DETAILED ACTION
This action is response to application number 18/784,441, dated on 07/25/2024.
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
Claim Rejections - 35 USC § 102
The following is a quotation of the appropriate paragraphs of 35 U.S.C. 102 that form the basis for the rejections under this section made in this Office action:
A person shall be entitled to a patent unless –
(a)(2) the claimed invention was described in a patent issued under section 151, or in an application for patent published or deemed published under section 122(b), in which the patent or application, as the case may be, names another inventor and was effectively filed before the effective filing date of the claimed invention.
In the event the determination of the status of the application as subject to AIA 35 U.S.C. 102 and 103 (or as subject to pre-AIA 35 U.S.C. 102 and 103) is incorrect, any correction of the statutory basis for the rejection will not be considered a new ground of rejection if the prior art relied upon, and the rationale supporting the rejection, would be the same under either status.
Claims 1-7, 11-16 and 20 are rejected under 35 U.S.C. 102(a)(2) as being anticipated or alternatively unpatentable over Li et al. (US 2026/0045990 A1).
Claims 1, 11, Li discloses an electronic device (UE; Fig. 2, el. 120) comprising: memory (Fig. 2, el. 282);
a communication module (Fig. 2, el. 254a-254r) comprising an antenna array (antenna array; Fig. 2, els. 252a-252r) configured to form a plurality of candidate beams (One or more antennas (for example, antennas 234a through 234t or antennas 252a through 252r) may include, or may be included within, one or more antenna panels, one or more antenna groups, one or more sets of antenna elements, or one or more antenna arrays, among other examples. An antenna panel, an antenna group, a set of antenna elements, or an antenna array may include one or more antenna elements (within a single housing or multiple housings), a set of coplanar antenna elements, a set of non-coplanar antenna elements, or one or more antenna elements coupled to one or more transmission or reception components, such as one or more components of FIG. 2; ¶54); and
a processor (Fig. 2, el. 280) operatively connected to the communication module (Fig. 2, el. 254a-254r) and the memory, the processor comprising a channel state classifier based on a neural network (AL/ML model; Fig. 6; may monitor long-term trends and patterns for performance and employ AI/ML models to perform corrective actions; ¶76; among other examples. In addition, the actor 508 may also depend on the type of tasks performed by the model inference host 504, type of inference data provided to the model inference host 504, and/or type of output produced by the model inference host 504. For example, if the output from the model inference host 504 is associated with beam management, the actor 508 may be a UE; ¶96; FIG. 6 is a diagram illustrating an example 600 of an AI/ML based beam management, in accordance with the present disclosure. As shown in FIG. 6, an AI/ML model 610 may be deployed at or on a UE 120. For example, a model inference host (such as a model inference host 504) may be deployed at, or on, a UE 120. The AI/ML model 610 may enable the UE 120 to determine one or more inferences or predictions based on data input to the AI/ML model 610; ¶100; an AI/ML model such as an artificial neural network for a Type-1 virtual measurement resource may be smaller than an AI/ML model for a Type-2 virtual measurement resource; ¶133),
wherein the processor (Fig. 2, el. 280) is configured to:
generate channel characteristic data based on information measured from the plurality of candidate beams for a signal received from a base station (generating a set of measurement values/characteristic data based on UE measurement from the plurality of beams from a signal received from the network node; For example, as shown by reference number 615, an input to the AI/ML model 610 may include measurements associated with a first set of beams. A measurement may be associated with a beam if the measurement is a measurement of a reference signal that is transmitted using the beam (where the beam is a transmit beam) or if the measurement is a measurement of a reference signal using the beam (where the beam is a receive beam). For example, a network node 110 may transmit one or more signals via respective beams from the first set of beams. The UE 120 may perform measurements (e.g., L1 RSRP measurements, L1 SINR measurements, or other measurements) of the first set of beams to obtain a first set of measurement values. For example, each beam, from the first set of beams, may be associated with one or more measurements performed by the UE 120. The UE 120 may input the first set of measurement values (e.g., L1 RSRP measurement values) into the AI/ML model 610 along with information regarding the first set of beams and/or a second set of beams, such as a beam direction (e.g., spatial direction), beam width, beam shape, and/or other characteristics of the first set of beams and/or the second set of beams; ¶101; In other words, the connections may be implicit connections defining beam characteristics associated with a given resource with respect to beams associated with other resources(s) that are included in a different set; ¶105; In other words, the UE 120 may use the connections between the first set of resources and the second set of resources to obtain beam characteristics or beam shapes associated with the first set of resources and the second set of resources. The UE 120 may use the beam characteristics or beam shapes associated with the first set of resources and the second set of resources to perform one or more AI/ML predictions associated with the first set of resources and the second set of resources; ¶106; As a result, the UE 120 may be enabled to perform improved predictive beam management by obtaining beam characteristics (e.g., beam shape and/or beam width) associated with the first set of resources and the second set of resources. Additionally, by using implicit connections between two sets of resources, the UE 120 and/or a network node 110 may conserve a signaling overhead, network resources, processing resources, and/or power associated with indicating the beam characteristics (e.g., beam shape and/or beam width) associated with the first set of resources and the second set of resources. For example, by using implicit connections between the two sets of resources, detailed beamforming information or implementations performed at a network node 110 do not need to be disclosed or indicated to the UE 120; ¶108),
