Compressive Sensing Based Channel Estimation Research Articles

A Low-Complexity Channel Estimation in Internet of Vehicles in Intelligent Transportation Systems for 5G Communication

The objective of utilizing mmWave/subTHz bands in next-generation wireless communications is to be achieved. Despite this, since reconfigurable intelligent surface (RIS)-assisted systems depend on the transmission channel configuration, the system architecture design, and the methods used to derive channel state information (CSI) on a base station (BS) and RIS, channel estimation continues to be the main problem with these systems. This research proposes an innovative RIS-based and compressed sensing-based channel estimation technique for the internet of vehicles. To obtain the best phase shift matrix, the communication model must first be constructed, and the angle-of-arrival and departure are utilized. Channel estimation is then performed based on the perception matrix. The training overhead and complexity of the channel estimation are reduced by considering the position information of the vehicles in the optimal phase shift matrix. Simulation results show that the proposed algorithm exhibits better channel estimation and low complexity performance compared with existing algorithms.

Channel estimation for reconfigurable intelligent surface-assisted mmWave based on Re‘nyi entropy function

This study focuses on channel estimation for reconfigurable intelligent surface (RIS)-assisted mmWave systems, in which the RIS is used to facilitate base-to-user data transfer. For beamforming to work with active and passive elements, a large-size cascade channel matrix should always be known. Low training costs are achieved by using the mmWave channels’ inherent sparsity. The research provides a unique compressive sensing-based channel estimation approach for reducing pilot overhead issues to a minimum. The proposed technique estimates channel data signals in a downlink for RIS-assisted mmWave systems. The mmWave systems often have a sparse distribution of signal sources due to the spatial correlations of the domains. This distribution pattern makes it possible to use compressive sensing methods to resolve the channel estimation issue. In order to decrease the pilot overhead, which is necessary to predict the channel, the proposed method extends the Re‘nyi entropy function as the sparsity-promoting regularizer. In contrast to conventional compressive sensing techniques, which necessitate an initial knowledge of the signal’s sparsity level, the presented method employs sparsity adaptive matching pursuit (SAMP) techniques to gradually determine the signal’s sparsity level. Furthermore, it introduces a threshold parameter based on the signal’s energy level to eliminate the sparsity level requirement. Extensive simulations show that the presented channel estimation approach surpasses the traditional OMP-based channel estimation methods in terms of normalized mean square error performance. In addition, the computational cost of channel estimation is lowered. Based on the simulations, our approach can estimate the channel well while reducing training overhead by a large amount.

Compressive sensing-based channel estimation for high mobile systems with delay Doppler effects

In this paper, channel overhead is reduced by exploiting channel sparsity for multiple input multiple output-orthogonal frequency division multiplexing (MIMO-OFDM) system. Where, compressive sensing (CS) based dictionary design algorithms has been adopted as a channel estimation technique in high mobile systems with minimal number of pilots, such as high-speed train (HST) systems. A novel framework design of the dictionary-based CS was proposed considering both delay and Doppler effects in order to correctly recover the channel response. The channel under consideration is a 2 by 2 space-time block code (STBC) MIMO channel. Simulation tests according to the international telecommunication union (ITU) channel model demonstrated the suitability of the proposed dictionary for estimating the channel impulse response (CIR) of a liner time varying (LTV) channel with a mobility approaches 675 Km/h related to a Doppler frequency of 1500 Hz and 2.4 GHz carrier frequency. Two CS recovery algorithms were applied; orthogonal matching pursuit (OMP) and basis pursuit (BP), where by about 7 dB gain in signal to noise ratio (SNR) was achieved with mobility of 675 Km/h using OMP as compared to BP at a bit error rate (BER) of with 128 OFDM subcarriers.

