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Quadratic forms with random kernel matrices are ubiquitous in applications of multivariate statistics, ranging from signal processing to time series analysis, biomedical systems design, wireless communications performance analysis, and other fields. Their statistical characterization is crucial to both design guideline formulation and efficient computation of performance indices. To this end, random matrix theory can be successfully exploited. In particular, recent advancements in spectral characterization of finite-dimensional random matrices from the so-called polynomial ensembles allow for the analysis of several scenarios of interest in wireless communications and signal processing. In this work, we focus on the characterization of quadratic forms in unit-norm vectors, with unitarily invariant random kernel matrices, and we also provide some approximate but numerically accurate results concerning a non-unitarily invariant kernel matrix. Simulations are run with reference to a peculiar application scenario, the so-called spectrum sensing for wireless communications. Closed-form expressions for the moment generating function of the quadratic forms of interest are provided; this will pave the way to an analytical performance analysis of some spectrum sensing schemes, and will potentially assist in the rate analysis of some multi-antenna systems. [ABSTRACT FROM AUTHOR]

Abstract Accurate detection of spectrum holes is a useful requirement for cognitive radios that improves the efficiency of spectrum usage. The authors propose three novel, simple, and entropy‐based detectors for spectrum sensing in cognitive radio. The authors evaluate the probability of detection of these three detectors: Vasicek's entropy detector, truncated Vasicek's entropy detector, and Van Es' entropy detector, over a predefined probability of false‐alarm. In particular, the authors provide the approximate and asymptotic test statistics for these detectors in the presence and absence of Nakagami‐m fading, noise variance uncertainty, and optimised detection threshold. Furthermore, the authors provide a detailed comparison study among all the detectors via Monte Carlo simulations and justify authors results through real‐world data. The authors’ experimental results establish a superior performance of truncated Vasicek's entropy detector over Vasicek's entropy detector, energy detector, differential entropy detector and Van Es' entropy detector in practically viable scenarios.

We investigate opportunistic transmissions in a time-slotted wireless network, emphasizing constraints arising from finite durations allocated to various network operations and the availability of energy for these operations. Each time frame (time slot) comprises three sub-frames: sensing, reporting, and either transmission or energy harvesting based on the presence or absence of the primary user. We assume a fixed duration for the transmission sub-frame within each time frame. Utilizing a time division multiple access (TDMA) protocol, we manage local sensing data reporting within each time frame; consequently, the reporting time is contingent on the number of users. As a result, with the total time allocated for sensing and reporting being fixed, a trade-off arises between the number of collaborating users and the number of samples. Additionally, energy limitations and causality lead to two scenarios for wireless network operation: energy-deficit and energy-surplus regimes. To address this complexity, we formulate an optimization problem aimed at maximizing overall network throughput while considering constraints imposed by finite durations for various network operations and energy availability. We provide analytical evidence of the convexity of the optimization problem in both energy-deficit and energy-surplus scenarios. Furthermore, we propose two algorithms designed to achieve optimal throughput for each scenario. The accuracy of our analyses is validated through Monte Carlo simulations. Numerical results demonstrate the effectiveness of our proposed approach.

Accurate detection of spectrum holes is a useful requirement for cognitive radios that improves the efficiency of spectrum usage. The authors propose three novel, simple, and entropy‐based detectors for spectrum sensing in cognitive radio. The authors evaluate the probability of detection of these three detectors: Vasicek's entropy detector, truncated Vasicek's entropy detector, and Van Es' entropy detector, over a predefined probability of false‐alarm. In particular, the authors provide the approximate and asymptotic test statistics for these detectors in the presence and absence of Nakagami‐m fading, noise variance uncertainty, and optimised detection threshold. Furthermore, the authors provide a detailed comparison study among all the detectors via Monte Carlo simulations and justify authors results through real‐world data. The authors' experimental results establish a superior performance of truncated Vasicek's entropy detector over Vasicek's entropy detector, energy detector, differential entropy detector and Van Es' entropy detector in practically viable scenarios. [ABSTRACT FROM AUTHOR]

