Blind Channel Estimation Research Articles

Signal Scrambling Based Joint Blind Channel Estimation, Activity Detection, and Decoding for Massive Random Access

Signal Scrambling Based Joint Blind Channel Estimation, Activity Detection, and Decoding for Massive Random Access

Blind Channel Estimation and Data Detection With Unknown Modulation and Coding Scheme

Blind Channel Estimation and Data Detection With Unknown Modulation and Coding Scheme

A Nonredundant SVD-based Precoding Matrix for Blind Channel Estimation in CP-OFDM Systems Over Channels with Memory

In this paper, we investigate blind channel estimation in cyclic, prefixed orthogonal frequency division multiplexing (OFDM) systems using precoding matrices. Many studies show how the performance of bit error rate (BER) and mean square error of channel estimation are impacted by using a circulant matrix as a precoding matrix. We investigate a new structure for the precoding matrix in which the first row is generated by an exponentially decreasing equation along with the Toeplitz function and deploying singular value decomposition (SVD) to generate the final precoder matrix. This type of SVD-based precoding matrix has been presented and simulated under various circumstances. The resulting precoding matrix is employed in two different OFDM systems to estimate the channel frequency response vector up to scalar ambiguity. The proof of the precoding matrix full rank property is presented, along with other criteria that help with the development of various precoding matrices.

Blind channel estimation in ZP-OFDM system using new precoding matrix over channels with memory

Using a new precoding matrix, a new method for blind channel estimation of OFDM systems with zero padding has been investigated. A single vector of the correlation matrix is employed to obtain the normalized estimated channel vector, and one pilot signal is used to resolve the scalar ambiguity. Two models of OFDM systems have been investigated, and their NMSE and BER results have been compared in various scenarios. In particular conditions, the new precoding matrix offers an advantage over the conventional circulant. To aid in the creation of novel precoding matrices, a proof of the full rank property is stated under some conditions.

A Grant-Based Random Access Scheme With Low Latency for mMTC in IoT Networks

The design of transmission schemes and receiver techniques with high reliability and low latency for the massive machine-type communications (mMTC) is an important challenge for the future Internet of Things (IoT) systems in the sixth-generation (6G) wireless communication networks. In this paper, we propose a new physical-layer transceiver scheme for mMTC, where users transmit their data based on a pre-designed sparse Tanner graph, and the base station (BS) employs blind channel estimation and message-passing decoding to decode user data. In particular, low latency is achieved by the construction of the transmission Tanner graph, as well as a hybrid modulation scheme that consists of BPSK symbols to facilitate blind channel estimation, and general M-ary modulation to reduce the transmission delay. Moreover, high reliability is achieved by the proposed message-passing decoder that fully exploits the diversity of the transmitted signal and offers soft demodulation and decoding capabilities. We provide both performance analyses based on density evolution, and simulation results, to demonstrate the superior performance of the proposed physical-layer transceiver solution for mMTC.

Least Square Estimation-Based Different Fast Fading Channel Models in MIMO-OFDM Systems

In cellular wireless communication systems, channel estimation (CE) plays a pivotal role as a crucial technique applied in orthogonal frequency division multiplexing (OFDM) modulation. CE utilizes a variety of methods, including decision-directed channel estimation, pilot-assisted channel estimation (PACE), and blind channel estimation. Among these options, PACE is widely favored for its remarkable stability and consistent superior performance. The idea of massive multiple-input multiple-output (MIMO) shows tremendous potential for the future of wireless communications. However, existing massive MIMO systems face challenges with their high computational complexity and intricate spatial structures, preventing efficient utilization of channel and sparsity features in these multiantenna systems. In communication channels, the signal received is often influenced by the characteristics of the channel and noise present at the receiver. To address this issue, an efficient dataset is utilized, employing the least square (LS) algorithm for minimization. OFDM is a commonly and widely used modulation method in communication systems utilized to specifically combat resonance fading in wireless channels. In wireless communication systems employing OFDM-MIMO, frequency selectivity and time-varying attributes due to multipath channels cause Intercarrier Interference (ICI) among symbols. Channel estimation is a vital aspect for mitigating the effects of fading channels. This investigation focuses on the application of a method examined in the study, which involves a block-type pilot symbol-assisted estimation technique for Rayleigh and Rician fading channel models. The research assesses the performance of the least square (LS) channel estimators in fast-fading channel models while employing various symbol mapping techniques focusing on bit error rate, throughput, and mean square error. The results indicate that the LS estimator exhibits excellent performance in Rayleigh and AWGN channels within the pedestrian A (PedA) model for both uplink and downlink scenarios. It outperforms the PedA model without channel estimation.

