Multiplicative multi-attribute auction based optimal network selection for heterogeneous wireless networks

One of the remarkable features of the next generation network is the integration of heterogeneous wireless networks, which enables mobile users with multi-mode terminals access to the best available network seamlessly. However, most of previous work only takes account of either maximizing single user＇s utility or the whole network＇s payoff, rarely considers the negotiation between them. In this paper, we propose a novel network selection approach using improved multiplicative multi-attribute auction （MMA）. At first, an improved MMA method is put forward to define the user＇s utility. Additionally, user cost is defined by considering allocated bandwidth, network load intensity and cost factor parameter. And last the best suitable network is selected according to the user＇s performance-cost-ration. Simulation results confirm that the proposed scheme outperforms the existing scheme in terms of network selection＇s fairness, user＇s performance-cost-ration, load balancing and the number of accommodated users.

Optimal resource allocation scheme for cognitive radio networks with relay selection based on game theory

In this paper, we present a non-transferable utility coalition graph game （NTU-CGG） based resource allocation scheme with relay selection for a downlink orthogonal frequency division multiplexing （OFDMA） based cognitive radio networks to maximize both system throughput and system faimess. In this algorithm, with the assistance of others SUs, SUs with less available channels to improve their throughput and fairness by forming a directed tree graph according to spectrum availability and traffic demands of SUs. So this scheme can effectively exploit both space and frequency diversity of the system. Performance results show that, NTU-CGG significantly improves system faimess level while not reducing the throughput comparing with other existing algorithms.