Search

A wireline receiver consisting of a linear equalizer, a decision-feedback equalizer (DFE), a clock and data recovery (CDR) circuit, and a demultiplexer (DMUX) employs new circuit and architecture techniques that afford substantial power savings. Realized in 28-nm technology, the 56-Gb/s receiver has a bit error rate (BER) of less than 10⁻¹² for a channel loss of 25 dB at 28 GHz.

To increase the storage capacity limit of magnetic recording channels, recent studies proposed multilayer magnetic recording (MLMR): the vertical stacking of magnetic media layers. MLMR readback waveforms consist of the superposition of signals from each layer recovered by a read head placed above the upper layer. This article considers the problem of equalization and detection for MLMR comprising two layers. To this end, we use MLMR waveforms generated using a grain switching probability (GSP) model that is trained on realistic micromagnetic simulations. We propose three systems for equalization and detection. The first is a convolutional neural network (CNN) equalizer followed by an MLMR Viterbi algorithm (VA) for detection. We show that this system outperforms the traditional 2-D linear minimum mean squared error (2-D-LMMSE) equalizer. The second system uses CNNs for equalization and separation of signals from each layer, which is followed by a regular VA. The third system contains CNNs trained to directly provide soft bit estimates. By interfacing the CNN detector with a channel decoder, we show that an areal density gain of 16.2% can be achieved by a two-layer MLMR system over a one-layer system.

The Underwater Acoustic Channel (UAC) is a time variant channel and its multipath effects create ISI. This is one of the most important obstacles in the UAC channel which reduces the transmission rate. To remove this obstacle, a proper filter has to be designed in the receiver section. In this article, optimal step size for equalizer is computed and compared the results with the known techniques namely Decision Feedback Equalizer with interleave division multiple access (DFE IDMA) and Cyclic Prefix - Orthogonal Frequency Division Multiplexing (CP-OFDM) Equalizer. Channels are modeled using ray tracing methods. The various factors considered are ambient noise, attenuation loss, bottom and surface loss. The overall path loss for channels is computed by summing up the attenuation loss, surface and bottom loss. Simulation results evident that for short range UAC channel, the BER in the order of 10-2 is achieved using proposed methodology with least Eb/No compared to standard DFE method.

A 30-Gb/s three-level pulse amplitude modulation (PAM-3) transceiver is designed with a one-tap tri-level decision feedback equalizer (DFE) to realize a high-speed dynamic random access memory (DRAM) interface via the 28-nm CMOS process. A 1.5-bit/pin bit efficiency is achieved by encoding and decoding 3-bit data in two unit intervals (UIs). The half-rate PAM-3 transmitter modulates single-ended pseudorandom binary sequence (PRBS) 7/15 data using a low-power encoding logic and an output driver. The receiver achieves a bit error rate (BER) of less than 1E-12 over an 80-mm FR-4 printed circuit board (PCB) channel. At the maximum data rate, the bit efficiency of the transceiver is 1.11 pJ/bit, consuming 33.4 mW. In the receiver, the attenuated PAM-3 data are equalized by a continuous-time linear equalizer (CTLE) and a one-tap tri-level DFE, which has the same complexity as that of non-return-to-zero (NRZ) signaling. The tri-state buffers, which have a floating PMOS switch, convert the output of the comparator into NRZ data, resulting in reduced delay and power dissipation. Four channels of the transceivers operate at data rates of up to 30 $\times $ 4 Gb/s, and the horizontal eye margin of the measured PAM-3 data is achieved at a UI of 0.14 for the PRBS-7 pattern at the maximum data rate.

The implementation of an unclocked DFE (UC-DFE) architecture for high-speed PAM-4 signals is investigated in this brief. Instead of clocked slicers and flip-flops, data-decision and feedback delay control are performed by saturated analog delay chains. As a result, the UC-DFE, previously exploited for NRZ signals, saves power consumption and silicon area while the simple implementation allows operation at high data-rate. A receiver front-end comprising a linear equalizer and the proposed 2-tap UC-DFE scheme is designed in 7 nm FinFET technology. From post-layout simulations, the receiver recovers a PAM-4 signal at 112 Gb/s after an 18 dB loss channel with a power efficiency of 0.47 pJ/bit. The receiver also works with NRZ signals at half the bit-rate equalizing 24 dB channel loss with a power efficiency of 0.70 pJ/bit.

This paper presents a linear complexity iterative rake detector for the recently proposed orthogonal time frequency space (OTFS) modulation scheme. The basic idea is to extract and coherently combine the received multipath components of the transmitted symbols in the delay-Doppler grid using maximal ratio combining (MRC) to improve the SNR of the combined signal. We reformulate the OTFS input-output relation in simple vector form by placing guard null symbols or zero padding (ZP) in the delay-Doppler grid and exploiting the resulting circulant property of the blocks of the channel matrix. Using this vector input-output relation we propose a low complexity iterative decision feedback equalizer (DFE) based on MRC. The performance and complexity of the proposed detector favorably compares with the state of the art message passing detector. An alternative time domain MRC based detector is also proposed for even faster detection. We further propose a Gauss-Seidel based over-relaxation parameter in the rake detector to improve the performance and the convergence speed of the iterative detection. We also show how the MRC detector can be combined with outer error-correcting codes to operate as a turbo DFE scheme to further improve the error performance. All results are compared with a baseline orthogonal frequency division multiplexing (OFDM) scheme employing a single tap minimum mean square error (MMSE) equalizer., arXiv admin note: text overlap with arXiv:2001.10703 Accepted for publication in IEEE Transactions on Vehicular Technology

The existing generalized frequency division multiplexing (GFDM) systems employ cyclic prefix (CP) to avoid inter-symbol interference (ISI) in frequency-selective fading channels. For multiple input multiple output GFDM (MIMO-GFDM) system, the existence of CP will result in the losses of spectrum efficiency, energy efficiency and transmission rate (SET). In this paper, we propose a novel MIMO-GFDM system without CP insertion. We design a decision feedback equalizer that is performed before fast Fourier transform operation to efficiently remove ISI. We also design a CP restoration unit to remove the inter-carrier interference in the MIMO-GFDM systems. The proposed CP-free MIMO-GFDM system offers a much higher SET than traditional MIMO-GFDM systems in frequency selective fading channels. Some simulation examples are given to demonstrate the effective of the proposed MIMO-GFDM system.

