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arXiv · 2411.09130

Analysis and Optimization of Multiple-STAR-RIS Assisted MIMO-NOMA with GSVD Precoding: An Operator-Valued Free Probability Approach

Siqiang Wang·Zhong Zheng·Jing Guo·Zesong Fei·Zhi Sun
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Abstract

Among the key enabling 6G techniques, multiple-input multiple-output (MIMO) and non-orthogonal multiple-access (NOMA) play an important role in enhancing the spectral efficiency of the wireless communication systems. To further extend the coverage and the capacity, the simultaneously transmitting and reflecting reconfigurable intelligent surface (STAR-RIS) has recently emerged out as a cost-effective technology. To exploit the benefit of STAR-RIS in the MIMO-NOMA systems, in this paper, we investigate the analysis and optimization of the downlink dual-user MIMO-NOMA systems assisted by multiple STAR-RISs under the generalized singular value decomposition (GSVD) precoding scheme, in which the channel is assumed to be Rician faded with the Weichselberger's correlation structure. To analyze the asymptotic information rate of the users, we apply the operator-valued free probability theory to obtain the Cauchy transform of the generalized singular values (GSVs) of the MIMO-NOMA channel matrices, which can be used to obtain the information rate by Riemann integral. Then, considering the special case when the channels between the BS and the STAR-RISs are deterministic, we obtain the closed-form expression for the asymptotic information rates of the users. Furthermore, a projected gradient ascent method (PGAM) is proposed with the derived closed-form expression to design the STAR-RISs thereby maximizing the sum rate based on the statistical channel state information. The numerical results show the accuracy of the asymptotic expression compared to the Monte Carlo simulations and the superiority of the proposed PGAM algorithm.

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Siqiang Wang, Zhong Zheng, Jing Guo, Zesong Fei, Zhi Sun. 2024-11-14. Analysis and Optimization of Multiple-STAR-RIS Assisted MIMO-NOMA with GSVD Precoding: An Operator-Valued Free Probability Approach. https://arxiv.org/abs/2411.09130

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