Time-varying Multipath Fading Channels Research Articles

Semantic communication for cooperative perception based on importance map

Cooperative perception, which has a broader perception field than single-vehicle perception, has played an increasingly important role in autonomous driving to conduct 3D object detection. Through vehicle-to-vehicle (V2V) communication technology, various connected automated vehicles (CAVs) can share their sensory information (LiDAR point clouds) for cooperative perception. We employ an importance map to extract significant semantic information and propose a novel cooperative perception semantic communication scheme with intermediate fusion. Meanwhile, our proposed architecture can be extended to the challenging time-varying multipath fading channel. To alleviate the distortion caused by the time-varying multipath fading, we adopt explicit orthogonal frequency-division multiplexing (OFDM) blocks combined with channel estimation and channel equalization. Simulation results demonstrate that our proposed model outperforms the traditional separate source-channel coding over various channel models. Moreover, a robustness study indicates that only part of semantic information is key to cooperative perception. Although our proposed model has only been trained over one specific channel, it has the ability to learn robust coded representations of semantic information that remain resilient to various channel models, demonstrating its generality and robustness.

A Double-Threshold Channel Estimation Method Based on Adaptive Frame Statistics

Channel estimation is an important module to enhance the performance of orthogonal frequency division multiplexing (OFDM) systems. However, the presence of a large amount of noise in time-varying multipath fading channels significantly affects the channel estimation accuracy and thus the recovery quality of the received signals. Therefore, this paper proposes a double-threshold (DT) channel estimation method based on adaptive frame statistics (AFS). The method first adaptively determines the number of statistical frames based on the temporal correlation of the received signals, and preliminarily detects the channel structure by analyzing the distribution characteristics of multipath sampling points and noise sampling points during adjacent frames. Subsequently, a multi-frame averaging technique is used to expand the distinction between multipath and noise sampling points. Finally, the DT is designed to better recover the channel based on the preliminary detection results. Simulation results show that the proposed adaptive frame statistics-double-threshold (AFS-DT) channel estimation method is effective and has better performance compared with many existing channel estimation methods.

Performance of STBC Based MIMO-OFDM Using Pilot-aided Channel Estimation

Many studies have been published to address the growing issues in wireless communication systems. Space-Time Block Coding (STBC) is an effective and practical MIMO-OFDM application that can address such issues. It is a powerful tool for increasing wireless performance by coding data symbols and transmitting diversity using several antennas. The most significant challenge is to recover the transmitted signal through a time-varying multipath fading channel and obtain a precise channel estimation to recover the transmitted information symbols. This work considers different pilot patterns for channel estimation and equalization in MIMO-OFDM systems. The pilot patterns fall under two general types: comb and block types, with a proper arrangement suitable to the multiple transmit antennas. The two main channel estimation methods, LS and MMSE, are compared by evaluating performance in terms of Bit Error Rate (BER) to analyze the performance of pilot-aided channel estimation for 2x2 and 4x4 MIMO arrangements utilizing LTE parameters and the effects of modifying different numbers of OFDM subcarriers under different channel models It has been discussed, a 4x4 system performs better than a 2x2 system in terms of BER with an acceptable amount of additional complexity.

Doppler Frequency Estimation Based on Time Diversity of Random Processes in Doubly-Selective Channels

This work introduces a new method to estimate Doppler frequency which carries important characteristics feature of time-variant multipath fading channels. The proposed method employs the time diversity (TD) of general random processes with rich correlation characteristics introduced by various time intervals, where a maximum-likelihood (ML) algorithm is utilized. A closed-form weighted least-square equation is derived with the help of basis expansion approximation. In addition, the Cram <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$\acute{\text{e}}$</tex-math></inline-formula> r-Rao lower bound (CRLB) of the estimation is analyzed. The results show that the proposed method performs well in terms of Doppler frequency estimation accuracy.

