IoT Connectivity Optimization in RIS-Assisted Full-Duplex Massive MIMO Networks

Abstract

With the continuous increase in the number of Internet-of-Things (IoT) devices, next-generation 6G networks are required to deploy efficient resource allocation schemes to support this increased demand. In this paper, we consider a single-cell reconfigurable intelligent surface (RIS) assisted full-duplex (FD) massive multiple-input-multiple-output (MIMO) network. The FD base station (BS) simultaneously supports macro-downlink (DL) users, macro-uplink (UL) users, and IoT-DL devices. However, effective analog SI cancellation techniques are necessary to ensure FD-feasibility and prevent the high implementation complexity of the analog self-interference (SI) cancellation circuit in massive MIMO networks. Additionally, the network is required to optimize the DL-IoT devices' connectivity to avoid unwanted degradation in the macro-DL users' transmissions. Accordingly, in this paper, to address these challenges, we propose a RIS-assisted SI-cancellation technique to help minimize the SI level in the analog domain. Additionally, we aim at maximizing the DL-IoT sum rate while avoiding any unintended deterioration in macro-DL users' transmissions. From numerical results, we verify the effectiveness of the proposed algorithm to decrease the SI level. Additionally, we further show that, as compared to systems that use zero-forcing precoding in SI cancellation, the proposed technique effectively boosts IoT connectivity.