Power-line communication (PLC) systems are used for data transmission through the mains. The deployment of intelligent networks and the high demand for broadband communications have made this technology spread worldwide in recent years. Multicarrier modulations, such as filter-bank multicarrier (FBMC), allow broadband links to be achieved by improving intercarrier interference, at the cost of adding complexity to the system. Nevertheless, the main disadvantage of PLC is the medium, commonly with significant noise and interference. To compensate these effects inserted by the channel, channel estimators and equalizers are often included at the reception stage, at the expense of increasing the computational load and complexity of the resulting receiver. This article proposes a hardware/software (HW/SW) architecture for the real-time implementation of an FBMC transmultiplexer with a channel estimator and an L-Adaptive sine/cosine modulated filter bank equalizer for transmultiplexers (L-ASCET) equalizer. The FBMC transmultiplexer and the L-ASCET equalizer are implemented in HW, whereas the channel estimation and the determination of the equalizer coefficients are specified in SW, all forming a system-on-chip architecture. The performance provided by the proposal has been evaluated in terms of the signal-to-noise ratio, the root-mean-square error, and the bit error rate.

In this article, a 1.62-to-10.8-Gb/s video interface receiver with jointly adaptive equalization is presented. Sign–sign least-mean-squares (SSLMS) algorithm is applied to adaptation for not only a decision feedback equalizer (DFE) but also a continuous-time linear equalizer (CTLE) to unify the adaptation methods. This approach facilitates concurrent adaptation that results in short adaptation time and also reduces the extra hardware and power consumption. An average value of fourth- and fifth-post-cursors is used as an indicator for CTLE adaptation, and the validity is substantiated by numerical analysis and simulation. Furthermore, the concept of a biased data-level reference (dLev) is proposed and analyzed to alleviate insufficient adaptation of the SSLMS algorithm in the presence of pre-cursor inter-symbol interference (ISI). The proposed dLev is acquired by simply adjusting up and down ratios of the dLev update equation. The prototype test chip is implemented in 65-nm CMOS technology and occupies an active area of 0.174 mm2. The proposed adaptive equalizers compensate for the channel loss of up to 34 dB at 10.8 Gb/s. Total power consumption of the receiver is 37.2 mW at 10.8 Gb/s, and the figure of merit (energy efficiency per channel loss at Nyquist frequency) of 0.1 pJ/b/dB is achieved.

Underwater acoustic communication often suffers from extended channel impulse response (CIR) and large Doppler spread such that signals become difficult to detect and decode. For the underwater Internet-of-Things (IoT) applications, the challenges are even bigger, because the IoT devices usually transmit very short messages due to severe power constraints, and the length of pilots are often shorter than that of CIR. Therefore, conventional pilot-assisted channel estimation and equalization approaches are incapable of detecting the information data. Moreover, the existing blind channel equalization algorithms, which do not require pilots, are not able to detect information data either, because the number of transmitted symbols is too small to approximate the expected loss with empirical loss, where the loss refers to as the error of estimated signal envelopes. In this article, a new equalization and decoding algorithm is proposed for the underwater IoT devices under harsh communication environments. Inspired by the recent blind deconvolution and compressive sensing techniques, we construct an optimization problem with the objective function rewarding sparsity of the estimated CIR using $l_{4}$ -norm and convexify the feasible set of the problem while guarantee the same solution. Then, we develop a pruned tree search initialization method and use gradient descent to find an optimal solution efficiently. The new algorithm is first verified by simulations, which show that the proposed algorithm outperforms conventional methods, such as the linear minimum mean square error (LMMSE) equalizer and a constant modulus algorithm (CMA). The proposed algorithm, along with a practical procedure for compensating large Doppler spread and carrier frequency offset, is further employed to process the real-world underwater IoT data collected in a fish-tag project. It shows that the proposed algorithm can equalize and detect the IoT data which were corrupted by channels whose lengths are longer than that of pilots, but the existing algorithms, such as LMMSE equalizer and CMA-based blind equalizer are not able to accomplish. It also shows that the proposed algorithm can provide a very impressive improvement compared to raw detection (no equalization or decoding) and decoding-only approaches.

Most of the non-linear transceivers, which are based on Tomlinson-Harashima (TH) precoding and have been proposed in the literature for two-way relay systems, assume perfect channel state information (CSI). In this paper, a novel and robust TH precoding scheme has been investigated for two-way relay systems, with multiple antennas at the transceiver and relay nodes. We assume imperfect CSI and the channel uncertainty is bounded by a spherical region. The minimum mean square error at the destinations is used as the design criterion and we develop an iterative method to solve this nonconvex problem to obtain TH precoding matrices at the transmitter, linear precoding matrix at the relay and linear equalizer at the destination nodes, where each subproblem is convex. Simulations are provided to evaluate the performance and to validate the efficiency of the proposed scheme.

A self-iterative vector approximate message passing (VAMP) soft frequency-domain equalizer (SFDE) is a preferred choice for turbo equalization due to its excellent complexity-performance tradeoff. To maximize its performance, the equivalent channel matrix is required to be right-rotationally invariant (RRI) which generally does not hold though. As a result, existing VAMP-SFDEs cannot reach the performance bound. To solve such a dilemma, we propose in this letter to introduce a precoding operation on the transmitter side such that a RRI equivalent channel matrix can be obtained. The designed precoding scheme is named the Hadamard-Haar random precoding (HHRP), which is the concatenation of an inverse fast Fourier transform matrix, a random interleaving matrix, and a Hadamard-Haar transform (HHT) matrix. Attributed to the HHRP, the VAMP-SFDE applied in turbo equalization achieves extra performance gain and enjoys faster convergence over existing VAMP-SFDEs.

