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arXiv 2205.00546eess.SP
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用于多载波分双工无蜂窝大规模MIMO系统功率分配的异构图神经网络

Heterogeneous graph neural network for power allocation in multicarrier-division duplex cell-free massive MIMO systems

Bohan Li, Lie-Liang Yang, Robert G Maunder, Songlin Sun, Pei Xiao

AI总结:

针对分布式无蜂窝大规模MIMO系统的干扰问题,提出多载波分双工方案及CF-HGNN优化功率分配,在接近传统算法性能的同时大幅降低运算时间,且具备良好可扩展性。

摘要:

带内全双工无蜂窝(CF)系统存在严重的自干扰和交叉链路干扰,在分布式运行模式下尤为突出。为此,我们提出多载波分双工作为实现分布式无蜂窝大规模MIMO系统全双工运行的使能技术,该系统中下行链路和上行链路传输在同一频段内同时进行,但使用相互正交的子载波。为最大化频谱效率(SE),我们提出一种专为无蜂窝系统设计的异构图神经网络(HGNN),称为CF-HGNN,用于优化功率分配(PA)。我们为CF-HGNN设计了自适应节点嵌入层,可适配不同数量的接入点(AP)、移动台(MS)和子载波。CF-HGNN的注意力机制使单个AP/MS节点能够以不同优先级聚合来自干扰路径和通信路径的信息。作为对比,我们提出了一种二次变换与连续凸逼近(QT-SCA)算法,以经典方法求解功率分配问题。数值结果表明,CF-HGNN能够达到QT-SCA可实现频谱效率的99%,但运行时间仅为后者的10^-4倍。在频谱效率性能方面,CF-HGNN显著优于传统的贪婪不公平方法。此外,CF-HGNN对具有不同节点数量和子载波数量的无蜂窝网络表现出良好的可扩展性,在用户聚类的辅助下对大规模无蜂窝网络也具备良好的可扩展性。

英文摘要:

In-band full duplex cell-free (CF) systems suffer from severe self-interference and cross-link interference, especially when CF systems are operated in distributed way. To this end, we propose the multicarrier-division duplex as an enabler for achieving full-duplex operation in the distributed CF massive MIMO systems, where downlink and uplink transmissions occur simultaneously in the same frequency band but on the mutually orthogonal subcarriers. To maximize the spectral-efficiency (SE), we introduce a heterogeneous graph neural network (HGNN) specific for CF systems, referred to as CF-HGNN, to optimize the power-allocation (PA). We design the adaptive node embedding layer for CF-HGNN to be scalable to the various numbers of access points (APs), mobile stations (MSs) and subcarriers. The attention mechanism of CF-HGNN enables individual AP/MS nodes to aggregate information from the interfering and communication paths with different priorities. For comparison, we propose a quadratic transform and successive convex approximation (QT-SCA) algorithm to solve the PA problem in classic way. Numerical results show that CF-HGNN is capable of achieving 99\% of the SE achievable by QT-SCA but using only $10^{-4}$ times of its operation time. CF-HGNN significantly outperforms the traditional greedy unfair method in terms of SE performance. Furthermore, CF-HGNN exhibits good scalability to the CF networks with various numbers of nodes and subcarriers, and also to the large-scale CF networks when assisted by user clustering.

机构、总结、中文摘要均由AI分析而来,可能存在错误,请以论文正文为准。

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