Orthogonal Frequency Division Multiplexing Receiver Research Articles

FPGA implementation of joint synchronization algorithm based on short training sequence

Synchronization processing is a vital component of the Orthogonal Frequency Division Multiplexing (OFDM) receiver, playing a decisive role in determining the performance of the OFDM receiver. Based on the repetitive structure of the Short Training Sequence (STS) in the IEEE 802.11 physical layer protocol data unit, this paper proposes and implements a joint synchronization algorithm to address issues related to data packet detection and coarse frequency offset correction during the synchronization process. Through software simulation and Field-Programmable Gate Array (FPGA) board-level validation, it is concluded that the algorithm ensures performance while, to some extent, conserving FPGA resources.

The Use of Deep Learning Techniques in OFDM Receivers for 5G NR: A Survey

The Fifth Generation (5G) New Radio (NR) wireless system is the most promising next-generation solution to meet the needs of the increasing demands of mobile market. Orthogonal Frequency Division Multiplexing (OFDM) is the fundamental transmission technique due to the great improvement in spectral efficiency and the flexibility in fast varying environments. The use of multiple-input-multiple-output (MIMO) OFDM and Massive MIMO (Ma-MIMO) OFDM promises the increase in quality, throughput, and capacity of the communication system. The use of Deep Learning techniques in wireless communication networks has shown significant achievements to overcome the rising challenges as compared to the conventional solutions. In this paper, first we give a brief background on OFDM, MIMO-OFDM and Ma-MIMO. Second, we provide a survey on the use of deep learning techniques in OFDM receivers. We categorize the state-of-the-art into Block based and End-to-End deep learning models. Third, we provide a survey on the use of deep learning techniques in MIMO-OFDM and Ma-MIMO-OFDM. We categorize the state-of-the-art as model driven and data driven deep learning models. Finally, we present future research directions.

Deep Learning OFDM Receivers for Improved Power Efficiency and Coverage

In this article, we propose multiple machine learning (ML) based physical-layer receiver solutions for demodulating orthogonal frequency-division multiplexing (OFDM) signals that are subject to high level of nonlinear distortion. Specifically, three novel deep learning based convolutional neural network receivers are devised, containing layers in time- and/or frequency-domains, allowing to demodulate and decode the transmitted bits reliably despite the high error vector magnitude (EVM) in the transmit signal. Applicable training procedures are also described, such that the learned layers in the receiver processing properly generalize over different nonlinear distortion and multipath channel characteristics. Extensive set of numerical results is provided, in the context of 5G NR uplink (UL) incorporating also measured terminal power amplifier (PA) characteristics. The obtained results show that the proposed receiver systems are able to clearly outperform the classical linear minimum mean-squared error (LMMSE) receiver as well as the existing ML receiver approaches, especially when the EVM is high compared to modulation order. This is particularly so when the devised ML receiver is of hybrid nature with layers both in time and frequency. The proposed ML receivers can thus facilitate pushing the terminal PA systems deeper into saturation, and thereon improve the terminal power-efficiency, radiated power and network coverage. Through combining the obtained radio link performance results with link budget calculations, all carried out at the 28 GHz mmWave band, it is shown that the proposed ML receivers can enhance the network coverage in terms of maximum UL link distances by close to 100%, when compared to classical LMMSE receiver based networks.

An Enhanced Channel Estimation for IEEE 802.15.4 OFDM Receiver in High-Speed Mobile IoT Communication Systems

Currently, orthogonal frequency division multiplexing (OFDM), standardized in IEEE 802.15.4, has attracted attention as a transmission scheme for higher data rates, long-distance transmissions, and mobile communications for the expansion of IoT applications as well as for legacy IoT applications, such as smart metering systems and home-area networks. In particular, receiving schemes using IEEE 802.15.4-based OFDM have been actively examined to realize mobile communication. Previous studies used the long training field (LTF) and pilot subcarriers for channel estimation of multipath fading, achieving mobile communication with data rates of 100 kbps or above at moving speeds of up to 40 km/h in an urban environment. In a previous study, channel estimates were obtained by repeating channel estimates from the LTF and pilot subcarriers in the time domain and by interpolating the estimates in the frequency domain. This study proposes a receiving scheme for IEEE 802.15.4 OFDM aimed at high-speed mobile IoT communication systems. In the time axis direction, the proposed scheme obtains channel estimates in the same manner as previous schemes. However, in the frequency axis, the proposed scheme obtains the channel estimates by copying estimates of neighboring subcarriers according to the coherence bandwidth of the multipath fading channel to update the channel estimates frequently. Computer simulation results show that the proposed scheme including delay spread estimation can achieve mobile communication with a data rate of 100 kbps at moving speed of 200 km/h in the urban environment.

