Weighted Energy Detection Research Articles

Intelligent Reflecting Surface-Aided Spectrum Sensing for Cognitive Radio

Spectrum sensing is a key enabling technique for cognitive radio (CR), which provides essential information on the spectrum availability. However, due to severe wireless channel fading and path loss, the primary user (PU) signals received at the CR or secondary user (SU) can be practically too weak for reliable detection. To tackle this issue, we consider in this letter a new intelligent reflecting surface (IRS)-aided spectrum sensing scheme for CR, by exploiting the large aperture and passive beamforming gains of IRS to boost the PU signal strength received at the SU to facilitate its spectrum sensing. Specifically, by dynamically changing the IRS reflection over time according to a given codebook, its reflected signal power varies substantially at the SU, which is utilized for opportunistic signal detection. Furthermore, we propose a weighted energy detection method by combining the received signal power values over different IRS reflections, which significantly improves the detection performance. Simulation results validate the performance gain of the proposed IRS-aided spectrum sensing scheme, as compared to different benchmark schemes.

Improved Spectrum Sensing Schemes Using Prewhitening and Weights Under Spatially Correlated Noise

Conventional spectrum sensing (SS) schemes in a multiantenna cognitive radio utilize matrix-inverse based prewhitening to decorrelate the spatially correlated received signals. However, the improved SS schemes can be proposed by efficiently exploiting the spatial correlation information. In this paper, two detectors—weighted cross-correlation absolute value detector (WCCAVD) and weighted energy detector (WED), are proposed by exploiting the spatial correlation of noise. It is shown that in the presence of high spatial correlation and low signal-to-noise ratio, the proposed WCCAVD and WED outperform the conventional cross-correlation absolute value detector and energy detector (ED), respectively, by more than 1 dB. For the spatial correlation below 0.5, both the proposed detectors are shown to have comparable performance with respect to the corresponding conventional detectors. The analytical expressions for the decision threshold, the false-alarm and the detection probabilities of the proposed detectors are derived. The analytical results are validated by Monte-Carlo simulations.

Double Threshold Weighted Energy Detection for Asynchronous PU Activities in the Presence of Noise Uncertainty

Cognitive Heterogeneous Network (CHN) is a network that provides heterogeneous services on a cognitive radio basis. The existence of such heterogeneous services introduces a syndrome of concurrent problems. Handling each problem separately may result in more consequences. This paper aims to mitigate the combined effects of noise uncertainty and asynchronous primary user occurrence within the sensing interval of the secondary user in cognitive heterogeneous network. The paper proposes a double threshold energy detection method based on unequal scale sampling criteria. An analytic formula is deduced for both thresholds employing the power scaling criteria. Simulation results show a complete agreement with the performance expectations in terms of increased probability of detection while keeping the false alarm probability within the required limits, which is guaranteed by plotting the ROC curves. Moreover, an optimal threshold is formulated to minimize the overall detection errors, that led to further enhancements in performance as illustrated also via simulations.

Primary signal detection algorithms for spectrum sensing at low SNR over fading channels in cognitive radio

The generalized detector (GD) can be implemented at the low signal-to-noise ratio (SNR) in cognitive radio (CR) systems to improve the spectrum sensing performance under correlated antenna array elements. The weighted GD (WGD) and the generalized likelihood ratio test for GD (GLRT-GD) are proposed to be used for coarse spectrum sensing when the noise power is known and unknown, respectively. The GD optimal detection threshold is defined based on the minimum probability of error criterion for various fading channels, namely, the additive white Gaussian noise (AWGN), Nakagami-m, and Rayleigh fading channels. The performance of the proposed algorithms are compared with the spectrum sensing performance of the energy detector (ED), weighted ED (WED), maximum-minimum eigenvalue (MME) detector, generalized likelihood ratio test for ED (GLRT-ED), matched filter (MF), arithmetic to geometric mean (AGM) detector, scaled largest eigenvalue (SLE) detector, moment based detector (MBD), covariance based detector (CBD), and others. The simulation results demonstrate superiority in the spectrum sensing performance of the proposed algorithms in comparison with the above-mentioned detectors. For example, the GLRT-GD achieves the SNR gain equal to 1.2 dB, 4.0 dB, and 4.5 dB in comparison with GLRT-ED, MME, and GM detectors, respectively, at the probability of false alarm PFA=0.1. The WGD and GLRT-GD implementation allows us to achieve a considerable spectrum sensing performance improvement at small number of samples under the low SNR and the correlated antenna array elements.

