Multiband Signals Research Articles

Photonics-based simultaneous measurement of distance and velocity using multi-band LFM microwave signals with opposite chirps.

We propose and demonstrate a photonics-based method for simultaneously measuring distance and velocity by adopting multi-band linear-frequency-modulated microwave signals with opposite chirps as transmitted signals. A dual-drive Mach-Zehnder modulator and photodetector (PD) are used to mix transmitted and echo signals. By analyzing the electrical spectra of the electrical signals generated by the PD, distance and the magnitude and direction of velocity can be derived simultaneously, which is significant for real-time radar applications. Proof-of-concept experiments using the proposed approach are also presented. The measured relative errors for distance and velocity are less than 0.005% and 0.59%, respectively.

Network-level multiband signal coordination scheme based on vehicle trajectory data

Traffic signal coordination has long been recognized as an efficient way of achieving smooth urban traffic. This paper proposes a network-level multiband signal coordination (NMBSC) scheme to provide progression bands for major traffic streams extracted from vehicle trajectory data in urban road networks. A mixed-integer linear programming (MILP) formulation is proposed to model the NMBSC problem considering both bandwidths and interband connectivity. A decomposition-based heuristic is adopted to find a near-optimal solution to the MILP in a real-size network. Extensive numerical experiments are conducted using the VISSIM simulator to evaluate the performance of the proposed scheme by comparing it with some existing approaches. Simulation results show that the timing plan derived from NMBSC can improve the network-level performance under both uniform and non-uniform demand patterns in terms of average delay and average number of stops. Sensitivity analyses are also conducted to illustrate the robustness of the proposed method with limited trajectory data.

Multiband sparse signal reconstruction through direct one-bit sampling.

Compressive sensing (CS) aims at decreasing sampling rate to reduce the needed number of samples. On the other hand, the one-bit CS is proposed to reduce the quantization bit. In this paper, we proposed a one-bit CS system aiming at acquiring the multiband sparse signal which is a very popular signal model in wireless communication, especially in cognitive radio. This proposed system, called direct one-bit sampler (DOS), is simple in hardware implementation, and it consists of only a comparator working at Nyquist rate. In the stage of signal reconstruction, it can be equivalent to a special multicoset sampler which is a popular scheme in CS. Moreover, we propose an enhanced binary iterative hard thresholding (BIHT), a popular one-bit recovery algorithm, to deal with the multiple measurement vectors in the one-bit CS framework. Both the theoretical model and experimental results demonstrate that the proposed DOS, with the help of the enhanced BIHT, can not only accurately recover the positions of active subbands of the multiband sparse signal but also roughly estimate the power of each active subband.

Successive‐phase correction calibration method for modulated wideband converter system

The modulated wideband converter (MWC) is a sub-Nyquist sampling system recently proposed for sampling sparse multi-band signals. However, due to non-ideal analogue components, transfer-matrix calibration is required for the successful reconstruction. In practice, it is difficult to control the phases of the consecutive sinusoidal inputs precisely which are used to calibrate the transfer-matrix. Here, the authors propose a method of transfer-matrix calibration for the MWC with digital expanded channels when the phases of the sinusoidal inputs are unknown. As the oblique elements of the MWC transfer-matrix are equal, the authors can estimate the unknown phases first. The authors then obtain the actual transfer-matrix by phase correction. Finally, the validity of this method is verified by simulation and hardware experiment. The system performance is measured by computing the signal-to-noise ratio and the mean-square error between the original and reconstructed signals.

A Novel Single Feedback Architecture With Time-Interleaved Sampling for Multi-Band DPD

With the increasing demand for multi-band signal transmission, power amplifier (PA) linearization has applied a variety of multi-band predistortion methods. However, as the number of carrier increases, the feedback path will be complex and high-sampling rate analog-to-digital converters (ADCs) will significantly increase the system cost. In this letter, to simplify the system structure and reduce the sampling rate for ADCs, a novel DPD method based on a single feedback architecture with time-interleaved sampling method is proposed. The time-interleaved sampling signals based on the low-sampling rate can be obtained by two ADCs, whose under-sampling factors are coprime. Then, the multi-band signals can be reconstructed due to its band sparsity to calculate the coefficients of the predistortion. If the frequency difference of the bands are bigger than the BW of the ADCs, the multi-band signals can be still reconstructed. Compared with previous approaches regarding the multi-band DPD, the proposed method can obtain good linearization performance and reduce the feedback sampling rate. Experimental results with the multi-band signals are presented to show the improvements.

