Joint Optimization Framework for Operational Cost Minimization in Green Coverage-Constrained Wireless Networks

In this work, we investigate the joint optimization of base station (BS) location, its density, and transmit power allocation to minimize the overall network operational cost required to meet an underlying coverage constraint at each user equipment (UE), which is randomly deployed following the binomial point process (BPP). As this joint optimization problem is nonconvex and combinatorial in nature, we propose a non-trivial solution methodology that effectively decouples it into three individual optimization problems. Firstly, by using the distance distribution of the farthest UE from the BS, we present novel insights on optimal BS location in an optimal sectoring type for a given number of BSs. After that we provide a tight approximation for the optimal transmit power allocation to each BS. Lastly, using the latter two results, the optimal number of BSs that minimize the operational cost is obtained. Also, we have investigated both circular and square field deployments. Numerical results validate the analysis and provide practical insights on optimal BS deployment. We observe that the proposed joint optimization framework, that solves the coverage probability versus operational cost tradeoff, can yield a significant reduction of about $65\%$ in the operational cost as compared to the benchmark fixed allocation scheme.Comment: 30 pages, 15 figures, submitted to IEEE Transactions on Green Communications and Networkin

Optimal Area Power Efficiency in Cellular Networks

International audience—In this paper, we study the problem of minimizing the area power consumption in wireless cellular networks. We focus on the downlink of a single-tier network, in which the locations of base stations (BSs) are distributed according to a homogeneous Poisson point process (PPP). Assuming that a mobile user is connected to its strongest candidate BS, we derive bounds on the optimal transmit power in order to guarantee a certain minimum coverage and data rate. Under the same quality of service constraints, we find the optimal network density that minimizes the area power density. Our results show that the existence of an optimal BS density for minimizing the power consumption depends on the value of the pathloss exponent