Symbol Detection Research Articles

Echo state network based symbol detection in chaotic baseband wireless communication

The Chaotic Baseband Wireless Communication System (CBWCS) is expected to eliminate the Inter-Symbol Interference (ISI) caused by multipath propagation by using the optimal decoding threshold that is the sum of the ISI caused by past decoded bits and the ISI caused by future transmitting bits. However, the current technique is only capable of removing partial effects of the ISI, because only past decoded bits are available for the sub-optimal decoding threshold calculation. The unavailability of the future information needed for the optimal decoding threshold is an obstacle to further improve the Bit Error Rate (BER) performance. In contrast to the previous method using Echo State Network (ESN) to predict one future bit, the proposed method in this paper predicts the optimal decoding threshold directly using ESN. The proposed ESN-based threshold prediction method simplifies the symbol decoding operation by avoiding the iterative prediction of the output waveform points using ESN and accumulated error caused by the iterative operation. With this approach, the calculation complexity is reduced compared to the previous ESN-based approach. The proposed method achieves better BER performance compared to the previous method. The reason for this superior result is twofold. First, the proposed ESN is capable of using more future symbols information conveyed by the ESN input to obtain more accurate threshold rather than the previous method in which only one future symbol was available. Second, the proposed method here does not need to estimate the channel information using Least Squared (LS) method, which avoids the extra error caused by inaccurate channel information estimation. Simulation results and experiment based on a wireless open-access research platform under a practical wireless channel show the effectiveness and superiority of the proposed method.

Deep Unfolded Sparse Refinement Network Based Detection in Uplink Massive MIMO

Massive multiple-input multiple-output (mMIMO) is a promising technique to realize the ever-increasing demand for high-speed data, quality of service (QoS), and energy efficiency for 5G and beyond wireless systems. However, the increased number of users in mMIMO systems significantly affects performance of the existing approximate matrix inversion based and matrix inversion less iterative symbol detection techniques. Conventional detection algorithms cannot learn the inter-relations of input-output parameters based on available data without having specific mathematical models of communication scenarios. Moreover, existing deep learning (DL) based symbol detection models lack in-network compression, resulting in large training time and high computational load while expected to be deployed in a low latency communication system. In this article, a sparse refinement architecture is proposed for symbol detection in uplink mMIMO. The proposed DL architecture requires less trainable parameters as compared to a conventional fully connected detection network and refines the estimated symbol vector in each layer. Convergence of the proposed symbol detection technique is analytically justified. An expression for the approximate upper bound on the BER is derived which is supported by simulations. The obtained results prove viability of the proposed symbol detection model as compared to the several existing state-of-art uplink mMIMO detection techniques, in terms of superior the error performance and low computational complexity.

A Novel Hybrid Modulation Scheme for Molecular Communication: Performance Analysis

A molecular communication (MC) system as compared to the traditional communication system differs in terms of signalling and the medium of communication. Design and selection of modulation scheme is one of the important aspect in realisation of a reliable communication system. In this letter, we propose a novel binary bit addition modulation scheme for diffusion based MC system which utilizes the simple binary addition principle and reduces the effective number of molecules which are released from the transmitter. We study the performance analysis in terms of the bit error rate (BER) and derive the closed-form expressions for the probabilities of symbol detection. We compare the proposed modulation scheme with the binary concentration shift keying scheme and show that the proposed scheme has a lower BER and the gap in the BER increases as the number of molecules increases showing the effectiveness when the number of transmitted molecules is large. Furthermore, through extensive simulation results, we show the performance of the proposed modulation scheme for the diffusion based MC system.

Message-Passing Receiver Design for Multiuser Multi-Backscatter-Device Symbiotic Radio Communications