Automatic detection of sleep apnea events based on inter-band energy ratio obtained from multi-band EEG signal.

Sleep apnea is a potentially serious sleep disorder characterised by abnormal pauses in breathing. Electroencephalogram (EEG) signal analysis plays an important role for detecting sleep apnea events. In this research work, a method is proposed on the basis of inter-band energy ratio features obtained from multi-band EEG signals for subject-specific classification of sleep apnea and non-apnea events. The K-nearest neighbourhood classifier is used for classification purpose. Unlike conventional methods, instead of classifying apnea patient and healthy person, the objective here is to differentiate apnea and non-apnea events of an apnea patient, which makes the task very challenging. Extensive experimentation is carried out on EEG data of several subjects obtained from a publicly available database. Comprehensive experimental results reveal that the proposed method offers very satisfactory classification performance in terms of sensitivity, specificity and accuracy.

An Optimized Channel Selection Method Based on Multifrequency CSP-Rank for Motor Imagery-Based BCI System.

Background Due to the redundant information contained in multichannel electroencephalogram (EEG) signals, the classification accuracy of brain-computer interface (BCI) systems may deteriorate to a large extent. Channel selection methods can help to remove task-independent electroencephalogram (EEG) signals and hence improve the performance of BCI systems. However, in different frequency bands, brain areas associated with motor imagery are not exactly the same, which will result in the inability of traditional channel selection methods to extract effective EEG features. New Method To address the above problem, this paper proposes a novel method based on common spatial pattern- (CSP-) rank channel selection for multifrequency band EEG (CSP-R-MF). It combines the multiband signal decomposition filtering and the CSP-rank channel selection methods to select significant channels, and then linear discriminant analysis (LDA) was used to calculate the classification accuracy. Results The results showed that our proposed CSP-R-MF method could significantly improve the average classification accuracy compared with the CSP-rank channel selection method.

Blind multiband signal detection with multirate snapshots in low SNRs

A novel multirate sampling scheme is proposed for wideband spectrum sensing. The signal is subsampled by shifted parallel channels working at same rate. However, the rate switches among several rates between neighboring snapshots where the spectrum aliases at different frequencies. Basing on this, we propose a blind multiband signal detection algorithm. In this method, we construct frequency locator polynomials (FLPs) to locate the nonzero components of the original spectrum from the subsampled signal for each snapshot. We compute the FLP evaluations, which differs in the snapshots sampled with distinct rates. The evaluations are statistically described by the complex Gaussian distributions for each detected frequency. The detection is then completed by fusing these probabilities contributed by the latest several snapshots. Simulation results show that the proposed algorithm can efficiently lower the false alarms.

Multiband signal acquisition using improved modulated wideband converter

In this study, the authors stress the problem of sampling and recovering sparse wideband signals. A new methodology for sparse multiband signal acquisition using sinusoidal frequency modulation (SMF) multiplier-based modulated wideband converter (MWC) is presented. The harmonic property of the SMF signals enables sparse multiband signal to be precisely recovered from the sub-Nyquist measurements without any high-speed devices. The proposed architecture offers very close recovery accuracy to the existing pseudorandom binary sequences (PRBS)-based MWC in the absence of the prior knowledge of sparse wideband signals, while it provides more accurate recovery performance than the PRBS-MWC in the presence of prior information concerning sparsity. Besides, the proposed architecture is easier for hardware implementation than the PRBS-MWC. The experimental results demonstrate the effectiveness of the proposed scheme.

