Reinforcement Learning Based Angle-of-Arrival Detection for Millimeter-Wave Software-Defined Radio Systems

Millimeter-wave (mmWave) signals experience severe environmental path loss. To mitigate the path loss, beam-forming methods are used to realize directional mmWave beams that can travel longer. Yet, advanced algorithms are needed to track these directional beams by detecting angle-of-arrival (AoA) and aligning the transmit and receive antennas. To realize these advanced beam-forming algorithms in real world scenarios, Software-Defined Radio (SDR) platforms that allow both high-level programming capability and mmWave beam-forming are needed. Using a low-cost mmWave SDR platform, we design and prototype two reinforcement learning (RL) algorithms for AoA detection, i.e., Q- and Double Q-learning. We evaluate these algorithms and study the trade-offs involved in their design.