Channel Mismatch Research Articles

Deep transfer learning for automated single-lead EEG sleep staging with channel and population mismatches.

Introduction: Automated sleep staging using deep learning models typically requires training on hundreds of sleep recordings, and pre-training on public databases is therefore common practice. However, suboptimal sleep stage performance may occur from mismatches between source and target datasets, such as differences in population characteristics (e.g., an unrepresented sleep disorder) or sensors (e.g., alternative channel locations for wearable EEG). Methods: We investigated three strategies for training an automated single-channel EEG sleep stager: pre-training (i.e., training on the original source dataset), training-from-scratch (i.e., training on the new target dataset), and fine-tuning (i.e., training on the original source dataset, fine-tuning on the new target dataset). As source dataset, we used the F3-M2 channel of healthy subjects (N = 94). Performance of the different training strategies was evaluated using Cohen's Kappa (κ) in eight smaller target datasets consisting of healthy subjects (N = 60), patients with obstructive sleep apnea (OSA, N = 60), insomnia (N = 60), and REM sleep behavioral disorder (RBD, N = 22), combined with two EEG channels, F3-M2 and F3-F4. Results: No differences in performance between the training strategies was observed in the age-matched F3-M2 datasets, with an average performance across strategies of κ = .83 in healthy, κ = .77 in insomnia, and κ = .74 in OSA subjects. However, in the RBD set, where data availability was limited, fine-tuning was the preferred method (κ = .67), with an average increase in κ of .15 to pre-training and training-from-scratch. In the presence of channel mismatches, targeted training is required, either through training-from-scratch or fine-tuning, increasing performance with κ = .17 on average. Discussion: We found that, when channel and/or population mismatches cause suboptimal sleep staging performance, a fine-tuning approach can yield similar to superior performance compared to building a model from scratch, while requiring a smaller sample size. In contrast to insomnia and OSA, RBD data contains characteristics, either inherent to the pathology or age-related, which apparently demand targeted training.

Robust Transmission using Efficient Adaptive Modulation and Coding in Modern OFDM Systems

Abstract: OFDM (Orthogonal Frequency Division Multiplexing) as a special multi-carrier transmission technology has good resistance to narrow-band interference and frequency selective fading ability. Using adaptive modulation and coding in an OFDM systems in comparison with traditional fixed modulation techniques, it becomes possible to take full advantage of spectrum resources enhancing bandwidth efficiency and system capacity making it suitable for the high-speed and reliable mobile communication systems in the future. In AMC (Adaptive Modulation and Coding) technique, modulation method, coding rate, and/or other signal transmission parameters are dynamically adapted to the channel conditions to increase the system performance in terms of Bit Error Rate (BER) and throughput (bps) in various conditions such as channel mismatch, Doppler spreads, fading, etc. The purpose of this paper is to investigate the effect of adaptive modulation and coding in an OFDM system in terms of bit error rate and SNR over fading communication channels. In this paper, we have considered fixed and adaptive modulation technique in an OFDM system over AWGN and RICEAN channel with convolutional and BCH coding. An OFDM system model is simulated in MATLAB programming environment to obtain the simulation result. From the comparative simulation result analysis in terms of BER v/s SNR, it can be concluded that Adaptive modulation with Convolutional coding over RICEAN channel significantly reduces bit error rate giving better trade-off between SNR and BER than all other options considered here.

Implementation of Channel Equalization for Large-scale Wideband Digital Array

Channel equalization is essential in wideband digital array radar to compensate the channel distortion and mismatch. Inverse Fourier transform (IFT) method and weighted least squares (WLS) algorithm are used to calculate the coefficients of the equalizer filter. However, the accuracy of equalizer coefficient calculation is affected by many non-ideal factors in the actual correction of large-scale digital TR channels. This paper aims to analyze the influence of various non-ideal factors in equalizer coefficient calculation and propose a test method to reduce or eliminate their influence. Finally, based on 90 channel scale FPGA-based system, the method proposed in this paper is tested and verified, and good performance is achieved.

