Signal-to-noise Ratio Region Research Articles

Carrier frequency synchronization for WLAN systems based on MIMO-OFDM-IM

Multiple-input multiple-output (MIMO) orthogonal frequency division multiplexing (OFDM) with index modulation (IM) MIMO-OFDM-IM is a modulation technique that has garnered significant interest in recent times owing to its attraction of smooth transition to greener communications, enhancing efficiency in terms of energy, spectrum and compatibility to previous existing standards without the need for drastic changes in the physical layer. It is also proved that even though OFDM-IM provides better immunity to carrier frequency offset compared to traditional OFDM, the sensitivity to frequency error is still a major issue and it has to be resolved on priority basis to fully exploit the potential offered by the IM technique. In this work, we propose a novel non-data-aided algorithm that efficiently estimates and eliminates the offset present in the received signal. The algorithm has been analyzed for the MIMO-OFDM-IMbased wireless local area network system using the high throughput task group (TGn) MIMO channel models in the presence of AWGN. In addition, it is compared with the other popular blind algorithms based on both pilots and inactive data tones. The simulation plots depict a clear improvement of the new estimator over the existing methods not only in the low signal-to-noise ratio (SNR) regions but also in the high SNR region.

OFDM sensing based on deep learning

Detection of OFDM signals has received paramount interest especially for spectrum sensing in cognitive radios. We present a deep learning-based OFDM signal detection method, namely OFDM-DetNet, which combines feature learning and classifier in a single densely connected convolutional network (DenseNet) structure. OFDM-DetNet completes the detection in an end-to-end manner and utilizes the raw in-phase (I) and quadrature (Q) components of the received signal as the input and the detection result as the output. In addition, a confidence-based fusion method, namely OFDM-DetNet-F, is proposed to further improve the detection performance under low signal-to-noise ratio (SNR) scenarios without retraining the network. We construct three datasets with simulations and three datasets with real-world signals to evaluate the performance of the proposed methods. Both simulation results and experimental results indicate the superior performance of the proposed OFDM-DetNet compared with two deep learning-based OFDM detection methods, i.e., stacked autoencoder (SAE)-based method and covariance matrix (CM)-based detection method, in terms of probability of detection. Furthermore, the performance can be further improved with our proposed fusion scheme in low SNR region.

Performance analysis of IRS-assist wireless communication system with Alamouti transmit diversity scheme

Intelligent Reflecting Surface (IRS) is an emerging and growing technology for upcoming B5G and 6G wireless standards to design a perfect smart radio environment. In this article, we try to enhance the physical layer performance of the IRS- assist wireless communication system by taking some helpful performance metrics such as the outage probability (OP), average bit error rate (ABER), and average capacity of the system. Here, we assume our system is mainly affected by phase shift noise and quantization noise due to a discrete number of phase shifts of the IRS elements. The Alamouti STBC transmitter diversity technique is used to design the IRS-assist MISO wireless communication system to mitigate the effect of these kinds of noise, leading to the works’ novelty. We have derived a new and most accurate mathematical framework of PDF and MGF of the corresponding proposed IRS-assist wireless communication system with an end-to-end signal-to-noise ratio (SNR). Behind this statistical distribution, we expressed unique and more precise closed-form expressions of each performance metric under IRS assist SISO and MISO wireless link for the entire SNR region and it is significantly observed that the proposed MISO system provides better outcomes than the general SISO system. Furthermore, we calculate the diversity order of both configurations and it shows that the diversity order of the links is associated with the number of reflecting elements, phase noise error, and average magnitudes of the system. We also validated our analytical outcomes using Monte Carlo simulation results from MATLAB.

