Directional Communication

Directional communication focuses on precisely controlling the transmission and reception of signals, maximizing efficiency and minimizing interference. Current research emphasizes developing advanced models and algorithms, such as those incorporating Gaussian processes, deep reinforcement learning, and Bayesian optimization, to optimize beamforming, beamsteering, and antenna design for various applications. This work is crucial for improving the performance of diverse systems, including vehicle-to-vehicle communication, brain-computer interfaces, and wireless virtual reality, by enabling higher data rates, lower latency, and enhanced spatial reuse. The resulting improvements in efficiency and reliability have significant implications across multiple scientific disciplines and technological sectors.