Abstract: A method for improved efficiency for massive MIMO adaptation. In an embodiment, method for operating a network component in a massive MIMO network includes obtaining, by the network component, one or more network parameters; determining, by the network component, a number of active antennas in a massive MIMO AP to use for transmitting in one time frame according to the one or more network parameters, the number of active antennas selected being less than a total number of antennas available in the massive MIMO AP; and serving one or more UEs using the determined number of active antennas. Other embodiments reduce the number of beamformers utilized according to network parameters. In still other embodiments, a number of data streams to transmit and a corresponding number of users to be served in a time frame by a massive MIMO AP is reduced.

Abstract: A method for compressive channel estimation in a massive multiple input, multiple output (MIMO) system using sectored random beams is provided. In an embodiment, a method in a massive multiple input, multiple output (MIMO) transceiver for channel estimation includes obtaining a sector. The sector includes less than a complete coverage area of the transceiver. The method also includes transmitting, by the transceiver, a plurality of random beams to a user equipment (UE) in the sector.

Abstract: A method for compressive channel estimation in a massive multiple input, multiple output (MIMO) system using sectored random beams is provided. In an embodiment, a method in a massive multiple input, multiple output (MIMO) transceiver for channel estimation includes obtaining a sector. The sector includes less than a complete coverage area of the transceiver. The method also includes transmitting, by the transceiver, a plurality of random beams to a user equipment (UE) in the sector.

Abstract: Method and apparatus for downlink (DL) channel estimation in massive MIMO are provided. A base station (BS) support massive MIMO may select a dictionary for a user equipment (UE) in its cell and transmit information for constructing the dictionary to the UE. The UE constructs the dictionary based on the information received and performs compressed sensing (CS)-based DL channel estimation for the BS. The UE also sends feedback information to the BS, which include information that is useful for determination of the dictionary by the BS.

Abstract: It is possible to reduce the overhead associated with downlink channel estimation in massive Multiple-Input-Multiple-Output (MIMO) networks by processing training sequences according to a transformation matrix. The transformation matrix maps a generic dictionary to a non-generic dictionary associated with an antenna geometry of a MIMO antenna array. The transformation matrix can be computed based on the two dictionaries. In one embodiment, the training reference signal is precoded to obtain a precoded training reference signal, which is then transmitted over a MIMO antenna array. The training precoder used to precode the training reference signal is designed according to the transformation matrix to mitigate a dependence that the training reference signal transmission has on the antenna geometry.

Abstract: It is possible to reduce the overhead associated with downlink channel estimation in massive Multiple-Input-Multiple-Output (MIMO) networks by processing training sequences according to a transformation matrix. The transformation matrix maps a generic dictionary to a non-generic dictionary associated with an antenna geometry of a MIMO antenna array. The transformation matrix can be computed based on the two dictionaries. In one embodiment, the training reference signal is precoded to obtain a precoded training reference signal, which is then transmitted over a MIMO antenna array. The training precoder used to precode the training reference signal is designed according to the transformation matrix to mitigate a dependence that the training reference signal transmission has on the antenna geometry.

Abstract: Method and apparatus for downlink (DL) channel estimation in massive MIMO are provided. A base station (BS) support massive MIMO may select a dictionary for a user equipment (UE) in its cell and transmit information for constructing the dictionary to the UE. The UE constructs the dictionary based on the information received and performs compressed sensing (CS)-based DL channel estimation for the BS. The UE also sends feedback information to the BS, which include information that is useful for determination of the dictionary by the BS.

Abstract: A method for improved efficiency for massive MIMO adaptation. In an embodiment, method for operating a network component in a massive MIMO network includes obtaining, by the network component, one or more network parameters; determining, by the network component, a number of active antennas in a massive MIMO AP to use for transmitting in one time frame according to the one or more network parameters, the number of active antennas selected being less than a total number of antennas available in the massive MIMO AP; and serving one or more UEs using the determined number of active antennas. Other embodiments reduce the number of beamformers utilized according to network parameters. In still other embodiments, a number of data streams to transmit and a corresponding number of users to be served in a time frame by a massive MIMO AP is reduced.

Abstract: A system and method is proposed for progressively quantizing channel state information for application in a MIMO (multiple input multiple output) communication system. A method includes computing an estimate of a communications channel between a subscriber unit and a base station, quantizing the estimate with a first codebook, thereby producing a first quantized estimate, quantizing an (n?1)-th quantized estimate with an n-th codebook, thereby producing an n-th quantized estimate, where n is an integer value ranging from 2 to R, R is a total number of quantizations of the estimate, wherein the n-th codebook is a localized codebook. The method also includes incrementing n, repeating the quantizing an (n?1)-th quantized estimate until n=R, and transmitting information based on the R quantized estimates to the base station.

Abstract: A system and method is proposed for progressively quantizing channel state information for application in a MIMO (multiple input multiple output) communication system. A method includes computing an estimate of a communications channel between a subscriber unit and a base station, quantizing the estimate with a first codebook, thereby producing a first quantized estimate, quantizing an (n?1)-th quantized estimate with an n-th codebook, thereby producing an n-th quantized estimate, where n is an integer value ranging from 2 to R, R is a total number of quantizations of the estimate, wherein the n-th codebook is a localized codebook. The method also includes incrementing n, repeating the quantizing an (n?1)-th quantized estimate until n=R, and transmitting information based on the R quantized estimates to the base station.

Abstract: An apparatus and method for beamforming in a Multiple Input Multiple Output (MIMO) wireless communication system are provided. A Base Station (BS) includes a calculator for generating relational expressions between beamforming vectors of Mobile Stations (MSs) using channel information of each MS, for determining beamforming vector candidates of each MS from the relational expressions using generalized Eigen-analysis, and for selecting beamforming vectors for each MS from the beamforming vector candidates, and a plurality of Transmit (Tx) beamformers for performing Tx beamforming on dedicated pilot symbols to be transmitted to each MS using the beamforming vectors for each MS.

Abstract: A system and method is proposed for progressively quantizing channel state information for application in a MIMO (multiple input multiple output) communication system. A method includes computing an estimate of a communications channel between a subscriber unit and a base station, quantizing the estimate with a first codebook, thereby producing a first quantized estimate, quantizing an (n?1)-th quantized estimate with an n-th codebook, thereby producing an n-th quantized estimate, where n is an integer value ranging from 2 to R, R is a total number of quantizations of the estimate, wherein the n-th codebook is a localized codebook. The method also includes incrementing n, repeating the quantizing an (n?1)-th quantized estimate until n=R, and transmitting information based on the R quantized estimates to the base station.

Abstract: An apparatus and method for beamforming in a Multiple Input Multiple Output (MIMO) wireless communication system are provided. A Base Station (BS) includes a calculator for generating relational expressions between beamforming vectors of Mobile Stations (MSs) using channel information of each MS, for determining beamforming vector candidates of each MS from the relational expressions using generalized Eigen-analysis, and for selecting beamforming vectors for each MS from the beamforming vector candidates, and a plurality of Transmit (Tx) beamformers for performing Tx beamforming on dedicated pilot symbols to be transmitted to each MS using the beamforming vectors for each MS.