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Practical Interference Exploitation Precoding Without Symbol-by-Symbol Optimization: A Block-Level Approach

delete2023-06-01
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OA
AI
A
Ang Li *
C
Chao Shen
廖学文 cover
廖学文 (Xuewen Liao)
C
Christos Masouros
A
A. Lee Swindlehurst
DOI:10.1109/TWC.2022.3222780delete
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Abstract

Abstract

En 中文
In this paper, we propose a constructive interference (CI)-based block-level precoding (CI-BLP) approach for the downlink of a multi-user multiple-input single-output (MU-MISO) communication system. Contrary to existing CI precoding approaches which have to be designed on a symbol-by-symbol level, here a constant precoding matrix is applied to a collection of symbols within a given transmission block, thus significantly reducing the computational costs over traditional CI-based symbol-level precoding (CI-SLP) as the CI-BLP optimization problem only needs to be solved once per block. For both PSK and QAM modulation, we formulate an optimization problem to maximize the minimum CI effect over the block subject to a block- rather than symbol-level power budget. We mathematically derive the optimal precoding matrix for CI-BLP as a function of the Lagrange multipliers in closed form. By formulating the dual problem, the original CI-BLP optimization problem is further shown to be equivalent to a quadratic programming (QP) optimization. Numerical results validate our derivations, and show that the proposed CI-BLP scheme achieves improved performance over the traditional CI-SLP method, thanks to the relaxed power constraint over the considered block of symbol slots.
Keywords:
Precoding
Symbols
Optimization
Interference
Quadrature amplitude modulation
Modulation
Phase shift keying
MU-MISO
symbol-level precoding
constructive interference
optimization
Lagrangian

Journal

IEEE Transactions on Wireless Communications cover
IEEE Transactions on Wireless Communications
IF:
10.7
Papers:
1.3W
Citations:
5.3W

Organization

X
xi'an jiaotong university
Scholars:
9.2W
Papers: 6.6W
Citations: 75
U
University College London
Scholars:
7.9W
Papers: 6.2W
Citations: 15.7W
S
Shenzhen Research Institute of Big Data
Scholars:
251
Papers: 349
Citations: 357
U
university of london
Scholars:
21.5W
Papers: 19.7W
Citations: 305
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