Cognitive radio is a promising technology that emerged as a potential solution to the spectrum shortage problem by enabling opportunistic spectrum access. In many cases, cognitive radios are required to sense a wide range of frequencies to locate the spectrum white spaces; hence, wideband spectrum comes into play, which is also an essential step in future wireless systems to boost the throughput. Cognitive radios are intelligent devices and therefore can be opted for the development of modern jamming and anti-jamming solutions. To this end, our article introduces a novel AI-enabled energy-efficient and robust technique for wideband radio spectrum characterization. Our work considers a wideband radio spectrum made up of numerous narrowband signals, which could be normal communications or signals disrupted by a stealthy jammer. First, the receiver recovers the wideband from significantly low sub-Nyquist rate samples by exploiting compressive sensing technique to decrease the overhead caused by the high complexity analog-to-digital conversion process. Once the wideband is recovered, each available narrowband signal is given to a cyclostationary feature detector that computes the corresponding spectral correlation function and extracts the feature vectors in the form of cycle and frequency profiles. Then profiles are concatenated and given as input features set to an artificial neural network which in turn classifies each NB signal as legitimate communication with a specific modulation or disrupted by a stealthy jammer. The results show a classification accuracy of about 0.99 is achieved. Moreover, the algorithm highlights significantly high performances in comparison to recently reported spectrum classification techniques. The proposed technique can be used to design anti-jamming systems for military communication systems. [ABSTRACT FROM AUTHOR]

Cognitive radio technology was introduced as a possible solution for spectrum scarcity by exploiting dynamic spectrum access. In the last two decades, most researchers focused on enabling cognitive radios for managing the spectrum. However, due to their intelligent nature, cognitive radios can scan the radio frequency environment and change their transmission parameters accordingly on-the-fly. Such capabilities make it suitable for the design of both advanced jamming and anti-jamming systems. In this context, our work presents a novel, robust algorithm for spectrum characterisation in wideband radios. The proposed algorithm considers that a wideband spectrum is sensed by a cognitive radio terminal. The wideband is constituted of different narrowband signals that could either be licit signals or signals jammed by stealthy jammers. Cyclostationary feature detection is adopted to measure the spectral correlation density function of each narrowband signal. Then, cyclic and angular frequency profiles are obtained from the spectral correlation density function, concatenated, and used as the feature sets for the artificial neural network, which characterise each narrowband signal as a licit signal with a particular modulation scheme or a signal jammed by a specific stealthy jammer. The algorithm is tested under both multi-tone and modulated stealthy jamming attacks. Results show that the classification accuracy of our novel algorithm is superior when compared with recently proposed signal classifications and jamming detection algorithms. The applications of the algorithm can be found in both commercial and military communication systems. [ABSTRACT FROM AUTHOR]

Cogitive radio networks (CRNs) require high capacity and accuracy to detect the presence of licensed or primary users (PUs) in the sensed spectrum. In addition, they must correctly locate the spectral opportunities (holes) in order to be available to nonlicensed or secondary users (SUs). In this research, a centralized network of cognitive radios for monitoring a multiband spectrum in real time is proposed and implemented in a real wireless communication environment through generic communication devices such as software-defined radios (SDRs). Locally, each SU uses a monitoring technique based on sample entropy to determine spectrum occupancy. The determined features (power, bandwidth, and central frequency) of detected PUs are uploaded to a database. The uploaded data are then processed by a central entity. The objective of this work was to determine the number of PUs, their carrier frequency, bandwidth, and the spectral gaps in the sensed spectrum in a specific area through the construction of radioelectric environment maps (REMs). To this end, we compared the results of classical digital signal processing methods and neural networks performed by the central entity. Results show that both proposed cognitive networks (one working with a central entity using typical signal processing and one performing with neural networks) accurately locate PUs and give information to SUs to transmit, avoiding the hidden terminal problem. However, the best-performing cognitive radio network was the one working with neural networks to accurately detect PUs on both carrier frequency and bandwidth. [ABSTRACT FROM AUTHOR]

Cognitive radio is a promising technology that emerged as a potential solution to the spectrum shortage problem by enabling opportunistic spectrum access. In many cases, cognitive radios are required to sense a wide range of frequencies to locate the spectrum white spaces; hence, wideband spectrum comes into play, which is also an essential step in future wireless systems to boost the throughput. Cognitive radios are intelligent devices and therefore can be opted for the development of modern jamming and anti-jamming solutions. To this end, our article introduces a novel AI-enabled energy-efficient and robust technique for wideband radio spectrum characterization. Our work considers a wideband radio spectrum made up of numerous narrowband signals, which could be normal communications or signals disrupted by a stealthy jammer. First, the receiver recovers the wideband from significantly low sub-Nyquist rate samples by exploiting compressive sensing technique to decrease the overhead caused by the high complexity analog-to-digital conversion process. Once the wideband is recovered, each available narrowband signal is given to a cyclostationary feature detector that computes the corresponding spectral correlation function and extracts the feature vectors in the form of cycle and frequency profiles. Then profiles are concatenated and given as input features set to an artificial neural network which in turn classifies each NB signal as legitimate communication with a specific modulation or disrupted by a stealthy jammer. The results show a classification accuracy of about 0.99 is achieved. Moreover, the algorithm highlights significantly high performances in comparison to recently reported spectrum classification techniques. The proposed technique can be used to design anti-jamming systems for military communication systems.

