GLRT based cooperative spectrum sensing with soft combination in heterogeneous networks

A Cognitive radio ad hoc network (CRAHN) is an ad hoc network of primary user (PU) nodes and secondaryuser (SU) nodes, wherein the SU nodes use the licensed channels of the PU nodes when not in use. As theavailability of the PU channels fluctuates with time, communication topologies spanning the SU nodes have tobe dynamically and frequently reconfigured. Given the complete knowledge of the availability of the PUchannels, we propose a generic benchmarking algorithm that determines a sequence of stable communicationtopologies spanning all of the SU nodes such that the number of transitions from one instance of the topology toanother is the global minimum. At any time instant t when we need a stable communication topology spanningthe entire network, we look for the largest value of k such that the intersection of the static SU graphs from timeinstants t to t+k, defined as the mobile graph Gt...t+k(SU) = Gt(SU) ? Gt+1(SU) ? .... ? Gt+k(SU), is connected andthat the mobile graph Gt...t+k+1(SU) is not connected. We repeat the above procedure for the entire network sessionto determine a sequence of longest-living instances of the mobile graphs and the corresponding instances of thecommunication topology of interest (say a shortest path tree rooted at a source SU node) such that the number oftopology transitions is the minimum. We prove the theoretical correctness of the algorithm and evaluate itseffectiveness by implementing it to determine a sequence of shortest path trees of the maximum lifetime.

Adopting cognitive radios (CRs) having multiple antennas in blind non-cooperative and cooperative spectrum sensing (CSS) under fading channels has gained attention due to higher detection performances provided by the spatial diversity gain of multi-sensors in different geographical locations and lower complexity, respectively. However, most studies do not consider sensing scenarios of more practical significance: for example, sometimes adopting only uncalibrated antenna arrays and sometimes only correlated signals at antenna arrays of CRs, but almost always, none of these impairments. Therefore, this paper studies these combined impairments on the performances of two blind techniques in centralized CSS with decision fusion (DF) and data fusion for different numbers of CRs and antennas per CR, both under frequency selective fading channels. One is the circular folding cooperative power spectral density split cancellation (CFCPSC), and the other is the generalized likelihood ratio test (GLRT). Extensive numerical results show, for instance, that with sample fusion (SF) and calibrated antennas, GLRT outperforms CFCPSC independently of correlation, numbers of antennas, or CRs. However, uncalibrated antennas severely penalize GLRT while surprisingly benefiting CFCPSC. Correlation is detrimental to GLRT and CFCPSC with SF but may help CFCPSC in DF and GLRT in DF or eigenvalue fusion. Generally, CFCPSC outperforms GLRT.

Two methods are proposed to improve error rate performance for reception of short packets with quadrature amplitude modulation (QAM). Both approaches are based on the same idea as generalized likelihood ratio test (GLRT) but have reduced computational cost that scales linearly in both packet length and signal constellation size. The first method, sequential GLRT, is based on demodulation of QAM symbols sequentially using GLRT decision rule and treating previously demodulated symbols as known data. The second method, partial GLRT, uses partial search over a portion of packet while remaining data symbols are demodulated with hard decisions, and Euclidean distance between these decisions and received signal samples is used as a decision metric to improve detection reliability. Computer simulation results showed that partial GLRT provides bit error rate (BER) very close to achievable limit even for very short QAM packets where the number of data symbols is equal to the QAM constellation size. The loss at high signal-to-noise ratio is about 0.3 dB for 16-QAM and about 0.085 dB for 64-QAM. For sequential GLRT, BER performance is worse, but this method has lower computational cost and can be useful for choosing a suitable trade-off between BER performance and complexity.

We consider the problem of detecting a primary user in a cognitive radio network by employing multiple antennas at the cognitive receiver. In vehicular applications, cognitive radios typically transit regions with differing densities of primary users. Therefore, speed of detection is key, so detection based on a small number of samples is particularly advantageous for vehicular applications. Without assuming any prior knowledge of the primary user's signaling scheme, the channels between the primary user and the cognitive user, and the variance of the noise seen at the cognitive user, a generalized likelihood ratio test (GLRT) is developed to detect the presence/absence of the primary user. Asymptotic performance analysis for the proposed GLRT is also presented. Performance comparison between the proposed GLRT and other existing methods such as the energy detector and several eigenvalue-based methods under the condition of unknown or inaccurately known noise variance is provided. Our results show that the proposed GLRT exhibits better performance than other existing techniques, especially when the number of samples is small, which is particularly critical in vehicular applications.

