Communication systems: An introduction to signals and noise in electrical communication

Spectrum scarcity and under-utilization of the apportioned spectral resources by the licensed users have led to the introduction of a new communication paradigm called Cognitive radio (CR). This allows the unlicensed users to exploit the existing wireless spectrum opportunistically. However, the technology offers many challenges and spectrum sensing is one among them. For instance, narrowband spectrum sensing techniques provide spectral opportunities over a narrow frequency range and cannot identify individual spectral opportunities over a wide frequency band. In order to achieve a higher opportunistic throughput, CR is required to have a broader spectral awareness that can potentially be achieved by exploiting more spectral opportunities over a wideband spectrum. Due to many challenges that wideband spectrum sensing offers, it has become a field of interest for many researchers. Conventional wideband spectrum sensing techniques using Nyquist sampling rates, for instance, are infeasible, as they require high-speed analog-to-digital converters (ADCs). A more innovative approach based on sub-Nyquist sampling has thus become an area of extensive research in both academia and industry. This paper is a careful study and evaluation of some of the major sub-Nyquist wideband sensing techniques. Further, we introduce two novel sub-Nyquist sensing techniques and finally some open research challenges in general.

Sub-Nyquist wideband spectrum sensing techniques for cognitive radio: A review and proposed techniques

The problem of wideband spectrum sensing/sampling in the sub-Nyquist domain is solved in this paper using sparse (low-density) binary-valued measurement matrices. Key objectives are (i) to achieve an efficient compression ratio, and (ii) improve the signal reconstruction performance. We propose a novel RF front-end with parallel branches that we have called Low-Density Wideband Converter (LDWC). We show that the LDWC implements a binary Low-Density Parity Check (LDPC) matrix as the compressive sensing (CS) measurement matrix. We evaluate, using an Information-Theoretic approach, the asymptotic bound on the required number of LDWC parallel branches for sparsity detection. We develop two new belief propagation (BP) algorithms that operate on the Tanner graph of the CS measurements. We have derived the first algorithm by assuming independence among the variable nodes (VNs) of the Tanner graph. For the second method, we have accounted for the joint probability distribution of the VNs. Analytical and simulated performance results prove the concepts of the LDWC and the proposed BP algorithms and quantify the attainment of objectives (i) and (ii) stated above.

Sub-Nyquist Spectrum Sensing of Sparse Wideband Signals Using Low-Density Measurement Matrices

https://ietresearch.onlinelibrary.wiley.com/doi/pdf/10.1049/iet-pel.2020.0181

Effects of the LED modelling on the output capacitance of power converters

Recently developed variable pulse width finite rate of innovation (VPW-FRI) theory offers an efficient way for sampling pulse streams with various shapes at the sub-Nyquist rate. Unfortunately, for real signals, noise, and model mismatch will induce inaccuracies to such scheme in the reconstruction process. In this paper, an optimization model-based sub-Nyquist sampling system for pulses with various shapes is proposed, which improves the performance of VPW-FRI scheme under noise and model mismatch situation. Since the real pulse streams with various shapes may be modeled as the sum of an unknown number of Lorentzian pulses and a model mismatch error signal, we build an optimization object function with the purpose of minimizing the energy of this model mismatch error signal. Then, for solving such function, we propose a two-channel sub-Nyquist sampling system to obtain a Fourier coefficients subset and several discrete samples from the input signal. We demonstrate that the best number of Lorentzian pulses and the corresponding pulse parameters can be found by using an improved particle swarm optimization algorithm. Finally, simulations with the electrocardiogram signals in MIT-BIH database have shown that the proposed method has better performance and stability than traditional VPW-FRI scheme.

Optimization Model Based Sub-Nyquist Sampling of Pulses With Various Shapes and Its Application to ECG Signals

A modulated wideband converter (MWC) has been introduced as a sub-Nyquist sampler that exploits a set of fast alternating pseudo random (PR) signals. Through parallel analog channels, an MWC compresses a multiband spectrum by mixing it with PR signals in the time domain, and acquires its sub-Nyquist samples. Previously, the ratio of compression was fully dependent on the specifications of PR signals. That is, to further reduce the sampling rate without information loss, faster and longer-period PR signals were needed. However, the implementation of such PR signal generators results in high power consumption and large fabrication area. In this paper, we propose a novel aliased modulated wideband converter (AMWC), which can further reduce the sampling rate of MWC with fixed PR signals. The main idea is to induce intentional signal aliasing at the analog-to-digital converter (ADC). In addition to the first spectral compression by the signal mixer, the intentional aliasing compresses the mixed spectrum once again. We demonstrate that AMWC reduces the number of analog channels and the rate of ADC for lossless sub-Nyquist sampling without needing to upgrade the speed or the period of PR signals. Conversely, for a given fixed number of analog channels and sampling rate, AMWC improves the performance of signal reconstruction.

Intentional Aliasing Method to Improve Sub-Nyquist Sampling System

The modulated wideband converter (MWC) is well-known for a sub-Nyquist wideband sampling capability based on compressed sensing (CS) theory. In this paper, our goal is to use the MWC as a base to design a sub-Nyquist radar electronic surveillance (ES) system. Our focus is then to extend the capabilities of the previous MWC system in order to meet the challenges, i.e., a very long acquisition time, a much larger simultaneous monitoring bandwidth, and a faster digital signal processing receiver. To this end, we present a new performance analysis framework and then a new digital domain receiver. The proposed performance analysis framework will be useful in comparing signal-acquisition performance of the proposed ES system with those of other sub-Nyquist receivers, including those of the classical Nyquist rate receivers, without resorting to extensive simulations. This framework can also be used to study the complex interplays of important system parameters of MWC, such as the sampling rate, the number of parallel channels, the period of Pseudo random sequence, and thus guides us in selecting the right system dimensions and parameters for desired performance. Radar surveillance application has its inherent needs for very long acquisition time and simultaneous monitoring of very large frequency range. To meet this challenge, a fast signal recovery system needs to be developed, so that radar signal logistics can be retained and recovered from compressed samples. We have proposed a split and synthesis process in which the radar signal recovery problem over a long signal acquisition time can be divided into many small CS signal recovery problems, and the solutions for small pieces are put together later on at the end. In addition, a sub-sampling method is proposed to have the multiple measurement vector problem complete signal recovery faster without noticeable performance loss.

Sanghun Im

Papers

Intentional Aliasing Method to Improve Sub-Nyquist Sampling System

A Sub-Nyquist Radar Electronic Surveillance System