input the channel characteristic data to the channel state classifier (neural network) to infer a channel state between the electronic device and the base station (inputting the beam measurement values/results to the AI/ML model (Fig. 6)/model inference host (Fig. 5) to infer the beam/channel between the UE and the BS; For example, as shown by reference number 615, an input to the AI/ML model 610 may include measurements associated with a first set of beams. A measurement may be associated with a beam if the measurement is a measurement of a reference signal that is transmitted using the beam (where the beam is a transmit beam) or if the measurement is a measurement of a reference signal using the beam (where the beam is a receive beam). For example, a network node 110 may transmit one or more signals via respective beams from the first set of beams. The UE 120 may perform measurements (e.g., L1 RSRP measurements, L1 SINR measurements, or other measurements) of the first set of beams to obtain a first set of measurement values. For example, each beam, from the first set of beams, may be associated with one or more measurements performed by the UE 120. The UE 120 may input the first set of measurement values (e.g., L1 RSRP measurement values) into the AI/ML model 610 along with information regarding the first set of beams and/or a second set of beams, such as a beam direction (e.g., spatial direction), beam width, beam shape, and/or other characteristics of the first set of beams and/or the second set of beams; ¶101; As shown by reference number 620, the AI/ML model 610 may output one or more predictions. The one or more predictions may include predicted measurement values (e.g., predicted L1 RSRP measurement values) associated with the second set of beams. This may reduce a quantity of beam measurements that are performed by the UE 120, thereby conserving power of the UE 120 and/or network resources that would have otherwise been used to transmit or measure all beams included in the first set of beams and the second set of beams. This type of prediction may be referred to as a codebook based spatial domain selection or prediction; ¶102; ¶103; In some examples, the first set of beams may be referred to as Set B beams and the second set of beams may be referred to as Set A beams. In some examples, the first set of beams (e.g., the Set B beams) may be a subset of the second set of beams (e.g., the Set A beams). In some other examples, the first set of beams and the second set of beams may be different beams and/or may be mutually exclusive sets. For example, the first set of beams (e.g., the Set B beams) may include wide beams (e.g., unrefined beams or beams having a beam width that satisfies a first threshold) and the second set of beams (e.g., the Set A beams) may include narrow beams (e.g., refined beams or beams having a beam width that satisfies a second threshold). In one example, the AI/ML model 610 may perform spatial-domain downlink beam predictions for beams included in the Set A beams based on measurement results of beams included in the Set B beams. As another example, the AI/ML model 610 may perform temporal downlink beam prediction for beams included in the Set A beams based on historic measurement results of beams included in the Set B beams; ¶104; In other words, the UE 120 may use the connections between the first set of resources and the second set of resources to obtain beam characteristics or beam shapes associated with the first set of resources and the second set of resources. The UE 120 may use the beam characteristics or beam shapes associated with the first set of resources and the second set of resources to perform one or more AI/ML predictions associated with the first set of resources and the second set of resources; ¶106), and
reform the plurality of candidate beams based on the inferred channel state (performing beam optimization, beam selection, beam refinement, beam management and beam shaping based on the beam/channel state; As shown in FIG. 4, example 420 depicts a third beam management procedure (e.g., P3 CSI-RS beam management). The third beam management procedure may be referred to as a beam refinement procedure, a UE beam refinement procedure, and/or a receive beam refinement procedure; ¶83; To ensure that the UE 120 and the network node 110 are communicating using a best beam or beam pair, beam management procedures (such as the beam management procedures described in connection with FIG. 4) may be performed by the UE 120 and/or the network node 110; ¶85; FIG. 6 is a diagram illustrating an example 600 of an AI/ML based beam management, in accordance with the present disclosure. As shown in FIG. 6, an AI/ML model 610 may be deployed at or on a UE 120. For example, a model inference host (such as a model inference host 504) may be deployed at, or on, a UE 120. The AI/ML model 610 may enable the UE 120 to determine one or more inferences or predictions based on data input to the AI/ML model 610; ¶100; As another example, an output of the AI/ML model 610 may include a point-direction, an angle of departure (AoD), and/or an angle of arrival (AoA) of a beam included in the second set of beams. This type of prediction may be referred to as a non-codebook based spatial domain selection or prediction. As another example, multiple measurement reports or values, collected at different points in time (e.g., time domain information regarding measurement reports or values), may be input to the AI/ML model 610. This may enable the AI/ML model 610 to output codebook based and/or non-codebook based predictions for a measurement value, an AoD, and/or an AoA, among other examples, of a beam at a future time. The output(s) of the AI/ML model 610, as described herein, may facilitate initial access procedures, secondary cell group (SCG) setup procedures, beam refinement procedures (e.g., a P2 beam management procedure or a P3 beam management procedure as described above in connection with FIG. 4), link quality (e.g., as represented by a predicted CQI or precoding matrix indicator (PMI)) or interference adaptation procedure, beam failure and/or beam blockage predictions, and/or radio link failure predictions, among other examples. This may lead to better management accuracy without excessive beam sweeping; ¶103).