Hardware Impairment Estimation in NB-IoT: A Parallel Multitask Learning Method

Orthogonal frequency-division multiplexing (OFDM) is widely adopted in narrowband Internet of Things (NB-IoT). Nevertheless, the OFDM system is highly sensitive to the impairments caused by imperfect radio-frequency hardwares, which may greatly jeopardize the orthogonality between different subcarriers and degrade the demodulation performance. Though large efforts have been devoted to the hardware impairment estimation, however, it is highly challenging to jointly estimate multiple hardware impairments due to their coupling effects, especially for the NB-IoT systems which usually work in low signal-to-noise ratio (SNR) regions with limited computing and radio resources. In this article, we propose a parallel multitask learning (MTL) estimator to jointly estimate carrier frequency offset and in- and quadrature-phase imbalance in NB-IoT systems. Specifically, MTL is introduced to extract the inherent correlations between different hardware impairments so as to address the coupling effect and average the noise impact. In addition, we propose a parallel structure and a sliding window scheme to reduce the network complexity and decrease the estimation bias. Numerical results show that our proposed parallel MTL estimator can jointly estimate multiple hardware impairments with short pilot sequences, and outperform the conventional methods in terms of estimation accuracy and computation time in the typical SNR regions of the IoT devices.

Semantic Feature-Enhanced Graph ATtention Network for Radar Target Recognition in Heterogeneous Radar Network

Radar target recognition (RTR), as a key technique of intelligent radar systems, has been widely investigated. Accurate RTR at low signal-to-noise ratios (SNRs) still remains an open challenge. Considering that most existing methods are based on a single radar or the homogeneous radar network, we extend RTR to the heterogeneous radar network to improve the robustness of RTR, which uses the radar cross Section (RCS) signals at low SNRs by further exploiting the frequency-domain information. In this article, a Semantic Feature-Enhanced Graph ATtention Network (SFE-GAT) is proposed, which extracts semantic features from both the source and transform domains via the long short-term memory (LSTM) and GAT layers, then fuses them in the semantic space using an attention mechanism, and further distills higher-level semantic features using a GAT layer before classification. Extensive experiments are carried out to validate that the proposed SFE-GAT model can greatly improve the RTR accuracy in the low SNR region.

Parameter Estimation of a Noisy Real Sinusoid Using Quartic Polynomial Approach

An analytical polynomial expression, for accurate and computationally efficient frequency estimation of a single real sinusoid under Additive White Gaussian Noise (AWGN), is derived and proposed in this paper. The method, which can be easily adapted for real time frequency estimation, is based on transforming the frequency estimation problem as the solution of a fourth order (quartic) expressed as powers of a trigonometric function containing the unknown frequency. The coefficients of the quartic polynomial can be found using the complex magnitudes of three Discrete Fourier Transform (DFT) bins, centered at the maximum magnitude value of the DFT coefficients. Simulated results illustrate that, the performance of the proposed estimator has a mean squared-error (MSE) performance which is very close to the Cramer Rao Lower Bound (CRLB) for high signal-to-noise ratio (SNR) region, as well as close to previously published estimators in the low SNR region.

Rapid coherent Raman hyperspectral imaging based on delay-spectral focusing dual-comb method and deep learning algorithm.

Rapid coherent Raman hyperspectral imaging shows great promise for applications in sensing, medical diagnostics, and dynamic metabolism monitoring. However, the spectral acquisition speed of current multiplex coherent anti-Stokes Raman scattering (CARS) microscopy is generally limited by the spectrometer integration time, and as the detection speed increases, the signal-to-noise ratio (SNR) of single spectrum will decrease, leading to a terrible imaging quality. In this Letter, we report a dual-comb coherent Raman hyperspectral microscopy imaging system developed by integrating two approaches, a rapid delay-spectral focusing method and deep learning. The spectral refresh rate is exploited by focusing the relative delay scanning in the effective Raman excitation region, enabling a spectral acquisition speed of 36 kHz, ≈4 frames/s, for a pixel resolution of 95 × 95 pixels and a spectral bandwidth no less than 200 cm-1. To improve the spectral SNR and imaging quality, the deep learning models are designed for spectral preprocessing and automatic unsupervised feature extraction. In addition, by changing the relative delay focusing region of the comb pairs, the detected spectral wavenumber region can be flexibly tuned to the high SNR region of the spectrum.

