Multichannel Receiver Research Articles

GPU-Accelerated Self-Calibrating GRAPPA Operator Gridding for Rapid Reconstruction of Non-Cartesian MRI Data

Self-calibrating GRAPPA operator gridding (SC-GROG) is a method by which non-Cartesian (NC) data in magnetic resonance imaging (MRI) are shifted to the Cartesian k-space grid locations using the parallel imaging concept of GRAPPA operator. However, gridding with SC-GROG becomes computationally expensive and leads to longer reconstruction time when mapping a large number of NC samples in MRI data to the nearest Cartesian grid locations. This work aims to accelerate the SC-GROG for radial acquisitions in MRI, using massively parallel architecture of graphics processing units (GPUs). For this purpose, a novel implementation of GPU-accelerated SC-GROG is presented, which exploits the inherent parallelism in gridding operations. The proposed method employs the look-up-table (LUT)-based optimized kernels of compute unified device architecture (CUDA), to pre-calculate all the possible combinations of 2D-gridding weight sets and uses appropriate weight sets to shift the NC signals from multi-channel receiver coils at the nearest Cartesian grid locations. In the proposed method, LUTs are implemented to avoid the race condition among the CUDA kernel threads while shifting various NC points to the same Cartesian grid location. Several experiments using 24-channel simulated phantom and (12 and 30 channel) in vivo data sets are performed to evaluate the efficacy of the proposed method in terms of computation time and reconstruction accuracy. The results show that the GPU-based implementation of SC-GROG can significantly improve the image reconstruction efficiency, typically achieving 6× to 30× speed-up (including transfer time between CPU and GPU memory) without compromising the quality of image reconstruction.

Fully-Digital Blind Compensation of Non-Linear Distortions in Wideband Receivers

Digital post-distortion is becoming an increasingly attractive solution to compensate for the non-linearities of RF receivers implemented in deep submicron CMOS technologies. A very promising technique which provides flexibility and robustness is the frequency spreading based digital post-distortion. This paper focuses on the analysis of this approach for wideband receivers and its main limitations, such as the anti-alias filter (AAF) impact. As a matter of fact, distortions filtered by the AAF can degrade the correction performance. To overcome this problem, a novel solution based on mimicking the AAF frequency response in the digital domain is proposed. Aspects regarding the estimation and the algorithm convergence are also studied: the noise effect and the choice of the free frequency band. Furthermore, two other techniques are developed to improve the modeling and the correction of the memory effect using, respectively, a bank of filters and two-step architecture. The proposed techniques are demonstrated by simulations and measurements on a multi-channel receiver suited for DVB-T applications. The receiver is based on a 1-GHz bandwidth RF front-end followed by a 2.7-GHz 13-bit analog-to-digital converter.

Extended C-band tunable multi-channel InP-based coherent receiver PICs.

Fully integrated monolithic, multi-channel InP-based coherent receiver PICs and transceiver modules with extended C-band tunability are described. These PICs operate at 33 and 44 Gbaud per channel under dual polarization (DP) 16-QAM modulation. Fourteen-channel monolithic InP receiver PICs show integration and data rate scaling capability to operate at 44 Gbaud under DP 16-QAM modulation for combined 4.9 Tb/s total capacity. Six channel simultaneous operation of a commercial transceiver module at 33 Gbaud is demonstrated for a variety of modulation formats including DP 16-QAM for >1.2Tbit/s aggregate data capacity.

A study of characteristics of underwater acoustic particle velocity channels measured by acoustic vector sensors

Acoustic vector sensors measure orthogonal components of acoustic particle velocity. When used in underwater communication systems, they act as multichannel receivers. One advantage of a vector receiver, compared to an array of spatially-separated scalar receivers such as hydrophones, is its compact size. Some characteristics of particle velocity channels are studied theoretically or via simulations (A. Abdi and H. Guo, “Signal correlation modeling in acoustic vector sensor arrays,” IEEE Transactions on Signal Processing, vol. 57, pp. 892-903, 2009; H. Guo, et al., “Delay and Doppler spreads in underwater acoustic particle velocity channels,” J. Acoust. Soc. Am., vol. 129, pp. 2015-2025, 2011). In this paper, we use data measured by a vector sensor to study various key characteristics of underwater particle velocity channels, including delay spreads, signal-to-noise ratios, and possible correlations among different channels. By inspecting the eigen structure of channel matrices, we also investigate how various measured particle velocity channel impulse responses can affect the performance of an equalizer to detect transmitted symbols. The results are useful for designing proper vector sensor-based multichannel receivers in underwater communication systems. [This work was supported in part by the National Science Foundation (NSF), Grant IIP-1500123.]

