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 .
Information Disclosure Statement
Acknowledgment is made of the information disclosure statement filed on April 9, 2024, and November 5, 2025. U.S. patent applications, foreign patents, and non-patent literature documents have been considered.
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.
Claims 1-6, 8-10, 13-15, 18-19, 21-22, 24, 26, and 27-30 are rejected under 35 U.S.C. § 103 as being unpatentable over Luo et. al. (WO 2020259832 A1), herein referred to as “Luo”, in view of Chava et. al. (U.S. Pat. Pub. 2021/0351885), herein referred to as “Chavva”. The Luo reference was provided in the information disclosure statement dated November 6, 2025.
Regarding Claim 1,
Luo discloses: An apparatus for wireless communication at a user equipment (UE), comprising: a memory; and at least one processor coupled to the memory and configured to: receive, from a base station, at least one channel status information (CSI) report setting associated with a CSI resource setting that configures a set of CSI reference signal (CSI-RS) resources
(Page 20, lines 18-25)
FIG. 10 shows a method 1000 for supporting beam power variation measurements according to an embodiment of the present invention. In particular, the method 1000 is performed by a user equipment. The method comprises: a step 1001 of receiving information indicating a first set of configuration parameters for performing a power variation pattern measurement per each beam of a determined set of beams; a step 1002 of performing a measurement for each beam of the determined set of beams; a step 1003 of generating a first measurements report based on the measurements; and a step 1004 of sending the first measurements report to a network device.
(Page 21, lines 4-21)
2. Configuration of SSB‘s, CSI-RS‘s, and DMRS for multi-stage measurements, comprising:
Combined usage of SSB and CSI-RS for RPVP measurement as the 1.sup.st stage. One option is the training mode with super-dense SSB;
Configuration of CSI-RS for RPVP measurement as 2.sup.nd stage, comprising: configuration of a training mode with super-dense CSI-RS, and a normal mode; and configuration of beam multiplexing and beam resource sets.
Configuration of Demodulation Reference Signal (DMRS) for RPVP measurement as the 3.sup.rd stage, comprising configuration of beam multiplexing pattern of DMRS incl. sampling period for measuring each beam and the number of observation durations that contain different beam sets.
and transmit, to the base station based on the at least one CSI report setting, the quantity change rate or a reliability information message based on the quantity change rate associated with the one or more predicted quantities over the at least one future time window.
(Page 20, lines 18-25)
FIG. 10 shows a method 1000 for supporting beam power variation measurements according to an embodiment of the present invention. In particular, the method 1000 is performed by a user equipment. The method comprises: a step 1001 of receiving information indicating a first set of configuration parameters for performing a power variation pattern measurement per each beam of a determined set of beams; a step 1002 of performing a measurement for each beam of the determined set of beams; a step 1003 of generating a first measurements report based on the measurements; and a step 1004 of sending the first measurements report to a network device.
(Page 20, line 29-Page 21 line 2)
1. Configuration of UE measurements to capture the RPVP per beam, comprising:
Signalling of network device to require UE to perform of RPVP measurement per beam;
Configuration of observation duration and occurrence repetition number of blockage/deep fade to identify periodic behaviour; Definition of quality metric (e.g. non-blocked/fade percentage) of each beam as down selection criteria for beam selection.
Luo does not disclose: the at least one CSI report setting including at least one quantity change rate associated with one or more predicted quantities over at least one future time window or one or more measured quantities over at least one past time window, the one or more predicted quantities or the one or more measured quantities being associated with the set of CSI-RS resources.
However, Chavva discloses: the at least one CSI report setting including at least one quantity change rate associated with one or more predicted quantities over at least one future time window or one or more measured quantities over at least one past time window, the one or more predicted quantities or the one or more measured quantities being associated with the set of CSI-RS resources.
[0195] At step 1802, the method includes obtaining training data. The training data can include PDSCH transmission statistics for each CSI report. When online training is triggered, in addition to performing CQI prediction for reporting, the UE 601 can collect PDSCH transmission statistics. The PDSCH transmission statistics includes block error rate for PDSCH reception for each CSI report. In an example, the block error rate can be determined based on Cyclic Redundancy Check (CRC) pass and CRC fail. Thus, the block error rate can contribute to the training data collection of the UE 601. The training data further includes and previous predicted values of CQI the parameters stored in the database 602c, optimal MCS evaluated by the UE 601, MCS used by the gNB 607 for encoding PDSCH, feedback delay and reporting periodicity.
[0198] At step 1804, the method includes utilizing the updated values of the weights for predicting, at a current time instant, the probable values of CQI at a future time instance. The predicted values of CQI can be sent to the gNB 607 in a CSI report. The gNB 607 can utilize the predicted values of CQI to schedule PDSCH. At step 1805, the method includes determining the block error rate pertaining to PDSCH reception. The UE 601 can receive the PDSCH and determine the block error rate. The block error rate is used for further refining of the weights of the neural network 602c.
Note: The predicted quantity is the CQI, which is part of the CSI report and a predicted value that occurs at a future time instance (“future time window”). The quantity rate then, can be interpreted as stored and previous predicted values of CQI in addition to the probable values of CQI at a future time distance.
Luo and Chavva are considered to be analogous because they pertain to wireless communications networks. It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify Luo to include the concept of a quantity change rate associated with predicted quantities over at least one future time window dropping Doppler information associated with priorities as taught by Chavva so as to improve communication within the wireless network.
Regarding Claim 2,
Luo discloses: The apparatus of claim 1, wherein the one or more predicted quantities or the one or more measured quantities include one or more of: a layer 1 (L1) reference signal received quality (RSRQ) quantity, a signal-to-interference plus noise (SINR) quantity, a channel quality indicator (CQI) quantity, or a rank indicator (RI) quantity, and wherein each of the L1 RSRQ quantity, the SINR quantity, the CQI quantity, or the RI quantity includes a drop rate or an improve rate.
