Sparse Channel Research Articles

A lightweight road traffic sign detection algorithm based on adaptive sparse channel pruning

Abstract The development of traffic sign recognition (TSR) has become increasingly important for enhancing the safety and convenience of assisted driving. To achieve high accuracy, faster inference speed, and a lightweight model, an improved lightweight TSR network, termed YOLOv8-ALWP, has been proposed. This network incorporates adaptive downsampling to replace the original convolution module in YOLOv8. By employing multiple pooling and convolution operations, it reduces the spatial resolution to extract additional feature information. To accommodate the varying scale characteristics of different traffic signs, large separable kernel attention is introduced to enhance spatial pyramid pooling-fast. Furthermore, the complete intersection over union loss has been improved, and a new Wise-Focaler-EIoU Loss has been proposed to accelerate model convergence and enhance generalization capabilities. Finally, layer-adaptive sparsity for magnitude-based pruning is employed to reduce the model’s parameters, decrease computational complexity, and improve inference speed. Experiments were conducted using the TT100K, Roadsign, CCTSDB, and GTSDB datasets. In the TT100K dataset, compared to the baseline model, the improved algorithm significantly reduced parameters by 64.67%, FLOPs by 44.44%, and increased mAP by 1.7%, precision by 5.5%, and FPS from 70.3 to 81.7, respectively. Under four specific conditions, the improved algorithm effectively addressed the shortcomings of the baseline model, such as missed detections and reduced accuracy. These experimental results indicate that the YOLOv8-ALWP algorithm achieves model lightweighting while enhancing detection accuracy.

Hybrid Heuristic‐Based Invariable Step‐Size Zero‐Attracting Modified Normalized Least Mean Square for Efficient Channel Estimation in Broadband Wireless Communication System

ABSTRACTThe stability of the broadband channel estimation capabilities is provided by the normalized least mean square (NLMS) approaches. When compared to compression sensing‐based algorithms, the NLMS is very effective to perform in the channel estimation model, and also, the computational complexity of these algorithms is very low. But the important drawback of these algorithms is the intrinsic sparsity of broadband communication channels is not effectively utilized in these systems. The channel sparsity is exploited using invariable step‐size zero‐attracting NLMS algorithms in the adaptive sparse channel estimation techniques. The best trade‐off between the computational complexity and the cost function cannot be obtained, and the convergence rate is low in the ISS‐ZA‐NLMS, and a new algorithm is developed with the help of existing ISS‐ZA‐NLMS. The objective of this research work is to develop a novel channel estimation mechanism for broadband wireless communication systems using the enhanced NLMS method with the usage of a hybrid heuristic strategy. This work designs and develops an efficient channel estimation technique named as hybrid heuristic‐based invariable step‐size zero‐attracting modified NLMS for enhancing efficiency. The heuristic algorithms like rat swarm optimizer and red deer algorithm are fused together to get hybrid rat red deer swarm optimization for estimating the channels with higher efficiency. The main scope of the implemented scheme is to offer better performance regarding the mean square error (MSE) measures. The simulation analysis is made finally to estimate the performance of the investigated HH‐ISS‐ZA‐MNLMS with the comparison of conventional approaches.

Analysis for sparse channel representation based on dictionary learning in massive MIMO systems

AbstractThe accuracy analysis of dictionary sparse representation for channels in massive MIMO systems is a relatively unexplored field. Existing research has primarily focused on investigating the accuracy of dictionary sparse representation using simulation in massive MIMO systems, but has not provided quantitative accuracy analysis. To address this gap, the correlation numerical proportional factor is proposed to represent the accuracy performance of non‐zero elements in the coefficient matrix. Additionally, a qualitative analytical formula for dictionary sparse representation accuracy is provided and an optimal upper bound for the correlation numerical proportional factor is established. Furthermore, the innovation indicates that the accuracy of dictionary sparse representation is mainly influenced by the cross‐correlation between the pilots matrix and the dictionary matrix, as well as sparsity. The author has also developed a method for minimizing the correlation numerical proportional factor. In order to obtain an optimal sparse representation coefficient matrix, a cross‐correlation matrix is constructed and an analytical expression is derived for it as well as its use as an optimal hard decision threshold is determined. Finally, a sparse representation coefficient optimization algorithm is proposed using this optimal threshold. Simulation results demonstrate that this algorithm can significantly improve channel sparse dictionary representation accuracy.

