Analysis of Optimal Weighted Cooperative Spectrum Sensing with Multiple Antenna Elements

In this paper, we address the problem of multiantenna spectrum sensing in Cognitive Radios (CRs) by considering the correlation between the received channels at different antennas. First, we derive the optimum genie-aided detector which assumes perfect knowledge of the antenna correlation coefficients, Primary User (PU) signal power and noise variance. This is used as a benchmark for comparing with more practical detectors when some or all of these parameters are unknown to the Secondary User (SU). Two scenarios are considered: 1) the antenna correlation coefficients and PU signal power are unknown to the SU; 2) the antenna correlation coefficients, PU signal power and noise variance are unknown to the SU. To derive sub-optimum detectors for these two scenarios, we apply the Rao test, an asymptotically equivalent test to the Generalized Likelihood Ratio Test (GLRT) that does not require the Maximum Likelihood (ML) estimates of unknown parameters. Additionally, we calculate analytical approximations to the detection and false-alarm probabilities of the proposed detectors and verify them with Monte-Carlo simulations. The simulation results show that these new detectors outperform several recently proposed detectors for CR using multiple antennas.

In an opportunistic spectrum access (OSA) based cognitive radio system, a secondary user (SU) is allowed to access the licensed spectrum of the primary user (PU) when it is inactive. Conventional power allocation strategies, which do not consider spectrum sensing information (SSI), may not be optimal in OSA based cognitive radio system because when the SU mis-detects the PU's presence, the interference from the PU will cause a lower data rate or a higher outage probability to the SU. In this paper, power allocation strategies for each frame are designed based on the SSI gathered by the SU during the sensing period of the frame. We consider both cooperative and non-cooperative spectrum sensing scenarios. In non-cooperative spectrum sensing, the SU transmitter (SU-Tx) has its SSI while in cooperative spectrum sensing, the SU-Tx has both its SSI and the SU receiver's (SU-Rx's) SSI. Using the SSIs, power allocation strategies are designed to either maximize the average data rate or minimize the outage probability of the SU. The proposed power allocation strategies have to ensure that the PU is sufficiently protected and the SU satisfies its long-term transmit power budget. Optimization of the sensing time is also considered to further enhance the performances of the system.

Cognitive Radio (CR) aims to provide efficient utilization of the scarcely available radio spectrum. Spectrum sensing is indispensable to identify spectrum holes. Inaccurate sensing results in interference to PU. Therefore, sensing accuracy is an important parameter. Cooperative Spectrum Sensing (CSS) is an efficient technique to improve the sensing accuracy. It makes use of cooperation between Secondary Users (SUs) to detect the presence of Primary User (PU). Game theoretical models help in understanding and modeling interaction among users. In this paper, a CR network with multiple PUs and SUs is considered. CSS is modeled as hedonic coalition formation game where SUs sensing the same channel form coalitions. The coalitions are formed on the basis of probability of detection. For each PU, the SUs which have maximum probability of detection form coalition and cooperatively make the final sensing decision by making use of OR rule. Channel estimation for ST-SR link is done using WARP testbed. The sensing accuracy and energy consumption for the coalitions when link is modeled using free space pathloss model and using WARP testbed are found out. Simulation results show that CSS modeled using hedonic coalition formation has better sensing accuracy than non-cooperative sensing.

Cognitive Radio Network (CRN) is an intelligent technique to access the radio spectrum. In CRN the Secondary Users (SUs) sense the spectrum hole of the Primary User (PU) band opportunistically. Spectrum sensing is the major step in CRN to observe the Primary User (PU) signal. For individual user, sensing ability is affected by fading and shadowing. Cooperative Spectrum Sensing (CSS) provide solution to the problem of fading and shadowing where multiple SUs cooperates with each other to catch the absence and presence of PU signal. However, it is vulnerable to a variety of malicious User (MU) attacks. These MUs are unfriendly to the sensing results that could be achieved by CSS. In the proposed work performance of hard combination schemes are tested under varying Signal to Noise Ratios (SNRs). The performance of the hard combination schemes such as majority voting, logical-AND and logical-OR are tested in the presence of Lazy Users (LUs) and Opposite Malicious Users (OMU) at different SNR levels. Results indicate that hard combination scheme such as majority voting outperforms all other schemes, such as logical-OR and logical-AND.

