面向6G低轨卫星物联网的能效优先的多波束鲁棒预编码设计

如今，物联网已经应用在经济社会的各个领域，但是由于空间、环境等限制，地面物联网在一些应用场景中表现出了服务能力严重不足的问题。针对这个问题，第六代移动通信技术（6th generation mobile networks， 6G），提出将卫星通信与地面通信融合，从而实现全球无缝覆盖。对于卫星通信，卫星通常由太阳能供电，导致能量有限，因此想要实现大规模设备高质量的通信，卫星的能量效率设计非常必要。本文为6G低轨（low earth orbit， LEO）卫星物联网（Internet of Things， IoT）设计了一个能量有效的非正交多址接入（non-orthogonal multiple access， NOMA）框架，以支持广域分布设备的大规模机器通信（massive machine-type communications， mMTC）。考虑到LEO卫星的能量有限性和信道状态信息（channel state information， CSI）不准确，本文建立了一个在功率和信干噪比（signal-to-interference-plus-noise ratio， SINR）约束下最大化能效的优化问题。通过将分数形式问题转换为等效的减法形式优化问题，提出了一种鲁棒的联合波束成形和功率分配算法，以在存在信道不确定性的情况下最大化能量效率。理论分析和仿真结果均验证了所提算法的有效性和鲁棒性。 

Energy-Efficient Design of Multibeam Robust Precoding for 6G LEO Satellite Internet of Things

? ?Nowadays， the Internet of Things （IoT） has been applied in various fields of the economy and society. However， due to the limitations of space and environment， the terrestrial IoT has shown a serious shortage of service capabilities in some application scenarios. To solve this problem， the 6th generation （6G） mobile networks， proposes to integrate satellite communication and terrestrial communication to achieve seamless global coverage. For satellite communications， satellites are usually powered by solar energy， resulting in limited energy. Therefore， the energy-efficient design of satellites is necessary for achieving high-quality communications in large-scale devices. This paper provides an energy-efficient non-orthogonal multiple access （NOMA） framework for 6G low earth orbit （LEO） satellite IoT， so as to support massive machine-type communications （mMTC） of IoT devices distributed over a very large area. Considering limited energy supply and partial channel state information （CSI） at the LEO satellite， an energy efficiency maximization optimization problem is formulated subject to power and signal-to-interference-plus-noise ratio （SINR） constraints. Since the formulated problem is mathematically intractable， by transforming the fractional-form problem to an equivalent subtractive-form optimization problem， an iterative-based robust joint beam design and power allocation algorithm is proposed to maximize the energy efficiency in the presence of channel uncertainty. Both theoretical analysis and simulation results validate the effectiveness and robustness of the proposed algorithm.