Compressive sensing-based channel estimation schemes for wideband millimeter wave wireless communication systems

The introduction of both hybrid precoder-combiner structure and low-bit resolution analog-to-digital converters (ADCs) makes channel estimation in millimeter (mm) Wave Massive MIMO systems to be challenging. In the literature, several channel estimation schemes presented focus on narrowband channels. However, few works are found to be presented for the case of wideband channels for this system. In this paper, two new channel estimation techniques are proposed with the first scheme based on the Projection-based Orthogonal Matching Pursuit (POMP) algorithm. The second estimation scheme is based on the Predictive Orthogonal Least Squares (POLS) algorithm. In all the simulation results, POLS shows the best performance but with the highest computational complexity cost, with POMB exhibiting the next best performance with a lower computational complexity cost. In terms of compromise between performance and computational complexity cost, the proposed POLS-based channel estimator is the best estimator for the wideband mmWave Massive MIMO systems.

Deep Compressed Sensing-Based Cascaded Channel Estimation for RIS-Aided Communication Systems

To reduce the pilot overhead of cascaded channel estimation for RIS-aided Massive MIMO communication system, we proposed a deep compressed sensing-based channel estimation scheme, where U-shaped network (U-Net), an encoder-decoder with skip connection, is used to recover the high-dimensional cascaded channel matrix from limited pilot overhead. The skip connections between encoder and decoder can fuse features of different scales and semantic by concatenating the feature map, which enhance the reconstruction performance of cascaded channel. To further improve the feature extraction ability of U-Net, we design a ResU-Net architecture with stacked residual units to increase the depth of network. Simulation results show the channel estimation of ResU-Net is more accurate than conventional algorithm and other network model. Meanwhile, ResU-Net has good generalization and robustness for different pilot lengths and phase quantization errors.

Channel Estimation for Broadband Millimeter Wave MIMO Systems Based on High-Order PARALIND Model

Channel state information (CSI) is important to improve the performance of wireless transmission. However, the problems of high propagation path loss, multipath, and frequency selective fading make it difficult to obtain the CSI in broadband millimeter-wave (mmWave) system. Based on the inherent multidimensional structure of mmWave multipath channels and the correlation between channel dimensions, mmWave multiple input multiple output (MIMO) channels are modelled as high-order parallel profiles with linear dependence (PARALIND) model in this paper, and a new PARALIND-based channel estimation algorithm is proposed for broadband mmWave system. Due to the structural property of PARALIND model, the proposed algorithm firstly separates the multipath channels of different scatterers by PARALIND decomposition and then estimates the channel parameters from the factor matrices decomposed from the model based on their structures. Meanwhile, the performance of mmWave channel estimation is analysed theoretically. A necessary condition for channel parameter estimation is given based on the uniqueness principle of PARALIND model. Simulation results show that the proposed algorithm performs better than traditional compressive sensing-based channel estimation algorithms.

Gridless Compressed Sensing Based Channel Estimation for UAV Wideband Communications With Beam Squint

Unmanned aerial vehicle (UAV) has become an appealing solution for a wide range of commercial and civilian applications thanks to its high mobility and flexible deployment. Due to the continuous UAV navigation, the channels between UAV based arial base station (BS) and the ground users are subject to the Doppler effect. Meanwhile, when UAV is equipped with massive number of antennas in high frequency band such as mmWave and Tera Hertz (THz), the non-negligible propagation delay across the array aperture would cause <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">beam squint</i> effect. In this paper, we first investigate the channel under both Doppler effect and beam squint effect for UAV communications with large antenna array and high frequency band. Then, we design a gridless compressed sensing (GCS) based channel tracking method, where the high dimension uplink channel can be derived by estimating a few physical parameters such as the direction of arrival (DOA), Doppler shift, and the complex gain information. Besides, with the Doppler shift reciprocity and angular reciprocity, the downlink channel can be derived by only one pilot symbol, which greatly decreases the downlink channel training overhead. Various simulation results are provided to verify the effectiveness of the proposed methods.

