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+近年来随着卫星互联网、手机直连卫星快速发展，国际标准组织3GPP、ITU等机构已将卫星通信纳入第六代无线通信的重要组成部分，卫星通信将步入飞速发展的新阶段，星通信将会得到更深入的融合应用，卫星通信业务及用户数量等将迎来快速增长。传统频率复用、多波束技术等有效提升了卫星资源使用效率，但随着用户数量的增加和服务质量需求的多样化，业务在时间和空间上的分布不均问题变得更加突出，这对系统灵活高效地分配资源提出了更高的挑战。论文针对卫星通信中资源分配面临的新问题，结合目前广受关注的深度强化学习技术，在信道分配问题上利用神经网络发掘时空关联性，在跳波束问题上利用多智能体等技术解决跳波束问题中信道与波束点亮联合分配的问题。
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+本文首先介绍了多波束卫星通信系统的基本架构、卫星的信道传输特性以及马尔科夫决策过程和强化学习等技术，建立了基于深度强化学习的信道分配问题的基本场景与用户通信模型。为了保证用户的通信质量，以用户通信的阻塞率作为优化目标，设计了基于深度强化学习的动态信道分配算法。该算法通过状态转换将用户的地理位置描述为多通道图像，并基于卷积神经网络和深度Q网络进行求解，从而获得实时的信道分配策略。仿真结果表明，基于深度强化学习的信道分配算法相比其他算法，能够有效保证系统的接通率，提高频谱效率。
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+进一步针对跳波束应用下的信道分配，以优化通信吞吐量和时延的公平性为优化目标函数，建立起基于深度强化学习的跳波束与信道联合分配算法。该算法包含两个独立的深度Q网络，分别对波束点亮方案和信道分配方式进行优化，采用多头输出的方法解决了跳波束分配问题中空间复杂度过高的问题。仿真结果表明，与传统算法相比，该方法能有效提高系统的吞吐量，并且能够保证系统的时延公平性，同时相比遗传算法等启发式算法能够有效减少系统的决策时间，能够应对动态变化的通信环境。
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+In recent years, with the rapid development of satellite internet and direct-to-mobile satellite communications, international standard organizations such as 3GPP and ITU have incorporated satellite communications as an essential component of the sixth generation of wireless communications. Satellite communications are entering a new phase of rapid development. There will be a deeper integration and application of satellite communications, and the satellite communications industry, along with the number of users, will experience rapid growth. Traditional frequency reuse and multi-beam technologies have effectively improved the efficiency of satellite resource utilization. However, with the increase in the number of users and the diversification of service quality demands, the uneven distribution of services in time and space has become more prominent. This poses higher challenges for the system to allocate resources flexibly and efficiently. This paper addresses the new challenges in resource allocation in satellite communications, combining the widely focused deep reinforcement learning technology. It uses neural networks to explore spatio-temporal correlations in channel allocation problems and employs multi-agent technologies to solve the joint allocation of channels and beam illumination in hop-beam problems.
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+This paper first introduces the basic architecture of multi-beam satellite communication systems, the channel transmission characteristics of satellites, and technologies such as the Markov decision process and reinforcement learning. It establishes the basic scenario and user communication model for the channel allocation problem based on deep reinforcement learning. To ensure the quality of user communications, the user communication blockage rate is used as the optimization target. A dynamic channel allocation algorithm based on deep reinforcement learning is designed. This algorithm uses state transitions to describe the user's geographic location as a multi-channel image and solves it based on convolutional neural networks and deep Q-networks to obtain real-time channel allocation strategies. Simulation results show that the channel allocation algorithm based on deep reinforcement learning can effectively ensure the system's connectivity rate and improve spectral efficiency compared to other algorithms.
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+Further, for channel allocation under hop-beam applications, with the optimization of communication throughput and the fairness of delay as the optimization objective function, a hop-beam and channel joint allocation algorithm based on deep reinforcement learning is established. This algorithm contains two independent deep Q-networks, which optimize the beam illumination scheme and channel allocation method, respectively. It uses a multi-head output method to solve the problem of high spatial complexity in hop-beam allocation. Simulation results show that compared to traditional algorithms, this method can effectively improve the system's throughput and ensure the system's delay fairness. Compared to heuristic algorithms like genetic algorithms, it can significantly reduce the system's decision-making time and cope with dynamically changing communication environments.