Blind multicarrier waveform recognition based on spatial-temporal learning neural networks

Blind multicarrier waveform recognition has become a more daunting task and open problem for the current and future radio surveillance and signals interception, with the advent of new multicarrier technologies such as the state-of-the-art F-OFDM, UFMC, FBMC, OTFS, GFDM and CP-OFDM techniques. Therefore, the practical recognition scheme for multicarrier waveforms is necessary to keep up with the pace. To tackle this challenge, we propose a novel multicarrier waveform recognition framework based on Spatial Temporal-Convolutional Long Short-Term Deep Neural Network (ST-CLDNN) in the entirely blind context. The complementary information of the raw in-phase, quadrature and amplitude samples are first extracted to provide more distinguishable features. Then ST-CLDNN collects the advantages of one-dimensional convolutional and long short-term memory (LSTM) to extract high-level spatial and temporal features for the recognition task. Later, we introduce the transfer learning strategy to put the computational resource to good use and obviate the retraining from scratch for a time-varying channel. Experimental results indicate that the proposed ST-CLDNN can perform better than the traditional feature-based classifiers and existing deep learning (DL)-based neural networks, and deliver a substantial recognition performance in a time-varying multipath fading channel.

Adaptive-Slope Squashing-Function-Based ANN for CSI Estimation and Symbol Detection in SFBC-OFDM System

This paper presents an adaptive-slope squashing-function (ASF)-based artificial neural network (ANN) for efficient estimation of smoothly time-varying multipath fading channels, in a $$4 \times 1$$ space-frequency-block-coded orthogonal-frequency-division-multiplexing (SFBC-OFDM) system using 64 subcarriers. The channel-state-information (CSI) estimated at first stage is further used for OFDM information symbol detection (through minimum mean square error criterion-based detection) at second stage. To combat the impact of smoothly time-varying environment, we emphasize on the utilization of ASF-ANN using backpropagation (BP) algorithm for the estimation of channel tap coefficients in frequency domain. The underlying ANN is modeled as feedforward multi-layered perceptron that updates the network weights. The major focus is on the gradient-descent algorithm-based adaptation of the slope of squashing-function (SF) along with other ANN parameters, which enhances the training efficiency of ASF-ANN in terms of the lower mean-squared channel estimation error in comparison with the traditional fixed-slope squashing-function (FSF) ANN technique. Simulation results corresponding to the underlying $$4 \times 1$$ SFBC-OFDM system are presented to depict that ASF-ANN-based approach outperforms the FSF-ANN technique by providing lower bit-error-rate (BER) due to the usage of well-estimated CSI. At 15 dB SNR and fade rate = 0.001, the average BER reduces to $$2.85 \times 10^{ - 4}$$ for the ASF-ANN based approach, due to improved CSI estimation, which accounts for approximately 5% improvement in the detection success rate as compared to the FSF-ANN-based approach.

Pseudo Convex Framework using Sparse Channel Estimation for Multipath Fading Channels in DS-CDMA Systems

A Direct-Sequence Code-Division Multiple-Access (DS-CDMA) with rake receiver for multiuser system sets up a new dimension in mobile communication system. We propose a pseudo convex framework using an optimum sparse channel estimation technique for DS-CDMA mobile communication systems. Further, the blind channel estimation problem will be examined for rake-based DS-CDMA communication framework with time variant multi-path fading channels This receiver accomplishes an effective assessment of channel according to a maximum convexity criterion, by means of the sparse technique. This estimation method requires a convenient representation of the discrete multipath fading channel based on the sparse theory. In this paper, we have defined a specialized interior-point method for solving large-scale ℓ1 - problem in multiuser detection. Our method can be generalized to handle a variety of extensions such as various channel conditions. A new solution to DS-CDMA based sparse channel estimation is presented in this paper that assures a global optimal solution. Also, it is proven that the said solution can be used as an apparent program that shall enable solutions utilizing interior point techniques involving polynomial time complexity. Through simulation the rationality of the techniques proposed in this paper has been highlighted by results obtained for various modulation schemes and channel parameters. The execution of a pseudo convex framework using sparse channel estimation technique with rake receiver in DS-CDMA framework for multipath fading channels is explored. The overall performance is evaluated in terms of bit error rate (BER) for a range of values of signal to noise ratio (SNR). This framework gives better performance under various modulation schemes using pseudo noise (PN) spreading code. Furthermore, performance of the proposed system is compared with different detectors.