Configuration Optimization and Channel Estimation in Hybrid Beamforming mmWave Systems With Channel Support Side Information

Hybrid beamforming (HBF) is a promising technique in millimeter-wave (mmWave) systems with a limited number of radio frequency (RF) chains. Acquiring channel state information is essential for HBF design. Various compressive-sensing-based channel estimation (CE) schemes that exploit the sparse nature of mmWave channels have been proposed. The design of an analog beamforming matrix plays a key role in providing high CE quality with low pilot overhead. In this paper, a novel and flexible HBF architecture called adaptive sum product network (ASPN) is proposed, which can realize more flexible analog beamforming with less hardwares. Based on the ASPN architecture, a novel variational-Bayesian-inference-based compressive CE algorithm is proposed, which can fully exploit the channel support side information (CSSI) at the base station under a general uncertain measurement matrix induced by the off-grid parameters for power leakage elimination. To optimally configure the degrees of freedom in the ASPN according to the CSSI, a dynamic configuration optimization algorithm is proposed by formulating the CE performance metric in terms of the configuration parameters of ASPN. The importance and effectiveness of the flexibility introduced in ASPN HBF architecture, the compressive CE algorithm as well as the configuration algorithm are illustrated by the superb performance of the proposed scheme compared to the representative state-of-the-art techniques through extensive simulation results.

Joint Nuclear Norm and ℓ1–2-Regularization Sparse Channel Estimation for mmWave Massive MIMO Systems

Millimeter-wave massive MIMO can effectively improve the signal-to-noise ratio, but the high-dimensional channel matrix significantly increases the complexity of the classic channel estimation algorithm. On the other hand, millimeter-wave massive MIMO has low rank and sparsity properties in the angle domain. Combining these two properties can effectively improve the channel estimation accuracy. This article proposes a novel millimeter-wave sparse channel estimation method based on joint nuclear norm and $\ell _{1-2}$ -regularization. The basic idea of the proposed algorithm is to formulate the channel estimation problem as a compressed sensing problem. This method constructs an objective function consisted of $\ell _{1-2} $ -regularization, and the resulting nuclear norm minimization problems is optimized via the alternating direction method of multipliers (ADMM) algorithm. The simulation results verified that the proposed method can provide better estimation accuracy compared with the state-of-the-art compressed sensing-based channel estimation methods.

Adaptive Compressive Sensing-Based Channel Estimation for 5G Massive MIMO Systems

massive numerous statistics notable yield (MIMO) is a key innovation in a long way off correspondences to get records paces of more than one instances. vast MIMO framework has pretty big variety of reception apparatuses at the base station (BS) worked for overhauling severa clients simultaneously. it is a promising innovation for acknowledgment of immoderatethroughput far off interchanges. titanic MIMO misuses the excessive degree of spatial opportunity with the purpose that it considerably improves the channel limit and power proficiency of the MIMO framework. The massive MIMO frameworks are comprehensively stated as a full-size putting in innovation for fifth era (5G) wireless correspondence frameworks. but, in massive MIMO frameworks, a careful assurance of the channel nation information (CSI)is needed for motivation in the back of feasible sign discovery, asset distribution and beamforming and so forth. those frameworks having an large range of reception apparatuses at the base Stations, clients want to gauge channels which can be associated with severa portions of transmit recieving wires. due to this, pilot overhead seems to be excessive. on this way, acknowledgment of the right channel state statistics with a base pilot overhead can be a tough issue. Reenactment outcomes finished demonstrates that the proposed calculation can swiftly and precisely determine huge MIMO channel of the difficult to understand channel sparsity and with excessive computational productiveness whilst contrasted and distinct beyond calculations

Performance Analysis of a Compressive Sensing–based Channel Estimation Algorithm in a Noisy Channel

Performance Analysis of a Compressive Sensing–based Channel Estimation Algorithm in a Noisy Channel

Compressive Sensing Based Channel Estimation for Massive MIMO Communication Systems

