Simultaneous Information and Control Signalling Protocol for RIS-Empowered Wireless Systems

Integration of RIS in radio access networks requires signaling between edge units and the RIS microcontroller (MC). Unfortunately, in several practical scenarios, the signaling latency is higher than the communication channel coherence time, which causes outdated signaling at the RIS. To counterbalance this, we introduce a simultaneous information and control signaling (SICS) protocol that enables operation adaptation through wireless control signal transmission. SICS assumes that the MC is equipped with a single antenna that operates at the same frequency as the RIS. RIS operates in simultaneous transmission and reflection (STAR) mode, and the source employs non-orthogonal multiple access (NOMA) to superposition the information signal to the control signal. To maximize the achievable user data rate while ensuring the MC's ability to decode the control signal, we formulate and solve the corresponding optimization problem that returns RIS's reflection and transmission coefficients as well as the superposition coefficients of the NOMA scheme. Our results reveal the robustness of the SICS approach.

Can graph neural network-based detection mitigate the impact of hardware imperfections?

Until recently, researchers used machine learning methods to compensate for hardware imperfections at the symbol level, indicating that optimum radio-frequency transceiver performance is possible. Nevertheless, such approaches neglect the error correcting codes used in wireless networks, which inspires machine learning (ML)-approaches that learn and minimise hardware imperfections at the bit level. In the present work, we evaluate a graph neural network (GNN)-based intelligent detector's in-phase and quadrature imbalance (IQI) mitigation capabilities. We focus on a high-frequency, high-directional wireless system where IQI affects both the transmitter (TX) and the receiver (RX). The TX uses a GNN-based decoder, whilst the RX uses a linear error correcting algorithm. The bit error rate (BER) is computed using appropriate Monte Carlo simulations to quantify performance. Finally, the outcomes are compared to both traditional systems using conventional detectors and wireless systems using belief propagation based detectors. Due to the utilization of graph neural networks, the proposed algorithm is highly scalable with few training parameters and is able to adapt to various code parameters.