Compressed Channel Sensing Research Articles

Instantaneous frequency measurement based on photonic compressive sensing with sub-Nyquist pseudo-random binary sequences.

We propose a novel, to the best of our knowledge, approach to realizing instantaneous frequency measurement with ultrahigh measurement bandwidth, which utilizes three-channel photonic compressive sensing (CS) with sub-Nyquist pseudo-random binary sequences (PRBSs). In each CS channel, an alias frequency is recovered due to the sub-Nyquist property of the applied PRBS. A frequency identification algorithm is employed to determine the frequency of the signal under measurement according to the three alias frequencies. The proposed approach significantly reduces the bit rate of the applied PRBSs and the sampling rate required by the digitizers in CS. A proof-of-concept experiment for measuring frequency in the Ku band is demonstrated using PRBSs at 1 Gb/s and digitizers with a sampling rate of 250 MS/s.

Hybrid mmWave MIMO Systems Under Hardware Impairments and Beam Squint: Channel Model and Dictionary Learning-Aided Configuration

Low overhead channel estimation based on compressive sensing (CS) has been widely investigated for hybrid wideband millimeter wave (mmWave) multiple-input multiple-output (MIMO) systems. The channel sparsifying dictionaries used in prior work are built from ideal array response vectors evaluated on discrete angles of arrival/departure. In addition, these dictionaries are assumed to be the same for all subcarriers, without considering the impacts of hardware impairments and beam squint. In this manuscript, we derive a general channel and signal model that explicitly incorporates the impacts of hardware impairments, practical pulse shaping functions, and beam squint, overcoming the limitations of mmWave MIMO channel and signal models commonly used in previous work. Then, we propose a dictionary learning (DL) algorithm to obtain the sparsifying dictionaries embedding hardware impairments, by considering the effect of beam squint without introducing it into the learning process. We also design a novel CS channel estimation algorithm under beam squint and hardware impairments, where the channel structures at different subcarriers are exploited to enable channel parameter estimation with low complexity and high accuracy. Numerical results demonstrate the effectiveness of the proposed DL and channel estimation strategy when applied to realistic mmWave channels.

Machine learning-enabled MIMO-FBMC communication channel parameter estimation in IIoT: A distributed CS approach

Compressed Sensing (CS) is a Machine Learning (ML) method, which can be regarded as a single-layer unsupervised learning method. It mainly emphasizes the sparsity of the model. In this paper, we study an ML-based CS Channel Estimation (CE) method for wireless communications, which plays an important role in Industrial Internet of Things (IIoT) applications. For the sparse correlation between channels in Multiple Input Multiple Output Filter Bank MultiCarrier with Offset Quadrature Amplitude Modulation (MIMO-FBMC/OQAM) systems, a Distributed Compressed Sensing (DCS)-based CE approach is studied. A distributed sparse adaptive weak selection threshold method is proposed for CE. Firstly, the correlation between MIMO channels is utilized to represent a joint sparse model, and CE is transformed into a joint sparse signal reconstruction problem. Then, the number of correlation atoms for inner product operation is optimized by weak selection threshold, and sparse signal reconstruction is realized by sparse adaptation. The experiment results show that the proposed DCS-based method not only estimates the multipath channel components accurately but also achieves higher CE performance than classical Orthogonal Matching Pursuit (OMP) method and other traditional DCS methods in the time-frequency dual selective channels.

Site-Specific Online Compressive Beam Codebook Learning in mmWave Vehicular Communication

Millimeter wave (mmWave) communication is one viable solution to support Gbps sensor data sharing in vehicular networks. The use of large antenna arrays at mmWave and high mobility in vehicular communication make it challenging to design fast beam alignment solutions. In this paper, we propose a novel framework that learns the channel angle-of-departure (AoD) statistics at a base station (BS) and uses this information to efficiently acquire channel measurements. Our framework integrates online learning for compressive sensing (CS) codebook learning and the optimized codebook is used for CS-based beam alignment. We formulate a CS matrix optimization problem based on the AoD statistics available at the BS. Furthermore, based on the CS channel measurements, we develop techniques to update and learn such channel AoD statistics at the BS. We use the upper confidence bound (UCB) algorithm to learn the AoD statistics and the CS matrix. Numerical results show that the CS matrix in the proposed framework provides faster beam alignment than standard CS matrix designs. Simulation results indicate that the proposed beam training technique can reduce overhead by 80% compared to exhaustive beam search, and 70% compared to standard CS solutions that do not exploit any AoD statistics.

