Lower Symbol Error Rate Research Articles

Hybrid optimization‐based deep neuro‐fuzzy network for designing m‐user multiple‐input multiple‐output interference channel

SummaryMultiple‐input multiple‐outputs (MIMOs) have eminent quality in maximizing the throughput of wireless communication models. In MIMO, the antenna arrays can be utilized for fulfilling the needs of 5G by utilizing various spatial signatures of users. Even though 5G communication is imminent, there exist issues, like network interference that arise due to reused frequency spectrum resources. This delving presents an optimized deep model for suppressing interference occurring in the Rayleigh channel in the multiple‐user MIMO (MU‐MIMO) model. Here, an MU‐MIMO model is employed with correlated interference wherein there exist various users around the base station (BS) with several antennas at the transmitter and receiver. Here, a deep neuro‐fuzzy network (DNFN) is used to upgrade the performance of detectors underneath correlated interference. Here, the model comprises zero forcing‐maximum likelihood detection (ZF‐MLD) that assists to generate an initial estimate of broadcasted signals in a particular time slot. The DNFN is used to capture latent correlation among several symbols. Here, the DNFN training is performed using developed autoregressive Henry gas spider monkey optimization (RHGSMO), which is the combination of conditional autoregressive value at risk (CAViaR), Henry gas solubility optimization (HGSO), and spider monkey optimization (SMO). With the lowest symbol error rate (SER), bit error rate (BER), and signal to interference and noise ratio (SINR), the suggested RHGSMO‐based DNFN performed better than existing approaches.

Bi‐directional LSTM based channel estimation in 5G massive MIMO OFDM systems over TDL‐C model with Rayleigh fading distribution

SummaryIn this work, a deep learning (DL)‐based massive multiple‐input multiple‐output (mMIMO) orthogonal frequency division multiplexing (OFDM) system is investigated over the tapped delay line type C (TDL‐C) model with a Rayleigh fading distribution at frequencies ranging from 0.5 to 100 GHz. The proposed bi‐directional long short‐term memory (Bi‐LSTM) channel state information (CSI) estimator uses online learning during training and offline learning during the practical implementation phase. The design of the estimator takes into account situations in which prior knowledge of channel statistics is limited and targets excellent performance, even with limited pilot symbols (PS). Three separate loss functions (mean square logarithmic error [MSLE], Huber, and Kullback–Leibler Distance [KLD]) are assessed in three classification layers. The symbol error rate (SER) and outage probability performance of the proposed estimator are evaluated using a number of optimization techniques, such as stochastic gradient descent (SGD), momentum, and the adaptive gradient (AdaGrad) algorithm. The Bi‐LSTM‐based CSI estimator is trained considering a specific number of PS. It can be readily seen that by incorporating a cyclic prefix (CP), the system becomes more resilient to channel impairments, resulting in a lower SER. Simulations show that the SGD optimization approach and Huber loss function‐trained Bi‐LSTM‐based CSI estimator have the lowest SER and very high estimation accuracy. By using deep neural networks (DNNs), the Bi‐LSTM method for CSI estimation achieves a superior channel capacity (in bps/Hz) at 10 dB than long short‐term memory (LSTM) and other conventional CSI estimators, such as minimum mean square error (MMSE) and least squares (LS). The simulation results validate the analytical results in the study.

A Wireless Channel Equalization Method Based on Hybrid Whale Optimization: For Constant Modulus Blind Equalization System

This paper proposes a wireless channel equalization method applied to the constant modulus blind equalization system, which addresses the slow convergence and strong randomness in the initialization of equalizer weights in the constant modulus blind equalization algorithm (CMA) by introducing a hybrid arithmetic whale optimization algorithm (HAWOA). The mean square error in the CMA is utilized as the cost function for the HAWOA to obtain a more effective initial weights for the equalizer. To validate the superiority of the hybrid arithmetic whale constant modulus blind equalization algorithm, tests are conducted on the equalization system using 16QAM and 64QAM signals. The simulation results demonstrate that the proposed algorithm achieved better initial weights compared to the CMA and the constant modulus blind equalization algorithm based on the whale optimization algorithm. It can obtain the desired mean square error with a lower symbol error rate in fewer iterations. Furthermore, the hybrid arithmetic whale constant modulus blind equalization algorithm exhibited faster convergence in optimizing initial weights, effectively enhancing the equalization performance of the CMA in wireless channels while ensuring timeliness.