Iterative Semi-Coherent Receiver for Coded LoRa Systems

This letter presents a novel design of an iterative receiver for low-power long-range (LoRa) systems with channel coding and semi-coherent receiver. To enable soft-input-soft-output (SISO) decoding, closed-form expressions for the exact distribution of the received signal samples and the resulting log-likelihood ratios of the coded bits are obtained. Next, we propose an iterative semi-coherent receiver that performs iterative processing among the soft-output demodulator, the SISO channel decoder, and the blind channel estimator, i.e., without any training overhead. Simulation results show that our proposed receiver outperforms non-coherent receiver, and closely approaches the ideal coherent receiver’s performance.

New precoding blind channel estimation and channel order estimation algorithm in OFDM systems with cyclic prefix

Using different precoding matrices, a new blind channel estimation in OFDM systems with cyclic prefix is presented in this paper. The proposed method employs one column of the correlation matrix directly, unlike the traditional precoding techniques where the elements of precoding matrix is been removed. Results show the impact of this method specially when using the type of precoding matrices which include the circulant property in its design. Since channel order estimation is an important task in blind methods, a new and simple algorithm is also investigated with no additional complexity been added to the system. A proof of diagonalizability property is mentioned which can be implemented for other investigations concerning this type of matrices.

Channel-Resilient Deep-Learning-Driven Device Fingerprinting Through Multiple Data Streams

Enabling accurate and automated identification of wireless devices is critical for allowing network access monitoring and ensuring data authentication for large-scale IoT networks. RF fingerprinting has emerged as a solution for device identification by leveraging the transmitters’ inevitable hardware impairments that occur during manufacturing. Although deep learning is proven efficient in classifying devices based on hardware impairments, the performance of deep learning models suffers greatly from variations of the wireless channel conditions, across time and space. To the best of our knowledge, we are the first to propose leveraging MIMO capabilities to mitigate the channel effect and provide a channel-resilient device classification framework. We begin by showing that for AWGN channels, combining multiple received signals improves the testing accuracy by up to 30%. We then show that for more realistic Rayleigh channels, blind channel estimation enabled by MIMO increases the testing accuracy by up to 50% when the models are trained and tested over the same channel, and by up to 69% when the models are tested on a channel that is different from that used for training.

Blind channel estimation for DCO-OFDM based vehicular visible light communication

Blind channel estimation (CE) methods for OFDM based RF communication provide high-rate transmission by eliminating pilot overhead in conventional pilot-based methods, at the cost of lower accuracy. However, there is no work on the blind CE for OFDM based visible light communication (VLC) systems. In this paper, we propose a novel blind CE method for vehicular VLC with the goal of improving CE accuracy based on the exploitation of the channel statistics derived, by utilizing extensive amount of data collected for different communication angles, distances, and ambient light conditions. First, the normalized channel frequency response (CFR) of the V2LC channel is demonstrated to be invariant of inter-vehicular distance, relative transmitter/receiver zenith angle and ambient light. Then, this channel characteristic is exploited in the blind CE to improve its accuracy with two-step estimation of normalization factor. Extensive simulations at different vehicle speeds show that the proposed method outperforms the pilot-based and superimposed training-based CE methods in terms of spectral efficiency both for all modulation schemes and at all relative speeds. The proposed blind channel estimation (CE) method provides 9.77% increase in the spectral efficiency, compared to the second best method, superimposed training-based CE, at 20 dB signal-to-noise ratio (SNR) and 160 km/h relative speed, for 64-Quadrature Amplitude Modulation (QAM) Direct Current-Biased Optical Orthogonal Frequency Division Multiplexing (DCO-OFDM). Moreover, the real-time performance of the proposed blind CE is demonstrated for a realistic vehicle mobility scenario extracted from SUMO.