Claims 2, 12, Li discloses wherein the channel characteristic data comprises information on at least one of a reference signal received power (RSRP), a power delay profile (PDP), and a channel impulse response (CIR) (As another example, the actor 508 may be a UE and the output from the model inference host 504 may be associated with beam management. For example, the output may be one or more predicted measurement values for one or more beams. The actor 508 (e.g., a UE) may determine that a measurement report (e.g., a Layer 1 (L1) RSRP report) is to be transmitted to a network node 110 based on the one or more predicted measurement values. For example, if the one or more predicted measurement values satisfy a threshold (such as a threshold relative to an actual measurement value, a measurement reporting threshold, or another form of threshold), the actor 508 may determine that a measurement report is to be transmitted to the network node 110 indicating the one or more predicted measurement values and/or the actual measurement value; ¶97; For example, as shown by reference number 615, an input to the AI/ML model 610 may include measurements associated with a first set of beams. A measurement may be associated with a beam if the measurement is a measurement of a reference signal that is transmitted using the beam (where the beam is a transmit beam) or if the measurement is a measurement of a reference signal using the beam (where the beam is a receive beam). For example, a network node 110 may transmit one or more signals via respective beams from the first set of beams. The UE 120 may perform measurements (e.g., L1 RSRP measurements, L1 SINR measurements, or other measurements) of the first set of beams to obtain a first set of measurement values. For example, each beam, from the first set of beams, may be associated with one or more measurements performed by the UE 120. The UE 120 may input the first set of measurement values (e.g., L1 RSRP measurement values) into the AI/ML model 610 along with information regarding the first set of beams and/or a second set of beams, such as a beam direction (e.g., spatial direction), beam width, beam shape, and/or other characteristics of the first set of beams and/or the second set of beams; ¶101; see also ¶113 of Tian et al. (US 2025/0323840 A1) in regard to AI/ML input including CIR, RSR, AOA, LOS information and NLOS information).
Claims 3, 13, Li discloses wherein the processor is further configured to:
determine whether channel information is obtained from the channel characteristic data, and select a method of the channel state classifier (neural network (AL/ML model; Fig. 6; model inference host; Fig. 5)) inferring the channel state based on a result the determination of whether the channel information is obtained from the channel characteristic data (selecting the neural network (AL/ML model; Fig. 6; model inference host; Fig. 5) method/output to infer the beam/channel characteristic; The model inference host 504 may be configured to run an AI/ML model based on inference data provided by the data sources 506, and the model inference host 504 may produce an output (e.g., a prediction) using the inference data input to the actor 508; ¶96; FIG. 6 is a diagram illustrating an example 600 of an AI/ML based beam management, in accordance with the present disclosure. As shown in FIG. 6, an AI/ML model 610 may be deployed at or on a UE 120. For example, a model inference host (such as a model inference host 504) may be deployed at, or on, a UE 120. The AI/ML model 610 may enable the UE 120 to determine one or more inferences or predictions based on data input to the AI/ML model 610; ¶100; The UE 120 may input the first set of measurement values (e.g., L1 RSRP measurement values) into the AI/ML model 610 along with information regarding the first set of beams and/or a second set of beams, such as a beam direction (e.g., spatial direction), beam width, beam shape, and/or other characteristics of the first set of beams and/or the second set of beams; ¶101; As another example, an output of the AI/ML model 610 may include a point-direction, an angle of departure (AoD), and/or an angle of arrival (AoA) of a beam included in the second set of beams. This type of prediction may be referred to as a non-codebook based spatial domain selection or prediction. As another example, multiple measurement reports or values, collected at different points in time (e.g., time domain information regarding measurement reports or values), may be input to the AI/ML model 610. This may enable the AI/ML model 610 to output codebook based and/or non-codebook based predictions for a measurement value, an AoD, and/or an AoA, among other examples, of a beam at a future time. The output(s) of the AI/ML model 610, as described herein, may facilitate initial access procedures, secondary cell group (SCG) setup procedures, beam refinement procedures (e.g., a P2 beam management procedure or a P3 beam management procedure as described above in connection with FIG. 4), link quality (e.g., as represented by a predicted CQI or precoding matrix indicator (PMI)) or interference adaptation procedure, beam failure and/or beam blockage predictions, and/or radio link failure predictions, among other examples. This may lead to better management accuracy without excessive beam sweeping; ¶103; In other words, the UE 120 may use the connections between the first set of resources and the second set of resources to obtain beam characteristics or beam shapes associated with the first set of resources and the second set of resources. The UE 120 may use the beam characteristics or beam shapes associated with the first set of resources and the second set of resources to perform one or more AI/ML predictions associated with the first set of resources and the second set of resources; ¶106).
