论文标题
多载波 - 划分双链细胞无细胞的巨大巨大MIMO系统中的功率分配的异质图神经网络
Heterogeneous graph neural network for power allocation in multicarrier-division duplex cell-free massive MIMO systems
论文作者
论文摘要
带内完全双链细胞（CF）系统患有严重的自身干扰和交联干扰，尤其是当CF系统以分布式操作时。为此，我们提出多载波划分双链体作为在分布式CF大型MIMO系统中实现全双工操作的推动力，在同一频段中，下行链路和上行链路传输在同一频段中同时发生，但在相互正交的子载波上。为了最大化光谱效率（SE），我们引入了针对CF系统（称为CF-HGNN）的异质图神经网络（HGNN），以优化功率分配（PA）。我们为CF-HGNN设计自适应节点嵌入层，以扩展到各种访问点（AP），移动站（MSS）和子载波。 CF-HGNN的注意机制使各个AP/MS节点能够从具有不同优先级的干扰和通信路径中汇总信息。为了进行比较，我们提出了一种二次变换和连续的凸近似（QT-SCA）算法，以以经典方式解决PA问题。数值结果表明，CF-HGNN能够实现QT-SCA可实现的99％\％，但仅使用$ 10^{ - 4} $ times的操作时间。就SE性能而言，CF-HGNN显着优于传统的贪婪不公平方法。此外，CF-HGNN对CF网络具有良好的可扩展性，该网络具有各种数量的节点和子载波，以及在用户群集辅助时，也可以对大规模CF网络。
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.
