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.

Analysis of Blocking in mmWave Cellular Systems: Characterization of the LOS and NLOS Intervals in Urban Scenarios

In the millimeter waves (mmWave) bands considered for 5G and beyond, the use of very high frequencies results in the interruption of communication whenever there is no line of sight between the transmitter and the receiver. Blockages have been modeled in the literature so far using tools such as stochastic geometry and random shape theory. Using these tools, in this paper, we characterize the lengths of the segments in line-of-sight (LOS) and in non-line-of-sight (NLOS) statistically in an urban scenario where buildings (with random positions, lengths, and heights) are deployed in parallel directions configuring streets.