Claims 4, 14, Li discloses wherein the processor is further configured to:
based on determining that the channel information corresponding to all channel characteristic data among the channel characteristic data is obtained from the channel characteristic data, select a method of classifying a channel class based on the channel information; based on determining that the channel information corresponding to all channel characteristic data among the channel characteristic data is not obtained from the channel characteristic data, select a method of predicting channel information corresponding to the channel characteristic data based on a distribution of the channel characteristic data; and based on determining that the channel information corresponding to partial channel characteristic data among the channel characteristic data is obtained from the channel characteristic data, select a method of inferring channel information corresponding to remaining channel characteristic data based on channel information corresponding to the partial channel characteristic data (selecting the neural network (AL/ML model; Fig. 6; model inference host; Fig. 5) method/output based on the channel information corresponding to all/partial/no channel characteristic, the selection including method of predicting channel information, and inferring remaining beam/channel characteristic based on beam/channel information of the partial beam/channel characteristic; FIG. 6 is a diagram illustrating an example 600 of an AI/ML based beam management, in accordance with the present disclosure. As shown in FIG. 6, an AI/ML model 610 may be deployed at or on a UE 120. For example, a model inference host (such as a model inference host 504) may be deployed at, or on, a UE 120. The AI/ML model 610 may enable the UE 120 to determine one or more inferences or predictions based on data input to the AI/ML model 610; ¶100; The UE 120 may input the first set of measurement values (e.g., L1 RSRP measurement values) into the AI/ML model 610 along with information regarding the first set of beams and/or a second set of beams, such as a beam direction (e.g., spatial direction), beam width, beam shape, and/or other characteristics of the first set of beams and/or the second set of beams; ¶101; As another example, an output of the AI/ML model 610 may include a point-direction, an angle of departure (AoD), and/or an angle of arrival (AoA) of a beam included in the second set of beams. This type of prediction may be referred to as a non-codebook based spatial domain selection or prediction. As another example, multiple measurement reports or values, collected at different points in time (e.g., time domain information regarding measurement reports or values), may be input to the AI/ML model 610. This may enable the AI/ML model 610 to output codebook based and/or non-codebook based predictions for a measurement value, an AoD, and/or an AoA, among other examples, of a beam at a future time. The output(s) of the AI/ML model 610, as described herein, may facilitate initial access procedures, secondary cell group (SCG) setup procedures, beam refinement procedures (e.g., a P2 beam management procedure or a P3 beam management procedure as described above in connection with FIG. 4), link quality (e.g., as represented by a predicted CQI or precoding matrix indicator (PMI)) or interference adaptation procedure, beam failure and/or beam blockage predictions, and/or radio link failure predictions, among other examples. This may lead to better management accuracy without excessive beam sweeping; ¶103; In some examples, the first set of beams may be referred to as Set B beams and the second set of beams may be referred to as Set A beams. In some examples, the first set of beams (e.g., the Set B beams) may be a subset of the second set of beams (e.g., the Set A beams). In some other examples, the first set of beams and the second set of beams may be different beams and/or may be mutually exclusive sets. For example, the first set of beams (e.g., the Set B beams) may include wide beams (e.g., unrefined beams or beams having a beam width that satisfies a first threshold) and the second set of beams (e.g., the Set A beams) may include narrow beams (e.g., refined beams or beams having a beam width that satisfies a second threshold). In one example, the AI/ML model 610 may perform spatial-domain downlink beam predictions for beams included in the Set A beams based on measurement results of beams included in the Set B beams. As another example, the AI/ML model 610 may perform temporal downlink beam prediction for beams included in the Set A beams based on historic measurement results of beams included in the Set B beams; ¶104; In other words, the UE 120 may use the connections between the first set of resources and the second set of resources to obtain beam characteristics or beam shapes associated with the first set of resources and the second set of resources. The UE 120 may use the beam characteristics or beam shapes associated with the first set of resources and the second set of resources to perform one or more AI/ML predictions associated with the first set of resources and the second set of resources; ¶106).
