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WO2024083319A1 - Sélection de faisceau - Google Patents

Sélection de faisceau Download PDF

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Publication number
WO2024083319A1
WO2024083319A1 PCT/EP2022/079023 EP2022079023W WO2024083319A1 WO 2024083319 A1 WO2024083319 A1 WO 2024083319A1 EP 2022079023 W EP2022079023 W EP 2022079023W WO 2024083319 A1 WO2024083319 A1 WO 2024083319A1
Authority
WO
WIPO (PCT)
Prior art keywords
beams
user equipment
subset
base station
determined
Prior art date
Application number
PCT/EP2022/079023
Other languages
English (en)
Inventor
Saeed Reza KHOSRAVIRAD
Tachporn SANGUANPUAK
Jakub SAPIS
Original Assignee
Nokia Solutions And Networks Oy
Priority date (The priority date is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the date listed.)
Filing date
Publication date
Application filed by Nokia Solutions And Networks Oy filed Critical Nokia Solutions And Networks Oy
Priority to PCT/EP2022/079023 priority Critical patent/WO2024083319A1/fr
Publication of WO2024083319A1 publication Critical patent/WO2024083319A1/fr

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Classifications

    • H—ELECTRICITY
    • H04—ELECTRIC COMMUNICATION TECHNIQUE
    • H04B—TRANSMISSION
    • H04B7/00—Radio transmission systems, i.e. using radiation field
    • H04B7/02—Diversity systems; Multi-antenna system, i.e. transmission or reception using multiple antennas
    • H04B7/04—Diversity systems; Multi-antenna system, i.e. transmission or reception using multiple antennas using two or more spaced independent antennas
    • H04B7/06—Diversity systems; Multi-antenna system, i.e. transmission or reception using multiple antennas using two or more spaced independent antennas at the transmitting station
    • H04B7/0686—Hybrid systems, i.e. switching and simultaneous transmission
    • H04B7/0695—Hybrid systems, i.e. switching and simultaneous transmission using beam selection
    • H04B7/06952—Selecting one or more beams from a plurality of beams, e.g. beam training, management or sweeping

Definitions

  • Various example embodiments relate to an apparatus and method for beam selection.
  • an apparatus comprising: at least one processor; and at least one memory storing instructions that when executed by the at least one processor cause the apparatus at least to perform: creating a subset of beams from beams available to a base station communicating with a user equipment; transmitting reference signals for the subset of beams; receiving feedback from the user equipment related to the reference signals for the subset of beams received by the user equipment; and selecting, based on the feedback, a beam from the beams available to the base station determined to be suitable for communication with the user equipment.
  • the creating may comprise creating, as the subset of beams, a specified maximum number of beams from a total number of the beams available to the base station.
  • the specified maximum number of beams may be less than the total number of the beams available to the base station.
  • the subset of beams may include a beam used or recently used by the user equipment.
  • the subset of beams may include beams proportionally selected from each panel grid of beams and/or each Reflective Intelligent Surface grid of beams.
  • the subset of beams may include beams biased to be selected from a panel grid of beams and/or Reflective Intelligent Surface grid based on location feedback from the user equipment.
  • the subset of beams may include beams randomly selected from each panel grid of beams and/or each Reflective Intelligent Surface grid of beams.
  • the subset of beams may include a beam least recently used from each panel grid of beams and/or each Reflective Intelligent Surface grid of beams.
  • the subset of beams may include every nth beam from each panel grid of beams and/or each Reflective Intelligent Surface grid of beams.
  • the subset of beams may include beams from a grid of beams having a wider coverage area than the beam.
  • the subset of beams may be determined using a Deep Reinforcement Learning model.
  • the subset of beams may be determined using neural network.
  • the subset of beams may be determined using a Long Short-Term Memory Recurrent Neural Network.
  • the subset of beams may be determined using an actor-critic methodology.
  • the actor-critic methodology may be based on a proximal policy optimisation.
