This paper introduces a unified framework for activity detection in IRS-assisted IoT networks that simultaneously addresses three critical practical challenges: asynchronous transmissions, heterogeneous power levels used by devices to report their local observations, and signal blockage in dynamic environments. The system leverages an intelligent reflecting surface (IRS) to enhance detection reliability, with optional incorporation of a direct line-of-sight (LoS) path. Departing from conventional approaches that handle these challenges in isolation, we formulate a comprehensive detection problem as a binary hypothesis test and develop a structured hierarchy of four detectors: an optimal detector alongside three computationally efficient detectors designed for practical scenarios with different levels of prior knowledge about noise variance, channel state information, and device transmit powers. This hierarchical approach systematically bridges the gap between theoretical optimality and implementation practicality. For each detector, we derive closed-form expressions for both detection and false alarm probabilities, establishing theoretical performance benchmarks and revealing fundamental scaling laws. Extensive simulations validate our analytical results and extract actionable design guidelines by systematically evaluating the impact of key system parameters including the number of antennas, samples, users, and IRS elements on detection performance, providing valuable insights for 6G IoT system designers. The proposed framework effectively bridges theoretical optimality with implementation practicality, providing a scalable solution for IRS-assisted IoT networks in emerging 6G systems.

This paper introduces a unified learning framework for Byzantine-resilient spectrum sensing and secure transmission in intelligent reflecting surface (IRS)-assisted networks under channel state information (CSI) uncertainty. The sensing module employs robust Bayesian belief updates with adversary-resistant aggregation and consensus, guaranteeing reliable primary user (PU) detection even when a bounded fraction of users are malicious. Based on the sensing outcome, the transmission module formulates the downlink design as a sum mean-squared error (MSE) minimization problem under transmit-power and signal-leakage constraints, jointly optimizing the base station (BS) precoder, IRS configuration, and user equalizers. For partial or known CSI, we develop a lightweight alternating optimization algorithm with provable sublinear convergence. For unknown CSI, we integrate constrained Bayesian optimization (BO) within a geometry-aware, low-dimensional latent space. Simulations demonstrate that the proposed framework achieves a higher probability of detection at a fixed false-alarm rate under adversarial attacks compared to state-of-the-art schemes. It also yields substantial reductions in user MSE, strong suppression of eavesdropper signal power, and fast convergence. This work provides a practical, resilient solution for coherent sensing–communication coordination in emerging sixth-generation (6G) applications such as vehicular, unmanned aerial vehicle (UAV), and Internet of Things (IoT) networks.

We propose a novel clustered random beamforming (RBF) scheme for multi-user multiple-input multiple-output (MIMO) downlink systems. The objective of this scheme is to maximize the number of served users while ensuring that each user meets a minimum quality of service (QoS) requirement. The system model comprises a base station (BS) equipped with multiple transmit antennas, serving multiple users. Hierarchical modulation (HM) is employed for each beam to improve spectral efficiency. To analyze the performance of the proposed scheme, we develop an analytical model by mapping the symbols of all users within a cluster onto different layers of a HM constellation. We demonstrate that the proposed scheme not only increases the number of served users but also enhances the sum-rate capacity of the system. Additionally, we discuss the selection of the optimal HM parameter based on predefined performance requirements, illustrated through a sample case. Simulation results indicate that the proposed hierarchical random beamforming (HRBF) scheme is highly promising for multi-user massive MIMO downlink systems, particularly in the context of 5G New Radio (NR) and future 6G networks and beyond.

In this paper, an agile smart attacker model in spectrum sensing of cognitive radio network (CRN) is introduced. This smart attacker does not make the channel busy all the time, instead it senses spectrum and when a primary user leaves, it occupies the spectrum by mimicking the signal characteristics of the primary users. To model such a smart attacker, we use a dependent Markov chain to model the primary user and primary user emulation attacker activities, simultaneously. We derive the transition probabilities for the assumed dependent Markov model. Then, the effect of the primary user and attacker traffic on a secondary user's throughput under Rayleigh flat fading channel is investigated and closed form expressions are derived for the average probability of detection and false alarm. Furthermore, the impact of this attacker on the performance of a CRN and the throughput of the secondary user network is studied analytically. We also derive a test rule based on the generalized likelihood ratio test, to detect the legitimate user from smart illegitimate user. In addition, launching primary user emulation attacker in the conventional and proposed smart attacking procedures are compared, where the results demonstrate that by using smart attacking, the deterioration in throughput of the secondary user's network is considerable. In fact, it is shown that under proper selection of the traffic parameters, the secondary user's network throughput may tend toward zero. Finally, the accuracy of the obtained results are analyzed and verified by the simulation results.

