Flexible Resource Optimization for D2D XL-MIMO Communication via Adversarial Multi-Armed Bandit
Zhaomin Jian, Chao Ma, Yunchao Song, Mengshuang Liu, Huibin Liang · Electronics · 2025
Extremely large-scale multi-input and multi-output (XL-MIMO) communication, compared to conventional massive multi-input multi-output communication, can support more users and higher data throughput, thereby significantly improving its spectral efficiency and spatial multiplexing capabilities. This paper investigates the optimization of resource allocation for device-to-device (D2D) multicast communication in XL-MIMO cellular networks. The “many-to-many” sharing model permits one subcarrier to be shared among multiple D2D groups (DGs) and each DG to reuse multiple subcarriers. The objective is to maximize the total multicast data rate of DGs while meeting the data rate requirements of cellular users. This optimization problem is formulated as a 0–1 mixed-integer nonlinear programming problem, with the challenge lying in the fact that adjusting the subcarriers and the power of the user equipment alters the network’s carrier occupation and interference relationships, thereby increasing computational complexity. To address this challenge, a phased strategy is proposed. Initially, subcarrier allocation and coarse power allocation are conducted for cellular users. Subsequently, an adversarial multi-player multi-armed bandit framework is employed, treating DGs as players and subcarrier and power combinations as arms, to maximize the total multicast data rate. An improved Exp3 algorithm is utilized for selecting the optimal combination of arms. Finally, precise power allocation for cellular users is conducted based on the allocation results of the DGs. A comparative analysis of various simulations confirms the superiority of our algorithm over the established heuristic subcarrier assignment and proposed power allocation (HSAPP) and the channel allocation scheme using full information of device locations (CAFIL) approaches.