Physical-Layer Authentication Based on Channel Phase Responses for Multi-Carriers Transmission

To defend against eavesdropping and spoofing attacks, the physical layer authentication (PLA) techniques utilize the unique attributes of channel or device for identifying attackers. Among these techniques, the PLA schemes based on channel phase responses use the secret key driven channel phase to authenticate the legitimate user, which have a better performance than the channel amplitude based schemes. However, the prior phase-based schemes only consider the perfect channel correlation coefficient between the two successive timeslots, which differs from the real scenarios. Meanwhile, the closed-form expressions of the theoretical analysis results are not comprehensive in the prior schemes, which are also not tight especially at low signal-to-noise ratio (SNR) regions. In this paper, we propose a PLA scheme based on the channel phase response, aiming to offset the performance loss introduced by the channel correlation coefficient. Moreover, we derive the closed-form expressions of the theoretical analysis results, such as the mean value, the variance and the probability density functions (PDFs), which can be utilized to provide the closed-form threshold for making decision instead of a great ideal of testing. Then, the security analysis is provided to verify the resistance of the proposed scheme under the attacks. Simulation results show that the proposed scheme outperforms the benchmarks and the proposed theoretical results match well with the simulated results even under low SNR regions.

Shape from focus using gradient of focus measure curve

Finding the best focused position is the key to shape from focus (SFF). Focus measure (FM) operators, sampling step, especially various noises usually cause unstable focused position searching and reduce the accuracy of SFF. Traditional methods usually use specific functions like Gaussian function and polynomial function to fit the focus measure curve and take the position of the peak of the fitting curve as the best focused position. However, due to the complexity of the imaging process and the noise, a fixed function is difficult to describe the focus measure curve. In this paper, a robust SFF method is proposed. In the process of finding the best focused position, the signal-to-noise ratio (SNR) of the values near the peak of the focus measure curve is relatively low, which usually leads to incorrect result. However, the rising and falling parts of the focus measure curve are more robust than the low SNR region, so we calculate the gradient of the focus measure curve with an adaptive derivative step. With the derivative step, we can reduce the influence of the SNR region. Then the zero point of the gradient curve and the derivative step are used to find the best focused position. Without any additional pre-work or constraints, our method can achieve high accuracy and strong robustness simultaneously. The effectiveness of the proposed method is evaluated by the simulated objects and real objects with different shapes. The 3D shape recovery results show that the proposed method is a superior alternative to enhance the focus volume in SFF. The RMSE of simulated objects is reduced by more than 25% when the proposed method is applied. For real objects, the RMSE of our method is 36%, 53% lower, compared with the Gaussian interpolation method and the polynomial fitting method, respectively.

Multihop Optical Wireless Communication Over ${\mathcal {F}}$-Turbulence Channels and Generalized Pointing Errors With Fog-Induced Fading

Multihop relaying is a potential technique to mitigate channel impairments in optical wireless communications (OWC). In this paper, multiple fixed-gain amplify-and-forward (AF) relays are employed to enhance the OWC performance under the combined effect of atmospheric turbulence, pointing errors, and fog. We consider a long-range OWC link by modeling the atmospheric turbulence by the Fisher-Snedecor <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">${\mathcal {F}}$</tex-math></inline-formula> distribution, pointing errors by the generalized non-zero boresight model, and random path loss due to fog. We also consider a short-range OWC system by ignoring the impact of atmospheric turbulence. We derive novel upper bounds on the probability density function (PDF) and cumulative distribution function (CDF) of the end-to-end signal-to-noise ratio (SNR) for both short and long-range multihop OWC systems by developing exact statistical results for a single-hop OWC system under the combined effect of <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">${\mathcal {F}}$</tex-math></inline-formula> -turbulence channels, non-zero boresight pointing errors, and fog-induced fading. Based on these expressions, we present analytical expressions of outage probability (OP) and average bit-error-rate (ABER) performance for the considered OWC systems involving single-variate Fox's H and Meijer's G functions. Moreover, asymptotic expressions of the outage probability in high SNR region are developed using simpler Gamma functions to provide insights on the effect of channel and system parameters. The derived analytical expressions are validated through Monte-Carlo simulations, and the scaling of the OWC performance with the number of relay nodes is demonstrated with a comparison to the single-hop transmission.