Efficient Optical MIMO–OFDM Channel Estimation Based on Correntropy Compressive Sensing

The compressive sensing (CS) technique is widely used in the field of radio-frequency channel estimation. However, in optical wireless channel (OWC) estimation, CS needed to be adapted to cope with the nonlinear distortion resultant from the electrical-to-optical and optical-to-electrical conversion process of the OWC. Therefore, a robust CS channel estimation technique is proposed for the optical MIMO–OFDM system. The proposed CS channel estimation technique is based on higher-order statistics (HOS). In which, the correntropy is exploited as a HOS similarity measurement instead of the second-order statistics euclidean distance (ED) used with the conventional CS to handle the OWC nonlinear distortion. Herein, the ED is the most popular iterative CS algorithms such as orthogonal matching pursuits, sparsity adaptive matching pursuit, and self-aware step size sparsity adaptive matching pursuit is efficiently replaced by the HOS correntropy based CS to show the effectiveness of the proposed algorithm. Moreover, the most recent visible light communication system GLIM-OFDM is considered here to test the proposed technique. However, the pre-estimated channel state information (CSI) obtained from the previous OFDM frame is exploited as initial information in estimating the CSI of the current OFDM frame, which improves overall system performance. A comparison with the conventional CS reconstruction algorithms is conducted via extensive simulation analysis. The simulation results show the superiority of the bit error rate of the proposed correntropy based CS channel estimation scheme compared to the conventional CS-based channel estimation algorithms.

Mm-Wave channel estimation with accelerated gradient descent algorithms

The availability of millimeter wave (mm-Wave) band in conjunction with massive multiple-input-multiple-output (MIMO) technology is expected to boost the data rates of the fifth-generation (5G) cellular systems. However, in order to achieve high spectral efficiencies, an accurate channel estimate is required, which is a challenging task in massive MIMO. By exploiting the small number of paths that characterize the mm-Wave channel, the estimation problem can be solved by compressed-sensing (CS) techniques. In this paper, we propose a novel CS channel estimation method based on the accelerated gradient descent with adaptive restart (AGDAR) algorithm exploiting a ℓ1-norm approximation of the sparsity constraint. Moreover, a modified re-weighted compressed-sensing (RCS) technique is considered that iterates AGDAR using a weighted version of the ℓ1-norm term, where weights are adapted at each iteration. We also discuss the impact of cell sectorization and tracking on the channel estimation algorithm. We compare the proposed solutions with existing channel estimations with an extensive simulation campaign on downlink third-generation partnership project (3GPP) channel models.

Structured Random Compressed Channel Sensing for Millimeter-Wave Large-Scale Antenna Systems

In millimeter-wave (mmWave) communications, large-scale antenna system (LSAS) is considered an essential technology to realize beamforming gain to compensate for huge propagation loss. However, channel estimation for LSASs poses a formidable challenge, especially when hybrid analog–digital structures are adopted for ensuring reasonable complexity and cost. To address this challenge, compressed channel sensing (CCS) is leveraged to measure mmWave channels via a random sensing codebook. By exploiting the sparse nature in mmWave channels, only a small number of measurements is required for channel recovery. However, signaling or storing the configuration of a full random sensing codebook leads to a huge burden. Moreover, because the sensing beam of a full random sensing codebook always spreads its power over the channel, the CCS has a stringent requirement on the signal-to-noise ratio (SNR) for robust channel recovery. To overcome these issues, we propose a structured random sensing codebook inspired by the random convolutional measurement process. Owing to its structured nature, signaling or storing overhead from the codebook configuration is significantly reduced. Additionally, the structured random sensing codebook can concentrate its power in a local angle coverage for a sectorized cell, thus improving the robustness in low-SNR regimes. Simulation results demonstrate that the recovery performance of the proposed structured random sensing codebook design is comparable to that of the full random design. Regarding the robustness in low-SNR regimes, the recovery performance is substantially improved by the structured random sensing codebook with power concentration for a local angle coverage.

