Proactive real-time interference avoidance in a 5G millimeter-wave over fiber mobile fronthaul using SARSA reinforcement learning

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We propose and experimentally demonstrate a proactive real-time interference avoidance scheme using SARSA reinforcement learning (RL) in a millimeter-wave over a fiber mobile fronthaul system. The RL consists of three core factors, including state, action, and reward. The state is defined as a discretized value from the center frequency, the left, right, and center sub-EVM of the signal. We use five actions to shift the signal frequency in the proposed scheme, which is -20, -10, 0, 10, and 20MHz, for the RL agent to choose the action to avoid the dynamic interference. For the agent to learn from the experience, the reward is defined as the log value of BER difference between the past and the present state. The RL-based approach is an online learning algorithm, which can learn in real time based on environmental feedback. Besides, the agent can learn from past experience owing to the Q-value table, which makes it act intelligently when facing a similar situation again. We verify the capability of the proposed scheme under both fixed and dynamic interference scenarios. The agent demonstrates an efficient intelligent mechanism to avoid the interference, which provides a promising solution for proactive interference mitigation in the 5G mobile fronthaul network.