A Sub-Channel Spatial Homogeneity-Based Channel Estimation Method for Underwater Optical Densely Arrayed MIMO Systems

The limited surface area and structural constraints of small underwater communication devices necessitate a dense placement of transmitting and receiving array elements in optical multiple-input multiple-output (MIMO) systems. The compact layout leads to the formation of sub-channels that exhibit notable spatial correlation and a tendency toward homogeneity. Although sub-channel spatial homogeneity (SSH) may diminish the communication capacity of MIMO systems, it provides a significant advantage by reducing the pilot overhead. In this study, we exploit the inherent SSH and the natural time-domain sparsity of channel impulse response (CIR) in the underwater optical densely arrayed MIMO (UODA-MIMO) system to propose an innovative SSH-based channel estimation (SSH-CE) method. We model the underwater optical CIR at Gbaud rates and integrate it with SSH characteristics. This approach transforms the reconstruction targets of compressive sensing (CS) from conventional CIR samples to prior CIR model parameters and the fitting residuals of the homogeneous sub-channels, reducing the pilot overhead. The simulation results of photon tracing for UODA-MIMO sub-channels in turbid harbor water indicate a monotonic, exponential decay in CIR at Gbaud rates, with transmission delays exceeding 5 nanoseconds for distances over 8 m. Moreover, the correlation coefficients among sub-channels reach a minimum of 0.975, confirming the presence of SSH in UODA-MIMO systems. In comparison to existing CS methods that rely on known sparsity, sparsity adaptation, and the structural sparsity of MIMO channels, the SSH-CE method achieves a lower degree of sparsity in reconstruction targets and a reduced lower bound for pilot requirements under the SPARK criterion. Specifically, the SSH-CE method achieves a reduction in the pilot overhead for reconstructing Nt sub-channels of K-sparse to 2Nt irrespective of CIR residual compensation.

An Automated Sparse Channel Estimation Framework for MU‐MIMO‐OFDM System With Adaptive Extreme Learning and Heuristic Mechanism

ABSTRACTCompressed sensing is used for channel estimation in Multiple Input Multiple Output‐Orthogonal Frequency Division Multiplexing (MIMO‐OFDM) systems, but large‐scale networks face challenges regarding the antenna elements and spatial non‐stationarities. To enhance spectral efficiency in Multi User‐MIMO (MU‐MIMO) systems, essential signals are collected by Quadrature Phase Shift Keying (QPSK) in the transformer phase. Further, the modulated signals are provided to the “Pulse Shaping Algorithm (PSA)” for neglecting the inter‐symbol interference rate along with inter‐carrier interference. Subsequently, in the transmitter phase, the “Inverse Fast Fourier Transforms (IFFT)” technique is performed to map the symbols and then provide the signal to the receiver side. The spare channel is validated using a semi‐blind sparse algorithm, with parameters tuned using the Opposition Mud Ring Algorithm (OMRA). Then, the estimated sparse channel values are used for generating the new data, and the Adaptive Extreme Learning Model (AELM) is trained to predict the spare channel outcome. The main objective is to reduce the Minimum Mean Square Error (MMSE) in the channel. Thus, the spare channel outcome is predicted automatically using the AELM. Then, diverse evaluations are executed in the suggested spare channel estimation approach over the different mechanisms to observe their effectualness rate.

Overcoming the Energy vs Power Dilemma in Commercial Li-Ion Batteries via Sparse Channel Engineering.

Improvements in both the power and energy density of lithium-ion batteries (LIBs) will enable longer driving distances and shorter charging times for electric vehicles (EVs). The use of thicker and denser electrodes reduces LIB manufacturing costs and increases energy density characteristics at the expense of much slower Li-ion diffusion, higher ionic resistance, reduced charging rate, and lower stability. Contrary to common intuition, we unexpectedly discovered that removing a tiny amount of material (<0.4 vol %) from the commercial electrodes in the form of sparsely patterned conical pores greatly improves LIB rate performance. Our research revealed that upon commercial production of high areal capacity electrodes, a very dense layer forms on the electrode surface, which serves as a bottleneck for Li-ion transport. The formation of sparse conical pore channels overcomes such a limitation, and the facilitated ion transport delivers much higher power without reduction in the practically attainable energy. Diffusion and finite element method-based simulations provide deep insights into the fundamentals of ion transport in such electrode designs and corroborate the experimental findings. The reported insights provide a major thrust to redesigning automotive LIB electrodes to produce cheaper, longer driving range EVs that retain fast charging capability.