Deep learning and expert knowledge based underwater acoustic OFDM receiver

In this paper, a deep learning and expert knowledge based receiver is proposed for underwater acoustic (UWA) orthogonal frequency division multiplexing (OFDM). Different from the existing deep learning based UWA OFDM receivers, the proposed receiver combines deep learning with the classical expert knowledge of block-based signal processing in UWA OFDM to improve system performance and interpretability. It performs joint channel estimation and signal detection by designing skip connection (SC) convolutional neural network (CNN) cascaded attention mechanism (AM) enhanced bi-directional long short-term memory (BiLSTM) network, abbreviated as SC-CNN-AM-BiLSTM network (SCABNet). Specifically, the channel estimation subnet is designed with SC-CNN to utilize the thought of image super-resolution to reconstruct the entire channel frequency response of all subcarriers. The signal detection subnet is designed with AM-BiLSTM to extract the correlations of received sequential data for signal detection. Especially with the AM, the signal detection subnet can focus more on effective information of the received distorted signal to train the optimal network weights to improve the accuracy of data recovery. The proposed SCABNet is evaluated by experimental data, and the results have demonstrated that the SCABNet has the lowest BER and robust performance compared to the traditional linear algorithm, deep learning based black-box receiver, and ComNet receiver. And the proposed SCABNet is effective and robust when multiple nonideal factors co-exist.

Performance Analysis of ACO/DCO-OFDM Hybrid PLC-VLC Receivers With Blanking Non-Linearity

Blanking non-linearity is broadly used in impulsive noise communication environments due to its efficiency and ease of deployment to mitigate such noise. During the blanking procedure, data samples with impulsive noise are identified and clipped to zero, which removes impulsive noise from the system thereby, improving the system performance. In this regard, this paper investigates the performance of hybrid power line communication-visible light communication (PLC-VLC) systems with blanking nonlinearity. The signal-to-noise (SNR) ratio at the output of the blanking device is deduced using a closed-form analytical expression. The results show that there is good agreement between simulation results and theory if there is a sufficiently large number of orthogonal frequency division multiplexing (OFDM) sub-carriers. In addition, the study also indicates that the blanking method is more effective in curbing impulsive noise in comparison to the clipping technique. According to the threshold optimisation investigations, there exists a worst-case SINR value that minimizes the output SNR at the OFDM receiver after blanking. Furthermore, the findings also revealed that proper blanking threshold selection is essential for obtaining the best performance.

Effect of DNN Approximation for Channel Estimation and Signal Detection on OFDM Applications

This paper offers a deep learning approximation to realize channel estimation and signal detection that creates the main communication structure skeleton for the orthogonal frequency-division multiplexing (OFDM) system known as an efficient modulation type on 5G. This letter offers an application of deep learning to handle the wireless OFDM channels' end-to-end conduct. First, channel state information (CSI) is predicted explicitly that differs from existing OFDM receivers, then detected the transmitted symbols utilizing the predicted CSI. In the end, CSI is predicted by the suggested deep learning approximation indirectly and transmitted symbols are directly recovered. The structure of the designed receiver occurs of a layer of DNN and soft decisions, which resolves the issues channel estimation error, time delay, and limitation of decoding between users in classic detection techniques. In the simulation results, it is observed that the receiver has powerful stability on the power distribution of user, not only convenient for the linear channel, but also for nonlinear channel when enhancement the number of users, also detection can be well on the receiver. Generally, the efficiency of the modulation system decreases with the features of the multipath channel utilized for transmission. Channel estimation and detection of symbols utilize to reduce the impacts of the channel, which needs high computation and bandwidth conventionally. This paper is used deep neural networks (DNN) for detecting the signal, in this way much effort in detecting the channel is prevented. The proposed method saves priceless bandwidth via used CP in OFDM with a big increase in SNR.

