Search

A recent study has revealed that incorporating intelligent reflecting surfaces (IRS) into wireless systems improves their performance within various signal propagation conditions. This research paper focuses on evaluating the spectrum-sensing abilities of cognitive radio in wireless environments enhanced by IRS technology. To achieve this, the paper develops a statistical model to describe the connections between the primary user (PU) and IRS, as well as between IRS and the secondary user (SU). This model calculates a precise approximation of the probability density function (PDF) of the end-to-end instantaneous signal-to-noise ratio (SNR). The SNR considers both the PU-to-IRS link, which follows a Rayleigh fading distribution, and the IRS-to-SU link, which follows an FTR fading distribution. By utilizing this PDF distribution, the paper derives analytical expressions for the average probability of detection (APD) for single and cooperative secondary users. These APD expressions are then employed to establish the exact closed-form expression for the average area under the receiver operating characteristics (AUC), system throughput, and transmission probability of the SU. To validate these theoretical findings, the paper conducts Monte Carlo simulations. The results from these simulations indicate an improved detection probability when operating in an IRS-enhanced wireless propagation environment. Consequently, the study suggests that incorporating IRS technology can significantly enhance the reliability of communication systems based on cognitive radio principles. [ABSTRACT FROM AUTHOR]

Spectrum sensing (SS) is substantial demand for enabling cognitive radio (CR) systems. In recent years, digital beamforming (DBF) is exploited for performance enhancement of multiple antenna elements or uniform linear array (ULA) based spectrum sensing techniques. Generally, a ULA suffers from its high side lobe level (SLL) and has a relatively low realized array gain. Subsequently, the ULA-based CR receiver is compelled to provide low detection performance. Furthermore, the efforts to increase the realized array gain are still not sufficient to dramatically increase the detection performance, because the problem of high SLL remains a challenging issue. In this paper, a highly efficient approach is introduced for performance enhancement of ULA-based SS techniques using side lobe level reduction (SLLR) beamforming that is applied to the receiving ULA. Worthy, a large SLLR combined with maintaining the same half power beamwidth (HPBW) as the original array, results in a synthesized array with a significantly improved gain. Thence, both the received signal-to-noise ratio (SNR) and signal-to-interference plus noise ratio (SINR) of the combined received signal are significantly enhanced at the CR receiver, which would considerably increase the detection capability of the proposed system. The proposed approach is investigated by integrating the SLLR-based antenna array with the generalized likelihood ratio test/direction of arrival (GLRT/DOA)-based SS technique that is denoted as GD technique. In contrast to the ULA, the synthesized array's excitation coefficients are non-uniform, necessitating special signal processing to incorporate it in the received signal model and derive closed-form expressions for the probability of detection, probability of false alarm, and decision threshold, all of which are already reported in this work. Several simulation scenarios are performed to validate the supremacy of the proposed SS approach compared to the traditional single antenna element based techniques and other state of the art beamforming based techniques. [ABSTRACT FROM AUTHOR]

In this paper, we investigate the bit error rate (BER) analysis of the scheduled transmit antenna selection (TAS) with maximal ratio combining (MRC) for the cognitive radio multiple-input multiple-output (CR-MIMO) systems. As a first step, this paper derives approximate probability density functions (PDFs) of the effective signal to noise ratio (SNR) of the proposed scheduled TAS with MRC in CR-MIMO systems. Next, the tight closed-form expressions of the BER for the M -ary QAM and M -ary PSK modulations are presented, respectively. Using asymptotic analysis, we approximate the BER for the scheduled system, and quantify the diversity order and SNR gain, respectively. Analytical results show that the diversity order of the scheduled system is the product of spatial diversity (SD) and multiuser diversity (MUD). Thus, BER performance of the proposed system is improved as the number of users increases by MUD regardless of modulation types. Also, the SNR gain is getting worse as the number of received antennas at the primary system increases regardless of modulation types. [ABSTRACT FROM AUTHOR]

