Optimized LTE Cell Planning With Varying Spatial and Temporal User Densities

Abstract

Base station deployment in cellular networks is one of the fundamental problems in network design. This paper proposes a novel method for the cell planning problem for the fourth generation (4G) cellular networks using meta-heuristic algorithms. In this approach, we aim to satisfy both cell coverage and capacity constraints simultaneously by formulating an optimization problem that captures practical planning aspects. The starting point of the planning process is defined through a dimensioning exercise that captures both coverage and capacity constraints. Afterwards, we implement a meta-heuristic algorithm based on swarm intelligence (e.g., particle swarm optimization or the recently-proposed grey wolf optimizer) to find suboptimal base station locations that satisfy both problem constraints in the area of interest which can be divided into several subareas with different spatial user densities. Subsequently, an iterative approach is executed to eliminate eventual redundant base stations. We also perform Monte Carlo simulations to study the performance of the proposed scheme and compute the average number of users in outage. Next, the problems of green planning with regards to temporal traffic variation and planning with location constraints due to tight limits on electromagnetic radiations are addressed, using the proposed method. Finally, in our simulation results, we apply our proposed approach for different scenarios with different subareas and user distributions and show that the desired network quality of service targets are always reached even for large-scale problems.

A Sharing- and Competition-Aware Framework for Cellular Network Evolution Planning

“…The research following this line is vast and includes various aspects. For example, in [15], [16], [17], [18], [19], [20], [21] the authors study the optimization of base station location in an area of interest. Some of these works [15], [16], [17], [18] deal with 3G systems and they are based on meta-heuristics aiming at minimizing the number of base stations to be deployed.…”

“…Some of these works [15], [16], [17], [18] deal with 3G systems and they are based on meta-heuristics aiming at minimizing the number of base stations to be deployed. Other more recent works [19], [20], [21] have focused on the same objective using similar approaches but on LTE networks.The work in [20] in particular uses a model based on stochastic geometry, and the coverage probability as the metric to optimize. The problem addressed in our paper is fundamentally different from the ones addressed by the aforementioned works since it includes sharing of already existing infrastructure by more than one operator and considers the competition regulation impact, modeled similarly as imposed by the Irish regulator recently [5], on network planning.…”

Green Virtualization for Multiple Collaborative Cellular Operators

“…During this procedure, a complete demand assessment is made and the coverage overlap is minimized to reduce the interference and subsequently, the transmit power needed to meet QoS requirements. In this regard, efforts have been made for effective cell planning [17] and BS deployment [18]. Nevertheless, the BS deployment is done according to estimates of peak demand levels.…”

“…In other words, the operators will agree to collaborate only if collaboration leads to an increase in their profit. The constraints (17) and (18) ensure that the BS power budget and its capacity are not exceeded. Hence, the collaboration decision is subject to two conditions: 1) the capacity and power budget of the host BSs are not exceeded,i.e., QoS is maintained (see equation (3)), and 2) both operators maintain profit levels that are at least equal to the ones before collaboration.…”

“…(15) to (18) reduces to optimizing the roaming prices between operators for this set of active BSs since the constraints (17) and (18) only depend on (c) . The reduced problem becomes a MOLPP and its solution is non-trivial since the objective of each operator is conflicting with the objective of other operators.…”