A New Method of Spectrum Sensing in Cognitive Radio for Dynamic and Randomly Modelled Primary Users

In this study, a new spectrum sensing method for cognitive radio (CR) is proposed for primary user (PU) signals which are modelled as random with unknown power. It is also assumed that, being dynamic, the PU might exhibit multiple status changes in the channel within the sensing period. As a novel approach for detecting dynamic and randomly modelled PUs, we propose a cumulative sum based weighted energy detector (CuS-WED) that only utilizes the samples between the last status change point (LSCP) of the PU and the end of the sensing period contrary to using all the observed data samples in the sensing period. It is shown that the proposed sensing strategy provides noticeable performance improvement in detecting dynamic and randomly modelled PUs. Probability of detection performance of the proposed sensing scheme is investigated for strictly one, at most two, and at most three status changes of the PU. Based on simulation results, it is concluded that the proposed method performs better than the other existing techniques, especially for low probability of false alarm values.

전자전 미약신호 환경에서 미상 위협 신호원의 검출 성능 향상을 위한 가중 에너지 검출 기법

위협의 핵심정보 추출을 위해 활용되는 전자전 지원시스템은 경로손실 등의 환경 상 제약으로 인해 수신신호의 전력이 낮은 미약신호 환경 하에서 운용될 수 있다. 이러한 상황에서 위협신호를 신속하고 정확하게 검출하기 위해서는 기존의 단일 샘플 에너지 검출 기법이 아닌 수신 신호의 전체 에너지를 최대한 활용함으로써 검출 성능을 향상시키는 기법이 요구된다. 하지만 신호원에 대한 사전정보가 존재하지 않는 전자전 환경에서 신호의 전체 에너지를 활용하기 위해서는 모든 신호원의 길이를 고려할 수 있도록 크기가 다양한 다수의 윈도우를 가지는 검출기를 설계해야 하므로 연산량이 과도하게 증가하는 문제점이 존재한다. 이러한 문제를 현실적으로 해결하기 위해 적은 수의 대표 윈도우를 사용하여 윈도우의 수를 줄이는 방법이 활용되지만 결과적으로 하나의 윈도우가 일정 구간의 미상신호를 고려해야 하므로 수신되는 신호의 길이와 검출기의 윈도우 크기의 불일치로 인해 검출 성능이 저하되는 문제점이 여전히 존재한다. 따라서 본 논문에서는 수신신호의 길이와 검출기의 윈도우 길이가 일치하지 않을 경우의 성능 저하를 분석하고, 성능향상이 가능한 상황인 수신신호의 길이가 검출기의 윈도우 길이보다 작은 경우에는 검출성능을 향상시킬 수 있고, 이외의 상황에는 기존의 에너지 검출기의 성능과 유사한 가중에너지 검출기를 제안하고 그 성능을 분석한다. Electronic warfare systems for extracting information of the threat signals can be employed under the circumstance where the power of the received signal is weak. To precisely and rapidly detect the threat signals, it is required to use methods exploiting whole energy of the received signals instead of conventional methods using a single received signal input. To utilize the whole energy, numerous sizes of windows need to be implemented in a detector for dealing with all possible unknown length of the received signal because it is assumed that there is no preliminary information of the uncooperative signals. However, this grid search method requires too large computational complexity to be practically implemented. In order to resolve this complexity problem, an approach that reduces the number of windows by selecting the smaller number of representative windows can be considered. However, each representative window in this approach needs to cover a certain amount of interval divided from the considering range. Consequently, the discordance between the length of the received signal and the window sizes results in degradation of the detection performance. Therefore, we propose the weighted energy detector which results in improved detection performance comparing with the conventional energy detector under circumstance where the window size is smaller than the length of the received signal. In addition, it is shown that the proposed method exhibits the same performance under other circumstances.

