  This paper presents a deep reinforcement learning (DRL) solution for power
control in wireless communications, describes its embedded implementation with
WiFi transceivers for a WiFi network system, and evaluates the performance with
high-fidelity emulation tests. In a multi-hop wireless network, each mobile
node measures its link quality and signal strength, and controls its transmit
power. As a model-free solution, reinforcement learning allows nodes to adapt
their actions by observing the states and maximize their cumulative rewards
over time. For each node, the state consists of transmit power, link quality
and signal strength; the action adjusts the transmit power; and the reward
combines energy efficiency (throughput normalized by energy consumption) and
penalty of changing the transmit power. As the state space is large, Q-learning
is hard to implement on embedded platforms with limited memory and processing
power. By approximating the Q-values with a DQN, DRL is implemented for the
embedded platform of each node combining an ARM processor and a WiFi
transceiver for 802.11n. Controllable and repeatable emulation tests are
performed by inducing realistic channel effects on RF signals. Performance
comparison with benchmark schemes of fixed and myopic power allocations shows
that power control with DRL provides major improvements to energy efficiency
and throughput in WiFi network systems.
