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 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 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 underutilized 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 may vary depending on the nature of the sharing players, the level of sharing and spectrum access scheme. In this survey, we study the main concepts of dynamic spectrum sharing, different sharing scenarios, as well as the major challenges associated with sharing of licensed bands. Finally, we conclude this survey with open research challenges and suggest some future research directions.

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

In order to provide meaningful data about spectrum use, occupancy measurements describing the utilization rate of a specific frequency band should be conducted over a specific area instead of a single location. This paper presents a comprehensive methodology for the measurement and analysis of spectrum occupancy. This paper surveys spectrum measurement campaigns and associated interference maps, introducing the latter as a tool for spectrum analysis and management based on measurement data. An interference map characterizes the spectrum use by defining the level of interference over an area of interest in a certain frequency band. Building on findings from practical measurement studies, guidelines for spectrum occupancy measurements are given. While many scientific spectrum occupancy measurement papers tend to be too optimistic about the significance and generality of the results, we propose a cautionary perspective on drawing strong conclusions based on the often limited amount of data gathered. The different phases of the spectrum occupancy measurement and analysis process are described and a thorough discussion of interpolation methods is provided. Means to improve the measurement accuracy are discussed, especially regarding spatial domain considerations and the impact of the sampling interval on the results. A practical example of an improved measurement system design covering all the phases of the measurement process and used at the Turku, Finland; Blacksburg, VA, USA; and Chicago, IL, USA, spectrum observatories is given. Using the improved design, more realistic spectrum occupancy data can be obtained to lay the foundation for spectrum management decisions.

Spectrum Occupancy Measurements: A Survey and Use of Interference Maps

Radio maps are expected to be an essential tool for the resource optimization and management of 5G automotive. In this paper, we consider the problem of radio map reconstruction using a wireless sensor network formed by sensor nodes in vehicles, access nodes from a smart city infrastructure, etc. Due to limited resource constraints in sensor networks, it is crucial to select a small number of sensor measurements for field reconstruction. In this context, we present a novel distributed incremental clustering algorithm based on the regression Kriging method for radio map reconstruction in terms of average received power at locations where no sensor measurements are available. The path-loss and shadowing components of the wireless channel are separately estimated. For shadowing estimation, clusters of sensors are adaptively formed and their size is optimized in terms of the least number of sensors by minimizing the ordinary Kriging variance. The complexity of the proposed algorithm is analyzed and simulation results are presented to showcase the algorithm efficacy to field reconstruction.

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Distributed Radio Map Reconstruction for 5G Automotive

Radio environment map (REM) has emerged as a crucial technology to improve the robustness of intelligent transportation systems (ITS) by enhancing network planning and spectrum resource utilization. To construct a precise REM, optimizing deployment of sensor nodes and increasing spatial interpolation accuracy are two main directions. Given the deployment of sensor nodes, high resolution (HR) spatial interpolation would still bring about huge computing overhead, which is not practical for realtime applications. In order to improve the efficiency and accuracy of REM construction, we propose a super-resolution (SR) based REM construction method, which is composed of Kriging interpolation, dictionary learning and random forest. In our method, both low resolution (LR) and HR REM image sets are generated and trained to obtain a random forest model. With spectrum data from the limited number of sensor nodes, a SR REM can be acquired by the proposed method. Simulation results demonstrate that our method can greatly shorten the construction time of REM while maintaining high accuracy.

/pdf/radio-environment-map-construction-using-super-resolution-4o8cyhuhp5.pdf

Radio Environment Map Construction Using Super-Resolution Imaging for Intelligent Transportation Systems

Increasing utilization of LTE-Advanced (LTE-A) to meet the rapid growth in wireless bandwidth demand is an important focus for current research. Dynamic spectrum access (DSA) is a promising approach that can be utilized to improve bandwidth utilization in LTE-A systems and networks. The application of DSA is not limited to commercial use but can also be applied to provide access to other systems including public safety communication systems and device to device communications. This paper provides a general overview of DSA and a review of the recent research into the use of DSA to improve bandwidth utilization in LTE-A networks. DSA is a flexible technique that is being applied to different network technologies including cognitive radio, mobile cellular femtocells and wireless relay.

A Survey on Dynamic Spectrum Access for LTE-Advanced

Cognitive Radio based Dynamic Spectrum Access (CR-DSA) has been envisaged as the foremost solution to eliminate the impending false scarcity of the radio frequency spectrum and to combat the dramatic surge in mobile data traffic. Furthermore, the revolutionary concepts - Carrier Aggregation (CA) and Licensed Shared Access (LSA) - have been conceived to support higher data rates by aggregating bandwidth from different parts of the spectrum, and to provide for a predictable Quality of Service (QoS) through spectrum sharing, respectively, so as to enhance the performance of the current generation mobile communication systems. However, an inevitable consequence of spectrum sharing is the interference induced upon the incumbent user. In this work, the prime focus of attention is on the mitigation of the interference caused by an LSA-licensed LTE-Advanced Radio Access Network (RAN) in the uplink (UL). To this end, we propose an interference management scheme based on Radio Environment Maps (REMs). Different approaches have been considered for the modeling of the requisite maps and a performance comparison between them by means of detection theory is presented. In conclusion, we prove the superior performance of the kriging spatial interpolation technique over the other approaches.

Juan Diego Naranjo

Papers

Modeling of interference maps for Licensed Shared Access in LTE-advanced networks supporting Carrier Aggregation