Performance Estimation and Selection Guideline of SiPM Chip within SiPM-Based OFDM-OWC System

The orthogonal frequency division multiplexing (OFDM), which has high spectral efficiency, is an attractive solution for silicon photomultiplier (SiPM)-based optical wireless communication (OWC) systems to boost data rates. However, the currently available SiPMs are not optimized for implementing the OFDM receiver. Incorporating different types of SiPM at the OFDM receiver results in different data rates at the same condition. Therefore, the receiver designer requires a method for predicting the performance of SiPMs and then selects the best one to build the optimum receiver. In this paper, we first investigate the origin of SiPM’s power-dependent frequency response. The investigation outcome is then used to create a method for predicting the subcarrier SNR. Combining the estimated subcarrier SNR with the bit-loading scheme, we finally propose a general approach for estimating the fundamental OFDM data rate an SiPM chip can support at a given received power. Results are then presented that can be used by the future receiver designer as a guideline to find the best type of SiPM to build the optimum OFDM receiver.

Neural Network-Based OFDM Receiver for Resource Constrained IoT Devices

Orthogonal Frequency Division Multiplexing (OFDM)-based waveforms are used for communication links in many current and emerging Internet of Things (IoT) applications, including the latest WiFi standards. For such OFDM-based transceivers, many core physical layer functions related to channel estimation, demapping, and decoding are implemented for specific choices of channel types and modulation schemes, among others. To decouple hard-wired choices from the receiver chain and thereby enhance the flexibility of IoT deployment in many novel scenarios without changing the underlying hardware, we explore a novel, modular Machine Learning (ML)-based receiver chain design. Here, ML blocks replace the individual processing blocks of an OFDM receiver, and we specifically describe this swapping for the legacy channel estimation, symbol demapping, and decoding blocks with Neural Networks (NNs). A unique aspect of this modular design is providing flexible allocation of processing functions to the legacy or ML blocks, allowing them to interchangeably coexist. Furthermore, we study the implementation cost-benefits of the proposed NNs in resource-constrained IoT devices through pruning and quantization, as well as emulation of these compressed NNs within Field Programmable Gate Arrays (FPGAs). Our evaluations demonstrate that the proposed modular NN-based receiver improves bit error rate of the traditional non-ML receiver by averagely 61 percent and 10 percent for the simulated and over-the-air datasets, respectively. We further show complexity-performance tradeoffs by presenting computational complexity comparisons between the traditional algorithms and the proposed compressed NNs.

Research of the Influence of the Quadrature Components Mismatch on the Noise Immunity of OFDM and UFMC Signals

The method of direct modulation using complex signals is used to implement signal paths of transmitters in base stations of cellular communication systems. In the process of modulation, there are mismatches of the gain coefficient and the phase of the quadrature components of the signal. Mismatch degrades the Error Vector Magnitude (EVM) at the receiver, which in turn results in an increased Bit Error Rate (BER). The quality of the received signal is expressed in bit error rate. The mismatch of the amplitude and phase of the quadrature components is one of the most important factors making the greatest contribution to the amplitude of the error vector, and which must be investigated. The paper presents a research of the influence of the mismatch on the OFDM (Orthogonal frequency-division multiplexing) and UFMC (universal filtered multi-carrier) technologies. A model of the transmitter, communication channel and receiver for OFDM and UFMC signals has been developed. The model was built in the MatLab software environment. In the work, by studying the simulation model, the dependence of the noise immunity of technologies was studied by changing the parameters of the communication channel, such as the amplitude and phase mismatch of the quadrature components of the signal, as well as the signal-to-noise ratio. Also, a comparative analysis of such signal parameters as the occupied bandwidth, peak to average ratio, frequency of occurrence of bits with an error was carried out. Based on the results of the study, graphs of the dependence of the error probability and the signal peak to average ratio on the mismatch of the quadrature components were obtained for two technologies, OFDM and UFMC. The study allows us to highlight the advantages of UFMC technology, which are expressed in spectral efficiency, noise immunity and the level of the signal peak to the average ratio.