Fully Digital Calibration Technique for Channel Mismatch of TIADC at Any Frequency

This paper presents a fully digital modulation calibration technique for channel mismatch of TIADC at any frequency. By pre-inputting a test signal in TIADC, the mismatch errors are estimated and stored, and the stored values will be extracted for compensation when the input signal is at special frequency which can be detected by a threshold judgement module, thus solving the problem that the traditional modulation calibration algorithm cannot calibrate the signal at special frequency. Then, by adjusting the operation order among the error estimation coefficient, modulation function and input signal in the calibration loop, further, the order of correlation and modulation in the error estimation module, the complexity of the proposed calibration algorithm is greatly reduced and it will not increase with the number of channels of TIADC. What's more, the hardware consumption of filters in calibration algorithm is greatly reduced by introducing a CSD (Canonical Signed Digit) coding technique based on Horner's rule and sub-expression sharing. Applied to a four-channel 14bit 560MHz TIADC system, with input signal at 75.6MHz, the FPGA verification results show that, after calibration, the spurious-free dynamic range (SFDR) improves from 33.47dB to 99.81dB and signal-to-noise distortion ratio (SNDR) increases from 30.15dB to 81.89dB.

Buffer-aided Relay Selection Technique in a Cooperative Communication Network

The goal of cooperative communication is to increase spatial diversity and wireless network coverage. However, in a classical relaying setup, where a single best relay is chosen to receive the source's data in the first time slot and retransmit in the second time slot regardless of channel circumstances, channel mismatch occurs. In this paper, we have developed an enhanced relay selection technique at the relay nodes that incorporates buffers to address the aforementioned problem. The outage probability and throughput computations are derived using closed-form formulas. The results obtained show that the enhanced relay selection technique gives lower values in outage probability and higher values in throughput when compared with the conventional technique, providing it a desirable strategy for use in technical communication.

Digital Post-processing Techniques for Time-interleaved ADCs

This paper presents an all-digital post-processing circuit for a high-speed and high-precision time-interleaved ADC (TIADC). By pre-inputting a test signal in the TIADC, channel mismatch errors are estimated and stored, and the stored values are extracted to compensate for errors when the input signal is at a special frequency. The frequency can be detected by a threshold judgement module. This solves the problem of the traditional modulation calibration algorithm, which cannot calibrate the signal at a special frequency. A high-speed low-complexity digital down converter (DDC) circuit with an optional decimation ratio of 1, 2, 4, or 8 times was designed to have different frequency outputs of the TIADC. We applied it to a four-channel 14-bit 360-MHz TIADC system with an input signal fin of 110 MHz. The FPGA verification results show that after calibration and decimation, the spurious-free dynamic range (SFDR) and signal-to-noise distortion ratio (SNDR) improve by about 40 dB and 30 dB, respectively.

Spoken Language Identification using CNN with Log Mel Spectrogram Features in Indian Context

This study demonstrates a novel application of Log Mel Spectrogram coefficients to image classification via Convolutional Neural Networks (CNN). The acoustic features obtained as log mel spectrogram images are used in this article. Log mel spectrogram pictures, a novel technique, ensure that the system is noise-resistant and free of channel mismatch. The majority of Indian languages from our own dataset were used.With the use of auditory features integrated in CNN, we hope to quickly and accurately detect a language. InceptionV3 and Resnet50 models are also used in this study for performance analysis. When compared to the existing system, these approaches achieved significant improvements in language identification accuracy.

Time-interleaved system mismatch estimation based on correlation function and particle swarm optimization algorithm.

The time-interleaved analog-to-digital converters (TIADCs) technique is an efficient solution to improve the sampling rate of the acquisition system with low-speed ADCs. However, channel mismatches such as gain mismatch, time skew mismatch, and offset mismatch may seriously degrade the performance of TIADC. Furthermore, for high-speed signal acquisition, the gain and time skew mismatches would vary with the signal frequency, and the traditional fixed model does not work any longer. In this paper, a series of sinusoidal signals are adopted to estimate the variable mismatches. First, an autocorrelation-based approach is presented to estimate the gain mismatch. The information about the gain mismatch is extracted from the autocorrelation function of sub-ADC output samples. Then, the time skew mismatch is estimated by utilizing the particle swarm optimization algorithm. The reported simulation results show that the mismatches can be accurately estimated. Finally, a commercial 12.5 GSPS four-channel TIADC system is utilized to verify the performance of the proposed method. The spurious free dynamic range of the system can be improved by about 20 dB, and the effectiveness of the proposed estimation method is demonstrated.