Adaptive estimation of sparse channel based on modified RLS for coherent underwater acoustics communications

A new channel estimation approach used in coherent underwater acoustic communication system is presented in this paper. The proposed method consists of two steps: (1) obtain the initial channel estimation based on least square (LS) and the sparse characteristics of underwater acoustic channels; (2) track the channels using the modified recursive least squares (RLS). In the first step, the threshold value to set all coefficients below it to zero is deduced. In the second step, a modified RLS based on the idea of RLS and Kalman filter terminology is proposed. Experimental results show that the proposed method can track underwater acoustic channels well. The lower symbol error rate (SER) and higher output SNR can be achieved.

Performance analysis of cooperative NOMA system for defense application with relay selection in a hostile environment

In the current scenario, where all the services have become online, the demand for increased capacity, low symbol error rate (SER), better data rate, and low latency with high quality of services has been expected from the service providers. Non-orthogonal multiple access (NOMA) is the key enabling technology by which all the above requirements could be achieved. In this work, the physical layer security of a cooperative NOMA system has been improved. The present approach of the decode-and-forward (DF) method consists of one base station (BS), N relays, and two users. Here, a two-stage relay method has been proposed, and analytical results are generated to show that this two-stage relay scheme has been found to achieve the lowest outage probability among all possible relay schemes with the highest attainable diversity gain. In a high signal-to-noise ratio (SNR), the asymptotic and closed-form expressions for outage probability have been obtained. The outage probability for N relays is also computed, which shows that the outage probability decreases as the number of relays get increased. Meanwhile, the Monte-Carlo simulation shows that cooperative NOMA with the proposed two-stage relay technique outperforms cooperative NOMA using the traditional max-min approach.

Performance analysis of space time coded generalized frequency division multiplexing system over generalized fading channels

AbstractDue to its low out‐of‐band emission and high spectral efficiency, the generalized frequency division multiplexing (GFDM) has been adopted as one of the most prominent waveform techniques for the 5G networks. To further combat the deep fading, the space time coding (STC) can be integrated with the GFDM system which provide high diversity gain that results into low symbol error rate at the receiver. In this article, a generalized model of space time coded GFDM system (STC‐GFDM) has been proposed. More precisely, a comprehensive analytical framework to compute the average symbol error rate (ASER) is introduced, which can be used for any MIMO configuration, code rate, and arbitrary fading channel. The proposed framework has wider applicability over generalized fading channels and having more accurate exact solution along with easy to evaluate approximate solution. Thus, the proposed framework extends the horizon of performance analysis of STC‐GFDM system over any fading scenario, that is not covered so far in the literature. In general, the closed from expressions of exact and approximate along with asymptotic ASER are derived for different fading channels using moment generating function (MGF) approach. Particularly, the ASER analysis has been performed analytically using derived expression over Rayleigh, TWDP, Nakagami‐q, and Nakagami‐m fading channels for ‐ary quadrature amplitude modulation (‐QAM). This analysis confirms that with the increase in transmit and receive antenna and decrease in modulation order, the ASER performance improves for all fading channels. Also, the improvement in ASER is observed as the power of specular components (ie, LOS paths) increases. Furthermore, these analytical results are validated using Monte‐Carlo simulation.

Design of a low cost and power efficient 200/400 Gbps optical interconnect using DAC-less simplified PAM4 architecture

Abstract Incoherent addition of three monochromatic laser sources is used to create four distinctive power levels to match the profile of standard quaternary pulse amplitude modulation (PAM4). The proposed PAM4 signal generation results in a lower symbol error rate as compared to standard PAM4 systems. This design improves the signal-to-noise ratio as compared to existing PAM4 schemes and can reduce the cost and power consumption of existing 200 G or higher optical transceivers.

