Multiband Spectrum Access: Great Promises for Future Cognitive Radio Networks

Cognitive radio has been widely considered as one of the prominent solutions to tackle the spectrum scarcity. While the majority of existing research has focused on single-band cognitive radio, multiband cognitive radio represents great promises towards implementing efficient cognitive networks compared to single-based networks. Multiband cognitive radio networks (MB-CRNs) are expected to significantly enhance the network's throughput and provide better channel maintenance by reducing handoff frequency. Nevertheless, the wideband front-end and the multiband spectrum access impose a number of challenges yet to overcome. This paper provides an in-depth analysis on the recent advancements in multiband spectrum sensing techniques, their limitations, and possible future directions to improve them. We study cooperative communications for MB-CRNs to tackle a fundamental limit on diversity and sampling. We also investigate several limits and tradeoffs of various design parameters for MB-CRNs. In addition, we explore the key MB-CRNs performance metrics that differ from the conventional metrics used for single-band based networks.Comment: 22 pages, 13 figures; published in the Proceedings of the IEEE Journal, Special Issue on Future Radio Spectrum Access, March 201

Cooperative sensing of spectrum opportunities

Reliability and availability of sensing information gathered from local spectrum sensing (LSS) by a single Cognitive Radio is strongly affected by the propagation conditions, period of sensing, and geographical position of the device. For this reason, cooperative spectrum sensing (CSS) was largely proposed in order to improve LSS performance by using cooperation between Secondary Users (SUs). The goal of this chapter is to provide a general analysis on CSS for cognitive radio networks (CRNs). Firstly, the theoretical system model for centralized CSS is introduced, together with a preliminary discussion on several fusion rules and operative modes. Moreover, three main aspects of CSS that substantially differentiate the theoretical model from realistic application scenarios are analyzed: (i) the presence of spatiotemporal correlation between decisions by different SUs; (ii) the possible mobility of SUs; and (iii) the nonideality of the control channel between the SUs and the Fusion Center (FC). For each aspect, a possible practical solution for network organization is presented, showing that, in particular for the first two aspects, cluster-based CSS, in which sensing SUs are properly chosen, could mitigate the impact of such realistic assumptions

Comprehensive survey on quality of service provisioning approaches in cognitive radio networks : part one

Much interest in Cognitive Radio Networks (CRNs) has been raised recently by enabling unlicensed (secondary) users to utilize the unused portions of the licensed spectrum. CRN utilization of residual spectrum bands of Primary (licensed) Networks (PNs) must avoid harmful interference to the users of PNs and other overlapping CRNs. The coexisting of CRNs depends on four components: Spectrum Sensing, Spectrum Decision, Spectrum Sharing, and Spectrum Mobility. Various approaches have been proposed to improve Quality of Service (QoS) provisioning in CRNs within fluctuating spectrum availability. However, CRN implementation poses many technical challenges due to a sporadic usage of licensed spectrum bands, which will be increased after deploying CRNs. Unlike traditional surveys of CRNs, this paper addresses QoS provisioning approaches of CRN components and provides an up-to-date comprehensive survey of the recent improvement in these approaches. Major features of the open research challenges of each approach are investigated. Due to the extensive nature of the topic, this paper is the first part of the survey which investigates QoS approaches on spectrum sensing and decision components respectively. The remaining approaches of spectrum sharing and mobility components will be investigated in the next part

Cooperative Spectrum Sensing in Cognitive Radio Networks Using Multidimensional Correlations

