The first network node 12 then transmits the DL control channel the determined number of repetitions within the cell 11.
Action 404. The wireless device 10 estimates a DL link quality of the DL control channel.).
Therefore, it would have been obvious to one of ordinary skill in the art, before the effective filing date of the claimed invention, to take the technique of M-PDCCH aggregation level and/or M-PDCCH power boosting modification of THANGARASA to the method of FDD-TDD carrier aggregation with TDD cell expansion of LEE in order to take the advantage of a technique for providing for TDD cell coverage enhancement in multi-RAT FDD-TDD carrier aggregation operation for a wireless device or UE (THANGARASA: Pages 25-26, 36).
LEE and THANGASRASA does not teach wherein the PDCCH-related policy determines the modifications by processing the collected data measurements of an FDD cell and the target TDD cell to output an updated aggregation level (AL) or power boosting level for the target TDD cell.
In an analogous art, XU teaches wherein the PDCCH-related policy determines the modifications by processing the collected data measurements of an FDD cell and the target TDD cell to output an updated aggregation level (AL) or power boosting level for the target TDD cell (
[0007] Coverage enhancement techniques may include repetition of transmissions within subframes, repetition across different subframes, power boosting …...
[0034] FIG. 3 shows a system 300 configured for effective deployment of coverage enhancement techniques, in accordance with various embodiments. The system 300 can include one or more eNBs 105 using one or more component carriers 325 (CC.sub.1-CC.sub.N) to communicate with UEs 115.
[0036] the system 300 may support TDD-FDD joint operation, including CA.
[0037] The various CCs 325 may each be transmitted according to various coverage enhancement techniques.
[0043] Differences in coverage enhancement needs may be determined by a network entity (e.g., a base station controller of base station 105-a or 105-b) based on the differences between CCs. Or, in some cases, UE 115-a may send reports, based on measurements references signals (e.g. reference signal received power (RSRP), reference signal received quality (RSRQ), etc.), for various CCs, which a base station 105 may use to determine coverage enhancement needs.
[0059] In some cases, to enhance coverage, channels may be repeated (e.g., bundled) over a number of subframes according to various repetition levels-e.g., low, medium, and/or high repetition levels. …. a low repetition level may include a repetition length of x copies of channel, a medium repetition level may include a repetition length of 2x copies, and a high repetition level may include a repetition length of 4x copies. In some embodiments, subsequent repetition levels use a greater transmission power as compared with a prior level.
[0060] Such a coverage enhancement technique may be applied to unicast channels 425-b (e.g., EPDCCH and PDSCH).
(It is obvious that when system operating in FDD-TDD CA mode, based the collected data measurements from UE reported RSRP/RSRQ for CCs of FDD-TDD CA, base station may determine, according to PDCCH policy, to enhance coverage applied to EPDCCH transmission, where the technique for enhance coverage include various repetition levels of transmissions or updated aggregation level (AL) and/or power boosting)).
Therefore, it would have been obvious to one of ordinary skill in the art, before the effective filing date of the claimed invention, to take the technique of using different level of repetitions and/or increased power for PDCCH transmission for different FDD and TDD Component Carriers (CCs) while operating in joint FDD-TDD Carrier Aggregation mode of XU to the method of FDD-TDD carrier aggregation with TDD cell expansion of LEE and THANGARASA in order to take the advantage of a technique for providing coverage enhancement to improve coverage for CCs in wireless systems (XU: [0003, 0059-0060]).
Regarding claim 2, LEE, in view of THANGARASA and XU, teaches the one or more non-transitory computer-readable media of claim 1.
LEE does not explicitly disclose wherein the detecting that the one or more UE devices have insufficient coverage as defined by the one or more metrics comprises determining that a respective Synchronization Signal (SS) Reference Signal Received Power (SS-RSRP) is below a predetermined value, determining that a respective SS Signal to Interference-plus-Noise Ratio (SS-SINR) is below a predetermined value, or both.
THANGARASA teaches wherein the detecting that the one or more UE devices have insufficient coverage as defined by the one or more metrics comprises determining that a respective Synchronization Signal (SS) Reference Signal Received Power (SS-RSRP) is below a predetermined value, determining that a respective SS Signal to Interference-plus-Noise Ratio (SS-SINR) is below a predetermined value, or both (
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The radio measurements are done for various purposes. Some example measurement purposes are: mobility, positioning, … and optimization etc. Examples of measurements in LTE are Cell identification aka Physical Cell ID (PCI) acquisition, Reference Symbol Received Power (RSRP), ….., Radio Link Monitoring (RLM), which RLM comprises: Out of Synchronization (out of sync) detection and In Synchronization (in-sync) detection etc. The detection of out of sync (OoS) and in-sync is based on the wireless device estimating the channel quality of the serving cell.
