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

Abstract

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. .in).A preliminary five-page conference version [1] of this work has been submitted to IEEE Int. Symp. Personal Indoor and Mobile Radio Commun. (PIMRC) 2017. Power Allocation: One of the method for reduction of power consumption is to dynamically turn BSs on/off based on the time and spatial distribution of the traffic load. Various methods for deciding the sleeping mode of the BSs have been discussed in [7]-[15]. Authors in [7] and [8] considered the switching of the BSs based on the traffic profile. Whereas in [9], both the traffic profile and density of the BSs are considered for deciding the switching. Authors in [10] proposed a switching-based energy saving algorithm, which can achieve energy savings of up to 80%. However, these works [7]-[10] did not consider any Quality of Service (QoS) constraint to be met while minimizing the energy cost. Authors in [11] discussed about the trade off between energy saving and spectral efficiency due to the switching of BSs, and thereby designed an optimal control mechanism to solve this trade off. In [12], both centralized and decentralized BS energy saving schemes have been proposed under the constraint of outage probability. Authors in [13] and [14] investigated the impact of sleep operation on the blocking probability and delay, respectively. A survey in [15] gives the state of the art on the proposals for reducing the power consumption at the BSs by implementing sleep operations. Although, the works in [7]-[15] optimize the BSs densities by efficiently controlling the switching operation, they have not discussed about the joint optimization of transmit power and location of the BSs.BS Localization: Energy efficient network designs by optimizing the BSs de...