Proceedings Article10.1109/CCE.2006.350820
MIMO Precoding Structures for Frequency-Flat and Frequency-Selective Fading Channels
Hamid Reza Bahrami,Tho Le-Ngoc +1 more
- 01 Oct 2006
- pp 193-197
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TL;DR: Simulation results show that the proposed precoders can improve the ergodic capacity of both frequency-flat and frequency-selective fading channels, especially for channels with high spatial correlation.
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Abstract: This paper presents optimum MIMO precoder designs based on transmit and receive correlation matrices to maximize the ergodic channel capacity for both frequency-flat and frequency-selective fading channels. It is shown that the optimum MIMO precoder in a frequency-flat fading channel is an eigen beamformer with beams referred to the eigen-modes of the transmit correlation matrix. By using the multi-path representation of a frequency-selective fading channel, the corresponding optimum precoding structure is derived. For a frequency-selective fading channel that can be represented by L uncorrelated effective paths, the optimum precoding structure consists of P+L parallel eigen-beamformers. Simulation results show that the proposed precoders can improve the ergodic capacity of both frequency-flat and frequency-selective fading channels, especially for channels with high spatial correlation.
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Citations
Advances in MIMO Techniques for Mobile Communications—A Survey
Farhan Khalid,Joachim Speidel +1 more
TL;DR: This paper provides a comprehensive overview of critical developments in the field of multiple-input multiple-output (MIMO) wireless communication systems and touches upon the topic of MU-M IMO capacity as well as the promising convex optimization approaches to MIMO system design.
Linear transceivers for mimo relays
Cheng Yu Andy Shang
- 01 Jan 2014
TL;DR: This thesis considers the leakage of the SI from the FD relay, and proposes different precoder and weight vector designs that may increase the end to end (e2e) signal to interference and noise ratio (SINR) at the destination.
Massive MIMO Data Detection Using Matrix Inversion Method and Deep Neural Network
Heydar A. Saleem,Mohammad A. Esmaeili,Mohamed K. M. Hassan,Mahmoud A. Albreem,Sam Ansari +4 more
- 10 Oct 2023
TL;DR: This paper proposes a hybrid data detection algorithm for massive MIMO systems, combining linear approximate matrix inversion with a deep neural network to achieve near-optimal performance with lower complexity, outperforming classical methods in various simulations.
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