Modeling and Performance Analysis of Clustered Device-to-Device Networks

Abstract

Device-to-device (D2D) communication enables direct communication between proximate devices thereby improving the overall spectrum utilization and offloading traffic from cellular networks. This paper develops a new spatial model for D2D networks in which the device locations are modeled as a Poisson cluster process. Using this model, we study the performance of a typical D2D receiver in terms of coverage probability under two realistic content availability setups: (i) content of interest for a typical device is available at a device chosen uniformly at random from the same cluster, which we term uniform content availability, and (ii) content of interest is available at the k th closest device from the typical device inside the same cluster, which we term k-closest content availability. Using these coverage probability results, we also characterize the area spectral efficiency (ASE) of the whole network for the two setups. A key intermediate step in this analysis is the derivation of the distributions of distances from a typical device to both the intra-and inter-cluster devices. Our analysis reveals that an optimum number of D2D transmitters must be simultaneously activated per cluster in order to maximize ASE.This can be interpreted as the classical tradeoff between more aggressive frequency reuse and higher interference power. The optimum number of simultaneously transmitting devices and the resulting ASE increase as the content is made available closer to the receivers. Our analysis also quantifies the best and worst case performance of clustered D2D networks both in terms of coverage and ASE. Index TermsDevice-to-device (D2D) communication, clustered D2D network, Poisson cluster process, Thomas cluster process, stochastic geometry.M. Afshang is with Wireless@VT,

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“…According to [8], the distribution of the distance between a random point in a cluster and the clusterhead is described by the Rayleigh distribution and it is given by f R (r, σ 2 ) = r σ 2 exp − r 2 2σ 2 .…”

“…In this way, if some interferers have not received enough energy during the HP, they will not contribute at the total interference. To that end, the Laplace transforms for the intra-cluster L intra (s) and the inter-cluster L inter (s) interference provided in [8] are modified to take into account the active nodes with probability p a and are given by L intra (s) = E exp − sn …”

“…For instance, smart city sensors are typically clustered in densely populated areas [6] or smart transportation sensors in cars form clusters during traffic hours and exchange data around gateways deployed on traffic lights [7]. Therefore, it is significant to take into account the clustered topology under fading conditions in the performance evaluation, since it affects the generated interference [8].…”

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“…Among various types of spatial point processes, Poisson cluster process (PCP), where daughter points form random clusters centered at points from a parent Poisson point process (PPP), are commonly used for modeling wireless networks with random cluster topologies arising from geographical factors or protocols for medium access control [17], [18]. In particular, in recent work on random networks, PCPs have been frequently used to model the phenomenons of user clustering at hotspots [19] and the clustering of small-cell base stations (BSs) around macro-cell BSs [20] for heterogeneous networks or distributed-node clustering in D2D networks [21]. In this work, the WP-BackCom network is also modeled as a PCP where PBs form the parent PPP and backscatter nodes are the clustered daughter points.…”

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