Sparse channel estimation for underwater acoustic OFDM systems with super-nested pilot design

Underwater acoustic channels are usually sparse and have large delay spread. In this paper, super-nested array structure in the field of array signal processing is borrowed to be the pilot design of underwater acoustic OFDM systems, in order to better estimate large delay spread channels with limited number of pilots. Specifically, by constructing the pilot subcarriers’ covariance matrix and the pilot position difference, the virtual pilot on the differential co-array are employed for sparse channel estimation. In order to reduce the error between the estimated pilot subcarriers’ covariance matrix and the ideal covariance matrix, the cross-correlation matrix of pilot subcarriers is estimated in advance for interference cancellation. Then the sparse iterative covariance estimation algorithm (SPICE) is adopted to further refine the covariance matrix and improve the channel estimation performance. Simulation, pool and sea experimental results show that the proposed method can effectively estimate the large delay spread sparse channels and improve the performance of underwater acoustic OFDM systems.

A switching norm based least mean Square/Fourth adaptive technique for sparse channel estimation and echo cancellation

To realize rapid data transmission, the broadband transmission technique is being extensively explored and applied in existing wireless communication systems. The multi-path channel in broadband wireless communication systems is sparse and this sparsity can be used as prior knowledge to estimate the channel. To make use of sparsity, this paper recommends a switching norm-based least mean square/fourth (SN-LMS/F) adaptive approach for sparse channel estimation and echo cancellation. The suggested SN-LMS/F is implemented by adding a soft parameter adjustment function (SPF) into the conventional LMS/F adaptive method's cost function and utilizes both the l0 and l1 norm to exploit system sparsity with reduced complexity. The simulated output indicates that the suggested SN-LMS/F adaptive technique provides a more desirable performance for sparse channel estimation and echo cancellation with reduced execution time.

Artificial neural network-based sparse channel estimation for V2V communication systems

Abstract Artificial neural networks (ANNs) have gained a lot of attention from researchers in the past few years and have been employed on a large scale. They have also been gaining momentum in wireless communication systems. For efficient vehicle-to-vehicle (V2V) channel communication, a sparse multipath channel issue must be studied. To minimize the multipath effect, a time reversal (TR) operation and time division synchronization orthogonal frequency division multiplexing (TDS-OFDM) have been appealing because of their fast synchronization and active spectral efficiency. To improve the transceiver's execution in a frequency-selective fading channel environment, an OFDM system is used to reduce inter- symbol interference (ISI). Simultaneous Orthogonal Matching Pursuit (SOMP) channel state estimator algorithm suffer from high computational cost and high computational complexity. The ANN algorithm has better performance than SOMP algorithm. The proposed neural network technologies have lower complexity than the SOMP algorithm. The application of ANN is capable of solving complex problems, such as those encountered in image, signal processing and have been implemented for channel estimation in OFDM. The proposed ANN outperformed the SOMP algorithm with regard to signal compensation. Overall, the ANN algorithm achieved the best performance. This study proposes an ANN-based sparse channel state estimator. Regarding the bit error rate (BER) metric, the proposed estimator outperforms the channel estimation approach based on the SOMP. The simulation results confirm the efficacy of the proposed approach.

How to Leverage Double-Structured Sparsity of RIS Channels to Boost Physical-Layer Authentication

How to Leverage Double-Structured Sparsity of RIS Channels to Boost Physical-Layer Authentication

Path loss, angular spread and channel sparsity modeling for indoor and outdoor environments at the sub-THz band

In this paper, we present new measurement results to model large-scale path loss, angular spread and channel sparsity at the sub-THz (141–145 GHz) band, for both indoor and outdoor scenarios. Extensive measurement campaigns have been carried out, taking into account both line-of-sight (LoS) and non line-of-sight (NLoS) propagation. For all considered propagation scenarios, omni-directional and directional path loss models have been developed, based on the so-called close-in (CI) free-space reference distance model. A power angular spread analysis is further presented. The sparsity of the wireless channel has also been studied by employing suitable metrics, namely the so-called Gini index (GI), the Ricean K-factor and the root mean square (RMS) delay spread.

Parametric channel estimation for RIS-assisted mmWave MIMO-OFDM systems with low pilot overhead

We consider the channel estimation problem in a millimeter-wave (mmWave) multiple-input multiple-output orthogonal frequency division multiplexing (MIMO-OFDM) system aided by a passive reconfigurable intelligent surface (RIS). Initially, leveraging the inherent sparsity of mmWave channels, we decouple the channel estimation into the recovery of multipath parameters, including spatial frequencies of angles, delays, and complex gains. Subsequently, we employ two training schemes: the first involves a full row rank constrained RIS phase shift pattern, and the second incorporates a Kronecker structure constrained RIS phase shift pattern. These schemes reform channel estimation into estimating parameters from two fifth-order tensor signals, which admit constrained Canonical Polyadic decomposition. Moreover, both tensor signals share Vandermonde-constrained factor matrices, enabling algebraic algorithms for tensor decomposition problems and recovering channel multipath parameters. Theoretical analyses show that the first training scheme has a lower signal processing complexity for channel estimation, while the second attains a higher estimation accuracy. Also, we prove that our proposed two schemes have the same minimum pilot overhead, which is as low as proportional to the product of the number of paths in the two MIMO channels (transmitter-RIS and RIS-receiver). Numerical results validate the effectiveness of our proposed methods.

