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

Park, Jeonghun; Andrews, Jeffrey G.; Heath, Robert W.

doi:10.1109/tccn.2018.2815615

Cited by 31 publications

(21 citation statements)

References 35 publications

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“…al. stand out, for example, those on mMIMO [38] and mmWave performance analysis [39], random blockage [40], mmWave ad-hoc networks [41], mmWave secure communications [42], shared mmWave spectrum [43] [44], and wireless power systems [45]. For further reference, and with regard to the analysis of other relevant network aspects, it is worth pointing out the studies of G Among all these results, one of the most important theoretical findings is that by J. G. Andrews and H. S. Dhillon et.…”

Section: The Old Understandingmentioning

confidence: 99%

Toward Ultradense Small Cell Networks: A Brief History on the Theoretical Analysis of Dense Wireless Networks

Liu

Ding

2019

Wiley Encyclopedia of Electrical and Electronics Engineering

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This article provides dives into the fundamentals of dense and ultra-dense small cell wireless networks, discussing the reasons why dense and ultra-dense small cell networks are fundamentally different from sparse ones, and why the wellknown linear scaling law of capacity with the base station (BS) density in the latter does not apply to the former. In more detail, we review the impact of the following factors on ultradense networks (UDNs), (i) closed-access operations and line-ofsight conditions, (ii) the near-field effect, (iii) the antenna height difference between small cell BSs and user equipments (UEs), and (iv) the surplus of idle-mode-enabled small cell BSs with respect to UEs. Combining all these network characteristics and features, we present a more realistic capacity scaling law for UDNs, which indicates (i) the existence of an optimum BS density to maximise the area spectral efficiency (ASE) for a given finite UE density, and (ii) the existence of an optimum density of UEs that can be simultaneously scheduled across the network to maximise the ASE for a given finite BS density.

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Section: The Old Understandingmentioning

confidence: 99%

Toward Ultradense Small Cell Networks: A Brief History on the Theoretical Analysis of Dense Wireless Networks

Liu

Ding

2019

Wiley Encyclopedia of Electrical and Electronics Engineering

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“…In [33], the feasibility of secondary licensing in licensed mmWave bands is established, yet the model that was used to represent the locations of the primary and secondary BSs did not generalize to scenarios where the primary and secondary networks share infrastructure. In [34], the probability of rate coverage of spectrum-shared mmWave networks with inter-operator coordination is determined.…”

Section: B Prior Workmentioning

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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“…Mathematically, CP i produces the linearly precoded signalsx i,1 according to (5) and (6), respectively, and obtains the quantized signalsx i,1 , whose covariance matrix depends on the correlation matrix Θ…”

Section: Multivariate Compressionmentioning

confidence: 99%

“…Spectrum pooling among multiple network operators, or tenants, is an emerging technique for meeting the rapidly increasing traffic demands over the available scarce spectrum resources [1]- [6]. Spectrum pooling can be implemented by means of orthogonal or non-orthogonal resource allocation.…”

Section: Introductionmentioning

confidence: 99%

Multi-Tenant C-RAN With Spectrum Pooling: Downlink Optimization Under Privacy Constraints

Park

Simeone

Shamai

2018

IEEE Trans. Veh. Technol.

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Spectrum pooling allows multiple operators, or tenants, to share the same frequency bands. This work studies the optimization of spectrum pooling for the downlink of a multi-tenant Cloud Radio Access Network (C-RAN) system in the presence of inter-tenant privacy constraints. The spectrum available for downlink transmission is partitioned into private and shared subbands, and the participating operators cooperate to serve the user equipments (UEs) on the shared subband. The network of each operator consists of a cloud processor (CP) that is connected to proprietary radio units (RUs) by means of finite-capacity fronthaul links. In order to enable inter-operator cooperation, the CPs of the participating operators are also connected by finite-capacity backhaul links. Inter-operator cooperation may hence result in loss of privacy. Fronthaul and backhaul links are used to transfer quantized baseband signals.Standard quantization is considered first. Then, a novel approach based on the idea of correlating quantization noise signals across RUs of different operators is proposed to control the trade-off between distortion at UEs and inter-operator privacy. The problem of optimizing the bandwidth allocation, precoding, and fronthaul/backhaul compression strategies is tackled under constraints on backhaul and 2 fronthaul capacity, as well as on per-RU transmit power and inter-operator privacy. For both cases, the optimization problems are tackled using the concave convex procedure (CCCP), and extensive numerical results are provided.

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

Cited by 31 publications

References 35 publications

Toward Ultradense Small Cell Networks: A Brief History on the Theoretical Analysis of Dense Wireless Networks

Toward Ultradense Small Cell Networks: A Brief History on the Theoretical Analysis of Dense Wireless Networks

Modeling Infrastructure Sharing in mmWave Networks With Shared Spectrum Licenses

Multi-Tenant C-RAN With Spectrum Pooling: Downlink Optimization Under Privacy Constraints

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