Page 4 Lines 16-34:
A DL subframe # 0 and DL subframe # 5 carry synchronization signals, i.e. both Primary Synchronization Signal (PSS) and Secondary Synchronization Signal (SSS). In order to identify an unknown cell, e.g. new neighbor cell, the wireless device has to acquire a timing of that cell and eventually the PCI… Subsequently the wireless device also measures RSRP and/or RSRQ of the newly identified cell in order to use the radio measurement itself and/or in order to report the radio measurement to the network node.
In an existing RLM procedure the wireless device performs radio measurements on downlink reference symbols to estimate downlink radio link quality e.g. Signal to Interference plus Noise Ratio (SINR). This radio measurement is then used by the wireless device to determine the hypothetical BLER of a DL control channel, e.g. the PDCCH … by using a pre-defined mapping between the estimated quality, e.g. SINR levels, and the BLER of the PDCCH taking into account PCFICH errors. There are two thresholds associated with RLM procedure, namely Q.sub.out and Qin. These two thresholds refer to a certain target BLER: Q.sub.out corresponds to 10 % target BLER of hypothetical PDCCH and Q.sub.in to 2% target BLER of hypothetical PDCCH…
See also Page 21 lines 32 – Page 22 Line 9:
Table 1 and Table 2 show the SNR to BLER mapping for Additive White Gaussian Noise (AWGN) for Q.sub.out in Table 1 and Qin in Table 2…… the same tables show the achievable BLER for different SNR values as function of aggregation level (AL) and number of repetitions.
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These tables can be stored in the memory of the wireless device 10 for determining Oin and Oout levels when operating at different coverage level, e.g. SNR or SINR. The tables can also be predefined in the specification to allow the first network node 12 to select an appropriate combination of transmission parameters, e.g. AL, bundle (repetitions) for transmitting M-PDCCH for enabling the wireless device 10 for performing RLM, e.g. determining Oin and Oout. These tables show that the BLER may be improved with higher aggregation level and repetition levels. It is shown that it is quite bad with low AL and no repetition, but then when repetition is applied the BLER goes down.
Page 26 Lines 8, 17-20:
The first network node 12 can also modify the aggregation level for M-PDCCH….
The transmit power of M-PDCCH may certainly affect the SNR of M-PDCCH. In cases with bad coverage, e.g. cell-edge, basements, it may be necessary to boost the transmit power of M-PDCCH to reach the wireless device 10. The power boosting level and the reception level used may affect the SNR-BLER of M-PDCCH.
Page 29 Lines 3-28:
Action 402. ..the first network node 12 may assume the wireless device 10 is in the enhanced coverage mode and ….. the first network node 12 may determine number of repetitions based on estimated quality at the first network node 12.
(It is obvious that UE reported synchronization signals RSRP and/or SINR measurement indicating less threshold values casing base station to modify PDCCH aggregation level or power boosting)).
Therefore, it would have been obvious to one of ordinary skill in the art, before the effective filing date of the claimed invention, to take the technique of M-PDCCH aggregation level and/or M-PDCCH power boosting modification of THANGARASA to the method of FDD-TDD carrier aggregation with TDD cell expansion of LEE and XU in order to take the advantage of a technique for providing for TDD cell coverage enhancement in multi-RAT FDD-TDD carrier aggregation operation for a wireless device or UE (THANGARASA: Pages 25-26, 36).
Regarding claim 8, LEE, in view of THANGARASA and XU, teaches the one or more non-transitory computer-readable media of claim 1, wherein the collected data measurements comprise CA status (
[0016] The wireless communication system can support the CA combining FDD carriers and TDD carriers. In particular, a TDD coverage, particularly, a TDD coverage using a high frequency is more restricted by an uplink coverage than an FDD coverage.
[0080] In step 1220, the terminal transmits measurement report information to the first cell of the base station. The terminal transmits information about whether to add the secondary cell, to the base station.).
LEE does not explicitly disclose wherein the collected data measurements comprise SS-RSRP, SS-SINR, aggregation level, boosting level, frequency domain resources, monitoring slot periodicity offset, precoder granularity, multi-TPR repetition support, cross-carrier scheduling support, or any combination thereof.
THANGARASA teaches wherein the collected data measurements comprise SS-RSRP, SS-SINR, aggregation level, boosting level, frequency domain resources, monitoring slot periodicity offset, precoder granularity, multi-TPR repetition support, cross-carrier scheduling support, or any combination thereof (
Page 4 Lines 16-34:
A DL subframe # 0 and DL subframe # 5 carry synchronization signals, i.e. both Primary Synchronization Signal (PSS) and Secondary Synchronization Signal (SSS). In order to identify an unknown cell, e.g. new neighbor cell, the wireless device has to acquire a timing of that cell and eventually the PCI… Subsequently the wireless device also measures RSRP and/or RSRQ of the newly identified cell in order to use the radio measurement itself and/or in order to report the radio measurement to the network node.