In two-dimensional magnetic recording (TDMR) channels, intersymbol interference (within and between tracks) and pattern-dependent media noise are impediments to reaching higher areal density. We propose a novel deep neural network (DNN)-based a posteriori probability (APP) detection system with parallel multi-track detection for TDMR channels. The proposed DNN-based APP detector replaces the trellis-based Bahl–Cocke–Jelinek–Raviv (BCJR) or Viterbi algorithm and pattern-dependent noise prediction (PDNP) in a typical TDMR scenario, in which it directly outputs log-likelihood ratios of the coded bits and iteratively exchanges them with a subsequent channel decoder to minimize bit error rate (BER). We investigate three DNN architectures—fully connected DNN, convolutional neural network (CNN), and long short-term memory (LSTM) network. The DNN’s complexity is limited by employing linear partial response (PR) equalizer pre-processing. The best performing DNN architecture, CNN, is selected for iterative decoding with a channel decoder. Simulation results on a grain-flipping-probability (GFP) media model show that all three DNN architectures yield significant BER reductions over a recently proposed 2D-PDNP system and a previously proposed local area influence probabilistic (LAIP)-BCJR system. On a GFP model with 18 nm track pitch and 11.4 Teragrains/in2, the CNN detection system achieves an information areal density of 3.08 Terabits/in2, i.e., a 21.72% density gain over a standard BCJR-based 1D-PDNP; the CNN-based system also has $3\times $ the throughput of 1D-PDNP, yet requires only 1/10th the computer run time.

Minimum mean-squared error (MMSE) equalizers are a viable solution to mitigate the frequency selectivity of the aeronautical telemetry channel. Because the MMSE equalizer filter coefficients are a function of the carrier frequency offset (CFO), the equivalent discrete-time channel, and the noise variance, reliable estimates of those parameters are required. The CFO is due primarily to the high velocity of the airborne transmitter. Because the equivalent discrete-time channel in aeronautical telemetry is sparse, the performance of the joint estimator based on a sparse channel estimator is superior to the performance of a traditional nonsparse maximum-likelihood-inspired joint estimator. The improvement is seen in lower estimator error variances for the parameters, particularly for the CFO and channel estimate, and in the postequalizer bit-error rate. Simulation results demonstrate that the postequalizer bit-error rate using the sparse estimator is almost as good as that using ideal estimators.

An efficient frequency-domain equalizer is developed for single-carrier (SC) aided spatial modulation (SM) systems over frequency-selective fading channels, by employing the idea of expectation propagation (EP) algorithms. Specifically, we design an EP-aided frequency domain turbo equalizer (EP-FDTE) for iterative information exchange between the equalizer and the soft decoder, leading to more reliable a posterior probability (APP) estimation for signal detection. Our simulation results demonstrate that the proposed equalizer outperforms conventional frequency-domain turbo equalizer (FDTE) and generalized approximate message passing (GAMP)-aided turbo equalizer for SC-SM transmission.

In this paper we propose a novel turbo equalizer based on the expectation propagation (EP) algorithm. Optimal equalization is computationally unfeasible when high-order modulations and/or large memory channels are used. In these scenarios, low-cost and suboptimal equalizers, such as those based on the linear minimum mean square error (LMMSE), are commonly used. The LMMSE-based equalizer can be efficiently implemented with a Kalman smoother (KS), i.e., a forward and backward Kalman filtering whose predictions are merged in a posterior smoothing step. Recently, it was shown that applying EP at the forward and backward stages of a KS equalizer could significantly improve its performance. In this paper, we investigate applying EP at the smoothing level instead. Also, we propose some further modifications to better exploit the information coming from the channel decoder in turbo equalization schemes. Overall, we remarkably reduce the computational complexity while highly improving the performance in terms of bit error rate.

This paper describes a three-step frequency-domain equalization scheme for multiple-input multiple-output (MIMO) underwater acoustic communication. First, an iterative least-squares channel estimation method is developed to enhance the accuracy of channel estimation in MIMO communication. The interference rejection combining method is then adopted to suppress co-channel interference based on the estimated MIMO channels. This technique exploits the correlation between the interference received through different channels. Finally, a decision feedback equalizer embedded with a digital phase-lock loop is cascaded before the final determination of equalized symbols to compensate the phase rotation. Experimental results show that the bit error rates of the proposed scheme can be several orders of magnitude lower than those of conventional frequency-domain equalization schemes.

Increased bandwidth per pin is necessary to support the growing data throughput in mobile systems. This article presents a low-power simultaneous bidirectional (SBD) transceiver employing a novel voltage-mode driver with a resistor-transconductance (R-gm) hybrid subtractor to enable concurrent transmission and reception of data on a single differential channel. A finite impulse response (FIR) filter-based adaptive echo cancellation system that cancels both near- and far-end echoes allows for the support of a wide range of channels, while a continuous-time linear equalizer (CTLE) efficiently compensates for channel loss. The quarter-rate source-synchronous transceiver utilizes a low-complexity phase interpolator-based clock and data alignment system to set the optimum data sampling point. Fabricated in 28-nm CMOS, the 32-Gb/s SBD transceiver achieves 1.83 mW/Gb/s and compensates 10.2-dB loss at a bit error rate (BER) < 10−12.

This correspondence explores the application of sparse equalizers in generalized time-reversed space-time block codes (GTR-STBCs) for a two-transmit/one-receive antenna aeronautical telemetry link. It is shown that the jointly optimum design of the sparse equalizer coefficients and the power sharing parameter $\rho$ in GTR-STBCs is computationally challenging because the active tap ratio of the sparse equalizers is a non-convex function of $\rho$ . A search algorithm based on a grid search followed by an interpolation is shown to yield impressive results. Our numerical results demonstrate that depending on signal-to-noise ratio and channel conditions, the number of non-zero taps of sparse equalizers for GTR-STBCs can be reduced by 53% to $86\%$ at the cost of 0.25 dB relaxation in the mean-squared error.

In this paper, an adaptive QR-based energy efficient low complexity signal detection scheme in multiple input multiple output-orthogonal frequency division multiplexing (MIMO-OFDM) systems with nearly optimal error performance is proposed for solving the disadvantage of path elimination QR decomposition-M algorithm (PEQRD- M ). For minimizing the loss of error performance, the proposed scheme uses suboptimal PEQRD- M , and hybrid PEQRD- M and lattice reduction-aided decision feedback equalizer (LR-aided DFE) adaptively by using channel state. The proposed scheme selects the signal detection scheme adaptively by calculating the condition number of wireless channel. The proposed scheme uses PEQRD- M when the channel state is poor where its condition number is high, and reversely uses the hybrid PEQRD- M and LR-aided DFE when the channel state is favorable where its condition number is low. The simulation results show that the proposed scheme has nearly the same error performance as PEQRD- M and low complexity at low signal to noise ratio (SNR).

