On beamforming with finite rate feedback in multiple-antenna systems

By Krishna Kiran Mukkavilli, Ashutosh Sabharwal, Elza Erkip, Behnaam Aazhang. IEEE Transactions on Information Theory, 2003

932 citations recorded by the source corpus.

Subfields: Computer Networks and Communications, Electrical and Electronic Engineering

View Paper: https://www.semanticscholar.org/paper/8ed5bd785db6b9c95e675359556fd7b0914fe44e

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Citations in the corpus

MIMO Wireless Linear Precoding (2007, background): “information can be, for example, selected and important channel information for precoding [33], the index of the precoder from a codebook [61], [62], or quantized channel information [63], [64] (and references therein).”
Achieving the Welch bound with difference sets (2005, background): “is mainly due to [ 19 ], although not explicitly recognized as a bound on therein.”
Grassmannian beamforming for multiple-input multiple-output wireless systems (2003, background): “The relationship between quantized beamforming and Grassmannian line packing was observed in [22], [29]–[31] in parallel and independently of our work in [32]–[35].”
What is the value of limited feedback for MIMO channels? (2004, methodology): “This model has been proposed for limited feedback beamforming [12] [13], precoded orthogonal space-time block codes [1] [14], precoded spatial multiplexing [15] [16], and transmit covariance optimization [17] [18].”
On the performance of random vector quantization limited feedback beamforming in a MISO system (2007, background): “Proof: Using results from [5, Theorem 1] and [4], the cdf of the squared absolute inner product between two uniformly distributed unit vectors is, for 2 [0; 1℄, given by 1 (1 )m 1: (11) To find the cdf of , we realize that the cdf of the maximum squared inner product of N independent beamforming vec”
High-Dimensional Stochastic Gradient Quantization for Communication-Efficient Edge Learning (2019, background): “In [12], targeting the multiple-input singleoutput (MISO) communication system, a Grassmannian codebook comprising a set of vector codewords with unitary norm is designed for quantizing the channel state information (CSI).”
High-Dimensional Stochastic Gradient Quantization for Communication-Efficient Edge Learning (2020, methodology): “In [13], for beamformer quantization and feedback, a randomly generated Grassmannian codebook, which comprises a set of unitary vectors uniformly distributed on the Grassmann manifold, is proved to be asymptotically optimal under the criterion of rate maximization as the codebook size grows.”
Network Beamforming Using Relays With Perfect Channel Information (2008, background): “If the transmitter has perfect or partial channel information, performance can be further improved through beamforming since it takes advantage of the channel information (both direction and strength) at the transmit side to obtain higher receive SNR [2].”
Multi-Antenna Downlink Channels with Limited Feedback and User Selection (2007, methodology): “Note that for a single user system, CQI is not necessary since there is no need of user selection.”
An overview of limited feedback in wireless communication systems (2008, methodology): “Work in [222] for the MISO case and [202] for the MIMO case showed that for a spatially uncorrelated Rayleigh fading channel, the outage minimizing, SN R maximizing, and rate maximizing design is to i) think of the setF as a collection of lines in the Euclidean space Ct and ii) maximize the angular ”
Fast transfer of channel state information in wireless systems (2006, methodology): “2 Reverse link CSI transmission Transmission of CSI in a communication link is often done digitally: the CSI is quantized and then channelcoded and modulated onto a standard PSK or QAM constellation [17, 18].”
MIMO Broadcast Channels With Finite-Rate Feedback (2006, background): “receiver quantizes its channel to the quantization vector that is closest to its channel vector, where closeness is measured in terms of the angle between two vectors or, equivalently, the inner product [4], [5].”
Multiuser MIMO Achievable Rates With Downlink Training and Channel State Feedback (2007, methodology): “Following [21], [20], [19], and [26], we consider a specific scheme for channel state quantization based on a quantization codebook of unit-norm vectors in .”