  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.