Throughput Enhancement for the Joint Radar-Communication Systems Based on Cognitive Closed-Loop Design

The majority of the current dual function radar-communication (DFRC) systems are producing only an angle-dependent transmit beampattern (BP) and imposing a constraint for the communication receiver to be present within the sidelobe (SL) directions to decode the transmitted information. In this paper, an efficient angle-range dependent DFRC system is proposed using frequency diverse array (FDA)-multiple-input multiple-output (MIMO) configuration. We developed a closed-loop design that improves the tracking task of the system and permits a cognitive selection for an optimum signaling strategy towards the intended communication receiver during each scan. We used two information embedding schemes, a proposed amplitude phase shift keying (APSK) and phase-shift keying (PSK), that utilize either the magnitude ratios and/or phase shifts between the radar waveform pairs towards the communication direction. Hence, the communication link is maintained and the system throughput is enhanced. The simulation results verify that the communication performance is enhanced in terms of low symbol error rate (SER), secure information transmission, and increased throughput, while for radar applications the performance is improved in terms of target detection and parameter estimation. It also showed that the proposed symbol mapping APSK scheme provides a high data rate transmission compared to the existing SL-based information embedding schemes while preserving a low bit error rate (BER). Besides, the Cramer-Rao lower bounds (CRLB) for range and angle estimation and the signal-to-interference-noise ratio (SINR) for the proposed DFRC system are analyzed.

Deep Learning‐Based Symbol‐Level Precoding for Large‐Scale Antenna System

In this work, we consider a multiple input multiple‐output system with large‐scale antenna array which creates unintended multiuser interference and increases the power consumption due to the large number of radio frequency (RF) chains. The antenna selective symbol level precoding design is developed by minimizing the symbol error rate (SER) with limits of available RF chains. The ℓ0‐norm constrained nonconvex problem can be approximated as ℓ1‐minimization, which is further solved by alternating direction method of multipliers (ADMM) approach. The basic ADMM scheme is mapped into iterative construction process where the optimum solution is obtained by taking deep learning network as building block. Moreover, because that the standard ADMM algorithm is sensitive to the selection of hyperparameters, we further introduce the back propagation process to train the parameters. Simulation results show that the proposed deep learning ADMM scheme can achieve significantly low SER performance with small activated subset of transmit antennas.

Deep learning-based code indexed modulation for autonomous underwater vehicles systems

The Multiuser Direct Sequence Spread Spectrum (DSSS) has been proposed for the autonomous underwater vehicles (AUV) communication systems in a long transmission distance to transmit multiple users over the same channel bandwidth. Unfortunately, the DSSS data rate is limited by four users as a maximum due to the extensive multipath arrivals. This paper proposes a new scheme for the AUV communication systems called, deep learning coded index modulation-spread spectrum (DL-CIM-SS), to overcome the increasing data rate restriction of limited users number. The proposed DL-CIM-SS transmits the majority of information bits via the index of spreading code instead of transmitting all information bits physical. That doesn't only harvest more energy efficiency as the majority of information bits are not transmitted physically anymore, but also provide almost perfect detection at the receiver end. To further save the AUV energy, a pre-processing stage is added before feeding the received signal into the DL-based detector; the DL-based detector becomes environment-independent and no more training will be required during the online deployment. The proposed DL-CIM-SS performance is evaluated in this paper over simulation and measured underwater acoustic channels. The simulation results show the ability of the proposed scheme to increase the underwater acoustic data rate with significant energy efficiency improvement and low system bit and symbol error rate.

Computational ghost imaging with spatiotemporal encoding pseudo-random binary patterns.

Computational ghost imaging (CGI) can reconstruct the pixelated image of a target without lenses and image sensors. In almost all spatial CGI systems using various patterns reported in the past, people often only focus on the distribution of patterns in the spatial dimension but ignore the possibility of encoding in the time dimension or even the space-time dimension. Although the random illumination pattern in CGI always brings some inevitable background noise to the recovered image, it has considerable advantages in optical encryption, authentication, and watermarking technologies. In this paper, we focus on stimulating the potential of random lighting patterns in the space-time dimension for embedding large amounts of information. Inspired by binary CGI and second-order correlation operations, we design two novel generation schemes of pseudo-random patterns for information embedding that are suitable for different scenarios. Specifically, we embed a total of 10,000 ghost images (64 × 64 pixels) of the designed Hadamard-matrix-based data container patterns in the framework of CGI, and these ghost images can be quantitatively decoded to two 8-bit standard grayscale images, with a total data volume of 1, 280, 000 bits. Our scheme has good noise resistance and a low symbol error rate. One can design the number of lighting patterns and the information capacity of the design patterns according to the trade-off between accuracy and efficiency. Our scheme, therefore, paves the way for CGI using random lighting patterns to embed large amounts of information and provides new insights into CGI-based encryption, authentication, and watermarking technologies.