Polarimetric inverse scattering using LSTM-aided association-learning sparse reconstruction framework

Conventional polarimetric inverse scattering is under the assumption of joint sparsity of different polarization channels. However, in practical situation, there are strong correlations among these multi-channel measurements, but the inherent dependencies are unknown; the deterministic joint sparsity assumption may cause insufficient utilization of association information among multi-polarization channels, and consequently lead to limited improvement of canonical scatterers extraction accuracy. Aimed at this problem, we propose a long short-term memory (LSTM)-aided association-learning sparse reconstruction framework for polarimetric inverse scattering, in which the dependencies among multi-channel measurements are captured via a data driven pattern. In the proposed scheme, instead of directly solving the full inverse problem, polarimetric inverse scattering with multi-attribute canonical scatterers extraction is implemented via hierarchical pattern, and consequently the required computational burden is reduced. Particularly, the LSTM network is utilized to perform atomic screening for estimating first type of attributes of canonical scatterers, and then the optimal adaptive filtering (OAF) process is carried out for estimating second and third types of attributes, followed by residual update process. Extensive experimental results demonstrate that the proposed approach is able to outperform existing algorithms in terms of reconstruction accuracy and computational speed.

Semi-blind sparse channel estimation using regularized expectation maximization

In Massive multiple-input multiple-output (MIMO) systems, channel estimation is crucial. The large size of the antennas causes a significant pilot and feedback overhead, making it challenging to estimate channels in massive MIMO systems. Besides, the studies have shown that mmWave channels are sparse due to the limited number of dominant propagation paths. Therefore, the motivation of this paper is to exploit the inherent sparsity of the massive mmWave MIMO channels to develop a semi-blind channel estimation with reduced number of pilots. To this goal, an expectation maximization (EM) based technique has been developed which leverages the sparsity of the underlying channel for its better estimation. An iterative approach is proposed to solve the modeled problem which simultaneously updates the channel coefficients and data symbols using available data and system structure at each iteration. The proposed method imposes sparsity with the aid of Smoothed L0 norm (SL0) in the M-step. The simulation results demonstrate the proposed method have quick convergence and lower channel estimation error compared to the existing methods. As a quantitative evaluation, the proposed method attains the normalized mean square error of 9×10−2 and 7×10−3 at SNR=5dB and SNR=15dB, respectively.

Time-Varying Channel Estimation Based on Distributed Compressed Sensing for OFDM Systems.

For orthogonal frequency division multiplexing (OFDM) systems in high-mobility scenarios, the estimation of time-varying multipath channels not only has a large error, which affects system performance, but also requires plenty of pilots, resulting in low spectral efficiency. To address these issues, we propose a time-varying multipath channel estimation method based on distributed compressed sensing and a multi-symbol complex exponential basis expansion model (MS-CE-BEM) by exploiting the temporal correlation and the joint delay sparsity of wideband wireless channels within the duration of multiple OFDM symbols. Furthermore, in the proposed method, a sparse pilot pattern with the self-cancellation of pilot intercarrier interference (ICI) is adopted to reduce the input parameter error of the MS-CE-BEM, and a symmetrical extension technique is introduced to reduce the modeling error. Simulation results show that, compared with existing methods, this proposed method has superior performances in channel estimation and spectrum utilization for sparse time-varying channels.

Millimeter Wave Multi-mode OAM-MIMO Channel Capacity Study

This study examines the theoretical aspects of channel capacity for millimeter wave multi-mode Orbital Angular Momentum (OAM) Multiple Input Multiple Output (MIMO) systems. It begins with a theoretical analysis of these channels, highlighting their importance and potential applications in wireless communications. A Sparse Bayesian Learning (SBL) based Joint Uplink and Downlink Channel Estimation (JUDCE) approach is then employed to estimate the downlink channel using uplink training signals, while accounting for inactive receiving antennas. Exploiting the sparsity of millimeter wave channels, the downlink channel estimation is modeled as a compressed sensing problem using a binary mask matrix. An SBL framework is developed to efficiently tackle this problem. Theoretical and simulation results indicate the proposed method significantly improves channel estimation accuracy and spectral efficiency of downlink transmissions, underscoring its importance for the advancement of future communication technologies.

