Multi-objective oriented resource allocation in reconfigurable intelligent surface assisted HCNs

Abstract

In order to achieve thousand-fold throughput improvements for future applications, heterogeneous cellular networks (HCNs) with ultra-densely deployed small cells have gained a great deal of attention in recent years. However, energy consumption is an increasing concern as base station deployment (BSs) becomes denser. In order to address this issue, a novel framework is proposed for integrating multiple reconfigurable intelligent surfaces (RISs) in heterogeneous cellular networks (HCNs), where the communication in each BS is enhanced by its associated RIS. The multi-objective optimization is formulated to obtain the optimal trade-off between throughput and energy consumption. Moreover, an improved non-dominated sorting genetic algorithm II (NSGA-II) based algorithm is proposed to optimize the sub-carrier assignment, user equipment (UE) association, power allocation, and RISs’ phase shift. The proposed algorithm’s potential solutions are first encoded as individuals and optimized with the conventional NSGA-II algorithm. In order to further tackle the coupling characters of the optimization variables, the Zoutendijk method is further employed for searching for better solutions. Numerical results illustrate that: (i) the proposed multiple-RIS-assisted HCNs outperform conventional HCNs without RIS. (ii) the performance can be improved by deploying the RIS closer to UEs or increasing the number of reflecting elements.