Robust Deep Learning-Based End-to-End Receiver for OFDM System With Non-Linear Distortion

In this letter, we propose a deep learning-based receiver named one-dimensional transmit and recovery net (1D-TRNet), which is robust to non-linear clipping distortion for orthogonal frequency-division multiplexing (OFDM) systems. The proposed scheme uses a 1D U-shape structure with multiple gated recurrent units (GRUs). The 1D-convolution kernel can extract both the low-level (local) and high-level (global) features in the non-linearly distorted OFDM signals. Simulation results show that the proposed 1D-TRNet receiver achieves significant bit-error rate (BER) performance gain over the traditional OFDM receivers. Moreover, the proposed 1D-TRNet receiver outperforms the state-of-the-art generalized approximate message passing (GAMP) receiver under the same computational complexity constraint.

Universal Filtered Multicarrier Receiver Complexity Reduction to Orthogonal Frequency Division Multiplexing Receiver

The Universal Filtered Multicarrier (UFMC) waveform technology is one of the promising waveforms for 5G and beyond 5G networks. Owing 2N-point Fast Fourier Transform (FFT) processor at the UFMC receiver, the computational and implementation complexity is two times more than the conventional Orthogonal Frequency Division Multiplexing (OFDM) receiver system. In this paper, we proposed a simplified UFMC receiver structure to reduce computational complexity as well as hardware requirements. The received UFMC symbol simplified exactly to its equivalent after performing 2N-point FFT and decimation operations. In which, the mathematical model of the frequency-domain UFMC signal is rederived after processing through 2N-point FFT and decimator, and the simplified signal is generated with an N-point FFT. Accordingly, the 2N-point FFT processor and decimator are replaced with a single N-point FFT processor. This approach reduces the 50% computational complexity at the FFT processor level hence the hardware and processing time. The computational complexity of the proposed receiver model is approximately equivalent to the OFDM receiver. Additionally, analyzed the mathematical model for the simplified UFMC receiver and the comparative performance of the UFMC system with the conventional model.

Massive MIMO-OFDM Systems with Low Resolution ADCs: Cramér-Rao Bound, Sparse Channel Estimation, and Soft Symbol Decoding

We consider the delay-domain sparse channel estimation and data detection/decoding problems in a massive multiple-input-multiple-output (MIMO) orthogonal frequency division multiplexing (OFDM) wireless communication system with low-resolution analog-to-digital converters (ADCs). The high non-linear distortion due to coarse quantization leads to severe performance degradation in conventional OFDM receivers, which necessitates novel receiver techniques. Firstly, we derive the Bayesian Cramr-Rao-lower-bound (CRLB) on the mean squared error (MSE) in recovering jointly compressible vectors from quantized noisy underdetermined measurements. Secondly, we formulate the pilot-assisted channel estimation as a multiple measurement vector (MMV) sparse recovery problem, and propose a variational Bayes (VB) algorithm to infer the posterior distribution of the channel. We benchmark the MSE performance of our algorithm with that of the CRLB, and numerically show that the VB algorithm meets the CRLB. Thirdly, we propose a soft symbol decoding algorithm that infers the posterior distributions of the data symbols given the quantized observations. We utilize the posterior statistics of the detected data symbols as virtual pilots, and propose an iterative soft symbol decoding and data-aided channel estimation procedure. Finally, we propose a variant of the iterative algorithm that utilizes the output bit log-likelihood ratios of the channel decoder to adapt the data prior to further improve the performance. We provide interesting insights into the impact of the various system parameters on the MSE and bit error rate of the proposed algorithms, and benchmark them against the state-of-the-art.

