What question did this study set out to answer?

The aim is to develop a federated deep reinforcement learning system to improve dynamic routing and resource allocation in cognitive radio networks.

March 29, 2026Open Access

Federated deep reinforcement learning for cross-layer congestion control and energy minimization in cognitive radio networks

MLM. Jhansi Lakshmi ABArram Mahesh Babu

Key Points

The aim is to develop a federated deep reinforcement learning system to improve dynamic routing and resource allocation in cognitive radio networks.
Developed Federated Deep Reinforcement Learning (F-DRL) framework for dynamic routing.
Incorporated multi-layer architecture including Network, Physical, and MAC Layer metrics.
Utilized federated learning to train local DRL models while maintaining privacy.
F-DRL outperformed traditional heuristic routing protocols in simulations.
Demonstrated higher packet delivery ratio and reduced overall delay.
Significantly improved network throughput compared to conventional DRL methods.

Abstract

Managing resources and executing collaborative routing in massive cognitive radio networks (CRNs) has been accomplished with the help of deep reinforcement learning (DRL). But it takes a lot of energy and more time for transmissions because it uses trial and error to interact with the surroundings. The underlying difficulty of dynamic cognitive radio networks (CRNs) is the volatility and unpredictability of spectrum availability. These shifts have occurred because of opportunistic access, which makes conventional routing and allocating resources very difficult. Our innovative Federated Deep Reinforcement Learning (F-DRL) system for Dynamic Routing with Cross-Layer Optimizer aims to tackle this issue. All of the secondary user (SU) nodes in our architecture take advantage of DRL. By taking into account the current state of the network, it smartly decides on the best next-hop and the associated distribution channel. This decision-making procedure is carried out in a multi-layer architecture. Queue length and path delay are some of the Network Layer characteristics that it incorporates. Along with that, it takes into account Physical and MAC Layer metrics like SINR, PU operation, and channel quality. In order to circumvent the drawbacks of centralized DRL, we employ a Federated Learning framework. Some of these restrictions include the fact that exchanging data in its entirety raises privacy concerns and has a significant transmission overhead. Because of this, SUs can train their local DRL models in private; only updating the central server on model updates is necessary for the global aggregate. In comparison to cutting-edge heuristic routing protocols, the suggested F-DRL-driven adaptive routing method performs far better in simulations. Additionally, it improves upon conventional routing methods that rely on DRL. It demonstrates high performance in relation to packet delivery ratio (PDR), overall delay, and network throughput. In addition, the plan offers a way to learn on a large scale. In addition, it provides a method that is crucial for widespread adaptive CRN deployments, one that preserves anonymity.

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