(Page 16, lines 20-28)
Optionally, the network device 100 may further configured to indicate the UE 101 to send the first measurements report, wherein the first measurements report includes information about at least one of: a starting/ending time of each beam blockage/deep fade interval for each beam, signal-to-noise ratio, SNR, value variations in intervals with SNR above a threshold, indices of complementary beams, and a periodic beam selection pattern.
Regarding Claim 3,
Luo discloses: The apparatus of claim 1, wherein the at least one quantity change rate is expressed based on curve fitting to at least one polynomial function or one or more reference signal received quality (RSRQ) values associated with one or more time instances within the at least one future time window or the at least one past time window.
(Page 16, lines 20-28)
Optionally, the network device 100 may further configured to indicate the UE 101 to send the first measurements report, wherein the first measurements report includes information about at least one of: a starting/ending time of each beam blockage/deep fade interval for each beam, signal-to-noise ratio, SNR, value variations in intervals with SNR above a threshold, indices of complementary beams, and a periodic beam selection pattern.
(Page 17, line 29-Page 18 line 7)
It should be noted that there are different options to select the subset of beams, and a combination of those options is also possible. For instance, the network device may inform the UE about a percentage of time portion where the SNR should be above a threshold, so that only beams fulfilling this percentage will be reported. Certainly, it is also possible that the UE identifies no periodic power variation in the determined set of beams. Therefore, the network device 100 may also be configured to receive a report from the UE indicating that no periodic power variation in the determined set of beams is identified. Consequently, any traditional beam measurement method can be used. In such case, the network device 110 may provide additional information to the UE 110, indicating the UE 110 to measure a RSRP or RSRQ of a series of resources that belong to each beam; and obtain a third measurements report from the UE 110.
Regarding Claim 4,
Luo discloses: The apparatus of claim 1, wherein the at least one quantity change rate is associated with Doppler or velocity information associated with a blockage estimated based on the set of CSI-RS resources.
(page 20, line 29-page 21 line 2)
1. Configuration of UE measurements to capture the RPVP per beam, comprising:
Signalling of network device to require UE to perform of RPVP measurement per beam;
Configuration of observation duration and occurrence repetition number of blockage/deep fade to identify periodic behaviour; Definition of quality metric (e.g. non-blocked/fade percentage) of each beam as down selection criteria for beam selection.
Note: Doppler effect/velocity here is being interpreted as the blocking and fading percentage, since a “block”, in a general sense, will affect the Doppler effect of the signal.
Regarding Claim 5,
Luo discloses: The apparatus of claim 1, wherein the reliability information message is further based on one or more of: a percentage value, a variance associated with the at least one quantity change rate, or one or more confidence levels or one or more confidence intervals associated with the at least one quantity change rate.
(page 20, line 29-page 21 line 2)
1. Configuration of UE measurements to capture the RPVP per beam, comprising:
Signalling of network device to require UE to perform of RPVP measurement per beam;
Configuration of observation duration and occurrence repetition number of blockage/deep fade to identify periodic behaviour; Definition of quality metric (e.g. non-blocked/fade percentage) of each beam as down selection criteria for beam selection.
Regarding Claim 6,
Luo discloses: The apparatus of claim 1, wherein at least one length, at least one starting point, or at least one ending point associated with the at least one future time window is configured by the base station or defined without the base station.
(Page 15, lines 10-14)
Additionally, the BS may further configure optional parameters:
- Different Thresholds for blockage and deep fade,
- Start of observation: Indicate the first CSI-RS,
- Duration of observation/training,
- Number of event re-occurrence to determine periodic behavior.
Regarding Claim 8,
Luo discloses: The apparatus of claim 1, wherein the set of CSI-RS resources are associated with a same serving cell, and wherein the at least one processor is further configured to: transmit, to the base station, a recommendation associated with the set of CSI-RS resources, wherein the set of CSI-RS resources is configured based on the recommendation.
(Page 20, lines 18-25)
FIG. 10 shows a method 1000 for supporting beam power variation measurements according to an embodiment of the present invention. In particular, the method 1000 is performed by a user equipment. The method comprises: a step 1001 of receiving information indicating a first set of configuration parameters for performing a power variation pattern measurement per each beam of a determined set of beams; a step 1002 of performing a measurement for each beam of the determined set of beams; a step 1003 of generating a first measurements report based on the measurements; and a step 1004 of sending the first measurements report to a network device.
(Page 20, line 29-Page 21 line 2)
1. Configuration of UE measurements to capture the RPVP per beam, comprising:
Signalling of network device to require UE to perform of RPVP measurement per beam;
Configuration of observation duration and occurrence repetition number of blockage/deep fade to identify periodic behaviour; Definition of quality metric (e.g. non-blocked/fade percentage) of each beam as down selection criteria for beam selection.
(Page 21, lines 4-21)
2. Configuration of SSB‘s, CSI-RS‘s, and DMRS for multi-stage measurements, comprising:
Combined usage of SSB and CSI-RS for RPVP measurement as the 1.sup.st stage. One option is the training mode with super-dense SSB;
Configuration of CSI-RS for RPVP measurement as 2.sup.nd stage, comprising: configuration of a training mode with super-dense CSI-RS, and a normal mode; and configuration of beam multiplexing and beam resource sets.
Configuration of Demodulation Reference Signal (DMRS) for RPVP measurement as the 3.sup.rd stage, comprising configuration of beam multiplexing pattern of DMRS incl. sampling period for measuring each beam and the number of observation durations that contain different beam sets.
Regarding Claim 9,
Luo discloses: The apparatus of claim 8, wherein the recommendation or the CSI resource setting is periodically, semi-persistently, or dynamically transmitted or configured.