  • the model may use Reference Signal Received Power of user equipment, Reference Signal Received Power of scheduled user equipment, an indication of a current or latest beam being used for communication with user equipment; location of user equipment, a quality indication of beams previously used for communication with user equipment and/or a channel estimate indicating a likelihood of a presence of line-of-sight with user equipment.
  • the model may include beams having greater than a predicted Reference Signal Received Power threshold in the subset of beams.
  • the feedback may include received power from user equipment, an indication of at least one preferred beam from user equipment and/or user equipment location.
  • the feedback may include received power from user equipment for all beams, for a best beam and/or for beams exceeding a received power threshold.
  • the selecting may comprise selecting the beam from all the beams available to the base station.
  • the selecting may comprise selecting the beam from other than the subset of beams.
  • the selecting may comprise selecting the beam from the beams available to the base station determined to have a predicted transmission characteristic.
  • the selecting may comprise selecting the beam from the beams available to the base station determined to have a highest predicted received power by the user equipment.
  • the selecting may comprise selecting the beam from the beams available to the base station determined to be predicted to have higher than a threshold received power.
  • the beam may be determined using a Deep Reinforcement Learning model.
  • the beam may be determined using a neural network.
  • the beam may be determined using a Long Short-Term Memory Recurrent Neural Network.
  • the model may use the indication of at least one preferred beam from user equipment, user equipment location and/or the quality indication of beams previously used for communication with user equipment and/or a channel estimate indicating a likelihood of a presence of line-of-sight with user equipment.
  • the model may interpolate a coverage space provided by the subset of beams to determine the beam.
  • the model may utilise reinforcement learning having a reward based on a relationship between a predicted and actual Reference Signal Received Power for the beam.
  • the likelihood of the presence of line-of-sight with user equipment may be determined from a channel response and relatively locations of the user equipment and the base station.
  • the likelihood of the presence of line-of-sight with user equipment may be determined based on a ray tracing simulation.
  • the likelihood of the presence of line-of-sight with user equipment may be determined using a neural network.
  • the at least one memory may store instructions that when executed by the at least one processor cause the apparatus at least to perform: using the beam for communication with the user equipment.
  • the apparatus may comprise a base station.
  • a method comprising: creating a subset of beams from beams available to a base station communicating with a user equipment; transmitting reference signals for the subset of beams; receiving feedback from the user equipment related to the reference signals for the subset of beams received by the user equipment; and selecting, based on the feedback, a beam from the beams available to the base station determined to be suitable for communication with the user equipment.
  • the creating may comprise creating, as the subset of beams, a specified maximum number of beams from a total number of the beams available to the base station.
  • the specified maximum number of beams may be less than the total number of the beams available to the base station.
  • the subset of beams may include a beam used or recently used by the user equipment.
  • the subset of beams may include beams proportionally selected from each panel grid of beams and/or each Reflective Intelligent Surface grid of beams.
  • the subset of beams may include beams biased to be selected from a panel grid of beams and/or Reflective Intelligent Surface grid based on location feedback from the user equipment.
  • the subset of beams may include beams randomly selected from each panel grid of beams and/or each Reflective Intelligent Surface grid of beams.
  • the subset of beams may include a beam least recently used from each panel grid of beams and/or each Reflective Intelligent Surface grid of beams.
  • the subset of beams may include every nth beam from each panel grid of beams and/or each Reflective Intelligent Surface grid of beams.
  • the subset of beams may include beams from a grid of beams having a wider coverage area than the beam.
  • the subset of beams may be determined using a Deep Reinforcement Learning model.
  • the subset of beams may be determined using neural network.
  • the subset of beams may be determined using a Long Short-Term Memory Recurrent Neural Network.
  • the subset of beams may be determined using an actor-critic methodology.
  • the actor-critic methodology may be based on a proximal policy optimisation.
  • the model may use Reference Signal Received Power of user equipment, Reference Signal Received Power of scheduled user equipment, an indication of a current or latest beam being used for communication with user equipment; location of user equipment, a quality indication of beams previously used for communication with user equipment and/or a channel estimate indicating a likelihood of a presence of line-of-sight with user equipment.