In this study, the optimisation of cooperation level and uplink spectrum sharing are analysed by employing physical layer security concept in the device to device communications underlaying cellular networks. It is assumed that two transmitting devices communicate with the same receiving device in the presence of a passive eavesdropper. Also, it is assumed that one of the two transmitting devices aids the other one as a relay node. The optimum cooperation level and channel assignment schemes are obtained through maximizing the secrecy rate under constraints of satisfying the quality of services for cellular users (CUs) and the receiving device. In this sense, the selected CU which shares its resource block acts as a friendly jammer to interfere the eavesdropper. Although the proposed approach initially leads to a non‐convex optimization problem, it can be solved efficiently by using GloptiPloy (global optimum polynomial) and SeDuMi software packages. Simulation results are provided to evaluate the performance of the proposed optimum cooperation level scheme and demonstrate the superiority of the proposed approach compared to the conventional fixed cooperation level schemes.

In this study, the authors study the collaborative data aggregation using multiple antennas sensors and fusion centre (FC) with energy harvesting capability in the wireless sensor network (WSN). The optimisation problem is formulated to improve the data transfer rate, based on the parameters of collaboration among sensors, the energy harvesting, and storage of each sensor. In particular, they observe several practical constraints for energy harvesting and capability battery energy storage to maintain network connectivity. They propose three scenarios based on the number of antennas for transferring, collecting, and sharing the data on sensor and FC. It is shown these optimisation problems are a non‐convex and to resolve this issue, the objective function is converted to a convex function using a relaxation method. The numerical results show the impact of different parameters on the data rate at FC and improvement in network connection and throughput by using proposed collaborative data aggregation techniques compared to their counterparts.

In this paper, we consider a cognitive radio system with some of secondary users (SUs) equipped with multiple antenna full-duplex transceivers. We design a hybrid system to sense and access the spectrum opportunistically such that the SU operates on both underlay and interweave hybrid methods while the primary user (PU) may be active or inactive, respectively, to perform the sensing as agile as possible. Moreover, we maximize the throughput by allocating the spectral resources assuming limited available energy during each time frame with constraints on the interference to the primary user. We show that this problem is convex and propose a simple solution algorithm for it. Our simulations confirm the accuracy of our analytical and theoretical results and illustrate that the total achieved throughput of full-duplex cognitive radio even by considering self-interference is maximized under the assumed conditions.

In this paper, we study the spectrum sensing problem when the sensing device is equipped with an uncalibrated multiantenna array. We consider an unknown Toeplitz structure for the covariance matrix of the signal of interest and unknown uncorrelated noise variances. We propose two new invariant constant false-alarm rate detectors with less computational cost than the generalized likelihood ratio test: (1) the Rao test and (2) a separating function estimation test (SFET). We calculate approximate expressions for the false-alarm and detection probabilities of the proposed Rao test-based detector. In addition, we derive the exact false-alarm probability of the proposed SFET for the case of two antennas. Moreover, we obtain an accurate approximation for the false-alarm probability of the proposed SFET for the general case using the method of moments. The accuracy of these expressions is validated by Monte-Carlo experiments. The simulation results show that the proposed detectors outperform state-of-the-art detectors.

Sharing a relay in cognitive radio (CR) networks in order to enhance the secondary user's (SU's) performance in an interference‐limited scheme is investigated. It is assumed, by utilising hierarchical modulation, a shared relay is used simultaneously between both primary user (PU) and SU. The proposed scheme relies on both power adaptation and rate scheduling. Based on channel side information and tolerable interference limitation at the PU, the SU adapts its power and the transmission data rates are regulated by the PU's status and channels condition. Theoretical analyses and simulation results are provided to evaluate the performance of the proposed dynamic spectrum sharing scheme. It is shown that rate adaptation leads to significant improvement of the throughput of the SU, at the cost of negligible degradation in performance of the PU. The results also demonstrate that sharing CR relay with the PU can compensate the side effects of spectrum sharing and interference caused by the SU, which for instance is equivalent to ∼8 dB (low signal‐to‐noise ratio (low‐SNR) regime) and to 15 dB (high‐SNR regime) SNR improvement.