Intelligent reflecting surface (IRS) technology has found extensive application in the wireless communication domain, offering significant enhancements in communication performance by manipulating the reflection of electromagnetic waves. This research article delves into the domain of UAV-assisted wideband cognitive radio networks augmented by IRS and underpinned by sensing-based spectrum sharing techniques. We introduce UAVs as alternative solutions for both primary and secondary base stations to optimize the management of spectral resources. Our primary objective centers around the joint optimization of both the trajectories of the primary and secondary UAVs, power allocation at the secondary UAVs, reflection coefficients of intelligent reflecting surfaces, sensing time, and subcarrier allocation, all aimed at maximizing the achievable rate of secondary users. Given that the problem at hand is non-convex, we employ deep reinforcement learning algorithms for resolution. To address the challenge of mixed-action spaces, we implement a novel Dueling DQN-Twin Delayed Deep Deterministic policy gradient (DDQN-TD3) algorithm. The simulation outcomes illustrate that the methodology introduced in this paper substantially amplifies the performance of CR system when contrasted with benchmark methods. This is evident in the improved spectrum efficiency, elevated data transmission rates, and the minimization of interference with the primary network. [ABSTRACT FROM AUTHOR]

The cognitive ability of Cognitive Radio (CR) can adjust appropriate transmission parameters according to different channel conditions and adapt to dynamic communication environments. Channel quality evaluation is a key technology of adaptive communication systems, and its real-time evaluation results provide a direct basis for adaptive communication transmission scheme switching. So it is necessary to study the estimation algorithms of signal-to-noise ratio (SNR). Several SNR estimation algorithms based on additive white Gaussian noise channel (AWGN) are studied in this paper, analyze and compare through theoretical derivation and computer simulation. The simulation results of the computer MATLAB software show that the algorithm introduced in the article has a high estimation accuracy in the entire range of the SNR simulation interval of 0 to 15dB, and can accurately reflect the current channel conditions to facilitate the CR system adjusts the communication strategy in time, provides a certain reference basis for CR channel estimation decision-making. [ABSTRACT FROM AUTHOR]

The sudden outbreak of the novel coronavirus disease in 2019, known as COVID-19 has impacted the entire globe and has forced governments of various countries to a partial or full lockdown in the fear of the rapid spread of this disease. The major lesson learned from this pandemic is that there is a need to implement a robust system by using non-pharmaceutical interventions for the prevention and control of new contagious viruses. This goal can be achieved using the platform of the Internet of Things (IoT) because of its seamless connectivity and ubiquitous sensing ability. This technology-enabled healthcare sector is helpful to monitor COVID-19 patients properly by adopting an interconnected network. IoT is useful for improving patient satisfaction by reducing the rate of readmission in the hospital. The presented work discusses the applications and technologies of IoT like smart and wearable devices, drones, and robots which are used in healthcare systems to tackle the Coronavirus pandemic This paper focuses on applications of cognitive radio-based IoT for medical applications, which is referred to as "Cognitive Internet of Medical Things" (CIoMT). CIoMT is a disruptive and promising technology for dynamic monitoring, tracking, rapid diagnosis, and control of pandemics and to stop the spread of the virus. This paper explores the role of the CIoMT in the health domain, especially during pandemics, and also discusses the associated challenges and research directions. [ABSTRACT FROM AUTHOR]

Non-Orthogonal multiple access (NOMA) and Cognitive radio (Cr) are seen as one of the most promising techniques, which improves the utilization of the spectrum in 5G. The expanding number of wireless applications like new gadgets, IOT brought about developing a block in the ISM groups. The FCC requested to permit unlicensed clients to work in the void area without obstruction to an authorized guest. Cr gives an answer for an extra range prerequisite issue for productive spectrum usage. The foremost condition for permitting CRs to utilize spectrum is not causing obstruction to licensed users. Spectrum sensing permit secondary users (Su) to separately recognize the idle portions of the spectrum, and thus evade obstruction to licensed users. In existing spectrum sensing techniques, SU can only utilize the unused spectrum when PU is not present. Therefore, spectrum exploitation of the conventional system is very low. In recent times NOMA has been projected to utilize the spectrum in an efficient manner. The proposed work permits the SU to utilize a spectrum of PU, both at its absence. Spectrum sensing in NOMA is not explored so far. Hence, in this paper, NOMA based matched filter detection is designed for QAM-64 and QAM-256. Matlab simulation is applied to study the operation of the proposed detection technique in NOMA in respect of several parameters like bit error rate (BER) Vs signal to noise ratio (SNR), the probability of detection (Pd), and probability of false alarm (Pfa). [ABSTRACT FROM AUTHOR]