Low Complexity Least Minimum Symbol Error Rate Based Post-Distortion for Vehicular VLC

Vehicular visible light communications (VLC) has emerged as a viable supplement for high speed next-generation vehicle to vehicle (V2V) communication systems. However, performance of a V2V-VLC link is impaired due to nonlinear transfer-characteristics of light emitting diodes (LEDs), and inter-symbol interference (ISI). In this article, a low-complexity least-squares based post-distortion algorithm is formulated over reproducing kernel Hilbert space (RKHS) for a multi-hop V2V-VLC link. The impairments encountered in V2V-VLC channels are mitigated in RKHS by a minimum symbol error-rate post-distorter using a low dimensional approximation of random Fourier features (RFF) (which is a soft approximation of the feature-map to RKHS), that facilitates computationally simple post-distortion under finite memory-budget. The convergence and the BER-performance of the proposed post-distorter is analyzed over realistic V2V VLC channels obtained via ray-tracing. From the analysis, and the presented computer-simulations, the proposed post-distorter is found to exhibit equivalent convergence characteristics and error-rate over reasonable distances, with much lower computational complexity.

FTrack: Parallel Decoding for LoRa Transmissions

LoRa has emerged as a promising Low-Power Wide Area Network (LP-WAN) technology to connect a huge number of Internet-of-Things (IoT) devices. The dense deployment and an increasing number of IoT devices lead to intense collisions due to uncoordinated transmissions. However, the current MAC/PHY design of LoRaWAN fails to recover collisions, resulting in degraded performance as the system scales. This article presents FTrack, a novel communication paradigm that enables demodulation of collided LoRa transmissions. FTrack resolves LoRa collisions at the physical layer and thereby supports parallel decoding for LoRa transmissions. We propose a novel technique to separate collided transmissions by jointly considering both the time domain and the frequency domain features. The proposed technique is motivated from two key observations: (1) the symbol edges of the same frame exhibit periodic patterns, while the symbol edges of different frames are usually misaligned in time; (2) the frequency of LoRa signal increases continuously in between the edges of symbol, yet exhibits sudden changes at the symbol edges. We detect the continuity of signal frequency to remove interference and further exploit the time-domain information of symbol edges to recover symbols of all collided frames. We substantially optimize computation-intensive tasks and meet the real-time requirements of parallel LoRa decoding. We implement FTrack on a low-cost software defined radio. Our testbed evaluations show that FTrack demodulates collided LoRa frames with low symbol error rates in diverse SNR conditions. It increases the throughput of LoRaWAN in real usage scenarios by up to 3 times.

Spectral efficiency enhancement using an antenna‐based orthogonal frequency division multiaccess technique

An approach to enhance antenna spectral efficiency is proposed based on combining spacetime electromagnetic (EM) models of Tx/Rx antennas with orthogonal frequency division multiaccess (OFDM), leading to the EM-OFDM, a technology capable of removing intersymbol interference (ISI) in high-data rate communication links caused by the EM-induced distortion antenna effects. The proposed approach differs from traditional OFDM in wireless communication in several aspects. First, the technique suggests a new decoupling approach by treating each given antenna transreceive device pair as a “stand-alone channel” with its own distortion mechanisms considered separately from the propagation channel. Moreover, the deterministic distortion caused by the nonflat pure antenna EM filtration effects is exploited to carefully design a specialized OFDM transmission techniques based on the antenna parameters, not the multipath fading channels often invoked in conventional uses of OFDM methods. (The EM-OFDM, however, can be combined with a traditional OFDM later if fading channels are present.) In this manner, a more efficient implementation of the wireless link equalization strategy may be enacted since the EM antenna origin of ISI is very different from the traditional propagation channel one. As a proof of concept, the proposed EM-OFDM method is implemented for a single-input-single-output link comprised of half-wavelength linear wire antennas. A careful use of finite difference time-domain to provide EM data allowed the construction of 64 decoupled “pure antenna OFDM subchannels.” Simulation results suggests that the antenna-based OFDM system is capable of completely neutralizing all ISI effects caused by the limited antenna matching bandwidth of the transreceive wires, therefore, supporting considerably higher data rates with low symbol error rate (SER). A concrete evaluation of the SER using quadrature phase-shift keying (QPSK) digital carrier modulation resulted in an increase of the effective antenna digital communication spectral efficiency by ratios up to 300%. Moreover, the EM-OFDM error rate was found to be close to the ideal QPSK level or the maximum possible theoretical limit. Thus, combining detailed EM knowledge with standard signal processing methods can lead to considerable improvement in system design without modifying the antenna physical layout. The proposed approach is expected to play a role in the forthcoming 5G/6G and millimetre wave technology systems currently under development where there is a trend toward integration of EM and digital signal processing at the physical layer level.