In an existing RLM procedure the wireless device performs radio measurements on downlink reference symbols to estimate downlink radio link quality e.g. Signal to Interference plus Noise Ratio (SINR). This radio measurement is then used by the wireless device to determine the hypothetical BLER of a DL control channel, e.g. the PDCCH … by using a pre-defined mapping between the estimated quality, e.g. SINR levels, and the BLER of the PDCCH taking into account PCFICH errors. There are two thresholds associated with RLM procedure, namely Q.sub.out and Qin. These two thresholds refer to a certain target BLER: Q.sub.out corresponds to 10 % target BLER of hypothetical PDCCH and Q.sub.in to 2% target BLER of hypothetical PDCCH…
See also Page 21 lines 32 – Page 22 Line 9:
Table 1 and Table 2 show the SNR to BLER mapping for Additive White Gaussian Noise (AWGN) for Q.sub.out in Table 1 and Qin in Table 2…… the same tables show the achievable BLER for different SNR values as function of aggregation level (AL) and number of repetitions.
Page 22 Lines 10-18:
These tables can be stored in the memory of the wireless device 10 for determining Oin and Oout levels when operating at different coverage level, e.g. SNR or SINR. The tables can also be predefined in the specification to allow the first network node 12 to select an appropriate combination of transmission parameters, e.g. AL, bundle (repetitions) for transmitting M-PDCCH for enabling the wireless device 10 for performing RLM, e.g. determining Oin and Oout. These tables show that the BLER may be improved with higher aggregation level and repetition levels. It is shown that it is quite bad with low AL and no repetition, but then when repetition is applied the BLER goes down.
Page 26 Lines 8, 17-20:
The first network node 12 can also modify the aggregation level for M-PDCCH….
The transmit power of M-PDCCH may certainly affect the SNR of M-PDCCH. In cases with bad coverage, e.g. cell-edge, basements, it may be necessary to boost the transmit power of M-PDCCH to reach the wireless device 10. The power boosting level and the reception level used may affect the SNR-BLER of M-PDCCH.).
Therefore, it would have been obvious to one of ordinary skill in the art, before the effective filing date of the claimed invention, to take the technique of M-PDCCH aggregation level and/or M-PDCCH power boosting modification of THANGARASA to the method of FDD-TDD carrier aggregation with TDD cell expansion of LEE and XU in order to take the advantage of a technique for providing for TDD cell coverage enhancement in multi-RAT FDD-TDD carrier aggregation operation for a wireless device or UE (THANGARASA: Pages 25-26, 36).
Regarding claim 9, LEE, in view of THANGARASA and XU, teaches one or more non-transitory computer-readable media of claim 1.
LEE does not explicitly disclose wherein the PDCCH settings modifications comprise updated PDCCH configuration parameters, adjacent-cell coordination parameters, PDCCH related feature triggers, or any combination thereof.
THANGARASA teaches wherein the PDCCH settings modifications comprise updated PDCCH configuration parameters, adjacent-cell coordination parameters, PDCCH related feature triggers, or any combination thereof (
Page 26 Lines 8, 17-20:
The first network node 12 can also modify the aggregation level for M-PDCCH….
The transmit power of M-PDCCH may certainly affect the SNR of M-PDCCH. In cases with bad coverage, e.g. cell-edge, basements, it may be necessary to boost the transmit power of M-PDCCH to reach the wireless device 10. The power boosting level and the reception level used may affect the SNR-BLER of M-PDCCH.).
Therefore, it would have been obvious to one of ordinary skill in the art, before the effective filing date of the claimed invention, to take the technique of M-PDCCH aggregation level and/or M-PDCCH power boosting modification of THANGARASA to the method of FDD-TDD carrier aggregation with TDD cell expansion of LEE and XU in order to take the advantage of a technique for providing for TDD cell coverage enhancement in multi-RAT FDD-TDD carrier aggregation operation for a wireless device or UE (THANGARASA: Pages 25-26, 36).
Claim 3-5, 14-15 and 18 are rejected under 35 U.S.C. 103 being unpatentable over Lee et al. (US 20170117956 A1, of record, hereinafter ‘LEE’) in view of Thangarasa et al. (WO 2017061939 A1, of record, hereinafter ‘THANGARASA’) in view of Xu et al. (EP 3097652 B1, herein after ‘XU’) and with further in view of Sokun et al. (WO 2024052717 A1, of record, hereinafter ‘SOKUN’).
Regarding claim 3, LEE, in view of THANGARASA and XU, teaches one or more non-transitory computer-readable media of claim 1.
LEE, THANGARASA and XU do not explicitly disclose wherein the determining of the modifications to the PDCCH settings for the one or more UE devices based on the PDCCH-related policy comprises:
selecting one or more Artificial Intelligence (AI) / Machine Learning (ML) model inferences that match the PDCCH-related policy; and
using the one or more selected AI/ML model inferences for the determining of the modifications to the PDCCH settings.
In an analogous art, SOKUN teaches wherein the determining of the modifications to the PDCCH settings for the one or more UE devices based on the PDCCH-related policy comprises:
selecting one or more Artificial Intelligence (AI) / Machine Learning (ML) model inferences that match the PDCCH-related policy, and using the one or more selected AI/ML model inferences for the determining of the modifications to the PDCCH settings (
Page 1 Lines 24-29:
efficient use of PDCCH resources, i.e., available bandwidth and power, has a direct impact on the overall network performance.