With outstanding features, machine learning (ML) has become the backbone of numerous applications in wireless networks. However, the conventional ML approaches face many challenges in practical implementation, such as the lack of labeled data, the constantly changing wireless environments, the long training process, and the limited capacity of wireless devices. These challenges, if not addressed, can impede the effectiveness and applicability of ML in wireless networks. To address these problems, transfer learning (TL) has recently emerged to be a promising solution. The core idea of TL is to leverage and synthesize distilled knowledge from similar tasks and valuable experiences accumulated from the past to facilitate the learning of new problems. By doing so, TL techniques can reduce the dependence on labeled data, improve the learning speed, and enhance the ML methods’ robustness to different wireless environments. This article aims to provide a comprehensive survey on the applications of TL in wireless networks. Particularly, we first provide an overview of TL, including formal definitions, classification, and various types of TL techniques. We then discuss diverse TL approaches proposed to address emerging issues in wireless networks. The issues include spectrum management, signal recognition, security, caching, localization, and human activity recognition, which are all important to next-generation networks, such as 5G and beyond. Finally, we highlight important challenges, open issues, and future research directions of TL in future wireless networks. [ABSTRACT FROM AUTHOR]

The development of spectral efficient solutions for internet of things (IoT) face challenges primarily due to the large-scale placement of an immense number of sensors and devices. Cognitive radio (CR) technology is considered as a potential solution to resolve the spectrum scarcity problems of IoT. Incorporation of CR in IoT encounters various challenges including fast response and efficient spectrum sensing even in low signal to noise ratio. In this study we integrate the basic functionalities of the both CR and IoT technology and present a five layered framework for CR enabled IoT. In addition to the framework we also proposed and develop a spectrum sensing algorithm for CR-based IoT architecture, meeting the efficiency and time sensitivity requirements. The proposed algorithm is more accurate, robust to noisy environment and four times faster than existing approaches. The developed algorithm is compared with existing blind spectrum sensing techniques in term of detection performance, optimization methods and computational complexity. Experimental evaluations with real wireless microphone signals demonstrate the effectiveness of the proposed scheme and show superiority over existing ones.

This paper adopts a relay among available secondary relays and exploits non-selected relays as jammers to maintain and secure legitimate secondary source-to-destination communications in energy harvesting underlay cognitive networks (EHUCNs) as their direct communications is in outage. All relays harvest radio frequency energy in signals transmitted by primary and secondary transmitters and consume the harvested energy for their jamming-and-relaying operations. Under Rayleigh fading, peak transmission power restriction, interference power restriction, primary interference and power splitting-based energy scavenging method, we propose exact closed-form formulas of outage and intercept probabilities to promptly assess both security and reliability performances of the suggested relay-and-jammers selection in EHUCNs. The suggested expressions are corroborated by computer simulations. Numerous results indicate the efficacy of the relay-and-jammers selection in improving both security and reliability for source–destination communications. Moreover, the security and reliability performances are saturated at large maximum transmit/interference powers and considerably degraded by the primary interference. Furthermore, the reliability performance can be optimized by splitting appropriately the received power for information decoding and energy harvesting at the relays. [ABSTRACT FROM AUTHOR]

We expect that 5G will support a large volume of data traffic to provide various services with a low latency. This will inevitably require an increased amount of energy. Wireless power transfer needs a higher receiver sensitivity than data decoding. Increased electromagnetic fields may introduce harmful effects on living organisms. Fortunately, massive multiple-input and multiple-output (MIMO) can provide significant gains in radiated energy and spectral efficiencies. Cognitive radio and 5G are emerging technologies. In this paper, we show that cognitive spectrum sensing and power harvesting can be accomplished simultaneously. Energy detection (ED) with energy harvesting has been widely investigated. However, ED may not work at a low signal-to-noise ratio with a harsh signal fluctuation in millimeter-wave in 5G. Therefore, we focus on cyclostationary spectrum sensing in this paper. We show that the maximum likelihood cyclostationary detection that can be used for power harvesting is the power squared. The cyclic power can be conveniently harvested and used for spectrum sensing.

We consider rate allocation for video users in the case where the available bandwidth fluctuates. Simply minimizing the objective distortion or optimizing the stability of video qualities does not optimize subjective quality. We formulate a utility-based solution, considering that a user's preference of video quality often varies over a range with upper and lower thresholds of quality. Our iterative pricing-based resource allocation procedure reallocates the bandwidth not only between different users within a time slot but also between different time slots, such that no user suffers quality degradation on average by participating in the multiplexing process. Experimental results show that, compared with equal resource allocation and existing rate allocation solutions, the subjective result becomes increasingly better with the increase of bandwidth fluctuation rate or bandwidth fluctuation range. Moreover, as the number of users increases, the results improve.