  • the model may include beams having greater than a predicted Reference Signal Received Power threshold in the subset of beams.
  • the feedback may include received power from user equipment, an indication of at least one preferred beam from user equipment and/or user equipment location.
  • the feedback may include received power from user equipment for all beams, for a best beam and/or for beams exceeding a received power threshold.
  • the selecting may comprise selecting the beam from all the beams available to the base station.
  • the selecting may comprise selecting the beam from other than the subset of beams.
  • the selecting may comprise selecting the beam from the beams available to the base station determined to have a predicted transmission characteristic.
  • the selecting may comprise selected the beam from the beams available to the base station determined to have a highest predicted received power by the user equipment.
  • the selecting may comprise selected the beam from the beams available to the base station determined to be predicted to have higher than a threshold received power.
  • the beam may be determined using a Deep Reinforcement Learning model.
  • the beam may be determined using a neural network.
  • the beam may be determined using a Long Short-Term Memory Recurrent Neural Network.
  • the model may use the indication of at least one preferred beam from user equipment, user equipment location and/or the quality indication of beams previously used for communication with user equipment and/or a channel estimate indicating a likelihood of a presence of line-of-sight with user equipment.
  • the model may interpolate a coverage space provided by the subset of beams to determine the beam.
  • the model may utilise reinforcement learning having a reward based on a relationship between a predicted and actual Reference Signal Received Power for the beam.
  • the likelihood of the presence of line-of-sight with user equipment may be determined from a channel response and relatively locations of the user equipment and the base station.
  • the likelihood of the presence of line-of-sight with user equipment may be determined based on a ray tracing simulation.
  • the likelihood of the presence of line-of-sight with user equipment may be determined using a neural network.
  • the method may comprise using the beam for communication with the user equipment.
  • a non-transitory computer readable medium comprising program instructions stored thereon for performing at least the method and its optional features set out above.
  • FIG. 1 illustrates a synchronization signal block (SSB) block transmission phase by a gNB for each of the beams in a grid of beams (GoB);
  • SSB synchronization signal block
  • FIG. 2 illustrates a flowchart detail the main steps performed by the gNB according to an example embodiment
  • FIG. 3 illustrates a beam sampling and ML-based interpolation processes according to an example embodiment
  • FIG. 4 illustrates where the sampling process could use beams that are not from the Ntot beamset according to an example embodiment
  • FIG. 5 illustrates index assignment between the sampled set and the total beamset according to an example embodiment
  • FIG. 6 illustrates Long Short-Term Memory Recurrent Neural Network (LSTM RNN) for down-selecting a subset of the total available beams (interleaving a sampled beam set) according to an example embodiment
  • FIG. 7 illustrates Deep reinforcement learning-based LSTM RNN for down-selecting a subset of the total available beams (interleaving a sampled beam set) according to an example embodiment
  • FIG. 8 illustrates offline training for predicting likelihood of LoS/NloS UEs
  • FIG. 9 illustrates training (left) and inference (right) flowcharts for the proposed ML entity in FIG. 7.
  • Some example embodiments provide an approach to selecting a beam for use for communication between a base station and user equipment. From all the beams that can be configured for communication between the base station and user equipment, a reduced number, subset or subgroup is selected. Typically, that reduced number of beams does not exceed that number which may be used in a measurement process using measurement or reference signals transmitted from the base station to the user equipment using the reduced number of beams. Feedback is received from the user equipment in relation to the signals received by the user equipment using the reduced number of beams. That feedback is used to select a beam that is suitable for communication with the user equipment.
  • the beam is selected from all the beams that can be configured for communication between the base station and user equipment and not just from the reduced number of beams used when transmitting the measurement or reference signals.
  • the beam that is selected differs from any of the reduced number of beams used when transmitting the measurement or reference signals.
  • Interpolation of the beam space of the reduced number of beams typically using a machine-learning model, enables a beam that is most suitable for communication with the user equipment to be selected.
  • the beam that is selected is typically other than a beam from the reduced number, subset or subgroup of beams.
