Live field trial of Licensed Shared Access (LSA) concept using LTE network in 2.3 GHz band

Palola, Marko; Matinmikko, Marja; Prokkola, Jarmo; Mustonen, Miia; Heikkilä, Matti; Kippola, Tero; Yrjölä, Seppo; Hartikainen, Vesa; Tudose, Lucia; Kivinen, Arto; Paavola, Jarkko; Heiska, Kari

doi:10.1109/dyspan.2014.6817778

Cited by 50 publications

(31 citation statements)

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“…Therefore, the SUs should accurately estimate the PUs' traffic distribution, i.e., classify the PU traffic correctly from a set of possible distributions, e.g., exponential, gamma, log-normal, and Weibull distributions as tested in [9]. Looking at the recent DSA/OSA applications, traffic classification can be used in Licensed Shared Access [10] (LSA) systems, where traffic classification would help in identifying the behavior of individual LSA licensees [11] and adapting licensing rules accordingly.…”

Section: Introductionmentioning

confidence: 99%

Primary User Traffic Classification in Dynamic Spectrum Access Networks

Liu

Pawełczak

Čabrić

2014

IEEE J. Select. Areas Commun.

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This paper focuses on analytical studies of the primary user (PU) traffic classification problem. Observing that the gamma distribution can represent positively skewed data and exponential distribution (popular in communication networks performance analysis literature) it is considered here as the PU traffic descriptor. We investigate two PU traffic classifiers utilizing perfectly measured PU activity (busy) and inactivity (idle) periods: (i) maximum likelihood classifier (MLC) and (ii) multi-hypothesis sequential probability ratio test classifier (MSPRTC). Then, relaxing the assumption on perfect period measurement, we consider a PU traffic observation through channel sampling. For a special case of negligible probability of PU state change in between two samplings, we propose a minimum variance PU busy/idle period length estimator. Later, relaxing the assumption of the complete knowledge of the parameters of the PU period length distribution, we propose two PU traffic classification schemes: (i) estimate-then-classify (ETC), and (ii) average likelihood function (ALF) classifiers considering time domain fluctuation of the PU traffic parameters. Numerical results show that both MLC and MSPRTC are sensitive to the periods measurement errors when the distance among distribution hypotheses is small, and to the distribution parameter estimation errors when the distance among hypotheses is large. For PU traffic parameters with a partial prior knowledge of the distribution, the ETC outperforms ALF when the distance among hypotheses is small, while the opposite holds when the distance is large.Comment: Accepted to IEEE Journal on Selected Areas in Communications; Preliminary version appeared in Proc. IEEE GLOBECOM, Dec. 9-13, 2013, Atlanta, GA, US

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Section: Introductionmentioning

confidence: 99%

Primary User Traffic Classification in Dynamic Spectrum Access Networks

Liu

Pawełczak

Čabrić

2014

IEEE J. Select. Areas Commun.

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“…In [145] and [150], the results from trials of the reference LSA framework (i.e., the conventional LSA architecture) in time-division LTE specifications are presented and discussed. The PMSE as an incumbent in 2.3-2.4GHz bands is considered.…”

Section: ) Lsa Trial Demonstrationsmentioning

confidence: 99%

“…Thus, this problem needs to be further considered when the LSA bands are dynamically reclaimed by the incumbent (e.g., the case of PMSE), in contrast to the case when the incumbent (e.g., the military) shares the bands in reasonable time scales such as months, years or in remote regions [52]. According to [145], the band evacuation phase in LSA requires appropriates optimisations that determine how fast parameters such as the antenna direction, frequency band or even power level, can be altered. Applying LSA bands for indoor scenarios with low power BSs may seem to be a reasonable solution for this problem [146] .…”

Section: Challenges Of Deployment In Mobile Cellular Networkmentioning

confidence: 99%

“…Therefore, any time the bands are requested by the incumbent, the BS should stop operating and a shutdown process must be carried out. The MNO needs to perform traffic steering and handover and serve the UEs through adjacent BSs, which are operating in typical exclusive bands [145]. In the case that the target BSs are heavily loaded and are not able to accommodate the UE right away, leading to increased queuing or even connection dropping.…”

