Joint optimization of cluster formation and power control for interference-limited machine-to-machine communications

Clustered communication has been considered as one key technology for supporting machine-to-machine (M2M) wireless networks with a large number of communicating devices. Unlike related work that focuses on clustering with simple or no wireless interference model at the physical layer, in this paper we investigate the optimization problem of cluster formation and power control for interference-limited M2M communications. We consider a scenario where machines that form in clusters are allowed to reuse the spectrum occupied by human devices through proper transmission power control. To maximize the number of machines that can communicate while meeting the data rate constraints of human devices and machines themselves, we formulate a mixed-integer non-linear programming (MINLP) problem. Since the MINLP problem becomes too complex when the number of machines increases, we propose an algorithm that transforms the problem into a coalition structure generation sub-problem embedded with a linear power control sub-problem. The proposed algorithm is an anytime algorithm and hence the length of the running time can be arbitrarily controlled while yielding a feasible solution with the desired quality. Compared with other approaches for solving the original MINLP problem, we show through numerical results that the proposed algorithm can effectively solve the target problem and allow machines to achieve better spatial reuse with human devices in interference-limited M2M communications.