OFDM Channel Research Articles

Wireless Adaptive Image Transmission Over OFDM Channels Based on Entropy Model

Wireless Adaptive Image Transmission Over OFDM Channels Based on Entropy Model

Enhanced channel prediction in large‐scale 5G MIMO‐OFDM systems using pyramidal dilation attention convolutional neural network

AbstractIn order to enhance communication while minimizing complexity in 5G and beyond, MIMO‐OFDM systems need accurate channel prediction. In order to enhance channel prediction, decrease Error Vector Magnitude, Peak Power, and Adjacent Channel Leakage Ratio, this study employs the Pyramidal Dilation Attention Convolutional Neural Network (PDACNN). Simplified clipping with filtering (SCF) reduces PAPR data, and this technique employs a PDACNN trained with the reduced data. By combining attention techniques with pyramidal dilated convolutions, the suggested PDACNN architecture is able to extract OFDM channel parameters across several scales. Attention approaches enhance channel prediction by allowing the model to dynamically concentrate on essential information. The primary objective is to make use of the network's ability to comprehend intricate spatial–temporal connections in OFDM channel data. The goal of these techniques is to make channel forecasts more accurate and resilient while decreasing concerns about EVM, Peak Power, and ACLR. To confirm the effectiveness of the suggested CP‐LSMIMO‐OFDM‐PDACNN, we measure its spectral efficiency, peak‐to‐average power ratio, bit error rate (BER), signal‐to‐noise ratio (SNR), and throughput. Throughput gains of 23.76%, 30.45%, and 18.97% are achieved via CP‐LSMIMO‐OFDM‐PDACNN, while bit error rates of 20.67%, 12.78%, and 19.56% are reduced. PAPRs of 21.66%, 23.09%, and 25.11% are also decreased.

Experimental investigations on the applicability of LASSO based sparse recovery for compressively sensed OFDM channel estimation

Experimental investigations on the applicability of LASSO based sparse recovery for compressively sensed OFDM channel estimation

Deep Residual Learning with Attention Mechanism for OFDM Channel Estimation

In this paper, we apply deep learning to the channel estimation problem of OFDM. Precisely speaking, to reduce the influence of noise on LS channel estimation, we design a channel estimation model based on attention mechanism and residual network. AttRNet-Conv and AttRNet-FC use attentional mechanism to change the weight of the feature to suppress noise. Compared with other channel estimation algorithms based on deep learning, AttRNet-Conv and AttRNet-FC avoid damaging channel information. The simulation results show that AttRNet-Conv and AttRNet-FC are superior to existing channel estimation algorithms based on deep learning in both high and low pilot frequency conditions. Even at low SNR, the estimation accuracy is higher than that of LMMSE. Experimental results show thatAttRNet-Conv and AttRNet-FC has strong robustness. In addition, we find that AttRNet-Conv is more suitable for channel estimation at low SNR in EPA, and the AttRNet-FC model is used in other cases.

A Spatial-Specific Neural Network-Based OFDM Channel Estimation Under Time-Varying Channels

A Spatial-Specific Neural Network-Based OFDM Channel Estimation Under Time-Varying Channels

An efficient and reliable OFDM channel state estimator using deep learning convolutional neural networks

An efficient and reliable OFDM channel state estimator using deep learning convolutional neural networks

Multi‐input fully CNN for joint pilot decontamination and symbol detection in 5G massive MIMO

AbstractThis paper presents a multi‐input deep learning‐based joint pilot decontamination and symbol detection (SD) technique for 5G massive multiple‐input multiple‐output (MAMIMO) systems. It consists of a fully convolutional neural network (FCNN) that finds the 5G channel coefficients using pre‐known sounding reference signals (SRSs) under pilot contamination and a symbol detector derived from projected gradient descent iterations. The study considers pilot contamination caused by inter‐cell interferences between SRSs during channel estimation (CE). The proposed scheme accepts entire 5G orthogonal frequency modulated (OFDM) data and least square estimates and produces the transmitted OFDM signal. Simulation experiments demonstrated that the proposed technique has better CE and SD performance with reduced trainable parameters. Moreover, it is faster due to the lowest elapsed time during end‐to‐end OFDM symbol detection. This paper proposes a joint pilot decontamination and signal detection for 5G MAMIMO systems. It achieves better detection performance with the lowest number of trainable parameters and memory requirements. It is applicable in 5G OFDM symbol detection and channel estimation under pilot contamination.

