This work presents an extensive simulation-based performance evaluation of the IEEE 802.11bf protocol for Wi-Fi sensing in the sub-7 GHz band, focusing on the impact of sensing on data communication.
The authors propose two solutions that allow a mobile network operator to dynamically multiplex unlicensed radio resources between a Wi-Fi network and a scheduled cellular network, such as LTE LAA or 5G NR-U, with different levels of resource sharing granularity.
Optimizing cell-edge throughput through beamforming in wireless networks is crucial for future advancements in network technology.
Optimizing user association in dense mmWave networks using Whittle index for improved performance metrics.
The author proposes an Adaptive Split Learning (ASL) scheme to dynamically select split points and allocate computing resources in wireless edge networks, aiming to reduce training latency while considering energy constraints.
The author proposes a solution using AI at the edge of the network to optimize resource allocation and performance in ORAN-based 6G networks through deep reinforcement learning (DRL).