Visible Light Communication (VLC) is a promising solution to meet the ever increasing demand for indoor data connectivity with high bit rates. VLC uses the license-free bands and provides high-speed connections unlike RF wireless communication. However; indoor VLC suffers from performance degradation due to either co-channel interference (CCI) or inter-symbol interference (ISI). In this paper, an interference management scheme is proposed for VLC Multi-Input Multi-Output (MIMO) based systems. The scheme reduces the number of interfered signals by using a constrained field-of-view angular diversity receiver CFOV-ADR and then uses least-square (LS) channel estimation with maximum-likelihood (ML) equalizer to resolve the interfered signals. The proposed interference mitigation scheme using constrained-FOV-ADR with channel estimation IM-CFOV-CE enables frequency reuse of one. The bit-error-rate (BER) is calculated at various room positions with different receiver's heights and rotation angles. The performance evaluation results reveal that the proposed system can achieve a higher number of downlink channels and superior BER performance than that of without CCI management. Also, the proposed scheme has been compared with Time Division Multiple Access (TDMA) technique, and achieves enhanced performance at all positions and orientations of the ADR.

Dynamic bit encoding and decoding of the magnetic recording process remain a challenge in that the process is restrained by the balance between reading and writing performance of the decoder's bit error rate (BER). Sequential neural networks offer data streamflow for processes to reproduce recoded bits from signal distribution, overcoming the limitation of codeword mapping designed for each specific bit-patterned magnetic recording (BPMR) channel. Here, we implement the vanilla long short-term memory (LSTM) for adaptive modulation decoders in various BPMR channel designs within a single network, which benefits multi-channel decoder calibration tools with the same standardization. Signal information from media readback, a two-dimensional (2D) equalizer, 2D Viterbi, and a 2D soft-output Viterbi algorithm (SOVA) detector is arranged as a tensor that enables sequence-to-sequence bit prediction even with a highly complex data arrangement. Our adaptive model can predict recorded bits from readback with accuracies of approximately 97% for rate 4/5 decoding and 75% for crossing platforms, using a recently proposed single-reader/two-track reading (SRTR) system at an areal density of 4 Tb/in2 in a signal-to-noise ratio range of 1 to 8 dB. We conducted a BER simulation with the relevant results from conventional decoders and the LSTM model. Ultimately, our approach may demonstrate the limitation of supervised learning designed for BPMR systems and reveal a sequence data focus on LSTM that paves the way for sequential-type, unsupervised, mechanism-based, next-generation magnetic recordings.

In this paper, we have experimentally demonstrated a secure 100 Gb/s intensity-modulation and direct-detection transmission over 100 km standard single-mode fiber (SSMF) based on quantum noise stream cipher (QNSC) for the first time. The original 4-level pulse-amplitude modulation (PAM4) data is mapped to M-level data sequence randomly, and quantum noise such as the generated amplified spontaneous emission noise and the shot noise in the channel can mask these adjacent signal levels. The legitimate receiver needs to distinguish not M-level encrypted data but 4-level data with the shared key. In our scheme, a calculated detection failure probability of 98.72% is achieved. Apparently, more quantum noise contributes to higher security but the worse signal to noise ratio. To balance algorithm complexity, transmission performance and security performance, a sparse $\ell _{1} $ regularization based on recursive least square (RLS) algorithm is proposed and firstly used in Volterra equalizer. To eliminate the power fading induced by fiber dispersion, a dual-drive Mach-Zehnder modulator is used to achieve single-sideband modulation, and no dispersion compensation is required. By these means, 100 Gb/s PAM4-QNSC signal transmission over 100 km SSMF with the bit error rate (BER) below the 7% overhead hard-decision forward error correction threshold of $3.8 \times 10^{-3}$ is achieved, and > 59% complexity reduction of Volterra equalizer is realized. Moreover, the measured BER of 150 Gb/s PAM8-QNSC signal transmission over 50 km SSMF could go below the 20% overhead soft-decision forward error correction threshold of $2.0\times 10^{-2}$ . The results validate that the proposed scheme is effective to realize low-cost, high-speed (> 100 Gb/s), and secure optical fiber transmission in the data center.

Power-Line Communication (PLC) employs multi-carrier modulations, such as Filter-Bank Multi-Carrier (FBMC), to improve communications through the PLC channel and provide an efficient use of the spectrum, thus allowing higher data rates. Since one of the main drawbacks is the noisy channel with multipath and frequency-selective fading, the receiver typically includes a channel estimator and equalizer, at the expense of increasing the computational load and complexity of that receiver and making it difficult to obtain real-time solutions. In this context, this work proposes a heterogeneous System-on-Chip (SoC) architecture for the real-time implementation of a FBMC transmultiplexer that involves the channel estimation and equalization at the reception stage. For that purpose, the performance of a multi-core approach is evaluated for both the Zynq® 7000 SoC and theZynq®UltraScale+ (US+) devices, by using a single-, dual- and quad-core solution to perform the channel estimation and the calculation of the equalizer coefficients in the ARM processor available in the proposed architecture. Two approaches have been analysed for the necessary synchronization among cores; one based on atomic instructions and the other one on interrupts. The dual-core proposal in both Zynq® 7000 and Zynq® US+ provides a x2 acceleration compared to the single-core proposal, whereas the quad-core one inZynq®US+ does not provide a x4 acceleration as expected, due to the timing overheads of the synchronization among cores imposed by the data dependencies existing in these tasks. Experimental results include the evaluation of the processing times for each task in the algorithm, as well as the acceleration obtained by each proposal. The proposed architecture can be easily applied to other processing algorithms that may take advantage of the parallelism provided by the multi-core approach in a more efficient way, depending on their data dependencies.

Document Information Language: English Accession Number: WOS:000527416700002, In Digital Video Broadcasting-Handheld (DVB-H) devices for cyber-physical social systems, the Discrete Fractional Fourier Transform-Orthogonal Chirp Division Multiplexing (DFrFT-OCDM) has been suggested to enhance the performance over Orthogonal Frequency Division Multiplexing (OFDM) systems under time and frequency-selective fading channels. In this case, the need for equalizers like the Minimum Mean Square Error (MMSE) and Zero-Forcing (ZF) arises, though it is excessively complex due to the need for a matrix inversion, especially for DVB-H extensive symbol lengths. In this work, a low complexity equalizer, Least-Squares Minimal Residual (LSMR) algorithm, is used to solve the matrix inversion iteratively. The paper proposes the LSMR algorithm for linear and nonlinear equalizers with the simulation results, which indicate that the proposed equalizer has significant performance and reduced complexity over the classical MMSE equalizer and other low complexity equalizers, in time and frequency-selective fading channels.