High Speed LED-to-Camera Communication using Color Shift Keying with Flicker Mitigation

LED-to-camera communication allows LEDs deployed for illumination purposes to modulate and transmit data which can be received by camera sensors available in mobile devices like smartphones, wearable smart-glasses, etc. Such communication has a unique property that a user can visually identify a transmitter (i.e., LED) and specifically receive information from the transmitter. It can support a variety of novel applications such as augmented reality through mobile devices, navigation using smart signs, fine-grained location specific advertisement, etc. However, the achievable data rate in current LED-to-camera communication techniques remains very low to support any practical application. In this paper, we present ColorBars, an LED-to-camera communication system that utilizes Color Shift Keying (CSK) to modulate data using different colors transmitted by the LED. It exploits the increasing popularity of Tri-LEDs (RGB) that can emit a wide range of colors. We show that commodity cameras can efficiently and accurately demodulate the color symbols. ColorBars ensures flicker-free and reliable communication even in the presence of inter-frame loss and diversity of rolling shutter cameras. We implement ColorBars on embedded platform and evaluate it with Android and iOS smartphones as receivers. Our evaluation shows that ColorBars can achieve a data rate of 7.7 Kbps on Nexus 5, 3.7 Kbps on iPhone 5S, and 2.9 Kbps on Samsung Note8. It is also shown that lower CSK modulations (e.g., four and eight CSK) provide extremely low symbol error rates (<; 10 <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">-3</sup> ), making them a desirable choice for reliable LED-to-camera communication.

Strategies and Demonstration to Support Multiple Wireless Protocols with a Single RF Front-End

In an increasingly interconnected world, the demand for smartphones, tablets, and other wireless devices in the IoT has surged over the last few years. This high demand has increased mobile data usage and wireless communication, resulting in an explosion of traffic demand in the limited 2.4 GHz frequency band. This traffic demand and the limitations of existing wireless devices operating in the same frequency range have resulted in a spectrum congestion problem. To utilize the available spectrum more efficiently, it becomes important to detect the desired signals and support flexible communications. After reviewing relevant characteristics of the protocols of interest, this article introduces a new approach for spectrum sharing with a hardware implementation to support the coexistence of WiFi, LTE, and ZigBee in the congested 2.4 GHz band using a single RF front-end. A detection method was developed to resample the preambles of both WiFi and ZigBee to the LTE sampling rate to avoid continuous resampling of the received signal. Further processing steps such as synchronization and demodulation for each protocol are described. Measurement results are provided to demonstrate the techniques showing a low symbol error rate tested over the air in a laboratory environment with interference.