Claims 5, 15, Li discloses wherein the processor is further configured to infer the channel state as one of a line-of-sight (LoS) state and a non-line-of-sight (NLoS) state, based on inferring the channel state as the LoS state, reform the plurality of candidate beams based on a narrow beam having a first beam width, and based on inferring the channel state as the NLoS state, reform the plurality of candidate beams based on a wide beam having a second beam width that is greater than the first beam width (inferring the beam states as LOS and not LOS, LOS beams having narrow beams and not LOS beams having wide beams and reforming candidate beams having wide beams; In some examples, the transmission path of a narrower beam may be more likely to be tailored to a receiver, such that the transmission may be more likely to meet a line-of-sight (LOS) condition as the narrower beam may be more likely to reach the receiver without being obstructed by obstacle(s). Also, as the transmission path may be narrow, reflection and/or refraction may be less likely to occur for the narrower beam; ¶84; While higher frequency bands may provide narrower beam structures and higher transmission rates, higher frequency bands may also encounter higher attenuation and diffraction losses, where a blockage of an LOS path may degrade a wireless link quality. For example, when two wireless devices are communicating with each other based on an LOS path at a higher frequency band and the LOS path is blocked by an obstacle, such as a pedestrian, building, and/or vehicle, among other examples, the received power may drop significantly; ¶85; In some examples, the first set of beams may be referred to as Set B beams and the second set of beams may be referred to as Set A beams. In some examples, the first set of beams (e.g., the Set B beams) may be a subset of the second set of beams (e.g., the Set A beams). In some other examples, the first set of beams and the second set of beams may be different beams and/or may be mutually exclusive sets. For example, the first set of beams (e.g., the Set B beams) may include wide beams (e.g., unrefined beams or beams having a beam width that satisfies a first threshold) and the second set of beams (e.g., the Set A beams) may include narrow beams (e.g., refined beams or beams having a beam width that satisfies a second threshold). In one example, the AI/ML model 610 may perform spatial-domain downlink beam predictions for beams included in the Set A beams based on measurement results of beams included in the Set B beams. As another example, the AI/ML model 610 may perform temporal downlink beam prediction for beams included in the Set A beams based on historic measurement results of beams included in the Set B beams; ¶104).
Claims 6, 16, Li discloses wherein the electronic device further comprises at least one sensor (UE’s sensors such as light sensor, battery charging sensor…etc; Fig. 2, el. 120),
wherein the processor is further configured to infer the channel state as one of an a line-of-sight (LoS) state, a non-line-of-sight (NLoS) state including an LoS path, and an NLoS state that does not include the LoS path, based on inferring the channel state as the LoS state, reform the plurality of candidate beams based on a narrow beam having a first beam width, based on inferring the channel state as the NLoS state including the LoS path, reform the plurality of candidate beams based on the narrow beam, and based on inferring the channel state as the NLoS state that does not include the LoS path, reform the plurality of candidate beams based on a wide beam having a second beam width that is greater than the first beam width (inferring the beam states as LOS and not LOS, LOS beams having narrow beams and not LOS beams having wide beams and reforming candidate beams having wide/narrow beams and performing beam shaping ; In some examples, the transmission path of a narrower beam may be more likely to be tailored to a receiver, such that the transmission may be more likely to meet a line-of-sight (LOS) condition as the narrower beam may be more likely to reach the receiver without being obstructed by obstacle(s). Also, as the transmission path may be narrow, reflection and/or refraction may be less likely to occur for the narrower beam; ¶84; While higher frequency bands may provide narrower beam structures and higher transmission rates, higher frequency bands may also encounter higher attenuation and diffraction losses, where a blockage of an LOS path may degrade a wireless link quality. For example, when two wireless devices are communicating with each other based on an LOS path at a higher frequency band and the LOS path is blocked by an obstacle, such as a pedestrian, building, and/or vehicle, among other examples, the received power may drop significantly; ¶85; In some examples, the first set of beams may be referred to as Set B beams and the second set of beams may be referred to as Set A beams. In some examples, the first set of beams (e.g., the Set B beams) may be a subset of the second set of beams (e.g., the Set A beams). In some other examples, the first set of beams and the second set of beams may be different beams and/or may be mutually exclusive sets. For example, the first set of beams (e.g., the Set B beams) may include wide beams (e.g., unrefined beams or beams having a beam width that satisfies a first threshold) and the second set of beams (e.g., the Set A beams) may include narrow beams (e.g., refined beams or beams having a beam width that satisfies a second threshold). In one example, the AI/ML model 610 may perform spatial-domain downlink beam predictions for beams included in the Set A beams based on measurement results of beams included in the Set B beams. As another example, the AI/ML model 610 may perform temporal downlink beam prediction for beams included in the Set A beams based on historic measurement results of beams included in the Set B beams; ¶104; ¶106; As a result, the UE 120 may be enabled to perform improved predictive beam management by obtaining beam characteristics (e.g., beam shape and/or beam width) associated with the first set of resources and the second set of resources. Additionally, by using implicit connections between two sets of resources, the UE 120 and/or a network node 110 may conserve a signaling overhead, network resources, processing resources, and/or power associated with indicating the beam characteristics (e.g., beam shape and/or beam width) associated with the first set of resources and the second set of resources. For example, by using implicit connections between the two sets of resources, detailed beamforming information or implementations performed at a network node 110 do not need to be disclosed or indicated to the UE 120; ¶108).