Symbiotic radio (SR) has emerged as a spectrum and energy-efficient communication paradigm for future passive Internet-of-Things (IoT). In this paper, we consider a multiuser multi- backscatter-device (BD) SR communication system to enhance the spectrum efficiency, by sharing a common time-frequency resource block. Due to the presence of inter-user and inter-BD interference, multiuser and multi-BD detection in the receiver design become much more challenging. Concretely, the detection problem involves several key components: direct-link channel estimation, backscatter-link channel estimation, user signal decoding, and BD symbol detection. A conventional way is to realise these components in two separate phases, in which a channel estimation phase is followed by a data decoding phase. However, channel state information (CSI) acquisition is very difficult for the multiuser multi-BD SR communication, as compared to the case of orthogonal multiple access. In addition, the backscatter-link is relatively weak, which further increases the difficulty of CSI acquisition. To address these issues, we propose a novel receiver design to perform joint channel estimation, user data decoding, and BD symbol detection. Based on the factor graph representation of the joint estimation problem, we design a message-passing receiver for the multiuser multi-BD SR system to iteratively refine the estimation outputs. Extensive simulation results demonstrate the effectiveness of the proposed receiver design.

LoRa Signal Synchronization and Detection at Extremely Low Signal-to-Noise Ratios

In recent years, LoRa has been deployed in countless Internet of Things (IoT) applications across the globe. However, as LoRa is a proprietary technology, research into its physical-layer performance has been challenging. Implementing LoRa on software-defined radio (SDR) platforms yields valuable insight into the physical layer of the LoRa standard and paves the way for improvements in packet reception capabilities for LoRa receivers. This article presents an independently developed packet reception algorithm, which drastically improves the physical performance of LoRa communication links. The advanced signal presence detection, synchronization, and symbol detection strategies are shown to significantly increase packet reception ratios in extremely adverse noise conditions. Multiple algorithm variations are presented and compared in terms of bit error rate (BER) performance and computational cost. In comparison to a theoretical system with perfect channel state information, the simulated BER performance of the best performing algorithm only requires an increase of 1.6 dB in signal-to-noise ratio (SNR) to exhibit the same performance. Finally, SDR implementations of the algorithms exhibit average SNR performance gains up to 4.7 dB when compared to commercially available hardware.

OTFS vs. OFDM in the Presence of Sparsity: A Fair Comparison

Many recent works in the literature declare that Orthogonal Time-Frequency-Space (OTFS) modulation is a promising candidate technology for high mobility communication scenarios. However, a truly fair comparison with its direct concurrent and widely used Orthogonal Frequency-Division Multiplexing (OFDM) modulation has not yet been provided. In this paper, we present such a fair comparison between the two digital modulation formats in terms of achievable communication rate. In this context, we explicitly address the problem of channel estimation by considering, for each modulation, a pilot scheme and the associated channel estimation algorithm specifically adapted to sparse channels in the Doppler-delay domain, targeting the optimization of the pilot overhead to maximize the overall achievable rate. In our achievable rate analysis we consider also the presence of a guard interval or cyclic prefix. The results are supported by numerical simulations, for different time-frequency selective channels including multiple scattering components and under non-perfect channel state information resulting from the considered pilot schemes. This work does not claim to establish in a fully definitive way which is the best modulation format, since such choice depends on many other features which are outside the scope of this work (e.g., legacy, intellectual property, ease and know-how for implementation, and many other criteria). Nevertheless, we provide the foundations to properly compare multi-carrier communication systems in terms of their information theoretic achievable rate potential, within meaningful and sensible assumptions on the channel models and on the receiver complexity (both in terms of channel estimation and in terms of soft-output symbol detection).

Training-based symbol detection with temporal convolutional neural network in single-polarized optical communication system

In order to reduce the physical impairment caused by signal distortion, in this paper, we investigate symbol detection with Deep Learning (DL) methods to improve bit-error performance in the optical communication system. Many DL-based methods have been applied to such systems to improve bit-error performance. Referring to the speech-to-text method of automatic speech recognition, this paper proposes a signal-to-symbol method based on DL and designs a receiver for symbol detection on single-polarized optical communications modes. To realize this detection method, we propose a non-causal temporal convolutional network-assisted receiver to detect symbols directly from the baseband signal, which specifically integrates most modules of the receiver. Meanwhile, we adopt three training approaches for different signal-to-noise ratios. We also apply a parametric rectified linear unit to enhance the noise robustness of the proposed network. According to the simulation experiments, the bit-error-rate performance of the proposed method is close to or even superior to that of the conventional receiver and better than the recurrent neural network-based receiver.