Cognitive Radio Network (CRN) is considered as a viable solution on Internet of Vehicle (IoV) where objects equipped with cognition make decisions intelligently through the understanding of both social and physical worlds. However, the spectrum availability and data sharing/transferring among vehicles are critical improving services and driving safety metrics where the presence of Malicious Devices (MD) further degrade the network performance. Recently, a blockchain technique in CRN-based IoV has been introduced to prevent data alteration from these MD and allowing the vehicles to track both legal and illegal activities in the network. In this paper, we provide the security to IoV during spectrum sensing and information transmission using CRN by sensing the channels through a decision-making technique known as Technique for Order Preference by Similarity to the Ideal Solution (TOPSIS), a technique that evokes the trust of its Cognitive Users (CU) by analyzing certain predefined attributes. Further, blockchain is maintained in the network to trace every activity of stored information. The proposed mechanism is validated rigorously against several security metrics using various spectrum sensing and security parameters against a baseline solution in IoV. Extensive simulations suggest that our proposed mechanism is approximately 70% more efficient in terms of malicious nodes identification and DoS threat against the baseline mechanism. [ABSTRACT FROM AUTHOR]

Summary: This paper describes a licensed shared access (LSA) testbed and field trials using a live network. The testbed includes real 4G base stations and up to 1000 virtual base stations, in the spectrum sharing scenario between satellite and cellular systems. The trials focus on 5G pioneer bands 3.4–3.8 GHz and 24.25–27.5 GHz where a satellite system is operating in the downlink direction and a cellular system is accessing the same band. The designed testbed supports both frequency bands. The performance evaluation concerns evacuation and frequency change times using different types of base stations in 3.6 GHz, that is, how fast the system relinquishes the shared band to the primary user and continues transmission using another band. We show that our LSA system is scalable and able to support large number of base stations. In addition, we investigate how satellite systems could reuse International Mobile Telecommunication (IMT) bands to offer enhanced satellite communication services for land, maritime, and aeronautical applications. Preliminary simulations and analysis confirm the possibility to reuse IMT spectrum for satellite systems without causing harmful interference to the terrestrial system. [ABSTRACT FROM AUTHOR]

This paper introduces a novel technique for access by a cognitive Secondary User (SU) using best-effort transmission to a spectrum with an incumbent Primary User (PU), which uses Type-I Hybrid ARQ. The technique leverages the primary ARQ protocol to perform Interference Cancellation (IC) at the SU receiver (SUrx). Two IC mechanisms that work in concert are introduced: Forward IC, where SUrx, after decoding the PU message, cancels its interference in the (possible) following PU retransmissions of the same message, to improve the SU throughput; Backward IC, where SUrx performs IC on previous SU transmissions, whose decoding failed due to severe PU interference. Secondary access policies are designed that determine the secondary access probability in each state of the network so as to maximize the average long-term SU throughput by opportunistically leveraging IC, while causing bounded average long-term PU throughput degradation and SU power expenditure. It is proved that the optimal policy prescribes that the SU prioritizes its access in the states where SUrx knows the PU message, thus enabling IC. An algorithm is provided to optimally allocate additional secondary access opportunities in the states where the PU message is unknown. Numerical results are shown to assess the throughput gain provided by the proposed techniques. © 1983-2012 IEEE.

Cognitive radio (CR) technology has been demonstrated as one of the key technologies that can provide the needed spectrum bands for supporting the emerging spectrum-hungry multimedia applications and services in next-generation wireless networks. Multicast routing technique plays a significant role in most of wireless networks that require multimedia data dissemination to a group of destinations through single-hop or multi-hop communication. Performing multimedia multicasting over CR networks can significantly improve the quality of multimedia transmissions by effectively exploiting the available spectrum, reducing network traffic and minimizing communication cost. An important challenge in this domain is how to perform a multi-cast transmissions over multiple hops in a dynamically varying CR environment while maintaining high-quality received video streaming to all multi-case CR receivers without affecting the performance of legacy primary radio networks (PRNs). In this paper, we investigate the problem of multicast multimedia streaming in multi-hop CR networks (CRNs). Specifically, we propose an intelligent multicast routing protocol for multi-hop ad hoc CRNs that can effectively support multimedia streaming. The proposed protocol consists of path selection and channel assignment phases for the different multi-cast receivers. It is based on the shortest path tree (SPT) that implements the expected transmission count metric (ETX). The channel selection is based on the ETX, which is a function of the probability of success (POS) over the different channels that depends on the channel-quality and availability. Simulation results verify the significant improvement achieved by the proposed protocol compared to other existing multicast routing protocols under different network conditions. [ABSTRACT FROM AUTHOR]