Spectrum sensing ranks amongst cognitive radio's most critical and demanding activities. For radio resource networks to use idle licensed bandwidth, secondary users need to identify the primary user's signal precisely. Cognitive radio networks (CRNs) have been proposed to improve the efficiency of spectrum use to meet the increasing demand for wireless bandwidth; they allow channel communication between secondary (unlicensed) and primary (licensed) users without interruption. A secondary user in a CRN must at all times track the presence of a primary user's signal to prevent the primary user (PU) from attempting to intervene whether the statistical variance of the PU data traffic would boost the prediction outcome provided by multiple current teaching algorithms in estimating the PU's average OFF time and the need for the best adaptive learning to predict accurate PU activities. The objective of the work has been modeled to identify the PUs signal. First, the data has been created via quadrature phase shift keying modulation and quadrature amplitude modulation by convolutional neural network (CNN). A dataset with different orthogonal frequency‐division (OFDM) multiple access signals is generated. Then cyclo‐stationary functions are extracted using the accumulation process fast Fourier Transform. This research aims to use glow worm swarm optimization (GWSO) to automatically check for the optimal design of CNNs without any manual work being required. Also, k‐Nearest Neighbors (kNN) replaces Softmax, as it outperforms conventional CNN architectures with noisy signals. A comparison of the proposed classifier with another machine learning classifier, namely support vector machine, Decision tree (DT), clearly shows that SVM and DT are outperformed by the improved convolution neural network‐glow worm swarm optimization (ICNN‐GWSO) classification. Results of simulation show that the improved scheme outperforms conventional spectrum sensing systems, both in fading and non‐fading settings, where efficiency is measured using measures such as chance, the total likelihood of error, and the ability to optimize data transfer possibilities.

Abstract5G and 6G promise to be catalysts for the Internet of Things (IoT), enabling ultra‐massive and mission‐critical IoT. The demands of new IoT applications and the large number of connected objects being announced will exacerbate the thorny issue of spectrum scarcity in wireless communications. Cognitive radio (CR) is a revolutionary technology that promises to mitigate the problem of spectrum scarcity through more efficient use of spectrum. Integrating CR into the IoT allows objects to opportunistically access spectrum resources already allocated to a Primary User (PU) without causing interference. Spectrum sensing (SS) allows objects to be aware of the PU's spectrum occupancy status. In radio environments where the PU signal is subject to multipath and shadowing effects that degrade the channel between the PU and objects, SS performed by a single object could be inaccurate and lead to incorrect decisions about the PU's status. Cooperative spectrum sensing (CSS) has been proposed to improve detection performance. However, this cooperation between objects has opened the way to a new form of attack known as the Spectrum Sensing Data Falsification (SSDF) or Byzantine attack. In a SSDF attack, attackers falsify their local sensing results before sharing them in the CSS. This attack is very harmful to the CSS and can lead to a loss of spectrum opportunities or interference with the PU. In this paper, from the attack point of view, a sophisticated Byzantine attack model that generalizes well the simple attack strategies has been proposed and allows an attacker to implement other attack strategies. From a defense point of view, a new and innovative Weighted Sequential Hypothesis Testing (WSPRT) scheme has been suggested. This ensures the security of the CSS while significantly reducing the average number of samples required for overall decision‐making in a very hostile IoT network. The results obtained from simulations carried out in various attacks scenarios show that the proposed secure CSS scheme requires at most six samples to detect the state of the PU without error when the proportion of attackers reaches 80%. This performance far exceeds that of other defense mechanisms such as classic WSPRT, SPRT, and majority rule, with which we have compared it under the same conditions. In general, for the classic WSPRT, SPRT, and majority rule mechanisms, the error rate starts to deteriorate at ratio 40% of attackers and the number of samples is greater than six and reaches 50.

Cognitive radio network provides an efficient solution for spectrum utilization. The cooperative spectrum sensing is one of the sensing techniques with which the secondary users of cognitive radio network (CRN) could learn about the availability of the spectrum. In this article, authors had designed and developed their own CRN simulation model in which the RF spectrum range of 54 MHz to 648 MHz is divided into various channels. Secondary users are kept moving in these various channels along with the primary user. The presence of primary user is detected by applying weights on the energy received by local secondary users. The information about the energy of primary user is collected locally by SU and provided to the fusion center for PU detection. This article discusses how the secondary user detects the energy of primary user using weights.KeywordsCognitive radio networks (CRN)Primary users (PU) Secondary users (SU)Dynamic spectrum sensing (DSA)Radio frequency (RF)Cooperative spectrum sensing (CSS) and Fusion center (FC)

