Enabling dense machine-to-machine communications through interference-controlled clustering

Clustering of machines for better spatial reuse has been considered as one key technology for supporting machine-to-machine (M2M) communications with a large number of communicating devices. Unlike related work that focuses on greedy clustering algorithms without interference control, in this paper we consider a scenario where machines through joint cluster formation and power control are allowed to opportunistically use the spectrum occupied by human devices for interference-limited M2M communications. To maximize the number of machines that can communicate without violating the QoS constraint of the human device, we formulate a mixed-integer non-linear programming (MINLP) problem to determine the optimal cluster structure and power control. We then propose an anytime algorithm based on simulated annealing to solve the MINLP problem under a high density of machines. Compared with the approach of directly solving the MINLP problem and the approach of separately performing cluster formation and power control, we show through numerical results that the proposed algorithm can effectively solve the target problem while striking a better performance tradeoff between complexity and optimality.