The problem of multiple antenna spectrum sensing in cognitive radio (CR) networks is studied in this paper. We propose two new invariant constant false-alarm rate eigenvalue-based (EVB) detectors, using the higher order moments of the sample covariance matrix eigenvalues, by exploiting the separating function estimation test framework. We find closed-form expressions for the false-alarm and detection probabilities of the proposed detectors by providing moment-based approximations of their statistical distributions. The accuracy of the obtained closed-form expressions is validated by Monte Carlo simulations. In addition, we compare the performance of the proposed detectors with that of their two counterparts, i.e., John's and the arithmetic to geometric mean (AGM) detectors, in terms of the asymptotic relative efficiency. This comparison enables us to demonstrate the superiority of our proposed detectors over those detectors within the typical range of signal-to-noise ratio in CR application. The comparative simulation results also illustrate the superiority of the proposed detectors over John's and the AGM detectors as well as some other state-of-the-art EVB algorithms given in the literature.

Performance analysis of selection combining (SC) for decode-and-forward (DF) relaying in case of general time-varying Rayleigh fading channels is investigated. While auto-regressive (AR) time series model is applied in channels, differential binary phase shift keying (DBPSK) modulation and non-coherent detection at relay and destination are used together. Due to SC scheme, DF relaying and DBPSK modulation no channel side information (CSI) is required to provide average bit-error-rate (BER), which is exactly calculated. Simulation results in different channel scenarios and fading rates, verifies the presented analysis. It is shown that SC is very close to semi-maximum-ratio-combining (semi-MRC) which is difficult to analyze and error floors occur at high signal-to-noise ratio (SNR) in both methods. The error floor in SC is obtained.

In this paper, we study a general practical stochastic energy harvesting (EH) model where in, an EH relay is able to harvest energy from the radio frequency (RF) radiation in a super capacitor. The different aspects of this stochastic energy harvesting model (SEHM) are discussed and the performance of a point-to-point (P2P) wireless link is evaluated in the presence of such an EH model and decode-and-forward (DF) relaying in terms of average error and outage probabilities. The performance limitations and practical applicability of the SEHM compared to the DF scenario in multi-relay case are studied. The simulation results show the performance improvement of this EH cooperative relaying compared to the traditional relay-based cooperative communications.

We study the probability of error optimization problem in collaborative spectrum sensing (CSS) with limited time and energy resources in energy harvesting cognitive radio (CR) networks. The frame structures for sensing and data transmission, if the spectrum is identified as vacant, are both supposed to be fixed. This implies that the time resource dedicated for CSS is limited and shared between spectrum sensing time and results reporting time, which depends on the number of sensing users. We consider an optimization problem containing network constraints on time and energy resources for an energy harvesting secondary user (SU) which cooperates with other users using an `OR' fusion rule. It is assumed that the SU, with arbitrary signal to noise ratio, is in energy deficit regime and a predefined constraint on probability of collision is satisfied to protect the cooperative network performance quality. We analytically prove convexity of the considered optimization problem ensuring a global solution can be obtained. Analytical results in addition to simulation results are provided to prove our claims.

We study a collaborative spectrum sensing (CSS) problem with limited time and energy resources in cognitive radio (CR) network. Time duration of sensing and data transmission, if the spectrum is identified as vacant, are both supposed to be fixed. This implies that the time resource dedicated for CSS is limited and shared between results reporting time (which depends on the number of sensing users) and spectrum sensing time. We consider an optimization problem containing network constraints on time and energy resources for an energy harvesting secondary user (SU) which cooperates with other users by an OR fusion rule. It is assumed that the SU with arbitrary signal to noise ratio is in energy deficit regime and also predefined constraint on probability of collision is satisfied to protect the cooperative network performance quality. We analytically prove the convexity of the considered optimization problem to ensure we obtain a global solution. Analytical results in addition to simulation results are provided to prove the claims.