Efficient Image Transmission Over OFDM Channel with Energy Conservation and LS Channel Estimation with Various Modulation Schemes

To send data like text, images, music, and videos, packetization procedures are commonly used in communication apps. Just a portion of carriers in OFDM systems may be suitable for transmitting images due to channel fading. To reduce high channel errors at the receiver, the transmitter can proactively map descriptions onto good subcarriers and delete remaining descriptions if it has access to channel status information. In this study, discrete wavelet processing as an energy-saving approach for transmitting compressed image frames via OFDM channels. By utilizing one-bit channel status information at the transmitter, the descriptions are sorted and allocated to high-quality channels in a descending order of importance. To conserve energy, faulty sub-channel descriptions are removed at the transmitter. In this project, image transmission across OFDM channels is packetized to enable efficient data transfer while minimizing energy consumption. We also use the LS Channel estimation method to get a more accurate channel estimate with different modulation schemes and SNR. We obtained energy savings of up to 38% and improved channel estimation with various modulation schemes by utilizing MATLAB simulations to test on a variety of modulation schemes and channel SNR ratios.

A systematic literature review on channel estimation in MIMO-OFDM system: performance analysis and future direction

Abstract Multiple Input Multiple Output-Orthogonal Frequency Division Multiplexing (MIMO-OFDM) is a familiar modern wireless broadband technology due to its resistance against multipath fading, high data transmission rate, and spectral efficiency. This technology delivers dependable communication as well as a large range of coverage. The precise recovery of Channel State Information (CSI) and synchronization among the receiver and transmitter are two major challenges for MIMO-OFDM systems. Several estimate procedures, like blind, pilot-aided, and semi-blind channel estimating, are used to recover channel state information. Yet, those systems have several flaws that cause them to perform poorly. Hence, this paper describes the basic introduction of the Channel Estimation (CE) process in the MIMO-OFDM system. The main goal of this survey is to study analyzing and categorizing the channel estimation algorithms, and simulation tools in different contributions. Further, the performance analysis with different performance metrics from diverse contributions is pointed out. Thus, this review article presents a detailed overview of the various channel estimation schemes that have been exploited in the OFDM channel to enhance the estimation of the CSI in the MIMO-OFDM systems. This work presents and discusses the relevant comparative results and computational complexity for all of these CE systems. Furthermore, there is a list of open study directions for further exploration.

DATA TRANSMISSION IN MIMO-OFDM TECHNOLOGIES FOR CURRENT AND FUTURE WIRELESS COMMUNICATION SYSTEMS: FROM THEORY TO PRACTICE

This paper presents a Multiple-Input Multiple-Output Orthogonal Frequency Division Multiplexing (MIMO-OFDM) communication system with space-time and space-Frequency block coded (STBC/SFBC) of present generation downlink channel. Space-time block coding (STBC) and space-frequency block coding are transmit diversity schemes used in a wireless communication system to mitigate the effect of channel fading. The system is aimed to reduce the error rate (BER) by mitigating the effect of multipath fading which increase the quality of the system. In the proposed system, 2 transmit and 2 receive antennas are used, to transmit modulated symbols of block N across an OFDM channel, using SFBC Alamouti’s based encoding algorithm (SFBC-OFDM) derives from the basic criteria for space-time block codes (STBC). The signals from the first and second antennas are encoded and transmitted simultaneously in space and frequency over frequency-selective Rayleigh fading channel using two adjacent carriers. The work examines the SFBC and spatial diversity benefits realized based on the BER performance. The result is simulated effectively using MATLAB application software and the BER performance is compared with the conventional STBC-OFDM. It is shown that both STBC and SFBC techniques achieve similar performance with a diversity gain of four (4) over a slow fading channel with 2 antennas at both ends of the link under simple processing across the transmit and receive antennas.

TensorRT acceleration based on deep learning OFDM channel compensation

In order to accelerate the DNN module computing speed and significantly improve the operation efficiency of the miniaturized deep learning communication platform. TensorRT-based DNN computing acceleration technology is studied and implemented. By performing model preservation, model parsing, engine optimization and inference deployment on trained DNNs, the DNN network structure and quantized data volume are effectively optimized. The performance of the TensorRT-accelerated models is tested, and the results show that the TensorRT-based DNN computation acceleration technique has shorter computation time and higher computation throughput with high performance.

Deep Learning for OFDM Channel Estimation in Impulsive Noise Environments

Deep Learning for OFDM Channel Estimation in Impulsive Noise Environments

The Optimal Power Allocation for Sum Rate and Energy Efficiency of Full-Duplex Two-Way Communication Network.

