Maximizing Mobile Coverage via Optimal Deployment of Base Stations and Relays

Xu, Li; Guo, Dongning; Grosspietsch, J.; Yin, Huarui; Wei, Guo

doi:10.1109/tvt.2015.2458015

Cited by 22 publications

(13 citation statements)

References 38 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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“…This network of a few eNb to serve a UE with minimum HO we called sparse network. For both cases, sparse and Dense, the eNB's are deployed and positioned at the specific location to maximize the minimum SNR in the area of interest [16], and we assigned average minimum SNR of 0 dB and 3 dB specified for sparse and dense network respectively. The number of HOs and RLF rates are analysed in the sparse network with a different value of TTT.…”

Section: Proposed Modelmentioning

confidence: 99%

“…In the simulation, we deployed eNB adhere to a model by [16] for base station modeling. Number of deployed eNB for sparse and dense are 3 and 7 respectively.…”

Section: A Base Station Modellingmentioning

confidence: 99%

Handover Management in Dense Networks with Coverage Prediction from Sparse Networks

Mollel

Öztürk

Kisangiri

et al. 2019

2019 IEEE Wireless Communications and Networking Conference Workshop (WCNCW)

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Millimeter Wave (mm-Wave) provides high bandwidth and is expected to increase the capacity of the network thousand-fold in the future generations of mobile communications. However, since mm-Wave is sensitive to blockage and incurs in a high penetration loss, it has increased complexity and bottleneck in the realization of substantial gain. Network densification, as a solution for sensitivity and blockage, increases handover (HO) rate, unnecessary and ping-pong HO, which in turn reduces the throughput of the network. On the other hand, to minimize the effect of increased HO rate, Time to Trigger (TTT) and Hysteresis factor (H) have been used in Long term Evolution (LTE). In this paper, we primarily present two different networks based on Evolved NodeB (eNB) density: sparse and dense. As their name also suggests, the eNB density in the dense network is higher than the sparse network. Hence, we proposed an optimal eNB selection mechanism for 5G intramobility HO based on spatial information of the sparse eNB network. In this approach, User equipment (UE) in the dense network is connected only to a few selected eNBs, which are delivered from the sparse network, in the first place. HO event occurs only when the serving eNB can no longer satisfy the minimum Signal-to-Noise Ratio (SNR) threshold. For the eNBs, which are deployed in the dense network, follow the conventional HO procedure. Results reveal that the HO rate is decreased significantly with the proposed approach for the TTT values between 0 ms to 256 ms while keeping the radio link failure (RLF) at an acceptable level; less than 2% for the TTT values between 0 ms to 160 ms. This study paves a way for HO management in the future 5G network.

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“…The authors were the first to propose a cost-effective network architecture where mobile base stations are dispatched with public safety personnels to support elevated traffic demand at incident scenes [6]. In [7], we proposed to let public safety users deploy relay nodes to extend the wireless coverage of the public safety network when necessary. In this paper, we consider situations where it is impossible or too risky to let public safety users deploy relays in some area.…”

Section: Introductionmentioning

confidence: 99%

Drone-assisted public safety wireless broadband network

Guo

Yin

et al. 2015

2015 IEEE Wireless Communications and Networking Conference Workshops (WCNCW)

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A nationwide interoperable public safety wireless broadband network is being planned by the First Responder Network Authority (FirstNet) under the auspices of the United States government. The public safety network shall provide the needed wireless coverage in the wake of an incident or a disaster. This paper proposes a drone-assisted multi-hop device-to-device (D2D) communication scheme as a means to extend the network coverage over regions where it is difficult to deploy a landbased relay. The resource are shared using either time division or frequency division scheme. Efficient algorithms are developed to compute the optimal position of the drone for maximizing the data rate, which are shown to be highly effective via simulations.

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Maximizing Mobile Coverage via Optimal Deployment of Base Stations and Relays

Cited by 22 publications

References 38 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

Handover Management in Dense Networks with Coverage Prediction from Sparse Networks

Drone-assisted public safety wireless broadband network

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