论文标题

速率分拆多重访问多端链路下行通信系统:光谱和能源效率折衷

Rate-Splitting Multiple Access for Multi-antenna Downlink Communication Systems: Spectral and Energy Efficiency Tradeoff

论文作者

Zhou, Gui, Mao, Yijie, Clerckx, Bruno

论文摘要

速率分解(RS)最近被认为是一种有前途的多体层广播通道(BC)的物理层技术。由于它能够部分解码干扰并部分将剩余干扰视为噪声,RS是强大的多重访问的推动力,即速率降低的多个访问(RSMA),已显示出比两个范围及其范围(N型范围)的频谱效率(SE)和能源效率(EE)更高的光谱效率(SE)和能源效率(EE)。由于SE最大化和EE最大化是两个相互矛盾的目标,因此对这两个标准之间的权衡的研究特别令人感兴趣。在这项工作中,我们通过研究发射机的依赖性电路功率消耗的多个输入单输出(MISO)BC中RSMA的联合SE和EE最大化问题来解决SE-EE折衷。为了应对来自多个目标功能和依赖速率的电路功耗带来的挑战,我们首先提出了两种方法,将原始问题转换为单目标问题,即加权-SUM方法和加权功率方法。然后提出了连续的凸近似值(SCA)算法,以共同优化转换问题的预编码器和RS消息拆分。数值结果表明,我们的算法比现有算法快得多。此外,就SE和EE及其权衡而言,RS的性能优于或等于非RS策略。

Rate-splitting (RS) has recently been recognized as a promising physical-layer technique for multi-antenna broadcast channels (BC). Due to its ability to partially decode the interference and partially treat the remaining interference as noise, RS is an enabler for a powerful multiple access, namely rate-splitting multiple access (RSMA), that has been shown to achieve higher spectral efficiency (SE) and energy efficiency (EE) than both space division multiple access (SDMA) and non-orthogonal multiple access (NOMA) in a wide range of user deployments and network loads. As SE maximization and EE maximization are two conflicting objectives, the study of the tradeoff between the two criteria is of particular interest. In this work, we address the SE-EE tradeoff by studying the joint SE and EE maximization problem of RSMA in multiple input single output (MISO) BC with rate-dependent circuit power consumption at the transmitter. To tackle the challenges coming from multiple objective functions and rate-dependent circuit power consumption, we first propose two methods to transform the original problem into a single-objective problem, namely, weighted-sum method and weighted-power method. A successive convex approximation (SCA)-based algorithm is then proposed to jointly optimize the precoders and RS message split of the transformed problem. Numerical results show that our algorithm converges much faster than existing algorithms. In addition, the performance of RS is superior to or equal to non-RS strategy in terms of both SE and EE and their tradeoff.

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