Massive multiple-input multiple-output (MIMO) is believed to be a key technology to get 1000x data rates in wireless communication systems. Massive MIMO occupies a large number of antennas at the base station (BS) to serve multiple users at the same time. It has appeared as a promising technique to realize high-throughput green wireless communications. Massive MIMO exploits the higher degree of spatial freedom, to extensively improve the capacity and energy efficiency of the system. Thus, massive MIMO systems have been broadly accepted as an important enabling technology for 5th Generation (5G) systems. In massive MIMO systems, a precise acquisition of the channel state information (CSI) is needed for beamforming, signal detection, resource allocation, etc. Yet, having large antennas at the BS, users have to estimate channels linked with hundreds of transmit antennas. Consequently, pilot overhead gets prohibitively high. Hence, realizing the correct channel estimation with the reasonable pilot overhead has become a challenging issue, particularly for frequency division duplex (FDD) in massive MIMO systems. In this paper, by taking advantage of spatial and temporal common sparsity of massive MIMO channels in delay domain, nonorthogonal pilot design and channel estimation schemes are proposed under the frame work of structured compressive sensing (SCS) theory that considerably reduces the pilot overheads for massive MIMO FDD systems. The proposed pilot design is fundamentally different from conventional orthogonal pilot designs based on Nyquist sampling theorem. Finally, simulations have been performed to verify the performance of the proposed schemes. Compared to its conventional counterparts with fewer pilots overhead, the proposed schemes improve the performance of the system.

Compressive Sensing based Channel Estimation in OFDM Systems

Compressive Sensing based Channel Estimation in OFDM Systems

Improving MIMO relay compressed sensing-based channel estimation and pilot allocation

Increasing the data rate of communication systems together with the performance are main goals of the communication networks in the future. Multiple-input multiple-output (MIMO)-orthogonal frequency division multiplexing (OFDM) relay networks as a key technology is one the main techniques to maintain the performance and data rate. Being influenced by fading channels, the MIMO-OFDM relay networks need some reliable approaches to estimate the channel response. Compressed sensing (CS) is one of the critical approaches to maintain the accuracy together with the spectral efficiency. In this paper, we have utilized CS-based approaches to estimate the MIMO-OFDM relay channel. Specifically, forward backward prediction (FBP) is used to estimate the fading channels. This approach benefits from backward correction which distinguishes it from other iterative approaches and helps interestingly in channel estimation accuracy. Moreover, in order to improve the channel estimation, pilot allocation approaches are proposed based on the system model and probability function. Furthermore, a cross entropy-based approach is utilized to propose two different approaches called sequential cross entropy self-coherence (SCE-SC) and parallel cross entropy self-coherence. Actually, mutual coherence is divided into two parts namely cross coherence and self coherence. It is demonstrated that in FBP-based approach self-coherence is important in mutual coherence. Consequently, the computations are interestingly decreased. The superiority of the method is represented using comparing simulations with other well-known approaches.

Compressive sensing-based channel estimation for SC-FDE system

The channel of wireless broadband communication system usually features time domain sparsity. The channel estimation of single carrier frequency domain equalization (SC-FDE) system was modeled as compressive sensing (CS)-based reconstruction of sparse signal. Matching pursuit algorithms based on greedy search were adopted to reconstruct channel information. Under the premise of reduced reference signal overhead, the system reliability can be effectively enhanced. This paper proposes a priori information aided scheme. By inserting the cyclic prefix of the first training sequence to a SC-FDE transmission frame, which is used as guard interval, the priori information including more accurate channel length and sparsity can be obtained with inter-block-interference (IBI)-free training sequence. The proposed scheme shows obvious superiority in slow fading channels. It effectively improves the reconstruction of probability, thereby improving the accuracy of channel estimation, meanwhile sacrificing less spectral efficiency.

Bayesian mmWave Channel Estimation via Exploiting Joint Sparse and Low-Rank Structures

We consider the problem of channel estimation for millimeter wave (mmWave) systems, where both the base station and the mobile station employ a single radio frequency (RF) chain to reduce the hardware cost and power consumption. Recent real-world channel measurements reveal that the mmWave channels incur a certain amount of spread over the angular domains due to the scattering clusters. The angular spreads give rise to a joint sparse and low-rank channel matrix in the angular domain. To utilize this joint sparse and low-rank structure, we address the channel estimation problem within a Bayesian framework. Specifically, we adopt a matrix factorization formulation and translate the problem of channel estimation into one of searching for two-factor matrices. To encourage a joint sparse and low-rank solution, independent sparsity-promoting priors are placed on entries of the two-factor matrices, which aims to promote sparse factor matrices with only a few non-zero columns. Based on the proposed prior model, we develop a variational Bayesian inference method for the mmWave channel estimation. The simulation results show that our proposed method presents a considerable performance improvement over the state-of-the-art compressed sensing-based channel estimation methods.

