Channel Equalization Algorithms Research Articles

CNN neural network temporal feature storage structure fusion for the visible channel equalization algorithm

The visible light communication channel has time-varying characteristics and is difficult to predict. This paper proposes an equalization algorithm based on the structure of a convolutional neural network (CNN), combining time series feature length and long short-term memory (LSTM), and adding a residual structure. It can be seen that the equalization coefficient vector of the optical channel is a time series, which can reflect the noise characteristics of the channel and has storage characteristics. The equalizer algorithm can accurately learn the complex channel characteristics and calculate the compensation coefficient according to the channel characteristics. It can also restore the original transmission signal. At the same time, this paper also examines the compensation method of the receiver in the mobile state. The long-term memory parameters of LSTM are used to represent the sequence causality in the memory channel, and CNN and residual structure are used to refine the results and improve the accuracy of the reconstruction. The simulation results show that the algorithm can effectively eliminate the influence of the fading characteristics of the visible optical channel, improve the bit error rate performance of system transmission, solve the overall problem of channel corruption, and precisely restore the original transmission signal with fast convergence speed. In addition, this method can achieve a better balance between performance and complexity compared to the traditional contention balancing method, which proves the potential and effectiveness of the proposed channel balancing method.

Equalization methods for Filter Bank Multicarrier OQAM

Filter bank multi-carrier offset-quadrature amplitude modulation (FBMC OQAM) is a hot topic in 5G multi-carrier research. In order to achieve high data rates and reliable wireless communication, receiver channel equalization is required. This is because of the equalizer's excellent spectral efficiency, exceptionally low side-lobe leakage, zero periodic cyclic prefixes (CP), and multiphase filter design. However, a number of degradations, including fading, Doppler shifts, and intermittent symbol interference (ISI), are added to the transmitted data symbols as they traverse the wireless channel, reducing the network's overall quality. To mitigate the negative effects of channel defects, many channel equalization algorithms have been developed for use in modern telecommunications networks. Six different equalizer approaches have been implemented. Using the Pedestrian A and Vehicular B channel in our simulations, we examine how well they function. The system was simulated using MATLAB's Communications Toolbox (an M file). Bit-error-rate (BER) and signal-to-noise ratio (SNR) were used to evaluate the outcomes. The Bit error rate was calculated by checking for differences between the sent and received bit sequences. The best value of the bit error ratio using frequency domain equalization and the time domain equalization 5.4 and 4.7 , respectively.

Orthogonal time-frequency space (OTFS) modulation for underwater mobile acoustic communications

This paper investigates the Orthogonal Time-Frequency Space (OTFS) Modulation for Underwater Mobile Acoustic Communications where the communication channel suffers from severe multipath and Doppler effects simultaneously. Practical OTFS modulation schemes with different parameters are designed for acoustic transmission at a center frequency of 115 kHz and a bandwidth of 11.5 kHz. The schemes are tested in lake experiments where the transmitter was anchored and the receiver was towed by a boat at a speed of approximately 1 m/s or 3.6 km/h. The receiver utilizes low-complexity channel estimation and equalization algorithms, such as NLMS (Normalized Least Mean Squares) and IPNLMS (improved proportionate NLMS) algorithms. The results show some insights of the OTFS scheme for acoustic communications. First, the mobile acoustic channel characteristics are different in the 2D delay-Doppler domain than those in the 1D time and frequency domains for the Single-Carrier Coherent Modulation (SCCM) and the Orthogonal frequency division modulation (OFDM), respectively. Second, in mobile scenarios, the OTFS scheme receiver effectively and significantly reduces the accuracy requirements of the Doppler compensation algorithm and provides better frequency-selective fading suppression and Doppler effect robustness compared to SCCM and OFDM schemes. Third, the OTFS scheme has better anti-multipath performance and reduces multipath interference by effectively differentiating signals on different paths in the 2D delay-Doppler domain.

Integrated Silicon Photonic Reservoir Computing With PSO Training Algorithm for Fiber Communication Channel Equalization

Integrated Silicon Photonic Reservoir Computing With PSO Training Algorithm for Fiber Communication Channel Equalization

End-to-End Underwater Acoustic Communication Based on Autoencoder with Dense Convolution

To address the problems of the high complexity and poor bit error rate (BER) performance of traditional communication systems in underwater acoustic environments, this paper incorporates the theory of deep learning into a conventional communication system and proposes data-driven underwater acoustic filter bank multicarrier (FBMC) communications based on convolutional autoencoder networks. The proposed system is globally optimized by two one-dimensional convolutional (Conv1D) modules at the transmitter and receiver, it realizes signal reconstruction through end-to-end training, it effectively avoids the inherent imaginary interference of the system, and it improves the reliability of the communication system. Furthermore, dense-block modules are constructed between Conv1D layers and are connected across layers to achieve feature reuse in the network. Simulation results show that the BER performance of the proposed method outperforms that of the conventional FBMC system with channel equalization algorithms such as least squares (LS) estimation and virtual time reversal mirrors (VTRM) under the measured channel conditions at a certain moment in the Qingjiang River.

