Performance of neural network based distributed radio resource usage optimization algorithms in realistic heterogeneous wireless networks

This paper investigates effectiveness of distributed radio access network (RAN) selection schemes to optimize radio resource usage, for the realistic heterogeneous wireless networks, in which the distribution of the base stations are non-uniform and the non-cognitive legacy terminals also connect to the same RANs. Our proposed algorithm is based on mutually-connected and higher-order neural networks, which can directly optimize any objective functions of entire heterogeneous wireless networks, by completely distributed procedure. Our simulation results show that the proposed scheme is effective even for non-uniform heterogeneous wireless network environments, especially for higher-order and complex optimization problems. Such improvements in the proposed method also can be seen also for the cases that the legacy non-cognitive terminals co-exist in the same heterogeneous wireless networks.