Claim 7, Li discloses wherein the processor is further configured to,
based on a determination the channel state is changed from the NLoS state including the LoS path to the NLoS state that does not include the LoS path, reform the plurality of candidate beams based on the wide beam (reforming the candidate beams based on the wide beam when beam state does not include LOS any longer and performing beam shaping; In some examples, the transmission path of a narrower beam may be more likely to be tailored to a receiver, such that the transmission may be more likely to meet a line-of-sight (LOS) condition as the narrower beam may be more likely to reach the receiver without being obstructed by obstacle(s). Also, as the transmission path may be narrow, reflection and/or refraction may be less likely to occur for the narrower beam; ¶84; While higher frequency bands may provide narrower beam structures and higher transmission rates, higher frequency bands may also encounter higher attenuation and diffraction losses, where a blockage of an LOS path may degrade a wireless link quality. For example, when two wireless devices are communicating with each other based on an LOS path at a higher frequency band and the LOS path is blocked by an obstacle, such as a pedestrian, building, and/or vehicle, among other examples, the received power may drop significantly; ¶85; In some examples, the first set of beams may be referred to as Set B beams and the second set of beams may be referred to as Set A beams. In some examples, the first set of beams (e.g., the Set B beams) may be a subset of the second set of beams (e.g., the Set A beams). In some other examples, the first set of beams and the second set of beams may be different beams and/or may be mutually exclusive sets. For example, the first set of beams (e.g., the Set B beams) may include wide beams (e.g., unrefined beams or beams having a beam width that satisfies a first threshold) and the second set of beams (e.g., the Set A beams) may include narrow beams (e.g., refined beams or beams having a beam width that satisfies a second threshold). In one example, the AI/ML model 610 may perform spatial-domain downlink beam predictions for beams included in the Set A beams based on measurement results of beams included in the Set B beams. As another example, the AI/ML model 610 may perform temporal downlink beam prediction for beams included in the Set A beams based on historic measurement results of beams included in the Set B beams; ¶104; ¶106; As a result, the UE 120 may be enabled to perform improved predictive beam management by obtaining beam characteristics (e.g., beam shape and/or beam width) associated with the first set of resources and the second set of resources. Additionally, by using implicit connections between two sets of resources, the UE 120 and/or a network node 110 may conserve a signaling overhead, network resources, processing resources, and/or power associated with indicating the beam characteristics (e.g., beam shape and/or beam width) associated with the first set of resources and the second set of resources. For example, by using implicit connections between the two sets of resources, detailed beamforming information or implementations performed at a network node 110 do not need to be disclosed or indicated to the UE 120; ¶108).
