Search

Cognitive radio is an effective technology to improve spectrum utilization through spectrum sharing among primary users (PUs) and secondary users (SUs). Developing accurate and efficient methods to determine the PU coverage is crucial in order to avoid potential interference from SU to PU. In this paper, we propose to use the estimated location of each SU, which is obtained using the self-organizing maps algorithm, to determine the boundary of PU coverage. Furthermore, the PU boundary detection issue considering the presence of malicious SUs is also investigated. Support vector machine (SVM) algorithm, considering both linear and kernel cases, is implemented as the PU boundary detection method. Numerical results show that the boundary detection accuracy based on the estimated network layout approaches the results obtained from an accurate network layout (i.e., each SU with precise location information) and the kernel-based SVM classification results outperform the linear SVM method. In addition, PU boundary detection performance in the presence of malicious SUs is evaluated, in which three different $K$ nearest neighbor based malicious SU detection algorithms are utilized. [ABSTRACT FROM PUBLISHER]

In this paper, we first explore a selective cooperation framework for secondary user transmissions in a cognitive radio network with single relay. In the selective cooperation framework, two transmission modes (i.e., relay diversity transmission and non-relay direct transmission) are considered. We study two specific selective cooperation schemes with and without an acknowledgement (ACK) from a cognitive destination as to if it succeeds in decoding or not, called ACK and non-ACK based selective cooperation, respectively. We derive closed-form outage probability expressions for the two schemes with imperfect spectrum sensing, showing that an outage probability floor occurs in high signal-to-noise ratio (SNR) regions due to mutual interference between primary and secondary users. We consider the use of the outage probability floor to generalize the traditional diversity-multiplexing tradeoff (DMT) definition, based on which a DMT analysis is conducted for the non-ACK and ACK based selective cooperation schemes. We then extend the selective cooperation framework to a multiple-relay cognitive radio network considering the best cognitive relay only to participate in assisting secondary transmissions, referred to as the selective best-relay cooperation. We also consider the non-ACK and ACK based selective best-relay cooperation schemes and develop their DMTs by using the generalized DMT definition. [ABSTRACT FROM PUBLISHER]

In this correspondence, we investigate a selective relay spectrum sensing and best relay data transmission (SRSS-BRDT) scheme for multiple-relay cognitive radio networks. Specifically, in the spectrum sensing phase, only selected cognitive relays are utilized to transmit/forward their initial detection results (without a dedicated sensing relay channel) to a cognitive source for fusion, where the dedicated sensing channel refers to the channel transmitting initial spectrum sensing results from cognitive relays to the cognitive source. In the data transmission phase, only the best relay is selected to assist the cognitive source for its data transmissions. By jointly considering the two phases, we derive a closed-form expression of the outage probability for the SRSS-BRDT scheme over Rayleigh fading channels. We show that the SRSS-BRDT scheme outperforms the traditional cognitive transmission scheme (with a limited dedicated sensing channel) in terms of the outage probability performance. In addition, numerical results illustrate that the outage probability of the SRSS-BRDT scheme can be minimized through an optimal allocation of the time durations between the spectrum sensing and data transmission phases. [ABSTRACT FROM AUTHOR]

In cognitive radio (CR) networks, cooperative spectrum sensing is utilized to improve the sensing performance to avoid potential interference to primary users (PUs) and increase spectrum access opportunities for secondary users (SUs). A cooperative spectrum sensing process is divided into three phases: individual sensing/detection, reporting/fusion, and data transmission. In the reporting phase, one or more reporting channels are needed to transmit individual sensing results to a fusion center (FC), and global spectrum sensing results are determined at the FC. The number of required reporting channels depends on the number of spectrum sensors or SUs, which relates to reporting channel efficiency and channel scheduling complexity. That is to say, the reporting channel design can be a challenge, especially when fixed assignment scheduling is used. Therefore, in this paper, we design a reporting channel scheme based on random access protocols, including slotted Aloha and reservation-Aloha. Performance evaluations in terms of PU detection probabilities and false alarm probabilities considering the proposed reporting channels are presented. In addition, the impact of soft/unquantized spectrum sensors or detectors (SUs) and malicious SUs is considered in this paper. Analytical and simulation results illustrate the effectiveness of the proposed reporting channel scheduling methods in improving the cooperative spectrum sensing performance. [ABSTRACT FROM AUTHOR]