  • the beam is typically not one of the reduced number, subset or subgroup of beams.
  • the beam that is selected is typically that beam suited to communication between the base station and user equipment.
  • the suitability can be established in a variety of ways. For example, that beam with the highest predicted Reference Signal Received Power (RSRP), any beam predicted to have higher than a threshold RSRP or any other predicted transmission characteristic can be used to determine a suitable beam.
  • RSRP Reference Signal Received Power
  • Some example embodiments invention relate to beam management in wireless access, especially in New Radio Frequency Range 2 (NR FR2 (mmWave)).
  • a mmWave phased array antenna is equipped with a grid of beam (GoB) which is a set of predefined beam patterns.
  • a beam management procedure is used in 5G NR in order to acquire and maintain a set of beams at the base station (gNB) GoB and/or user equipment (UE) GoB, which can be used for downlink (DL) and uplink (UL) transmission/reception.
  • gNB base station
  • UE user equipment
  • This is important technology for mmWave, deployed in multi- Transmission/ Reception Point (TRP) operation, as well as single panel access points.
  • TRP Transmission/ Reception Point
  • synchronization signal blocks are transmitted by the gNB at regular intervals (e.g., at periodicities of every 5/10/20/40/80/160 ms). Multiple SSBs are carried successively in an SSB burst.
  • SSBs carry a Primary Synchronization Signal (PSS), Secondary Synchronization Signal (SSS) and a Physical Broadcast Channel (PBCH) with a Demodulation Reference Signal (DMRS). Therefore, it spans over 4 Orthogonal Frequency Division Multiplexing (OFDM) symbols.
  • PSS Primary Synchronization Signal
  • SSS Secondary Synchronization Signal
  • PBCH Physical Broadcast Channel
  • DMRS Demodulation Reference Signal
  • OFDM Orthogonal Frequency Division Multiplexing
  • the maximum number of SSBs is frequency dependant where for mmWave frequencies (e.g., 24-39 GHz) the number is 64.
  • the UE measures the Reference Signal Received Power (RSRP) from those beams and identifies the best beam for its
  • the beam management process described above is specified by 3GPP for users in idle mode as well as in connected mode and upon a beam failure.
  • the overhead corresponding to the SSB burst increases with the number of beams in the GoB.
  • 3GPP has specified a maximum 64 size for the SSB burst.
  • gNBs may be equipped with a large number of beams in their GoB, e.g., 32 beams multiplied by the number of phased array panels.
  • a gNB with 3 TRPs could have more than 90 beams in their GoB.
  • the number can normally be much larger than that especially if narrow beams are favourable.
  • some gNBs could potentially have beams as narrow as 7 degrees width which could increase the GoB size to over 100 beams.
  • reflective intelligent surfaces (RISs) with beam-based operation are deemed as a low- complexity and feasible solution.
  • Integrating one or more RIS with GoB into a gNB could further increase the aggregated size of the beam set for gNB.
  • the total number of SSBs required for the gNB also increases.
  • a gNB with 3 panels and 3 RIS each e.g., operating 30 beams
  • the RIS technology it is desirable to have many more of narrow beams in the GoB since those panels are passive and only reflecting the existing radio waves in the air. Therefore, using wide beams with RIS could dramatically decrease their gain.
  • Some example embodiments provide a sampling and interleaving solution to this problem.
  • the approach is to sample the beam set; operate SSB burst on the sampled beam set and train a Machine Learning Model (ML) to infer the best beam for each user out of the larger set of beams only given the sampled observation.
  • ML Machine Learning Model
  • Two methods are used which are (i) long short-term memory recurrent neural networks LSTM RNN predictive training and (ii) deep reinforcement learning for choosing the proper subset of the beams for gNB and then allocate the beam to each UE from the selected subset of beams.
  • offline training is used for predicting the likelihood of Non-Line-of- Sight/Line-of-Sight (NLoS/LoS) users (UEs) and then the obtained likelihood of NLoS/LoS users is fed as one of the inputs for both LSTM RNN and deep reinforcement learning implementations.