Cooperative spectrum sensing (CSS) scheme is employed to detect the primary user (PU) more quickly and accurately in cognitive radio (CR) systems. The performance of CSS scheme is usually significantly degraded when the inter-user and reporting channels experience path loss or deep fading. Meanwhile, the reporting channels are usually bandwidth limited. When the number of the secondary users (SUs) is very large, the sensing results need enormous bandwidth in the transmission. To solve this problem, a dynamical clustering CSS scheme with bandwidth constraints is proposed, where the SUs with better reporting channels are chosen as the cluster heads and the other SUs are dynamically grouped according to the inter-user channel conditions. In each cluster, the proposed scheme adopts the double thresholds scheme to make sure that only the SUs with high quality can send their sensing results. Simulation results demonstrate that the average number of sensing bits of the proposed scheme decreases greatly with a little loss in the sensing performance. This advantage is more obvious compared with the STBC based cooperative spectrum sensing scheme.

In cognitive radio networks, secondary users need to access the licensed spectrum while neither disturbing the transmissions of the primary users nor introducing much interference to other secondary users. Hence, proper power controlling is of paramount importance to ensure efficient system operation. The previous work on power control in cognitive networks mainly focuses on the interactions among a set of secondary users. The primary users' influence is rarely considered. In this paper, a new power control game model is formulated, by including the primary users into the player set of the game. The primary users are rewarded for sharing their licensed spectrum with secondary users by setting a reasonable interference cap for the secondary transmissions. However, we ensure a minimum required Quality of Service (QoS) for primary users by severely penalizing their utility if their required transmission quality is not met. Simultaneously, the secondary users achieve energy efficient transmissions while not introducing much interference to both the primary and other secondary users. We prove that the proposed cognitive power control game has a unique Nash equilibrium (NE). The numerical examples show that the proposed game theoretical power control algorithm can provide the secondary users with energy efficient transmissions and the primary users with reasonable monetary rewards, while not compromising their required QoS.

Cooperative spectrum sensing (CSS) has the ability to accurately identify the activities of the primary users (PUs). As the secondary users’ (SUs) sensing performance is disturbed in the fading and shadowing environment, therefore the CSS is a suitable choice to achieve better sensing results compared to individual sensing. One of the problems in the CSS occurs due to the participation of malicious users (MUs) that report false sensing data to the fusion center (FC) to misguide the FC’s decision about the PUs’ activity. Out of the different categories of MUs, Always Yes (AY), Always No (AN), Always Opposite (AO) and Random Opposite (RO) are of high interest these days in the literature. Recently, high sensing performance for the CSS can be achieved using machine learning techniques. In this paper, boosted trees algorithm (BTA) has been proposed for obtaining reliable identification of the PU channel, where the SUs can access the PU channel opportunistically with minimum disturbances to the licensee. The proposed BTA mitigates the spectrum sensing data falsification (SSDF) effects of the AY, AN, AO and RO categories of the MUs. BTA is an ensemble method for solving spectrum sensing problems using different classifiers. It boosts the performance of some weak classifiers in the combination by giving higher weights to the weak classifiers’ sensing decisions. Simulation results verify the performance improvement by the proposed algorithm compared to the existing techniques such as genetic algorithm soft decision fusion (GASDF), particle swarm optimization soft decision fusion (PSOSDF), maximum gain combination soft decision fusion (MGCSDF) and count hard decision fusion (CHDF). The experimental setup is conducted at different levels of the signal-to-noise ratios (SNRs), total number of cooperative users and sensing samples that show minimum error probability results for the proposed scheme.

An allocation of sensing and reporting times is proposed to improve the sensing performance by scheduling them in an efficient way for cognitive radio networks with cluster-based cooperative spectrum sensing. In the conventional cooperative sensing scheme, all secondary users (SUs) detect the primary user (PU) signal to check the availability of the spectrum during a fixed sensing time slot. The sensing results from the SUs are reported to cluster heads (CHs) during the reporting time slots of the SUs and the CHs forward them to a fusion center (FC) during the reporting time slots of the CHs through the common control channels for the global decision, respectively. However, the delivery of the local decision from SUs and CHs to a CH and FC requires a time which does not contribute to the performance of spectrum sensing and system throughput. In this paper, a super-allocation technique, which merges reporting time slots of SUs and CHs to sensing time slots of SUs by re-scheduling the reporting time slots, has been proposed to sense the spectrum more accurately. In this regard, SUs in each cluster can obtain a longer sensing duration depending on their reporting order and their clusters except for the first SU belonged to the first cluster. The proposed scheme, therefore, can achieve better sensing performance under -28 dB to -10 dB environments and will thus reduce reporting overhead.