A Pilot-Based Integration Method of Ranging and LS Channel Estimation for OFDM Systems

In the design of unmanned aerial vehicle (UAV) communication systems, orthogonal frequency division multiplexing (OFDM) is a commonly used communication technology. An efficient channel estimation and equalization algorithm is required to recover the amplitude, phase, and frequency of the signal in OFDM systems. At present, the more precise channel estimation method is based on the pilot. However, its spectrum utilization is relatively low. Therefore, this paper presents the design of a new pilot based on the LS channel estimation, which extends the role of the traditional pilot and improves the utilization of the spectrum. In addition to the channel estimation and equalization, the new pilot can also be utilized for ranging. Simulation results show that the proposed scheme can achieve both channel estimation and communication ranging functions by using the new pilot, and it outperforms the conventional method in channel estimation performance. The proposed method can complete ranging when the bit error rate (BER) is above 0 dB. Moreover, compared with the traditional channel estimation, it reduces the requirement for SNR by about 1 dB under the same BER.

Performance Analysis of MIMO Detection Algorithms with BPSK and PAM Modulation

One of the best strategies for worthwhile Communication in wireless technologies is MIMO i.e., Multiple input and Multiple output systems where the information exchange is happened through multiple antennas. The Wireless transmission of data/signal suffers from fading and interference effects which creates a problem for signal recovery in wireless communication hence these effects can be controlled by the equalizer. The MIMO system uses multiple antennas at the transmitter and receiver to exploit multipath propagation. The paper emphasizes various channel equalization algorithms recognized for sub-optimum MIMO detection which include Zero Forcing (ZF), Minimum Mean Square Error (MMSE), ZF-SIC, and MMSE-SIC equalizers, the performance is evaluated for 2*2 and 4*4 MIMO wireless channels using various modulation schemes in which BER is taken for consideration. The implementation work is carried out through MATLAB toolbox version 2018b.

Joint message-passing-based accurate bidirectional channel estimation and generalized-approximation-message-passing-based low-complexity perfect equalization with underwater moving transceivers

Moving underwater acoustic communications incur the fast time-varying multipath interference (FTMI), leading to a decrease in decoding performance or even decoding failure. In this study, we address the FTMI issue using the superimposed training (ST) scheme and segment strategy with a joint channel estimation and equalization algorithm. The ST scheme refers to the linear superposition of the training sequence with a small power to the symbol sequence; the segment strategy slices a data block into the segments. Thus, the tracking capability of time-varying channels is significantly improved. To realize their full potential, a “novel” joint message-passing-based accurate bidirectional channel estimation and generalized-approximation-message-passing-based low-complexity perfect equalization (BCE-LPE) algorithm is developed. The Gaussian channel estimates of the segments can be parameterized by their means and variances, thereby enabling fusion of the channel estimates. The correlation of the channel estimates for the segments is established through a channel correlation coefficient, so that the whole data block can be used to estimate the channel of each segment, leading to an accurate channel estimation. Unlike the Gaussian approximate process of the input symbol sequence of the traditional linear equalization, the proposed low-complexity perfect equalization maintains the discrete and independent form of the input symbol sequence, avoiding the equalizer’s input information loss. The a priori, a posteriori, extrinsic means and variances of interleaved encoded bits are computed using the low-complexity diagonal-matrix message passing, leading to the low-complexity perfect equalization, with computational complexity in a logarithmic order per symbol. Moreover, channel estimation, equalization, and decoding are jointly performed in an iterative manner to significantly enhance the overall system performance. Additionally, the parallel processing strategy is used to improve the computation speed, in which all the data blocks are separately and parallelly processed. Field experiments with moving transceivers (the communication distance was about 5.5 km, the bandwidth was 4 kHz, and the transmission rate was 3765 bits/s) were performed in Jiaozhou Bay in 2021, with the experimental results verifying the effectiveness of the BCE-LPE algorithm.