  • NLoS/LoS Non-Line-of- Sight/Line-of-Sight
  • the main steps taken in some example embodiments are as illustrated in FIGs. 2 and 3.
  • the gNB selects Nr out of the Ntot beams (this is referred to as a ‘sampling process’) and at step S20 the SSB indexes for the selected Nr beams are stored in memory.
  • Nr is chosen to satisfy the specifications limit and guarantee a good sampling of the coverage area.
  • the gNB can consider the future scheduling plans in selecting the Nr beams; e.g., selecting beams that are being used or latest used by the users on the queue for scheduling.
  • the sampling process could be random or based on any sampling algorithm (e.g., every other beam, every third beam, a preconfigured sampling pattern, etc.).
  • the gNB instead of SSB transmission for the Ntot beams, the gNB transmits SSB for the Nr selected beams.
  • the gNB gathers the following: beam selection feedback from users (including beam RSRP), information about the position of the users, and beam indices.
  • the RSRP could be per beam-UE, or best beam for each UE (in our case, we assume that the gNB allocates one beam for each UE).
  • each RSRP is allocated using the original beam indexing for the beams.
  • the gNB uses a trained ML (e.g., LSTM RNN) where its input includes:
  • the beam selection feedback (e.g., including RSRP) from user;
  • the beam index identifier logical vector of size Ntot (0/1 depending on beam selection in the Nr beam set or not - in the previous sequence);
  • the estimated channel of the user e.g., from UL channel estimation
  • the UE location information is used to detect presence of a dominant path and predict likelihood of LoS/NLoS.
  • the likelihood value is fed also to the trained ML
  • the output of the trained ML includes:
  • a likelihood value for all the Ntot beams for the user representing how useful the beam is going to be for the user (or, alternatively the RSRP predicted for the beams);
  • the best beam for a user is then selected based on the output of the ML above; the beam index from Ntot beams with highest likelihood value is selected for the user.
  • This approach of sampling and interpolation of the beam space provides the analog beamforming with many benefits: Thanks to the limited number of beams in the sampled subset, the operation will be compliant with beam management specifications and the maximum number of allowed SSBs in a burst; The operation doesn’t impose any limitation on the total number of beams Ntot.
  • Ntot can be arbitrarily large as long as Ntot can be sampled with sufficient precision using 64 (or other specified number) beams.
  • 64 or other specified number
  • the gNB can have an arbitrary number of beams, a mix of narrow and wide, where those beams could have overlapping coverage too, but at each instance, this approach chooses the best out of all the beams for each user; It is important to decouple the beam set of the SSB operation from the data transmission beam set. Data transmission benefits from having a large number of very narrow beams, while SSB transmission requires a small number of beams to curb the overhead level. This approach enables such decoupling.
  • the network further enables the network to sample the beam set differently at each time, e.g., depending on the location of the users connected to the network;
  • adding one new RRH or RIS to the gNB in essence adds more beams to gNB’s GoB.
  • This approach enables the beam based operation to extend by adding more RRHs and RISs to the gNB, without increasing the SSB burst overhead.
  • Future scheduling plans e.g., gNB can increase the number of selected beams from one of the panels, knowing that the same set of beams will be used for communication with users in certain area (based on their location information) - optional.
  • Nr beams it is helpful to dynamically or semi-statically change the choice of Nr beams over time. Changing the selected subset of Nr beams has the following benefits: • It can improve the performance of the ML entity, especially in case of an online learning such as reinforcement learning
  • Nr Ntot » Nr
  • some or all of the Nr beams may be chosen from among beams outside of the Ntot beam set (the Nr selected beams still need beams that cover points in the same coverage area).
  • the Nr sampling beams are wide beams while the Ntot beams are all narrow beams. This is illustrated in Fig. 4.
  • the sampling process can be implemented using deep reinforcement learning (DRL).
  • DRL deep reinforcement learning
  • the gNB can use online training (deep reinforcement learning) to down-select a Nr set of beams dynamically.
  • deep reinforcement learning online training
  • an LSTM RNN for neural network inside DRL is implemented so that the network will have memory for DRL implementation.
