Uplink Performance Analysis and Optimization for Dense Clustered Wireless Networks With Nearest-BS Association

Abstract

While Poisson Cluster Process (PCP) has been used extensively in the literature for analyzing large-scale clustered wireless networks, related work differs in terms of how the BS (Base Station) distribution and UE (User Equipment) association strategy are modeled in this context. Different from related work that places BSs via Poisson Point Process (PPP) independent of the UE distribution, we consider a more practical scenario where the BSs are located at the parent points of PCP. Furthermore, while a lot of analysis often assumes each UE to be associated with the BS at the parent point of the cluster it belongs to, such a model may result in bias for dense networks where clusters of UEs may overlap with each other. To address such a pitfall, in this work we consider “nearest-BS” association such that a UE may be associated with a nearby BS that is the parent point of another cluster. While there does exist analysis on downlink transmissions for such a network scenario, we find that analysis for uplink performance is more challenging and has yet to be developed in the literature. To proceed, we start from the analytical framework established by related work but derive new expressions for the aggregate uplink interference from different UE clusters in the network. We derive accurate analytical upper and lower bounds for the transmission success probability and we are able to arrive at a closed-form expression for the special case with a path loss exponent of 4. Furthermore, we derive the asymptotic performance bound for ultra-dense networks and investigate the problem of optimizing the area spectral efficiency (ASE) with respect to the design of the signal-to-interference-plus-noise ratio (SINR) threshold. By leveraging the closed-form expression that we have derived, the problem can be solved effectively to obtain the optimal ASE in ultra-dense networks. Evaluation results show that our analysis is correct and the derived bounds are accurate, thus motivating further investigation based on the derived results.