Error Detection and Correction of Mismatch Errors in M-Channel TIADCs Based on Genetic Algorithm Optimization

In order to achieve higher system performance, a digital calibration technique for the sub-channel mismatches of time-interleaved ADCs (TIADCs) is proposed in this paper. The sine-fit-based estimation algorithm is introduced to estimate the channel mismatches and a calibration algorithm is proposed to compensate for the mismatches. Subsequently, the genetic algorithm (GA) is firstly utilized to detect the mismatch errors of the outputs of sub-channels after frequency domain filtering. The detected offset error and gain error are then corrected by performing the calibration algorithm, and the time errors are corrected by fractional delay filters based on Farrow structure. The spurious-free dynamic range (SFDR) is enhanced from 19.69 dB to 108.12 dB, and the signal to noise and distortion ratio (SNDR) is enhanced from 16.02 dB to 97.63 dB. The proposed technique is further validated on the FPGA. Compared with existing calibration techniques, the proposed technique has the advantages of simple algorithm structure, low hardware resource consumption, and high calibration accuracy and can be applied to the calibration of high-resolution (18-bit) TIADCs for low-frequency inputs.

Error Correction Method of TIADC System Based on Parameter Estimation of Identification Model

The performance of analog-to-digital converters (ADCs) has reached a bottleneck due to the limitations of the manufacturing process and testing environment. Time-interleaved ADC (TIADC) technology can increase the sampling rate without changing the resolution. However, channel mismatch severely degrades the dynamic performance of the TIADC system. For the channel mismatch problem of TIADC, most of the current solutions have preconditions, such as eliminating only some kind of error or increasing the complexity of the hardware. A few methods can estimate multiple errors without changing the hardware circuit. To improve the dynamic performance of the TIADC system, on the basis of an in-depth study of the channel mismatch error of TIADC, according to the system identification theory, an identification model is designed to characterize the frequency characteristics of TIADC. Using the system observation data, the transfer function parameters of the system are recursively estimated. By constructing and verifying the identification model of the TIADC system, and then through the frequency domain correction method, a digital compensation filter is established to complete the error correction of the system. The test results of the four-channel TIADC high-speed data acquisition system show that the actual input and output characteristics of the test system are consistent with the nature of the identification model. The four channels of the TIADC system are provided by four sub-channels of two AD9653 chips, and the highest sampling rate of a single channel is 125MSPS. For sinusoidal input signals from 20 MHz to 150 MHz, the sampling system can achieve a signal-to-noise ratio (SNR) above 56.8 dB and spurious free dynamic range (SFDR) above 69.7 dB. The dynamic performance of the sampling system is nearly equivalent to that of its sub-ADC; the feasibility of the model identification method and the effectiveness of error correction are verified in simulation and experiment.

Channel Randomness-Based Adaptive Cyclic Prefix Selection for Secure OFDM System

A novel method for cyclic prefix (CP) adaptation based on wireless channel randomness is proposed for orthogonal frequency-division multiplexing (OFDM) systems in urban areas. Especially, a quantization algorithm and an adaptive window-based strong subcarriers selection are designed on the sorted channel frequency response of the legitimate party to select randomly the secure CP (SCP) length for each OFDM symbol without the need to share any information between legitimate nodes. The proposed method creates a problem for illegitimate user’s synchronization as it does not have any information about SCP length for every OFDM symbol, even if it uses blind synchronization techniques. The effectiveness of the proposed mechanism is evaluated through representative metrics, such as bit error rate (BER), throughput in both perfect and imperfect channel estimation scenarios, mismatch probability (MP), and cyclic autocorrelation function (CAF). Also, the performance for the presence of a correlated eavesdropper (Eve) in the system is evaluated. Simulation results show a huge secrecy gap between BER and throughput performance of legitimate and illegitimate users in all scenarios.

A sidelobe suppression algorithm for 77 GHz MIMO radars

77 GHz radar has become a promising approach to enhance automotive safety by quickly detecting and identifying targets around the vehicle, especially in harsh weather conditions. This requires 77 GHz radars to provide environmental imaging with high resolution and reliability. However, radar images are easily blurred by sidelobes and background noises, which makes it difficult to extract real target information. In this paper, a sidelobe suppression algorithm based on the point spread function (PSF) and complex-valued neural network has been proposed to discriminate and suppress unwanted sidelobes while maintaining mainlobes referring to targets. To overcome the scarcity of real-world 77 GHz multiple-input multiple-output (MIMO) radar datasets, this paper derives the formula of PSF for 77 GHz MIMO radars in detail and exploits the PSF to generate simulated datasets for training. In addition, to be compatible with the complex-valued radar datasets, a customized neural network model has also been established in this paper. The well-trained neural network is further adopted to suppress sidelobes on real-world radar images. Comprehensive simulations and measurements have proved the superior performance of the proposed method, especially in cases with low signal-to-noise ratio (SNR), large channel mismatches, small target Radar Cross-Section (RCS) and large observation angle.