The basic structure for PDCCH is a control channel element (CCE). The number of CCEs for a PDCCH is referred to as the aggregation level (AL). A network node may transmit PDCCH on 1, 2, 4, 8, or 16 CCE ALs. Using higher CCE ALs may increase the PDCCH coverage by using a lower coding rate.
Page 2 Lines 1-8:
Similar to CCE AL allocation, allocation of power over PDCCH CCEs has a critical impact on PDCCH capacity and PDCCH coverage. Using a higher amount of transmit power per CCE may increase PDCCH coverage by improving channel estimation accuracy. ….. attaining the best PDCCH performance requires optimizing CCE-AL assignment and power allocation over CCEs jointly to maximize not only PDCCH capacity but also PDCCH coverage.
Page 2 Lines 20 – Page 3 Line 25:
certain challenges currently exist with physical downlink control channel (PDCCH) resource allocation …..particular embodiments overcome the implementation-related challenges given above using a two-step approach. In the first step, particular embodiments employ an optimization framework. This framework maximizes the number of accommodated scheduling entities (SEs) per slot and also to minimizes the total number of control channel element (CCE) consumption, while meeting several practical constraints such as total amount of power available, total amount of CCEs available and power boosting threshold.
In the second step, a machine learning technique is trained on the training set and learns the complex mapping between the inputs and outputs. Afterwards, the trained machine learning algorithm may be used for predicting CCE assignments and power allocation for PDCCH per slot.
…. a method is performed by a network node for PDCCH resource allocation (e.g., offline learning model). ….. The method further comprises generating a machine learning training set for online CCE and power allocation based on the determined number of CCEs and power allocation for the CCEs for each of the SEs of the plurality of SEs.
In particular embodiments, determining the number of CCEs and power allocation for the CCEs for each of the SEs of the plurality of SEs is based on a plurality of signal quality, priority, and DCI size combinations associated with each of the SEs.).
Therefore, it would have been obvious to one of ordinary skill in the art, before the effective filing date of the claimed invention, to take the technique of using trained machine learning algorithm may be used for predicting CCE assignments and power allocation for PDCCH per slot of SOKUN to the method of FDD-TDD carrier aggregation with TDD cell expansion of LEE, THANGARASA and XU in order to take the advantage of a technique for providing efficient use of PDCCH resources, i.e., available bandwidth and power for overall network performance while increasing PDCCH coverage (SOKUN: Pages 1-2).
Regarding claim 4, LEE, in view of THANGARASA, XU and SOKUN, teaches one or more non-transitory computer-readable media of claim 3.
LEE, THANGARASA and XU do not explicitly disclose send the collected data measurements to a network core for retraining a respective AI/ML model associated with a respective AI/ML model inference of the one or more AI/ML model inferences, receive an updated AI/ML model from the network core, and use the updated AI/ML model for the respective AI/ML model inference.
SOKUN teaches send the collected data measurements to a network core for retraining a respective AI/ML model associated with a respective AI/ML model inference of the one or more AI/ML model inferences, receive an updated AI/ML model from the network core, and use the updated AI/ML model for the respective AI/ML model inference (
Page 3 Lines 9-16:
For poor PDCCH performance, the training set may be updated offline. During this time period, particular embodiments may resort to baseline algorithms for CCE assignment and power allocation.
According to some embodiments, a method is performed by a network node for PDCCH resource allocation (e.g., offline learning model). The method comprises obtaining a data set representing a plurality of SEs). Each of the SEs is associated with at least one of a signal quality, a priority, and a DCI size…….
Page 4 Lines 9-13
…. The machine learning training set is determined offline based on a number of CCEs and power allocation for the CCEs for each model SE of a plurality of model SEs based on at least one of a signal quality, a priority, and a DCI size associated with each of the model SEs and at least one of a model total power available, a model power boosting threshold, and a model total number of CCEs available.
Page 5 Lines 3-5
…. the method further comprises determining a performance of the machine learning training set is degraded and obtaining an updated machine learning training set.).
Therefore, it would have been obvious to one of ordinary skill in the art, before the effective filing date of the claimed invention, to take the technique of using trained machine learning algorithm may be used for predicting CCE assignments and power allocation for PDCCH per slot of SOKUN to the method of FDD-TDD carrier aggregation with TDD cell expansion of LEE, THANGARASA and XU in order to take the advantage of a technique for providing efficient use of PDCCH resources, i.e., available bandwidth and power for overall network performance while increasing PDCCH coverage (SOKUN: Pages 1-2).
Regarding claim 5, LEE, in view of THANGARASA, XU and SOKUN, teaches one or more non-transitory computer-readable media of claim 3.
LEE, THANGARASA and XU do not explicitly disclose wherein the one or more AI/ML model inferences comprise a PDCCH Aggregation Level (AL) change, a PDCCH power boost, PDCCH beamforming and precoding changes, inter-cell PDCCH coordination, cross-carrier scheduling, multi-Transmission Reception Point (TRP) PDCCH transmission, or any combination thereof.
