Abstract-Machine-to-Machine (M2M) communication in cellular network is the driver for the future Internet of Things (IoT). The main challenge of M2M communication is the possibility of huge traffic in the uplink network that can cause problem in the network. This paper considers the problem of resource allocation among machines connecting in uplink to different femto base stations (FBSs). Resource allocation problem is analyzed through both non-cooperative and cooperative game to maximize their data rate and minimize utilization of power. Numerical result shows that by adapting non-cooperative game, all machines are getting data rate as per Nash Equilibrium (NE) or either they can set their strategy to maximize their data rate selfishly. On the other hand for coalitional game theory approach machines who participate in game are getting fair resource allocations.

Abstract-Interference Management (1M) is one of the major challenges of next generation wireless communication. Fractional Frequency Reuse (FFR) has been acknowledged as an efficient 1M technique, which offers significant capacity enhancement and improve cell edge coverage with low complexity. In literature, FFR has been analyzed mostly with cellular networks described by Hexagon Grid Model, which is neither tractable nor scalable to the dense deployment of next generation wireless networks.Moreover, the perfect geometry based grid model tends to overestimate the system performance and not able to reflect the reality. In this paper, we use the stochastic geometry approach, FFR is analyzed with cellular network modeled by homogeneous Poisson Point Process (PPP). A dynamic frequency allocation scheme is proposed which take into account the randomness of the cell coverage area describe by Voronoi tessellation. It is shown that the proposed scheme outperforms the traditional fixed frequency allocation schemes in terms of per user capacity and capacity density.

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