Space-Time/Frequency Line Coded OFDM: System Design and Practical Implementation

In this paper, we investigate a class of new multiple-input multiple-output (MIMO) transmission schemes, dubbed space time line code (STLC), in the context of different mobility situations. We extend its original structure into space and frequency domains and propose a novel frequency line code (SFLC) for combining with orthogonal frequency division multiplexing (OFDM) in order to achieve potential diversity gains in time-varying multipath fading channels. Specifically, at the transmitter, the STLC/SFLC-OFDM scheme exploits the channel state information (CSI) to linearly encode successive modulated symbols in the time/frequency domain to achieve full diversity gain. At the receiver, it can retrieve the transmit symbols without full CSI, thanks to its special structure. We analyse the impact of time-variant channels to STLC and SFLC to show the expected performance degradation in practical channel, especially in the case of different mobile speeds. Our analysis and simulations show that the BER performance degrades with increasing correlation coefficient, which makes STLC-OFDM more robust in channels with abundant multipath spread, and SFLC-OFDM more robust in channels with high mobility speed. Meanwhile, a guideline is provided for switching when the communication environment changes. Finally, we carry out the practical implementation of STLC-OFDM and SFLC-OFDM schemes and characterize their performance with both computer simulations and an experimental testbed.

Timing and Integer Frequency Offset Estimation for DLMT System over Time-Varying Rayleigh Fading Channel

In this paper, we study the timing and integer carrier frequency offset (ICFO) estimation problem for dynamic lattice multicarrier transmission (DLMT) system over time-varying Rayleigh fading channel. Firstly, a novel preamble structure based on two constant amplitude zero auto-correlation (CAZAC) sequences is designed for DLMT system. Then, by using the designed dual-CAZAC preamble, a discrete cross ambiguity function (DCAF) based timing and ICFO estimation algorithm is proposed. Simulation results show that the proposed DCAF based timing and ICFO estimation algorithm can mitigate the impact of time-varying multipath Rayleigh fading channel and outperforms traditional estimators on the correct estimation probability performance.

Iterative Joint Channel Estimation and Signal Detection for OFDM System in Double Selective Channels

Intercarrier interference caused by fast time-varying multipath fading channels degrades the system performance of high-mobility orthogonal frequency division multiplexing systems. This study consi...

Fast Time-Varying Multipath Channel Estimation and Signal Detection for of OFDM Systems with Interference Compensation

Orthogonal frequency division multiplexing (OFDM) is a widely adopted system for broadband wireless transmission, However, time-varying channels degrade the performance owing to the intercarrier interference (ICI). This study discusses the performance enhancement problems of channel estimation and signal detection in view of fast time-varying multipath fading channel characteristics of high mobility. A two-stage method is presented. In the coarse stage, initial channel estimation is performed using the signals at pilot subcarriers based on least-square (LS) estimate is performed. However, the initial estimate still needs to be improved because of the data-induced ICI. The coarse stage improves estimate accuracy by compensating the interferences. The refining stage, applies interference compensation and q-banded successive interference suppression (qSIS) methods to enhance the detection and re-estimate accuracy. Based on robustness, the detected data are called the “pseudo-pilots”, which together with the pilots are treated as a “visual-training” (VT) to improve channel re-estimation. Simulation results indicate that the proposed method is more robust than the traditional method in fast time-varying multipath fading channels with lower computational complexity. The proposed method is independent of the delay and fading properties of the channel.

Support Vector Machine Regression and Artificial Neural Network for Channel Estimation of LTE Downlink in High-Mobility Environments

In this paper we apply and assess the performance of support vector machine regression (SVR) and artificial neural network (ANN) channel estimation algorithms to the reference signal structure standardized for LTE Downlink system. SVR and ANN where applied to estimate real channel environment such as vehicular A channel defined by the International Telecommunications Union (ITU) in the presence of nonlinear impulsive noise.The proposed algorithms use the information provided by the received reference symbols to estimate the total frequency response of the time variant multipath fading channel in two phases. In the first phase, each method learns to adapt to the channel variations, and in the second phase it predicts all the channel frequency responses. Finally, in order to evaluate the capabilities of the designed channel estimators, we provide performance of SVR and ANN, which is compared with traditional Least Squares (LS) and Decision Feedback (DF). The simulation results show that SVRhas a better accuracy than other estimation techniques.

Robust signal recognition algorithm based on machine learning in heterogeneous networks

There are various heterogeneous networks for terminals to deliver a better quality of service. Signal system recognition and classification contribute a lot to the process. However, in low signal to noise ratio (SNR) circumstances or under time-varying multipath channels, the majority of the existing algorithms for signal recognition are already facing limitations. In this series, we present a robust signal recognition method based upon the original and latest updated version of the extreme learning machine (ELM) to help users to switch between networks. The ELM utilizes signal characteristics to distinguish systems. The superiority of this algorithm lies in the random choices of hidden nodes and in the fact that it determines the output weights analytically, which result in lower complexity. Theoretically, the algorithm tends to offer a good generalization performance at an extremely fast speed of learning. Moreover, we implement the GSM/WCDMA/LTE models in the Matlab environment by using the Simulink tools. The simulations reveal that the signals can be recognized successfully to achieve a 95% accuracy in a low SNR (0 dB) environment in the time-varying multipath Rayleigh fading channel.

