12 papers · 1 filter
AoI-Aware Machine Learning for Constrained Multimodal Sensing-Aided Communications
Abolfazl Zakeri, Nhan Thanh Nguyen, Ahmed Alkhateeb +1
Using environmental sensory data can enhance communications beam training and reduce its overhead compared to conventional methods. However, the availability of fresh sensory data…
Deep Reinforcement Learning for Dynamic Sensing and Communications
Abolfazl Zakeri, Nhan Thanh Nguyen, Ahmed Alkhateeb +1
Environmental sensing can significantly enhance mmWave communications by assisting beam training, yet its benefits must be balanced against the associated sensing costs. To this en…
Data-Free Knowledge Distillation for LiDAR-Aided Beam Tracking in MmWave Systems
Abolfazl Zakeri, Nhan Thanh Nguyen, Ahmed Alkhateeb +1
We propose a data-free knowledge distillation (DF- KD) framework for LiDAR-aided mmWave beam tracking, where the objective is to predict the optimal current and future beams from a…
Attention-Enhanced Learning for Sensing-Assisted Long-Term Beam Tracking in mmWave Communications
Mengyuan Ma, Nhan Thanh Nguyen, Nir Shlezinger +2
Beam training and prediction in millimeter-wave communications are highly challenging due to fast time-varying channels and sensitivity to blockages and mobility. In this context,…
Knowledge Distillation for Sensing-Assisted Long-Term Beam Tracking in mmWave Communications
Mengyuan Ma, Nhan Thanh Nguyen, Nir Shlezinger +3
Infrastructure-mounted sensors can capture rich environmental information to enhance communications and facilitate beamforming in millimeter-wave systems. This work presents an eff…
Constrained Multimodal Sensing-Aided Communications: A Dynamic Beamforming Design
Abolfazl Zakeri, Nhan Thanh Nguyen, Ahmed Alkhateeb +1
Using multimodal sensory data can enhance communications systems by reducing the overhead and latency in beam training. However, processing such data incurs high computational comp…