An improved underwater wireless sensor network communication using Internet of Things and signal to noise ratio analysis

AbstractUnderwater acoustic (UWA) channels are widely regarded as one of the most challenging communication mediums. Low frequencies are best for acoustic propagation, and the bandwidth available for communication is extremely small. The worst channel situation often limits the efficiency of UWA communication systems due to the difficulty and time‐varying nature of the UWA channel. To solve the existing limitations related with underwater signal transmission and communication, an improved underwater communication system incorporated with Internet of Things (IoT) is proposed in this work. The proposed system utilizes the orthogonal signal division multiplexing modulation technique. Here the signal transmission process is achieved with the help of an IoT network system. An improved AdaBoost channel estimation algorithm is used to obtain the channel information. This work utilizes an improved stochastic gradient descent optimization method for selecting the suitable mode of signal transmission based on the estimated channel. Further, an adaptive recursive least square channel equalization algorithm is used for channel compensation. The simulation results are obtained with the help of the MATLAB platform. The performance is analyzed in terms of parameters such as signal to noise ratio, bit error rate, and mean square error. A comparison of the proposed method is also made with the existing methods. The evaluation results show that the proposed method performs better than the existing methods.

Adaptive algorithms for blind channel equalization in impulsive noise

The unsupervised adaptive mitigation of intersymbol interference in an additive impulsive noise environment, modeled as generalized Gaussian, is dealt in this work. The theory of statistical invariance, Wijsman’s theorem, is used to develop a maximal-invariant test to discriminate equally-likely pulsed signals against impulsive disturbance leading to an admissible cost function for blind equalization. The cost function is optimized to realize two adaptive equalizers capable of not only mitigating intersymbol interference but also robust to impulsive disturbance. Numerical simulations, obtained on a baseband digital microwave radio system for amplitude-phase shift keying signaling in an additive (generalized Gaussian and symmetric-alpha stable) impulsive environment, confirm the admissibility of the proposed equalizers in terms of robustness and steady convergence.

Research on Equalization Technology of Broadband Satellite Communication Channel

Satellite channel is the representative non-linear channel with limited frequency band. The random change of the channel causes the communication stability to be affected. With the development of satellite communication technology, the bandwidth of satellite communication has increased and the data rate has also increased significantly, resulting in more serious code interference, which has become a key factor affecting communication quality. Finding an efficient equalization technology to eliminate channel inter-code interference is the key to ensuring satellite broadband communications. This article first briefly discusses the principle of eliminating inter-symbol interference and common equalizer algorithms, and summarizes the main factors that affect the performance of channel equalization algorithms. On this basis, an improved LSTM blind equalization algorithm is proposed.

Assessment of Different Channel Equalization Algorithms for a Converged OFDM-Based 5G mm-wave A-RoF System at 60 GHz

In this article, we simulate a converged 5G mm-wave analogue radio-over-fiber (A-RoF) system at 60 GHz, and perform offline signal processing to equalize the dispersive optical link with the three most frequently employed algorithms, i.e., the simple least mean square (LMS) algorithm, the constant modulus algorithm (CMA) and the adaptive median filtering (AMF), which are implemented in Matlab. The performances of the different algorithms are compared for various optical fiber lengths with respect to the EVM values obtained before and after equalization. In the case of QPSK in OFDM subcarriers, it is observed that the CMA algorithm performs better than the LMS and MF algorithms, with 2% and 1.4% EVM improvement respectively, while for 16QAM in OFDM subcarriers it is observed that the LMS algorithm has a very small improvement of 0.2% EVM compared to the MF algorithm, while CMA is not suitable for 16QAM modulation in the proposed converged 5G mm-wave A-RoF system at 60 GHz.

Performance Evaluation of Machine Learning-Based Channel Equalization Techniques: New Trends and Challenges

Wireless communication systems have evolved and offered more smart and advanced systems like ad hoc and sensor-based infrastructure fewer networks. These networks are evaluated with two fundamental parameters including data rate and spectral efficiency. To achieve a high data rate and robust wireless communication, the most significant task is channel equalization at the receiver side. The transmitted data symbols when passing through the wireless channel suffer from various types of impairments, such as fading, Doppler shifts, and Intersymbol Interference (ISI), and degraded the overall network performance. To mitigate channel-related impairments, many channel equalization algorithms have been proposed for communication systems. The channel equalization problem can also be solved as a classification problem by using Machine Learning (ML) methods. In this paper, channel equalization is performed by using ML techniques in terms of Bit Error Rate (BER) analysis and comparison. Radial Basis Functions (RBFs), Multilayer Perceptron (MLP), Support Vector Machines (SVM), Functional Link Artificial Neural Network (FLANN), Long-Short Term Memory (LSTM), and Polynomial-based Neural Networks (NNs) are adopted for channel equalization.