(Page 15, lines 10-14)
Additionally, the BS may further configure optional parameters:
- Different Thresholds for blockage and deep fade,
- Start of observation: Indicate the first CSI-RS,
- Duration of observation/training,
- Number of event re-occurrence to determine periodic behavior.
Regarding Claim 10,
Luo discloses: The apparatus of claim 8, wherein the recommendation or the CSI resource setting further indicates a timing relationship associated with a stop or resume associated with a monitoring of the set of CSI-RS resources.
(Page 15, lines 10-14)
Additionally, the BS may further configure optional parameters:
- Different Thresholds for blockage and deep fade,
- Start of observation: Indicate the first CSI-RS,
- Duration of observation/training,
- Number of event re-occurrence to determine periodic behavior.
Regarding Claim 13,
Luo discloses: The apparatus of claim 8, wherein the at least one processor is further configured to: receive, from the base station, one or more RSs not associated with the one or more predicted quantities and associated with the at least one CSI report setting, the one or more RSs being CSI-RS resources or cell-specific demodulation reference signals (DM-RSs), the one or more RSs being associated with a cell associated with the one or more predicted quantities or a different cell.
(Page 20, lines 18-25)
FIG. 10 shows a method 1000 for supporting beam power variation measurements according to an embodiment of the present invention. In particular, the method 1000 is performed by a user equipment. The method comprises: a step 1001 of receiving information indicating a first set of configuration parameters for performing a power variation pattern measurement per each beam of a determined set of beams; a step 1002 of performing a measurement for each beam of the determined set of beams; a step 1003 of generating a first measurements report based on the measurements; and a step 1004 of sending the first measurements report to a network device.
(Page 20, line 29-Page 21 line 2)
1. Configuration of UE measurements to capture the RPVP per beam, comprising:
Signalling of network device to require UE to perform of RPVP measurement per beam;
Configuration of observation duration and occurrence repetition number of blockage/deep fade to identify periodic behaviour; Definition of quality metric (e.g. non-blocked/fade percentage) of each beam as down selection criteria for beam selection.
(Page 21, lines 4-21)
2. Configuration of SSB‘s, CSI-RS‘s, and DMRS for multi-stage measurements, comprising:
Combined usage of SSB and CSI-RS for RPVP measurement as the 1.sup.st stage. One option is the training mode with super-dense SSB;
Configuration of CSI-RS for RPVP measurement as 2.sup.nd stage, comprising: configuration of a training mode with super-dense CSI-RS, and a normal mode; and configuration of beam multiplexing and beam resource sets.
Configuration of Demodulation Reference Signal (DMRS) for RPVP measurement as the 3.sup.rd stage, comprising configuration of beam multiplexing pattern of DMRS incl. sampling period for measuring each beam and the number of observation durations that contain different beam sets.
Regarding Claim 14,
Luo discloses: The apparatus of claim 1, wherein the one or more predicted quantities are associated with a periodicity of at least one frame, wherein the one or more predicted quantities or the one or more measured quantities include one or more of: a beam blockage prediction, a layer 1 (L1) reference signal received quality (RSRQ) quantity, a signal-to-interference plus noise (SINR) quantity, a channel quality indicator (CQI) quantity, or a rank indicator (RI) quantity, one or more lengths associated with the one or more time windows, one or more curve-fitting parameters, one or more variance values, one or more confidence levels or confidence intervals, and wherein the one or more predicted quantities are associated with one time window of the at least one time window or associated with an entirety of the at least one time window.
Primary (Page 16, lines 20-28)
Optionally, the network device 100 may further configured to indicate the UE 101 to send the first measurements report, wherein the first measurements report includes information about at least one of: a starting/ending time of each beam blockage/deep fade interval for each beam, signal-to-noise ratio, SNR, value variations in intervals with SNR above a threshold, indices of complementary beams, and a periodic beam selection pattern.
Regarding Claim 15,
Luo discloses: The apparatus of claim 1, wherein the one or more predicted quantities associated with multiple CSI-RS resources of the set of CSI-RS resources or multiple time windows of the at least one time window is associated with one CSI report setting of the at least one CSI report setting or multiple CSI report settings of the at least one CSI report setting.
(Page 20, lines 18-25)
FIG. 10 shows a method 1000 for supporting beam power variation measurements according to an embodiment of the present invention. In particular, the method 1000 is performed by a user equipment. The method comprises: a step 1001 of receiving information indicating a first set of configuration parameters for performing a power variation pattern measurement per each beam of a determined set of beams; a step 1002 of performing a measurement for each beam of the determined set of beams; a step 1003 of generating a first measurements report based on the measurements; and a step 1004 of sending the first measurements report to a network device.
(Page 21, lines 4-21)
2. Configuration of SSB‘s, CSI-RS‘s, and DMRS for multi-stage measurements, comprising:
Combined usage of SSB and CSI-RS for RPVP measurement as the 1.sup.st stage. One option is the training mode with super-dense SSB;
Configuration of CSI-RS for RPVP measurement as 2.sup.nd stage, comprising: configuration of a training mode with super-dense CSI-RS, and a normal mode; and configuration of beam multiplexing and beam resource sets.
Configuration of Demodulation Reference Signal (DMRS) for RPVP measurement as the 3.sup.rd stage, comprising configuration of beam multiplexing pattern of DMRS incl. sampling period for measuring each beam and the number of observation durations that contain different beam sets.
Regarding Claim 18,
Luo discloses: The apparatus of claim 1, wherein the one or more predicted quantities include a beam blockage prediction associated with one or more candidate beams associated with one or more candidate RSs for the at least one future time window.
(Page 20, line 29-Page 21 line 2)
1. Configuration of UE measurements to capture the RPVP per beam, comprising:
Signalling of network device to require UE to perform of RPVP measurement per beam;
Configuration of observation duration and occurrence repetition number of blockage/deep fade to identify periodic behaviour; Definition of quality metric (e.g. non-blocked/fade percentage) of each beam as down selection criteria for beam selection.
