Accelerating Iteratively Linear Detectors in Multi-User (ELAA-)MIMO Systems with UW-SVD
Jiuyu Liu, Yi Ma, Jinfei Wang, Rahim Tafazolli

TL;DR
This paper introduces UW-SVD, a novel method that accelerates iterative MIMO detectors by transforming the channel into a better-conditioned equivalent model, significantly speeding up convergence especially in challenging ELAA-MIMO scenarios.
Contribution
The paper proposes user-wise SVD to improve convergence speed of iterative MIMO detectors in ill-conditioned channels, particularly for ELAA-MIMO systems.
Findings
UW-SVD improves convergence speed by up to 10 times.
The e-channel matrix is better conditioned than the original channel.
Simulation confirms effectiveness in both uncoded and coded systems.
Abstract
Current iterative multiple-input multiple-output (MIMO) detectors suffer from slow convergence when the wireless channel is ill-conditioned. The ill-conditioning is mainly caused by spatial correlation between channel columns corresponding to the same user equipment, known as intra-user interference. In addition, in the emerging MIMO systems using an extremely large aperture array (ELAA), spatial non-stationarity can make the channel even more ill-conditioned. In this paper, user-wise singular value decomposition (UW-SVD) is proposed to accelerate the convergence of iterative MIMO detectors. Its basic principle is to perform SVD on each user's sub-channel matrix to eliminate intra-user interference. Then, the MIMO signal model is effectively transformed into an equivalent signal (e-signal) model, comprising an e-channel matrix and an e-signal vector. Existing iterative algorithms can be…
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Taxonomy
TopicsDistributed Sensor Networks and Detection Algorithms · Ultra-Wideband Communications Technology · Microwave Imaging and Scattering Analysis