Reservoir Computing Meets Extreme Learning Machine in Real-Time MIMO-OFDM Receive Processing

In this paper, we consider a real-time deep learning-based symbol detection approach for MIMO-OFDM systems. To exploit the temporal correlation of the wireless channel and the time-frequency structure of OFDM signals, a recurrent neural network (RNN) with deep feedforward output layers is introduced, where the recurrent layers and feedforward output layers are designed to process time-domain and frequency-domain information respectively. Reservoir computing (RC), a special type of RNN, and extreme learning machine (ELM), a special type of feedforward neural network, are chosen as the corresponding building blocks to facilitate over-the-air training. An online training loss objective is introduced to recursively update the neural weights in real-time. We believe this is the first work in the literature to realize real-time machine learning for MIMO-OFDM symbol detection, i.e., conducting NN-based symbol detection on an OFDM symbol basis. We demonstrate that (1) the <i>IEEE</i> standardized WiFi training sequence can be directly applied as the real-time training sequence (2) the symbol detection performance can be further improved by using our theoretically derived pilot pattern. Evaluation results show that our RC-ELM-based symbol detection method outperforms traditional model-based techniques as well as state-of-the-art learning-based approaches in highly dynamic channel environments for real-time symbol detection.

A Low-Complexity Joint Estimation and Detection Scheme of OFDM System With Combined BP-MF and VAMP

Considering orthogonal frequency division multiplexing (OFDM) system, we propose a joint channel estimation, noise precision estimation, symbol detection and bit decoding algorithm. In this algorithm, we incorporate the recently proposed vector approximate message passing (VAMP) with the belief propagation and mean field (BP-MF) framework, where BP is applied within subgraph of demodulation and decoding, and MF is used to handle nonlinear observations for estimating unknown noise precision, and VAMP is employed to estimate channels in densely connected factor graph. Furthermore, we use expectation propagation (EP) to demonstrate that the proposed approach converges after only one iteration. Compared to the state-of-the-art algorithm which exploits generalized approximate message passing (GAMP), the proposed scheme achieves the same bit error ratio (BER) performance, but lower per-iteration complexity and faster convergence speed.

ASAYAR: A Dataset for Arabic-Latin Scene Text Localization in Highway Traffic Panels

The extraction of text information from traffic panels is one of the challenging problems in computer vision. Although the past decade has seen a promising shift and important progress in object detection, few works and a limited number of datasets focus specifically on extracting text from traffic signs. To address the lack of data for text detection in traffic panels, especially those with Arabic scripts, this paper introduces a new multilingual and multipurpose dataset named ASAYAR. It consists of three sub-datasets: Arabic-Latin scene text localization, traffic sign detection, and directional symbol detection. The dataset contains 1763 images collected on different Moroccan highways, and annotated manually, using 16 object categories. The fully annotated ASAYAR images contains more than 20000 bounding box objects. The paper also investigates the usability of the dataset, by evaluating the performance of the <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">state-of-the-art</i> algorithms for object and text detection. Experimental results show good detection scores, demonstrating the potential contribution of ASAYAR in the development of methods for text extraction from traffic panels.

On the interference arising from random spatial fields of interferers utilizing multiple subcarriers

Effective symbol detection, channel estimation and decoding of channel codes require an accurate characterization of the noise probability distribution. In many systems, notably the internet of things, noise is largely in the form of interference, arising from a massive number of simultaneous transmissions from uncoordinated devices. Obtaining the probability distribution of the interference is a challenging problem due to the use of non-orthogonal multiple access schemes over several subcarriers (leading to multivariate statistical models) and the heavy-tailed nature of the interference due to the random locations of devices. In this paper, we derive a novel tractable characterization of the interference probability distribution based on an application of Sklar’s theorem to develop a combination of alpha-stable and t-copula dependence models. We demonstrate that this formulation produces an accurate statistical modeling framework that admits efficient parameter estimation methods. As an illustration of the utility of our models, we develop a simple-to-implement nonlinear receiver when a binary signal is transmitted over all subcarriers by the desired transmitter, which is effective in a range of scenarios and can significantly outperform existing approaches.