Cognitive radio (CR) has been classified as a promising technology which can significantly mitigate the impact of spectrum underutilization and spectrum scarceness arising from massive expansion of wireless applications. That is as a result of the CR capability to improve both the spectral and energy management efficiently. Spectrum sensing is the most important technological requirement for accomplishment of cognitive radio systems. For a known primary user (PU) signal, matched filtering is the common technique used for signal detection over a specific band of spectrum, therefore, detection performance enhancement of the matched filter detector has gained great attention. In this paper, a new multiple antenna elements (MAE) and matched filtering (MF) based spectrum sensing technique named as MAE/MF is proposed for detection capability enhancement. Multiple antenna elements utilization improves the received signal to noise ratio (SNR) in proportional to the achieved antenna array gain. The likelihood ratio test is used to decide the presence/absence of PU signal. Simulation results showed that the proposed MAE/MF technique outperforms the single antenna element based matched filter (SAE/MF) and other existing techniques in the state-of-the-art, especially at extremely low signal to noise ratios and limited number of received signal samples. [ABSTRACT FROM AUTHOR]

Abstract: A power allocation problem to maximize the capacity of the secondary user subject to an interference constraint in OFDM-based cognitive radio networks is studied in this paper. One main problem is how to select subcarriers for allocating power. In this paper, a water-level approach is proposed to obtain the optimal solution by using the linear search method. The proposed optimal scheme is free of the log operation and easily implemented to achieve optimum performance. We also propose two suboptimal schemes with a lower complexity which can select subcarriers of relatively better channel states and causing the less amount of mutual interference. A novel ratio parameter is introduced to indicate the quality of a subcarrier. The superiority of the proposed schemes is verified by simulations. Their performances are better than those of the existing heuristic schemes and very close to that of the optimal solution. [Copyright &y& Elsevier]

A novel energy detection scheme for improved dynamic spectrum access is proposed in this paper for cognitive radio applications. To maximize the access for secondary user (SU), the availability of inactive primary user (PU) is determined based on spectrum sensing and channel state estimation. A novel energy detection scheme has been introduced to collect the spectrum sensing data of the secondary users during collection period and the length of the collection period is determined adaptively based on the number sensing data using random access. Thus, complex slot management is not necessary and computational complexity is reduced. Sensing data collection using adaptive determination is formulated as a AND rule based decision problem. Simulation results exhibit that the proposed scheme significantly outperforms the existing adaptive double threshold (ADT) technique in terms of probability of false alarm and probability of detection for various signal to noise ratio (SNR) values. [ABSTRACT FROM AUTHOR]

Recently, cyclostationarity (CS) based detection methods exploiting the autocorrelation periodicity property of the orthogonal frequency division multiplexing (OFDM) signals attracted a lot of attention. These detection methods are more complex than energy detection but they have better detection performance in low-SNR regimes. The drawback, however, is their extensive computational complexity. In this paper, we propose a computationally efficient spectrum sensing method for detecting unsynchronized OFDM signals in additive white Gaussian noise (AWGN). The proposed method exploits the second-order CS property of OFDM signals to set an adaptive threshold, which achieves the desired constant false alarm rate (CFAR) property. The complexity of the proposed method is significantly reduced compared with the scheme proposed in [22]. Monte-Carlo simulations illustrate that the performance of the proposed detector outperforms the classical energy detector (ED), the sliding window detector (SW) and Axell's detector in addition to guaranteeing a low sensing time. [ABSTRACT FROM AUTHOR]