OFDM Performance Assessment for Traffic Surveillance in Drone Small Cells

In this paper, a novel compressed-sensing-based channel-estimation (CE) method is proposed to estimate the sparse and doubly selective air-to-ground wireless channel for drones. These drones are supposed to be utilized for data transmission in cellular networks as well as traffic monitoring. The proposed method utilizes time domain training sequence for CE. The Monte Carlo simulation results indicate that the proposed CE method performs better in high Doppler shift conditions compared with other recently proposed methods. We also present a modification of the proposed method for sparse channels where the location of multipath receptions does not change as fast as their amplitude and phase over time. Exploiting this fact, we separate estimation of the sparse channel's nonzero elements locations from estimating their amplitudes and phases and therefore improve bandwidth efficiency and performance. The proposed methods are used in simulation of a traffic surveillance scenario where frames of a real video which were taken by a drone from a highway are used as the input to the communication system and the received and demodulated signal is analyzed for car detection using a faster region-based-convolutional-network algorithm. The computational-complexity and the bandwidth-efficiency of the CE methods are compared within this scenario. Simulation results by using normalized-mean-square-error and bit-error-rate calculations show that using the proposed CE methods increases the CE and data demodulation accuracy whilst compared with the least-square method, increases the bandwidth-efficiency up to 5% and reduces the computational-complexity by a factor of two or more.

Distributed Compressed Sensing-Based Channel Estimation and Pilot Allocation for MIMO Relay Networks

Distributed compressed sensing (DCS)-based channel estimation of multiple-input–multiple-output (MIMO) orthogonal frequency-division multiplexing for relay communication is considered in this paper. Specifically, the pilot allocation is addressed to optimize the channel estimation performance. Pilot placement in all the existing works based on Compressed Sensing (CS), address the mean square error (MSE) probabilistically via mutual coherence. On the contrary, we try to address the MSE of the estimate directly and optimize the MSE directly and design a pilot pattern to maximize the performance of the estimation. By taking into account the optimization approach, a combinatorial stochastic algorithm has been presented. Simulation results represent that the DCS-based MIMO relay channel estimation using optimized pilot placements will increase the performance from 3 to 10 dB as compared with the conventional least squares (LS) method. Moreover, the DCS-based MIMO relay channel estimation shows 2.3% and 45% improvement in spectrum efficiency under the same bit error rate performance over the compressed sensing (CS)-based channel estimation and traditional LS-based channel estimation approach, respectively.

Location-Based Millimeter Wave Multi-Level Beamforming Using Compressive Sensing

In this letter, a novel millimeter wave (mmWave) multi-level beamforming (BF) is proposed. User positioning is used for roughly defining the area within which the mobile station (MS) is properly located. Based on this, a multi-level beam search is conducted using compressive sensing-based channel estimation to find out the transmit/receive beams for establishing the mmWave link. The estimated MS location-uncertainty area is used to determine the number of beams along with the beamwidth required for constructing the sensing matrix used in each beam searching level. Mathematical and simulation analysis confirm the superiority of the proposed BF scheme over the conventional ones in both BF complexity and performance.

Pilot-based parametric channel estimation algorithm for DCO-OFDM-based visual light communications

Due to wide modulation bandwidth in optical communication, multipath channels may be non-sparse and deteriorate communication performance heavily. Traditional compressive sensing-based channel estimation algorithm cannot be employed in this kind of situation. In this paper, we propose a practical parametric channel estimation algorithm for orthogonal frequency division multiplexing (OFDM)-based visual light communication (VLC) systems based on modified zero correlation code (ZCC) pair that has the impulse-like correlation property. Simulation results show that the proposed algorithm achieves better performances than existing least squares (LS)-based algorithm in both bit error ratio (BER) and frequency response estimation.