In this paper, collaborative spectrum sensing to detect random signals corrupted by Gaussian noise in the presence of Primary User Emulation Attackers (PUEAs) is studied. We consider smart PUEAs which aims at increasing the false alarm probability and constitute a PUEA network on a Cognitive Radio (CR) network by impersonating Primary Users (PUs). In addition, we propose two security schemes in which sensing nodes get assistance from the Secondary Users (SUs) using two different approaches. In the first approach, the proposed scheme requires having some knowledge about the PUEA network similar to most of the schemes available in the literature. In our second proposed scheme, information about the PUEA network is not required yielding a scheme which is robust to the strategy of attackers. In both proposed approaches, we propose an algorithm to incorporate the SUs assistance in spectrum sensing. The final collaborative decision is made through solution of an optimization problem in order to achieve the best performance and protect the CR predefined requirements. Furthermore, in order to evaluate the performance of the proposed detector at the SUs and at the employed detector in the Fusion Center (FC), the closed form expressions for detection and false alarm probabilities are computed analytically. The provided closed-form analytical results in addition to simulation results show that the proposed schemes significantly outperform the existing secure spectrum sensing schemes.

In this paper, we propose a new detector for multiantenna spectrum sensing in cognitive radios (CR) by exploiting the Separating Function Estimation Test (SFET) framework. Specifically, we consider a blind scenario for multiantenna spectrum sensing in which both the channel gains and noise variance are assumed to be unknown. For such a scenario, we find an appropriate Separating Function (SF) whose Maximum Likelihood Estimate (MLE) leads us to a SFET-based detector which uses the second order moments of the eigenvalues of the Sample Covariance Matrix (SCM). We also find closed-form expressions for the detection and false-alarm probabilities of the proposed detector. The performance of the proposed detector asymptotically tends to that of the Uniformly Most Powerful Unbiased (UMPU) detector as the number of independent and identically distributed observations increases. In addition, simulation results show that the proposed detector outperforms the state-of-art eigenvalue- based detectors because of using the second order moments of the SCM eigenvalues.

In this paper, we propose a novel downlink system that is able to maximize the number of users which could be served with high correct decoding probability while, minimizing the total consumed energy for data reconstruction by whole users simultaneously. We deploy 2/4/…/2N-ASK hierarchical modulation that enables multi-resolution transmission along with an energy harvesting system to implement a wise and efficient downlink system. In our scheme, there is one information sender and two decode-and-forward access-points each of them equipped with one transmission antenna that wish to successfully transmit data to the users based on their link quality. In order to minimize the total consumed energy by the users, we prepare energy access-point which is an harvester with the capability of harvesting energy from radio frequency radiations. Each time the transmission commences, our scheme based on the weighted difference relay selection method selects the best access-point that mutually is able to maximize the number of serving users via a smart users number-defining algorithm and minimize the total consumed energy of the users. We investigate the performance of such a system in terms of average error probability and provide upper and lower bounds on them. We also discuss about careful hierarchical parameter selection that maximizes the number of serving users.

We address the problem of spectrum sensing in cognitive radios (CRs) when the secondary user (SU) is equipped with a multiantenna receiver. We consider scenarios with correlation between the received channels at different antennas and unequal per-antenna noise variances to accommodate calibration errors. First, we derive the exact as well as the asymptotic performance of the genie-aided (benchmark) detector with perfect knowledge of the antenna correlation coefficients, the primary user (PU) signal power and the noise covariance matrix. Then, we consider the sensing problem in which the SU is non-cognizant of the per-antenna noise variances, the PU signal power and the correlation of channel gains, starting with a specific treatment of the two-antenna case. For a general multiantenna receiver, we propose combining the derived test statistics among all antenna pairs. The related optimization problem to obtain the optimum combination weights is analyzed, which requires the analytical performance characterization of the constituent two-antenna detector. Thus, we compute the exact performance of the proposed detector in a special case (a particular case of the Hadamard ratio test) in terms of its detection and false alarm probabilities. Performance analyses are verified with simulations, showing that the proposed detector outperforms several recently-proposed multiantenna detectors for CR in the scenarios considered.

In this paper, we investigate relay-sharing in cognitive radio (CR) networks to improve the secondary user's (SU's) performance in the presence of interference. It is assumed that the relay is shared simultaneously between both the primary user (PU) and the SU by using hierarchical modulation at the relay node while mapping the PU's and the SU's symbols onto two different layers of the hierarchical constellation. The proposed scheme relies on power adjusting, where the SU power adjustment is based on channel side information (CSI) and tolerable interference limit at the PU. Theoretical analyses along with simulation results are provided to evaluate the performance of the proposed dynamic spectrum sharing scheme. The results demonstrate that the proposed relay-sharing scheme may result in about 7dB and 15dB signal to noise ratio (SNR) improvement for the SU in low-SNR and high-SNR regimes, respectively.