Our aim with this paper is to present a solution suitable for vehicle-to-everything (V2X) communications, particularly, when employing single-carrier modulations combined with frequency-domain equalization (SC-FDE). In fact, we consider the V2X channel to be doubly-selective, where the variation of the channel in time is due to the presence of a Doppler term. Accordingly, the equalization procedure is dealt by a low-complexity iterative frequency-domain equalizer based on the iterative block decisionfeedback equalization (IB-DFE) while the tracking procedure is conducted employing an extended Kalman filter (EKF). The proposed system is very efficient since it allows a very low density of training symbols, even for fast-varying channels. Furthermore only two training symbols are required to initialize the tracking procedure. Thus, ensuring low latency together with reduced channel estimation overheads. published

Quadrature spatial modulation (QSM) is one of the most prevalent transmission techniques for future wireless mobile network due to its high spectral efficiency (SE) and low complexity. However, it is restrictive to downlink of the cellular network deployments. Therefore, this paper proposes QSM for the multiuser uplink data transmission that increases the SE of the network by deploying multiple antennas at the mobile user with only two radio frequency (RF) chains. Maximum likelihood (ML) decoder is used to detect the received signal and attains the optimal detection performance but it is impractical because of its high computational complexity. Consequently, this paper proposes compressed sensing (CS) based orthogonal matching pursuit (OMP) detection as it has a suboptimal performance with a low computational complexity which can be a practical solution in the high-density uplink multiuser network. However, conventional OMP algorithm has a low estimation performance due to multiuser interference which corrupt channel matrix. Thus, this work design an equalizer that mitigates the multiuser interference and improve the detection performance by orthonormalizing the columns of channel matrix. Simulation analysis confirm that the proposed QSM technique based on CS detector outperforms the conventional schemes in terms of SE and bit error rate (BER).

In this treatise, we conceive a hardware impairment aware receiver design of a large multiple-input multiple-output (MIMO) orthogonal frequency division multiplexing (OFDM) system, taking into account the error statistics of parameter estimations. The hardware impairment includes the quantization noise, phase noise etc. There are two key contributions in this work. Firstly, consideration of error statistics of residual parameters in the receiver design improves receiver performance. In this work, we propose a linear equalizer based data detector at the receiver for a large MIMO-OFDM system and the parameters of interest are the channel impulse response (CIR) and carrier frequency offset (CFO) along with the hardware impairments awareness both at transmitter and receiver. The design criterion chosen for designing the detector equalizer is based on the linear minimum mean square error (LMMSE) one and it will consider the statistics of estimation error and hardware impairments. However, in any practical communication system, the availability of statistics for these parameter errors is inconceivable. Hence, we propose further to consider the Bayesian Crammer Rao lower bound (BCRLB) as the variance of the error with the key assumption of zero mean. Numerical simulation demonstrates that, consideration of such statistics attains an SNR gain of $2$ dB with respect to a fixed symbol error ratio (SER). It also shows that the SER performance with BCRLB matches very closely with the case, when exact error variance is used. For the second contribution, an extensive theoretical analysis has been performed to evaluate the MSE improvement of data detection with the LMMSE criterion with parameter estimation error statistics consideration in the large MIMO-OFDM system. This theoretical analysis of improved MSE has been conceived with the usage of large dimension random matrix theory and a closed-form expression has been obtained. Numerical simulation demonstrates that the theoretical analysis for MSE improvement matches closely with the simulated one.

Wireless power transfer (WPT) systems can potentially provide simultaneous power and data transfer for Internet-of-Things devices, such as smart speakers, glasses, and watches. However, due to the high quality factor of coils, the intrinsically narrow WPT channel bandwidth severely limits the data transmission capability of the system, especially for data downloading links requiring higher data rates. In addition, the channel bandwidth varies with power transfer distances, which is also undesirable for wireless communication. In this study, the channel features of a two-coil series–series matching magnetic resonance wireless power and data transfer (MWPDT) system, including the bandwidth and the roll-off slope are characterized analytically. Based on the characterization results, a receiver front-end circuit with a three-stage cascaded equalizer (EQ) is proposed and implemented to extend the bandwidth of a MWPDT system at different distances. The proposed EQ can provide a frequency response with a variable roll-up slope from 10 to 45 dB/dec to compensate for the distance-dependent channel response of an MWPDT system. A complete MWPDT system is built and tested to verify the performance of the proposed method. Experimental results demonstrate that the data rates can be extended from 650, 500, and 350 kbps to 850, 700, and 650 kbps at 0.4, 0.5, and 0.6-m distances, respectively. The highest data rate extension ratio is 85% at a transmission distance of 0.6 m, which is 2.4 times the radii of the coil employed in the system.

A deep neural network (DNN) equalizer for 16-QAM signal in free space-optics (FSO) transmission system is proposed and experimentally demonstrated in this paper. When the fiber and FSO links in fiber-wireless system are cascaded, the nonlinearity will become serious. Therefore, a DNN equalizer is designed to reduce the nonlinear effect in the FSO system based on the channel nonlinearity. The result shows that DNN equalizer approach can minimize the nonlinear and reduces error vector magnitude (EVM).

Equalization is typically used to counteract the channel loss for signal integrity. The separate optimization for tap coefficients of feed forward equalizer (FFE) and the transfer function of continuous time linear equalizer (CTLE) may not give the optimal result for the channel with both FFE and CTLE applied. A method of combined optimization of FFE and CTLE equalizations based on the analysis of pulse response is proposed in this paper. The simulation result of eye diagrams proves the collaboration of FFE and CTLE equalizations can be optimized with the proposed method.