Experimental results on synchronization with chirp signals using a vector sensor receiver

Chirp signals, also known as linear frequency modulated signals, are widely used for synchronization, signal acquisition, and frame detection in underwater communication systems. This is due to the peak at the output of the chirp matched filter at the receive side. In low signal-to-noise ratio (SNR) scenarios, however, this peak can be buried in noise, which results in major synchronization errors and system performance loss. While a scalar array of spatially separated hydrophones can increase SNR to improve synchronization, the size of the array may not be suitable for small platforms. Acoustic vector sensors, on the other hand, are small-size devices that can serve as multichannel communication receivers. In this paper, performance of a vector sensor receiver for synchronization using a chirp signal is studied. Our experimental results indicate that a compact vector sensor receiver can significantly enhance the output of the filter matched to the chirp signal. This is because the proposed vector matched filter significantly suppresses the noise and provides a sharp peak at the output. This is particularly important for synchronization and signal acquisition in underwater communication systems operating in low SNR environments. [This work was supported in part by the National Science Foundation (NSF), Grant IIP-1500123.]

A multiple-input multiple-output orthogonal frequency division multiplexing underwater communication system using vector transducers

Vector sensors and transducers are compact multichannel devices that can be used for underwater communication via acoustic particle velocity channels (A. Abdi and H. Guo, “A new compact multichannel receiver for underwater wireless communication networks,” IEEE Transactions on Wireless Communications, vol. 8, pp. 3326-3329, 2009). In this paper, a multiple-input multiple-output (MIMO) underwater acoustic communication system is presented using orthogonal frequency division multiplexing (OFDM) modulation. Upon transmitting multiple independent data streams simultaneously over several channels, this MIMO system can increase the transmission rate, whereas the OFDM modulation mitigates the highly frequency selective underwater channels. Various components of the system including vector transducers and algorithms for synchronization, channel estimation, MIMO detection, channel coding, etc., are designed and implemented. Using this system, experiments are conducted to measure and study acoustic particle velocity channels in the MIMO setup. Additionally, system performance parameters such as bit error rate and spectral efficiency are measured and discussed for various conditions and configurations, to understand the performance of the developed vector MIMO-OFDM system. [The work was supported in part by the National Science Foundation (NSF), Grant IIP-1500123.]

Improving Passive Time Reversal Underwater Acoustic Communications Using Subarray Processing.

Multichannel receivers are usually employed in high-rate underwater acoustic communication to achieve spatial diversity. In the context of multichannel underwater acoustic communications, passive time reversal (TR) combined with a single-channel adaptive decision feedback equalizer (TR-DFE) is a low-complexity solution to achieve both spatial and temporal focusing. In this paper, we present a novel receiver structure to combine passive time reversal with a low-order multichannel adaptive decision feedback equalizer (TR-MC-DFE) to improve the performance of the conventional TR-DFE. First, the proposed method divides the whole received array into several subarrays. Second, we conduct passive time reversal processing in each subarray. Third, the multiple subarray outputs are equalized with a low-order multichannel DFE. We also investigated different channel estimation methods, including least squares (LS), orthogonal matching pursuit (OMP), and improved proportionate normalized least mean squares (IPNLMS). The bit error rate (BER) and output signal-to-noise ratio (SNR) performances of the receiver algorithms are evaluated using simulation and real data collected in a lake experiment. The source-receiver range is 7.4 km, and the data rate with quadrature phase shift keying (QPSK) signal is 8 kbits/s. The uncoded BER of the single input multiple output (SIMO) systems varies between and for the conventional TR-DFE, and between and for the proposed TR-MC-DFE when eight hydrophones are utilized. Compared to conventional TR-DFE, the average output SNR of the experimental data is enhanced by 3 dB.

Adaptive noise abatement in automatic cab signaling receivers

Under conditions of a priori noise uncertainty, it is effective to use adaptive noise-abatement techniques to improve noise protection of continuous automatic cab signaling (CACS) receivers. The purpose of this article is to study the possibilities of an adaptive signal-processing technique, adaptive noise filtration (AF), providing better noise resistance of the CACS signals. To achieve this goal, the following problems are solved: a mathematical model of the CACS receivers' input signals is developed, a functional circuit and adaptation algorithm of a basic and a modified multichannel CACS receivers' adaptive filters is substantiated, and a computer simulation of adaptive filters is performed, confirming the accuracy of the obtained results and effectiveness of such filters for noise reduction. A distinctive feature of the proposed filters, which significantly expands their functionality, is a priori information on the presence of breaks in the CACS signals used in the adaptive algorithms.

