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Adaptive CAC using NeuroEvolution to maximize throughput in mobile networks

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4 Author(s)
Xu Yang ; MPI-QMUL Information Systems Research Centre, Macao Polytechnic Institute, Macao SAR, China ; Yapeng Wang ; John Bigham ; Laurie Cuthbert

This paper proposes a learning approach to solve adaptive Connection Admission Control (CAC) schemes in future wireless networks. Real time connections (that require lower delay bounds than non-real-time) are subdivided into hard realtime (requiring constant bandwidth capacity) or adaptive (that have flexible bandwidth requirements). The CAC for such a mix of traffic types is a complex constraint reinforcement learning problem with noisy fitness. Noise deteriorates the final location and quality of the optimum, and brings a lot of fitness fluctuation in the boundary of feasible and infeasible region. This paper proposes a novel approach that learns adaptive CAC policies through NEAT combined with Superiority of Feasible Points. The objective is to maximize the network revenue and also maintain predefined several QoS constraints.

Published in:

2011 IEEE Wireless Communications and Networking Conference

Date of Conference:

28-31 March 2011