Hybrid mmWave MIMO-OFDM Channel Estimation Based on the Multi-Band Sparse Structure of Channel

This paper presents a hybrid channel estimator for multiple-input multiple-output orthogonal frequency-division multiplexing mmWave systems based on the sparse nature of the angular channel and leveraging the compressed sensing (CS) tools. The angular support is recovered without any discretization by treating the angular channel in a continuous framework which resolves the limited angular resolution of the discrete sparse channel models used in the previous CS-based channel estimators. The power leakage problem is also addressed by modeling the continuous angular channel as a multi-band signal with the bandwidth of each sub-band being proportional to the amount of power leakage caused by the limited antenna array length. The RF combiner is implemented using a network of low-power switches for antenna subset selection based on a multi-coset sampling pattern. Simulation results validate the low reconstruction error and good performance of the proposed channel estimator.

A Novel CAP-WDM-PON Employing Multi-Band DFT-Spread DMT Signals Based on Optical Hilbert-Transformed SSB Modulation

We propose and experimentally demonstrate a novel carrier-less amplitude/phase modulation wavelength-division-multiplexing passive optical network (CAP-WDM-PON) with its bandwidth efficiency enhanced by using a multi-band discrete Fourier transform spread (DFT-spread) discrete multi-tone (DMT) signal based on Hilbert-transformed single-sideband (HB-SSB) modulation. The DFT-spread technique is utilized to extend orthogonal subcarriers, which effectively restrain the high-peak signal formation probability, and thereby, it can significantly reduce the high peak-to-average power ratio of the DMT signal. The proposed optical line terminal (OLT) in CAP-WDM-PON suggests five downlink optical subcarriers. For each optical subcarrier, there are as many as $6\times20$ Gb/s SSB digital DMT channels CAP modulated on them, which can be efficiently generated by using only one set of optical transmitter hardware. The experimental results show that the proposed system is capable of accommodating $5\times6$ channels (20 Gb/s per channel) within an optical distribution network (ODN) loss budget of 19.4 dB, and there is a 2.4-dB ODN loss budget improvement by utilizing the DFT-spread scheme. More importantly, the proposed spectral- and bandwidth-efficient CAP-WDM-PON system requires six times less downlink optical transmitters at the OLT, which can be translated into savings in cost, power consumption, and footprint. In addition, key techniques, such as pre-equalization for high-frequency components and digital HB-SSB modulation format for generation of multi-band DMT signals, are also studied.

A ROF Access Network for Simultaneous Generation and Transmission Multiband Signals Based on Frequency Octupling and FWM Techniques

We report a radio-over-fiber (ROF) access network with multiple high-repetive frequency mm-wave signals generation utilizing a dual-parallel Mach-Zehnder modulator (DP-MZM) and an semiconductor optical amplifier (SOA) for multiple base stations (BSs). In the scheme, at the central station (CS), signal and pump with frequency interval of 8fRF are generated by properly adjusting the parameters of the DP-MZM. After FWM in a SOA, new converted optical signals are obtained. Two tones of the optical signals are selected by using tunable optical filter (TOF), which are then sent into a photodiode (PD) to generate multiple mm-wave signals with different frequencies (8fRF, 16fRF, and 24fRF) for different BSs. Based on the proposed scheme, the mm-wave signals with frequencies of 20, 40, and 60 GHz carrying 2.5 Gb/s signal by a 2.5GHz RF signal have been generated by numerical simulation. Simulation results show that the proposed ROF system architecture with multiple-frequency millimeter-wave signals generation serving multiple BSs can work well. This scheme can raise the capacity of ROF system, reduce the requirement of the repetitive frequency of the driven RF signal, and support multiple mm-wave wireless access for BSs.

All-Digital Blind Background Calibration Technique for Any Channel Time-Interleaved ADC

This paper proposes a novel digital adaptive blind background calibration technique for the gain, timing skew, and offset mismatch errors in a time-interleaved analog-to-digital converter (TI-ADC). Based on the frequency-shifted basis functions generated only from the measured TI-ADC output, the three mismatch errors can be represented, extracted, and then subtracted from the TI-ADC output adaptively, which is quite different from the conventional methods (e.g., using a bank of adaptive FIR filters). The advantage of the proposed technique is that it only needs to know the measured output signal and the TI-ADC channel number, without needing any additional information; and it is applicable to any TI-ADCs with no limitations to the channel number, sub-ADC sampling rate, signal type, and so on. Specifically, the frequency-shifted signal generator employs the Hilbert transform and does not need extra finite impulse response filters. Extensive simulation and measurement results have been presented to demonstrate the effectiveness and superiority of the proposed technique, for single-tone, multi-tone, wideband modulated, and multi-band modulated signals. In the measurement for a 32GS/s 16-channel TI-ADC system, it shows that the proposed technique can improve the effective number of bit by 2 ~ 3 bit and the SFDR by 30 ~ 35 dB.

