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EP-4154424-B1 - METHOD AND APPARATUS OF FUSING RADIO FREQUENCY AND SENSOR MEASUREMENTS FOR BEAM MANAGEMENT

EP4154424B1EP 4154424 B1EP4154424 B1EP 4154424B1EP-4154424-B1

Inventors

  • ALI, Anum
  • NGUYEN, KHUONG N.
  • MO, Jianhua
  • NG, BOON LOONG
  • VA, VUTHA

Dates

Publication Date
20260506
Application Date
20210713

Claims (11)

  1. A user equipment, UE, (111-116) for beam management in a wireless communication system, the UE comprising: a transceiver (310) configured to receive signals from one or more base stations; a motion sensor (365) configured to obtain motion information; and a processor (340) operably connected to the transceiver (310) and the motion sensor (365), the processor (340) configured to: determine reference signal measurements from the signals, obtain the motion information of the UE, generate beam management information for the beam management by combining the reference signal measurements and the motion information based at least in part on a comparison of a parameter associated with the motion information to a threshold, identify a beam based on the generated beam management information, and perform wireless communication based on the identified beam.
  2. The UE (111-116) of Claim 1, wherein a particle filter (802, 804) is used to combine the reference signal measurements and the motion information to generate the beam management information; and the processor (340) is further configured to: identify a plurality of particles associated with the particle filter, based on an angle of arrival and a channel gain, update the plurality of particles based on the reference signal measurements and the motion information, identify one or more new particles to be included in the particle filter, and identify the beam based on the plurality of particles and the one or more new particles for the wireless communication.
  3. The UE (111-116) of Claim 2, wherein to identify the beam further the processor (340) is configured to : identify a mean direction of the plurality of particles and the one or more new particles; compare the identified mean direction to regions of a beam decision map (834), wherein each of the regions represent a gain associated with one of multiple beams; identify a region of the beam decision map that corresponds with the identified mean direction, wherein the identified region represents gain of a first beam of the multiple beams; and identify the first beam as the beam for performing the wireless communication.
  4. The UE (111-116) of Claim 2, wherein the processor (340) is further configured to: identify a first beam from one or more beams, wherein the first beam corresponds to a region of the particle filter that includes a number of particles more than any other of the one or more beams; and identify the first beam as the beam for performing the wireless communication.
  5. The UE (111-116) of Claim 1, wherein the processor (340) is further configured to: identify one or more parameters associated with at least one of the reference signal measurements and the motion information; determine whether to combine the reference signal measurements and the motion information to generate the beam management information based on the one or more parameters; and identify the beam based on the reference signal measurements, in response to a determination not to combine the reference signal measurements and the motion information based on the one or more parameters.
  6. The UE (111-116) of Claim 5, wherein to identify the one or more parameters, the processor (340) is configured to perform at least one of: compare a rotational speed of the UE based on the motion information to a first threshold; compare an error level associated with the motion information based on the motion sensor to a second threshold; compare error level associated with the reference signal measurements to a third threshold; and compare an update rate of the reference signal measurements to an update rate of the motion information.
  7. The UE (111-116) of Claim 6, wherein: when the rotational speed of the UE is above the first threshold, the processor (340) is configured to combine the reference signal measurements and the motion information; when the rotational speed of the UE is below the first threshold, the processor (340) is configured to not combine the reference signal measurements and the motion information; when the error level associated with the motion information is below the second threshold, the processor (340) is configured to combine the reference signal measurements and the motion information; when the error level associated with the motion information is above the second threshold, the processor (340) is configured to not combine the reference signal measurements and the motion information; when the error level associated with the reference signal measurements is below the second threshold, the processor (340) is configured to combine the reference signal measurements and the motion information; when the error level associated with the reference signal measurements is above the second threshold, the processor (340) is configured to not combine the reference signal measurements and the motion information; when the update rate of the reference signal measurements is less than the update rate of the motion information, the processor (340) is configured to combine the reference signal measurements and the motion information; and when the update rate of the reference signal measurements is greater than the update rate of the motion information, the processor (340) is configured to not combine the reference signal measurements and the motion information.
  8. The UE (111-116) of Claim 1, wherein the processor (340) is configured to: modify a format of the reference signal measurements and the motion information into a vector; identify, using a neural network, a pattern from the reference signal measurements and the motion information; and identify the beam for the wireless communication based on the pattern.
  9. The UE (111-116) of Claim 8, wherein: to modify the reference signal measurements and the motion information into the vector the processor (340) is configured to: convert the reference signal measurements into a one dimensional vector, transform the motion information into rotational matrix, and generate the vector corresponding to one time step, wherein the vector is based on a combination of the one dimensional vector and the rotational matrix; and to identify the pattern the processor (340) is configured to: identify, using a long short-term memory, LSTM, a first beam based on the vector, the vector corresponds to a first time step, perform the wireless communication, at the first time step, based on the first beam, generate a second vector corresponding to a subsequent time step, identifying, using the LSTM, a second beam based on the second vector and the first beam, and perform the wireless communication, at the subsequent time step, based on the first beam.
  10. The UE (111-116) of Claim 8, wherein the processor (340) is configured to: generate feedback for a beam adjustment decision based on criteria, wherein the criteria includes at least one of a communication quality, power consumption, temperature, avoiding frequent beam changes, and maintaining certain signal quality, wherein the feedback provides a reward or a punishment to the neural network based on whether the identified beam satisfies the criteria; rate one or more beams to be measured for a subsequent time step base on the feedback from a previous time step; and recommend, at the subsequent time step, the one or more beams for measurement based on the rating.
  11. A method for beam management implemented by a user equipment, UE, (111-116) in a wireless communication system as claimed in any of claims 1 to 10.