In this paper, a multidimensional-correlation-based sensing scheduling algorithm, (CORN)2, is developed for cognitive radio networks to minimize energy consumption. A sensing quality metric is defined as a measure of the correctness of spectral availability information based on the fact that spectrum sensing information at a given space and time can represent spectrum information at a different point in space and time. The scheduling algorithm is shown to achieve a cost of sensing (e.g., energy consumption, sensing duration) arbitrarily close to the possible minimum, while meeting the sensing quality requirements. To this end, (CORN)2 utilizes a novel sensing deficiency virtual queue concept and exploits the correlation between spectrum measurements of a particular secondary user and its collaborating neighbors. The proposed algorithm is proved to achieve a distributed and arbitrarily close to optimal solution under certain, easily satisfied assumptions. Furthermore, a distributed Selective-(CORN)2 (S-(CORN)2) is introduced by extending the distributed algorithm to allow secondary users to select collaboration neighbors in densely populated cognitive radio networks. In addition to the theoretically proved performance guarantees, the algorithms are evaluated through simulations

Quality and Availability of spectrum based routing for Cognitive radio enabled IoT networks

With the recent emergence and its wide spread applicability Internet of Things (IoT) is putting pressure on network resources and most importantly on availability of spectrum. Spectrum scarcity is the issue to be addressed in networking within IoT. Cognitive radio is the technology which addresses the problem of spectrum scarcity in an efficient way. Equipping the IoT devices with cognitive radio capability will lead to a new dimension called cognitive radio enabled IoT devices. To achieve ON-demand IoT solutions and interference free communications cognitive radio enabled IoT devices will become an effective platform for many applications. As there is high dynamicity in availability of spectrum it is challenging for designing an efficient routing protocol for secondary users in cognitive device networks. In this work we are going to estimate spectrum quality and spectrum availability based on two parameters called global information about spectrum usage and instant spectrum status information. Enhanced energy detector is used at each and every node for better probability of detection. For estimating spectrum quality and availability we are introducing novel routing metrics. To have restriction on the number of reroutings and to increase the performance of routing in our proposed routing metric only one retransmission is allowed. Then, two algorithms for routing are designed for evaluating the performance of routing and we find that the bit error rates of proposed algorithms (nodes are dynamic) have decreased a lot when compared to conventional methods (Nodes are static) and throughput of proposed algorithm also improved a lot

Opportunistic Spectrum Utilization by Cognitive Radio Networks: Challenges and Solutions

Cognitive Radio Network (CRN) is an emerging paradigm that makes use of Dynamic Spectrum Access (DSA) to communicate opportunistically, in the un-licensed Industrial, Scientific and Medical bands or frequency bands otherwise licensed to incumbent users such as TV broadcast. Interest in the development of CRNs is because of severe under-utilization of spectrum bands by the incumbent Primary Users (PUs) that have the license to use them coupled with an ever-increasing demand for unlicensed spectrum for a variety of new mobile and wireless applications. The essence of Cognitive Radio (CR) operation is the cooperative and opportunistic utilization of licensed spectrum bands by the Secondary Users (SUs) that collectively form the CRN without causing any interference to PUs\u27 communications. CRN operation is characterized by factors such as network-wide quiet periods for cooperative spectrum sensing, opportunistic/dynamic spectrum access and non-deterministic operation of PUs. These factors can have a devastating impact on the overall throughput and can significantly increase the control overheads. Therefore, to support the same level of QoS as traditional wireless access technologies, very closer interaction is required between layers of the protocol stack. Opportunistic spectrum utilization without causing interference to the PUs is only possible if the SUs periodically sense the spectrum for the presence of PUs\u27 signal. To minimize the effects of hardware capabilities, terrain features and PUs\u27 transmission ranges, DSA is undertaken in a collaborative manner where SUs periodically carry out spectrum sensing in their respective geographical locations. Collaborative spectrum sensing has numerous security loopholes and can be favorable to malicious nodes in the network that may exploit vulnerabilities associated with DSA such as launching a spectrum sensing data falsification (SSDF) attack. Some CRN standards such as the IEEE 802.22 wireless regional area network employ a two-stage quiet period mechanism based on a mandatory Fast Sensing and an optional Fine Sensing stage for DSA. This arrangement is meant to strike a balance between the conflicting goals of proper protection of incumbent PUs\u27 signals and optimum QoS for SUs so that only as much time is spent for spectrum sensing as needed. Malicious nodes in the CRN however, can take advantage of the two-stage spectrum sensing mechanism to launch smart denial of service (DoS) jamming attacks on CRNs during the fast sensing stage. Coexistence protocols enable collocated CRNs to contend for and share the available spectrum. However, most coexistence protocols do not take into consideration the fact that channels of the available spectrum can be heterogeneous in the sense that they can vary in their characteristics and quality such as SNR or bandwidth. Without any mechanism to enforce fairness in accessing varying quality channels, ensuring coexistence with minimal contention and efficient spectrum utilization for CRNs is likely to become a very difficult task. The cooperative and opportunistic nature of communication has many challenges associated with CRNs\u27 operation. In view of the challenges described above, this dissertation presents solutions including cross-layer approaches, reputation system, optimization and game theoretic approaches to handle (1) degradation in TCP\u27s throughput resulting from packet losses and disruptions in spectrum availability due non-deterministic use of spectrum by the PUs (2) presence of malicious SUs in the CRN that may launch various attacks on CRNs\u27 including SSDF and jamming and (3) sharing of heterogeneous spectrum resources among collocated CRNs without a centralized mechanism to enforce cooperation among otherwise non-cooperative CRN