This paper considers the forward error correction (FEC) code design for approaching the capacity of a dynamic multiple access channel (MAC) where both the number of users and their respective signal powers keep constantly changing, resembling the scenario of an actual wireless cellular system. To obtain a low-complexity non-orthogonal multiple access (NOMA) scheme, we propose a serial concatenation of a low-density parity-check (LDPC) code and a repetition code (REP), this way achieving near Gaussian MAC (GMAC) capacity performance while coping with the dynamics of the MAC system. The joint optimization of the LDPC and REP codes is addressed by matching the analytical extrinsic information transfer (EXIT) functions of the sub-optimal multi-user detector (MUD) and the channel code for a specific and static MAC system, achieving near-GMAC capacity. We show that the near-capacity performance can be flexibly maintained with the same LDPC code regardless of the variations in the number of users and power levels. This flexibility (or elasticity) is provided by the REP code, acting as “user-load and power equalizer”, dramatically simplifying the practical implementation of NOMA schemes, as only a single LDPC code is needed to cope with the dynamics of the MAC system.

A 20-Gbps receiver bridge chip featuring auto-skew calibration and continuous-time linear equalization is proposed to support the mobile industry processor interface D-PHY version 2.0 specification with four data lanes and one clock lane. The proposed receiver bridge chip performs byte synchronization and 1-to-8 deserialization for converting high-speed scalable low-voltage signals into low-speed low-voltage complementary metal-oxide semiconductor signals. The proposed auto-skew calibration has a simple architecture and is insensitive to dynamic noise owing to the use of the multiple bits supplied from the deserializer as a result of the phase detector for the skew calibration. It is performed via a four-step sequential process to use the minimum time delay. The proposed receiver bridge chip is implemented using a 0.11 $\mu \text{m}$ CMOS process with a 1.2 V supply. The measured peak-to-peak time jitter of the signal recovered using the proposed receiver is 50 ps at a data rate of 5.0 Gbps/lane on a printed circuit board FR-4 10 inch channel. The proposed skew calibration reduces the time skew among the four data lanes and one clock lane to less than 10 ps.

Visible light communication (VLC) has emerged as a viable supplement to the existing radio frequency based communication systems. However, its performance is severely impaired by a) nonlinear characteristics of light emitting diode (LED), which reduces the Euclidean distance between neighboring constellation points, and b) intersymbol interference (ISI) due to time-dispersive nature of VLC channel, which reduces the eye opening of the received symbols and degrades the bit error rate (BER) performance. In this paper, we propose a novel kernel least mean square (KLMS) with adaptive decision feedback equalizer based post-distorter for VLC for joint mitigation of LED nonlinearity and ISI. In this work, minimum mean square error criterion based joint optimization problem is formulated in a reproducing kernel Hilbert space for optimization of the feedforward and feedback filter coefficients. Simulations indicate that the proposed post-distorter yields better BER performance as compared to existing algorithms.

In future Internet-of-Things networks, sensors or even access points can be mounted on ground/aerial vehicles for smart-city surveillance or environment monitoring. For such high-mobility sensing , it is impractical to collect data from a large population of sensors using any traditional orthogonal multi-access scheme due to the excessive latency. To tackle the challenge, a technique called over-the-air computation (AirComp) was recently developed to enable a data-fusion center to receive a desired function of sensing data from concurrent sensor transmissions, by exploiting the superposition property of a multi-access channel. This paper aims at further developing multiple-input-multiple output (MIMO) AirComp for enabling high-mobility multimodal sensing . Specifically, we design MIMO-AirComp equalization and channel feedback techniques for spatially multiplexing multifunction computation. Given the objective of minimizing the computation error, a close-to-optimal equalizer is derived in closed-form using differential geometry. The solution can be computed as the weighted centroid of points on a Grassmann manifold, where each point represents the subspace spanned by the channel matrix of a sensor. As a by-product, the problem of MIMO-AirComp equalization is proved to have the same form as the classic problem of multicast beamforming, establishing the AirComp-multicasting duality . Its significance lies in making the said Grassmannian-centroid solution transferable to the latter problem which otherwise is solved using the computation-intensive semidefinite relaxation method. Last, building on the AirComp architecture, an efficient channel-feedback technique is designed for direct acquisition of the equalizer at the access point from simultaneous feedback by all sensors. This overcomes the difficulty of provisioning orthogonal feedback channels for many sensors.

In diffusion-based communication, for molecular systems, the achievable data rate depends on the stochastic nature of diffusion, which exhibits a severe inter-symbol-interference (ISI). Multiple-input multiple-output (MIMO) multiplexing improves the data rate at the expense of an inter-link interference (ILI). This paper investigates training-based channel estimation schemes for diffusive MIMO (D-MIMO) systems and corresponding equalization methods. Maximum likelihood and least-squares estimators of mean channel are derived, and the training sequence is designed to minimize the mean square error (MSE). The numerical validations in terms of MSE are compared with Cramer–Rao bound derived herein. Equalization is based on decision feedback equalizer (DFE) structure as this is effective in mitigating diffusive ISI/ILI. Zero-forcing, minimum MSE, and least-squares criteria have been paired to DFE, and their performances are evaluated in terms of bit error probability. D-MIMO time interleaving is exploited as an additional countermeasure to mitigate the ILI with remarkable performance improvements. The configuration of nano-transceivers is not static but affected by a Brownian motion. A block-type communication is proposed for D-MIMO channel estimation and equalization, and the corresponding time-varying D-MIMO MC system is numerically evaluated.

Generalized frequency division multiplexing (GFDM) is a new waveform that can play an important role in some 5th generation (5G) mobile wireless networks scenarios, such as 5G for remote areas. It offers low out-of-band emission, good spectral efficiency and has the flexibility to cover the 5G requirements in remote areas applications. The GFDM receiver implementation complexity poses restrictions to the use of this powerful waveform in constrained resources devices such as the ones used in the Internet of Things (IoT). This letter proposes, an innovative frequency-domain equalizer showing complexity close to the zero-forcing (ZF) receiver while achieving performance close to the Minimum Mean Square Error (MMSE) receiver. The study also evaluates the proposed equalizer performance under frequency selective fading channel for the single-input/single-output (SISO) and multiple-input/multiple-output (MIMO) cases. The results show that with a small increment in complexity a performance gain can be obtained.

