Interference Alignment in Two-Tier Randomly Distributed Heterogeneous Wireless Networks Using Stochastic Geometry Approach

With the massive increase in wireless data traffic in recent years, multi-tier wireless networks have been deployed to provide much higher capacities and coverage. However, heterogeneity of wireless networks bring new challenges for interference analysis and coordination due to spatial randomly distributed transmitters. In this paper, we present a distance-dependent interference alignment (IA) approach for a generic two-tier heterogeneous wireless network, where transmitters in the first and second tiers are distributed as poisson point process (PPP) and poisson cluster process, respectively. The feasibility condition of the IA approach is used to find upper bound of the number of interference streams that can be aligned. The proposed IA scheme maximizes the second-tier throughput by using the tradeoff between signal-to-interference ratio and multiplexing gain. It is shown that acquiring accurate knowledge of the distance between the receiver in the second-tier and the nearest cross-tier transmitter only brings insignificant throughput gain compared with statistical knowledge of distance. Furthermore, the remaining cross-tier and intercluster interferences are modeled and analyzed using stochastic geometry technique. Numerical results validate the derived expressions of success probabilities and throughput, and show that the distance-dependent IA scheme significantly outperforms the traditional IA scheme in the presence of path-loss effect.