Claim 20, Li discloses a method of operating an electronic device, the method comprising:
forming a plurality of candidate beams for performing wireless communication with an external device (Fig. 4 shows forming a plurality of candidate beams for performing wireless communication with an network node/BS; The second beam management procedure may be referred to as a beam refinement procedure, a network node beam refinement procedure, a TRP beam refinement procedure, and/or a transmit beam refinement procedure. As shown in FIG. 4 and example 410, CSI-RSs may be configured to be transmitted from the network node 110 to the UE 120. The CSI-RSs may be configured to be aperiodic (e.g., using DCI). The second beam management procedure may include the network node 110 performing beam sweeping over one or more transmit beams. The one or more transmit beams may be a subset of all transmit beams associated with the network node 110 (e.g., determined based at least in part on measurements reported by the UE 120 in connection with the first beam management procedure). The network node 110 may transmit a CSI-RS using each transmit beam of the one or more transmit beams for beam management. Thus, beam refinement may include measuring CSI-RSs on one or more transmit beams, that are selected based on measurements reported by the UE 120, using a single receive beam of the UE 120. In some examples, the one or more transmit beams may be more granular than the N transmit beams of the first beam management procedure (e.g., may have a narrower spatial separation from one another than the N transmit beams). The UE 120 may measure each CSI-RS using a single (e.g., a same) receive beam (e.g., determined based at least in part on measurements performed in connection with the first beam management procedure). The second beam management procedure may enable the network node 110 to select a best transmit beam based at least in part on measurements of the CSI-RSs (e.g., measured by the UE 120 using the single receive beam) reported by the UE 120; ¶82);
generating, based on information measured from the plurality of candidate beams, first channel characteristic data representing channel characteristics in a spatial domain and second channel characteristic data representing channel characteristics in a time domain (generating spatial domain and time domain beam characteristic; ¶101; ¶102; As another example, an output of the AI/ML model 610 may include a point-direction, an angle of departure (AoD), and/or an angle of arrival (AoA) of a beam included in the second set of beams. This type of prediction may be referred to as a non-codebook based spatial domain selection or prediction. As another example, multiple measurement reports or values, collected at different points in time (e.g., time domain information regarding measurement reports or values), may be input to the AI/ML model 610. This may enable the AI/ML model 610 to output codebook based and/or non-codebook based predictions for a measurement value, an AoD, and/or an AoA, among other examples, of a beam at a future time. The output(s) of the AI/ML model 610, as described herein, may facilitate initial access procedures, secondary cell group (SCG) setup procedures, beam refinement procedures (e.g., a P2 beam management procedure or a P3 beam management procedure as described above in connection with FIG. 4), link quality (e.g., as represented by a predicted CQI or precoding matrix indicator (PMI)) or interference adaptation procedure, beam failure and/or beam blockage predictions, and/or radio link failure predictions, among other examples. This may lead to better management accuracy without excessive beam sweeping; ¶103; In some examples, the first set of beams may be referred to as Set B beams and the second set of beams may be referred to as Set A beams. In some examples, the first set of beams (e.g., the Set B beams) may be a subset of the second set of beams (e.g., the Set A beams). In some other examples, the first set of beams and the second set of beams may be different beams and/or may be mutually exclusive sets. For example, the first set of beams (e.g., the Set B beams) may include wide beams (e.g., unrefined beams or beams having a beam width that satisfies a first threshold) and the second set of beams (e.g., the Set A beams) may include narrow beams (e.g., refined beams or beams having a beam width that satisfies a second threshold). In one example, the AI/ML model 610 may perform spatial-domain downlink beam predictions for beams included in the Set A beams based on measurement results of beams included in the Set B beams. As another example, the AI/ML model 610 may perform temporal downlink beam prediction for beams included in the Set A beams based on historic measurement results of beams included in the Set B beams; ¶104);
applying data obtained by combining the first channel characteristic data (spatial domain beam characteristic) with the second channel characteristic data (time domain beam characteristic) in a two-dimensional form to a pre-trained channel state inference model (to the neural network (AL/ML model; Fig. 6; model inference host; Fig. 5)) to infer a channel state between the electronic device (UE) and the external device (network node/BS) (applying the spatial domain beam characteristic and time domain beam characteristic to the AL/ML model (Fig. 6) or the model inference host (Fig. 5) to infer beam state between the UE and the network node; ¶101; ¶102; ¶103; ¶104); and