Energy harvesting has lately been of particular attention to researchers. In addition, cognitive radio networks (CRNs) are recognized as an attainable measure for the problem of radio spectrum shortage in next generation radio access. A combination of these two technologies, which forms energy harvesting CRNs (EHCRNs), allows wireless communication terminals to prolong their operation time in limited spectrum scenarios. Nonetheless, that CRNs create opportunities for secondary users to access primary users' spectrum induces vulnerability of message security. So far, security capability analysis of EHCRNs has been limited to Rayleigh fading whilst Nakagami-m fading is more common than Rayleigh fading and better reflects distinct fading severity degrees in practical scenarios. Accordingly, this paper firstly offers the precise security capability analysis of EHCRNs under interference power constraint, Nakagami-m fading, maximum transmit power constraint, and primary interference. Then, the offered analysis is ratified by computer simulations. Ultimately, multiple results reveal that the security capability is considerably improved with smaller primary interference and lower required security threshold. Moreover, the security capability is significantly impacted by channel severity and is optimized with appropriate selection of time percentage. [ABSTRACT FROM AUTHOR]

In cognitive radios, the spectrum sensing method is used to detect primary user signals in frequency spectrum. In received signals, due to statistical covariance's primary signal is present with noise. But this noise power is not fixed and varies due to random noise and interference occurred in the networks. This noise power may lead to noise uncertainty problems in energy detection performance. So, in this paper proposed a bias compensated normalized least mean square (BNLMS) algorithm which includes noise power used for adjusting the threshold, then noise power is adjusted at each sensing point of the licensed spectrum. By the proposed method noisy inputs are estimated and they are eliminated. Further stability is also analyzed by calculating mean deviation and mean square deviation analysis, then convergence and steady state error rate performance is seen for variable step size with stabilization. Then probability detection, false alarm probability is calculated to reduce sensing errors for improving energy detection performance by avoiding noise uncertainty problems, we get a better probability of detection for different SNR values when compared to other algorithms and this concept is used in remote health care monitoring for avoiding noise interferences occurred with wireless communications. [ABSTRACT FROM AUTHOR]

Autonomous vehicles are becoming a reality in places with advanced infrastructure to support their operations. In crowded places, harsh environments, missions that require these vehicles to be aware of the context in which they are operating, and situations requiring continuous coordination with humans such as in disaster relief, advanced-vehicle systems (AVSs) need to be better contextually aware. The vast literature referring to “context-aware systems” is still sparse, focusing on very limited forms of contextual awareness. It requires a structured approach to bring it together to truly realize contextual awareness in AVSs. This paper uses a human-AVSs (HAVSs) lens to polarize the literature in a coherent form suitable for designing distributed HAVSs. We group the relevant literature into two categories: contextual-awareness related to the vehicle infrastructure itself that enables AVSs to operate, and contextual-awareness related to HAVSs. The former category focuses on the communication backbone for AVSs including ad-hoc networks, services, wireless communication, radio systems, and the cyber security and privacy challenges that arise in these contexts. The latter category covers recommender systems, which are used to coordinate the actions that sit at the interface of the human and AVSs, human-machine interaction issues, and the activity recognition systems as the enabling technology for recommender systems to operate autonomously. The structured analysis of the literature has identified a number of open research questions and opportunities for further research in this area.

Cognitive radio is more than just radio environment awareness, and more importantly, has the ability to interact with the environment in the best way possible. Ideally, cognitive radios will form a self-regulating society of mobile radios achieving maximum spectrum utilization. However, challenges arise as mobile radios tend to compete with one another for spectrum, generating harmful interference, and damaging performance individually and for the network as a whole. In this paper, we present a framework that allows competing radios to teach and learn from each other’s action so that a desirable equilibrium can be reached. The heart of cognition to establish this is the forward-looking ability, which enables competing radios to see beyond the present time, negotiate and optimize their actions toward a more agreeable equilibrium. Technically speaking, we adopt a belief-directed game where each mobile radio, regarded as player, formulates a belief function to project how the radio environment as a whole would respond to any of its action. This model facilitates engineering of the equilibrium by different choices of the players’ belief functions. Under this model, players will negotiate naturally through a sequence of calculated competition (i.e., cycles of teaching and learning with each other). We apply this methodology to a cognitive orthogonal frequency-division multiple-access radio network where mobile users are free to access any of the subcarriers and thus compete for radio resources to maximize their rates. The results reveal that the proposed negotiation-by-forward-looking competition mechanism guides users to converge to an equilibrium that benefits not only the individual users but the entire network approaching the maximum achievable sum-rate.

