Resource Optimization for Device-to-Device and Small Cell Uplink Communications Underlaying Cellular Networks

This paper investigates the joint power control and subchannel allocation problem for device-to-device and small cell uplink communications in a cellular network, with the aim of improving the network throughput. For this throughput maximization problem, we leverage a game-theoretic learning approach to solve it. However, there is an intractable issue for the feasible region of the optimization problem, which is intermixed with the integer nature. To tackle this problem, we first present the closed-form expressions of the optimal power under different scenarios by presetting the subchannel allocation profile. Based on the optimal power control, we then formulate the subchannel allocation problem as a game theoretical framework, and define a welfare function which has the same optimum as the optimization objective. To optimize the welfare function, a distributed trial and error learning algorithm is proposed to converge to a stochastically stable state. Since the achieved stable state cannot guarantee to be the optimal solution, we reformulate this problem as an exact potential game model and propose another distributed learning algorithm to find a better Nash equilibrium which is the global optimal solution (or a superior solution). Moreover, to accelerate the convergence, these two algorithms are modified by getting rid of these unavailable strategic profiles. Finally, numerical results verify the effectiveness of the proposed schemes.