Contextual Bandits and Reconfigurable Intelligent Surfaces for Predictive LTM Handover Decisions

This article addresses the challenge of optimizing handover (HO) in next-generation wireless networks by integrating Reconfigurable Intelligent Surfaces (RIS), predicting received signal power, and utilizing learning-based decision-making. A conventional reactive HO mechanism, such as lower-layer triggered mobility (LTM), is enhanced through linear prediction to anticipate link degradation. Additionally, the use of RIS helps to mitigate signal blockage and extend coverage. An online trained non-linear Contextual Multi-Armed Bandit (CMAB) agent selects target gNBs based on context features, which reduces unnecessary HO and signaling overhead. Extensive simulations evaluate eight combinations of these techniques under realistic mobility and channel conditions. Results show that CMAB and RSRP prediction consistently reduce the number of HO, ping-pong rate and cell preparations, while RIS improves link reliability.

Beyond Diagonal RIS-assisted MIMO Transmission: Beamforming Gain and Capacity Optimization

Reconfigurable Intelligent Surfaces (RIS) have emerged as a transformative technology in wireless communications, offering unprecedented control over signal propagation. This study focuses on passive beyond diagonal reconfigurable intelligent surface (BD-RIS), which has been proposed to generalize conventional diagonal RIS, in Multiple-Input Multiple-Output (MIMO) downlink (DL) communication systems. We compare the performance of transmit beamforming (TxBF) and MIMO capacity transmission with waterfilling power allocation in the millimeter wave (mmWave) band, where propagation primarily occurs under line-of-sight (LOS) conditions. In the lack of closed-form expressions for the optimal RIS elements in either case, our approach adopts a gradient-based optimization approach requiring lower complexity than the solution in arXiv:2406.02170. Numerical results reveal that BD-RIS significantly outperforms traditional diagonal RIS in terms of spectral efficiency and coverage