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This paper proposes a new spectrum sensing technique, referred to as autonomous compressive sensing (CS)-augmented spectrum sensing, which can be developed to provide more efficient spectrum opportunity identification than geolocation database methods. First, we propose an autonomous CS-based sensing algorithm that enables the local secondary users (SUs) to automatically choose the minimum sensing time without knowledge of spectral sparsity or channel characteristics. The compressive samples are collected block-by-block in time, while the spectral is gradually reconstructed until the proposed stopping criterion is reached. Moreover, a CS-based blind cooperating user selection algorithm is proposed to select the cooperating SUs via indirectly measuring the degeneration of the signal-to-noise ratio experienced by different SUs. Numerical and real-world test results demonstrate that the proposed algorithms achieve high detection performance with reduced sensing time and number of cooperating SUs in comparison with the conventional compressive spectrum sensing algorithms. [ABSTRACT FROM AUTHOR]

In this paper, we investigate the performance of cooperative spectrum sensing (CSS) with multiple-antenna nodes over generalized and composite fading channels. To this end, we approximate the probability density function (pdf) of the signal-to-noise ratio (SNR) of various fading channels using the mixture Gamma (MG) distribution. Based on this, we derive an exact closed-form expression and a generic infinite series representation for the corresponding probability of energy detection, along with a finite upper bound for the involved truncation error. Both expressions have a relatively simple algebraic form that gives them convenience in handling both analytically and numerically. Furthermore, the composite effect of multipath fading and shadowing scenarios in CSS is mitigated by applying an optimal fusion rule that minimizes the total error rate (TER), where the optimal number of nodes is derived under the Bayesian criterion, assuming erroneous feedback channels. We also extend the derived average detection probability to include diversity reception techniques, namely, maximal-ratio combining, square-law combining, and square-law selection (SLS). For the SLS, we demonstrate the existence of an error rate floor as the number of antennas of the cognitive radio nodes increases in erroneous decision feedback channels. Accordingly, we derive the optimal rule for the number of antennas that minimizes the TER in the SLS framework. Monte Carlo simulations are presented to corroborate the analytical results and to provide illustrative performance comparisons and insights between different composite fading channels. [ABSTRACT FROM PUBLISHER]

By cooperative spectrum sharing schemes, a primary network (PN) leases a part of the licensed band to a secondary network (SN). A secondary transmitter (ST) acts as a relay for the primary transmitter (PT) to improve the end-to-end link quality of the PN, and as a reward, the ST gets the opportunity to transmit its own data by superposition coding. In this paper, a new cooperative spectrum sharing (CSS) scheme taking into account malicious eavesdropping (wiretapping) by a PN is proposed. In such scheme, licensed band is a decoy to an SN to wiretap the secondary transmission. Thus, secrecy in secondary transmission becomes critical to the SN. To get achievable secrecy (data) rate in secondary transmission and to fulfill the duty as a relay for primary transmission, the SN looks for a smart power-allocation (SPA) parameter that satisfies both goals. SPA parameter \alpha\mathrm{smart} provides the target secrecy data rate, and the achieved data rate at the PN is greater than or equal to the data rate obtained by the direct transmission (DT). Considering a Rayleigh block-fading channel, the SPA parameter is evaluated for each secondary transmission. Details on the evaluation of the SPA parameter are presented, and theoretical outage probabilities of the PN and the SN are derived and validated by simulations. [ABSTRACT FROM AUTHOR]

Cognitive radio (CR) is proposed as an efficient way to address the issue of spectrum shortage and underutilization, in which cooperative spectrum sensing (CSS) is used to enhance sensing performance. One of the most fundamental problems of CSS is how to appropriately assign the secondary users (SUs) to sense the primary user (PU) channels. In this paper, we study the CSS problem under a more practical scenario where we take the heterogeneous characteristics of both SUs and PU channels into consideration. With the objective to maximize the achievable throughput for SUs, we propose a cluster-based CSS to obtain a proper assignment policy, in which all the SUs in the same cluster cooperate in sensing the same set of PU channels. Moreover, the cluster-based CSS problem is formulated as a Maximum-Weight one-sided Biclique (MWB) problem. We prove that the MWB problem is NP-complete, and a greedy heuristic algorithm with polynomial computational complexity is developed to find the suboptimal assignment policy. Evaluation results show that our proposed greedy algorithm can achieve a close-to-optimal solution with far less complexity. [ABSTRACT FROM PUBLISHER]

Spectrum sensing is an important aspect of cognitive radio networks (CRNs). Secondary users (SUs) should periodically sense the channels to ensure primary-user (PU) protection. Sensing with cooperation among several SUs is more robust and less error prone. However, cooperation also increases the energy spent for sensing. Considering the periodic nature of sensing, even a small amount of savings in each sensing period leads to considerable improvement in the long run. In this paper, we consider the problem of energy-efficient (EE) spectrum sensing scheduling with satisfactory PU protection. Our model exploits the diversity of SUs in their received signal-to-noise ratio (SNR) of the primary signal to determine the sensing duration for each user/channel pair for higher energy efficiency. We model the given problem as an optimization problem with two different objectives. The first objective is to minimize the energy consumption, and the second objective is to minimize the spectrum sensing duration to maximize the remaining time for data transmission. We solve both problems using the outer linearization method. In addition, we present two suboptimal but efficient heuristic methods. We provide an extensive performance analysis of our proposed methods under various numbers of SUs, average channel SNR, and channel sampling frequency. Our analysis reveals that all proposals with an energy minimization perspective provide significant energy savings compared with a pure transmission-time maximization (TXT) technique. [ABSTRACT FROM PUBLISHER]