SOKUN teaches wherein the one or more AI/ML model inferences comprise a PDCCH Aggregation Level (AL) change, a PDCCH power boost, PDCCH beamforming and precoding changes, inter-cell PDCCH coordination, cross-carrier scheduling, multi-Transmission Reception Point (TRP) PDCCH transmission, or any combination thereof (
Page 2 Lines 1-8:
Similar to CCE AL allocation, allocation of power over PDCCH CCEs has a critical impact on PDCCH capacity and PDCCH coverage. Using a higher amount of transmit power per CCE may increase PDCCH coverage by improving channel estimation accuracy. ….. attaining the best PDCCH performance requires optimizing CCE-AL assignment and power allocation over CCEs jointly to maximize not only PDCCH capacity but also PDCCH coverage.
Page 3 Lines 17-25:
…. a method is performed by a network node for PDCCH resource allocation (e.g., offline learning model). ….. The method further comprises generating a machine learning training set for online CCE and power allocation based on the determined number of CCEs and power allocation for the CCEs for each of the SEs of the plurality of SEs….. ).
Therefore, it would have been obvious to one of ordinary skill in the art, before the effective filing date of the claimed invention, to take the technique of using trained machine learning algorithm may be used for predicting CCE assignments and power allocation for PDCCH per slot of SOKUN to the method of FDD-TDD carrier aggregation with TDD cell expansion of LEE, THANGARASA and XU in order to take the advantage of a technique for providing efficient use of PDCCH resources, i.e., available bandwidth and power for overall network performance while increasing PDCCH coverage (SOKUN: Pages 1-2).
Regarding claim 14, LEE teaches one or more computing systems, comprising:
memory storing computer program instructions (Fig. 19, Storage Unit 1930 of a base station) for performing Time Division Duplexing (TDD) coverage enhancement in Frequency Division Duplexing (FDD)-TDD Carrier Aggregation (CA), and at least one processor configured to execute the computer program instructions (
Fig. 3A, Fig. 3B, [0049] FIGS. 3A and 3B depict coverage of an FDD cell and a TDD Cell in a wireless communication system according to an embodiment of the present invention. FIG. 3A depicts a case where a macro base station 320 offers both of the FDD cell and the TDD cell, and FIG. 3B depicts a case where the macro base station 320 offers the FDD cell and a small base station 330 offers the TDD cell. Referring to FIGS. 3A and 3B, TDD coverage including an uplink is smaller than the TDD coverage according to an embodiment of the present invention. That is, a system according to various embodiments of the present invention can expand the TDD cell coverage by performing uplink communication over the FDD cell.
Fig. 19, a storage unit 1930, and a control unit 1940, [0113] FIG. 19 depicts a block diagram of a base station apparatus for performing CA in a wireless communication system according to an embodiment of the present invention.
[0114] Referring to FIG. 19, the apparatus includes a wireless communication unit 1910, a backhaul communication unit 1920, a storage unit 1930, and a control unit 1940.
[0117] The storage unit 1930 stores a basic program for the operations of the base station apparatus for the CA execution, an application program, and data such as configuration information. … The storage unit 1930 provides the stored data according to a request of the control unit 1940.
[0118] The control unit 1940 controls general operations of the base station apparatus for the CA execution. For example, the control unit 1940 transmits a signal to the terminal through the wireless communication unit 1910. The control unit controls the apparatus for the CA execution to perform the procedures of FIG. 8, FIG. 9, FIG. 10, FIG. 11, FIG. 12, and FIG. 13.
See also [0121] programs stored in the computer-readable storage medium can be configured for execution by one or more processors….
[0122] Program … stored to …. a non-volatile memory), wherein the computer instructions are configured to cause the at least one processor to:
collect data measurements from one or more Radio Access Network (RAN) nodes (
Fig. 12 step 1220, [0080] In step 1220, the terminal transmits measurement report information to the first cell of the base station),
detect one or more User Equipment (UE) devices using FDD-TDD CA in a target TDD cell that have insufficient coverage as defined by one or more metrics (
[0015] According to an embodiment of the present invention, a terminal capable of supporting Time Division Duplex (TDD)-Frequency Division Duplex (FDD) can effectively achieve Carrier Aggregation (CA) in a wireless communication system.
[0016] The wireless communication system can support the CA combining FDD carriers and TDD carriers. In particular, a TDD coverage, particularly, a TDD coverage using a high frequency is more restricted by an uplink coverage than an FDD coverage.
Fig. 3A, Fig. 3B, [0049] Referring to FIGS. 3A and 3B, TDD coverage including an uplink is smaller than the TDD coverage according to an embodiment of the present invention. That is, a system according to various embodiments of the present invention can expand the TDD cell coverage by performing uplink communication over the FDD cell.
