Spectrum sharing models in cognitive radio networks

Spectrum scarcity demands thinking new ways to manage the distribution of radio frequency bands so that its use is more effective. The emerging technology that can enable this paradigm shift is the cognitive radio. Different models for organizing and managing cognitive radios have emerged, all with specific strategic purposes. In this article we review the allocation spectrum patterns of cognitive radio networks and analyse which are the common basis of each model.We expose the vulnerabilities and open challenges that still threaten the adoption and exploitation of cognitive radios for open civil networks.L'escassetat de demandes d'espectre fan pensar en noves formes de gestionar la distribució de les bandes de freqüència de ràdio perquè el seu ús sigui més efectiu. La tecnologia emergent que pot permetre aquest canvi de paradigma és la ràdio cognitiva. Han sorgit diferents models d'organització i gestió de les ràdios cognitives, tots amb determinats fins estratègics. En aquest article es revisen els patrons d'assignació de l'espectre de les xarxes de ràdio cognitiva i s'analitzen quals són la base comuna de cada model. S'exposen les vulnerabilitats i els desafiaments oberts que segueixen amenaçant l'adopció i l'explotació de les ràdios cognitives per obrir les xarxes civils.La escasez de demandas de espectro hacen pensar en nuevas formas de gestionar la distribución de las bandas de frecuencia de radio para que su uso sea más efectivo. La tecnología emergente que puede permitir este cambio de paradigma es la radio cognitiva. Han surgido diferentes modelos de organización y gestión de las radios cognitivas, todos con determinados fines estratégicos. En este artículo se revisan los patrones de asignación del espectro de las redes de radio cognitiva y se analizan cuales son la base común de cada modelo. Se exponen las vulnerabilidades y los desafíos abiertos que siguen amenazando la adopción y la explotación de las radios cognitivas para abrir las redes civiles

Interference Alignment for Cognitive Radio Communications and Networks: A Survey

© 2019 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (http://creativecommons.org/licenses/by/4.0/).Interference alignment (IA) is an innovative wireless transmission strategy that has shown to be a promising technique for achieving optimal capacity scaling of a multiuser interference channel at asymptotically high-signal-to-noise ratio (SNR). Transmitters exploit the availability of multiple signaling dimensions in order to align their mutual interference at the receivers. Most of the research has focused on developing algorithms for determining alignment solutions as well as proving interference alignment’s theoretical ability to achieve the maximum degrees of freedom in a wireless network. Cognitive radio, on the other hand, is a technique used to improve the utilization of the radio spectrum by opportunistically sensing and accessing unused licensed frequency spectrum, without causing harmful interference to the licensed users. With the increased deployment of wireless services, the possibility of detecting unused frequency spectrum becomes diminished. Thus, the concept of introducing interference alignment in cognitive radio has become a very attractive proposition. This paper provides a survey of the implementation of IA in cognitive radio under the main research paradigms, along with a summary and analysis of results under each system model.Peer reviewe

Hybrid Spectrum Sharing in mmWave Cellular Networks

While spectrum at millimeter wave (mmWave) frequencies is less scarce than at traditional frequencies below 6 GHz, still it is not unlimited, in particular if we consider the requirements from other services using the same band and the need to license mmWave bands to multiple mobile operators. Therefore, an efficient spectrum access scheme is critical to harvest the maximum benefit from emerging mmWave technologies. In this paper, we introduce a new hybrid spectrum access scheme for mmWave networks, where data is aggregated through two mmWave carriers with different characteristics. In particular, we consider the case of a hybrid spectrum scheme between a mmWave band with exclusive access and a mmWave band where spectrum is pooled between multiple operators. To the best of our knowledge, this is the first study proposing hybrid spectrum access for mmWave networks and providing a quantitative assessment of its benefits. Our results show that this approach provides major advantages with respect to traditional fully licensed or fully unlicensed spectrum access schemes, though further work is needed to achieve a more complete understanding of both technical and non technical implications