Wavenumber-Domain Multiband Signal Fusion With Matrix-Pencil Approach for High-Resolution Imaging

In this paper, a wavenumber-domain matrix-pencil-based multiband signal fusion approach was proposed for multiband microwave imaging. The approach proposed is based on the Born approximation of the field scattered from a target resulting in the fact that in a given scattering direction, the scattered field can be represented over the whole frequency band as a sum of the same number of contributions. Exploiting the measured multiband data and taking advantage of the parametric modeling for the signals in a radial direction, a unified signal model can be estimated for a large bandwidth in the wavenumber domain. It can be used to fuse the signals at different subbands by extrapolating the missing data in the frequency gaps between them or coherently integrating the overlaps between the adjacent subbands, thus synthesizing an equivalent wideband signal spectrum. Taking an inverse Fourier transform, the synthesized spectrum results in a focused image with improved resolution. Compared with the space–time domain fusion methods, the proposed approach is applicable for radar imaging with the signals collected by either collocated or noncollocated arrays in different frequency bands. Its effectiveness and accuracy are demonstrated through both numerical simulations and experimental imaging results.

Blind Sub-Nyquist Spectrum Sensing With Modulated Wideband Converter

Rooted in the compressed sensing theory, sub-Nyquist spectrum sensing (SNSS) has been considered as a promising approach to dealing with difficulties and limitations of conventional wideband spectrum sensing in cognitive radio (CR) networks. Most existing SNSS methods require some prior knowledge of the monitored frequency bands, such as the spectrum occupancy/sparsity level and/or the noise power, to determine a termination condition used by an underlying iterative signal recovery process. However, such prior knowledge may be difficult to acquire in practical CR scenarios. To address this problem, we propose a blind SNSS algorithm, referred to as the residual energy ratio based detector (RERD), which bypasses the need for the above-mentioned prior knowledge and performs spectrum sensing in a more autonomous way. The RERD algorithm, which is based on the modulated wideband converter (MWC) sub-Nyquist sampling framework, employs energy ratios of adjacent channels of the MWC as test statistics. We derive closed-form expressions of the decision threshold and the false alarm probability following the Neyman-Pearson criterion. Simulation results show that, without requiring the aforementioned prior knowledge, the RERD algorithm can accurately determine the support of a multiband signal contaminated by background noise in a wide range of signal-to-noise ratio. Moreover, the RERD algorithm is shown to be robust to a range of sparsity orders and different number of sampling channels.

Determination of the minimum sampling frequency in bandpass sampling by geometric approach and geometric programming

This paper presents a simple and fast approach to find a minimum sampling frequency for multi-band signals. Instead of neighbor and boundary conditions, constraints on the sampling frequency were derived by using the geometric approach to the bandpass sampling theorem. Reformulation of the constraints on the minimum sampling frequency enabled to represent the problem as an optimization problem which was structured by the geometric programming and mixed-integer nonlinear programming methods. The convex optimization problem was then solved by the proposed algorithm applying interior point approach in the line search framework. It was demonstrated that this unified structure directly linked the geometric approach of the bandpass sampling theorem to the optimization problem. The proposed method was verified through numerical simulations in terms of the minimum sampling frequency and the computational efficiency. Results illustrated the feasibility of the geometric approach and the proposed algorithm in the determination of the minimum sampling frequency by providing the savings in the number of iterations and the decrease in the valid minimum sampling frequency.