직교주파수분할다중화 시스템에서 저 복잡도의 구멍 뚫린 커널 기반 직교 매칭 퍼슛에 의한 압축 채널 센싱

#Channel estimation #compressed sensing #kernel #OFDM (orthogonal frequency division multiplexing) #OMP (orthogonal matching pursuit) #puncturing

Matrix Completion-Based Channel Estimation for MmWave Communication Systems With Array-Inherent Impairments

Hybrid massive MIMO structures with reduced hardware complexity and power consumption have been widely studied as a potential candidate for millimeter wave (mmWave) communications. Channel estimators that require knowledge of the array response, such as those using compressive sensing (CS) methods, may suffer from performance degradation when array-inherent impairments bring unknown phase errors and gain errors to the antenna elements. In this paper, we design matrix completion (MC)-based channel estimation schemes which are robust against the array-inherent impairments. We first design an open-loop training scheme that can sample entries from the effective channel matrix randomly and is compatible with the phase shifter-based hybrid system. Leveraging the low-rank property of the effective channel matrix, we then design a channel estimator based on the generalized conditional gradient (GCG) framework and the alternating minimization (AltMin) approach. The resulting estimator is immune to array-inherent impairments and can be implemented to systems with any array shapes for its independence of the array response. In addition, we extend our design to sample a transformed channel matrix following the concept of inductive matrix completion (IMC), which can be solved efficiently using our proposed estimator and achieve similar performance with a lower requirement of the dynamic range of the transmission power per antenna. Numerical results demonstrate the advantages of our proposed MC-based channel estimators in terms of estimation performance, computational complexity and robustness against array-inherent impairments over the orthogonal matching pursuit (OMP)-based CS channel estimator.

Non-orthogonal pilot pattern for sparse channel estimation in large-scale MIMO-OFDM system

From the perspective of compressed sensing (CS) theory, the channel estimation problem in large-scale multiple input multiple output (MIMO)-orthogonal frequency division multiplexing (OFDM) system is investigated. According to the theory, the smaller mutual coherence the reconstruction matrix has, the higher success probability the estimation can obtain. Aiming to design a pilot that can make the system reconstruction matrix having the smallest mutual coherence, this paper proposes a low complexity joint algorithm and obtains a kind of non-orthogonal pilot pattern. Simulation results show that compared with the conventional orthogonal pilot pattern, applying the proposed pattern in the CS channel estimation can obtain the better normalized mean square error performance. Moreover, the bit error rate performance of the large-scale MIMO-OFDM system is also improved.

Improved Asymptotic Capacity Lower Bound for OFDM System with Compressed Sensing Channel Estimation for Bernoulli Gaussian Channel

We propose an improved capacity lower bound for OFDM system with compressed sensing channel estima- tion for Bernoulli-Gaussian channel. We improve the known capacity lower bound which is based on Lasso compressed sensing channel estimation, by replacing the Lasso based estimate with an MMSE estimate, known to be optimal in the MMSE sense and achievable with practical algorithms for a broad range of system parameters setup. Additionally, for the system with equi-powered pilot subcarriers we opti- mize the capacity lower bound byfinding the optimal average fraction of pilot subcarriers used for channel estimation and optimal pilot to data power ratio given the average symbol powerpersubcarrier,andproposeanoptimizationprocedure with polynomial complexity.