Quad channel software defined receiver for passive radar application

Abstract In recent times the growing utilization of the electromagnetic environment brings the passive radar researches more and more to the fore. For the utilization of the wide range of illuminators of opportunity the application of wideband radio receivers is required. At the same time the multichannel receiver structure has also critical importance in target direction finding and interference suppression. This paper presents the development of a multichannel software defined receiver specifically for passive radar applications. One of the relevant feature of the developed receiver platform is its up-to-date SoC (System on hip) based structure, which greatly enhance the integration and signal processing capacity of the system, all while keeping the costs low. The software defined operation of the discussed receiver system is demonstrated with using DVB-T (Digital Video Broadcast – Terrestrial) signal as illuminator of opportunity. During this demonstration the multichannel capabilities of the realized system are also tested with real data using direction finding and beamforming algorithms.

120 Gb/s Multi-Channel THz Wireless Transmission and THz Receiver Performance Analysis

A photonic multi-channel terahertz (THz) wireless transmission system in the 350-475 GHz band is experimentally demonstrated. The employment of six THz carriers modulated with 10 Gbaud Nyquist quadrature phase-shift keying baseband signal per carrier results in an overall capacity of up to 120 Gb/s. The THz carriers with high-frequency stability and low phase noise are generated based on photonic photomixing of 25-GHz spaced six optical tones and a single optical local oscillator derived from a same optical frequency comb in an ultrabroadband uni-travelling carrier photodiode. The bit-error-rate performance below the hard decision forward error correction threshold of 3.8×10 -3 for all the channels is successfully achieved after wireless delivery. Furthermore, we also investigate the influence of the harmonic spurs in a THz receiver on the performance of transmission system, and the experimental results suggest more than 30 dB spur suppression ratio in downconverted intermediate frequency signals for obtaining less than 1 dB interference.

Modified Cramér‐Rao lower bounds for joint position and velocity estimation of a Rician target in OFDM‐based passive radar networks

AbstractOwing to the increased deployment and the favorable range and Doppler resolutions, orthogonal frequency‐division multiplexing (OFDM)‐based L band digital aeronautical communication system type 1 (LDACS1) stations have become attractive systems for target surveillance in passive radar applications. This paper investigates the problem of joint parameter (position and velocity) estimation of a Rician target in OFDM‐based passive radar network systems with multichannel receivers placed on moving platforms, which are composed of multiple OFDM‐based LDACS1 transmitters of opportunity and multiple radar receivers. The modified Cramér‐Rao lower bounds (MCRLBs) on the Cartesian coordinates of target position and velocity are computed, where the received signal from the target is composed of dominant scatterer (DS) component and weak isotropic scatterers (WIS) component. Simulation results are provided to demonstrate that the target parameter estimation accuracy can be improved by exploiting the DS component. It also shows that the joint MCRLB is not only a function of the transmitted waveform parameters, target radar cross section, and signal‐to‐noise ratio but also a function of the relative geometry between the target and the passive radar networks. The analytical expressions of MCRLB can be utilized as a performance metric to access the target parameter estimation in OFDM‐based passive radar networks in that they enable the selection of optimal transmitter‐receiver pairs for target estimation.

Challenges in Clock Synchronization for On-Site Coding Digital Beamformer

Typical radio frequency (RF) digital beamformers can be highly complex. In addition to a suitable antenna array, they require numerous receiver chains, demodulators, data converter arrays, and digital signal processors. To recover and reconstruct the received signal, synchronization is required since the analog-to-digital converters (ADCs), digital-to-analog converters (DACs), field programmable gate arrays (FPGAs), and local oscillators are all clocked at different frequencies. In this article, we present a clock synchronization topology for a multichannel on-site coding receiver (OSCR) using the FPGA as a master clock to drive all RF blocks. This approach reduces synchronization errors by a factor of 8, when compared to conventional digital beamformer.

Iterative Schemes to Solve Low-Dimensional Calibration Equations in Parallel MR Image Reconstruction with GRAPPA.