A systematic approach to I / Q mismatch calibration in image-reject receivers is presented in this paper. A new error detection algorithm is proposed, which automatically calibrates for phase and gain mismatches limiting the performance of image-reject receivers. A dual-loop feedback is employed which looks for the minimum phase/gain error using a 2-dimensional analog-based search algorithm and then finds the minimum value for the error. An experimental CMOS prototype RF front-end for cognitive radio applications operating at 400–800 MHz is proposed and simulated in the 0.18 μm CMOS technology, achieving an image rejection ratio (IRR) better than 55-dB in post-layout simulation. The comprehensive mathematical model of this analog algorithm is thoroughly studied in the appendix and the convergence is verified theoretically. [ABSTRACT FROM AUTHOR]

A significant challenge in spectrum sensing is to lessen the signal to noise ratio needed to detect the presence of primary users while the noise level may also be unknown. To meet this challenge, multi-antenna based techniques possess a greater efficiency compared to other algorithms. In a typical compact multi-antenna system, due to small interelement spacing, mutual coupling between thermal noises of adjacent receivers is significant. In this paper, unlike most of the spectrum sensing algorithms which assume spatially uncorrelated noise, the noises on the adjacent antennas can have arbitrary correlations. Also, in contrast to some other algorithms, no prior assumption is made on the temporal properties of the signals. We exploit low-rank/sparse matrix decomposition algorithms to obtain an estimate of noise and received source covariance matrices. Given these estimates, a Semi-Constant False Alarm Rate (S-CFAR) detector, in which the probability of false alarm is constant over the scaling of the noise covariance matrix, to examine the presence of primary users is proposed. In order to analyze the efficiency of our algorithm, we derive approximate probability of detection. Numerical simulations show that the proposed algorithm consistently and considerably outperforms state-of-the-art multi-antenna based spectrum sensing algorithms. [ABSTRACT FROM AUTHOR]

Avoiding collision among contending customer premise equipments (CPEs) attempting to associate with a base station (BS) in a wireless regional area network (WRAN) is a challenging issue. The collision probability is highly dependent upon the size of the initial contention window and the number of contending CPEs. To reduce the collision probability among CPEs in order to start the ranging process in an IEEE 802.22 network, the BS needs to adjust the initial contention window size. This paper provides an analytical framework to estimate the ranging request collision probability depending upon the size of the initial contention window and the number of CPEs attempting to join the IEEE WRAN cell. The accuracy of the estimated curve is analyzed for various numbers of contention CPEs on the basis of the relative errors. The numerical results confirm that the approximation works reasonably well for finding the ranging request collision probability for any number of contention CPEs at a particular value of initial contention window size. Moreover, this approximation provides the threshold size for a contention window to start the initial ranging process for any number of CPEs in an IEEE 802.22 network. [ABSTRACT FROM AUTHOR]

In this paper, we consider the problem of spectrum sensing in cognitive radios (CRs) by exploiting inherent polarization characteristics of signal. Since polarization vector can completely describe the radiation's polarization characteristics, we first derive the probability density function (PDF) of received polarization vector and its moments. Then we find the fact that both component and serial correlations of received polarization vector (i.e., the mixed polarization vector of primary signal and noise) are different from those of noise with high probability. This distinctive difference can be used to decide whether the primary signal exists or not. Therefore, component correlation sensing (CCS) algorithm and serial correlation sensing (SCS) algorithm are proposed respectively. Furthermore the closed-form expressions of probabilities of false alarm and detection are available for CCS and SCS algorithms. Simulations show that both CCS and SCS detectors achieve better performance with higher cross-polar discrimination (XPD) and polarization channel correlation coefficients. We also show that, if channel is highly depolarized, CCS performs better than SCS. Otherwise, the latter shows better performance. Compared with existing polarization based detectors, both CCS and SCS detectors perform better in the presence of noise power uncertainty. [ABSTRACT FROM AUTHOR]

