Robust tower location for code division multiple access networks

Abstract

Designing Code Division Multiple Access networks includes determining optimal locations of radio towers and assigning customer markets to the towers. In this paper, we describe a deterministic model for tower location and a stochastic model to optimize revenue given a set of constructed towers. We integrate these models in a stochastic integer programming problem with simple recourse that optimizes the location of towers under demand uncertainty. We develop algorithms using Benders' reformulation, and we provide computational results.

A nested benders decomposition approach for telecommunication network planning

“…Olinick and Rosenberger [25] extend the work of Kalvenes et al [14] and solve a stochastic model for the power-based PC problem. Stochastic models are also solved by Heikkinen and Prékopa [13] and Rosenberger and Olinick [28]. Based on the stochastic model, Olinick and Rosenberger [25] state that the SIR-based PC is nonlinear and "an exact solution procedure appears to be beyond the capabilities of the current state-of-the art of mathematical programming techniques."…”

A Matheuristic for Joint Optimal Power and Scheduling Assignment in DVB-T2 Networks

“…Also, the adoption of bio-inspired and genetic heuristics is not new: if we focus on genetic and evolutionary algorithms, we can report the remarkable cases of: [15], which addresses the problem of the optimal positioning of base stations in a mobile network, encoding the location of base stations in the chromosomes of the genetic algorithm; [16], which addresses the decision problem of how establishing the optimal assignment of users to deployed transmitters, in particular in the context of WiMAX networks, encoding the assignment in the chromosomes; [17], which focuses on the frequency assignment problem (FAP), proposing a permutation-based genetic algorithm to solve minimum span and fixed spectrum variants of the FAP; [18], which focuses on the problem of setting the power emissions of base stations, proposing two distributed power control algorithms that are based on evolutionary computation techniques to fast solve the linear equation systems associated with power updates of the stations; [19], which proposes a genetic algorithm for addressing the joint problem of power, frequency and modulation scheme assignment in fixed networks based on the WiMAX technology. Moreover, it is interesting to note that other works have also tried to tackle sources of data uncertainty within wireless network design problems, such as [20,21], which adopt a stochastic optimization approach to find a robust location plan of base stations to deal with fluctuations in traffic demand, [22], which deals with the stochastic scheduling of 5G multimedia services and [23][24][25], which propose stochastic programming and robust optimization approaches to deal with signal propagation uncertainty of wireless technologies in real-world environments.…”

GUB Covers and Power-Indexed Formulations for Wireless Network Design

“…The MILPs have been also tailored to cope with uncertainty affecting parameters of the model: in (Rosenberger and Olinick 2007) and (Olinick and Rosenberger 2008), two stochastic optimization approaches are presented to establish a robust location plan of the transmitters to tackle fluctuations in the traffic demand; in (Heikkinen and Prekopa 2004) and (Bienstock and D'Andreagiovanni 2009), Stochastic and Robust Optimization are respectively adopted to tackle the uncertainty affecting the fading coefficients.…”