This paper addresses jamming attack detection issue in a millimeter Wave (mmWave) massive MIMO system under noise uncertainty. Specifically, we apply the generalized likelihood ratio test (GLRT) to develop a one-step GLRT scheme for detecting jamming attack under the homogeneous and partially homogeneous noise environments, and exploit training data to estimate the unknown noise statistical information and replace it with resulting estimation in deriving GLRT procedure to obtain adaptive GLRT detection scheme. We also design a two-step GLRT scheme, where we first assume the unknown noise statistical information is known, and derive GLRT based on test data, and then replace it by the sample covariance matrix based on training data only to achieve a fully adaptive jamming attack detector. With the help of statistical signal subspace analysis and composite hypothesis testing theories, we further examine the statistical distributions of the jamming attack detection schemes and present the closed-form expressions of false alarm and detection probabilities for the proposed schemes under different noise environments. Finally, we implement extensive simulations to validate the theoretical results and evaluate the detection efficiency under various parameters.

The adaptive matched filter, like many other adaptive detection schemes, uses in its test statistic the data under test whitened by the sample covariance matrix S of the training samples. Actually, it is a generalized likelihood ratio test (GLRT) based on the conditional (i.e., for given S) distribution of the adaptively whitened data. In this letter, we investigate detection of a Gaussian rank-one signal using the marginal (unconditional) distribution of the adaptively whitened data. A first contribution is to derive the latter and to show that it only depends on a scalar parameter, namely the signal to noise ratio. Then, a GLRT is formulated from this unconditional distribution and shown to have the constant false alarm rate property. We show that it bears close resemblance with the plain GLRT based on the whole data set (data under test and training samples). The new detector performs as well as the plain GLRT and even better with multiple cells under test and low training sample support.

Although cooperative spectrum sensing improves sensing accuracy in cognitive radio systems, additional reporting time is required to report the local test statistics until the existence of the primary user is verified, resulting in detection delay. To reduce the reporting time, we propose controlling the reporting order of the local test statistics, so that the local test statistics are reported to the fusion center in descending order of magnitude. As a result, the global test statistic at the fusion center accumulates faster than the case without reporting order control, reducing the reporting time needed to determine when the threshold for detecting the primary user has been exceeded. To do this, we propose, based on distributed access control, a combination of order identification, which finds the order of the local test statistic, and transmission probability calculation according to the identified order. Simulation results show that the proposed reporting order control significantly reduces the reporting time for detecting the primary user.

We propose a blind method to detect interference in GNSS signals whereby the algorithms do not require knowledge of the interference or channel noise features. A sample covariance matrix is constructed from the received signal and its eigenvalues are computed. The generalized likelihood ratio test (GLRT) and the condition number test (CNT) are developed and compared in the detection of sinusoidal and chirp jamming signals. A computationally-efficient decision threshold is developed for the CNT.

In this paper, a novel two-stage coexistence detection scheme for 60-GHz millimeter-wave (mmW) WLAN/WPAN is proposed. Specifically, orthogonal frequency division multiplexing (OFDM) is employed as the transmission mode. The energy detector (ED) is used at the first stage to perform coarse scan. If required, the arithmetic mean to geometric mean detector (AGD) is invoked at the second stage to refine the overall detection performance. The second-stage AGD does not rely on any prior information of the channel gains, transmitted signal pattern or noise variance. Instead, it investigates the inherent correlation of the received data samples and is derived strictly following the generalized likelihood ratio test (GLRT) paradigm.

Spectrum Sensing (SS) is expected to play a crucial role in forthcoming 6G Cognitive Radio Networks (CRNs), where unlicensed users will be able to dynamically access the spectrum and perform opportunistic transmissions without generating interference for licensed users. In this work, we investigate multiple-antenna SS techniques by analyzing the performance of several widely used detection schemes-namely, Roy's Largest Root Test (RLRT), the Generalized Likelihood Ratio Test (GLRT), the Eigenvalue Ratio Detector (ERD), and the Energy Detector (ED)-under varying false-alarm probabilities and signal-to-noise ratios (SNRs). We assume there are a fixed number of sensors at the secondary-user receiver, namely, four. To evaluate the behavior of these detectors in realistic conditions, we developed a software-defined radio (SDR) testbed using Universal Software Radio Peripherals (USRPs), enabling both primary-user signal transmission and secondary-user data acquisition. The experimental results, illustrated through Receiver Operating Characteristic (ROC) and performance curves, are compared with simulation outcomes. The analysis is complemented by a detailed state-of-the-art listing of the available analytical characterizations of the false-alarm probabilities for the considered SS schemes. In particular, the GLRT false-alarm probability, previously unavailable in explicit form for a four-antenna equipped receiver, is computed as well. These results validate the superior detection capability of RLRT over the other schemes tested, confirming its effectiveness not only in theoretical analysis but also in practical SDR-based implementations.