Deep Learning Channel Estimation for OFDM 5G Systems with Different Channel Models

At cellular wireless communication systems, channel estimation (CE) is one of the key techniques that are used in Orthogonal Frequency Division Multiplexing modulation (OFDM). The most common methods are Decision‐Directed Channel Estimation, Pilot-Assisted Channel Estimation (PACE) and blind channel estimation. Among them, PACE is commonly used and has a steadier performance. Applying deep learning (DL) methods in CE is getting increasing interest of researchers during the past 3 years. The main objective of this paper is to assess the efficiency of DL-based CE compared to the conventional PACE techniques, such as least-square (LS) and minimum mean-square error (MMSE) estimators. A simulation environment to evaluate OFDM performance at different channel models has been used. A DL process that estimates the channel from training data is also employed to get the estimated impulse response of the channel. Two channel models have been used in the comparison: Tapped Delay Line and Clustered Delay Line channel models. The performance is evaluated under different parameters including number of pilots (64 pilots or 8 pilots), number of subcarriers (64), the length of cyclic prefix (16 or 0 samples) and carrier frequency (4 GHz) through computer simulation using MATLAB. From the simulation results, the trained DL estimator provides better results in estimating the channel and detecting the transmitted symbols compared to LS and MMSE estimators although, the complexity of the proposed LSTM estimator exceeds the equivalent LS estimator. Furthermore, the DL estimator also demonstrates its effectiveness with various pilot densities and with different cyclic prefix periods.

Blind detection for spatial modulation uplink multi‐user massive MIMO communications: A sparse Bayesian learning‐expectation maximization approach

AbstractMassive spatial modulation (SM) multi‐input multi‐output (MIMO) system is a promising technique in uplink communications for future mobile communication, due to its power and spectral efficiencies. However, these systems, like other MIMO communications, face the challenge of channel estimation. Pilot‐based channel estimation methods result in data rate reduction as well as imposing additional complexity at the receiver side, which is intensified in time‐varying channels. Therefore, blind channel estimation is an alternative way to avoid pilot transmission. Considering a time‐varying channel and taking the advantages of machine learning techniques, blind channel estimation and data detection for SM uplink multi‐user massive MIMO communications is presented in this article. In this regard, a blind multi‐user detection based on the expectation‐maximization (EM) algorithm, called BMU‐EM, is presented first; however, this detector suffers from high computational complexity. In order to mitigate the complexity problem, a blind multi‐user detection based on sparse Bayesian learning and expectation‐maximization, called BMU‐SBEM, is proposed. Simulation results show that the BMU‐SBEM detector performs almost close to the optimum detector where the perfect channel information is available. Furthermore, the computational complexity of the BMU‐SBEM detector increases linearly with the number of users, making it suitable for massive communications in time‐varying channels.

Detection and Blind Channel Estimation for UAV-Aided Wireless Sensor Networks in Smart Cities Under Mobile Jamming Attack

Unmanned aerial vehicles (UAVs) can be integrated into wireless sensor networks (WSNs) for smart city applications in several ways. Among them, a UAV can be employed as a relay in a "store-carry and forward" fashion by uploading data from ground sensors and metering devices and, then, downloading it to a central unit. However, both the uploading and downloading phases can be prone to potential threats and attacks. As a legacy from traditional wireless networks, the jamming attack is still one of the major and serious threats to UAV-aided communications, especially when also the jammer is mobile, e.g., it is mounted on a UAV or inside a terrestrial vehicle. In this paper, we investigate anti-jamming communications for UAV-aided WSNs operating over doubly-selective channels in the downloading phase. In such a scenario, the signals transmitted by the UAV and the malicious mobile jammer undergo both time dispersion due to multipath propagation effects and frequency dispersion caused by their mobility. To suppress high-power jamming signals, we propose a blind physical-layer technique that jointly detects the UAV and jammer symbols through serial disturbance cancellation based on symbol-level post-sorting of the detector output. Amplitudes, phases, time delays, and Doppler shifts - required to implement the proposed detection strategy - are blindly estimated from data through the use of algorithms that exploit the almost-cyclostationarity properties of the received signal and the detailed structure of multicarrier modulation format. Simulation results corroborate the anti-jamming capabilities of the proposed method, for different mobility scenarios of the jammer.