Regarding Claim 19,
Luo discloses: The apparatus of claim 18, wherein the one or more candidate beams associated with the one or more candidate RSs are configured by the CSI resource setting, and wherein the one or more RSs correspond with or do not correspond with the set of CSI-RS resources.
(Page 20, line 29-Page 21 line 2)
1. Configuration of UE measurements to capture the RPVP per beam, comprising:
Signalling of network device to require UE to perform of RPVP measurement per beam;
Configuration of observation duration and occurrence repetition number of blockage/deep fade to identify periodic behaviour; Definition of quality metric (e.g. non-blocked/fade percentage) of each beam as down selection criteria for beam selection.
Regarding Claim 21,
Luo discloses: The apparatus of claim 18, wherein the one or more candidate beams are reported in a CSI report carrying the one or more predicted quantities.
(Page 20, line 29-Page 21 line 2)
1. Configuration of UE measurements to capture the RPVP per beam, comprising:
Signalling of network device to require UE to perform of RPVP measurement per beam;
Configuration of observation duration and occurrence repetition number of blockage/deep fade to identify periodic behaviour; Definition of quality metric (e.g. non-blocked/fade percentage) of each beam as down selection criteria for beam selection.
Regarding Claim 22,
Luo discloses: The apparatus of claim 1, wherein a number associated with at least one CSI processing unit (CPU) associated with a beam blockage prediction associated with the one or more predicted quantities is based on a number associated with the set of CSI-RS resources.
(Page 15, lines 10-14)
Additionally, the BS may further configure optional parameters:
- Different Thresholds for blockage and deep fade,
- Start of observation: Indicate the first CSI-RS,
- Duration of observation/training,
- Number of event re-occurrence to determine periodic behavior.
Regarding Claim 24,
Luo discloses: The apparatus of claim 1, wherein a number associated with at least one CSI processing unit (CPU) associated with a beam blockage prediction associated with the one or more predicted quantities is based on a difference between a starting point of the at least one future time window and a present time.
(Page 15, lines 10-14)
Additionally, the BS may further configure optional parameters:
- Different Thresholds for blockage and deep fade,
- Start of observation: Indicate the first CSI-RS,
- Duration of observation/training,
- Number of event re-occurrence to determine periodic behavior.
Regarding Claim 26,
Luo discloses: The apparatus of claim 1, wherein a number associated with at least one CSI processing unit (CPU) associated with a beam blockage prediction associated with the one or more predicted quantities is based on a number of RSs associated with the beam blockage prediction and not associated with the one or more predicted quantities.
(Page 15, lines 10-14)
Additionally, the BS may further configure optional parameters:
- Different Thresholds for blockage and deep fade,
- Start of observation: Indicate the first CSI-RS,
- Duration of observation/training,
- Number of event re-occurrence to determine periodic behavior.
Regarding Claim 27,
Luo discloses: The apparatus of claim 1, further comprising a transceiver coupled to the at least one processor.
A processor is inherent in a wireless communication system.
Regarding Claim 28,
Claim 28 is rejected on the same grounds of rejection set forth in Claim 1.
Luo discloses: An apparatus for wireless communication at a base station, comprising: a memory; and at least one processor coupled to the memory and configured to: transmit, to a user equipment (UE), a channel status information (CSI) report setting associated with a CSI resource setting that configures a set of CSI reference signal (CSI-RS) resources
(Page 20, lines 18-25)
FIG. 10 shows a method 1000 for supporting beam power variation measurements according to an embodiment of the present invention. In particular, the method 1000 is performed by a user equipment. The method comprises: a step 1001 of receiving information indicating a first set of configuration parameters for performing a power variation pattern measurement per each beam of a determined set of beams; a step 1002 of performing a measurement for each beam of the determined set of beams; a step 1003 of generating a first measurements report based on the measurements; and a step 1004 of sending the first measurements report to a network device.
(Page 21, lines 4-21)
2. Configuration of SSB‘s, CSI-RS‘s, and DMRS for multi-stage measurements, comprising:
Combined usage of SSB and CSI-RS for RPVP measurement as the 1.sup.st stage. One option is the training mode with super-dense SSB;
Configuration of CSI-RS for RPVP measurement as 2.sup.nd stage, comprising: configuration of a training mode with super-dense CSI-RS, and a normal mode; and configuration of beam multiplexing and beam resource sets.
Configuration of Demodulation Reference Signal (DMRS) for RPVP measurement as the 3.sup.rd stage, comprising configuration of beam multiplexing pattern of DMRS incl. sampling period for measuring each beam and the number of observation durations that contain different beam sets.
and receive, from the UE based on the at least one CSI report setting, the predicted quantities or a reliability information message based on the one or more predicted quantities over the at least one future time window.
(Page 20, lines 18-25)
FIG. 10 shows a method 1000 for supporting beam power variation measurements according to an embodiment of the present invention. In particular, the method 1000 is performed by a user equipment. The method comprises: a step 1001 of receiving information indicating a first set of configuration parameters for performing a power variation pattern measurement per each beam of a determined set of beams; a step 1002 of performing a measurement for each beam of the determined set of beams; a step 1003 of generating a first measurements report based on the measurements; and a step 1004 of sending the first measurements report to a network device.
(Page 20, line 29-Page 21 line 2)
1. Configuration of UE measurements to capture the RPVP per beam, comprising:
Signalling of network device to require UE to perform of RPVP measurement per beam;
Configuration of observation duration and occurrence repetition number of blockage/deep fade to identify periodic behaviour; Definition of quality metric (e.g. non-blocked/fade percentage) of each beam as down selection criteria for beam selection.
