Optimizing revenue in CDMA networks under demand uncertainty

Olinick, Eli V.; Rosenberger, Jay M.

doi:10.1016/j.ejor.2007.02.048

Cited by 18 publications

(21 citation statements)

References 21 publications

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“…In Section 2 we review the deterministic model for CDMA network design from Kalvenes, Kennington, and Olinick [7] and the stochastic subscriber assignment model from Olinick and Rosenberger [10]. These models lay the foundation for the new model that is presented in Section 3.…”

Section: Introductionmentioning

confidence: 98%

“…We refer to these locations as markets, and we develop a model to maximize the expected profit of a CDMA system given probability distributions for the peak demands at each market. Our solution procedure exploits an algorithm presented in Olinick and Rosenberger [10] for maximizing the expected revenue of a CDMA system given that the tower location decisions have already been made.…”

Section: Introductionmentioning

confidence: 99%

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Robust tower location for code division multiple access networks

Rosenberger

Olinick

2006

Naval Research Logistics

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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.

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Section: Introductionmentioning

confidence: 98%

Section: Introductionmentioning

confidence: 99%

Robust tower location for code division multiple access networks

Rosenberger

Olinick

2006

Naval Research Logistics

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“…The topic of data uncertainty in wireless network design and robust wireless network design has been considered important and significant works have been carried out [56][57][58][59][60][61][62]. In [56], the authors discuss how to apply robust optimization for tackling traffic uncertainty in telecommunication network design, considering capacitated network design problems.…”

Section: Proposed Energy Saving Schemementioning

confidence: 99%

“…In [61], the authors propose an optimal transmitter power allocation in a wireless communication network when the useful link coefficients and interference coefficients are random. In [62], the authors introduce a yield management model and develop a stochastic programming model for channel allocation to optimize revenue under demand uncertainty.…”

Section: Proposed Energy Saving Schemementioning

confidence: 99%

An Efficient Energy Saving Scheme for Base Stations in 5G Networks with Separated Data and Control Planes Using Particle Swarm Optimization

Kang

Chung

2017

Energies

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Abstract:Reducing energy consumption of mobile communication networks has gained significant attentions since it takes a major part of the total energy consumption of information and communication technology (ICT). In this paper, we consider 5G networks with heterogeneous macro cells and small cells, where data and control planes are separated. We consider two types of data traffic, i.e., low rate data traffic and high rate data traffic. In basic separation architecture, a macro cell base station (MBS) manages control signals, while a small cell base station (SBS) manages both low rate data traffic and high rate data traffic. In the considered modified separation architecture, an MBS manages control signals and low rate data traffic, while an SBS manages high rate data traffic. Then, an efficient energy saving scheme for base stations (BSs) is proposed, where the state of a BS is determined depending on the number of user equipments (UEs) that request high rate data traffic and the number of UEs that exist under the overlapping areas commonly covered by the considered BS and the neighbor BSs. We formulate an optimization problem for the proposed energy saving scheme and obtain the solution using particle swarm optimization (PSO). Numerical results show that the proposed energy saving scheme in the modified separated network architecture has better energy efficiency compared to the conventional energy saving schemes in both basic and modified separated network architectures. Also, the proposed energy saving scheme has lower aggregate delay.

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“…The trade-off between infrastructure cost and profit has been dealt with by integer programming by Kalvenes [15]. In [18], Olinick and Rosenberger approached this trade-off by a stochastic optimization model to take into account demand uncertainty.…”

Section: Introductionmentioning

confidence: 99%

Solving a minimum-power covering problem with overlap constraint for cellular network design

Chen¹,

Yuan²

2010

European Journal of Operational Research

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We consider a type of covering problem in cellular networks. Given the locations of base stations, the problem amounts to determining cell coverage at minimum cost in terms of the power usage. Overlap between adjacent cells is required in order to support handover. The problem we consider is NP -hard. We present integer linear models and study the strengths of their continuous relaxations. Preprocessing is used to reduce problem size and tighten the models. Moreover, we design a tabu search algorithm for finding near-optimal solutions effectively and time-efficiently. We report computational results for both synthesized instances and networks originating from real planning scenarios. The results show that one of the integer models leads to tight bounds, and the tabu search algorithm generates highquality solutions for large instances in short computing time.

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Optimizing revenue in CDMA networks under demand uncertainty

Cited by 18 publications

References 21 publications

Robust tower location for code division multiple access networks

Robust tower location for code division multiple access networks

An Efficient Energy Saving Scheme for Base Stations in 5G Networks with Separated Data and Control Planes Using Particle Swarm Optimization

Solving a minimum-power covering problem with overlap constraint for cellular network design

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