GRAPPA (Generalized Autocalibrating Partially Parallel Acquisition) is a widely used parallel MRI reconstruction technique. The processing of data from multichannel receiver coils may increase the storage and computational requirements of GRAPPA reconstruction. Random projection on GRAPPA (RP-GRAPPA) uses random projection (RP) method to overcome the computational overheads of solving large linear equations in the calibration phase of GRAPPA, saving reconstruction time. However, RP-GRAPPA compromises the reconstruction accuracy in case of large reductions in the dimensions of calibration equations. In this paper, we present the implementation of GRAPPA reconstruction method using potential iterative solvers to estimate the reconstruction coefficients from the randomly projected calibration equations. Experimental results show that the proposed methods withstand the reconstruction accuracy (visually and quantitatively) against large reductions in the dimension of linear equations, when compared with RP-GRAPPA reconstruction. Particularly, the proposed method using conjugate gradient for least squares (CGLS) demonstrates more savings in the computational time of GRAPPA, without significant loss in the reconstruction accuracy, when compared with RP-GRAPPA. It is also demonstrated that the proposed method using CGLS complements the channel compression method for reducing the computational complexities associated with higher channel count, thereby resulting in additional memory savings and speedup.

Cramer-Rao Lower Bound Evaluation for Linear Frequency Modulation Based Active Radar Networks Operating in a Rice Fading Environment.

This paper investigates the joint target parameter (delay and Doppler) estimation performance of linear frequency modulation (LFM)-based radar networks in a Rice fading environment. The active radar networks are composed of multiple radar transmitters and multichannel receivers placed on moving platforms. First, the log-likelihood function of the received signal for a Rician target is derived, where the received signal scattered off the target comprises of dominant scatterer (DS) component and weak isotropic scatterers (WIS) components. Then, the analytically closed-form expressions of the Cramer-Rao lower bounds (CRLBs) on the Cartesian coordinates of target position and velocity are calculated, which can be adopted as a performance metric to access the target parameter estimation accuracy for LFM-based radar network systems in a Rice fading environment. It is found that the cumulative Fisher information matrix (FIM) is a linear combination of both DS component and WIS components, and it also demonstrates that the joint CRLB is a function of signal-to-noise ratio (SNR), target’s radar cross section (RCS) and transmitted waveform parameters, as well as the relative geometry between the target and the radar network architectures. Finally, numerical results are provided to indicate that the joint target parameter estimation performance of active radar networks can be significantly improved with the exploitation of DS component.

Referenceless one-dimensional Nyquist ghost correction in multicoil single-shot spatiotemporally encoded MRI

Single-shot spatiotemporally encoded (SPEN) MRI is a novel fast imaging method capable of retaining the time efficiency of single-shot echo planar imaging (EPI) but with distortion artifacts significantly reduced. Akin to EPI, the phase inconsistencies between mismatched even and odd echoes also result in the so-called Nyquist ghosts. However, the characteristic of the SPEN signals provides the possibility of obtaining ghost-free images directly from even and odd echoes respectively, without acquiring additional reference scans. In this paper, a theoretical analysis of the Nyquist ghosts manifested in single-shot SPEN MRI is presented, a one-dimensional correction scheme is put forward capable of maintaining definition of image features without blurring when the phase inconsistency along SPEN encoding direction is negligible, and a technique is introduced for convenient and robust correction of data from multi-channel receiver coils. The effectiveness of the proposed processing pipeline is validated by a series of experiments conducted on simulation data, in vivo rats and healthy human brains. The robustness of the method is further verified by implementing distortion correction on ghost corrected data.

Effects of IQ imbalance for simultaneous transmit and receive based cognitive anti-jamming receiver

In order to enhance the anti-jamming capability of Cyberspace data-link (CDL) in the complex electromagnetic environment, the performance of jamming sensing of simultaneous transmit and receive based cognitive anti-jamming (SCAJ) receiver impaired by IQ imbalance is investigated. Firstly, energy detection (ED) based the jamming detection and false-alarm probabilities in closed-form for single- and multi-channel SCAJ receiver are derived. Then, the image channel interference cancellation (IC) scheme is proposed to mitigate the image channel crosstalk. Simulation results show that the false-alarm probability is increased by IQ imbalance, and the proposed interference cancellation scheme can reduce the adverse effect of IQ imbalance, thus the anti-jamming performance of SCAJ receiver in CDL system can be improved.

Cramér–Rao bound analysis for joint target location and velocity estimation in frequency modulation based passive radar networks

With the wide spread availability and the favourable Doppler resolution, the frequency modulation (FM) commercial radio signals have become attractive for passive radar applications. Passive radar networks using multiple illuminators of opportunity and multichannel receivers have been shown to offer significant performance improvement owing to their advantage of signal and spatial diversities. In this study, the authors compute the joint Cramer–Rao lower bound (CRLB) for the target parameter (delay and Doppler) estimation error utilising FM commercial radio signals as illuminators of opportunity for passive radar network systems, where the non-coherent and coherent processing scenarios are considered. The numerical simulations are provided to show that the joint CRLB is not only a function of the transmitted waveforms but also of the relative geometry between the target and the passive radar networks for both non-coherent and coherent cases. The expressions for joint CRLB are an important performance metric for target parameter estimation in FM-based passive radar networks.