Luo does not disclose: the at least one CSI report setting including at least one quantity change rate associated with one or more predicted quantities over at least one future time window or one or more measured quantities over at least one past time window, the one or more predicted quantities or the one or more measured quantities being associated with the set of CSI-RS resources.
However, Chavva discloses: the at least one CSI report setting including at least one quantity change rate associated with one or more predicted quantities over at least one future time window or one or more measured quantities over at least one past time window, the one or more predicted quantities or the one or more measured quantities being associated with the set of CSI-RS resources.
[0195] At step 1802, the method includes obtaining training data. The training data can include PDSCH transmission statistics for each CSI report. When online training is triggered, in addition to performing CQI prediction for reporting, the UE 601 can collect PDSCH transmission statistics. The PDSCH transmission statistics includes block error rate for PDSCH reception for each CSI report. In an example, the block error rate can be determined based on Cyclic Redundancy Check (CRC) pass and CRC fail. Thus, the block error rate can contribute to the training data collection of the UE 601. The training data further includes and previous predicted values of CQI the parameters stored in the database 602c, optimal MCS evaluated by the UE 601, MCS used by the gNB 607 for encoding PDSCH, feedback delay and reporting periodicity.
[0198] At step 1804, the method includes utilizing the updated values of the weights for predicting, at a current time instant, the probable values of CQI at a future time instance. The predicted values of CQI can be sent to the gNB 607 in a CSI report. The gNB 607 can utilize the predicted values of CQI to schedule PDSCH. At step 1805, the method includes determining the block error rate pertaining to PDSCH reception. The UE 601 can receive the PDSCH and determine the block error rate. The block error rate is used for further refining of the weights of the neural network 602c.
Note: The predicted quantity is the CQI, which is part of the CSI report and a predicted value that occurs at a future time instance (“future time window”). The quantity rate then, can be interpreted as stored and previous predicted values of CQI in addition to the probable values of CQI at a future time distance.
Luo and Chavva are considered to be analogous because they pertain to wireless communications networks. It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify Luo to include the concept of a quantity change rate associated with predicted quantities over at least one future time window dropping Doppler information associated with priorities as taught by Chavva so as to improve communication within the wireless network.
Regarding Claim 29,
Claim 29 is rejected on the same grounds of rejection set forth in Claim 1.
Luo discloses: A method for wireless communication at a user equipment (UE), comprising: receiving, from a base station, at least one channel status information (CSI) report setting associated with a CSI resource setting that configures a set of CSI reference signal (CSI-RS) resources
(Page 20, lines 18-25)
FIG. 10 shows a method 1000 for supporting beam power variation measurements according to an embodiment of the present invention. In particular, the method 1000 is performed by a user equipment. The method comprises: a step 1001 of receiving information indicating a first set of configuration parameters for performing a power variation pattern measurement per each beam of a determined set of beams; a step 1002 of performing a measurement for each beam of the determined set of beams; a step 1003 of generating a first measurements report based on the measurements; and a step 1004 of sending the first measurements report to a network device.
(Page 21, lines 4-21)
2. Configuration of SSB‘s, CSI-RS‘s, and DMRS for multi-stage measurements, comprising:
Combined usage of SSB and CSI-RS for RPVP measurement as the 1.sup.st stage. One option is the training mode with super-dense SSB;
Configuration of CSI-RS for RPVP measurement as 2.sup.nd stage, comprising: configuration of a training mode with super-dense CSI-RS, and a normal mode; and configuration of beam multiplexing and beam resource sets.
Configuration of Demodulation Reference Signal (DMRS) for RPVP measurement as the 3.sup.rd stage, comprising configuration of beam multiplexing pattern of DMRS incl. sampling period for measuring each beam and the number of observation durations that contain different beam sets.
and transmitting, to the base station based on the at least one CSI report setting, the quantity change rate or a reliability information message based on the one or more predicted quantities over the at least one future time window.
(Page 20, lines 18-25)
FIG. 10 shows a method 1000 for supporting beam power variation measurements according to an embodiment of the present invention. In particular, the method 1000 is performed by a user equipment. The method comprises: a step 1001 of receiving information indicating a first set of configuration parameters for performing a power variation pattern measurement per each beam of a determined set of beams; a step 1002 of performing a measurement for each beam of the determined set of beams; a step 1003 of generating a first measurements report based on the measurements; and a step 1004 of sending the first measurements report to a network device.
(Page 20, line 29-Page 21 line 2)
1. Configuration of UE measurements to capture the RPVP per beam, comprising:
Signalling of network device to require UE to perform of RPVP measurement per beam;
Configuration of observation duration and occurrence repetition number of blockage/deep fade to identify periodic behaviour; Definition of quality metric (e.g. non-blocked/fade percentage) of each beam as down selection criteria for beam selection.
Luo does not disclose: the at least one CSI report setting including at least one quantity change rate associated with one or more predicted quantities over at least one future time window or one or more measured quantities over at least one past time window, the one or more predicted quantities or the one or more measured quantities being associated with the set of CSI-RS resources.
However, Chavva discloses: the at least one CSI report setting including at least one quantity change rate associated with one or more predicted quantities over at least one future time window or one or more measured quantities over at least one past time window, the one or more predicted quantities or the one or more measured quantities being associated with the set of CSI-RS resources.
[0195] At step 1802, the method includes obtaining training data. The training data can include PDSCH transmission statistics for each CSI report. When online training is triggered, in addition to performing CQI prediction for reporting, the UE 601 can collect PDSCH transmission statistics. The PDSCH transmission statistics includes block error rate for PDSCH reception for each CSI report. In an example, the block error rate can be determined based on Cyclic Redundancy Check (CRC) pass and CRC fail. Thus, the block error rate can contribute to the training data collection of the UE 601. The training data further includes and previous predicted values of CQI the parameters stored in the database 602c, optimal MCS evaluated by the UE 601, MCS used by the gNB 607 for encoding PDSCH, feedback delay and reporting periodicity.
