Mobile network operators are facing the difficult task of significantly increasing capacity to meet projected demand while keeping CAPEX and OPEX down. We argue that infrastructure sharing is a key consideration in operators' planning of the evolution of their networks, and that such planning can be viewed as a stage in the cognitive cycle. In this paper, we present a framework to model this planning process while taking into account both the ability to share resources and the constraints imposed by competition regulation (the latter quantified using the Herfindahl index). Using real-world demand and deployment data, we find that the ability to share infrastructure essentially moves capacity from rural, sparsely populated areas (where some of the current infrastructure can be decommissioned) to urban ones (where most of the next-generation base stations would be deployed), with significant increases in resource efficiency. Tight competition regulation somewhat limits the ability to share but does not entirely jeopardize those gains, while having the secondary effect of encouraging the wider deployment of next-generation technologies.
The classic network planning problem usually considers a single operator that aims at minimizing operational costs for a specific technology (e.g. 3G, LTE) while maintaining acceptable users satisfaction both in terms of coverage and capacity. The research following this line is vast and includes various aspects. For example, in @cite_28 @cite_25 @cite_14 @cite_13 @cite_26 @cite_2 @cite_1 the authors study the optimization of base station location in an area of interest. Some of these works @cite_28 @cite_25 @cite_14 @cite_13 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 @cite_26 @cite_2 @cite_1 have focused on the same objective using similar approaches but on LTE networks.The work in @cite_2 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 @cite_11 , on network planning.
@cite_14 @cite_26 @cite_28 @cite_1 @cite_2 @cite_13 @cite_25 @cite_11
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Base station (BS) 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 fourth-generation (4G) cellular networks using metaheuristic 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. Afterward, we implement a metaheuristic algorithm based on swarm intelligence (e.g., particle swarm optimization or the recently proposed gray-wolf optimizer) to find suboptimal BS 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 BSs. 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 regard 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 (QoS) targets are always reached, even for large-scale problems. The classic optimal transportation problem consists in finding the most cost-effective way of moving masses from one set of locations to another, minimizing its transportation cost. The formulation of this problem and its solution have been useful to understand various mathematical, economical, and control theory phenomena, such as, e.g., Witsenhausen's counterexample in stochastic control theory, the principal-agent problem in microeconomic theory, location and planning problems, etc. In this work, we incorporate the effect of network congestion to the optimal transportation problem and we are able to find a closed form expression for its solution. As an application of our work, we focus on the mobile association problem in cellular networks (the determination of the cells corresponding to each base station). In the continuum setting, this problem corresponds to the determination of the locations at which mobile terminals prefer to connect (by also considering the congestion they generate) to a given base station rather than to other base stations. Two types of problems have been addressed: a global optimization problem for minimizing the total power needed by the mobile terminals over the whole network (global optimum), and a user optimization problem, in which each mobile terminal chooses to which base station to connect in order to minimize its own cost (user equilibrium). This work combines optimal transportation with strategic decision making to characterize both solutions. The cell planning problem with capacity expansion is examined in wireless communications. The problem decides the location and capacity of each new base station to cover expanded and increased traffic demand. The objective is to minimize the cost of new base stations. The coverage by the new and existing base stations is constrained to satisfy a proper portion of traffic demands. The received signal power at the base station also has to meet the receiver sensitivity. The cell planning is formulated as an integer linear programming problem and solved by a tabu search algorithm. In the tabu search intensification by add and drop move is implemented by short-term memory embodied by two tabu lists. Diversification is designed to investigate proper capacities of new base stations and to restart the tabu search from new base station locations. Computational results show that the proposed tabu search is highly effective. A 10 cost reduction is obtained by the diversification strategies. The gap from the optimal solutions is approximately 1 spl sim 5 in problems that can be handled in appropriate time limits. The proposed tabu search also outperforms the parallel genetic algorithm. The cost reduction by the tabu search approaches 10 spl sim 20 in problems: with 2500 traffic demand areas (TDAs) in code division multiple access (CDMA). The planning of today's telecommunication networks is a very complex task, because the users and the demanded services as well as the costs to deploy and operate the network are evolving over time. Because of this reason, forecasts are made, which are inaccurate in nature. The dynamic and uncertain character of the input parameters needs to be taken into account during the network planning process. This PhD dissertation studies some important techno-economic aspects of the planning process, focusing on optical telecommunication networks, which form the basis of a lot of core networks of important telecom operators. Concerning the dynamic character of network traffic and equipment costs, our research compares the use of single period planning, anticipated planning and multi-period planning and their relation with investment selection techniques. Concerning the uncertain character of traffic demand and equipment costs, the well-known techniques using safety margins is evaluated and the use of Real Options thinking (originating from a stock option valuation technique) is illustrated. Furthermore, a lot of attention is given to the development of a cost model for a network operator, paying special attention to operational expenses. An activity-based cost model allows to quantify the operational expenses of the network operator. Therefore, different operational processes like maintenance, repair, service provisioning and pricing billing are distinguished. The process-based operational expenditures together with the continuous costs of infrastructure (costs for floor space rental, energy consumption, etc.) constitute the total operational expenditures for the telecom operator. Finally, we indicate how the suggested cost models can be applied in a liberalized telecom market.