As the advent of cognitive radios,blind encoder identification has attracted increasingly attentions since it plays an important role.The existing works mainly focus on additive white Gaussian noise (AWGN) channel,while the blind identification in multipath scenarios has not been sufficiently investigated.Considering the blind low density parity-check (LDPC) codes identification in the presence of unknown multipath fading channel,a likelihood-based classifier was proposed using the expectation maximization (EM) algorithm to obtain the maximum likelihood estimates of the unknown parameters.Then,an average log-likelihood ratio (LLR) estimator was adopted to classify the unknown encoder.Numerical results show that the proposed algorithm provides promising identification performance in multipath channels,especially in the low signal-to-noise ratio region.

Routing in Cognitive Radio Ad Hoc Network (CRAHN) is a challenging task due to limited spectrum availability. To overcome this problem several researchers have proposed various routing schemes based on Quality of Service (QoS) and spectrum availability. These schemes choose a path that gives maximum QoS and spectrum level ignoring the required QoS level for a particular application which may be quite less than the maximum level. Thus, this paper proposes an integrated service model having eight classes instead of two for CRAHN to envisage the admission control at Primary User (PU) nodes thus reducing overhead on Secondary Users (SU) for various applications. The PU node first examines the spectrum availability and then checks whether the received packet can be granted user specific Quality of Service (QoS) or not to support Elastic plus Real Time Applications for (SU) nodes. To prove the efficacy of proposed scheme comparison with Cognitive Ad hoc On-demand Distance Vector (CAODV) or shortest spectrum aware path routing mechanism is done. The result exhibits that high performance rate for good reliability, low latency and high throughput with fair load distribution among all the nodes of the network. [ABSTRACT FROM AUTHOR]

In this paper, performance analysis of an asynchronous distributed space time block coded system with optimum combining at the cognitive radio receiver (CR-Rx) has been done in a cooperative underlay cognitive Internet of Things network under the influence of multiple co-channel primary users (PUs) interferers. The transmit power strategy being employed at the CR-Tx considers the PIP (peak interference power) at the PU-Rx and peak transmit power constraints at the CR-Tx, respectively. We have derived the probability density function of the signal-to-interference ratio (SIR) at the destination for the proposed system. The system is numerically and mathematically analyzed in terms of average post processed SIR (APPSIR), ergodic capacity ( C erg ), average bit error rate (ABER) and outage probability. All the numerical outcomes are in corroboration with its simulation counterpart. [ABSTRACT FROM AUTHOR]

Cognitive radio (CR) is introduced to efficiently and opportunistically use the limited spectrum to minimize the spectrum scarcity issues. Usually, service providers purchase the spectrum license from the policy-makers and spectrum regulator authority. The non-licensed users cannot utilize the licensed bands even though primary user is not transmitting. It sometimes results in under-utilization of frequency bands, which in turn decreases the spectral efficiency. The CR can opportunistically use the freely available licensed spectrum. To solve the spectrum scarcity problem, we implemented CR using software defined radio on frequency modulated radio band (88–108MHz), because of ease in access and availability. The proposed solution is equally valid for other frequency bands as well, because the printed circuit board adopted here supports frequency from 50MHz to 2.2GHz. The proposed solution uses energy-detection based spectrum-sensing technique for primary user (PU) detection, and afterwards secondary user starts the data transmission over the vacant frequency band. We presented real-time experimental results for the detection probabilities by testing under various false alarm probabilities to verify the efficiency of the designed system. Experimental results show that we can achieve high detection probability even at low receiver sensitivity. When PU becomes active, then the proposed system quickly shift the public-safety user to other available white spaces to avoid halting the licensed user transmission. This solution is beneficial for security and defense organization such as army, traffic wardens, city wardens, and police. [ABSTRACT FROM AUTHOR]