Deep learning-based symbol detection for time-varying nonstationary channels

The highly dynamic channel (HDC) in an extremely dynamic environment mainly has fast time-varying nonstationary characteristics. In this article, we focus on the most difficult HDC case, where the channel coherence time is less than the symbol period. To this end, we propose a symbol detector based on a long short-term memory (LSTM) neural network. Taking the sampling sequence of each received symbol as the LSTM unit's input data has the advantage of making full use of received information to obtain better performance. In addition, using the basic expansion model (BEM) as the preprocessing unit significantly reduces the number of neural network parameters. Finally, the simulation part uses the highly dynamic plasma sheath channel (HDPSC) data measured from shock tube experiments. The results show that the proposed BEM-LSTM-based detector has better performance and does not require channel estimation or channel model information.

Application of Emotional Factors of Ink Symbols Evaluated by Network Model in Modern Visual Image Design

Accurate and fast recognition of ink symbols can enhance the perception ability of intelligent ink symbols in the environment, provide information input for the operation of intelligent ink symbols, and improve the perfection of ink painting. This paper proposes an end-to-end deep neural network model based on YOLOv3 for overall and individual recognition of ink symbols from a computer vision perspective. Ink symbol images generated by the simulation software are used for learning the overall and individual ink symbol detection models. Experiments demonstrate that the YOLOv3 detection algorithm has a good detection effect on ink symbolic targets, and the recognition of individual ink symbols has higher accuracy and flexibility, which provides a preliminary solution idea to solve the ink symbol information perception problem.

High mobility enabled spatial and media‐based modulated orthogonal frequency division multiplexing systems for beyond 5G wireless communications

SummaryThis paper introduces the concept of spatial and media‐based modulated (SMBM) orthogonal frequency division multiplexing (OFDM) as a potential candidate for highly mobile next generation beyond 5G (B5G) wireless communications. The proposed SMBM‐OFDM technique utilizes not only the transmit antenna and channel state indices but also OFDM subcarriers to improve the system performance under high mobility. In addition, this study sheds light on challenging fast time‐varying channel estimation problem of MBM‐based systems by using the linear minimum mean square error (LMMSE) approach due to its optimality to investigate the achievable system performance with the aid of basis expansion modeling. The minimum lower bound on the channel estimation error (Bayesian Cramer–Rao bound) is derived theoretically and shown to be attainable by the considered LMMSE estimator. Moreover, symbol detection performance is provided for different modulation types and higher mobile velocities. Simulation results demonstrate that SMBM‐OFDM system under high mobility is able to provide around 12‐dB performance gains in terms of both channel estimation and symbol detection error compared to conventional spatial modulation (SM)‐OFDM systems without MBM. The presented framework is important due to addressing the high mobility support of SMBM‐OFDM systems for B5G wireless communications in terms of achievable channel estimation and data detection performance.

Symbol Detection Based on Back Tracking Search Algorithm in MIMO-NOMA Systems

One of the most important methods used to cope with multipath fading effects, which cause the symbol to be received incorrectly in wireless communication systems, is the use of multiple transceiver antenna structures. By combining the multi-input multi-output (MIMO) antenna structure with non-orthogonal multiple access (NOMA), which is a new multiplexing method, the fading effects of the channels are not only reduced but also high data rate transmission is ensured. However, when the maximum likelihood (ML) algorithm that has high performance on coherent detection, is used as a symbol detector in MIMO NOMA systems, the computational complexity of the system increases due to higher-order constellations and antenna sizes. As a result, the implementation of this algorithm will be impractical. In this study, the backtracking search algorithm (BSA) is proposed to reduce the computational complexity of the symbol detection and have a good bit error performance for MIMO-NOMA systems. To emphasize the efficiency of the proposed algorithm, simulations have been made for the system with various antenna sizes. As can be seen from the obtained results, a considerable reduction in complexity has occurred using BSA compared to the ML algorithm, also the bit error performance of the system is increased compared to other algorithms.

Symbol detection based on temporal convolutional network in optical communications

Deep learning (DL) is one of the fastest developing areas in artificial intelligence, it has been recently gained studies and application in computer vision, automatic driving, automatic speech recognition, and communication. This paper uses the DL method to design a symbol detection algorithm in receiver for optical communication systems. The proposed DL based method is implemented by a non-causal temporal convolutional network (ncTCN), which is a convolutional neural network and appropriate for sequence processing. Meanwhile, we adopt three methods to realize the training process for multiple signal-to-noise ratios of the AWGN channel. Furthermore, we apply two nonlinear activation functions for the noise robustness to the proposed ncTCN. Without losing generality, we apply the ncTCN-based receiver to the 16-ary quadrature amplitude modulation optical communication system in the simulation experiment. According to the experiment results, the proposed method can obtain some bit error rate performance gain compared to some conventional receivers.