Two Stage Detector Comprising of Weighted-ED and Correlated-GLRT for Cognitive Radio Networks

Cognitive radio network is one of the prime solutions of band width crises problem. To resolve band width crises firstly we have to sense primary user (PU) licensed signal. To sense PU signal there are various sensing techniques which have been proposed by researchers, but each of them are having some limitations. In this paper we present a two stage detector for spectrum sensing (SS) in cognitive radio (CR). It comprises of a weighted-energy detector (W-ED) and a correlated-generalized likelihood ratio test (CGLRT). The first stage detects the energy and then if required, the C-GLRT makes the final decision in second stage. Performance of the proposed two stage detector is compared with the existing energy detector (ED), generalized likelihood ratio test (GLRT), and adaptive spectrum Sensing (ASS) detectors. The numerical results show that proposed sensing technique has better detection performance and require less sensing time.

Matched signal detection on graphs: Theory and application to brain imaging data classification

Motivated by recent progress in signal processing on graphs, we have developed a matched signal detection (MSD) theory for signals with intrinsic structures described by weighted graphs. First, we regard graph Laplacian eigenvalues as frequencies of graph-signals and assume that the signal is in a subspace spanned by the first few graph Laplacian eigenvectors associated with lower eigenvalues. The conventional matched subspace detector can be applied to this case. Furthermore, we study signals that may not merely live in a subspace. Concretely, we consider signals with bounded variation on graphs and more general signals that are randomly drawn from a prior distribution. For bounded variation signals, the test is a weighted energy detector. For the random signals, the test statistic is the difference of signal variations on associated graphs, if a degenerate Gaussian distribution specified by the graph Laplacian is adopted. We evaluate the effectiveness of the MSD on graphs both with simulated and real data sets. Specifically, we apply MSD to the brain imaging data classification problem of Alzheimer's disease (AD) based on two independent data sets: 1) positron emission tomography data with Pittsburgh compound-B tracer of 30 AD and 40 normal control (NC) subjects, and 2) resting-state functional magnetic resonance imaging (R-fMRI) data of 30 early mild cognitive impairment and 20 NC subjects. Our results demonstrate that the MSD approach is able to outperform the traditional methods and help detect AD at an early stage, probably due to the success of exploiting the manifold structure of the data.

A Survey on Various Coherent and Non-coherent IR-UWB Receivers

The recent developments in radio technologies, paves its way to impulse radio (IR) ultra-wideband (UWB) communication, which is used for low power, short range and high bandwidth communication, thereby exploiting a large portion of radio spectrum. In this paper, a brief review of the work done by various researchers on coherent and non-coherent IR-UWB receivers has been analysed, based on their bit error rate (BER) performances, as well as pros and cons of using these receivers. An in depth study on the receivers concludes that, non-coherent IR-UWB receiver is preferred over its counterpart coherent IR-UWB receiver even though it comes at the expense of poor BER performance. The simulation results prove that, though the performances are same, the low complexity of energy detector (ED) receivers gives an edge over the autocorrelation receivers. Further, ED receiver suffers from noise, which paves way to using weighted ED (WED) receiver. The superiority of WED receivers over all the other non-coherent UWB receivers is further confirmed by the simulation performed in AWGN and IEEE 802.15.4a UWB channels. It can also be concluded from the review that, some special receivers such as generalized likelihood ratio test, multi-symbol differential detector and decision feedback differential transmitted reference, when clubbed with UWB systems, lead to further improvement in BER performance.