Effective Capacity Analysis of AmBC-NOMA Communication Systems

In this paper, we propose an ambient backscatter communication non-orthogonal multiple access (AmBC-NOMA) system framework and derive exact expressions for the effective capacity (EC) of two NOMA users and the backscatter device (BD). For more insights, we provide asymptotic analysis by invoking <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">high signal-to-noise ratio (SNR) slope</i> and <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">high SNR power offset</i> . The simulation indicates that: 1) the ECs of users tend to be the constants in the high SNR region while the EC of BD increases linearly with SNR; 2) comparing the ECs of two users, the EC advantage of near user is more obvious when QoS constraint is loose; 3) the proposed system yields better user fairness than the orthogonal multiple access case; 4) the EC of BD strengthens with the increase of BD’s reflection coefficient, while the ECs of NOMA users weaken.

A Power and Spectrum Efficient Uplink Transmission Scheme for QoS-Constrained IoT Networks

Nonorthogonal multiplexing (NOM) is a novel superposition coding scheme that has been recently proposed to improve the throughput of wireless systems. However, restricting the number of multiplexed packets to two limits the throughput improvement of nonorthogonal multiplexing (NOM) to 100% in best-case scenarios. Therefore, this work presents a generalized NOM (GNOM) design with an unlimited number of multiplexed packets. In the multiplexing process, new and retransmitted packets due to arq are combined while considering the impact of channel conditions on the power assigned per packet. The proposed GNOM employs an efficient heuristic algorithm to perform the power assignment and multiplexing decisions. Moreover, the complexity can be controlled by enforcing a limit on the maximum number of multiplexed packets per transmission, making it suitable for iot nodes with diverse computational capabilities and quality of service requirements. The obtained results demonstrate the effectiveness of the proposed scheme, which offers up to 200% throughput improvement at moderate signal to noise ratios (SNRs), and up to 700% at high SNRs. Furthermore, the new scheme can reduce the transmission power consumption by up to 6 dB in the high SNR region.

Low complexity deep neural network based transmit antenna selection and signal detection in SM-MIMO system

In this paper, with the investigation of the conventional Euclidean distance based transmit antenna selection (ED-AS) and the maximum likelihood detection (MLD) in spatial modulation (SM) multiple-input multiple-output (MIMO) system, we propose the deep neural network (DNN) based transmit antenna selection (DNN-AS) and signal detection (DNN-SD), respectively, to effectively balance the system performance and the complexity. For the proposed DNN-AS, we transform the existing problem of AS into a prediction problem and design a low dimension multi-output classifier to achieve the low complexity solution of AS. For the DNN-SD, we present two sub-DNNs to recover the transmitted SM signal. Numerical results reveal that the proposed DNN-AS scheme gets a better average bit error rate (ABER) performance than the typical AS schemes in SM-MIMO system with lower complexity. In contrast to the ED-AS approach, it attains the optimal and suboptimal ABER performance at low-and-moderate signal-to-noise ratio (SNR) region and high SNR region, respectively. The proposed DNN-AS scheme achieves obvious SNR gains compared with the conventional SM system and gets about 3.5 dB gains over the typical maximum-norm based AS (Norm-AS) algorithm. Furthermore, the proposed DNN-SD scheme obtains a superior detection performance compared with the conventional linear detection methods and provides the same ABER performance as the optimum MLD scheme in the presence of correlated noise.

NOMA-based cognitive radio network with hybrid FD/HD relay in industry 5.0

The continued growth of the industrial Internet of Things (IoT) paves the way for the fifth industrial revolution (Industry 5.0), which is expected to further enhance the wireless connectivity and human machine collaboration. Nonetheless, Industry 5.0 will inevitably face the great challenges such as large scale connectivity and explosive data traffic because of the proliferation of IoT devices. To meet these challenges, we propose a novel non-orthogonal multiple access (NOMA)-based overlay cognitive radio (OCR) network for Industry 5.0. Specifically, the hybrid full-duplex (FD)/ half-duplex (HD) mode at the secondary user (SR) is adopted and in-phase and quadrature phase imbalance (IQI) is introduced to better comply with the practical scenarios. We investigate the outage probability (OP) and ergodic sum rate (ESR) of the considered network with the Nakagami-m channels. Extensive simulation experiments are performed to verify the accuracy of the analysis. From the simulation results, we can conclude: i) Increasing the signal-to-noise ratio (SNR), OP decreases but ESR increases and tends to a fixed value at high SNR regimes, indicating that the existence of IQI leads to a ceiling for the ESR; ii) The primary system has higher reliable performance at low SNR regions when SR is in FD mode, and for ergodicity, the system with FD relay is superior to HD relay; iii) The reliable performance of OMA system for primary users will be better since there is no interference at the relay.