(Construed that Base Station detects TDD cell that have insufficient coverage as defined by one or more metrics based on the CA coverage and balance check compared to FDD coverage and modifies TDD operation for TDD coverage enhancement)),
determine modifications to Physical Downlink Control Channel (PDCCH) settings for the one or more UE devices based on a PDCCH-related policy and one or more Artificial Intelligence (AI) / Machine Learning (ML) model inferences, and
transmit the PDCCH settings modifications to at least one of the one or more RAN nodes to implement the modifications to the PDCCH settings for the one or more UE devices to improve coverage for the one or more UE devices in the target TDD cell.
LEE does not explicitly disclose determine modifications to Physical Downlink Control Channel (PDCCH) settings for the one or more UE devices based on a PDCCH-related policy and one or more Artificial Intelligence (AI) / Machine Learning (ML) model inferences, wherein the PDCCH related policy determines the modifications by processing the collected data measurements of an FDD cell and the target TDD cell to output an updated aggregation level (AL) or power boosting level for the target TDD cell, and
transmit the PDCCH settings modifications to at least one of the one or more RAN nodes to implement the modifications to the PDCCH settings for the one or more UE devices to improve coverage for the one or more UE devices in the target TDD cell.
In an analogous art, THANGARASA teaches determine modifications to Physical Downlink Control Channel (PDCCH) settings for the one or more UE devices based on a PDCCH-related policy (
Page 3 Lines 4-7:
advanced techniques in the wireless device and/or in the access node for enhancing the coverage. Some non-limiting examples of such advanced techniques are, but not limited to, transmit power boosting, repetition of transmitted signal….
Page 10 Lines 21-25:
A scenario herein comprises at least one network node such as the first network node 12 serving the first cell 11 , say Primary Cell (PCell) aka serving cell etc. The wireless device 10 may also be configured with one or more additional cells on a need basis e.g. second cell 14, e.g. a Secondary Cell (SCell) in a carrier aggregation (CA)…..
Page 36, Lines 17-18, 28-30:
The embodiments are applicable to single carrier as well as to multicarrier or carrier aggregation (CA) operation of the UE …..
the embodiments are applicable to any RAT or multi-RAT systems, where the UE receives and/or transmit signals (e.g. data) e.g. LTE FDD/TDD…
(It is obvious the UE is operating in multi-RAT FDD-TDD carrier aggregation)
Page 21 lines 32 – Page 22 Line 9:
Table 1 and Table 2 show the SNR to BLER mapping for Additive White Gaussian Noise (AWGN) for Q.sub.out in Table 1 and Qin in Table 2…… the same tables show the achievable BLER for different SNR values as function of aggregation level (AL) and number of repetitions. AL indicates what aggregation level is assumed and bundle indicates the number of repetitions that have been assumed. …. No power boosting is used for PDCCH and M-PDCCH.
Page 25 Lines 11-24:
TDD operation includes different TDD-configurations …. measurements over multiple consecutive subframes are essential to achieve good measurement accuracy in coverage enhancement ….. for TDD the mapping tables may be different compared to those used for FDD. Furthermore one set of mapping tables may be associated with one particular TDD configuration.
Page 26 Lines 8, 17-20:
The first network node 12 can also modify the aggregation level for M-PDCCH….
The transmit power of M-PDCCH may certainly affect the SNR of M-PDCCH. In cases with bad coverage, e.g. cell-edge, basements, it may be necessary to boost the transmit power of M-PDCCH to reach the wireless device 10. The power boosting level and the reception level used may affect the SNR-BLER of M-PDCCH.
Fig. 4, Steps 402-404,
Page 29 Lines 3-28:
Action 402. ..the first network node 12 may assume the wireless device 10 is in the enhanced coverage mode and ….. the first network node 12 may determine number of repetitions based on estimated quality at the first network node 12.
(Base Station receives measurement of one or more cells/RAN nodes, and determine modifications to Physical Downlink Control Channel (PDCCH) settings for the one or more UE devices, modification of M-PDCCH based on mapping Table for repetition/aggregation)
Action 403. The first network node 12 then transmits the DL control channel the determined number of repetitions within the cell 11.
Action 404. The wireless device 10 estimates a DL link quality of the DL control channel.); and
transmit the PDCCH settings modifications to at least one of the one or more RAN nodes to implement the modifications to the PDCCH settings for the one or more UE devices to improve coverage for the one or more UE devices in the target TDD cell (
Fig. 4, Steps 403-404,
Page 29 Lines 6-28:
Action 403. The first network node 12 then transmits the DL control channel the determined number of repetitions within the cell 11.
Action 404. The wireless device 10 estimates a DL link quality of the DL control channel.).
Therefore, it would have been obvious to one of ordinary skill in the art, before the effective filing date of the claimed invention, to take the technique of M-PDCCH aggregation level and/or M-PDCCH power boosting modification of THANGARASA to the method of FDD-TDD carrier aggregation with TDD cell expansion of LEE in order to take the advantage of a technique for providing for TDD cell coverage enhancement in multi-RAT FDD-TDD carrier aggregation operation for a wireless device or UE (THANGARASA: Pages 25-26, 36).