Increasing areal density in bit patterned media recording systems increases the 2-D interference from the adjacent islands in the down-track and cross-track directions. Also, nonlinear effects due to the track mis-registration (TMR) and media noise become severe as the areal density increases and degrade the bit error rate (BER) performance. In this paper, we proposed a new detection scheme based on multi-layer perceptrons (MLPs) to alleviate the nonlinear effects. Two MLPs are used in the proposed detection scheme and learn the channel with the nonlinear effects in the training procedure. One MLP is used for TMR estimation and the other MLP is used for data detection. The MLP-based TMR estimator estimates the TMR from the readback signals. Then, the MLP-based data detector equalizes the channel reflecting the estimated TMR and media noise and detects the data bit. Therefore, the detection scheme is robust to the effects of the TMR and media noise. The proposed detection scheme provides the improved BER performances compared to the conventional detection scheme using the 2-D equalizer filter, 1-D generalized partial response target, and Viterbi detector in the channel with the TMR and media noise.

To fulfill the increasing demand for high speed Variable Bit Rate (VBR) broadcast services with reliable Quality of Service, Group Orthogonal-Orthogonal Frequency Division Multiple Access (GO-OFDMA) and Variable Spreading Length Multicarrier Code Division Multiple Access transmission schemes are examined over Land Mobile Satellite (LMS) channel. This paper presents the performance analysis of aforementioned multiple access transmission schemes in terms of computation complexity, Peak-to-Average-Power-Ratio (PAPR) and the average Bit Error Rate (BER) performance over L-band LMS channel. Minimum Mean Square Equalizer (MMSE) equalizer is employed at the receiver. Based on the simulation results, GO-OFDMA stems out to be a better variable rate transmission scheme in respect of both PAPR and BER performance for higher data rate users. Further, GO-OFDMA based VBR transmitter and MMSE equalizer are prototyped on commercially available virtex5 xc5vlx110t–1ff1136 Field Programmable Gate Array (FPGA) device. A block-by-block analysis of hardware resource utilization and power dissipation for the VBR GO-OFDMA transmitter is provided. The achieved baud rate for VBR GO-OFDMA transmission scheme on FPGA is 103.874 Msps. Thus, GO-OFDMA transmission scheme could be appropriate for VBR communication over LMS channel.

There have been a number of researches on block transmission systems via MIMO channels due to really high data transmission rate. However, because of the existence of inter-symbol interference (ISI) in the systems, guard intervals are added to eliminate the ISI, leading to a reduction in channel energy and bandwidth efficiency. In order to optimize these systems, there are many solutions in which a combining design of precoder and equalizer appears as a potential candidate. In this paper, a jointly optimal design for precoder and equalizer for ISI multiple input multiple output (MIMO) channels based on sharing redundancy is proposed. Theory analysis and simulation results demonstrate that the proposed design produces a significant improvement in the system performance such as a reduction in bit error rate, a decrease in channel energy loss and an increase in system throughput.

In this paper, a local area influence probabilistic (LAIP) detector for estimating magnetic grain interactions with coded data bits in two-dimensional magnetic recording is combined with a 2-D Bahl–Cocke–Jelinek–Raviv (BCJR)-based detector for joint removal of intertrack interference (ITI) and intersymbol interference (ISI). The LAIP detector sends log-likelihood ratio estimates of coded bits and an estimate of the local ISI/ITI convolution mask to a BCJR-based ISI/ITI detector followed by an irregular-repeat-accumulate decoder. Simulation results on a random Voronoi grain media model with ISI and ITI show that the concatenated LAIP/BCJR system, which detects three tracks simultaneously, achieves user information bit areal densities competitive or higher than results reported in a previous paper that employed the LAIP detector alone on a Voronoi grain channel without ISI/ITI. Simulation results on a grain-flipping probability media model based on micromagnetic simulations show that the proposed detector achieves an 11.3% bit error rate reduction compared to a recently proposed system with a 2-D linear equalizer followed by a two-track BCJR detector with 2-D pattern-dependent noise prediction.

An approximate maximum likelihood sequence estimator (MLSE) equalizer for shaped offset QPSK, version TG (SOQPSK-TG) is derived based on the pulse amplitude modulation (PAM) approximation of SOQPSK-TG using the two principal PAM pulses. The resulting equalizer is a generalization of the equalizer based on the Ungerboeck observation model, where the generalization is to two parallel, cross-correlated symbol streams over parallel intersymbol interference channels. The bit error rate (BER) performance and computational complexity of the approximate MLSE equalizer operating over three example channels, motivated by multipath propagation in aeronautical telemetry and with increasing selectivity, are presented. The BER performance was evaluated using computer simulations and shown to be approximately 5–8 dB better than the best frequency-domain minimum mean-squared error (MMSE) equalizers published in the open literature. The computational complexity, measured by the number of real-valued multiplications, is comparable to that of the frequency-domain MMSE equalizers.

This paper presents a four-level pulse amplitude modulation (PAM4) quarter-rate receiver that efficiently compensates for moderate channel loss in a robust manner through background adaptation of the receiver thresholds and equalization taps. The front-end utilizes an input single-stage continuous-time linear equalizer (CTLE) to boost the main cursor and relax the pre-cursor cancellation requirement, requiring only a 2-tap pre-cursor feed-forward equalizer (FFE) on the transmitter side. A 2-tap decision feedback equalizer (DFE) follows that includes one finite impulse response (FIR) tap and one infinite impulse response (IIR) tap to cancel first post-cursor and long-tail inter-symbol interference (ISI), respectively. In addition to the per-slice main three data samplers, a single error sampler is utilized for background threshold control and an edge-based sampler performs both phase-locked loop (PLL)-based clock and data recovery (CDR) phase detection and generates information for background DFE tap adaptation. Fabricated in general purpose (GP) 65-nm CMOS, the 56-Gb/s receiver achieves 4.63 mW/Gb/s and compensates for up to 20.8-dB loss at a bit error rate (BER) < 10−12 when operated with a 2-tap FFE transmitter.

In this brief, a 0.9-V 12-Gb/s quarter-rate two finite impulse response tap direct decision feedback equalizer (DFE) is presented. For a high-speed incorporated operation of both DFE summing and slicing, a common-mode-controlled charge-based latch (CMCCBL) is proposed. In a CMCCBL-based DFE, the common mode of the first tap feedback signal is changed to adjust the first tap feedback weight. Therefore, CMCCBL does not need the conventional first tap weighting transistors which increase the DFE feedback delay. The DFE core compensates for the FR4 printed circuit board channel loss of −22 dB at 6 GHz and consumes 4.16 mW achieving 0.347 pJ/bit for PRBS7 input. The active area of DFE is 0.0036 mm2 in a 65-nm CMOS process.