An Improved Azimuth Signal Reconstruction Algorithm for Wide-Beam Distributed SAR

Distributed multichannel synthetic aperture radar (MC-SAR) is a system in which transmitting or receiving arrays are distributed on multiple platforms or at different locations on one platform. The along-track component of the baseline makes distributed SAR promising in high-resolution wide-swath (HRWS) imaging such as azimuth MC-SAR. However, the additional channel mismatch introduced by the cross-track baseline (CTB) is considered for the distributed SAR. When the azimuth beam is wide, the azimuth-variant channel mismatch caused by the CTB must be compensated before SAR imaging. First, an improved azimuth signal reconstruction algorithm for distributed wide-beam SAR is proposed in this paper. The azimuth variance of the channel mismatch is considered in a reconstruction filter to further suppress the ambiguity, and the computational consumption is decreased by approximately decomposing the mismatch matrix. Second, the ambiguity suppression performance of the proposed method is analyzed quantitatively. Finally, a simulation and real data processing are provided to demonstrate the effectiveness of the proposed method.

Doppler Centroid Estimation for Ground Moving Target in Multichannel HRWS SAR System

In multichannel high-resolution and wide-swath (HRWS) synthetic aperture radar (SAR) ground moving target indication (GMTI) systems, Doppler centroid (DC) is an essential parameter for spectrum reconstruction and image focusing. However, the conventional DC estimator faces many problems in moving target with multichannel SAR system, such as nonuniform data in azimuth, subsignal usage, and channel mismatch. To estimate the DC of moving target, the modified cross-correlation coefficient (MCCC) method for multichannel SAR system is proposed in this letter, which is especially suitable for nonuniform and undersampled data. Simulations with real spaceborne data demonstrate the effectiveness of this method.

A Principle Solution for Enroll-Test Mismatch in Speaker Recognition

Mismatch between enrollment and test conditions causes serious performance degradation on speaker recognition systems. This paper presents a statistics decomposition (SD) approach to solve this problem. This approach decomposes the PLDA score into three components that corresponding to enrollment, prediction and normalization respectively. Given that correct statistics are used in each component, the resultant score is theoretically optimal. A comprehensive experimental study was conducted on three datasets with different types of mismatch: (1) physical channel mismatch, (2) long-term speaker characteristics mismatch, (3) near-far recording mismatch. The results demonstrated that the proposed SD approach is highly effective, and outperforms the ad-hoc multi-condition training approach that is commonly adopted but not optimal in theory.

Fully Digital On-Chip Wideband Background Calibration for Channel Mismatches in Time-Interleaved Time-Based ADCs

This letter presents a fully integrated on-chip digital mismatch compensation system for time-based time-interleaved (TI) data converters. The proposed digital compensation features blind calibration of gain, offset, and timing mismatches. The implemented system uses time-based sampling clock mismatch detection, achieving convergence within 32K samples, which is on par with analog-assisted background methods. A specialized filter structure compensates for timing mismatches of magnitude up to 0.21 of the sampling period, nearly triple the range of other published digital compensation methods, and is effective for input signals up to 0.92 Nyquist bandwidth. The on-chip digital correction achieves suppression of all mismatch tones to levels below −60 dBc while running fully in the background. The operation is demonstrated with an <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$8\times $ </tex-math></inline-formula> TI 2-GS/s analog-to-digital converter (ADC) prototype chip implemented in a 28-nm CMOS process.

A 120-MHz Broadband Differential Linear Driver With Channel Mismatch Cancellation and Bandwidth Extension for B-PLC Applications