LEE and THANGARASA do not explicitly disclose determine modifications to Physical Downlink Control Channel (PDCCH) settings for the one or more UE devices based on a PDCCH-related policy and one or more Artificial Intelligence (AI) / Machine Learning (ML) model inferences, wherein the PDCCH related policy determines the modifications by processing the collected data measurements of an FDD cell and the target TDD cell to output an updated aggregation level (AL) or power boosting level for the target TDD cell.
In an analogous art, XU teaches wherein the PDCCH-related policy determines the modifications by processing the collected data measurements of an FDD cell and the target TDD cell to output an updated aggregation level (AL) or power boosting level for the target TDD cell (
[0007] Coverage enhancement techniques may include repetition of transmissions within subframes, repetition across different subframes, power boosting …...
[0034] FIG. 3 shows a system 300 configured for effective deployment of coverage enhancement techniques, in accordance with various embodiments. The system 300 can include one or more eNBs 105 using one or more component carriers 325 (CC.sub.1-CC.sub.N) to communicate with UEs 115.
[0036] the system 300 may support TDD-FDD joint operation, including CA.
[0037] The various CCs 325 may each be transmitted according to various coverage enhancement techniques.
[0043] Differences in coverage enhancement needs may be determined by a network entity (e.g., a base station controller of base station 105-a or 105-b) based on the differences between CCs. Or, in some cases, UE 115-a may send reports, based on measurements references signals (e.g. reference signal received power (RSRP), reference signal received quality (RSRQ), etc.), for various CCs, which a base station 105 may use to determine coverage enhancement needs.
[0059] In some cases, to enhance coverage, channels may be repeated (e.g., bundled) over a number of subframes according to various repetition levels-e.g., low, medium, and/or high repetition levels. …. a low repetition level may include a repetition length of x copies of channel, a medium repetition level may include a repetition length of 2x copies, and a high repetition level may include a repetition length of 4x copies. In some embodiments, subsequent repetition levels use a greater transmission power as compared with a prior level.
[0060] Such a coverage enhancement technique may be applied to unicast channels 425-b (e.g., EPDCCH and PDSCH).
(It is obvious that when system operating in FDD-TDD CA mode, based the collected data measurements from UE reported RSRP/RSRQ for CCs of FDD-TDD CA, base station may determine, according to PDCCH policy, to enhance coverage applied to EPDCCH transmission, where the technique for enhance coverage include various repetition levels of transmissions or updated aggregation level (AL) and/or power boosting)).
Therefore, it would have been obvious to one of ordinary skill in the art, before the effective filing date of the claimed invention, to take the technique of using different level of repetitions and/or increased power for PDCCH transmission for different FDD and TDD Component Carriers (CCs) while operating in joint FDD-TDD Carrier Aggregation mode of XU to the method of FDD-TDD carrier aggregation with TDD cell expansion of LEE and THANGARASA in order to take the advantage of a technique for providing coverage enhancement to improve coverage for CCs in wireless systems (XU: [0003, 0059-0060]).
LEE, THANGARASA and XU do not explicitly disclose determine modifications to Physical Downlink Control Channel (PDCCH) settings for the one or more UE devices based on a PDCCH-related policy and one or more Artificial Intelligence (AI) / Machine Learning (ML) model inferences
In an analogous art, SOKUN teaches determine modifications to Physical Downlink Control Channel (PDCCH) settings for the one or more UE devices based on a PDCCH-related policy and one or more Artificial Intelligence (AI) / Machine Learning (ML) model inferences (
Page 1 Lines 24-29:
efficient use of PDCCH resources, i.e., available bandwidth and power, has a direct impact on the overall network performance.
The basic structure for PDCCH is a control channel element (CCE). The number of CCEs for a PDCCH is referred to as the aggregation level (AL). A network node may transmit PDCCH on 1, 2, 4, 8, or 16 CCE ALs. Using higher CCE ALs may increase the PDCCH coverage by using a lower coding rate.
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Similar to CCE AL allocation, allocation of power over PDCCH CCEs has a critical impact on PDCCH capacity and PDCCH coverage. Using a higher amount of transmit power per CCE may increase PDCCH coverage by improving channel estimation accuracy. ….. attaining the best PDCCH performance requires optimizing CCE-AL assignment and power allocation over CCEs jointly to maximize not only PDCCH capacity but also PDCCH coverage.
Page 2 Lines 20 – Page 3 Line 25:
certain challenges currently exist with physical downlink control channel (PDCCH) resource allocation …..particular embodiments overcome the implementation-related challenges given above using a two-step approach. In the first step, particular embodiments employ an optimization framework. This framework maximizes the number of accommodated scheduling entities (SEs) per slot and also to minimizes the total number of control channel element (CCE) consumption, while meeting several practical constraints such as total amount of power available, total amount of CCEs available and power boosting threshold.
In the second step, a machine learning technique is trained on the training set and learns the complex mapping between the inputs and outputs. Afterwards, the trained machine learning algorithm may be used for predicting CCE assignments and power allocation for PDCCH per slot.
