Fair Non-Orthogonal Multiple Access Communication Systems with Reconfigurable Intelligent Surface

Reconfigurable intelligent surface (RIS) is a promising solution to improve spectrum efficiency and promote cost- effectively wireless communication in the future. In this paper, RIS is deployed between a single-antenna base station (BS) and multiple single-antenna users to assist downlink non-orthogonal multiple access (NOMA) transmission. Considering the fairness among users, our goal is jointly optimizing the power allocation, decoding order, and the phase shifts to maximize the minimum user rate under total power constraint. To solve this minimum rate maximization problem, the optimal power allocation and the optimal fair rate are first revealed with a given phase shift vector. Then, the phase shift vector is optimized via maximizing the worst channel gain, which can determine the lower bound of the fair rate. The phase shift vector optimization problem is relaxed to a convex semidefinite program (SDP) and an efficient algorithm is proposed to obtain a rank-one solution. Simulation results show that our proposed algorithm can enhance the fair rate compared to the conventional scheme.

[1]  Zhiguo Ding,et al.  A Survey of Multi-Access Edge Computing in 5G and Beyond: Fundamentals, Technology Integration, and State-of-the-Art , 2019, IEEE Access.

[2]  Walid Saad,et al.  A Vision of 6G Wireless Systems: Applications, Trends, Technologies, and Open Research Problems , 2019, IEEE Network.

[3]  Pingzhi Fan,et al.  On the Performance of Non-Orthogonal Multiple Access in 5G Systems with Randomly Deployed Users , 2014, IEEE Signal Processing Letters.

[4]  Jiaru Lin,et al.  Exploiting Intelligent Reflecting Surfaces in Multi-Antenna Aided NOMA Systems. , 2019, 1910.13636.

[5]  Ming Chen,et al.  On the Optimality of Power Allocation for NOMA Downlinks With Individual QoS Constraints , 2017, IEEE Communications Letters.

[6]  Erik G. Larsson,et al.  Weighted Sum-Rate Optimization for Intelligent Reflecting Surface Enhanced Wireless Networks. , 2019, 1905.07920.

[7]  Chau Yuen,et al.  Reconfigurable Intelligent Surfaces for Energy Efficiency in Wireless Communication , 2018, IEEE Transactions on Wireless Communications.

[8]  Walid Saad,et al.  A Joint Learning and Communications Framework for Federated Learning Over Wireless Networks , 2021, IEEE Transactions on Wireless Communications.

[9]  H. Vincent Poor,et al.  Energy-Efficient Wireless Communications With Distributed Reconfigurable Intelligent Surfaces , 2020, IEEE Transactions on Wireless Communications.

[10]  Zhiguo Ding,et al.  A Simple Design of IRS-NOMA Transmission , 2019, IEEE Communications Letters.

[11]  Anthony Man-Cho So,et al.  On approximating complex quadratic optimization problems via semidefinite programming relaxations , 2005, IPCO.

[12]  Hui Tian,et al.  Resource Allocation for Multi-Cell IRS-Aided NOMA Networks , 2020, IEEE Transactions on Wireless Communications.

[13]  Wei Xu,et al.  Energy Efficient Resource Allocation in Machine-to-Machine Communications With Multiple Access and Energy Harvesting for IoT , 2017, IEEE Internet of Things Journal.

[14]  Lajos Hanzo,et al.  Intelligent Reflecting Surface Aided MIMO Broadcasting for Simultaneous Wireless Information and Power Transfer , 2019, IEEE Journal on Selected Areas in Communications.

[15]  Lajos Hanzo,et al.  Reconfigurable Intelligent Surface Aided NOMA Networks , 2020, IEEE Journal on Selected Areas in Communications.