Description

Technical Field This disclosure relates generally to wireless communication systems. More specifically, this disclosure relates to a beam selection operation at a terminal or user equipment (UE) based on combining radio frequency (RF) and sensor measurements. Background Art To meet the demand for wireless data traffic having increased since deployment of 4G communication systems, efforts have been made to develop an improved 5G or pre-5G communication system. Therefore, the 5G or pre-5G communication system is also called a 'Beyond 4G Network' or a 'Post LTE System'. The 5G communication system is considered to be implemented in higher frequency (mmWave) bands, e.g., 60GHz bands, so as to accomplish higher data rates. To decrease propagation loss of the radio waves and increase the transmission distance, the beamforming, massive multiple-input multiple-output (MIMO), Full Dimensional MIMO (FD-MIMO), array antenna, an analog beam forming, large scale antenna techniques are discussed in 5G communication systems. In addition, in 5G communication systems, development for system network improvement is under way based on advanced small cells, cloud Radio Access Networks (RANs), ultra-dense networks, device-to-device (D2D) communication, wireless backhaul, moving network, cooperative communication, Coordinated Multi-Points (CoMP), reception-end interference cancellation and the like. In the 5G system, Hybrid FSK and QAM Modulation (FQAM) and sliding window superposition coding (SWSC) as an advanced coding modulation (ACM), and filter bank multi carrier (FBMC), non-orthogonal multiple access(NOMA), and sparse code multiple access (SCMA) as an advanced access technology have been developed. The Internet, which is a human centered connectivity network where humans generate and consume information, is now evolving to the Internet of Things (IoT) where distributed entities, such as things, exchange and process information without human intervention. The Internet of Everything (IoE), which is a combination of the IoT technology and the Big Data processing technology through connection with a cloud server, has emerged. As technology elements, such as 'sensing technology', 'wired/wireless communication and network infrastructure', 'service interface technology' and 'Security technology'have been demanded for IoT implementation, a sensor network, a Machine-to-Machine (M2M) communication, Machine Type Communication (MTC), and so forth have been recently researched. Such an IoT environment may provide intelligent Internet technology services that create a new value to human life by collecting and analyzing data generated among connected things. IoT may be applied to a variety of fields including smart home, smart building, smart city, smart car or connected cars, smart grid, health care, smart appliances and advanced medical services through convergence and combination between existing Information Technology (IT) and various industrial applications. In line with this, various attempts have been made to apply 5G communication systems to IoT networks. For example, technologies such as a sensor network, Machine Type Communication (MTC), and Machine-to-Machine (M2M) communication may be implemented by beamforming, MIMO, and array antennas. Application of a cloud Radio Access Network (RAN) as the above-described Big Data processing technology may also be considered to be as an example of convergence between the 5G technology and the IoT technology. Disclosure of Invention Technical Problem The 5G/NR communication system is considered to be implemented in higher frequency (mmWave) bands, e.g., 28 GHz or 60GHz bands, so as to accomplish higher data rates or in lower frequency bands, such as 6 GHz, to enable robust coverage and mobility support. To decrease propagation loss of the radio waves and increase the transmission distance, the beamforming, massive multiple-input multiple-output (MIMO), Full Dimensional MIMO (FD-MIMO), array antenna, an analog beam forming, large scale antenna techniques are discussed in 5G/NR communication systems. A channel can change due to movement of the UE such as when a user changes the orientation of the UE which can cause a misalignment. A channel can change due to movement of the UE such as when a user moves the UE from its current location to a new location. As such, the UE will need to find the best beam again. However, measuring all of the beams can create a large latency in finding the best beam for reception and/or transmission which can reduce reception/transmission. US 2018/0191422 discloses techniques for detecting and recovering from beam-failure events. Motion sensor information generated by motion sensors on a UE is used to detect, predict and recover from a beam failure event resulting from movement of the UE. The motion sensor information may be used to adjust a current beam direction used by the UE, or to determine a recommendation for adjusting the beam direction. The UE further moni