Improving Decodability of Polar Codes by Adding Noise

This paper presents an online perturbed and directed neural-evolutionary (Online-PDNE) decoding algorithm for polar codes, in which the perturbation noise and online directed neuro-evolutionary noise sequences are sequentially added to the received sequence for re-decoding if the standard polar decoding fails. The new decoding algorithm converts uncorrectable received sequences into error-correcting regions of their decoding space for correct decoding by adding specific noises. To reduce the decoding complexity and delay, the PDNE decoding algorithm and sole neural-evolutionary (SNE) decoding algorithm for polar codes are further proposed, which provide a considerable tradeoff between the decoding performance and complexity by acquiring the neural-evolutionary noise in an offline manner. Numerical results suggest that our proposed decoding algorithms outperform the other conventional decoding algorithms. At high signal-to-noise ratio (SNR) region, the Online-PDNE decoding algorithm improves bit error rate (BER) performance by more than four orders of magnitude compared with the conventional simplified successive cancellation (SSC) decoding algorithm. Furthermore, in the mid-high SNR region, the average normalized complexity of the proposed algorithm is almost the same as that of the SSC decoding algorithm, while preserving the decoding performance gain.

Deep Neural Network: An Alternative to Traditional Channel Estimators in Massive MIMO Systems

Fifth-generation (5G) requires a highly accurate estimate of the channel state information (CSI) to exploit the benefits of massive multiple-input-multiple-output (MaMIMO) systems. 5G systems use pilot sequences to estimate channel behaviour using traditional methods like least squares (LS), or minimum mean square error (MMSE) estimation. However, traditional methods do not always obtain reliable estimations: LS exhibits a poor estimation when inadequate channel conditions (i.e., low- signal-to-noise ratio (SNR) region) and MMSE requires prior statistical knowledge of the channel and noise (complex to implement in practice). We present a deep learning framework based on deep neural networks (DNNs) for fifth-generation (5G) MaMIMO channel estimation. After a first preliminary scheme with which we verify the good estimation capacity of our DNN-based approach, we propose two different models, which differ in the information processed by the DNN and benefit from lower computational complexity or greater flexibility for any reference signal pattern, respectively. The results show that, compared to the LS-based channel estimation, the DNN approach decreases the mean square error (MSE) and the system’s spectral efficiency (SE) increases, especially in the low- SNR region. Our approach provides results close to optimal MMSE estimation but benefits from not requiring any prior channel statistics information.

Performance analysis of wireless powered sensor network with opportunistic scheduling over generalized [formula omitted] shadowed fading channels

This paper investigates the performance of a multi-node wireless powered sensor network (WPSN) with an opportunistic scheduling scheme over κ−μ shadowed fading channels. The system assumes that all the sensor nodes (SNs) are energy constrained and harvest energy from a hybrid access point (HAP) in the downlink. In contrast, the node with the best end-to-end instantaneous signal-to-noise ratio (SNR) is scheduled for information transmission to HAP in the uplink. For the underlying system model, approximate closed-form analytical expressions is developed with the help of the moment matching method, which is then used to evaluate the system performances such as outage probability, effective throughput, and average bit error rate (BER). In addition, we also perform an asymptotic analysis by assuming that the system operates at a high SNR region, which gives us valuable insights about the diversity order and coding gain. The accuracy of the analysis is further confirmed with Monte-Carlo simulations, which validate the correctness of the theoretical analysis.