Generic Multiuser Coordinated Beamforming for Underlay Spectrum Sharing

The beamforming techniques have been recently studied as possible enablers for underlay spectrum sharing. The existing beamforming techniques have several common limitations: they are usually system model specific, cannot operate with arbitrary number of transmit/receive antennas, and cannot serve arbitrary number of users. Moreover, the beamforming techniques for underlay spectrum sharing do not consider the interference originating from the incumbent primary system. This work extends the common underlay sharing model by incorporating the interference originating from the incumbent system into generic combined beamforming design that can be applied on interference, broadcast or multiple access channels. The paper proposes two novel multiuser beamforming algorithms for user fairness and sum rate maximization, utilizing newly derived convex optimization problems for transmit and receive beamformers calculation in a recursive optimization. Both beamforming algorithms provide efficient operation for the interference, broadcast and multiple access channels, as well as for arbitrary number of antennas and secondary users in the system. Furthermore, the paper proposes a successive transmit/receive optimization approach that reduces the computational complexity of the proposed recursive algorithms. The results show that the proposed complexity reduction significantly improves the convergence rates and can facilitate their operation in scenarios which require agile beamformers computation.Comment: 30 pages, 5 figure

Separation Framework: An Enabler for Cooperative and D2D Communication for Future 5G Networks

Soaring capacity and coverage demands dictate that future cellular networks need to soon migrate towards ultra-dense networks. However, network densification comes with a host of challenges that include compromised energy efficiency, complex interference management, cumbersome mobility management, burdensome signaling overheads and higher backhaul costs. Interestingly, most of the problems, that beleaguer network densification, stem from legacy networks' one common feature i.e., tight coupling between the control and data planes regardless of their degree of heterogeneity and cell density. Consequently, in wake of 5G, control and data planes separation architecture (SARC) has recently been conceived as a promising paradigm that has potential to address most of aforementioned challenges. In this article, we review various proposals that have been presented in literature so far to enable SARC. More specifically, we analyze how and to what degree various SARC proposals address the four main challenges in network densification namely: energy efficiency, system level capacity maximization, interference management and mobility management. We then focus on two salient features of future cellular networks that have not yet been adapted in legacy networks at wide scale and thus remain a hallmark of 5G, i.e., coordinated multipoint (CoMP), and device-to-device (D2D) communications. After providing necessary background on CoMP and D2D, we analyze how SARC can particularly act as a major enabler for CoMP and D2D in context of 5G. This article thus serves as both a tutorial as well as an up to date survey on SARC, CoMP and D2D. Most importantly, the article provides an extensive outlook of challenges and opportunities that lie at the crossroads of these three mutually entangled emerging technologies.Comment: 28 pages, 11 figures, IEEE Communications Surveys & Tutorials 201

A Study Of Cooperative Spectrum Sharing Schemes For Internet Of Things Systems

The Internet of Things (IoT) has gained much attention in recent years with the massive increase in the number of connected devices. Cognitive Machine-to-Machine (CM2M) communications is a hot research topic in which a cognitive dimension allows M2M networks to overcome the challenges of spectrum scarcity, interference, and green requirements. In this paper, we propose a Generalized Cooperative Spectrum Sharing (GCSS) scheme for M2M communication. Cooperation extends the coverage of wireless networks as well as increasing their throughput while reducing the energy consumption of the connected low power devices. We study the outage performance of the proposed GCSS scheme for M2M system and derive exact expressions for the outage probability. We also analyze the effect of varying transmission powers on the performance of the system

Optimal Bandwidth and Power Allocation for Sum Ergodic Capacity under Fading Channels in Cognitive Radio Networks

This paper studies optimal bandwidth and power allocation in a cognitive radio network where multiple secondary users (SUs) share the licensed spectrum of a primary user (PU) under fading channels using the frequency division multiple access scheme. The sum ergodic capacity of all the SUs is taken as the performance metric of the network. Besides all combinations of the peak/average transmit power constraints at the SUs and the peak/average interference power constraint imposed by the PU, total bandwidth constraint of the licensed spectrum is also taken into account. Optimal bandwidth allocation is derived in closed-form for any given power allocation. The structures of optimal power allocations are also derived under all possible combinations of the aforementioned power constraints. These structures indicate the possible numbers of users that transmit at nonzero power but below their corresponding peak powers, and show that other users do not transmit or transmit at their corresponding peak power. Based on these structures, efficient algorithms are developed for finding the optimal power allocations.Comment: 28 pages, 6 figures, submitted to the IEEE Trans. Signal Processing in June 201