A Sub-Nyquist Sampling Algorithm for Fractional Bandlimited Signals Based on AIC

Sampling theorems for fractional Fourier bandlimited signals have been developed due to the advantages of the fractional Fourier transformation. It may be difficult to put those theories into practice, because the sampling rate must be no less than twice the maximum fractional “frequency” of signal. This brief investigates sampling theorems of the fractional bandlimited signal, then introduces a sub-Nyquist sampling method which extends the modulated wideband converter to the fractional Fourier domain by a fractional shift invariant operator. The proposed method can effectively reduce the sampling rate for the fractional multiband signal. The sampled signals can be recovered by a matching pursuit algorithm. Simulation results verify the effectiveness of the proposed algorithm by discussing the robustness, recovery accuracy, and influence of the fractional order.

Sparse multiband signal spectrum sensing with asynchronous coprime sampling

Cognitive radio requires spectrum sensing over a broad frequency band and leads to a high sampling rate. In this paper, we propose an asynchronous coprime sampling technique for capturing and reconstructing of sparse multiband signals that occupy a small part of a given broad frequency band. The band locations of signal are not known a priori. In this proposed approach, we use a sub-Nyquist sampling rate by exploiting a low-dimensional representation of the original high-dimensional signal. A common input sparse multiband signal is digitized using a pair of uniform samplers, which are (not necessarily synchronously) clocked at coprime sampling rates. The captured samples are then re-sequenced and the multi-coset signal processing algorithm is employed. We derive the system model in the frequency domain, where the phase mismatch is compensated. Compared to the conventional multi-coset sampling, the proposed approach needs fewer samplers and does not require synchronous clock phase. Simulation results are provided to demonstrate the feasibility and effectiveness of the proposed asynchronous coprime sampling for sparse multiband signal.

Non-Uniform Wavelet Sampling for RF Analog-to-Information Conversion

Feature extraction, such as spectral occupancy, interferer energy and type, or direction-of-arrival, from wideband radio-frequency (RF) signals finds use in a growing number of applications as it enhances RF transceivers with cognitive abilities and enables parameter tuning of traditional RF chains. In power and cost limited applications, e.g., for sensor nodes in the Internet of Things, wideband RF feature extraction with conventional, Nyquist-rate analog-to-digital converters is infeasible. However, the structure of many RF features (such as signal sparsity) enables the use of compressive sensing (CS) techniques that acquire such signals at sub-Nyquist rates; while such CS-based analog-to-information (A2I) converters have the potential to enable low-cost and energy-efficient wideband RF sensing, they suffer from a variety of real-world limitations, such as noise folding, low sensitivity, aliasing, and limited flexibility. This paper proposes a novel CS-based A2I architecture called non-uniform wavelet sampling. Our solution extracts a carefully-selected subset of wavelet coefficients directly in the RF domain, which mitigates the main issues of existing A2I converter architectures. For multi-band RF signals, we propose a specialized variant called non-uniform wavelet bandpass sampling (NUWBS), which further improves sensitivity and reduces hardware complexity by leveraging the multi-band signal structure. We use simulations to demonstrate that NUWBS approaches the theoretical performance limits of $\ell_{1}$ -norm-based sparse signal recovery. We investigate hardware-design aspects and show ASIC measurement results for the wavelet generation stage, which highlight the efficacy of NUWBS for a broad range of RF feature extraction tasks in cost- and power-limited applications.

A Modified All-Digital Polar PWM Transmitter

This paper presents an all-digital polar pulsewidth modulated (PWM) transmitter for wireless communications. The transmitter combines baseband PWM and outphasing to compensate for the amplitude error in the transmitted signal due to aliasing and image distortion. The PWM is implemented in a field programmable gate array (FPGA) core. The outphasing is implemented as pulse-position modulation using the FPGA transceivers, which drive two switch-mode power amplifiers fabricated in 130-nm standard CMOS. The transmitter has an all-digital implementation that offers the flexibility to adapt it to multi-standard and multi-band signals. As the proposed transmitter compensates for aliasing and image distortion, an improvement in the linearity and spectral performance is observed as compared with a digital-PWM transmitter. For a 20-MHz LTE uplink signal, the measurement results show an improvement of up to 6.9 dBc in the adjacent channel leakage ratio.