Asymptotic Capacity Lower Bound for an OFDM System With Lasso Compressed Sensing Channel Estimation for Bernoulli-Gaussian Channel

We analyze the asymptotic capacity of an OFDM system with pilot aided channel estimation over a Bernoulli-Gaussian sparse channel, with Lasso compressed sensing (CS) used for channel estimation. In the analysis, we utilize the CS asymptotic performance similarity when using Haar and DFT measurement matrices. We evaluate the mean square estimation error of an augmented Lasso estimator using the replica method results and then use it to obtain an asymptotic capacity lower bound for the OFDM system. For the considered system with equi-power pilot symbols, we optimize the average fraction of pilot subcarriers used for channel estimation and the pilot to data power ratio, given an average symbol power per subcarrier, and evaluate the asymptotic capacity bound increase due to CS.

Sparse channel estimation and pilot optimization for underwater acoustic orthogonal frequency division multiple access uplink communications

Considering that the conventional channel interpolation method with sparse and irregular spaced pilots will lead to an error floor in underwater acoustic (UWA) orthogonal frequency division multiple access (OFDMA) uplink communications, a method for sparse channel estimation and pilot optimization is proposed in this paper. A compressed sensing (CS) algorithm is utilized for sparse channel impulse response estimation, which performs well in sparse and irregular spaced pilots and significantly decreases the channel estimation error. Besides, the pilots’ pattern and power joint optimization algorithm based on the random search technique is proposed for the minimum mutual coherence criterion in CS theory, which further improves the performance of CS estimation algorithm. During each iteration step, we randomly pick a pilots’ pattern from the subcarrier index set and a pilots’ power subset from the available power set. Then we perform this step iteratively within a certain searching time. Finally, the local optimal solution of the objective function for minimizing mutual coherence is considered as the feasible pilots’ pattern and power. Simulation results show that the convergence performance of the pilots’ pattern and power joint optimization algorithm is much better than that of the pilots’ pattern optimization algorithm. Furthermore, the channel estimation error of the proposed method is much lower than that of conventional least-squares channel estimator based on linear interpolation, CS channel estimator without pilot optimization, and CS channel estimator merely with pilots’ pattern optimization in channels of different multipath delay spreads. Finally, performance of the proposed method is demonstrated in the UWA uplink OFDMA systems with interleaved and generalized carrier assignment schemes respectively in the two-user case in a pool experiment. Experimental results show that the proposed method decreases dramatically the bit error rate in both carrier assignment schemes, and simultaneous reception for two users is achieved when signal noise ratio is larger than 10 dB.

Compressed Channel Sensing Algorithm Based on Cluster Sparsity

According to cluster structure feature for wideband sparse channel, the Cluster Regularized Adaptive Matching Pursuit (CRAMP) algorithm is presented based on the study of the original compressed channel sensing estimation algorithm. The proposed algorithm can accurately reconstruct the channel by both the adaptive process which chooses the number of candidate clusters and the regularization process which Support for secondary screening of candidate clusters, although the cluster sparsity of the channel is unknown. Simulation results show that the proposed algorithm’ performance is better than the BP, OMP, BOMP algorithm.

Compressed Channel Sensing using Designated Null Subcarriers in Full Duplex Wireless OFDM Systems

In this paper, we examine the digital cancellation of self-interference in full duplex wireless orthogonal frequency division multiplexing systems. Since digital cancellation highly depends on the accurate estimation of self-interference channels, which tends to be sparse, we propose a compressed sensing based channel estimator that uses designated null subcarriers. Our numerical results show that, by providing more accurate estimates of self-interference channels, the proposed scheme outperforms the conventional least square methods in terms of the self-interference suppression.