  • Actor-Critic method which can be implemented based on proximal policy optimization (PRO) can be used for DRL implementation, not just typical deep Q- learning/double deep Q-learning as in existing approaches. Therefore, LSTM RNN is implemented to help the system to have memory for DRL computation.
  • PRO proximal policy optimization
  • the inputs include:
  • Reference signal received power (RSRP) of each UE the gNB can use RSRP information of all UEs, alternatively it can limit itself to UEs scheduled in upcoming time slots.
  • Beam index identifier logical vector of size Ntot (0/1 depending on beam selection in the Nr beam set or not - in the previous sequence).
  • Sequence of previous CQI for the UE previous sequence of beams used for the user with operation quality value corresponding to each beam in the sequence, where the operation quality value is a metric of how good the selected beams has been for the user (examples are set out in more detail below).
  • the estimated channel of the user e.g., from Uplink (UL) channel estimation
  • UL Uplink
  • the likelihood value is fed also to the trained ML. Such information is useful for the ML to give higher likelihood to TRP panel beams for LoS UEs and higher likelihood to RIS and repeater beams for NLoS UEs.
  • the output includes:
  • the gNB transmits SSB for the Nr selected beams. Note that there are two sets of indexes for beams:
  • Total gNB GoB beam index which is from 1 to Ntot (with the possibility of varying Ntot over time, e.g., when new beams are added or removed to the GoB).
  • the selected Nr beams are going to be used for the burst transmission.
  • the true indexes of those beams i.e., from the index set #2
  • the mapping of those to the SSB indexes i.e., index set #1
  • Fig. 5 An example embodiment of how such mapping between beam indexes could be stored over different time instances is shown in Fig. 5.
  • the stored mapping is then used by the gNB to find the correct beam index from the total GoB, when the feedback from the users has arrived.
  • the gNB then collects, at step S40, the beam selection feedback from users and, at step S50, reverts the mapping as described above.
  • the RSRP feedback from the UE could be per beam, or the UE may send only the RSRP of its best beam. Although both may be used, the former could provide a better interpolation outcome.
  • gNB After collecting the feedback from the UE based on the SSB burst over sampled beamset, gNB uses, at step S60, a trained ML (e.g., Long-short term memory (LSTM) recurrent neural network as illustrated in FIG. 6) to derive, at step S70, the best beam index out of the total GoB for the UE.
  • ML Long-short term memory
  • operation quality value is a metric of how good the selected beams has been for the user
  • the output includes:
  • gNB picks the highest likelihood value and uses that for communications with the UE.
  • the ML is trained to predict RSRP for each beam
  • the best beam is chosen out of the Ntot, based on which one is predicted to have the highest RSRP for the user equipment.
  • the inference process is typically done for each UE that is going through the beam selection or beam tracking.
  • Training of the RNN is typically done offline based on collected data from the operator of the same network.
  • the coverage area can be modelled as digital twin, where ray-tracing and simulated network operation can be used to generate training data in large datasets.
  • the NN is trained in this model to properly ‘interpolate’ the beam space for each user equipment based on the limited sampled input (Nr sample points). Therefore, an offline training module as illustrated in FIG. 6 is trained for a specific coverage area and its dynamics. To use the NN for a different coverage area, it needs to be trained again. However, the NN training isn’t necessarily dependant on the GoB. In fact, one example embodiment of this NN can operate independently from the GoB.
  • the beam codebooks are fed in instead of the beam indexes.
  • the beam codebook could be fed in in form of a weight matrix (phase shifter codebook), or the gain pattern of the beam over the desired coverage area. In such case, at the output, instead of likelihood values for beams, the NN can be trained to feed out the optimal beam codebook (or gain pattern) for each user equipment. Best beam selection using reinforcement learning
  • the ML entity described above could also be designed as a reinforcement learning (RL) entity.
  • RL reinforcement learning
  • the pretrained model can be used, however the deep RL network can be trained during the deployment for the specific network coverage area that it is deployed in.