Cognitive radios have emerged as a key enabler for opportunistic spectrum access, in order to tackle the wireless spectrum scarcity and under utilization problems over the past two decades. In this paper, we aim to enhance the secondary user (SU) performance while maintaining the desired average packet delay for the primary user (PU). In particular, we investigate the trade-off between delay-constrained primary and secondary users in cog- nitive radio systems. In the first part of this work, we use the hard-sensing scheme to make a decision on the PU activity and maximize the SU effective capacity subject to an average PU delay constraint. Second, we propose a soft-sensing scheme by dividing the PU energy interval where the PU is decided to be idle into multiple decision. We also maximize the SU effective capacity subject to an average primary user delay constraint; then, we present three modifications for the proposed soft-sensing scheme to allow for low complexity implementation that is comparable to the complexity of the hard-sensing scheme, but with better performance. The numerical results reveal interesting insights comparing our soft sensing to the hard-sensing models in terms of the optimal performance obtained from our optimization solution compared to the unconstrained PU delay baseline system studied earlier in the literature. For instance, the hard sensing system in Akin and Gursoy (IEEE Trans Wirel Commun 9(11):3354–3364, 2010) and Abdel-Malek et al. (CrownCom 156:30–42, 2015) yields a SU effective capacity of only 50 % of the ideal, perfect sensing system. On the other hand, we show that the soft sensing system yields almost 87 % of the perfect sensing performance (at a primary user arrival rate of λ p = 0.1 ), which further increases for a larger number of decision sub-intervals. [ABSTRACT FROM AUTHOR]

The explosive emergence of wireless technologies and standards, covering licensed and unlicensed spectrum bands, has triggered the appearance of a huge amount of wireless technologies, with many of them competing for the same spectrum band instead of harmoniously sharing it. Unfortunately, the wireless spectrum is a scarce resource, and the available frequency bands will not scale with the foreseen demand for new capacity. Certain parts of the spectrum, in particular the license-free ISM bands, are overcrowded, while other parts, mostly licensed bands, may be significantly underutilized. As such, there is a need to introduce more advanced techniques to access and share the wireless medium, either to improve the coordination within a given band or to explore the possibilities of intelligently using unused spectrum in underutilized (e.g., licensed) bands. Therefore, in this paper, we present an open source software-defined radio-based framework that can be employed to devise disruptive techniques to optimize the sub-optimal use of radio spectrum that exists today. We describe three use cases where the framework can be employed along with intelligent algorithms to achieve improved spectrum utilization. In addition, we provide several experimental results showing the performance and effectiveness of the proposed framework.

Cognitive radios (CRs) mainly aim to reuse the spectrum holes in order to efficiently utilize the available scarce radio spectrum. However, current CRs techniques have a throughput limitation problem which ultimately limits telecommunication applications horizons nowadays. Moreover, achieving high throughput will overcome the bottleneck of CRs application limitations to the reporting and browsing applications only. To tackle this emerging throughput limitation issue in the CRs, this paper proposes the online greedy throughput maximization (OGTM) algorithm which overcomes the throughput limitations. OGTM allows the sensing cycle frame to have a variable length according to the assumed decision validity interval. Then, OGTM varies the decision validity interval of secondary users (SUs) based on the primary users (PUs) historical behavior. As a proof of concept, we developed a simulator in order to evaluate the performance of the proposed OGTM technique. The simulation results show that SUs benefit from the limited PU historical behavior learning, which resultantly increases the throughput up to 95% and at the same time decreases the miss detection probability by 50%.

Abstract This paper proposes an inter-frame dynamic double threshold (IF-DDT) spectrum sensing algorithm in order to improve the sensing performance based on energy detection (ED) in cognitive radios (CRs). Based on both the activity model of the primary user (PU) and the sensing mechanism of the secondary user (SU), the proposed algorithm exploits the relationship between two adjacent sensing frames and designs dynamic double thresholds for each sensing frame to enhance spectrum sensing performance when the received energy cannot give a reliable local decision. The detection probability and false alarm probability of the proposed sensing scheme are analyzed, and an algorithm for searching the optimal dynamic double thresholds is derived with very low complexity according to the Neyman-Pearson (NP) test criterion. Theoretical analysis and simulation results show that the proposed algorithm outperforms the ED algorithm.

Spectrum detecting is the essential and crucial mechanisms of cognitive radio (CR) to the invention the unemployed spectrum. CR system has been suggested as a conceivable resolution for enhancing the spectrum use by empowering unprincipled spectrum sharing. The principal prerequisite for enabling CR to utilize authorized range on an optional premise is not making interfering to primary users. The principal goal of CR is to use rare and limited natural resource efficiently with no obstruction to the primary users (PUs). This work presents an overview of CR architecture, discusses the characteristics and benefits of a CR. Energy identification, matched channel filter detection, and cyclostationary recognitions are most conventional techniques for spectrum sensing. The explanation behind picking energy detection procedure, it did not need any previous info from the primary user transmission. Additionally, the particular result of energy detection technique corrupts with a lower sign to noise ratio (SNR) level signal area. General detection performance of energy detection exceptionally depends upon noise, mainly while the SNR is low for PU. To consider this issue, this paper shows a remarkable, augmented threshold model for efficient energy detection procedure to improve the detection execution at low SNR level. The simulation results demonstrate the energy detection performance utilizing proposed system model is excellent than a fixed threshold at low SNR signal areas. [ABSTRACT FROM AUTHOR]