Full-duplex (FD) transmission holds a great potential of improving the sum data rate of wireless communication systems. However, the self-interference introduced by the full-duplex transmitter brings a big challenge to enhance the energy efficiency. This paper investigates the power allocation problem in a full-duplex two-way (FDTW) communication network over an OFDM channel, aiming at improving the sum data rate and energy efficiency. We first characterize the sum rate and energy efficiency achieved in a single-carrier FDTW system. The optimal transmit power that achieves the maximal sum data rate is presented. The energy efficiency maximization problem is solved by using fractional programming. Then we further formulate sum rate and energy efficiency maximization problem in a multi-subcarrier FDTW system. In particular, the sub-optimal transmit power allocation which achieves a decent sum rate improvement is found by using a proposed iterative algorithm. By combining the iterative algorithm and fractional programming, we further maximize the energy efficiency of the multi-subcarrier system. With our proposed algorithm, we can easily obtain an optimal transmit power that approximates the global optimal solution. Simulation results show that using the obtained optimal transmit power allocation algorithm can significantly improve the sum rate and energy efficiency in both single-carrier and multi-subcarrier systems.

Low-Complexity Adaptive Frequency-Domain Nonlinear Equalization for Analog RoF Mobile Fronthaul Using FFT/IFFT-Assisted Channel Aggregation

Addressing the nonlinearity induced signal degradation is of critical importance for harvesting the transparent benefits of an analog radio over fiber (A-RoF) architecture, to support mobile fronthaul (MFH) with high spectral efficiency and reduced latency. Here, for linearizing the low-latency A-RoF MFH using digital-domain fast Fourier transform /inverse fast Fourier transform (FFT/IFFT) assisted channel aggregation/de-aggregation (CA/DA), we propose and experimentally demonstrate a compatible adaptive frequency-domain (FD) nonlinear equalization scheme. This scheme is characterized by a much lower computational complexity compared with the popular time-domain Volterra nonlinear equalizer while delivering a comparable performance. The proposed equalization scheme is implemented using the FD two-box cascaded Hammerstein structure. It enables simplified coefficients estimation, computationally efficient equalization, FFT/IFFT calculation reusing in channel DA, and shared legacy one-tap FD linear equalization in the OFDM system. Experimental validations of the proposed nonlinear equalization approach are carried out via typical electroabsorption modulator (EAM) and Mach-Zehnder modulator (MZM) based intensity modulation and direct detection (IMDD) A-RoF links. Cost-effective fiber-optic (with EAM) and optical up-conversion enabled V-band millimeter-wave (mm-wave) fiber-wireless (with MZM) transmissions are demonstrated. Experimental results show that the proposed scheme can effectively improve the performances of both EAM and MZM based 10-km fiber-optic A-RoF MFH links, in terms of the (>8 dB) reduction of adjacent channel leakage ratio, (>10 dB) improvement of error-vector magnitude (EVM) and (>5 dB) enhancement of input intermediate-frequency (IF) power range, which retains an acceptable (below 8 %) EVM performance for the 64-QAM modulation. The input IF signal aggregates 8 125-MHz 64-QAM OFDM channels. Furthermore, the proposed scheme enables the transmission of a 5-Gbit/s aggregated IF 64-QAM signal over a 10-km fiber link and 1.2-m 60-GHz wireless channel, wherein a >12 dB improved input power margin is observed.

Block-Structured Deep Learning-Based OFDM Channel Equalization

This letter considers equalization of an optical signal under the Orthogonal Frequency Division Multiplexing modulation scheme. The equalizer mitigates nonlinear effects caused by a power amplifier (PA) in the transmitter and a transimpedance amplifier (TIA) in the receiver. We compare the Convolutional Neural Network (CNN) and the Wiener-Hammerstein (WH) linearization models using two PA and three TIA devices. We are first to demonstrate a great boost in CNN effectiveness if sequentially combined with a linear equalization. The optimal sequence of linear and nonlinear blocks depends on the device profiles, and is found to be the same between CNN and WH. We develop a new block-structured CNN-based solution that utilizes the optimal sequence and brings up to 2.88 dB Q-factor gain over the traditional WH approach.

OFDM Channel Estimation Along with Denoising Approach under Small SNR Environment using SSA

In this paper, a de-noising approach in conjunction with channel estimation (CE) algorithm for OFDM systems using singular spectrum analysis (SSA) is presented. In the proposed algorithm, the initial CE is computed with the aid of traditional linear minimum mean square error (LMMSE) algorithm, and then further channel is evaluated by considering the low rank eigenvalue approximation of channel correlation matrix related to channel using SSA. Simulation results on bit error rate (BER) revealed that the method attains an improvement of 7 dB, 5 dB and 3 dB compared to common LSE, MMSE and SVD based methods respectively. With the help of statistical correlation coefficient (C) and kurtosis (k), the SSA method utilized to de-noise the received OFDM signal in addition to CE. In the process of denoising, the received OFDM signal will be decomposed into different empirical orthogonal functions (EOFs) based on the singular values. It was established that the correlation coefficients worked well in identifying useful EOFs only up to moderate SNR $\geq$ 12dB. For low SNR