Design and Testbed Implementation of Blind Parameter Estimated OFDM Receiver

Designing an intelligent or adaptive transceiver system is becoming a promising technology for upcoming generations of wireless communication systems due to its adaptivity, spectrum efficiency, and low-latency characteristics. However, there is no work available until now that characterizes and demonstrates complete adaptation in the physical layer for orthogonal frequency-division multiplexing (OFDM) systems. In this article, we propose and implement sequential blind parameter estimation methods for OFDM signals using radio frequency (RF) testbed setup in a realistic scenario. The estimations include the number of subcarriers, symbol duration, cyclic prefix, oversampling factor, symbol timing offset (STO), and carrier frequency offset (CFO). The proposed algorithms also include blind modulation classification for linearly modulated signals over a frequency-selective fading channel. The parameter estimation has been carried out through a cyclic cumulant process. The modulation formats are classified by using normalized fourth-order cumulant in the frequency domain. The STO and CFO are estimated by a proposed modified maximum likelihood algorithm. The performances of parameter estimations, modulation classification, and synchronization are measured through analytical, simulation, and measurement studies. The overall performance of the OFDM system is provided in terms of the received constellation diagram and bit error rate (BER) over an indoor propagation environment.

GPU-Accelerated Partially Linear Multiuser Detection for 5G and Beyond URLLC Systems

In this feasibility study, we have implemented a recently proposed partially linear multiuser detection algorithm in reproducing kernel Hilbert spaces (RKHSs) on a GPU-accelerated platform. Partially linear multiuser detection, which combines the robustness of linear detection with the power of nonlinear methods, has been proposed for a massive connectivity scenario with the non-orthogonal multiple access (NOMA). This is a promising approach, but detecting payloads within a received orthogonal frequency-division multiplexing (OFDM) radio frame requires the execution of a large number of inner product operations, which are the main computational burden of the algorithm. Although inner-product operations consist of simple kernel evaluations, their vast number poses a challenge in ultra-low latency (ULL) applications, because the time needed for computing the inner products might exceed the sub-millisecond latency requirement. To address this problem, this study demonstrates the acceleration of the inner-product operations through massive parallelization. The result is a GPU-accelerated real-time OFDM receiver that enables sub-millisecond latency detection to meet the requirements of 5th generation (5G) and beyond ultra-reliable and low latency communications (URLLC) systems. Moreover, the parallelization and acceleration techniques explored and demonstrated in this study can be extended to many other signal processing algorithms in Hilbert spaces, such as those based on projection onto convex sets (POCS) and adaptive projected subgradient method (APSM) algorithms. Experimental results and comparisons with the state-of-art confirm the effectiveness of our techniques.

Deep learning aided OFDM receiver for underwater acoustic communications

In this study, we propose a deep learning (DL)-based orthogonal frequency division multiplexing (OFDM) receiver for underwater acoustic (UWA) communications. Compared to existing deep neural network (DNN) OFDM receivers composed of fully connected (FC) layers, our model tailors complex UWA communications with precision. To this end, it utilizes a convolutional neural network with skip connections to perform signal recovery. The stacks of convolutional layers with skip connections can effectively extract promising features from received signals and reconstruct the original transmitted symbols. Then, a multilayer perceptron is used for demodulation. To demonstrate the performance of the proposed DL-based UWA-OFDM communication system, the training and testing sets are generated using the strength of the measured-at-sea WATERMARK dataset. The experimental results show that the proposed model with skip connections can outperform the existing approaches (i.e., traditional UWA-OFDM with least squares channel estimation, and FC-DNN-based framework) in terms of both accuracy and efficiency. This is prominent in harsh UWA environments with strong multipath spread and rapid time-varying characteristics.

AI-Aided Online Adaptive OFDM Receiver: Design and Experimental Results

Orthogonal frequency division multiplexing (OFDM) has been widely applied in many wireless communi- cation systems. The artificial intelligence (AI)-aided OFDM receivers are currently brought to the forefront to replace and improve the traditional OFDM receivers. In this paper, we first compare two AI-aided OFDM receivers, namely, data-driven fully connected deep neural network and model-driven ComNet, through extensive simulation and real-time video transmission using a 5G rapid prototyping system for an over-the-air (OTA) test. We find a performance gap between the simulation and the OTA test caused by the discrepancy between the channel model for offline training and the real environment. We develop a novel online training system, which is called SwitchNet receiver, to address this issue. This receiver has a flexible and extendable architecture and can adapt to real channels by training only several parameters online. From the OTA test, the AI-aided OFDM receivers, especially the SwitchNet receiver, are robust to OTA environments and promising for future communication systems. At the end of this paper, we discuss potential challenges and future research inspired by our initial study in this paper.