A Very Low Complexity QRD-M MIMO Detection Based on Adaptive Search Area

We propose a low complexity QR decomposition (QRD)-M multiple input multiple output (MIMO) detection algorithm based on adaptive search area. Unlike the conventional QRD-M MIMO detection algorithm, which determines the next survivor path candidates after searching over the entire constellation points at each detection layer, the proposed algorithm adaptively restricts the search area to the minimal neighboring constellation points of the estimated QRD symbol according to the instantaneous channel condition at each layer. First, we set up an adaptation rule for search area using two observations that inherently reflect the instantaneous channel condition, that is, the diagonal terms of the channel upper triangle matrix after QR decomposition and Euclidean distance between the received symbol vector and temporarily estimated symbol vector by QRD detection. In addition, it is found that the performance of the QRD-M algorithm degrades when the diagonal terms of the channel upper triangle matrix instantaneously decrease. To overcome this problem, the proposed algorithm employs the ratio of each diagonal term and total diagonal terms. Moreover, the proposed algorithm further decreases redundant complexity by considering the location of initial detection symbol in constellation. By doing so, the proposed algorithm effectively achieves performance near to the maximum likelihood detection algorithm, while maintaining the overall average computation complexity much lower than that of the conventional QRD-M systems. Especially, the proposed algorithm achieves reduction of 76% and 26% computational complexity with low signal to noise ratio (SNR) and high SNR, compared with the adaptive QRD-M algorithm based on noise power. Moreover, simulation results show that the proposed algorithm achieves both low complexity and lower symbol error rate compared with the fixed QRD-M algorithms.

Receivers for VBLAST FBMC-OQAM Systems

We consider vertical Bell laboratories layered space-time architecture for offset quadrature amplitude modulation based filter bank multicarrier (FBMC) systems, and propose two different receivers. We first investigate a low-complexity QR decomposition (QRD) based conventional minimum mean square error (MMSE) successive interference cancellation (C-MMSE-SIC) receiver. The intrinsic interference in FBMC systems severely degrades the symbol error rate (SER) of the C-MMSE-SIC receiver. By exploiting the overlapping nature of neighboring symbols in an FBMC system, and the fact that the desired symbol in an FBMC system interferes with the symbols in a small neighborhood, we propose novel low-complexity intrinsic interference estimation (IIE) based sequential MMSE-SIC (IIE-SMMSE-SIC) receivers with QRD and its sorted version. We show that the IIE-SMMSE-SIC receivers, with similar complexity order as that of the C-MMSE-SIC, have lower SER.

Cognitive Waveform Optimization for Phase-Modulation-Based Joint Radar-Communications System

A dual-function radar communication (DFRC) system enables the implementation of a primary radar operation and a secondary communication function concurrently. A bank of transmit beamforming weight vectors are guaranteed to have the same transmitted radiation pattern to satisfy in the target detection requirements, while the phase symbol is selected from a preset dictionary so that communication information can be embedded. However, as the radar channel is time-variant due to the fluctuation in the radar cross-section (RCS) of the target and the Doppler shift that results from the relative motion of the target, it is necessary for a successive waveform design and selection scheme to continually obtain target information. Our work aims at enhancing the target detection performance by maximizing the relative entropy (RE) between two hypotheses (in the first hypothesis we assume the target is not present in the echoes while in the second hypothesis we assume the target exists in the echoes) and by minimizing the mutual information (MI) between successive target echoes. The proposed scheme overcomes the coexisting communication and radar detection problems in intelligent transportation systems (ITSs), where it is necessary to extract the features of target information that is obtained from a vehicle-mounted sensor. Our simulation results demonstrate an improvement in the target detection performance by the proposed two-stage approach. In addition, the system can transmit data of several Mbps with low symbol error rates.

Phase Transmittance RBF Neural Network Beamforming for Static and Dynamic Channels

This letter proposes a novel beamforming technique for self-organizing wireless networks, such as those encountered in fifth generation (5G) and the Internet of Things (IoT). The proposed beamformer is based on a complex radial basis function artificial neural network (ANN), which allows phase transmittance between the input and output nodes. The beamforming algorithm is applied to a six-element uniform circular array, considering the mutual coupling between elements. The proposed technique is evaluated over critical static and dynamic scenarios. The static scenario approaches a multiuser environment, highly polluted with electromagnetic interference and plenty of severe in-band undesired signals. The dynamic scenario emulates a close air support military operation, in which an aircraft moves around the ground station at a high velocity. In both scenarios, nonlinearities at the receiver analog RF front end with a –10 dB signal-to-interference ratio (SIR) are considered. Results show that the proposed technique presents significantly a superior performance when compared to other solutions. For the static scenario, the novel beamformer is able to focus the array radiation pattern in the direction of the desired signal, achieving a zero symbol error rate. For the dynamic scenario, the beamforming is able to track the moving station, achieving a low symbol error rate for a wide angular range.