智能反射面辅助的星地认知网络多播传输鲁棒优化设计
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1.南京邮电大学贝尔英才学院;2.南京邮电大学通信与信息工程学院

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基金项目:

国家自然科学基金重点国际合作(61720106003),南京邮电大学引进人才科研启动基金(NY220111)。


Robust Optimization Design for Multicast Transmission in IRS-Aided Cognitive Satellite and Terrestrial Network
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1.Nanjing University of Posts and Telecommunications Bell Honors School;2.Nanjing University of Posts and Telecommunications School of Communications and Information Engineering

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    摘要:

    针对智能反射面辅助的星地认知网络,提出了一种基于用户非完美信道状态信息的鲁棒多播传输算法,以进一步提高系统频谱效率。具体地,卫星采用多播技术服务多个主用户,同时共享频谱资源的地面网络通过空分多址和智能反射面分别服务直达用户和遮挡用户。然后,建立了以地面网络发射功率最小化为优化目标,并以地面用户信干噪比中断约束,以及主用户干扰功率中断概率为约束的联合优化问题。针对此非凸问题,首先借助指数分布的累积分布函数将非凸的中断约束转化为确定形式。接着,提出了一种结合交替优化与半正定松弛的鲁棒波束成形算法,以获得较优性能的解。计算机仿真结果证明了所提出的算法的鲁棒性和优越性。

    Abstract:

    In order to improve spectrum efficiency, this paper proposes a robust multicast transmission algorithm for intelligent reflecting surface (IRS)-aided cognitive satellite and terrestrial network (CSTN). Specifically, the satellite uses multicast technology to serve multiple primary users, while the terrestrial base station (BS), sharing spectrum resources with the satellite network, serves direct users and blocked users through space division multiple access technique and intelligent reflecting surfaces, respectively. Then, a joint optimization problem is formulated to minimize the BS transmit power, while satisfying the outage constraints of both the signal-to-noise ratio of terrestrial users and the interference power of the primary users. To address this nonconvex problem, the nonconvex outage constraint is first transformed into a deterministic form with the assistance of the cumulative distribution function of the exponential distribution. Then, a robust beamforming algorithm combining alternating optimization with semi-positive definite relaxation is proposed to obtain a solution having better performance. Computer simulation results demonstrate the robustness and superiority of the proposed algorithm.

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  • 收稿日期:2023-05-14
  • 最后修改日期:2024-03-02
  • 录用日期:2024-03-04
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