Physical Layer Security of Two-Way Ambient Backscatter Communication Systems

Achieving high network traffic demand in limited spectrum resources is a technical challenge for the beyond 5G and 6G communication systems. To this end, ambient backscatter communication (AmBC) is proposed for Internet-of-Things (IoT), because the backscatter device (BD) can realize communication without occupying extra spectrum resources. Moreover, the spectral efficiency can be further improved by using two-way (TW) communication. However, secure communication is a great challenge for accessing massive IoT devices due to the broadcasting nature of wireless propagation environments. In light of this fact, this article proposed a two-way ambient backscatter communication (TW-AmBC) network with an eavesdropper. Specifically, the physical layer security (PLS) is studied through deriving the analytical/asymptotic expressions of the outage probability (OP) and intercept probability (IP). Moreover, the outage probabilities (OPs) in high signal-to-noise ratio (SNR) regions are studied for the asymptotic behavior and the intercept probabilities (IPs) in high main-to-eavesdropping ratio (MER) regions as well. Through analysis and evaluation of simulation performance, the results show that (i) when considering the target node, the OP of the BD decreases with increasing SNR, that is, enhancing the reliability; (ii) an optimal value of BD’s reflection coefficient that maximize the reliability of backscatter link can be obtained; (iii) in high SNR regions, the OPs approach a constant; thus, the diversity orders are zero; (iv) when increasing the MER, the IP of target node decreases, suggesting the security enhances; (v) a trade-off exists between reliability and security which can be optimized by carefully designing the parameters.

AoD-Adaptive Channel Feedback for FDD Massive MIMO Systems With Multiple-Antenna Users

In this paper, we propose an efficient feedback scheme for an angle of departure (AoD) based channel estimation in frequency division duplex (FDD) massive multiple-input multiple-output (MIMO) systems with multiple antennas at the users. The channel feedback scheme is based on zero-forcing block diagonalization (BD) and it is proposed for two distinct design cases; in case I, the number of streams intended for a user equals the number of antennas at that user; in case II, the number of streams is less than the number of receive antennas. Case I is applicable in scenarios where high data rate requirements are needed as it transmits data symbols over all of the available degrees of freedom of the system. Diversely, case II is applicable when reliability is a priority in the system as it uses the additional <i>receive</i> antennas at the user to achieve spatial diversity to enhance the link performance. The proposed scheme is analyzed for the two cases by quantifying the downlink rate gap from the case of perfect channel state information (CSI). Moreover, we design structured feedback codebooks based on optimal subspace packing in the Grassmannian manifold and show that these codes achieve close performance to the perfect CSI case. Additionally, a vector quantization scheme is proposed to quantize the user&#x2019;s channel matrix when optimal power allocation across multiple streams is adopted in the low signal-to-noise ratio (SNR) region. The feedback codebooks are based on optimal line packing in the Grassmannian manifold, where every vector of the user&#x2019;s channel matrix is quantized and sent to the BaseStation. The results demonstrate a fundamental trade-off between vector quantization, with power optimization across the data streams, and subspace quantization. Specifically, vector quantization codebooks outperform subspace-based codebooks in the low SNR region, while the situation is reversed in the high SNR region.

Signal Preprocessing Technique With Noise-Tolerant for RF-Based UAV Signal Classification

Since the beginning of the COVID-19 pandemic, the demand for unmanned aerial vehicles (UAVs) has surged owing to an increasing requirement of remote, noncontact, and technologically advanced interactions. However, with the increased demand for drones across a wide range of fields, their malicious use has also increased. Therefore, an anti-UAV system is required to detect unauthorized drone use. In this study, we propose a radio frequency (RF) based solution that uses 15 drone controller signals. The proposed method can solve the problems associated with the RF based detection method, which has poor classification accuracy when the distance between the controller and antenna increases or the signal-to-noise ratio (SNR) decreases owing to the presence of a large amount of noise. For the experiment, we changed the SNR of the controller signal by adding white Gaussian noise to SNRs of -15 to 15 dB at 5 dB intervals. A power-based spectrogram image with an applied threshold value was used for convolution neural network training. The proposed model achieved 98% accuracy at an SNR of -15 dB and 99.17% accuracy in the classification of 105 classes with 15 drone controllers within 7 SNR regions. From these results, it was confirmed that the proposed method is both noise-tolerant and scalable.