Intelligent Reflective Surfaces vs. Mobile Relays | IEEE Globecom 2022 | Dr. Filip Lemic скачать в хорошем качестве

Intelligent Reflective Surfaces vs. Mobile Relays | IEEE Globecom 2022 | Dr. Filip Lemic

Title: Intelligent Reflective Surface vs. Mobile Relay-supported NLoS Avoidance in Indoor mmWave Networks Conference: IEEE Globecom 2022 Authors: Maria Bustamante Madrid, Jeroen Famaey, Filip Lemic Abstract: The 6th generation of wireless communication (6G) is envisioned to give rise to various technologies for improving the end-to-end communication performance, where the communication is envisioned to utilize wireless signals in the millimeter wave (mmWave) frequencies and above. Among others, these technologies comprise Intelligent Reflective Surfaces (IRSs) and Mobile Relays (MRs), whose envisaged roles include mitigating the negative effects of Non-Line-of-Sight (NLoS) connectivity, in particular at mmWave and higher frequencies. The core idea behind these technologies is to use cooperative networking where the source sends a signal to a repeater, in this case the IRS or the MR, which is upon reception forwarded to the destination. When comparing the two technologies, it is important to realize that the IRSs are primarily envisioned to be static entities attached to various objects in the environment such as walls and furniture. In contrast, the MRs will feature a higher degree of freedom, as they will be able to position themselves seamlessly in the environment. Based on the above assumptions, we derive an approach for determining the optimal position of the IRS and MR in indoor environments, i.e., the one that maximizes the end-to-end link quality between the source and the destination. We follow by capturing the communication quality indicators for both IRS- and MR-supported NLoS avoidance in indoor mmWave communication in a number of scenarios. Our results show that, from the end-to-end link quality perspective, the MRs generally outperform the IRSs, suggesting their utilization potential for throughput-optimized NLoS avoidance scenarios.