In this paper, we study the problem of multiple antenna spectrum sensing by using cyclostationary features of Primary Users (PUs) signals in Cognitive Radios (CRs). We consider the general case of multiple antenna sensing in the presence of spatially and temporally correlated noise when the PU signal has more than one cyclic frequency. We model and formulate the multiple antenna sensing problem as a composite hypothesis testing problem and use the Generalized Likelihood Ratio Test (GLRT) to derive a detector for the general model mentioned above. Then, we also propose the GLRT-based detectors for the two special cases of: 1) spatially uncorrelated but colored noise; 2) spatially white noise. Moreover, in order to calculate the decision threshold, the asymptotic performance of the proposed detectors under the null hypothesis is given. The provided simulation results show the superiority of the performance of the proposed detectors compared to the recently-proposed cyclostationary-based detectors.

In this paper, we study the performance of hybrid cognitive radio (CR) system in the presence of channel estimation error (CER) in terms of bit-error-probability (BEP) and outage probability. We assume simultaneous switching between the underlay and overlay modes of CR where in, the secondary user (SU) probabilistically accesses the primary user's (PU) channel in both underlay and overlay modes to utilize the benefits of hybrid CR system. The PU uses two-layer 2/4 — ASK constellation with unequal level of bit protection against the interference imposed by the SU's transmitter. The SU wishes to opportunistically use the PU's channel to transmit its own data while maintaining the performance of the PU's layer with the worst level of protection at a desired value. We derive the SU's maximum allowable transmit power in the overlay mode to guarantee the PU reliable communication then, we discuss about the optimum value of the hybrid switching rate that minimizes the outage probability of the SU along with, deriving the exact BEP and outage probability expressions for the secondary system as well as that of the first and the second layers of the PU system.

The collaborative Spectrum Sensing (SS) to detect random signals contaminated by additive Gaussian noise is studied. A novel soft decision rule based on the Lp-norm of square root of the Secondary Users (SUs) energies is proposed. The proposed scheme considers the bandwidth limitation for sharing sensing information between the SUs. Whereas the existing literature on Lp-norm detectors have assumed independent observations of the SUs, they would be highly correlated when Primary User (PU) is active. Therefore, we consider the correlated observations of the distributed SUs for the both cases of equal and unequal received Signal-to-Noise Ratios (SNRs). We derive the optimal Lp-norm vector parameter based on minimization of the overall error probability while meeting the spectral efficiency and sensing performance requirements. To this end, we use modified deflection coefficient metric to simplify optimization problem. Furthermore, in order to evaluate the performance of the proposed detector, the closed form expressions for detection and false alarm probabilities are computed analytically. The provided closed-form analytical results in addition to simulation results show that the proposed detector outperforms significantly the traditional energy combining detector.

Vehicle-to-vehicle (V2V) communications have recently received growing attentions due to the fact that it enables dynamic wireless data exchange between nearby vehicles, offering the opportunity for safety improvements and information sharing. In this paper, we investigate the performance analysis of a two-way amplify-and-forward (AF) multi-relay cooperative V2V communication networks with MPSK modulation, for two cases where the relays are vehicles or they are access-points of the road. In the mobile-to-mobile communication system considered in this paper, the communication channel is modeled as double-Rayleigh fading channel. We derive the exact symbol error probability (SEP) expressions under the two-phase and three-phase scenarios followed by their upper bound expressions. Then we examine the effect of the relays' position with respect to the source terminals, on their SEP and lastly, the maximum achievable diversity order under each scenario is studied.

In this paper, we investigate the use of time-frequency analysis for improvement of spectrum sensing in cognitive radios and exploit compressed sensing (sampling) to reduce the extremely high sampling rate of signal in time-frequency plane. We suggest a non-parametric spectrum sensing technique similar to energy detection utilizing time-frequency analysis to generally compromise between accuracy and sensing time, though the computational cost is significantly increased. As the representation of signals on the time-frequency plane is intrinsically sparse, thus we use the compressed sensing to achieve a significant reduction in the number of measurements. We propose using of different time-frequency representation such as short time Fourier transform, wavelet, Wigner-Ville and pseudo Wigner-Ville distribution in conduction of compressed sampling technique. The simulation results evaluate the performance of the proposed time-frequency compressed detectors compared to other time-frequency and energy detectors using basis pursuit and Bayesian compressive sensing reconstruction algorithms for AWGN and also Rayleigh and Rician fading channels.

