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Abstract-Enabling underlay direct Device-to-Device (D2D) communication mode in future cellular networks has good potential for spectrally-efficient and low-latency support of local media services. Recently, it has become evident that shrinking the reuse distance over which wireless resources are reused is a key enabler for achieving high spectral efficiency. Moreover, Interference Alignment (IA) based transmission can enhance the capacity of a wireless network by providing more degrees of freedom. In this work, we exploit clustering of D2D users, frequency reuse over clusters and then using IA to enhance the sum rate. Specifically, we show that in a D2D environment, it is possible to achieve significant gains in attainable rates by constructing clusters of D2D pairs and reuse the available radio resources over the clusters. Moreover, within a cluster, it is possible to further enhance the spectral efficiency by constructing small-sized groups of D2D pairs over which IA is applied to offer additional degrees of freedom. We show that resource reuse over the clusters offer overall rate increase proportional to the number of formed clusters. In addition, interference alignment offers up to 33% increase in the overall rates in the high transmission power regimes compared to the normal Point-toPoint (P2P) communication.

We propose analytical models for the interference power distribution in a cellular system employing MIMO beamforming in rich and limited scattering environments, which capture non line-of-sight signal propagation in the microwave and mmWave bands, respectively. Two candidate models are considered:the Inverse Gaussian and the Inverse Weibull, both are two-parameter heavy tail distributions. We further propose a mixture of these two distributions as a model with three parameters. To estimate the parameters of these distributions, three approaches are used: moment matching, individual distribution maximum likelihood estimation (MLE), and mixture distribution MLE with a designed expectation maximization algorithm. We then introduce simple fitted functions for the mixture model parameters as polynomials of the channel path loss exponent and shadowing variance. To measure the goodness of these models, the information-theoretic metric relative entropy is used to capture the distance from the model distribution