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A Symbol-Level Optimization Method for Block Precoding in Wireless Communication

delete2024-01-15
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PRE
AI
W
Wang, Qi
C
Chen Zhuang
周
周武旸 (Wuyang Zhou) *
DOI:10.1109/JIOT.2023.3296067delete
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Abstract

Abstract

En 中文
Compared to block precoding (BP), interference exploitation symbol-level precoding (IESLP) can effectively reduce transmit power in multiantenna wireless communication systems. However, because most existing IESLP methods are symbol-level optimizations of zero-forcing (ZF) BP which is usually not the best BP, the performance and application range of IESLP are restricted. In this article, we investigate the symbol-level optimization of any given BP method to reduce transmit power. We first define generalized constructive interference (GCI) as a new constructive interference metric for any received BP signals, where the interference is constructive as long as it will not impair the symbol error rate (SER). Then, convex GCI regions (GCIRs) of received BP signals for both phase shift keying (PSK) and quadrature amplitude modulation (QAM) constellations are designed. With the aid of GCIR, we propose BP-based symbol-level precoding (BPSLP) methods to reduce transmit power. Moreover, due to the difficulty of obtaining perfect channel state information (CSI), we also propose robust BPSLP (RBPSLP) methods to reduce transmit power for PSK and QAM constellations, where outage probability (OP) is constrained for robustness. Numerical results show that compared to existing precoding methods, our proposed precoding methods effectively reduce transmit power. It is worth noting that due to the bounded or even one-point constructive interference region (CIR), there are usually no feasible solutions for existing robust IESLP methods under QAM constellations, but our proposed RBPSLP method for QAM constellations is feasible as long as a feasible robust BP matrix exists.
Keywords:
Constructive interference (CI)
power minimization
robust precoding
symbol-level precoding (SLP)

Journal

IEEE Internet of Things Journal cover
IEEE Internet of Things Journal
IF:
8.9
Papers:
1.4W
Citations:
7.8W

Organization

C
chinese academy of sciences
Scholars:
56.7W
Papers: 45.0W
Citations: 704
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