Secrecy Coding Analysis of Short-Packet Full-Duplex Transmissions with Joint Iterative Channel Estimation and Decoding Processes.

This paper studies the secrecy coding analysis achieved by the self-jamming technique in the presence of an eavesdropper by considering a short-packet Full-Duplex (FD) transmission developed based on iterative blind or semi-blind channel estimation and advanced decoding algorithms. Indeed, the legitimate receiver and eavesdropper can simultaneously receive the intended signal from the transmitter and broadcast a self-jamming or jamming signal to the others. Unlike other conventional techniques without feedback, the blind or semi-blind algorithm applied at the legitimate receiver can simultaneously estimate, firstly, the Self-Interference (SI) channel to cancel the SI component and, secondly, estimate the propagation channel, then decode the intended messages by using 5G Quasi-Cyclic Low-Density Parity Check (QC-LDPC) codes. Taking into account the passive eavesdropper case, the blind channel estimation with a feedback scheme is applied, where the temporary estimation of the intended channel and the decoded message are fed back to improve both the channel estimation and the decoding processes. Only the blind algorithm needs to be implemented in the case of a passive eavesdropper because it achieves sufficient performances and does not require adding pilot symbols as the semi-blind algorithm. In the case of an active eavesdropper, based on its robustness in the low region of the Signal-to-Noise Ratio (SNR), the semi-blind algorithm is considered by trading four pilot symbols and only requiring the feedback for channel estimation processes in order to overcome the increase in noise in the legitimate receiver. The results show that the blind or semi-blind algorithms outperform the conventional algorithm in terms of Mean Square Error (MSE), Bit Error Rate (BER) and security gap (). In addition, it has been shown that the blind or semi-blind algorithms are less sensitive to high SI and self-jamming interference power levels imposed by secured FD transmission than the conventional algorithms without feedback.

Channel Estimation for DS-CDMA Rake Receiver using Sparse Recovery Approach

In the literature of direct-sequence code-division multiple-access (DS-CDMA), for issues related to channel estimation, some exclusive architectures have been proposed, which are characterized by known channel noise statistics and noise observation. But in reality, the channel parameters are frequently assessed utilizing training sequences that lead to difficulty in obtaining the channel noise statistics. Channel estimation quality has been proved to play an important role in the performance of rake receiver. This paper addresses the issues of optimizing DS–CDMA rake receiver channel estimation equipped with an Iterative least square sparse recovery (IL2SR) channel estimator. Moreover, the ambient noises corrupt the signal received and multiple-access interference further aggravates it. Because of this observation noises become hard to acquire. Hence this paper proposes as an iterative least square structure for channel estimation algorithm in rake receiver employed in DS-CDMA communication systems. Further, examination of blind channel estimation problem for rake-based DS-CDMA communication framework having multi-path fading channels with time variation is also attempted. The validity of the proposed techniques has been verified through results obtained from simulation for different channel parameters and spreading codes. Further exploration has been carried out with execution of the IL2SR with Rake receiver in DS-CDMA framework for multi-path fading channels. It is found that better performance is obtained with this framework under various channels with different spreading codes. The proposed system is compared with Kalman based techniques and it was found that DS-CDMA framework under additive white gaussian noise (AWGN)channel with IL2SR receiver reveals better outcomes in terms of bit error rate (BER). Also, there has been improvement in video quality while using the proposed IL2SR receiver with increase in the values of ratio of signal to noise ratio.

Unsourced Multiple Access Based on Sparse Tanner Graph—Efficient Decoding, Analysis, and Optimization

We propose novel sparse-graph-based transmission schemes and receiver algorithms for unsourced multiple access (UMA) in MIMO channels. The channel coherence interval is divided into a number of sub-slots and each active transmitter selects certain sub-slots to repeatedly transmit its codeword according to a sparse Tanner graph. We propose iterative receiver algorithms that at each iteration decode either a single codeword, or two or three codewords jointly, and then subtract the decoded codewords from received signals during all sub-slots. The keys to these decoders are novel blind channel estimation algorithms when the received signal contains one, two, or three codewords. We perform density evolution analysis on the proposed UMA systems to obtain the asymptotic upper bounds on the maximum achievable rates for different decoders under both regular and irregular Tanner graphs. Extensive simulation results are provided to illustrate the performance of the proposed UMA systems, and its advantages over existing compressed-sensing (CS)-based UMA schemes.