[0198] At step 1804, the method includes utilizing the updated values of the weights for predicting, at a current time instant, the probable values of CQI at a future time instance. The predicted values of CQI can be sent to the gNB 607 in a CSI report. The gNB 607 can utilize the predicted values of CQI to schedule PDSCH. At step 1805, the method includes determining the block error rate pertaining to PDSCH reception. The UE 601 can receive the PDSCH and determine the block error rate. The block error rate is used for further refining of the weights of the neural network 602c.
Note: The predicted quantity is the CQI, which is part of the CSI report and a predicted value that occurs at a future time instance (“future time window”). The quantity rate then, can be interpreted as stored and previous predicted values of CQI in addition to the probable values of CQI at a future time distance.
Luo and Chavva are considered to be analogous because they pertain to wireless communications networks. It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify Luo to include the concept of a quantity change rate associated with predicted quantities over at least one future time window dropping Doppler information associated with priorities as taught by Chavva so as to improve communication within the wireless network.
Regarding Claim 30,
Claim 29 is rejected on the same grounds of rejection set forth in Claim 1.
Luo discloses: A method for wireless communication at a base station, comprising:
transmitting, to a user equipment (UE), at least one channel status information (CSI) report setting associated with a CSI resource setting that configures a set of CSI reference signal (CSI-RS) resources
(Page 20, lines 18-25)
FIG. 10 shows a method 1000 for supporting beam power variation measurements according to an embodiment of the present invention. In particular, the method 1000 is performed by a user equipment. The method comprises: a step 1001 of receiving information indicating a first set of configuration parameters for performing a power variation pattern measurement per each beam of a determined set of beams; a step 1002 of performing a measurement for each beam of the determined set of beams; a step 1003 of generating a first measurements report based on the measurements; and a step 1004 of sending the first measurements report to a network device.
(Page 21, lines 4-21)
2. Configuration of SSB‘s, CSI-RS‘s, and DMRS for multi-stage measurements, comprising:
Combined usage of SSB and CSI-RS for RPVP measurement as the 1.sup.st stage. One option is the training mode with super-dense SSB;
Configuration of CSI-RS for RPVP measurement as 2.sup.nd stage, comprising: configuration of a training mode with super-dense CSI-RS, and a normal mode; and configuration of beam multiplexing and beam resource sets.
Configuration of Demodulation Reference Signal (DMRS) for RPVP measurement as the 3.sup.rd stage, comprising configuration of beam multiplexing pattern of DMRS incl. sampling period for measuring each beam and the number of observation durations that contain different beam sets.
and receiving, from the UE based on the at least one CSI report setting, the predicted quantities or a reliability information message based on the one or more predicted quantities over the at least one future time window.
(Page 20, lines 18-25)
FIG. 10 shows a method 1000 for supporting beam power variation measurements according to an embodiment of the present invention. In particular, the method 1000 is performed by a user equipment. The method comprises: a step 1001 of receiving information indicating a first set of configuration parameters for performing a power variation pattern measurement per each beam of a determined set of beams; a step 1002 of performing a measurement for each beam of the determined set of beams; a step 1003 of generating a first measurements report based on the measurements; and a step 1004 of sending the first measurements report to a network device.
(Page 20, line 29-Page 21 line 2)
1. Configuration of UE measurements to capture the RPVP per beam, comprising:
Signalling of network device to require UE to perform of RPVP measurement per beam;
Configuration of observation duration and occurrence repetition number of blockage/deep fade to identify periodic behaviour; Definition of quality metric (e.g. non-blocked/fade percentage) of each beam as down selection criteria for beam selection.
Luo does not disclose: the at least one CSI report setting including at least one quantity change rate associated with one or more predicted quantities over at least one future time window or one or more measured quantities over at least one past time window, the one or more predicted quantities or the one or more measured quantities being associated with the set of CSI-RS resources.
However, Chavva discloses: the at least one CSI report setting including at least one quantity change rate associated with one or more predicted quantities over at least one future time window or one or more measured quantities over at least one past time window, the one or more predicted quantities or the one or more measured quantities being associated with the set of CSI-RS resources.
[0195] At step 1802, the method includes obtaining training data. The training data can include PDSCH transmission statistics for each CSI report. When online training is triggered, in addition to performing CQI prediction for reporting, the UE 601 can collect PDSCH transmission statistics. The PDSCH transmission statistics includes block error rate for PDSCH reception for each CSI report. In an example, the block error rate can be determined based on Cyclic Redundancy Check (CRC) pass and CRC fail. Thus, the block error rate can contribute to the training data collection of the UE 601. The training data further includes and previous predicted values of CQI the parameters stored in the database 602c, optimal MCS evaluated by the UE 601, MCS used by the gNB 607 for encoding PDSCH, feedback delay and reporting periodicity.
[0198] At step 1804, the method includes utilizing the updated values of the weights for predicting, at a current time instant, the probable values of CQI at a future time instance. The predicted values of CQI can be sent to the gNB 607 in a CSI report. The gNB 607 can utilize the predicted values of CQI to schedule PDSCH. At step 1805, the method includes determining the block error rate pertaining to PDSCH reception. The UE 601 can receive the PDSCH and determine the block error rate. The block error rate is used for further refining of the weights of the neural network 602c.
Note: The predicted quantity is the CQI, which is part of the CSI report and a predicted value that occurs at a future time instance (“future time window”). The quantity rate then, can be interpreted as stored and previous predicted values of CQI in addition to the probable values of CQI at a future time distance.
Luo and Chavva are considered to be analogous because they pertain to wireless communications networks. It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify Luo to include the concept of a quantity change rate associated with predicted quantities over at least one future time window dropping Doppler information associated with priorities as taught by Chavva so as to improve communication within the wireless network.
