Energy Optimization of Distributed Power Communication Networks by DC Programming*

This paper proposes a novel distributed power optimization framework using DC programming to enhance energy efficiency and interference management in wireless networks. Traditional power control methods, including control-theoretic, game-theoretic, and geometric programming approaches, struggle with nonconvex optimization challenges, limiting scalability. DC programming overcomes these limitations by decomposing complex nonconvex problems into tractable convex subproblems, ensuring efficient and convergent solutions. Leveraging recent advances in DC-based optimization for impulse-to-peak control and buffer networks, our approach optimally balances power allocation and communication reliability in a fully distributed manner. Simulations confirm superior performance in energy efficiency, interference mitigation, and convergence speed, demonstrating DC programming’s potential for next-generation wireless networks.