Design of pipeline R2MDC FFT for implementation of MIMO OFDM transceivers using FPGA

In this paper, an area-efficient low power Fast Fourier Transform (FFT) processor is proposed for Multi Input Multi Output--Orthogonal Frequency Division Multiplexing (MIMO-OFDM) that consists of a modified architecture of radix-2 algorithm which is described as Radix-2 multipath delay commutation (R2MDC). Orthogonal frequency-division multiplexing is a popular method for high-data-rate wireless transmission. OFDM may be combined with multiple antennas at both the access point and mobile terminal to increase diversity gain and/or Enhance system capacity on a time-varying multi path fading channel, resulting in a multiple-input multiple-output OFDM system. This paper describes the design of R2MDC FFT for implementation of MIMO OFDM transceiver using FPGA targeted to future wireless LAN systems. The proposed system is pipeline Radix 2multipath delay commutation FFT has been designed for MIMO OFDM. The MIMO OFDM transceivers have been designed according to the proposed OFDM parameters. A low-power efficient and full-pipeline architecture enables the real-time operations of MIMO OFDM transceivers. The FPGA board has been developed to verify their circuit behavior and implementation of MIMO OFDM Transceivers.

Design of MOD-R2MDC FFT for MIMO OFDM in wireless telecommunication system

In this paper, an area-efficient low power Fast Fourier Transform (FFT) processor is proposed for multi input multi output-orthogonal frequency division multiplexing (MIMO-OFDM) in wireless telecommunication system that consists of a modified architecture of radix-2 algorithm which is described as modified radix-2 multipath delay commutation (MOD-R2MDC). Orthogonal frequency-division multiplexing is a popular method for high-data-rate wireless transmission. OFDM may be combined with multiple antennas at both the access point and mobile terminal to increase diversity gain and/or enhance system capacity on a time-varying multi path fading channel, resulting in a multiple-input multiple-output OFDM system. This paper describes the VLSI design of an area efficient MOD-R2MDC FFT for MIMO OFDM system targeted to future wireless telecommunication LAN systems. The proposed system is pipeline modified radix 2 multipath delay commutation FFT has been designed for MIMO OFDM. A low-power efficient and full-pipeline architecture enables the real-time application of MIMO OFDM system.

Adaptive Filtering Techniques using Cyclic Prefix in OFDM Systems for Multipath Fading Channel Prediction

This paper presents adaptive channel prediction techniques for wireless orthogonal frequency division multiplexing (OFDM) systems using cyclic prefix (CP). The CP not only combats intersymbol interference, but also precludes requirement of additional training symbols. The proposed adaptive algorithms exploit the channel state information contained in CP of received OFDM symbol, under the time-invariant and time-variant wireless multipath Rayleigh fading channels. For channel prediction, the convergence and tracking characteristics of conventional recursive least squares (RLS) algorithm, numeric variable forgetting factor RLS (NVFF-RLS) algorithm, Kalman filtering (KF) algorithm and reduced Kalman least mean squares (RK-LMS) algorithm are compared. The simulation results are presented to demonstrate that KF algorithm is the best available technique as compared to RK-LMS, RLS and NVFF-RLS algorithms by providing low mean square channel prediction error. But RK-LMS and NVFF-RLS algorithms exhibit lower computational complexity than KF algorithm. Under typical conditions, the tracking performance of RK-LMS is comparable to RLS algorithm. However, RK-LMS algorithm fails to perform well in convergence mode. For time-variant multipath fading channel prediction, the presented NVFF-RLS algorithm supersedes RLS algorithm in the channel tracking mode under moderately high fade rate conditions. However, under appropriate parameter setting in $$2\times 1$$2?1 space---time block-coded OFDM system, NVFF-RLS algorithm bestows enhanced channel tracking performance than RLS algorithm under static as well as dynamic environment, which leads to significant reduction in symbol error rate.