This paper introduces a broadband linear driver used in B-PLC system. The driver consists of two identical current-feedback amplifiers to realize a fully differential topology. A cross-current injection method between the noninverting and the inverting channels is proposed to reduce the even-order harmonics caused by the circuit mismatch. In addition, in order to increase the stability of the GBW to the changing load and maximize the bandwidth, a output stage implemented by a unit gain closed-loop amplifier is proposed to stabilize the open-loop gain of the overall circuit, meanwhile improve the linearity deteriorated by the crossover distortion. The proposed linear driver is fabricated in a SOI CBJT process and it can provide a maximum drive current of 500 mA under a 12-V power supply. The measurement results show that this linear driver is capable of driving a load of <inline-formula> <tex-math notation="LaTeX">$50~\Omega $ </tex-math></inline-formula> while achieving a output swing of <inline-formula> <tex-math notation="LaTeX">$16~{V_{pp}}$ </tex-math></inline-formula> with a second-order harmonic distortion (HD₂) of &#x2212;72 dBm and a third-order harmonic distortion (HD₃) of &#x2212;55 dBm. The bandwidth can reach 120 MHz. A complete in-system measurement also has been passed and the results show that the proposed driver chip can well meet the need of the B-PLC system.

“Orthogonalized” sparsity enhanced mismatch models for wireless heterogeneous networks

Performance of MIMO precoder for heterogeneous networks can be hindered by a lack of accurate channel state information. The sparsity enhanced mismatch model (SEMM) has been proposed recently to account for the channel estimate mismatch problem by exploiting the inherent sparse characteristics of MIMO interference channels. When (single user-MIMO) SU-MIMO precoder design takes into account the SEMM, it was shown to have better transmission performance compared to the conventional norm ball mismatch model (NBMM) in a single-user multi-victims scenario. However, when communicating and interference channels are highly correlated, which can happen frequently in ultra-dense heterogeneous networks, performance of the SEMM precoder degrades and in some cases, underperforms the NBMM precoder. An “orthogonalized” SEMM (OSEMM) is proposed herein to modify the SEMM such that it is better suited for correlated channels. The concept of orthogonalization of channels is not new but this work uses it to enhance the SEMM, which creates synergy between transmission performance and robustness toward channel mismatch error. Two variants of the OSEMM are proposed, namely the OSEMM-LQ and OSEMM-SVD, to modify the basis expansion model that is an integral part of the SEMM. The resulting mismatch model influences the design of the SU-MIMO precoder that aims to maximize certain transmission criterion. Even though SU-MIMO precoding is considered, a new channel correlation definition, which acts as a metric for the OSEMM, is given that allows for user selection in a multiuser scenario such that optimal performance can be attained for the targeted user. Analytical and simulation results are given that highlight the difference in performance between the two variants of the OSEMM.

Knowledge-Aided Ground Moving Target Relocation for Airborne Dual-Channel Wide-Area Radar by Exploiting the Antenna Pattern Information

This paper addresses the problem of ground moving target relocation (GMTR) for airborne dual-channel wide-area radar systems. The monopulse technique can be utilized to perform GMTR. However, in real conditions, the GMTR performance degrades greatly due to the effect of channel mismatch. To tackle this problem, prior knowledge of the antenna pattern information is fully utilized to improve the GMTR performance, and a knowledge-aided GMTR algorithm (KA-GMTR) for airborne dual-channel wide-area radar is proposed in this paper. First, the GMTR model for the two receiving channels is analyzed. The channel mismatch model is constructed, and its expression is derived. Then, the channel mismatch phase error is well estimated by exploiting the prior antenna pattern information based on the least squares (LS) method. Meanwhile, the knowledge-aided monopulse curve (KA-MPC) is derived to perform the direction of arrival (DOA) estimation for potential targets. Finally, KA-GMTR, based on the KA-MPC, is performed to estimate the azimuth offsets and relocate the geometry positions of the potential targets when channel mismatch occurs. Moreover, the target relocation performance is analyzed, and the intrinsic reason that degrades the target relocation accuracy is figured out. The performance assessment based on airborne real-data, also in comparison to the conventional GMTR method, has demonstrated that our proposed KA-GMTR algorithm offers preferable target relocation results under channel mismatch scenarios.

The Effect of Audio-Visual Smiles on Social Influence in a Cooperative Human–Agent Interaction Task

Emotional expressivity is essential for human interactions, informing both perception and decision-making. Here, we examine whether creating an audio-visual emotional channel mismatch influences decision-making in a cooperative task with a virtual character. We created a virtual character that was either congruent in its emotional expression (smiling in the face and voice) or incongruent (smiling in only one channel). People (N = 98) evaluated the character in terms of valence and arousal in an online study; then, visitors in a museum played the “lunar survival task” with the character over three experiments (N = 597, 78, 101, respectively). Exploratory results suggest that multi-modal expressions are perceived, and reacted upon, differently than unimodal expressions, supporting previous theories of audio-visual integration.