Training Strategy of Fuzzy-Firefly Based ANN in Non-Linear Channel Equalization

Channel equalization is remaining a challenge for the researcher. Especially for the non-linear channel as well as the extremely dispersive channel, an effective channel equalizer is required. It is common knowledge that non-linear channel equalizers based on the neural networks (NN) outperform adaptive filter-based linear equalizers. To train NN equalizers, gradient-descent-based approaches like the back-propagation algorithm are often utilized, although they have drawbacks such as trapping of local minima, slower convergence, and compassion to log in. In this work, we presented a novel training strategy using a fuzzy firefly algorithm (FFA) for channel equalization. By using proper network topology and parameters, the suggested training system offers stronger exploitation and exploration skills, as well as the ability to solve the local minima issue. The performance of the equalizer can be analyzed by estimating two parameters i.e. MSE and BER. To exhibit the suggested technique’s resilience in performance, the burst error situation was used, and the outcomes showed that the strategy is more effective in managing such situations than previous methods. The outcomes of the proposed method are presented through simulation, Furthermore, it proved that the suggested method validates a wide range of SNR, and also it outperforms the existing NN-based equalizers.

Multi-core fiber channel equalization algorithm based on K nearest neighbor method

Aiming at the channel damage problems of multi-core fiber (Multi-core Fiber, MCF), the AKNN (Adapted K-Nearest Neighbor) algorithm is proposed. The MATLAB software was used to simulate the channel of the seven-core fiber, both the inter-core crosstalk (Crosstalk, XT) and nonlinear effects were considered. Then the KNN algorithm was adapted to equalize the damaged signal at the receiver. The simulation results show that the AKNN algorithm possesses better BER performance and lower OSNR requirements with the extension of transmission distance, compared to traditional KNN algorithm.

Parametric Bilinear Iterative Generalized Approximate Message Passing Reception of FTN Multi-Carrier Signaling

A low-complexity parametric bilinear generalized approximate message passing (PBiGAMP)-based receiver is conceived for multi-carrier faster-than-Nyquist (MFTN) signaling over frequency-selective fading channels. To mitigate the inherent ill-conditioning problem of MFTN signaling, we construct a segment-based frequency-domain received signal model in the form of a block circulant linear transition matrix, which can be efficiently calculated by applying a two dimensional fast Fourier transform. Based on the eigenvalue decomposition of the block circulant matrices, we can diagonalize the covariance matrix of the complex-valued colored noise process imposed by the associated two dimensional non-orthogonal matched filtering. Building on this model, a PBiGAMP-based parametric joint channel estimation and equalization (JCEE) algorithm is proposed for MFTN systems. In this algorithm, we introduce a pair of additive terms for characterizing the interferences arising from adjacent segments and employ the exact discrete <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">a priori</i> probabilities of the transmitted symbols for improving the bit error rate (BER) performance. To further enhance the system’s robustness in the presence of ill-conditioned matrices, we develop a refined PBiGAMP-based JCEE algorithm by introducing a series of scaled identity matrices. Moreover, the proposed PBiGAMP-based JCEE algorithms may be readily decomposed into GAMP-based equalization algorithms, when the channel state information is perfectly known. The overall complexity of the proposed algorithms only increases logarithmically with the total number of transmitted symbols. Our simulation results demonstrate the benefits of the proposed PBiGAMP-based iterative message passing receiver conceived for MFTN signaling.

Channel Estimation and Equalization for Alamouti SF-Coded OFDM-UWA Communications

In this paper, a new channel estimation and equalization algorithm for underwater acoustic (UWA) communications is presented. The proposed algorithm is developed to meet the requirements of underwater time-varying sparse channels that undergo Rayleigh fading. In addition, the algorithm takes into consideration a path-based channel model which describes each received path with significant power by an attenuation factor, a Doppler scale, and a delay. Transmit diversity enabled by Alamouti space-frequency block coding coupled with orthogonal frequency division multiplexing is employed in the form of two transmitters and multiple receivers. The proposed, non-data-aided, expectation-maximization (EM)-based maximum a posteriori probability sparse channel estimation first estimates the channel transfer functions from each transmit antenna to the receiver. Then, the estimation performance is greatly improved by taking into account the sparseness of the UWA channel and refining the estimation based on the sparse solution that best matches the frequency-domain channel estimates obtained during the first phase of the estimation process. Sparse channel path delays and Doppler scaling factors are estimated by a novel technique called delay focusing . After that, slow time-varying, complex-valued channel path gains are estimated using a basis expansion model based on the discrete Legendre polynomial expansion. Computer simulation results show that the resulting channel estimation algorithm can achieve excellent mean-square error and symbol error rate for both generated data and semi-experimental data taken at Sapanca Lake in Turkey and is capable of handling some mismatch due to different fading models.

