Abstract
We present a general framework for distributed wireless information flow allocation problem in multiple access networks, where the end users (EUs) can seek wireless flows from multiple access points (APs). We aim to minimize the power consumption while satisfying each EU's minimum data rate requirement but not violating peak power constraint of each AP and interference constraint monitored by regulatory agents. Toward this end, we model the flow allocation problem as a game which is proved to be a best-response potential game. Then based on potential game theory, we show the existence and uniqueness of Nash equilibrium in the formulated game. Moreover, we demonstrate that the Nash equilibrium is actually the globally optimal solution to our problem. Besides, we propose two distributed algorithms along with convergence analysis for the network to obtain the Nash equilibrium. Meanwhile, we reveal the interesting layered structure of the problem in question. Extensive numerical results are conducted to demonstrate the benefits obtained by flow allocation, as well as the effectiveness of our proposed algorithms.
Highlights
During the past two decades, we have witnessed an ever increasing demand of high data rate services in wireless communications
More flexible Wireless Local Area Networks (WLANs), where end user (EU) can be associated with multiple access point (AP) to get access to the Internet, are drawing increasingly interests from both academia and industry
Protocol evaluation We investigate the gain obtained by multi-AP based scheme compared to single-AP based scheme
Summary
During the past two decades, we have witnessed an ever increasing demand of high data rate services in wireless communications. To appreciate the EUs’ flow distributions, let us consider a special scenario, where only one EU exists and interference constraints are relaxed Under this setting, we have the following proposition. An admission control scheme aiming at identifying EUs who require infeasible minimum flow rate requirements may be needed This feasibility identification problem is interesting and will be our future work. From the proof of Proposition 3, it is interesting to note that the unique NE R* maximizes i∈IJi(Ri) This is a very desirable result which implies that the social optimum can be obtained if we can find a scheme to reach the unique NE R* by playing game G, which is the very topic of the section.
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