…. a method is performed by a network node for PDCCH resource allocation (e.g., offline learning model). ….. The method further comprises generating a machine learning training set for online CCE and power allocation based on the determined number of CCEs and power allocation for the CCEs for each of the SEs of the plurality of SEs.
In particular embodiments, determining the number of CCEs and power allocation for the CCEs for each of the SEs of the plurality of SEs is based on a plurality of signal quality, priority, and DCI size combinations associated with each of the SEs.).
Therefore, it would have been obvious to one of ordinary skill in the art, before the effective filing date of the claimed invention, to take the technique of using trained machine learning algorithm may be used for predicting CCE assignments and power allocation for PDCCH per slot of SOKUN to the method of FDD-TDD carrier aggregation with TDD cell expansion of LEE, THANGARASA and XU in order to take the advantage of a technique for providing efficient use of PDCCH resources, i.e., available bandwidth and power for overall network performance while increasing PDCCH coverage (SOKUN: Pages 1-2).
Regarding claim 15, the claim is interpreted and rejected for the same reason as set forth for claim 5.
Regarding claim 18, the claim is interpreted mutatis mutandis of claims 14-15 and rejected for the same reason as set forth for claims 14-15.
Allowable Subject Matter
Claims 6-7, 10-13, 16-17 and 19-20 are objected to as being dependent upon a rejected base claim, but would be allowable if rewritten in independent form including all of the limitations of the base claim and in intervening claims.
The following is a statement of reasons for the indication of allowable subject matter:
Regarding claim 6, LEE, THANGARASA, XU, SOKUN, or any prior art of record either alone or in combination fails to teach the one or more non-transitory computer-readable media of claim 5, wherein the inputs to the one or more AI/ML model inferences comprise a CA status and UE measurements for cross-carrier scheduling, PDCCH beamforming and wideband precoding, and/or multi-TRP repetition for PDCCH, a current AL and boosting level for PDCCH AL management and PDCCH power boosting, frequency domain resources and monitoring slot periodicity and offset of neighboring cells for inter-cell PDCCH coordination, capabilities for cross-carrier scheduling, multi-TRP capabilities, or any combination thereof.
Regarding claim 7, LEE, THANGARASA, XU, SOKUN, or any prior art of record either alone or in combination fails to teach the one or more non-transitory computer-readable media of claim 5, wherein the outputs from the one or more AI/ML model inferences comprise an updated AL and/or boosting level for PDCCH AL management and PDCCH power boosting, updated time-frequency parameters for inter-cell PDCCH coordination, cross-carrier scheduling on/off triggering, precoder granularity for PDCCH beamforming and wideband precoding, multi-TRP transmission or repetition on/off triggering, or any combination thereof.
Regarding claim 10, LEE, THANGARASA, XU, SOKUN, or any prior art of record either alone or in combination fails to teach the one or more non-transitory computer-readable media of claim 1, wherein the one or more computer programs are further configured to cause the at least one processor to:
construct a PDCCH allocation map between the target TDD cell and one or more adjacent cells;
exchange PDCCH configuration information between the target TDD cell and the one or more adjacent cells; and
use the exchanged PDCCH configuration information to coordinate the PDCCH settings modifications between the target TDD cell and the one or more adjacent cells.
Regarding claim 11, LEE, THANGARASA, XU, SOKUN, or any prior art of record either alone or in combination fails to teach one or more non-transitory computer-readable media of claim 1, wherein the performing the CA coverage and balance check comprises determining whether an expected TDD-to-FDD coverage ratio a link budget and a TDD-to-FDD Primary Cell (PCell) UE ratio from the collected data measurements are similar within a predetermined metric.
Regarding claim 12, the claim being dependent on claim 11 is interpreted same as claim 11.
Regarding claim 13, LEE, THANGARASA, XU, SOKUN, or any prior art of record either alone or in combination fails to teach the one or more non-transitory computer-readable media of claim 1, wherein
the one or more computer programs are respective xApps running on a Near-Real Time Ran Intelligent Controller (NT RIC) in the RAN, and
the NT RIC is configured to control the one or more RAN nodes to implement the PDCCH settings modifications.
Regarding claim 16, the claim with similar features as in claim 6 is interpreted same as claim 6.
Regarding claim 17, the claim with similar features as in claim 11 is interpreted same as claim 11.
Regarding claim 19, the claim with similar features as in claim 6 is interpreted same as claim 6.
Regarding claim 20, the claim with similar features as in claim 10 is interpreted same as claim 10.
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure:
Gross et al. (US 20160218850 A1), describing METHOD AND APPARATUS FOR MANAGEMENT OF FREQUENCY-DIVISION-DUPLEX AND TIME-DIVISION-DUPLEX CARRIER AGGREGATION
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 nonprovisional extension fee (37 CFR 1.17(a)) 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 mailing date of this final action.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to SHAH M RAHMAN whose telephone number is (571)272-8951. The examiner can normally be reached 9:30AM-5:30PM PST.
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/SHAH M RAHMAN/Primary Examiner, Art Unit 2413
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