To identify the user’s presence by using non-cooperative detection methods in the Cognitive Radio (CR), networks used are Energy Detection (ED), Matched Filter Detection (MFD), and Cyclostationary Feature Detection (CFD). The signal power threshold is the critical parameter to identify whether the user is present or absent in the spectrum. In the literature, various authors proposed their research on spectrum sensing using CFD with static and predefined dynamic thresholds. In this paper, authors proposed the novel CFD with inverse covariance approach method for dynamic threshold estimated by using Generalized Likelihood Ratio Test (GLRT) and Neyman–Pearson (NP) observer detection criteria. The results show the performance of the proposed method as the probability of detection (PD) is increased and the probabilities of false alarm (Pfa) and missed detection (Pmd) have been reduced when compared with the existing methods. The results are simulated using Matlab software and the results are analyzed among the three parameters.

In this paper, we consider the detection of orthogonal frequency-division multiplexing (OFDM) signals under frequency-selective fading channels for cognitive radio. First, we propose a new decision statistic for the signal detection based on the special feature-cyclic prefix embedded in OFDM signals. The probability distribution function (pdf) of the statistic is derived under the hypothesis of signal presence or absence. Then, the generalized likelihood ratio test (LRT) and the pdf's under the two hypotheses are derived to obtain the theoretical false-alarm rate (FAR) and the probability of detection (PD). After that, a suboptimal test is proposed to resolve the interference/noise uncertainty problems met for many detection algorithms such as the energy detection in practice. The theoretical FAR and PD are derived, and the theoretical threshold for any given FAR according to Neyman-Pearson criterion is accordingly given. Finally, extensive simulations are performed to confirm the derived theoretical results and demonstrate the promising detection.

In a cognitive radio ad hoc network (CRAHN), the secondary user node has a restricted range for sensing other users within the network. To enhance the performance of nodes (secondary and primary users) in a CRAHN, the problem of limited channel sensing range and interference should be reduced which would enable the simultaneous transmission of primary and secondary users. In this paper, a cooperative MAC (medium access control) protocol is proposed—MLCP, which exploits the concept of multichannel network, for obtaining high system throughput. The main purpose of the proposed protocol is to introduce cooperation among secondary users, using the common control channel. The presence of the primary user is detected and the information is shared among neighbouring secondary users within the network proactively. This protocol is simulated using Network Simulator 2 and analysed with respect to system throughput, packet delivery ratio, and end-to-end delay experienced by primary users. The performance of the proposed protocol is also compared with existing cooperative MAC protocol and non-cooperative MAC protocol network scenario.

The authors propose a computationally efficient spectrum sensing solution for an orthogonal frequency division multiplexing (OFDM) signal in a frequency-selective fading channel with additive white Gaussian noise (AWGN). The authors' assumption is that the data symbols, channel coefficients and the noise variance are all unknown. The nature of the problem leads them to find an invariant detector. The optimum one is uniformly most powerful invariant (UMPI); their effort shows that this test does not exist, as the final decision statistic depends on some unknown parameters; although, they derive the most powerful invariant (MPI) detector, implanting these parameters, to provide an upper bound for invariant detectors. Instead, the authors develop the generalised likelihood ratio test (GLRT), substituting the unknown parameters by their maximum likelihood (ML) estimates in the Neyman–Pearson likelihood ratio. Furthermore, they propose a computationally efficient implementation of the resulting detector. Simulation results show a slight decrease in efficiency against gaining so much computational complexity (CC) improvement. For more comparisons they also derive the conventional energy detector, namely, generalised energy detector (GED) for a binary phase shift keying (BPSK) signal, to show the adequacy of the GLR detector performance which would be demonstrated by simulation results.

This paper analyzes the ergodic capacity in Rayeigh fading case and derives the signal-to-noise ratio (SNR) conditions for achieving the lower bound on ergodic capacity when nodes have different number of antennas, especially when the number of source antennas is larger than that of the destination's and the relay transmitter and receiver's. Then we verify it through simulations. Besides, simulation results show that with the increase of the number of the transmitter antennas, the lower bound on ergodic capacity raises linearly. Because the source node and the relay node can function as a "virtual" transmit antenna array when the relay node is located close to the source node.