Identity Authentication for Ground Control Station Based on MmWave MIMO Channel Sparsity

Identity Authentication for Ground Control Station Based on MmWave MIMO Channel Sparsity

Multiplicative Basis Function Based Adaptive DOA Estimation in Optimal MIMO Sparse Antenna Reconfiguration Model

Direction of arrival (DOA) estimation using sparse signal representation has gained significant attention in recent decades. The spatial signals utilized for DOA estimation are reconstructed through a novel compressive sensing (CS) approach. In this study, we used a CS approach to ascertain the adaptive underdetermined DOA for multiple inputs and multiple output (MIMO) sparse media. Accurate DOA estimation through the CS approach contributes to enhanced signal investigation and the suppression of undesired noise in a sparse channel. The method proposed herein encompasses the development and execution of a novel multiplicative basis function known as the multi-kernel supported non-negative sparse Bayesian learning (NNSBL) algorithm, which is implemented on predefined grid values. Concurrently, stochastic grey wolf optimization (GWO) is virtually applied to increase the degrees of freedom (DOF) on a minimum redundancy array (MRA) while considering an optimal antenna reconfiguration model. The advantages of this proposed approach include the generation of a distinct manifold matrix for precise beam steering, resulting in improved DOA estimation accuracy. Additionally, an augmentation of DOF using GWO was observed, with low computational complexity. The simulated result of proposed algorithm leads to the attainment of optimal minimum root mean square error (RMSE) across various optimal wavelengths of the stochastic sources. Finally, a comparative analysis of RMSE and convergence plots confirms the superior performance and high potentiality of the proposed method in comparison to existing techniques across different SNR values. Notably, there is significant reduction in RMSE which is suitable for precise DOA estimation in signal processing applications.

Implementing intelligent‐based sparse channel estimation in Multi User‐Multiple Input Multiple Output‐Orthogonal Frequency Division Multiplexing system with hybridized optimization algorithm

SummaryThe channel plays a significant role as the multipath in Multiple Input Multiple Output‐Orthogonal Frequency Division Multiplexing (MIMO‐OFDM) systems. The precoding‐based channel estimation algorithms estimate the channel capacity at the receiver node and they provide efficient channel estimates under worst case channel scenarios. To estimate the channel capacity in the mobile node, semi‐blind scenario is a crucial task in Multi User (MU) MIMO systems. Therefore, a semi blind sparse channel estimation algorithm is demonstrated in the MU‐MIMO system to provide better performance. In the transmitter side, the signal modulation is performed with the help of Quadrature Phase Shift Keying (QPSK), and the modulated signal is given to Pulse Shaping Algorithm (PSA) to reduce the inter‐carrier interference as well as inter symbol interference. At each transmitter, the Inverse Fast Fourier Transforms (IFFT) technique is used for mapping the symbols and then the mapped signal is send to the receiver. In the receiver side, the FFT, pulse reshaping, and QPSK demodulation blocks are serially connected and perform inverse operations of the transmitter in the receiver. Then, the sparse channel is estimated at the receiver by using the semi blind sparse algorithm, where the parameters are optimized by using the Hybridized Drosophila with Virus Colony Search Optimization (HD‐VCSO). The objective function of the developed sparse channel estimation is the reduction of Minimum Mean Square Error (MMSE) in the channel. The performance of the developed sparse channel estimation algorithm is analyzed through different conventional algorithms to validate the effectiveness of the proposed algorithm.

Channel Knowledge Map Construction Based on a UAV-Assisted Channel Measurement System

With the fast development of unmanned aerial vehicles (UAVs), reliable UAV communication is becoming increasingly vital. The channel knowledge map (CKM) is a crucial bridge connecting the environment and the propagation channel that may visually depict channel characteristics. This paper presents a comprehensive scheme based on a UAV-assisted channel measurement system for constructing the CKM in real-world scenarios. Firstly, a three-dimensional (3D) CKM construction scheme for real-world scenarios is provided, which involves channel knowledge extraction, mapping, and completion. Secondly, an algorithm of channel knowledge extraction and completion is proposed. The sparse channel knowledge is extracted based on the sliding correlation and constant false alarm rate (CFAR) approaches. The 3D Kriging interpolation is used to complete the sparse channel knowledge. Finally, a UAV-assisted channel measurement system is developed and CKM measurement campaigns are conducted in campus and farmland scenarios. The path loss (PL) and root mean square delay spread (RMS-DS) are measured at different heights to determine CKMs. The measured and analyzed results show that the proposed construction scheme can effectively and accurately construct the CKMs in real-world scenarios.