reforming the plurality of candidate beams based on the channel state, wherein the first channel characteristic data comprises information on a reference signal received power (RSRP) measured from the plurality of candidate beams, wherein the second channel characteristic data includes information on one of a power delay profile (PDP) and a channel impulse response (CIR) measured from the plurality of candidate beams (performing beam optimization, beam selection, beam refinement, beam management and beam shaping based on the beam/channel state; As shown in FIG. 4, example 420 depicts a third beam management procedure (e.g., P3 CSI-RS beam management). The third beam management procedure may be referred to as a beam refinement procedure, a UE beam refinement procedure, and/or a receive beam refinement procedure; ¶83; To ensure that the UE 120 and the network node 110 are communicating using a best beam or beam pair, beam management procedures (such as the beam management procedures described in connection with FIG. 4) may be performed by the UE 120 and/or the network node 110; ¶85; FIG. 6 is a diagram illustrating an example 600 of an AI/ML based beam management, in accordance with the present disclosure. As shown in FIG. 6, an AI/ML model 610 may be deployed at or on a UE 120. For example, a model inference host (such as a model inference host 504) may be deployed at, or on, a UE 120. The AI/ML model 610 may enable the UE 120 to determine one or more inferences or predictions based on data input to the AI/ML model 610; ¶100; As another example, an output of the AI/ML model 610 may include a point-direction, an angle of departure (AoD), and/or an angle of arrival (AoA) of a beam included in the second set of beams. This type of prediction may be referred to as a non-codebook based spatial domain selection or prediction. As another example, multiple measurement reports or values, collected at different points in time (e.g., time domain information regarding measurement reports or values), may be input to the AI/ML model 610. This may enable the AI/ML model 610 to output codebook based and/or non-codebook based predictions for a measurement value, an AoD, and/or an AoA, among other examples, of a beam at a future time. The output(s) of the AI/ML model 610, as described herein, may facilitate initial access procedures, secondary cell group (SCG) setup procedures, beam refinement procedures (e.g., a P2 beam management procedure or a P3 beam management procedure as described above in connection with FIG. 4), link quality (e.g., as represented by a predicted CQI or precoding matrix indicator (PMI)) or interference adaptation procedure, beam failure and/or beam blockage predictions, and/or radio link failure predictions, among other examples. This may lead to better management accuracy without excessive beam sweeping; ¶103; beam/channel characteristic data comprising RSRP, SINR, spatial domain and time domain beam characteristic; For example, as shown by reference number 615, an input to the AI/ML model 610 may include measurements associated with a first set of beams. A measurement may be associated with a beam if the measurement is a measurement of a reference signal that is transmitted using the beam (where the beam is a transmit beam) or if the measurement is a measurement of a reference signal using the beam (where the beam is a receive beam). For example, a network node 110 may transmit one or more signals via respective beams from the first set of beams. The UE 120 may perform measurements (e.g., L1 RSRP measurements, L1 SINR measurements, or other measurements) of the first set of beams to obtain a first set of measurement values. For example, each beam, from the first set of beams, may be associated with one or more measurements performed by the UE 120. The UE 120 may input the first set of measurement values (e.g., L1 RSRP measurement values) into the AI/ML model 610 along with information regarding the first set of beams and/or a second set of beams, such as a beam direction (e.g., spatial direction), beam width, beam shape, and/or other characteristics of the first set of beams and/or the second set of beams; ¶101; ¶102; As another example, an output of the AI/ML model 610 may include a point-direction, an angle of departure (AoD), and/or an angle of arrival (AoA) of a beam included in the second set of beams. This type of prediction may be referred to as a non-codebook based spatial domain selection or prediction. As another example, multiple measurement reports or values, collected at different points in time (e.g., time domain information regarding measurement reports or values), may be input to the AI/ML model 610. This may enable the AI/ML model 610 to output codebook based and/or non-codebook based predictions for a measurement value, an AoD, and/or an AoA, among other examples, of a beam at a future time. The output(s) of the AI/ML model 610, as described herein, may facilitate initial access procedures, secondary cell group (SCG) setup procedures, beam refinement procedures (e.g., a P2 beam management procedure or a P3 beam management procedure as described above in connection with FIG. 4), link quality (e.g., as represented by a predicted CQI or precoding matrix indicator (PMI)) or interference adaptation procedure, beam failure and/or beam blockage predictions, and/or radio link failure predictions, among other examples. This may lead to better management accuracy without excessive beam sweeping; ¶103; In some examples, the first set of beams may be referred to as Set B beams and the second set of beams may be referred to as Set A beams. In some examples, the first set of beams (e.g., the Set B beams) may be a subset of the second set of beams (e.g., the Set A beams). In some other examples, the first set of beams and the second set of beams may be different beams and/or may be mutually exclusive sets. For example, the first set of beams (e.g., the Set B beams) may include wide beams (e.g., unrefined beams or beams having a beam width that satisfies a first threshold) and the second set of beams (e.g., the Set A beams) may include narrow beams (e.g., refined beams or beams having a beam width that satisfies a second threshold). In one example, the AI/ML model 610 may perform spatial-domain downlink beam predictions for beams included in the Set A beams based on measurement results of beams included in the Set B beams. As another example, the AI/ML model 610 may perform temporal downlink beam prediction for beams included in the Set A beams based on historic measurement results of beams included in the Set B beams; ¶104; see also ¶113 of Tian et al. (US 2025/0323840 A1) in regard to AI/ML input including CIR, RSR, AOA, LOS information and NLOS information).
Allowable Subject Matter
Claims 8-10 and 17-19 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 any intervening claims.
Conclusion
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/KOUROUSH MOHEBBI/Primary Examiner, Art Unit 2471
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