Spectrum Sensing for Self-Organizing Network in the Presence of Time-Variant Multipath Flat Fading Channels and Unknown Noise Variance

Cognitive radio, as an important means of implementing spectrum-awareness and dynamic sharing, is of great significance to the future deployment of self-organizing networks. Given its practical application, cognitive radio technology may operate in various adverse self-organizing networks environments. For example, in wireless mobile communication, the multipath propagation with time-varying fading coefficients and unknown noise variance becomes inevitable. Unfortunately, most existing spectrum sensing methods could hardly acquire good performance in this adverse situation. To overcome this difficulty, in this paper we present a novel spectrum sensing algorithm in realistic cognitive radio applications. Firstly, we establish a dynamic state-space model which gives full consideration to the evolution characteristics of primary user's state and time-variant multipath flat fading channel, while the received signal processed by matched filtering is viewed as the observation. On this basis, spectrum sensing is realized by estimating the primary user's state, multipath channel impulse response and noise variance jointly and iteratively. This formulated problem is solved based on maximum a posteriori probability criterion and marginal particle filtering technology. Simulations demonstrate that the sensing performance achieved by proposed algorithm is satisfactory and at the same time, the channel response and noise variance are estimated accurately.

An Improved DFT-Based Channel Estimation Algorithm for OFDM System on Time-Varying Multipath Fading Channels

An improved discrete Fourier transform DFT-based channel estimation for OFDM systems is proposed. Conventional DFT-based channel estimator improved its performance by suppressing noise, but it does not completely suppress noise. In order to overcome the disadvantage, this paper proposed a gate method without requiring any channel statistical information. This method comprehensively considersthe effects of the power of the strongest path and the noises. Computer simulation demonstrates the performance of the proposed algorithms in terms of bit error rate.

Blind spectrum sensing for cognitive radio over time-variant multipath flat-fading channels

Abstract Cognitive radio has more extensive application in recent years, and it may operate in complex wireless environmental condition such as communication systems with time-variant multipath flat-fading channel. As an essential technology for cognitive radio, most existing spectrum sensing methods are designed for time-invariant propagation channel; thus, it could be extremely difficult to achieve acceptable sensing performance when we apply them to deal with time-variant multipath fading channel. In order to overcome this obstacle, we design a novel spectrum sensing method in this investigation. Firstly, a dynamic state-space model is proposed in which two different hidden Markov models are employed to abstract the evolution of primary user state and time-variant multipath flat-fading channel gain. Based on the dynamic state-space model, the spectrum sensing problem is formulated as blind estimation problem. Relying on maximum a posteriori probability criterion and particle filtering technology, a joint estimation algorithm of the time-variant channel gain and primary user state is presented. Experimental simulations demonstrate the superior performance of our presented sensing scheme, which could be used potentially in realistic cognitive radio systems.

Channel interpolation for LTE uplink systems with high mobility using Slepian sequences

Single Carrier Frequency Division Multiple Access (SC-FDMA) has been adopted for 3rd Generation Partnership Project (3GPP) Long Term Evolution (LTE) uplink communication since it combines the low peak to average power ratio feature of single-carrier transmission systems with the multipath resistance and flexible frequency allocation of orthogonal frequency-division multiple access (OFDMA). Unfortunately, SC-FDMA transmission over time-varying multipath fading channels for very high speed environments the subcarrier orthogonality is destroyed giving rise to intercarrier interference (ICI) due to channel variation within an SC-FDMA symbol. This paper is, therefore, focused on the use of time-domain channel interpolator for LTE uplink systems in large Doppler spread scenarios to improve channel tracking performance. The resulting interpolation algorithm is based on the discrete prolate spheroidal wave functions (DPSWF) which is particularly well suited to represent the rapidly time-varying fading channel due to optimum finite time and frequency support feature. The variations of the channel taps are tracked first by a Kalman filter in time domain during training symbols. Then, a DPSWF based channel interpolator is applied to recover the time variation of channel taps between training symbols within two consecutive slots in a single subframe. The performance of the proposed interpolator is compared with a polynomial interpolator whose order is adjusted adaptively to achieve the best fit under particular user mobilities. Moreover, the analytical mean square error (MSE) performance of DPSWF is derived and compared with the simulations. The results show that the DPSWF represents the time-varying fading channel very effectively and the proposed algorithm has excellent channel estimation performance in high SNR even with a very small number of channel interpolation parameters employed in the algorithm.