Abstract: Cognitive radio-based Long Term Evolution (LTE) networks benefit from the powerful features of cognitive Radios (CR), such as learning and reconfigurability, enabling them to perform, if required, channel switching to a channel with higher quality. This process of searching and sensing other channels is a restoration (recovery) process where the objective is to find the best channel in the shortest time. We propose in this paper a history-aware greedy restoration scheme triggered not only when the quality of the current operating channel of the user goes below a threshold, but at regular intervals. Based on the state of the current channel, our scheme computes the optimal number of channels to be sensed in this restoration period and this number is dynamically updated after each channel sensing result. Intrinsic features of learning and history-awareness of CRs are used to create the list of channels to be sensed based on the channels’ background and historical information. The sensing order improves the restoration mechanism by providing a shorter restoration time or a restored channel with a higher quality. We show that the proposed combined scheme provides improvements for the CR-based LTE network’s throughput, compared to other restoration schemes which work based on only greediness or history-awareness. [Copyright &y& Elsevier]

Abstract: In this paper, the performance of cognitive radio (CR) code division multiple access (CDMA) networks is analyzed in the presence of receive beamforming at the base stations (BSs). More precisely, we analyze, through simulations, the performance achievable by a CR user, with and without spectrum sensing, in a three-cell scenario. Uplink communications are considered. Three different schemes for spectrum sensing with beamforming are presented, together with a scheme without spectrum sensing. CR users belong to a cognitive radio network (CRN) which is coexisting with a primary radio network (PRN). Both the CRN and the PRN are CDMA based. The CRN is assumed to utilize beamforming for its CR users. Soft hand-off (HO) and power control are considered in both the CRN and the PRN. The impact of beamforming on the system performance is analyzed, considering various metrics. In particular, we evaluate the performance of the proposed systems in terms of outage probability, blocking probability, and average data rate of CR users. The results obtained clearly indicate that significant performance improvements can be obtained by CR users with the help of beamforming. The impact of several system parameters on the performance of the three considered spectrum sensing schemes with beamforming is analyzed. Our results, in terms of probability of outage, show that the relative improvement brought by the use of beamforming is higher in the absence of spectrum sensing (reduction of 80%) than in the presence of spectrum sensing (reduction of 42%). [Copyright &y& Elsevier]

The interplay between the non-orthogonal multiple access (NOMA) and the opportunistic cognitive radio (CR)-based orthogonal frequency multiple access (OFDMA) has been recently realized as a promising paradigm to support the unprecedented massive connectivity demands of future beyond fifth-generation (B5G) wireless communication systems. In such systems, which are called multi-carrier NOMA CR-based systems, each licensed band reserved for primary users can be opportunistically utilized based on power-domain NOMA to serve a group of secondary users simultaneously. An important challenge in this domain is how to provide energy-efficient resource allocation techniques that attempt to strike a balance between the total throughput (i.e., the achieved sum-rate) and the power required to achieve that rate while satisfying network QoS demands and being aware of the unique characteristics of the CR operating environment. In this paper, we propose an energy-efficient resource allocation technique for multi-carrier NOMA CR-based systems, which aims at maximizing the overall energy efficiency (EE) of the system under a set of CR and NOMA constraints. The EE maximization problem is shown to be a fractional non-convex optimization, which is, in general, hard to optimize. To deal with the fractional and the non-convexity nature of the formulated EE maximization problem, we exploit the Dinkelbach's algorithm to transfer the EE problem to a parameterized optimization problem. Then we use an iterative optimization approach to obtain the solution for the EE maximization problem. Simulation results reveal that this EE maximization-based resource allocation technique outperforms the existing resource allocation techniques in terms of the overall EE of the system while striking a good balance between the sum-rate and the transmit power consumption. [ABSTRACT FROM AUTHOR]

Abstract: In cognitive radio communications, the coexistence of a secondary network is allowed as long as the interference power from the secondary network measured at the primary user (PU) is kept below a given threshold. In this paper, we consider a communication scenario for cognitive radio networks where the channel state information (CSI) between the secondary base station (SBS) and the PU is perfectly known at the SBS transmitter whereas only the covariance information is known for the channel of the SBS-secondary user (SU) link at the SBS transmitter. A closed-form precoder design method is proposed for the purpose of maximizing the upper bound of the expected achievable rate of the SU. Even though the proposed scheme uses only partial CSI of the SBS-SU link, the simulation results show that the expected achievable rate of the proposed scheme approaches to that of a conventional scheme with full CSI as the channel correlation increases. [Copyright &y& Elsevier]