Due to the energy constraint of each cognitive user and potential secondary transmission errors, energy efficiency is very important for cognitive nodes in spectrum sensing and cooperative transmissions. In this paper, an energy awareness optimal relay selection (EAORS) scheme is proposed, in which the number of optimal relays is determined by a weighted objective function considering energy consumption in both spectrum sensing and the cooperative transmission phases, under the constraints of the global misdetection requirement and secondary cooperative transmission bit error rate (BER). In the proposed EAORS scheme, we investigate the tradeoff between detection accuracy and energy efficiency and construct the objective function as a weighted convex function of relay numbers with the consideration of global missing detection probability and cooperative transmission BER. This nonlinear convex problem is solved by applying numerical analysis to obtain the optimal relay numbers. Energy consumption analysis is conducted to show the effectiveness of the proposed EAORS scheme. Numerical results show that the proposed scheme outperforms a compressed sensing (CS)-based energy-efficient cooperative detection scheme and provides a tradeoff between detection accuracy and energy efficiency. [ABSTRACT FROM PUBLISHER]

This article investigates the physical-layer security of CR networks, which are vulnerable to various newly arising attacks targeting the weaknesses of CR communications and networking. We first review a range of physical-layer attacks in CR networks, including primary user emulation, sensing falsification, intelligence compromise, jamming, and eavesdropping attacks. Then we focus on the physical-layer security of CR networks against eavesdropping and examine the secrecy performance of cognitive communications in terms of secrecy outage probability. We further consider the use of relays for improving CR security against eavesdropping and propose an opportunistic relaying scheme, where a relay node that makes CR communications most resistant to eavesdropping is chosen to participate in assisting the transmission from a cognitive source to its destination. It is illustrated that the physical- layer secrecy of CR communications relying on opportunistic relaying can be significantly improved by increasing the number of relays, showing the security benefit of exploiting relay nodes. Finally, we present some open challenges in the field of relay-assisted physical-layer security for CR networks. [ABSTRACT FROM PUBLISHER]

A range of applications in cognitive radio networks, from adaptive spectrum sensing to predictive spectrum mobility and dynamic spectrum access, depend on our ability to foresee the state evolution of radio spectrum, raising a fundamental question: To what degree is radio spectrum state (RSS) predictable? In this article we explore the fundamental limits of predictability in RSS dynamics by studying the RSS evolution patterns in spectrum bands of several popular services, including TV bands, ISM bands, cellular bands, and so on. From an information theory perspective, we introduce a methodology of using statistical entropy measures and Fano inequality to quantify the degree of predictability underlying real-world spectrum measurements. Despite the apparent randomness, we find a remarkable predictability, as large as 90 percent, in real-world RSS dynamics over a number of spectrum bands for all popular services. Furthermore, we discuss the potential applications of prediction-based spectrum sharing in 5G wireless communications. [ABSTRACT FROM PUBLISHER]

This paper investigates the issue of cooperative spectrum sensing with a crowd of low-end personal spectrum sensors (such as smartphones, tablets, and in-vehicle sensors), where the sensing data from crowd sensors that may be unreliable, untrustworthy, or even malicious. Moreover, due to either unexpected equipment failures or malicious behaviors, every crowd sensor could sporadically and randomly contribute with abnormal data, which makes the existing cooperative sensing schemes ineffective. To tackle these challenges, we first propose a generalized modeling approach for sensing data with an arbitrary abnormal component. Under this model, we then analyze the impact of general abnormal data on the performance of the cooperative sensing, by deriving closed-form expressions of the probabilities of global false alarm and global detection. To improve sensing data quality and enhance cooperative sensing performance, we further formulate an optimization problem as stable principal component pursuit, and develop a data cleansing-based robust spectrum sensing algorithm to solve it, where the under-utilization of licensed spectrum bands and the sparsity of nonzero abnormal data are jointly exploited to robustly cleanse out the potential nonzero abnormal data component from the original corrupted sensing data. Extensive simulation results demonstrate that the proposed robust sensing scheme performs well under various abnormal data parameter configurations. [ABSTRACT FROM PUBLISHER]