Elaborate analysis and design of filter-bank-based sensing for wideband cognitive radios

The successful operation of a cognitive radio system strongly depends on its ability to sense the radio environment. With the use of spectrum sensing algorithms, the cognitive radio is required to detect co-existing licensed primary transmissions and to protect them from interference. This paper focuses on filter-bank-based sensing and provides a solid theoretical background for the design of these detectors. Optimum detectors based on the Neyman-Pearson theorem are developed for uniform discrete Fourier transform (DFT) and modified DFT filter banks with root-Nyquist filters. The proposed sensing framework does not require frequency alignment between the filter bank of the sensor and the primary signal. Each wideband primary channel is spanned and monitored by several sensor subchannels that analyse it in narrowband signals. Filter-bank-based sensing is proved to be robust and efficient under coloured noise. Moreover, the performance of the weighted energy detector as a sensing technique is evaluated. Finally, based on the Locally Most Powerful and the Generalized Likelihood Ratio test, real-world sensing algorithms that do not require a priori knowledge are proposed and tested.

Random Correlation-Based Receiver for Impulse Radio Communications in UWB Channels

Impulse radio is a low-complexity ultra-wideband system which is suitable for highly dispersive multipath channel. In this paper, we propose a random correlation-based receiver for impulse radio communications. The proposed receiver correlates the received pilot symbols with the randomly generated base functions according to channel statistics and forms the detection template by combining several base functions which have larger correlation coefficients with the pilot symbols. The proposed receiver demodulates the received signal by employing a symbol rate sampling. Computer simulation results have shown that the proposed receiver outperforms the conventional correlator-based receiver, compressive sensing correlator-based receiver, weighted energy detector and autocorrelation receiver.

Gaussian Target Detection in Multipath Clutter With Single-Antenna Time Reversal

We derive the time reversal likelihood ratio optimal detector for a target in stationary random multipath clutter, extending prior work where both are assumed to be deterministic. We suppress the stationary clutter through adaptive power allocation. We show that the Time Reversal Likelihood Ratio Test performs much better than the conventional Weighted Energy Detector. We provide analytical results on the performance of the time reversal detector by approximating it with the Time Reversal Linear Quadratic detector, which models the received signal as a Complex Gaussian. Our simulations show that the Linear Quadratic detector is a good approximation to the Time Reversal Likelihood Ratio Test and that both show a significant improvement of 4 to 7 dB effective signal-to-noise ratio gain over the Weighted Energy Detector. This gain is dependent on the target and clutter power spectral densities.

Cooperative Spectrum Sensing over Mixture-Nakagami Channels

We propose novel detectors for cooperative spectrum sensing in mixture-Nakagami fading channels, namely 1) the Neyman-Pearson Detector (NPD), 2) a Locally Optimum Detector for weak signals that exploit the correlation between the observations and transmitted signals, 3) a weak signal detector for unknown parameters and 4) two Generalized-Likelihood-Ratio detectors (GLRDs) that exploit the received energies. They significantly outperform energy and cyclostationary detectors in practical scenarios. We also analyze the performance of the NPD and GLRD for unknown transmitted signal over Rayleigh channels, where they reduce to a linear weighted-correlator and a weighted-energy detector respectively.

Optimal BER-Balanced Combining for Weighted Energy Detection of UWB OOK Signals

For the demodulation of ultra-wideband (UWB) on-off keying (OOK) signals, some weighted energy detection (WED) schemes have been proposed. However, the schemes available suffer from unbalanced bit error rates (BERs) for bits "0" and "1". Therefore, in this letter we will address the optimal BER-balanced WED. Specifically, we incorporate the BER balance requirement into the WED formulation and obtain the optimal weighting vector by solving a nonlinear convex optimization problem. To bypass the computational complexity required by the convex optimization, we derive a simple but near-optimal weighting vector. Simulations illustrate that the proposed BER-balanced WED schemes can achieve almost the same BER performance as the maximum likelihood (ML) demodulator, which is optimal in terms of BER performance but produces unbalanced BERs for different bits.