In this study, the authors address the problem of multiple antenna spectrum sensing in cognitive radios by exploiting the prior information about unknown parameters. Specifically, under assumption that unknown parameters are random with the given proper distributions, the authors use a Bayesian generalised likelihood ratio test (B‐GLRT) in order to derive the corresponding detectors for three different scenarios: (i) only the channel gains are unknown to the secondary user (SU), (ii) only the noise variance is unknown to the SU, (iii) both the channel gains and noise variance are unknown to the SU. For the first and third scenarios, the authors use the iterative expectation maximisation algorithm for estimation of unknown parameters and the authors derive their convergence rate. It is shown that the proposed B‐GLRT detectors have low complexity and besides are optimal even under the finite number of samples. The simulation results demonstrate that the proposed B‐GLRT detectors have an acceptable performance even under the finite number of samples and also outperform the related recently proposed detectors for multiple antenna spectrum sensing.

In this study, the authors consider the problem of multiple antenna spectrum sensing by exploiting the prior information about unknown parameters. Under assumption that additional statistical side information is available about unknown parameters, a novel framework and some detectors are proposed, which are optimal for finite number of received samples. locally most powerful, average likelihood ratio and the novel generalised likelihood ratio detectors are calculated for the different practical conditions in wireless channels. In addition, the analytical performance evaluation for proposed detectors is provided whenever possible. The simulation and analytical results show that the a‐priori information can be used for better spectrum sensing using finite number of samples.

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 this paper, we consider the problem of multiple antenna spectrum sensing in Cognitive Radios (CR) when some or all parameters are unknown. The Generalized Likelihood Ratio (GLR) test is the convectional method to solve the composite hypothesis testing problem in which the detection and estimation sub-problems are considered separately. In this paper, the multiple spectrum sensing problem is solved using a novel approach in which the the detection and estimation sub-problems considered jointly and the resulted detectors are optimal under finite number of samples. We assume some additional side statistical information is available for unknown parameters and as theoretical results of the novel GLR detector imply, the optimal way of using this additional side information, is to use them in the Maximum A-Posteriori (MAP) or Minimum Mean Square Error (MMSE) estimation of unknown parameters for constructing the GLR tests. The simulation results show that the newly derived GLR detectors outperform traditional GLR detectors significantly. Also for the situation that all parameters are unknown the proposed detectors are compared with the Energy Detector (ED), where the simulation results indicate that the proposed detectors not only have significantly better performance, but also are robust to practical noise mismatch.

In this paper, we investigate the use of multiple antennas at the cognitive radio receiver for improved wideband spectrum sensing and exploit compressed sensing to reduce the extremely high sampling rate of wideband signal. Using a priori statistical knowledge about the non-zero coefficients of the sparse primary user signal, we propose two novel multiband compressive detectors to detect the presence of primary user signal within frequency subbands while the occupancies of these are correlated. Therefore, we model the occupancy of each subband as a binary random variable and assume subband energies are linearly combined. The simulation results evaluate the performance of the proposed compressive detectors compared to existing compressive detectors and the traditional energy detectors.

In this paper, we consider the problem of wideband spectrum sensing by using the correlation among the observation samples in different subbands. The Primary User (PU) signal samples in occupied subbands are assumed to be zero-mean correlated Gaussian random variables and additive noise is modeled as colored zero-mean Gaussian random variables independent of the PU signal. It is also assumed that there is at least a minimum given number of subbands that are vacant of PU signals. First we derive the optimal detector and the Generalized Likelihood Ratio (GLR) detector for the case that the covariance matrix of PUs signal samples is unknown and the noise variance in the different subbands is known. Then, we propose an iterative algorithm for GLR test when both the covariance matrix of the PUs signal samples and the noise variances in the different subbands, are unknown. For analytical performance evaluation, we derive some closed-form expressions for detection and false alarm probabilities of the proposed detectors in low Signal to Noise Ratio (SNR) regime. The simulation results are further presented to compare the performance of the proposed detectors.