The evolving 5G and beyond 5G (B5G) wireless technologies are envisioned to provide ubiquitous connectivity and great heterogeneity in communication infrastructure by connecting diverse devices and providing multifarious services. Recently, the Internet of Things (IoT) and unmanned aerial vehicles (UAVs) are realized as an essential component of the upcoming 5G/B5G networks, enabling enhanced communication capacity, high reliability, low latency, and massive connectivity. However, one limiting factor in the expansion of 5G/B5G technology is the finite radio spectrum, which necessitates managing the anticipated spectrum crunch for future wireless networks. One potential solution is to develop intelligent cognitive methods to dynamically optimize the use of spectrum in 5G/B5G networks to solve the imminent problem of spectrum congestion and improve radio efficiency. This paper addresses the opportunistic spectrum access (OSA) problem in the 5G/B5G cognitive radio (CR) network of IoTs and UAVs through the novel deep learning-based detector, dubbed as Deep-CRNet. The proposed detector employs residual connections with cascaded multi-kernel convolutions to identify the primary user (PU) spectrum usage by extracting the inherent multi-scale signal and noise features in the sensed transmission patterns. Thereby, Deep-CRNet intelligently learns and locates the spectrum holes so that secondary users (SUs) and PUs can dynamically share network spectrum resources. The efficacy of Deep-CRNet is validated through simulation results, where it achieved 99.74% accuracy with 99.65% precision and 99.83% recall in accurately classifying the PU status. In addition, the average correct detection probability of Deep-CRNet in the low signal-to-noise ratio (− 20 dB to − 15 dB) range is 38.21% higher than the second best-performing detector. • A DL-based framework is presented for OSA in CR-enabled 5G/B5G networks (Deep-CRNet). • Deep-CRNet OSA model deploys UAVs as aerial BS to serve remote IoT users (PUs & SUs). • It captures intrinsic multiscale signal-noise features in PU transmission patterns. • Using residual learning, it dynamically exploits the unfilled spectrum holes for OSA. • Deep-CRNet outperforms other benchmark detectors in correct detection probability. [ABSTRACT FROM AUTHOR]

The proposed MIMO-based cognitive radio wireless sensor network (CR-WSN). [Display omitted] • Robust PU detection schemes are proposed for MIMO-based CR-WSN. • Optimal spectrum sensing method with GLRT when there is noise uncertainty in low SNR. • CHD detector is derived for unknown noise uncertainty statistics. • ECD and GLD detectors are derived for known noise uncertainty statistics. • Closed-form expressions given for threshold, detection & false-alarm probabilities. Noise uncertainty can severely deteriorate a primary user (PU) detector's sensing performance, so robustness against the noise uncertainty is of fundamental significance in cognitive radio networks. In this paper, we investigate robust detection schemes in the presence of noise uncertainty for multiple-input multiple-output (MIMO) based cognitive radio wireless sensor networks (CR-WSN) with multiple spectrum sensing scenarios. In practice, it is very seldom that accurate statistical characterization of noise is known a priori. In this context, we propose the generalized likelihood ratio test (GLRT) paradigm and estimator-correlator based optimal detectors to sense the unoccupied primary bands in the presence of noise uncertainty. First, we compute the estimator-correlator based detector (ECD) and generalized likelihood detector (GLD) for known noise uncertainty statistics. Next, a composite hypothesis based detector (CHD) is proposed using the GLRT framework for unknown noise uncertainty statistics. The proposed detection schemes provide robustness against uncertainty in the available noise power estimates using a finite number of observations (i.e., fast spectrum sensing) and particularly in critical areas of low Signal-to-Noise Ratio (SNR). Theoretical analysis is performed based on statistical theory. Closed-form expressions for detection and false-alarm probability, and analytic decision threshold are derived to demonstrate spectrum sensing performance using receiver operating characteristic (ROC) curves. Simulation results validate that proposed PU detection schemes are robust and achieve an improved sensing performance when there is noise uncertainty and corroborate our theoretical findings. [ABSTRACT FROM AUTHOR]