Coverage-constrained base station deployment and power allocation for operational cost minimization

Prasad, Ganesh; Mishra, Deepak; Hossain, Ashraf

doi:10.1109/pimrc.2017.8292392

Cited by 3 publications

(1 citation statement)

References 15 publications

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“…Theoretical algorithms were proposed in [15]- [17] to address the nonconvex and combinatorial optimization problem of BS deployment. Nevertheless, these works assumed simple propagation model and did not consider practical scenarios of BS deployment, which cannot be applied in practical systems.…”

Section: A Related Workmentioning

confidence: 99%

Propagation-Model-Free Base Station Deployment for Mobile Networks: Integrating Machine Learning and Heuristic Methods

Dai

Zhang

2020

IEEE Access

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As densification is the promising trend of future mobile networks, deployment of base stations (BSs) becomes increasingly difficult due to the laborious procedures in network planning; besides, unreasonable layout may lead to poor coverage performance. Hence, this paper firstly trains a propagationmodel-free received signal strength (RSS) predictor based on machine learning (ML) models, and then optimizes coverage performance of BS deployment via multi-objective heuristic methods. Specifically, many practical features that affect signal propagation like geographical types and operating parameters of BS, are fed into ML models to predict RSS in a rasterized area; then based on the trained model, a well-designed multi-objective genetic algorithm (GA) is proposed to minimize the number of deployed BSs with coverage constraint. For the practical considerations of fast convergence and output-consistence, greedy algorithm with fixed initial solution and searching direction is also carried out. Moreover, the typical scenarios of incremental deployment (the mobile operator needs to deploy more BSs on the basis of the existing deployment) and BS outage compensation (one BS fails and other BSs need to adjust their configurations to fill the coverage gap), are also investigated for practical needs. Simulations show that multi-layer perceptron outperforms other ML algorithms in terms of RSS prediction with mean absolute error (MAE) yielded to 3.78 dB; and numerical results verify the convergence and availability of the proposed algorithms, which shows 18.5% gain than the real-world deployment in terms of coverage rate.

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Section: A Related Workmentioning

confidence: 99%

Propagation-Model-Free Base Station Deployment for Mobile Networks: Integrating Machine Learning and Heuristic Methods

Dai

Zhang

2020

IEEE Access

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Energy-Efficient Base Station Deployment in Heterogeneous Communication Network

Chen

Wang

et al. 2019

2019 IEEE SmartWorld, Ubiquitous Intelligence &Amp; Computing, Advanced &Amp; Trusted Computing, Scalable Computing &Amp; Commu

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Joint Optimization Framework for Operational Cost Minimization in Green Coverage-Constrained Wireless Networks

Prasad

Mishra

Hossain

2018

IEEE Trans. on Green Commun. Netw.

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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. .in).A preliminary five-page conference version [1] of this work has been submitted to IEEE Int. Symp. Personal Indoor and Mobile Radio Commun. (PIMRC) 2017. Power Allocation: One of the method for reduction of power consumption is to dynamically turn BSs on/off based on the time and spatial distribution of the traffic load. Various methods for deciding the sleeping mode of the BSs have been discussed in [7]-[15]. Authors in [7] and [8] considered the switching of the BSs based on the traffic profile. Whereas in [9], both the traffic profile and density of the BSs are considered for deciding the switching. Authors in [10] proposed a switching-based energy saving algorithm, which can achieve energy savings of up to 80%. However, these works [7]-[10] did not consider any Quality of Service (QoS) constraint to be met while minimizing the energy cost. Authors in [11] discussed about the trade off between energy saving and spectral efficiency due to the switching of BSs, and thereby designed an optimal control mechanism to solve this trade off. In [12], both centralized and decentralized BS energy saving schemes have been proposed under the constraint of outage probability. Authors in [13] and [14] investigated the impact of sleep operation on the blocking probability and delay, respectively. A survey in [15] gives the state of the art on the proposals for reducing the power consumption at the BSs by implementing sleep operations. Although, the works in [7]-[15] optimize the BSs densities by efficiently controlling the switching operation, they have not discussed about the joint optimization of transmit power and location of the BSs.BS Localization: Energy efficient network designs by optimizing the BSs de...

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Coverage-constrained base station deployment and power allocation for operational cost minimization

Cited by 3 publications

References 15 publications

Propagation-Model-Free Base Station Deployment for Mobile Networks: Integrating Machine Learning and Heuristic Methods

Propagation-Model-Free Base Station Deployment for Mobile Networks: Integrating Machine Learning and Heuristic Methods

Energy-Efficient Base Station Deployment in Heterogeneous Communication Network

Joint Optimization Framework for Operational Cost Minimization in Green Coverage-Constrained Wireless Networks

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