In this paper, we consider the problem of spectrum sensing by using multiple antenna in cognitive radios when the noise and the primary user signal are assumed as independent complex zero-mean Gaussian random signals. The optimal multiple antenna spectrum sensing detector needs to know the channel gains, noise variance, and primary user signal variance. In practice some or all of these parameters may be unknown, so we derive the generalized likelihood ratio (GLR) detectors under these circumstances. The proposed GLR detector, in which all the parameters are unknown, is a blind and invariant detector with a low computational complexity. We also analytically compute the missed detection and false alarm probabilities for the proposed GLR detectors. The simulation results provide the available traded-off in using multiple antenna techniques for spectrum sensing and illustrates the robustness of the proposed GLR detectors compared to the traditional energy detector when there is some uncertainty in the given noise variance.

In this paper, we divide a wide frequency range into multiple subbands and in each subband detect whether in a primary user (PU) is active or not. We assume that PU signal at each subband and the additive noise are white zero-mean independent Gaussian random processes with unknown variances. We also assume that at least a minimum given number of subbands is vacant of PU signal and propose an invariant generalized likelihood ratio (GLR) detector. The concept of the grouping of subbands allows faster spectrum sensing of a subset of subbands which may be occupied by a specific PU. Also, we evaluate trade-offs involved in the proposed algorithms by simulation.

The spectrum sensing of a wideband frequency range is studied by dividing it into multiple subbands. It is assumed that in each subband either a primary user (PU) is active or absent in a additive white Gaussian noise environment with an unknown variance. It is also assumed that at least a minimum given number of subbands are vacant of PUs. In this multiple interrelated hypothesis testing problem, the noise variance is estimated and a generalised likelihood ratio detector is proposed to identify possible spectrum holes at a secondary user (SU). Provided that it is known that a specific PU can occupy a subset of subbands simultaneously, a grouping algorithm which allows faster spectrum sensing is proposed. The collaboration of multiple SUs can also be considered in order to enhance the detection performance. The collaborative algorithms are compared in terms of the required exchange information among SUs in some collaboration methods. The simulation results show that the proposed detector outperforms the energy detector in the presence of noise variance mismatch above 2.3 dB. Some involved trade-offs in the spectrum sensing using the proposed detector are discussed.

This paper considers a cooperative space-frequency (SF) coded OFDM system and evaluates its performance over quasi-static frequency selective Rayleigh fading channels. An expression for exact outage error probability is derived and its tight closed form lower bound is presented. The tightness of the lower bound is demonstrated through the Monte Carlo simulations. The asymptotic analysis indicates that the proposed protocol achieves full spatial and frequency diversities available in the cooperative communication system. The theoretical analysis of the proposed space-frequency coded cooperation protocol is further confirmed by computer simulations.

Recent measurements suggest the possibility of sharing spectrum among different parties subject to interference-protection constraints. In order to enable access to an unused licensed spectrum, a secondary user has to monitor licensed bands and opportunistically transmit whenever no primary signal is detected. In this paper, we study spectrum-sharing between a primary licensee and a group of secondary users. The structure of an asymptotically optimum detector based on the measurements of all secondary users is derived and the effect of the quantization error in such a system is evaluated. The results show the superiority of the proposed detector to other schemes.

A cooperative space–frequency (SF) coded orthogonal frequency division multiplexing system is considered and its performance over quasi-static frequency-selective Rayleigh fading channels is evaluated. An expression for exact outage error probability is derived and its tight closed-form lower bound is presented. The tightness of the lower bound is demonstrated through Monte Carlo simulation. Asymptotic analysis indicates that the proposed protocol achieves full spatial and frequency diversities available in the cooperative communication system. The theoretical analysis of the proposed SF coded cooperation protocol is further confirmed by computer simulation using a previously introduced SF block code that is capable of achieving full spatial and frequency diversities.

Traditionally, the frequency spectrum is licensed to users by government agencies in a fixed manner where the licensee has exclusive right to access the allocated band. However, with increasing demand for the spectrum and scarcity of vacant bands, a spectrum policy reform seems inevitable. Meanwhile, recent measurements suggest the possibility of sharing spectrum among different parties subject to interference-protection constraints. In order to enable access to an unused licensed spectrum, a secondary user has to monitor licensed bands and opportunistically transmit whenever no primary signal is detected. Spectrum-sharing between a primary licensee and a group of secondary users has been studied. The structure of an asymptotically optimum detector based on the measurements of all secondary users is derived and the effect of the quantisation error in such a system is evaluated. Also, it is shown that by using the proposed detector in a sequential detection structure, it is possible to shorten the decision time needed by the detector. The results show the superiority of the proposed detector to other schemes.