Section: Challenges Of Deployment In Mobile Cellular Networkmentioning

confidence: 99%

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Licensed Spectrum Sharing Schemes for Mobile Operators: A Survey and Outlook

Tehrani

Vahid

Triantafyllopoulou

et al. 2016

IEEE Commun. Surv. Tutorials

191

119

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The ongoing development of mobile communication networks to support a wide range of superfast broadband services has led to massive capacity demand. This problem is expected to be a significant concern during the deployment of the 5G wireless networks. The demand for additional spectrum to accommodate mobile services supporting higher data rates and having lower latency requirements, as well as the need to provide ubiquitous connectivity with the advent of the Internet of Things (IoT) sector, is likely to considerably exceed the supply, based on the current policy of exclusive spectrum allocation to mobile cellular systems. Hence, the imminent spectrum shortage has introduced a new impetus to identify practical solutions to make the most efficient use of the scarce licensed bands in a shared manner. Recently, the concept of dynamic spectrum sharing has received considerable attention from regulatory bodies and governments globally, as it could potentially open new opportunities for mobile operators to exploit spectrum bands whenever they are underutilised by their owners, subject to service level agreements. Although various sharing paradigms have been proposed and discussed, the impact and performance gains of different schemes can be scenario-specific and vary depending on the nature of the sharing parties, the level of sharing and spectrum access scheme. In this survey, we describe the main concepts of dynamic spectrum sharing, different sharing scenarios, as well as the major challenges associated with sharing licensed bands. Finally, we conclude this survey paper with open research challenges and suggest some future research directions

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“…A novel Licensed Shared Access (LSA) [16,17] concept enables advanced spectrum sharing between a limited number of end-users with at least two entities: the incumbent (i.e., the current holder of the spectrum usage rights) and the LSA licensee (i.e., the temporary user of spectrum, which could be a Mobile Network Operator, MNO) [18]. More specifically, the LSA framework [19] permits for controlled spectrum sharing between a Primary User (PU) and a Secondary User (SU) both having access to a portion of spectrum at a given location and time [20,21], as it is demonstrated in Figure 1.…”

Section: Spectrum Sharing With Licensed Shared Accessmentioning

confidence: 99%

Dynamic Resource Sharing in 5G with LSA: Criteria‐Based Management Framework

Sadreddini

Mašek

Çavdar

et al. 2018

Wireless Communications and Mobile Computing

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Owing to a steadily increasing demand for efficient spectrum utilization as part of the fifth-generation (5G) cellular concept, it becomes crucial to revise the existing radio spectrum management techniques and provide more flexible solutions for the corresponding challenges. A new wave of spectrum policy reforms can thus be envisaged by producing a paradigm shift from static to dynamic orchestration of shared resources. The emerging Licensed Shared Access (LSA) regulatory framework enables flexible spectrum sharing between a limited number of users that access the same frequency bands, while guaranteeing better interference mitigation. In this work, an advanced user satisfaction-aware spectrum management strategy for dynamic LSA management in 5G networks is proposed to balance both the connected user satisfaction and the Mobile Network Operator (MNO) resource utilization. The approach is based on the MNO decision policy that combines both pricing and rejection rules in the implemented processes. Our study offers a classification built over several types of users, different corresponding attributes, and a number of MNO's decision scenarios. Our investigations are built on Criteria-Based Resource Management (CBRM) framework, which has been specifically designed to facilitate dynamic LSA management in 5G mobile networks. To verify the proposed model, the results (spectrum utilization, estimated Secondary User price for the future connection, and user selection methodology in case of user rejection process) are validated numerically as we yield important conclusions on the applicability of our approach, which may offer valuable guidelines for efficient radio spectrum management in highly dynamic and heterogeneous 5G environments.

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Live field trial of Licensed Shared Access (LSA) concept using LTE network in 2.3 GHz band

Cited by 50 publications

References 1 publication

Primary User Traffic Classification in Dynamic Spectrum Access Networks

Primary User Traffic Classification in Dynamic Spectrum Access Networks

Licensed Spectrum Sharing Schemes for Mobile Operators: A Survey and Outlook

Dynamic Resource Sharing in 5G with LSA: Criteria‐Based Management Framework

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