Claims 7, 23, and 25 are rejected under 35 U.S.C. § 103 as being unpatentable over Luo in view of Yang and Jiang (U.S. Pat. Pub. 2023/0007522), herein referred to as “Yang.”
Regarding Claim 7,
Luo does not disclose all the limitations of Claim 7.
However, Yang discloses: The apparatus of claim 1, wherein the one or more predicted quantities are further based on a layer 1 (L1) filtering method, wherein the filtering method is based on a definition without signaling from the UE or the base station, a configuration by the base station, or a recommendation of the UE.
[0079] To be specific, in this case, after a signal change, there is a signal that remains unchanged. In other words, the first target signal and the second target signal correspond to signals that exist before and after a change. In this case, the terminal configures a configuration parameter and status information of a signal before a signal change to the signal that remains unchanged after the signal change. The configuration parameter may be a layer 1 filter parameter and a configuration of a synchronization signal block-based radio resource management measurement timing configuration SMTC window period; and the status information may be an L1 filter result.
Note: Here, regardless of the signal change (before and after) configured by the UE, the L1 filter parameter will be unaffected.
Luo and Yang are considered to be analogous because they pertain to wireless communications networks. It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify Luo to include the concept of having L1 filtering method is based on a definition without signaling from the UE as taught by Yang so as to improve communication within the wireless network.
Regarding Claim 23,
Claim 23 is rejected on the same grounds of rejection set forth in claim 7.
Regarding Claim 25,
Claim 25 is rejected on the same grounds of rejection set forth in claim 7.
Claims 11 and 12 are rejected under 35 U.S.C. § 103 as being unpatentable over Luo in view of Murata et. al. (U.S. Pat. Pub. 2022/0201570), herein referred to as “Murata.”
Regarding Claim 11,
Luo does not disclose all the limitations of Claim 11.
However, Murata discloses: The apparatus of claim 10, wherein the CSI resource setting further indicates the timing relationship associated with the stop or resume associated with the monitoring of the set of CSI-RS resources, and wherein the at least one processor is further configured to: receive, from the base station, an expire timer associated with resume monitoring or monitoring of a resume command.
[0068] The value of the resume timer (expiration value) may be configured (set) by a network using RRC, MAC CE, or an L1 signal. Further, the expiration value of the resume timer may be a single value, or may be configured (set) in accordance with the prioritized operation or with the source cell/target cell information (RAT, eNB, gNB, frequency, cell, BWP, beam (e.g., SSB, CSI-RS, TCI), PHR, CSI, RSRP (Reference Signal Received Power), RSRQ (Reference Signal Received Quality), and SINR (Signal to Interference plus Noise Ratio)).
Luo and Murata are considered to be analogous because they pertain to wireless communications networks. It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify Luo to include the concept of having an expire timer associated with the resume command as taught by Murata so as to improve communication within the wireless network.
Regarding Claim 12,
Luo does not disclose all the limitations of Claim 12.
However, Murata discloses: The apparatus of claim 11, wherein the at least one processor is further configured to: receive, from the base station, a transmission configuration indicator (TCI) state associated with a resource comprising the resume command.
[0068] The value of the resume timer (expiration value) may be configured (set) by a network using RRC, MAC CE, or an L1 signal. Further, the expiration value of the resume timer may be a single value, or may be configured (set) in accordance with the prioritized operation or with the source cell/target cell information (RAT, eNB, gNB, frequency, cell, BWP, beam (e.g., SSB, CSI-RS, TCI), PHR, CSI, RSRP (Reference Signal Received Power), RSRQ (Reference Signal Received Quality), and SINR (Signal to Interference plus Noise Ratio)).
Luo and Murata are considered to be analogous because they pertain to wireless communications networks. It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify Luo to include the concept of having a TCI states associated with a resume command as taught by Murata so as to improve communication within the wireless network.
Claim 16 is rejected under 35 U.S.C. § 103 as being unpatentable over Luo in view of Nigam et. al. (U.S. Pat. Pub. 2018/0302864), herein referred to as “Nigam.”
Regarding Claim 16,
Luo does not disclose all the limitations of Claim 16.
However, Nigam discloses: The apparatus of claim 1, wherein the one or more predicted quantities or the one or more measured quantities include one or more of: a beam blockage prediction, a layer 1 (L1) reference signal received quality (RSRQ) quantity, a signal-to-interference plus noise (SINR) quantity, a channel quality indicator (CQI) quantity, or a rank indicator (RI) quantity, and wherein each of the beam blockage prediction, the L1 RSRQ quantity, the SINR quantity, the CQI quantity, or the RI quantity is associated with one or more priorities associated with a dropping rule configured by the base station or the UE, wherein each of the L1 RSRQ quantity, the SINR quantity, the CQI quantity, or the RI quantity includes a drop rate, an improve rate, a variance, a confidence level or interval, or Doppler information, wherein each of the drop rate, the improve rate, the variance, the confidence level or interval, or the Doppler information is associated with one priority of the one or more priorities, and wherein a first priority of the one or more priorities associated with a first time window closer to a present time of the at least one time window is higher than a second priority of the one or more priorities associated with a second time window further to the present time of the at least one time window.
