Joint Beamforming and Dynamic Beam Hopping Based on MAPPO for LEO Satellite Communication System

IF 5.5 3区计算机科学 Q1 COMPUTER SCIENCE, INFORMATION SYSTEMS IEEE Wireless Communications Letters Pub Date : 2025-02-21 DOI:10.1109/LWC.2025.3544635

Meng Meng;Bo Hu;Shanzhi Chen;Shaoli Kang

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Abstract

To meet time-varying and nonuniform service requirements, how to flexibly allocate resource-constrained to meet the traffic demands is an important topic in LEO satellite communication system. We introduce joint beamforming and dynamic beam hopping (BH) algorithm to support hybrid wide-spot beam coverage in LEO satellite communication system, which can transmit control signalling and user data simultaneous. Considering that the joint decision of beamforming and BH will lead to explosive growth of the action space dimension, a cooperative Multi-Agent Proximal Policy Optimization (MAPPO) algorithm is presented to solve this problem in hybrid wide-spot beam coverage scenario. The beamforming problem is decomposed into two subproblems, power allocation and analog beamforming problems. The analog beamforming problem is solved by ZF beamforming algorithm, and the power allocation and BH problems are solved by MAPPO algorithm. In MAPPO algorithm, each agent only undertakes the BH or power allocation decision of one beam. The agents learn to cooperate with others via shared rewards to achieve a common goal of maximize the system throughput and minimize the delay fairness (DF) while ensuring the minimum rate requirement of the control beam. Simulation results show that the MAPPO algorithm can achieve real-time BH and power allocation to match time-varying traffic requests. In addition, the proposed algorithm can achieve about 8 Mbps throughput gain compared with GABH.

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基于MAPPO的低轨卫星通信系统联合波束形成和动态波束跳变

为满足时变、非均匀的业务需求，如何灵活分配资源约束以满足业务需求是低轨道卫星通信系统中的一个重要课题。为了支持低轨道卫星通信系统的混合广域波束覆盖，提出了联合波束形成和动态波束跳变算法，实现了控制信号和用户数据的同步传输。考虑到波束形成和BH的联合决策将导致行动空间维数的爆炸式增长，提出了一种多智能体合作的近端策略优化（MAPPO）算法来解决混合广点波束覆盖场景下的这一问题。将波束形成问题分解为功率分配和模拟波束形成两个子问题。模拟波束形成问题采用ZF波束形成算法解决，功率分配和BH问题采用MAPPO算法解决。在MAPPO算法中，每个agent只承担一个波束的BH或功率分配决策。智能体学习通过共享奖励与其他智能体进行合作，以实现系统吞吐量最大化和延迟公平性（DF）最小化的共同目标，同时保证控制束的最小速率要求。仿真结果表明，MAPPO算法可以实现实时的BH和功率分配，以匹配时变的业务请求。此外，与GABH相比，该算法可获得约8 Mbps的吞吐量增益。

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来源期刊

IEEE Wireless Communications Letters Engineering-Electrical and Electronic Engineering

CiteScore

12.30

自引率

6.30%

发文量

481

期刊介绍： IEEE Wireless Communications Letters publishes short papers in a rapid publication cycle on advances in the state-of-the-art of wireless communications. Both theoretical contributions (including new techniques, concepts, and analyses) and practical contributions (including system experiments and prototypes, and new applications) are encouraged. This journal focuses on the physical layer and the link layer of wireless communication systems.