  • the observation space is then as defined in FIG. 7.
  • the action space may include likelihood value for the Ntot beams in GoB or a predicted RSRP value for each beam in the GoB.
  • the reward is designed in a way to minimize convergence time and improve prediction accuracy. Therefore, a reward function is based on the selected beam being above the expected RSRP threshold. Therefore, the actual RSRP of the beam (measured during the communications process between the UE and the gNB) can be compared against the predicted RSRP and the delta is used in a cost function as follows: Reward: funct/on(predicted RSRP for a beam - actual RSRP of the beam).
  • the gNB creates a likelihood value for the channel to the UE having a dominant LoS element.
  • the Estimated channel in the time domain and the frequency domain can be used for this purpose.
  • the impact of the beamforming is deconvolved from the estimated channel first.
  • the gNB uses a simple neural network (e.g., 2 hidden layers) and uses the estimated channel from uplink pilot transmission to create a likelihood value for LoS/NLoS.
  • the output can be set to predict the likelihood of having a dominant path (equivalent to having strong LoS).
  • Such network can be readily trained using ray-tracing based simulations; the environments of the ray-tracing simulations don’t need to be similar or the same as the deployment environment - the reason is that the likelihood of LoS is directly related to the ratio of received power in the initial taps of the sampled channel. It is helpful to train such network using training data from multitude of environment to increase robustness of the training to variations in the environment.
  • FIG. 8 illustrates the steps of training and of inference of the ML entity shown in FIG. 7.
  • the isotropic channel between the UE and the gNB is simulated or measured.
  • the channel impulse response/and or the channel frequency response is extracted.
  • the neural network is trained using the channel impulse response/and or the channel frequency response, with the ground truth as an output.
  • the channel impulse response/and or the channel frequency response is estimated.
  • step S120 beamforming is deconvolved.
  • the resulting isotropic channel impulse response/and or the channel frequency response is fed to the neural network, as is the relative distance or the path loss if available.
  • step S140 the likelihood value representing the chance of the channel having a dominant line-of-sight element is extracted.
  • program storage devices e.g., digital data storage media, which are machine or computer readable and encode machine-executable or computerexecutable programs of instructions, wherein said instructions perform some or all of the steps of said above-described methods.
  • the program storage devices may be, e.g., digital memories, magnetic storage media such as a magnetic disks and magnetic tapes, hard drives, or optically readable digital data storage media.
  • the embodiments are also intended to cover computers programmed to perform said steps of the above-described methods.
  • circuitry may refer to one or more or all of the following:
  • circuit(s) and or processor(s) such as a microprocessor(s) or a portion of a microprocessor(s), that requires software (e.g., firmware) for operation, but the software may not be present when it is not needed for operation.
  • software e.g., firmware
  • circuitry also covers an implementation of merely a hardware circuit or processor (or multiple processors) or portion of a hardware circuit or processor and its (or their) accompanying software and/or firmware.
  • circuitry also covers, for example and if applicable to the particular claim element, a baseband integrated circuit or processor integrated circuit for a mobile device or a similar integrated circuit in server, a cellular network device, or other computing or network device.

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  • Computer Networks & Wireless Communication (AREA)
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Abstract

L'invention concerne un appareil comprenant : au moins un processeur ; et au moins une mémoire stockant des instructions qui, lorsqu'elles sont exécutées par le(s) processeur(s), amènent l'appareil à au moins : créer un sous-ensemble de faisceaux à partir de faisceaux disponibles pour une station de base communiquant avec un équipement utilisateur ; transmettre des signaux de référence pour le sous-ensemble de faisceaux ; recevoir une rétroaction de l'équipement utilisateur associée aux signaux de référence pour le sous-ensemble de faisceaux reçus par l'équipement utilisateur ; et d'après la rétroaction, sélectionner un faisceau parmi les faisceaux disponibles pour la station de base, déterminé comme étant approprié pour une communication avec l'équipement utilisateur.
PCT/EP2022/079023 2022-10-19 2022-10-19 Sélection de faisceau WO2024083319A1 (fr)

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