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The effective utilization of the spectrum has become an essential goal in the communications field, which is addressed by the Cognitive Radio (CR) systems. The primary task in a CR system is to sense the spectrum to identify its holes to be exploited by the secondary users. In this paper, we tackle the compressed spectrum sensing problem in a cooperative manner. The CRs distributed in an area take the samples of the signal that has been reached to them through a wireless fading channel. The spectrum has the block-sparse structure. Moreover, the spectrum observed by different CRs in an area share the same block-sparse support. Therefore, we suggest to exploit the joint block-sparsity structure of the spectrum sensed by various CRs to reconstruct the underlying spectrum more efficiently. To this end, we have suggested a simultaneous block-sparse recovery technique based on the iterative adaptive thresholding scheme. In addition, we have given the convergence analysis of the proposed method. The suggested scheme has the advantage of reducing the number of required samples at each CR. The simulation results have also validated the superiority of the suggested scheme over the other state-of-the-art methods. [ABSTRACT FROM AUTHOR]

Underlay mode of cognitive radio networks (CRNs) permits secondary users (SUs) to simultaneously operate with primary users (PUs), inducing mutual interference between them. Nevertheless, primary interference (interference from PUs to SUs) is either neglected or treated as Gaussian noise in existing works. Moreover, artificial noise, which is deliberately introduced to harm signal reception of wire-tappers, can enhance information securing capability of CRNs. This paper analyzes a gap between reliability probability (successful decoding probability of legitimate receiver) and intercept probability (successful decoding probability of wire-tappers), which accounts for artificial noise, peak transmit power constraint, interference power constraint, and fading channels, and considers primary interference as non-Gaussian noise. Towards this end, exact closed-form expressions of reliability probability and intercept probability are first derived and then validated by computer simulations. Numerous results illustrate that both probabilities are saturated at either large peak interference power or large peak transmit power, the primary interference dramatically deteriorates them while the artificial noise creates a large gap between them, demonstrating its effectiveness in securing legitimate information. [ABSTRACT FROM AUTHOR]

This paper proposes a jamming technique which employs a self-powered secondary jammer to interfere a wire-tapper, who eavesdrops communications between a self-powered secondary source and a secondary destination in energy harvesting cognitive radio networks (EHCRNs). For generality, interference from a primary source, maximum transmit power constraint and interference power constraint are considered in analyzing security performance of the proposed jamming technique in terms of security-reliability compromise. Towards this end, exact expressions of detection/eavesdropping outage probabilities at the destination/the wire-tapper are first proposed and then verified by computer simulations. Finally, results are provided to demonstrate the efficacy of the jamming technique and the key effects (interference from the primary user, power constraints, interference power distribution factor, and time splitting factor) on security performance of EHCRNs. [ABSTRACT FROM AUTHOR]

In this work, a novel multiband spectrum sensing technique is implemented in the context of cognitive radios. This technique is based on multiresolution analysis (wavelets), machine learning, and the Higuchi fractal dimension. The theoretical contribution was developed before by the authors; however, it has never been tested in a real-time scenario. Hence, in this work, it is proposed to link several affordable software-defined radios to sense a wide band of the radioelectric spectrum using this technique. Furthermore, in this real-time implementation, the following are proposed: (i) a module for the elimination of impulsive noise, with which the appearance of sudden changes in the signal is reduced through the detail coefficients of the multiresolution analysis, and (ii) the management of different devices through an application that updates the information of each secondary user every 100 ms. The performance of these linked devices was evaluated with encouraging results: 95% probability of success for signal-to-noise ratio (SNR) values greater than 0 dB and just five samples (mean) in error of the edge detection (start and end) for a primary user transmission.

This paper presents an enhanced cooperative spectrum sharing scheme in a cognitive radio network, which consists of two pairs: a primary transmitter-primary receiver pair and a secondary base station-secondary receiver pair. The developed scheme is based on a three time slots cooperative spectrum sharing, which exploits the maximum diversity gain for both the primary user (PU) and secondary user (SU). The performance of the developed scheme is assessed through the derivation of new closed-form expressions for both the bit error rate and achievable rate for arbitrary signal-to-noise ratio (SNR). In addition, we carry out an asymptotic analysis in the high SNR regime, which proves that the proposed scheme achieves a full diversity order for the PU and SU. Results of analytical expressions and numerical simulations both verify that the proposed scheme achieves significant gains for error and rate performance, for both the PU and SU, in comparison to other baseline schemes.