Self-Adaptive Game Theoretic Approaches for Resource Allocation in SC-FDMA Based Heterogeneous Networks

We investigate the resource blocks (RBs) allocation problem in the Single-Carrier Frequency Division Multiple Access (SC-FDMA) based heterogeneous uplink networks from a game-theoretical viewpoint. First, a general network model that considers the co-tier, cross-tier, and inter-cell interference caused by the co-channel deployment is presented. Then, we design an effective throughput-optimizing utility function and prove it an exact potential game. The
$\gamma$
-logit is a learning algorithm which commonly used to achieve the optimal solution in potential game and is very sensitive to the changes of the network structures, making it hard in practice. To obtain the optimal solution under the different network structures stably, we propose a Self-adaptive logit algorithm, which can achieve the optimal solution automatically and is a variant of the well-established
$\gamma$
-logit learning algorithm. Additionally, to speed up the convergence, we propose the corresponding suboptimal algorithm based on the better response principle. Simulation results show that the proposed Self-adaptive logit algorithm is throughput optimal and network structure adaptive, outperforming the existing Binary-logit, Max-logit, and Round Robin algorithm. Moreover, the proposed suboptimal algorithm is near-optimal throughput achieved and converges efficiently. In addition, we also study the impact of the initial point selection on our proposed two alogithms.