Distributed Wireless Network Optimization With Stochastic Local Search

Recent technological advances allow modification and fine-tuning of the wireless network characteristics. By modifying wireless properties such as transmission timeslots or frequencies, the wireless links quality can be optimized in order to reach optimal communication at the network level. In this paper, we approach the wireless network optimization problem as a distributed constraint optimization problem. As an inherently distributed task, the number of constraints, variables, and their domain sizes can be very large. Therefore, incomplete and local-search solutions such as the Distributed Stochastic Algorithm (DSA) are best suited to solve this class of problems. In this work, we study the wireless network optimization procedure of such solvers considering wireless messaging cost. Furthermore, we introduce Weighted-DSA a stochastic algorithm for wireless optimization. By reducing the search-space of the variables and re-exploring periodically, results show that this algorithm is able to reach optimal solution quality under minimal messaging costs.