Ehab Mahmoud Mohamed

Intelligent reconfigurable surface (IRS) is a competitive relaying technology to widen the WiGig coverage range, as it offers an effective means of addressing blocking issues. However, selecting the optimal IRS relay for maximum attainable data rate is a time-consuming process, as it requires WiGig beamforming training (BT) to tune the phase shifts (PSs) for WiGig base station (WGBS) and IRS relays. This paper proposes a self-learning-based budgeted Thomson sampling approach for IRS relay probing (BTS-IRS) to address this challenge. The BT time cost of probing the IRS relay is incorporated into the main BTS formula, where both payoff and cost posterior distributions are sampled separately, their ratio is estimated, and the arm/IRS relay with the highest ratio is decided. This enables the IRS relay to be chosen with the lowest BT time cost. Numerical results demonstrate the improved performance of the BTS-IRS relaying technique regarding BT time consumption/cost, spectral efficiency, and attainable data rate when compared to other benchmarks.