Extended Successive Convex Approximation for Phase Retrieval With Dictionary Learning

Phase retrieval aims at recovering unknown signals from magnitude measurements of linear mixtures. In this paper, we consider the phase retrieval with dictionary learning problem, which includes another prior information that the signal admits a sparse representation over an unknown dictionary. The task is to jointly estimate the dictionary and the sparse representation from magnitude-only measurements. To this end, we study two complementary formulations and develop efficient parallel algorithms by extending the successive convex approximation framework using a smooth majorization. The first algorithm is termed <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">compact-SCAphase</i> and is preferable in the case of moderately diverse mixture models with a low number of mixing components. It adopts a compact formulation that avoids auxiliary variables. The proposed algorithm is highly scalable and has reduced parameter tuning cost. The second algorithm, referred to as <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">SCAphase</i> , uses auxiliary variables and is favorable in the case of highly diverse mixture models. It also renders simple incorporation of additional side constraints. The performance of both methods is evaluated when applied to blind channel estimation from subband magnitude measurements in a multi-antenna random access network. Simulation results show the efficiency of the proposed techniques compared to state-of-the-art methods.

Channel Estimation and Prediction in a Pilot-Less Massive MIMO TDD Using Non-Coherent DMPSK

A novel time division duplex massive MIMO technique is proposed based on performing a pilot-less channel estimation in the uplink (UL) utilizing reconstructed differentially encoded data. Spatial multiplexing and differentially encoded data is applied both in the UL and in the downlink (DL). In this system, a reference signal is the first one of the differentially encoded streams in the UL and DL, and the pilots for data estimation are avoided while maintaining spatial multiplexing capabilities. To improve the channel estimation we propose to use a linear Wiener filter and we also propose different symbols placing strategies in an OFDM grid. We also propose a detection improvement of the UL data utilizing the predicted channels. We perform an analysis of the MSE of the blind channel estimation using the differentially encoded data and analyze the symbol-error-rate for both the UL and the DL when channel aging is considered. The analysis is corroborated via numerical results and the proposed scheme is shown to outperform its pilot-based counterpart.

Joint Blind Channel Estimation, Channel Equalization, and Data Detection for Underwater Visible Light Communication Systems

In this letter, we jointly consider the problems of blind channel estimation (BCE), channel equalization and data detection in underwater visible light communication (UVLC) systems. Specifically, we propose to solve them with a model-driven deep learning (DL) approach. Inspired by the module-based signal processing methodology and communication theory, we devise a new blind receiver architecture called blind detection network (BDNet), which consists of three modules corresponding to BCE, channel equalization, and symbol demapping, respectively. Different from existing BCE schemes, the BDNet does not directly estimate the channel itself, but instead estimates the inverse channel regardless of the scalar ambiguity issue by learning the latent channel features from the received signal only. The UVLC channel effects can then be partially compensated by the zero-forcing equalization module of BDNet. Finally, the symbol demapping module recovers the transmitted symbols with the aid of the learned decision thresholds. Simulation results show the competitive bit error rate performance of BDNet in comparison to conventional pilot-aided schemes.

Smart green ocean underwater IoT network by ICA-based acoustic blind MIMO OFDM transceiver

Multi-Input Multi-Output (MIMO) telecommunication systems with Orthogonal Frequency Division Modulation (OFDM) scheme support high rate data exchange which can be efficiently applied to the fast-growing networks of Internet of Things (IoT). In the case of Underwater IoT (UIoT) where the strength of electromagnetic waves rapidly falls off, the MIMO network can be effectively implemented by substitute of acoustic OFDM instead of the normal one. Due to these advantages, this research work suggests ICA based acoustic MIMO OFDM for UIoT wherein the data rate is further improved as the MIMO OFDM is implemented based on blind channel estimation and multi-user identification without the need for using any pilot and preamble data. Also, theoretical analysis shows the data rate increases and energy consumption decreases due to ICA-based MIMO acoustic channel estimation. In the case of the under-work UIoT network, it achieves 52.38% higher data rate, and 41.67% less energy consumption. As another specific result, the proposed method shows high efficiency on sparse underwater channels and as the channel sparseness gets lower, the efficiency decreases.