Cooperative Spectrum Sensing in Cognitive Radio Networks Using Multidimensional Correlations

In this paper, a multidimensional-correlation-based sensing scheduling algorithm, (CORN)2, is developed for cognitive radio networks to minimize energy consumption. A sensing quality metric is defined as a measure of the correctness of spectral availability information based on the fact that spectrum sensing information at a given space and time can represent spectrum information at a different point in space and time. The scheduling algorithm is shown to achieve a cost of sensing (e.g., energy consumption, sensing duration) arbitrarily close to the possible minimum, while meeting the sensing quality requirements. To this end, (CORN)2 utilizes a novel sensing deficiency virtual queue concept and exploits the correlation between spectrum measurements of a particular secondary user and its collaborating neighbors. The proposed algorithm is proved to achieve a distributed and arbitrarily close to optimal solution under certain, easily satisfied assumptions. Furthermore, a distributed Selective-(CORN)2 (S-(CORN)2) is introduced by extending the distributed algorithm to allow secondary users to select collaboration neighbors in densely populated cognitive radio networks. In addition to the theoretically proved performance guarantees, the algorithms are evaluated through simulations

Spectrum Estimation and Optimal Secondary User Selection in Cognitive Radio Networks

The high-speed development of wireless communication technology has emerged in the surging insistence on optimal spectrum resources. Nevertheless, in consonance to a contemporary study, most of the assigned frequency encounters notable underutilization as far as Cognitive Radio Network (CRN) is concerned. One important issue correlated with spectrum management is how to properly estimate and allocate the spectrum to a Secondary User (SU) for a highly dynamic environment in an optimal manner with minimum sensing delay. In this paper, a Chebyshev Vector Dynamic Spectrum and Kolmogorov-Smirnov Convolutional Network (CVDS-KSCN) method for dynamic spectrum estimation and optimal secondary user selection in CRN is developed. First, it is proposed to tackle the dynamic spectrum access issue with minimum sensing delay in CRN attaining robust spectrum channel throughput with minimum sensing delay. The spectrum estimation is modeled using the Chebyshev distance-based Harmonious Vector Spectrum Estimation model in a dynamic manner. With the dynamic spectrum estimated results, a Kolmogorov-Smirnov Convolutional Neural Network-based Secondary User Selection model is applied to retrieve optimal secondary users in CRN. The performance of CVDS-KSCN is assessed over numerous key aspects, where simulation results confirm the efficiency of the proposed method in achieving high reliable spectrum estimation and Secondary User selection. It is expressive in the simulation results that the proposed CVDS-KSCN method can achieve a good probability of throughput and reduction in sensing delay during Secondary User Selection with low probability of false alarm. The results show that the proposed method outperfroms the DRS and EFAHP algorithms quantitatively in terms of four parameters, namely throughput, sensing delay, false alarm percentage and Secondary User Selection Time