In this paper, the performance of cooperative spectrum sensing (CSS) with absolute value cumulation detection (AVCD) based improved energy detector (IED) in additive Laplacian noise environment is proposed. Each cognitive radio (CR) uses improved AVCD (i-AVCD) as a test statistic. Decision from each CR is then forwarded to the fusion center (FC) where the final decision on the presence or absence of primary user (PU) is made. Performance of CSS in the form of receiver operating characteristic (ROC) and the total error probability \((P_{\text{e}} )\) is discussed. Besides this, the optimum value of p, optimum number of CRs (n) out of K CRs, and optimum number of K to achieve a specified target total error probability (sum of false alarm probability and missed detection probability is also obtained. Results are presented here using Monte Carlo simulations and it is concluded that by carefully choosing the optimum value of p, performance can be improved in case of CSS with OR rule while the same is not necessarily applicable in case of CSS with AND and majority rule \((n/K)\) rule.

In order to mitigate the problem of crowded radio spectrum, dynamic spectrum access (DSA) has become the solution and a research focal point. The number of works in wideband spectrum sensing is dramatically increasing recently due to its importance in cognitive radio (CR) networks. Most works assume that secondary users (SUs) are synchronous with the primary users' (PUs) network. However, in many practical systems the SUs will have no information about the PUs' communication protocols. Furthermore, it is also possible that communications among PUs are not based on synchronous operation. In order to address such problems, a contention-based wideband DSA algorithm with asynchronous cooperative spectrum sensing (CSS), called the DySac, is proposed. In this scheme, the SUs need not maintain synchronism with the PUs, and the SUs who intend to access the primary network compete among each other for available channels. Performance analysis and simulations are conducted to evaluate the proposed DySac.

Dynamic spectrum access has been proposed to address the spectrum scarcity. Based on this access mode, secondary users are allowed to opportunistically operate when primary users are absent. Secondary Users are, however, required to relinquish the spectrum upon return of the incumbent spectrum holder. Although it improves spectrum utility, opportunistic spectrum access limits secondary users' access to exogenous spectrum holes vacated by primary users. We argue that spectrum holes should be endogenous, enabled by incentives to incumbent users to increase spectrum availability, while mitigating risk. To this end, we take a different perspective on dynamic spectrum sharing and propose a coopetive framework, whereby primary and secondary users engage in a cooperative, yet competitive, auction-driven spectrum sharing to enhance spectrum usage. In this framework, the primary users'spectrum is organized in three bands, namely exclusive usage, usage right and management right bands. Based on this structure, spectrum is traded as financial options to increase trading flexibility and capture the value primary and secondary users attach to spectrum access and usage. Spectrum trading between primary and secondary users is formalized as a utility maximization problem. Approximate solutions to this problem are derived and their performance is analyzed. The results show that spectrum trading, which combines revocable and exclusive leasing, provides higher utilities for both primary and secondary users.

In cognitive radio network, the secondary users (SUs) perform cooperative spectrum sensing (CSS) to improve the sensing performance when the primary user (PU) is shadowed or in severe fading. However, the CSS may deplete some electric power including sensing power and cooperative power, thus yielding to decrease the transmission power. In this paper, a simultaneous CSS and wireless power transfer (WPT) is proposed by using a power splitter to split the received PU signal into a harvesting signal stream and a sensing signal stream. The sensing signal stream is used to perform CSS, while the radio frequency energy of the harvesting signal stream is converted into the electric power to supply the CSS simultaneously. A joint optimisation problem of sensing time, cooperative SUs and power splitting is formulated, which seeks to maximise the total spectrum access probability of the SU over all the subchannels, subject to the constraints of the detection probability, the harvested energy and the number of cooperative SUs. The simulation results have shown that the proposed simultaneous CSS and WPT can improve the transmission energy compared with the traditional CSS, and there is a trade‐off between spectrum access and energy harvesting. Copyright © 2015 John Wiley & Sons, Ltd.