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In this work, we investigate the joint optimization of base station (BS)location, its density, and transmit power allocation to minimize the overallnetwork operational cost required to meet an underlying coverage constraint ateach user equipment (UE), which is randomly deployed following the binomialpoint process (BPP). As this joint optimization problem is nonconvex andcombinatorial in nature, we propose a non-trivial solution methodology thateffectively decouples it into three individual optimization problems. Firstly,by using the distance distribution of the farthest UE from the BS, we presentnovel insights on optimal BS location in an optimal sectoring type for a givennumber of BSs. After that we provide a tight approximation for the optimaltransmit power allocation to each BS. Lastly, using the latter two results, theoptimal number of BSs that minimize the operational cost is obtained. Also, wehave investigated both circular and square field deployments. Numerical resultsvalidate the analysis and provide practical insights on optimal BS deployment.We observe that the proposed joint optimization framework, that solves thecoverage probability versus operational cost tradeoff, can yield a significantreduction of about $65\%$ in the operational cost as compared to the benchmarkfixed allocation scheme.

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