Distributive wireless network resource allocation with nash equilibrium and internal-regret-learning of non-stationary actions

This paper presents an iterative method in solving distributive wireless network resource allocation at the shared link with bottleneck. We propose a utility function considering trade-off between transmission bit rate and power efficiency. Given other players' transmission strategies, the utility function of each player is a concave function. Next, we formulate resource allocation problem as a game, where each player compete to use network resource under its own power constraint. All players utilize the Modified Internal-Regret-Learning algorithm to find their own transmission strategies, which finally form a Nash equilibrium point. The convergence and rate of convergence of the proposed algorithm are proven. Then, we study the results of distributive resource allocation under partial knowledge of other players' strategies. Simulations are conveyed to show the results of resource allocation under various setup environments.