Consensus ADMM-Based Distributed Simultaneous Imaging & Communication

This paper takes the first steps toward enabling wireless networks to perform both imaging and communication in a distributed manner. We propose Distributed Simultaneous Imaging and Symbol Detection (DSISD), a provably convergent distributed simultaneous imaging and communication scheme based on the alternating direction method of multipliers. We show that DSISD achieves similar imaging and communication performance as centralized schemes, with order-wise reduction in computational complexity. We evaluate the performance of DSISD via 2.4 GHz Wi-Fi simulations.

Data Combining Schemes for a Detector Array Receiver in Free-Space Optical Communications

An array of detectors is widely used in deep space optical communications for both fine beam tracking and symbol detection. In this paper, we have analyzed and compared the performance of different combining schemes&#x2014;such as equal gain combiner (EGC), selection combiner (SC) and the maximal ratio combiner (MRC)&#x2014;that are used to fuse the outputs from the elements of such arrays. The three combining schemes are compared in terms of error rate performance and computational complexity. Additionally, for the limiting case of continuous arrays of circular configuration, the optimization of two important parameters&#x2014;the spot size and the array size&#x2014;is also discussed in order to achieve the best error rate performance.

Two-Stage ML Detector Using Absolute Value of IQ Components and SVM for Adaptive OFDM-IM

Among various index modulation technology, adaptive orthogonal frequency division multiplexing with index modulation (A-OFDM-IM) improves reliability by solving the problem of deep fading. However, the maximal likelihood (ML) detector of A-OFDM-IM has high complexity by simultaneously performing active subcarrier detection and quadrature amplitude modulation (QAM) symbol demodulation. A simpler type of ML detector can be applied since the A-OFDM-IM has independent active states of each subcarrier and uses a single QAM constellation. In this paper, we propose two low-complexity detectors for the A-OFDM-IM. The proposed detectors have a two-stage receiving process, active subcarrier detection, and QAM demodulation. In active subcarrier detection, the presence or absence of the QAM symbol is more important than its information. Therefore we derive the thresholds using the absolute value of the in-phase and quadrature components of the received signal in the frequency domain. Using the absolute value, the distribution of noise added to inactive subcarrier is half normal. This is different from noise distribution of the ML detector, and the proposed detectors select the threshold considering the corresponding noise distribution. The first proposed detector uses a threshold derived by the ML estimation method. The second detector estimates the active subcarriers via a support vector machine. The proposed detectors have lower complexity than the ML detector because of the divided receiving process. Moreover, the theoretical analysis and simulation results show that the proposed detectors have better reliability than the ML detector due to different noise distribution and thresholds.

A Variational Bayesian Inference-Inspired Unrolled Deep Network for MIMO Detection

The great success of deep learning (DL) has inspired researchers to develop more accurate and efficient symbol detectors for multi-input multi-output (MIMO) systems. Existing DL-based MIMO detectors, however, suffer several drawbacks. To address these issues, in this paper, we develop a model-driven DL detector based on variational Bayesian inference. Specifically, the proposed unrolled DL architecture is inspired by an inverse-free variational Bayesian learning framework which circumvents matrix inversion via maximizing a relaxed evidence lower bound. Two networks are respectively developed for independent and identically distributed (i.i.d.) Gaussian channels and arbitrarily correlated channels. The proposed networks, referred to as VBINet, have only a few learnable parameters and thus can be efficiently trained with a moderate amount of training samples. The proposed VBINet-based detectors can work in both offline and online training modes. An important advantage of our proposed networks over state-of-the-art MIMO detection networks such as OAMPNet and MMNet is that the VBINet can automatically learn the noise variance from data, thus yielding a significant performance improvement over the OAMPNet and MMNet in the presence of noise variance uncertainty. Simulation results show that the proposed VBINet-based detectors achieve competitive performance for both i.i.d. Gaussian and realistic 3GPP MIMO channels.