Simplified structure of weighted energy detector for UWB-IR systems

Proposed is a simplified weighted energy detector (WED) receiver structure for ultra-wideband impulse radio (UWB-IR) systems. The performance improvement of the WED comes with the cost of increased hardware complexity when performing linear combination over the fractional energies. In this proposed scheme, multiplication for the combination process is replaced with a register-shifting operation based on simplified weighting coefficients. Simulations show that the proposed WED structure achieves the same BER performance as a multiplier-using WED structure with much lower hardware complexity.

Weighted Energy Detection for Noncoherent Ultra-Wideband Receiver Design

For ultra-wideband (UWB) impulse radios, noncoherent energy detectors are motivated for their simple circuitry and effective capture of multipath energy. A major performance-degrading factor in energy detection is the noise floor, which is aggravated for low-duty-cycle UWB signals with a large time-bandwidth product. In this paper, weighted energy detection (WED) techniques are developed for effective noise suppression. The received signal is processed by a set of parallel integrators, each corresponding to a different integration time-window within a symbol period. The outputs of these integrators are weighted and linearly combined to generate decision statistics, while the weights are determined by the signal power collected from the corresponding integrators to improve the effective signal to noise ratio. The WED principle is applied to all phases of receiver processing, including signal detection, timing synchronization and data demodulation. For each phase, the optimal linear detector parameters, including decision thresholds and weighting coefficients, are derived analytically. Simulations show that the proposed noncoherent WED receiver enhances the bit-error-rate performance compared to conventional energy detectors.

Spectrum sensing for cognitive radio networks with correlated multiple antennas

A weighted energy detector and correlated-generalised likelihood ratio test detector are proposed for spectrum sensing assisted with correlated multiple antennas in cognitive radio. Simulations are presented to show that the detection performance of the proposed algorithms is improved significantly in the correlated antennas scenario.

Blind combining for weighted energy detection of UWB signals

Two blind schemes for weighted energy detection of ultra-wideband signals are proposed. The first yields the estimates of transmitted symbols by using the generalised likelihood ratio test (GLRT) paradigm, which is referred to as GLRT-WED. The second scheme first estimates the weighting vector of the WED detector by means of singular value decomposition (SVD), and then uses the obtained vector for data detection, which is referred to as SVD-WED. Both GLRT-WED and SVD-WED can achieve a performance very near to that of the ideal WED, while SVD-WED is significantly less complex than GLRT-WED.

Weighted Acquisition of UWB Signals Based on Energy Detection

Due to the low complexity and cost characteristics of ultra-wideband (UWB) systems, a weighted acquisition algorithm based on energy detection is proposed in this paper. This method is divided into two steps to acquire the direct path (DP) component. Firstly, weighted energy detection is applied to determine which energy block the DP lies in by generalized likelihood ratio test (GLRT). A sub-optimal weighted vector is obtained, by which the closed form of detection performance is proposed. In the second step, the precise position of DP within the detected energy block is obtained by the statistical characteristics of the channel energy distributions. Key parameters that affect acquisition performance are studied by analytical and numerical methods. Simulations and experiments are carried out for performance and complexity comparison with traditional ones. The results show that weighted acquisition achieves better performance under relative low complexity conditions.

Performance analysis of cumulant‐based detection of non‐gaussian signals

We consider the problem of detecting a linear stationary non-Gaussian signal in additive Gaussian noise. Detection is assumed to be done in two steps : first a selected set of sample cumulants and/or moments of the data is computed ; next a detection statistic based on these sample moments/cumulants is evaluated. Three types of detectors are considered : a likelihood ratio (optimal) detector, a weighted energy detector and a simple energy detector. The paper provides asymptotic performance analysis of these detectors for MA and ARMA signal and noise models. A simple example is worked out in detail.