Authors - Jaykumar Gandharva, Hardika Menghani, Tilak Brahmbhatt, Nischay Agrawal Abstract - Modern Electronic Warfare (EW) environments are very dynamic, crowded, and hostile, which makes static or centralised spectrum-allocation strategies useless. To tackle these issues, this paper introduces a completely adaptable Federated Deep Q-Network (A-FDQN) framework for each node, which is built onto a high-fidelity ns-3.40 EW simulation environment. In this simulation each tactical radio has been configured to work as an independent federated client which trains a local DQN within itself based on metrics obtained from SINR, congestion, delay, jitter, and interference caused by jamming. A federated server then periodically collects client models using Federated Averaging (FedAvg) or Median method. This lets global learning happen without needing centralised state visibility or constant connectivity, which is very important for networks on contested battlefields. Our framework is different from earlier RL and FL studies because it combines per-node reinforcement learning, periodic federated aggregation, partial client participation, tuneable synchronisation frequency, and realistic ns-3 modelling of mobility, sweep jamming, bursty traffic, congestion hotspots, and Wi-Fi PHY/MAC interactions. Our A-FDQN system dynamically changes the channel assignments at each node, which greatly improves the packet delivery ratio, minimum SINR, fairness, and delay when faced with challenging EW scenarios. This first of its kind end-to-end FRL architecture offers a decentralised, resilient, and operationally relevant way to manage the spectrum for next-generation military communications.
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