A 25 Gb/s Hybrid-Integrated Silicon Photonic Source-Synchronous Receiver With Microring Wavelength Stabilization

Single-mode wavelength-division multiplexing (WDM) optical links are an attractive technology to meet the growing interconnect bandwidth demand in data center applications. This paper presents a multi-channel hybrid-integrated photonic receiver based on microring drop filters and waveguide photodetectors implemented in a 130 nm SOI process and high-speed optical front-ends designed in 65 nm CMOS. The source-synchronous receiver utilizes an LC injection-locked oscillator (ILO) in the clock path for improved jitter filtering, while maintaining correlated jitter tracking with the data channels. Receiver sensitivity is improved with a large input-stage feedback resistor transimpedance amplifier (TIA) cascaded with an adaptively-tuned continuous-time linear equalizer (CTLE). In order to stabilize the microring drop filter resonance wavelength, a peak-detector-based thermal tuning loop is implemented with a 0.7 nm range at $43~\mu \text {W}$ /GHz efficiency. When tested with a waveguide photodetector with 0.45 A/W responsivity, the receiver achieves -8.0 dBm OMA sensitivity at a BER $= 10^{\mathrm {-12}}$ with a jitter tolerance corner frequency near 20 MHz and a per-channel power consumption of 17 mW including amortized clocking power.

A robust method for suppressing motion-induced coil sensitivity variations during prospective correction of head motion in fMRI

Prospective motion correction is a promising candidate solution to suppress the effects of head motion during fMRI, ideally allowing the imaging plane to remain fixed with respect to the moving head. Residual signal artifacts may remain, however, because head motion in relation to a fixed multi-channel receiver coil (with non-uniform sensitivity maps) can potentially introduce unwanted signal variations comparable to the weak fMRI BOLD signal (~1%–4% at 1.5–3.0T). The present work aimed to investigate the magnitude of these residual artifacts, and characterize the regime over which prospective motion correction benefits from adjusting sensitivity maps to reflect relative positional change between the head and the coil. Numerical simulations were used to inform human fMRI experiments. The simulations indicated that for axial imaging within a commonly used 12-channel head coil, 5° of head rotation in-plane produced artifact signal changes of ~3%. Subsequently, six young adults were imaged with and without overt head motions of approximately this extent, with and without prospective motion correction using the Prospective Acquisition CorrEction (PACE) method, and with and without sensitivity map adjustments. Sensitivity map adjustments combined with PACE strongly protected against the artifacts of interest, as indicated by comparing three metrics of data quality (number of activated voxels, Dice coefficient of activation overlap, temporal standard deviation of baseline fMRI timeseries data) across the different experimental conditions. It is concluded that head motion in relation to a fixed multi-channel coil can adversely affect fMRI with prospective motion correction, and that sensitivity map adjustment can mitigate this effect at 3.0T.

Transmitter Subset Selection in FM-Based Passive Radar Networks for Joint Target Parameter Estimation

Passive radar network systems utilize multiple transmitters of opportunity and multichannel receivers to offer remarkable performance improvement due to the advantage of signal and spatial diversities. The frequency modulation (FM) commercial radio signals have become attractive for passive radar applications owing to their wide-spread availability and the favourable Doppler resolution. In this paper, two transmitter subset selection schemes, balancing the trade-off between target parameter estimation accuracy and infrastructure utilization, are proposed for FM-based passive radar networks. In the first, the subset size of selected transmitters employed in the estimation process is minimized by effectively selecting a subset of transmitters, such that the required target parameter estimation mean-square error (MSE) threshold is attained. In the second, an optimal subset of transmitters of a predetermined size $\kappa $ is selected, such that the estimation MSE is minimized. These problems are formulated as a knapsack problem, where the coherent Cramer–Rao lower bound (CRLB) is used as a performance metric. Both transmitter subset selection schemes are tackled with greedy selection algorithms by successively selecting transmitters so as to minimize the performance gap between the CRLB and a predetermined MSE threshold or a predetermined subset size. Numerical simulations demonstrate that the problem of transmitter subset selection is not only a function of the transmitted waveforms but also of the relative geometry between the target and the passive radar network systems, which leads to reductions in both computational load and signal processing costs.