arXivDaily arXiv每日学术速递 周一至周五更新
arXiv 2005.10995eess.SP
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可重构智能表面增强型认知无线电网络

Reconfigurable Intelligent Surface Enhanced Cognitive Radio Networks

Jinglian He, Kaiqiang Yu, Yong Zhou, Yuanming Shi

摘要:

认知无线电(CR)网络是一种很有前途的网络架构,以满足提高稀缺的无线电频谱利用率的要求。同时,可重构智能表面(RIS)通过调节大量的无源反射单元来适当改变信号传播,从而提高无线网络的能量和频谱效率,是一种很有前途的解决方案。在本文中,我们研究了下行链路发射功率最小化问题的RIS增强的单小区认知无线电(CR)网络与单小区主无线电(PR)网络共存,通过联合优化发射波束形成器在次级用户(SU)发射机和相位移矩阵在RIS。研究的问题是一个高度棘手的,由于耦合的优化变量和单位模约束,其中一个替代的最小化框架。此外,一个新的差分凸(DC)算法被开发来解决由此产生的非凸二次规划提升到一个低秩矩阵优化问题。然后,我们利用迹范数和谱范数之间的差异,将非凸秩一约束表示为DC函数。仿真结果验证了我们提出的算法优于现有的国家的最先进的方法。

英文摘要:

The cognitive radio (CR) network is a promising network architecture that meets the requirement of enhancing scarce radio spectrum utilization. Meanwhile, reconfigurable intelligent surfaces (RIS) is a promising solution to enhance the energy and spectrum efficiency of wireless networks by properly altering the signal propagation via tuning a large number of passive reflecting units. In this paper, we investigate the downlink transmit power minimization problem for the RIS-enhanced single-cell cognitive radio (CR) network coexisting with a single-cell primary radio (PR) network by jointly optimizing the transmit beamformers at the secondary user (SU) transmitter and the phase shift matrix at the RIS. The investigated problem is a highly intractable due to the coupled optimization variables and unit modulus constraint, for which an alternative minimization framework is presented. Furthermore, a novel difference-of-convex (DC) algorithm is developed to solve the resulting non-convex quadratic program by lifting it into a low-rank matrix optimization problem. We then represent non-convex rank-one constraint as a DC function by exploiting the difference between trace norm and spectral norm. The simulation results validate that our proposed algorithm outperforms the existing state-of-the-art methods.

机构、总结、中文摘要均由AI分析而来,可能存在错误,请以论文正文为准。

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