An Adaptive Multi-Mode Underwater Acoustic Communication System Using OSDM and Direct Sequence Spread Spectrum Modulation

Dynamic underwater acoustic network is a research hotspot. Due to the complexity and time-varying of underwater acoustic channel, the performance of single-mode underwater acoustic communication system is often limited by the worst channel situation. Orthogonal signal division multiplexing (OSDM) is a new signal modulation technology, which can be regarded as a bridge between single carrier modulation and orthogonal frequency division multiplexing (OFDM) modulation. In order to improve the channel adaptive ability of underwater acoustic communication system and obtain a unified architecture of different carrier modulation, an adaptive multi-mode underwater acoustic communication system based on OSDM and direct sequence spread spectrum modulation (AMMUAC/OS) is proposed in this paper. Combined with spread spectrum modulation, the system can realize single carrier time domain spread spectrum (SC-TDSS), single carrier system with cyclic prefix (SC-CP), OSDM, multicarrier frequency domain spread spectrum (MC-FDSS), and OFDM communication system by adjusting the vector length M of OSDM. The addition of a variety of digital mapping methods enables the system to achieve multi-mode communication with a unified architecture. Based on the relationship between the OSDM vector length M and the underwater acoustic channel delay, a joint adaptive decision criterion based on the maximum channel delay and the channel signal-to-noise ratio (SNR) is proposed. Firstly, the OSDM vector length M is determined according to the maximum channel delay, and then the system communication mode is selected according to the channel SNR. Orthogonal matching pursuit (OMP) channel estimation algorithm is used to obtain channel information and minimum mean square error (MMSE) channel equalization algorithm is used for channel compensation. An adaptive multi-mode data frame structure is designed for the interaction between the sender and the receiver. The simulation results show that the proposed AMMUAC/OS can adapt to underwater acoustic channels with different channel delay and SNR.

Joint Channel Estimation and Generalized Approximate Messaging Passing-Based Equalization for Underwater Acoustic Communications

Acquiring channel state information and mitigating multi-path interference are challenging for underwater acoustic communications under time-varying channels. We address the issues using a superimposed training (ST) scheme with a least squares (LS) based channel estimation algorithm. The training sequences with a small power are linearly superimposed with the symbol sequences, and the training signals are transmitted over all time, resulting in enhanced tracking capability to deal with time-varying underwater acoustic channels at the cost of only a small power loss. To realize the full potentials of the ST scheme, we develop a LS based channel estimation algorithm with superimposed training, where the Toeplitz matrix is used, which is formed by the training sequences, enabling channel estimation with superimposed training. In particular, a low-complexity channel equalization algorithm based on generalized approximate messaging passing (GAMP) is proposed, where the a priori, a posteriori, extrinsic means and variances of interleaved coded bits are computed, and then convert them into extrinsic log likelihood ratios for BCJR decoding. Its computational complexity is only in a logarithmic order per symbol. Moreover, the channel estimation, GAMP equalization and decoding are performed jointly in an iterative manner, so that the estimated symbol sequences can also be used as virtual training sequences to improve the channel estimation and tracking performance, thereby remarkably enhance the overall system performance. Moving communication experiments in Jiaozhou Bay (communication frequency 12 kHz, bandwidth 6 kHz, sampling frequency 96 kHz, symbol transmission rate 4 ksym/s) were carried out, and the experimental results verify the effectiveness of the proposed technique.

Modified constant modulus algorithm based on bat algorithm

In this work, modified constant modulus algorithm based on bat algorithm is proposed for wireless sensor communications systems. The bat algorithm is a swarm intelligence optimization algorithm, which mainly used to solve optimization problems. The proposed algorithm focused on modified constant modulus algorithm, which is also applicable to the constant modulus algorithm. The error function of blind equalization algorithm is used as the evaluation function of the bat algorithm, and then the initial value of the weight vector is calculated adaptively by the bat algorithm. Theoretical analysis is provided to illustrate that the proposed algorithm has a faster convergence speed than the original one and is suitable for almost all blind channel equalization algorithms. The simulation results support the newly proposed improved algorithm. The proposed algorithm could be applied to some more complex wireless channel environments to improve the reception performance of sensor communication systems.