Deep-Waveform: A Learned OFDM Receiver Based on Deep Complex-Valued Convolutional Networks

The (inverse) discrete Fourier transform (DFT/ IDFT) is often perceived as essential to orthogonal frequency-division multiplexing (OFDM) systems. In this paper, a deep complex-valued convolutional network (DCCN) is developed to recover bits from time-domain OFDM signals without relying on any explicit DFT/IDFT. The DCCN can exploit the cyclic prefix (CP) of OFDM waveform for increased SNR by replacing DFT with a learned linear transform, and has the advantage of combining CP-exploitation, channel estimation, and intersymbol interference (ISI) mitigation, with a complexity of <i xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">O</i> (N <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup> ). Numerical tests show that the DCCN receiver can outperform the legacy channel estimators based on ideal and approximate linear minimum mean square error (LMMSE) estimation and a conventional CP-enhanced technique in Rayleigh fading channels with various delay spreads and mobility. The proposed approach benefits from the expressive nature of complex-valued neural networks, which, however, currently lack support from popular deep learning platforms. In response, guidelines of exact and approximate implementations of a complex-valued convolutional layer are provided for the design and analysis of convolutional networks for wireless PHY. Furthermore, a suite of novel training techniques are developed to improve the convergence and generalizability of the trained model in fading channels. This work demonstrates the capability of deep neural networks in processing OFDM waveforms and the results suggest that the FFT processor in OFDM receivers can be replaced by a hardware AI accelerator.

Bayesian Channel Estimation and Data Detection in Oversampled OFDM Receiver With Low-Resolution ADC

This paper focuses on a novel orthogonal frequency division multiplexing (OFDM) receiver architecture, which uses a low-resolution analog-to-digital converter (ADC) to oversample the received signal in time domain. Although attractive in terms of power consumption and hardware cost, low-resolution ADC incurs severe nonlinear quantization distortion and thus destroys the orthogonality between the OFDM subcarriers. The loss of orthogonality, together with the oversampling-caused correlation in noise samples, poses a great challenge to the OFDM receiver design. This paper aims to tackle this challenge. For the proposed receiver, we consider an often-used two-phase transmission protocol. In the first phase, training OFDM symbols are transmitted for channel estimation. In the second phase, information-conveying OFDM symbols are transmitted and detected using the previously obtained channel estimate. Therefore, the proposed receiver consists of two key components: channel estimator and data detector. These components are elaborately derived in the framework of Bayesian inference. Due to the diversity gain resulting from the oversampling operation, the proposed receiver can remarkably outperform the counterparts, including the conventional OFDM receiver with perfect infinite-precision quantization.

Design and Implementation of a Farrow-Interpolator-Based Digital Front-End in LTE Receivers for Carrier Aggregation

A digital front-end decimation chain based on both Farrow interpolator for fractional sample-rate conversion and a digital mixer is proposed in order to comply with the long-term evolution standards in radio receivers with ten frequency modes. Design requirement specifications with adjacent channel selectivity, inband blockers, and narrowband blockers are all satisfied so that the proposed digital front-end is 3GPP-compliant. Furthermore, the proposed digital front-end addresses carrier aggregation in the standards via appropriate frequency translations. The digital front-end has a cascaded integrator comb filter prior to Farrow interpolator and also has a per-carrier carrier aggregation filter and channel selection filter following the digital mixer. A Farrow interpolator with an integrate-and-dump circuitry controlled by a condition signal is proposed and also a digital mixer with periodic reset to prevent phase error accumulation is proposed. From the standpoint of design methodology, three models are all developed for the overall digital front-end, namely, functional models, cycle-accurate models, and bit-accurate models. Performance is verified by means of the cycle-accurate model and subsequently, by means of a special C++ class, the bitwidths are minimized in a methodic manner for area minimization. For system-level performance verification, the orthogonal frequency division multiplexing receiver is also modeled. The critical path delay of each building block is analyzed and the spectral-domain view is obtained for each building block of the digital front-end circuitry. The proposed digital front-end circuitry is simulated, designed, and both synthesized in a 180 nm CMOS application-specific integrated circuit technology and implemented in the Xilinx XC6VLX550T field-programmable gate array (Xilinx, San Jose, CA, USA).