[0044] In one example, the power management component 350 has a plurality of modules that may evaluate and determine a suitable PM for the UE. First, the selection of the power management mode may be based on determination of the channel condition (e.g., based on channel quality indicator (CQI)) that identifies the SNR and Doppler parameters associated with the channel 125 between the UE 115 and the base station 105. This module will look at ambient channel conditions and determine the best PM for the UE and vote for one of the three PMs. In one example, the module may be referred to as ChannelConditionPMEval. The module may estimate the SNR and Doppler detected by the UE on this component carrier (CC) and prioritize performance by choosing PM1 over PM2 in challenging channel conditions such as low SNR or high Doppler so as to improve performance. Additionally or alternative, the power management component 350 may select PM3 if the channel grant are not received for a period of time. Additionally or alternatively, the second module of the power management component 350 may monitor the channel grant inactivity period (e.g., channel grant elapsed time period) that maintains a time period since a last channel grant was received by the UE 115, that is, the module keeps track of how often the UE 115 has been scheduled with SCH grants. As such, the power management component 350 may maintain the periodicity of the UE 115 receiving channel grants. If the UE 115 has a low probability of receiving a grant based on the frequency (or infrequency) of prior channel grants received, the power management component 350 may select a power management mode that maximizes power saving properties over UE performance. The present module monitors the time elapsed since the last SCH grant and allows PM3 if this monitored time is greater than a threshold. In one example, the module may be referred to as SCHInactivityPMEval.
Luo and Nigam are considered to be analogous because they pertain to wireless communications networks. It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify Luo to include the concept of having CQI and Doppler information associated with priorities and time windows as taught by Nigam so as to improve communication within the wireless network.
Claim 17 is rejected under 35 U.S.C. § 103 as being unpatentable over Luo in view of Nigam, held further in view of Lindoff et. al. (U.S. Pat. Pub. 2014/0194121), herein referred to as “Lindoff.”
Regarding Claim 17,
Luo in view of Nigam does not disclose all the limitations of Claim 17.
However, Lindoff discloses: The apparatus of claim 16, wherein the at least one processor is further configured to: drop one or more of the drop rate, the improve rate, the variance, the confidence level or interval, or the Doppler information based on associated priorities of the one or more priorities.
[0054] In some embodiments, the prioritization of events may be done taking an expected validity period of the measurements that have caused the events into consideration. In some embodiments, an estimation of the speed with which the communications device moves relative to the network cells may be used as an input to the prioritisation. Another suitable measure for the expected validity period of measurements may be the estimated speed of the communications device (or a parameter indicative of the estimated speed, e.g. the measured Doppler shift) multiplied by the rate at which HO events are triggered. Other external knowledge, e.g. regarding the local cell planning may also be used to improve the performance, since the problem is more significant in denser networks. This is typically not known to the terminal; instead it may e.g. use statistics on the event occurrence rate to adapt to the current environment. A measure of the validity period may e.g. be used to determine whether a prioritisation of HO events is to be performed (e.g. when the expected validity period is long, e.g. longer than a suitable threshold value, a prioritisation may be omitted). Alternatively or additionally, an estimated validity period may be used in order to change the prioritisation criterion, e.g. whether to use the signal strength as a main criterion or the timing of events.
Luo in view of Nigam and Lindoff are considered to be analogous because they pertain to wireless communications networks. It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify Luo in view of Nigam to include the concept of dropping Doppler information associated with priorities as taught by Lindoff so as to improve communication within the wireless network.
Claim 20 is rejected under 35 U.S.C. § 103 as being unpatentable over Luo in view of Nigam, held further in view of Xi et. al. (WO 2018232090), herein referred to as “Xi.”
Regarding Claim 20,
Luo does not disclose all the limitations of Claim 20.
However, Xi discloses: The apparatus of claim 18, wherein the one or more candidate beams are reported in a second CSI report or medium access control (MAC) control element (MAC-CE) linked with a CSI report carrying the one or more predicted quantities, and wherein the CSI report setting identifies the link.
[00233] Spatial QCL relationships among different CCs may be obtained by a WTRU from SI in initial access. A QCL relationship may be related to WTRU capability, CCs related to a WTRU, or the like. QCL information may be obtained after a WTRU capability report is received from the network. A WTRU in RRC connected mode may be configured to measure and report on multiple CCs. Due to the dynamically changed propagation environment observed by a WTRU, including mobility, blockage, multi-path, reflection, or the like, a gNB may utilize WTRU feedback to dynamically update or indicate the spatial QCL information among different CCs. Updating may be performed by a higher layer signal, RRC signaling, a RRC message, a MAC-CE, a DCI, or the like. A measurement report or spatial QCL information may be signaled on a higher layer signal, RRC signaling, a RRC message, a MAC-CE, a UCI, or the like from the WTRU to the gNB. Similarly, a WTRU may be RRC configured with the spatial QCL parameter across multiple BWPs so that the WTRU may perform joint or independent L1-RSRP reporting using a QCL SS block and CSI-RS.
Luo and Xi are considered to be analogous because they pertain to wireless communications networks. It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify Luo to include the concept of a MAC CE linked with CSI carrying a predicted quantity as taught by Xi so as to improve communication within the wireless network.
Conclusion
Any inquiry concerning this communication or earlier communications from the examiner should be directed to JESSE P. SAMLUK whose telephone number is (571)270-5607. The examiner can normally be reached M-F 9-5.
Examiner interviews are available via telephone, in-person, and video conferencing using a USPTO supplied web-based collaboration tool. To schedule an interview, applicant is encouraged to use the USPTO Automated Interview Request (AIR) at http://www.uspto.gov/interviewpractice.
If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Derrick Ferris can be reached on 571-272-3123. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
Information regarding the status of published or unpublished applications may be obtained from Patent Center. Unpublished application information in Patent Center is available to registered users. To file and manage patent submissions in Patent Center, visit: https://patentcenter.uspto.gov. Visit https://www.uspto.gov/patents/apply/patent-center for more information about Patent Center and https://www.uspto.gov/patents/docx for information about filing in DOCX format. For additional questions, contact the Electronic Business Center (EBC) at 866-217-9197 (toll-free). If you would like assistance from a USPTO Customer Service Representative, call 800-786-9199 (IN USA OR CANADA) or 571-272-1000.
/JESSE P. SAMLUK/Examiner, Art Unit 2411
/DERRICK W FERRIS/Supervisory Patent Examiner, Art Unit 2411