Light-emitting diode (LED)-based communications, such as visible light communications and infrared communications, are candidate techniques to provide short-range and high-speed data transmission. In this letter, $M$ -ary pulse amplitude modulation (M-PAM), used as a high bandwidth efficiency scheme, is compared with three well-known optical orthogonal frequency-division multiplexing (OFDM) schemes. Considering the bandwidth limit and constrained peak transmitted power characteristics of LEDs, a bit loading algorithm and a single-tap equalizer with an optimized modulation index are used for the optical OFDM schemes tested. To reduce the inter-symbol interference caused by the bandlimited channel, an optimized pulse shape and a minimum mean squared error equalizer are applied to the M-PAM system. From numerical results, M-PAM can provide a substantially higher data rate than OFDM for bandlimited channels. When the channel bandwidth is ample compared with the symbol rate, optical OFDM outperforms M-PAM.

Filter bank multi-carrier based on offset quadrature amplitude modulation (FBMC/OQAM) suffers from inter-symbol interference and inter-subchannel interference in multi-path fading channels. By exploiting the special structures of the FBMC/OQAM signal and the resulting correlation matrix and assuming perfect estimation of channel and noise power, we study the properties of the decision feedback equalizer (DFE) designed by the minimum mean square error criterion. The study leads to efficient and mathematically equivalent computation of DFE coefficients. Consequently, the DFE requires a smaller complexity in computing the equalizer coefficients than the linear equalizer, while achieving a significant performance gain in highly frequency-selective channels. Building upon the proposed efficient computation of DFE, we develop DFE-based detectors—namely, two-stage DFE and multi-band DFE—so that a better detection performance can be obtained more efficiently. Complexity analysis and simulation results demonstrate the efficacy of the proposed computation method and the DFE-based detectors.

Single-carrier frequency-domain equalization (SC-FDE) is a promising technique for coherent high-rate underwater acoustic (UWA) communications. In this paper, a time-frequency domain turbo equalization (TFD-TE) scheme is proposed for single-carrier modulation. The TFD-TE scheme is to couple a multi-channel frequency domain equalizer with phase rotation compensation and a low-order single-channel time-domain turbo equalizer. To mitigate the error propagation and further enhance receiver performance, a bidirectional equalizer that combined the soft outputs of a conventional equalizer and a backward equalizer is introduced into the proposed TFD-TE scheme (Bi-TFD-TF). The sparse channel is estimated by the improved proportionate normalized least mean squares (IPNLMS) algorithm block by block. Both the simulated and experimental results show the proposed TFD-TE can effectively improve system performance with acceptable complexity. The proposed TFD-TE scheme is evaluated using communication data collected from the DJK15 lake experiment. For the $1\times 4$ single-input multiple-output (SIMO) communication system using quadrature phase shift keying (QPSK) and 8-phase shift keying (8PSK) modulations, the proposed scheme achieves error-free transmission after a few iterations. Our receiver achieves a maximum raw data rate of 12 kbit/s and a reliable net data rate of 4 kbit/s within 4-kHz bandwidth at a range of 10.8 km. Compared with conventional frequency domain turbo equalization (FD-TE), the proposed TFD-TE scheme can enhance 0.8–1.3 dB in terms of output signal-to-noise ratio (OSNR). The related bi-TFD-TE provides an additional 0.8–1.7-dB OSNR improvements.

Visible light communication (VLC) systems working as high-speed and short-range wireless connection methods have attracted more and more attention. This paper proposes a variable pulse width unipolar optical orthogonal frequency-division multiplexing (VPW-OFDM) scheme for indoor VLC systems. Similar to the polar-based optical OFDM, Hermitian symmetric data are not required to generate real signals. Instead, the magnitude and phase components of the complex-valued OFDM signal are transmitted successively. Unlike the polar-based optical OFDM, the proposed VPW-OFDM allows using variant pulse widths to reduce the effects of the noise added on the phase component. By adaptively optimizing the pulse widths, the proposed VPW-OFDM outperforms the polar-based OFDM and other state-of-the-art optical OFDM techniques, such as dc-biased optical OFDM, asymmetrically clipped optical OFDM, and unipolar-OFDM. For indoor VLC systems, illumination requirement is crucial and needs to be considered. In this paper, we analyze the effects of the illumination requirements on system performance. Bit error rate and transmitted bit rate are used as criteria to evaluate the communication performances for the techniques tested. In addition, the system channel with bandwidth limitation is taken into account. To enhance the data throughput, a single-tap equalizer at the receiver and the bit loading algorithm are applied. From the numerical results, the proposed VPW-OFDM outperforms DCO-, ACO-, U-, and polar-based OFDM for a wide channel bandwidth.

Future wireless communication systems, including fifth-generation (5G) networks and the Internet of Things (IoT), require a massive number of inexpensive transceivers. These transceivers come with various hardware impairments, such as phase noise and in-phase/quadrature phase (I/Q) imbalance. This piece of work studies the performance of underlay cognitive radio (CR) networks, considering the joint effect of I/Q imbalance and imperfect channel-state information (CSI) at the secondary user. In order to mitigate the effect of I/Q imbalance, an optimal maximum likelihood (ML) receiver design is proposed and analyzed. Specifically, a closed-form expression of the average pairwise error probability (APEP) and a tight upper bound of the average bit error rate (ABER) are derived. In addition, a widely linear equalization (WLE) receiver that has performance close to the optimal receiver with a computational complexity close to the traditional blind receiver is proposed. In particular, the exact PEP of this WLE receiver is obtained and its APEP is calculated numerically. Moreover, an exact expression is derived for Cramer-Rao lower bound (CRLB) of the secondary system receiver channel estimation error in the presence of I/Q imbalance at the secondary transmitter/receiver (STx/SRx) sides. Computer simulations prove the analytical results of the proposed receivers. The obtained results show that the optimal receiver has the best performance and the WLE receiver outperforms the traditional ML receiver in most cases. In addition, the analysis shows that the best estimator that reaches the CRLB is not affected by the I/Q imbalance at STx/SRx.