Radio Access Network and Spectrum Sharing in Mobile Networks: A Stochastic Geometry Perspective

Kibiłda, Jacek; Kaminski, Nicholas J.; DaSilva, Luiz A.

doi:10.1109/twc.2017.2666140

Cited by 21 publications

(11 citation statements)

References 34 publications

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“…Since our key lemma regarding the K-th strongest BS's link power (Lemma 1) does not depend on a specific channel model, one can use our results to characterize the performance by using realistic channel models. Finally, by using the developed analytical framework, one can consider more sophisticated sharing scenario, for example sharing with a Wi-Fi [31], satellite service [32], and radar [33], or access and infrastructure sharing with other operators [8].…”

Section: Discussionmentioning

confidence: 99%

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Inter-Operator Base Station Coordination in Spectrum-Shared Millimeter Wave Cellular Networks

Park

Andrews

Heath

2018

IEEE Trans. Cogn. Commun. Netw.

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We characterize the rate coverage distribution for a spectrum-shared millimeter wave downlink cellular network. Each of multiple cellular operators owns separate mmWave bandwidth, but shares the spectrum amongst each other while using dynamic inter-operator base station (BS) coordination to suppress the resulting cross-operator interference. We model the BS locations of each operator as mutually independent Poisson point processes, and derive the probability density function (PDF) of the K-th strongest link power, incorporating both line-of-sight and non line-of-sight states. Leveraging the obtained PDF, we derive the rate coverage expression as a function of system parameters such as the BS density, transmit power, bandwidth, and coordination set size. We verify the analysis with extensive simulation results. A major finding is that inter-operator BS coordination is useful in spectrum sharing (i) with dense and high power operators and (ii) with fairly wide beams, e.g., 30 • or higher.

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Section: Discussionmentioning

confidence: 99%

“…Although inter-operator BS coordination may currently seem impractical, it could be reasonable in future networks which are trending towards ever-increasing infrastructure aggregation [4], [8].…”

mentioning

confidence: 99%

Inter-Operator Base Station Coordination in Spectrum-Shared Millimeter Wave Cellular Networks

Park

Andrews

Heath

2018

IEEE Trans. Cogn. Commun. Netw.

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“…Their findings report on the effectiveness of capacity sharing, which performs better and is simpler to implement than the other forms of sharing that they studied. In [24], the potential of infrastructure and spectrum sharing is studied in the light of specific spatial characteristics of multi-operator networks, such as clustering, which is often found in real-world deployments [6]. On the operational side of network sharing, [25] shows that MNOs can achieve significant energy savings by jointly switching off BSs in multi-operator networks while guaranteeing quality-ofservice (QoS) to users.…”

Section: Related Work and Contributionsmentioning

confidence: 99%

Multi-Operator Connectivity Sharing for Reliable Networks: A Data-Driven Risk Analysis

Gomes

Kibiłda

Farhang

et al. 2021

IEEE Trans. Netw. Serv. Manage.

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A key distinction between today's and future networks is the appetite for reliable communication to support emerging critical-communication services. In this paper, we study multi-operator connectivity as a form of redundancy to support the design of reliable networks and investigate its trade-offs. This approach is motivated by 3GPP standardisation initiatives of dual-connectivity and similar techniques in industrial wired networks. We deploy a risk awareness performance metric to assess reliability: this superquantile metric accounts for periods of connectivity shortfalls. Our analysis shows that multi-operator connectivity brings significant reliability gains, in particular when network deployments by different operators exhibit high complementarity in coverage. We also explore the effects of multi-connectivity on spectral efficiency in times of high demand for bandwidth. Our study is based on a real-world dataset comprising signal strength indicators of three mobile operators in Dublin, Ireland.

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“…In work related to infrastructure sharing, a statistical approach to model multi-operator networks with shared deployment patterns was presented in [27], but performance of the models therein was evaluated only through simulation. In a subsequent paper by the same authors [28], the impact of spatial clustering, network density, and spectrum access coordination on network coverage in a multioperator system was studied analytically. In [29], different configurations of infrastructure and spectrum sharing were considered, and corresponding SINR and rate coverage probabilities were compared and evaluated against different channel, antenna, and BS patterns.…”

Section: B Prior Workmentioning

confidence: 99%

“…follows from Tonelli's theorem, step (30) follows from Campbell's mean value formula for the second power of a point process, and step(31)is the result of integrating with respect to the second moment measure given in(28). Hence, ρ reduces toρ = E [Φ 1 (A)Φ 2 (B)] − E [Φ 1 (A)] E [Φ 2 (B)] E [Φ(A ∩ B)] = (a − b)λvol (A ∩ B) λvol (A ∩ B) = a − b.…”

mentioning

confidence: 99%

Modeling Infrastructure Sharing in mmWave Networks With Shared Spectrum Licenses

Jurdi

Gupta

Andrews

et al. 2018

IEEE Trans. Cogn. Commun. Netw.

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Competing cellular operators aggressively share infrastructure in many major US markets. If operators also were to share spectrum in next-generation millimeter-wave (mmWave) networks, intra-cellular interference will become correlated with inter-cellular interference. We propose a mathematical framework to model a multioperator mmWave cellular network with co-located base-stations (BSs). We then characterize the signal-tointerference-plus-noise ratio (SINR) distribution for an arbitrary network and derive its coverage probability.To understand how varying the spatial correlation between different networks affects coverage probability, we derive special results for the two-operator scenario, where we construct the operators' individual networks from a single network via probabilistic coupling. For external validation, we devise a method to quantify and estimate spatial correlation from actual base-station deployments. We compare our two-operator model against an actual macro-cell-dominated network and an actual network primarily comprising distributed-antenna-system (DAS) nodes. Using the actual deployment data to set the parameters of our model, we observe that coverage probabilities for the model and actual deployments not only compare very well to each other, but also match nearly perfectly for the case of the DAS-node-dominated deployment. Another interesting observation is that a network that shares spectrum and infrastructure has a lower rate coverage probability than a network of the same number of BSs that shares neither spectrum nor infrastructure, suggesting that the latter is more suitable for low-rate applications.

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Radio Access Network and Spectrum Sharing in Mobile Networks: A Stochastic Geometry Perspective

Cited by 21 publications

References 34 publications

Inter-Operator Base Station Coordination in Spectrum-Shared Millimeter Wave Cellular Networks

Inter-Operator Base Station Coordination in Spectrum-Shared Millimeter Wave Cellular Networks

Multi-Operator Connectivity Sharing for Reliable Networks: A Data-Driven Risk Analysis

Modeling Infrastructure Sharing in mmWave Networks With Shared Spectrum Licenses

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