Distributed Differential Evolution for Joint Power–Rate Control in Interference-Limited Cognitive Radio Networks

• Nabin Keshav Gautam

  Department of Computer Science and Engineering, Mid-West University, 74 Birendranagar–Uttarganga Road, Surkhet 21703, Nepal

  Author

• Pramita Laxmi Shrestha

  Department of Computer Science and Engineering, Far Western University, 21 Dhangadhi–Hasuliya Avenue, Kailali 10900, Nepal

  Author

Cognitive radio networks allow secondary systems to access licensed spectrum under the condition that harmful interference to primary systems is avoided. In interference-limited deployments, the key performance determinants are the transmission powers and coding rates selected by secondary users, which jointly shape the experienced interference pattern and achievable throughput. Joint power and rate control in such environments is typically modeled as a coupled, nonconvex optimization problem with stringent interference constraints toward primary receivers and, in some cases, quality-of-service requirements for secondary links. Centralized solutions to this problem usually require global channel state information and tight coordination, which may be difficult to realize in large-scale or infrastructure-less networks. Distributed metaheuristic approaches offer an alternative by leveraging local computations and limited message exchange, at the cost of approximate optimality. This work examines a distributed differential evolution scheme tailored to joint power and rate control in interference-limited cognitive radio networks. The design focuses on linear constraint representations of interference coupling, a power-feasible encoding of individuals, and neighborhood-based information exchange. The resulting scheme aims to respect primary interference limits and secondary power budgets while exploring feasible tradeoffs between spectral efficiency and interference. Numerical investigations are described for multiuser scenarios with heterogeneous link budgets and channel conditions. The behavior of the distributed evolutionary dynamics is discussed with respect to convergence, robustness to initialization, and communication overhead. The study emphasizes structural aspects of the formulation and algorithm rather than specific system gains, with an aim to clarify the interplay between linear interference models and population-based distributed optimization.

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