Sparse channel estimation of MIMO-OFDM systems with unconstrained smoothed l0-norm-regularized least squares compressed sensing

This paper investigates the sparse channel estimation issue of multiple-input multiple-output orthogonal frequency division multiplexing (MIMO-OFDM) systems. Beginning with the formulation of least squares (LS) solution to sparse MIMO-OFDM channel estimation, a compressed channel sensing (CCS) framework based on the new smoothed l0-norm-regularized least squares (l2-Sl0) algorithm is proposed. Three methods, namely quasi-Newton, conjugate gradient, and optimization in the null and complement spaces of the measurement matrix, are then proposed to solve the l2-Sl0 unconstrained optimization problem. Moreover, the two former are also applied to solve the l2-Sl0 channel estimation. A number of computer simulation-based experiments are conducted showing a better reconstruction accuracy of the l2-Sl0 algorithm as compared with the smoothed l0-norm (Sl0) algorithm in the presence of noise. The proposed CCS approach can save nearly 25% pilot signals to maintain the same mean square error (MSE) and bit error rate (BER) performances as given by the conventional LS method.

Joint Interference mitigation with channel estimated in Underwater Acoustic OFDM system

Multicarrier modulation in the form of orthogonal frequency-division-multiplexing (OFDM) has been actively pursued in underwater acoustic communications. However, carrier frequency offset (CFO) can cause significant performance degradation in OFDM systems. Although underwater acoustic communication was recently the focus of research, literatures about joint interference mitigation with channel estimation using compressed sensing (CS) has been very limited in underwater acoustic OFDM. In the Cyclic prefix orthogonal frequency division multiplexing (CP-OFDM) system, a novel method was proposed which combined interference mitigation with channel estimation based on compressed sensing (CS).The novel joint optimization model combined CFO estimation with CS-based channel estimation based on the sparse in the delay-Doppler domain by using the null sub-carriers. Simulations and tank experiments have verified the performance of the receiver based on the joint interference mitigation and CS channel estimation. This method enables the OFDM system is effective against the CFO, and has better robustness.

A Joint Mechanism of Adaptive Bayesian Compressed Channel Sensing Based on Optimized Measurement Matrix

该文采用基于概率模型的贝叶斯压缩感知方法，从最大后验概率角度，给出了压缩信道感知的一般流程。在此基础上，利用自适应贝叶斯压缩感知将信号的重构和观测矩阵的设计结合，使这两个环节不再相互独立。同时，提出一种基于最优观测矩阵的自适应贝叶斯压缩感知联合机制，通过减少观测矩阵的相关度以及对观测矩阵的自适应设计，使得信道的重构效果更佳。另外可利用重构过程中得到的差错栏，对重构精确度进行衡量。仿真表明：在相同的实验条件下，该联合机制相比传统的重构算法，具有更好的抗噪声能力和重构精度。

Do Cooperative Radios Collide?

When cooperative terminals transmit concurrently, the receiver experiences additional distortion by the presence of radios carrier offsets and time delays. With the inevitable inaccuracy in estimating the channel at the receiving end, is it realistic to assume that relays transmissions do not collide? To address this question, we provide an analytical framework to model the effect of channel estimation methods that treat the channel as deterministic and unknown, and derive the average symbol error rate (SER) of a maximum likelihood sequence decoder (MLSD) and of a linear minimum mean squared error (LMMSE) equalizer using such estimates. We select the appropriate architecture by mapping two different channel estimation techniques onto the proposed analytical framework, and derive their diversity gain. First we analyze the well known linear least square channel estimation (LLSE), then, we show how to cast a compressed channel sensing (CCS) method onto our analytical model, by a novel approximation of the estimator noise and modeling mismatch. We observe that the latter is the key bottleneck in LLSE whereas the CCS appears sufficiently flexible to deliver the promised gains. This conclusion shows that collisions models are overly pessimistic, but it also underscores the need for an advanced receiver synchronization design.

A Useful Performance Metric for Compressed Channel Sensing

Recently, new progress has been made in using basis expansion models for system identification with compressed sensing. To aid the application of these methodologies, we introduce a metric, called localized coherence, for choosing input signals that result in better estimation performance. Its definition is motivated through the analysis of the normalized mean Euclidean error of the channel estimate and its efficacy is demonstrated through numerical simulations.