Joint Channel and AoA Estimation in OFDM Systems: One Channel Tap With Multiple AoAs Problem

Recently, a joint channel and angle-of-arrival (AoA) estimation scheme for pilot-assisted multiple-input multiple-output orthogonal frequency division multiplexing (MIMO-OFDM) systems with digital arrays has been proposed; in that scheme, compressive sensing-based time-domain channel estimation is applied. In the scheme, it is assumed that each channel tap has only one AoA. However, in reality, one channel tap may contain responses from multiple paths with different AoAs. In this letter, we propose an improved approach to solve the aforementioned problem. The underlying concept is to transmit multiple OFDM symbols with different beamforming vectors, rendering the time-invariant channel equivalently time-variant. Subsequently, the correlation matrix, imbedded with AoA information, can be estimated. Finally, subspace-based algorithms, such as multiple signal classification (MUSIC), can be used for AoA estimation. The advantage of the proposed method is that the number of detectable AoAs is much larger than that in conventional MUSIC. The performance of the proposed method was also theoretically analyzed. Simulation results demonstrated that with a small number of OFDM symbols, both channel gains and AoAs could be satisfactorily estimated and the theoretical analysis results were accurate.

Bilinear Channel Estimation for MIMO OFDM: Lower Bounds and Training Sequence Optimization

Time-varying narrowband Multiple-Input Multiple- Output Orthogonal Frequency Division Multiplexing (MIMO OFDM) channel estimation is considered. The effect of finite bandwidth and practical pulse shapes renders the sparse MIMO-OFDM channel, non-sparse - this effect is denoted leakage. It is shown that the leaked MIMO OFDM channel is effectively separable in the delay and Doppler domains. A convex optimization approach, based on the atomic norm, is posed to estimate the channel parameters. With respect to the mean-squared error, the proposed scheme offers a 4 dB improvement over methods that ignore leakage and a 1.3 dB gain over a method that does consider leakage. The Cramér Rao bound (CRB) for the leaked channel parameters is derived and also exhibits decoupling in delay and Doppler. Training sequences that optimize the CRBs for delay and Doppler parameters are determined via solving key fixed-point equations. The optimized random sequences offer a performance gain on the order of 5-2.5 dB over purely random sequences. The proposed channel estimation algorithm nearly achieves the CRB, suggesting near optimality. Finally, the proposed estimation strategy, when employed on experimental data (SPACE'08), provides an average performance gain of 3 dB with respect to the probability of error in comparison to a traditional sparse channel estimation scheme.

Toward Programmable DOCSIS 4.0 Networks: Adaptive Modulation in OFDM Channels

The sixth generation of DOCSIS standard is currently under development for the provisioning of multi-Gbps services over cable networks. Building upon DOCSIS 3.1 (D3.1), DOCSIS 4.0 (D4) introduces several features including full-duplex transmission and extended-spectrum, which benefit from subcarrier-level OFDM modulation configurations to adapt to varying channel conditions. To exploit the full potential of D4, we propose a softwarized adaptive subcarrier modulation management framework. The optimization system consists of (i) a clustering mechanism that classifies CMs (Cable Modems) with a similar channel condition into the same group using a sparsified K-means algorithm and (ii) an efficient profile generation mechanism to balance achieved channel throughput and packet error rate within the same group. Then, we implement key elements of the softwarized system using a virtualized network function in our DOCSIS experimental testbed that enables the programmatic control of OFDM channels using D4 performance parameters. Our experimental results show that the proposed optimization function offers significant improvements in OFDM channel throughput over current industry management practices. Furthermore, we confirm via simulations that using a novel clustering algorithm for the classification of CM populations and a new bit-loading method measurably enhances channel performance in large-scale distributed deployment scenarios.

ParEst: joint estimation of the OFDM channel state information in MIMO systems

OFDM channel state information (CSI) is needed for determining key parameters in MIMO transmissions. In this paper, a novel CSI estimation method, ParEst, is proposed, which estimates the CSI from multiple transmitting antennas simultaneously. ParEst is based on a recent discovery that the CSI can be approximated very well by the linear combination of complex-based sinusoids on constant frequencies. ParEst finds the CSI of all antennas jointly by solving an optimization problem and achieves higher accuracy than existing heuristic methods. As the base sinusoids are on constant frequencies, ParEst pre-computes most key steps and reduces the run-time computation complexity to just a modest number of vector dot products equivalent to a few FFT calculations. ParEst can be applied to MIMO links in networks such as LTE, 5G, or Wi-Fi.