Abstract:
Systems and techniques are disclosed to enhance the efficiency of available bandwidth between UEs and base stations. A UE transmits a sounding reference signal (SRS) to the base station. The base station characterizes the uplink channel based on the SRS received and, using reciprocity, applies the channel characterization for the downlink channel. As part of applying the channel information, the base station forms the beam to the UE based on the uplink channel information obtained from the SRS. The UE may include an array of antennas, each UE transmitting a different SRS that the base station receives and uses to characterize the downlink. Multiple UEs (or a single UE with multiple antennas) transmit SRS at the same time and frequency allocation (non-orthogonal), but with each sending its own unique SRS. Further, multiple UEs (or a single UE with multiple antennas) may send their SRS at unique time/frequency allocations (orthogonal).
Abstract:
Various aspects of the present disclosure generally relate to wireless communication. In some aspects, a user equipment may determine channel state information for a communication link, wherein the channel state information is based at least in part on a linear combination associated with a plurality of beams of the communication link, wherein a first set of beams used for a first subband, of a plurality of subbands of the communication link, is different than a second set of beams used for a second subband of the plurality of subbands, and wherein the plurality of beams includes the first set of beams and the second set of beams; and transmit the channel state information. Numerous other aspects are provided.
Abstract:
Various embodiments include methods performed in receiver circuitry of a wireless communication device for demodulating wireless transmission waveforms to reconstruct data tones, which may include receiving, from a transmitter, wireless transmission waveforms that includes peak reduction tones (PRTs) that were inserted by a PRT neural network in the transmitter, and demodulating the received wireless transmission waveforms using a decoder neural network that has been trained based on outputs of the transmitter to output a reconstruction of the data tones. Further embodiments include exchanging information between the transmitter and receiver circuitry to coordinate the PRT neural network used for inserting PRTs in the transmitting wireless communication device and the decoder neural network used in the receiving wireless communication device for demodulating transmission waveforms received from the transmitting wireless communication device.
Abstract:
Disclosed are techniques for receive beam selection for measuring a reference radio frequency (RF) signal. In an aspect, a first node determines a type of measurement to be performed on the reference RF signal, selects a receive beam based on the type of measurement to be performed on the reference RF signal, generates the selected receive beam, receives, from a second node, using the generated receive beam, the reference RF signal transmitted on a wireless channel, and performs one or more measurements on the received reference RF signal according to the type of the measurement to be performed.
Abstract:
Certain aspects of the present disclosure relate to methods and apparatus for a UE to flexibly indicate a preferred precoding resource block group (PRG) size to a base station (e.g., an eNB).
Abstract:
Aspects of the present disclosure relate to wireless communications and, more particularly, to reference signals (RS) and link adaptation for massive multiple-input multiple-output (MIMO). In one aspect, a method is provided which may be performed by a wireless device such as a base station (BS). The method generally includes receiving sounding reference signals (SRS) and at least one of: feedback regarding interference or a whitening matrix from one or more user equipments (UEs), determining beamforming parameters for transmissions to a group of one or more UEs based, at least in part, on the SRS and at least one of: the feedback regarding interference or the whitening matrix, and transmitting channel state information reference signals (CSI-RS) to UEs in the group using the determined beamforming parameters.
Abstract:
A method, an apparatus, and a computer program product for wireless communication are provided. The apparatus determines an observed bit rate based on uplink transmissions of the UE, estimates an available link capacity for the UE, selects an estimate factor, and estimates available uplink throughput for future uplink transmissions of the UE as a function of the observed bit rate, the estimated available link capacity, and the estimate factor.
Abstract:
A method and apparatus for determining available downlink bandwidth are described. The described aspects may include estimating an available link capacity of a cell for a user equipment. The described aspects may include estimating an available fraction of cell resources for the user equipment. The described aspects may include estimating available bandwidth of the cell for the user equipment as a function of the estimated available link capacity and the estimated available fraction of cell resources. Available bandwidth may be estimated for a cell in a Universal Mobile Telecommunications System (UMTS) system when the user equipment is in an idle mode and/or a connected mode. Available bandwidth may be estimated for a cell in a Long Term Evolution (LTE) system when the user equipment is in an idle mode and/or a connected mode.
Abstract:
Aspects described herein relate to using machine learning (ML) models for performing channel state information (CSI) encoding or decoding, CSI-reference signal (RS) optimization, channel estimation, etc. The ML models can be trained by a user equipment (UE), separately by the UE and a network node (e.g., base station), or jointly by the UE and network node.
Abstract:
Aspects described herein relate to encoding, based on an estimated channel matrix of a reference signal received from a base station, channel state information (CSI) using a machine learning (ML)-based CSI encoder, transmitting, to the base station, an output of the ML-based CSI encoder and assistance information related to the estimated channel matrix, and receiving, from the base station, a scheduling grant for a downlink channel having at least one parameter that is based on the output of the ML-based CSI encoder and the assistance information. Other aspects relate to receiving the CSI encoder output and assistance information, and transmitting the scheduling grant.