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

A powerful forward error correction (FEC) scheme based on the serial concatenation of Bose-Chaudhuri-Hocquenghen (BCH) and low-density parity-check (LDPC) codes has been adopted by the second generation digital video broadcast (DVB-S2) standard due to their near Shannon limit performance. This paper proposes an efficient FEC encoder core to support different DVB-S2 codes for variable coding modulation (VCM) schemes of low earth orbit (LEO) satellite-ground communications. By exploring the properties of different concatenated BCH-LDPC codes and reusing the computation units and memories, the compatibility is achieved and the utilization of hardware resources is improved. Besides, parallel computing is performed to speed up the acquisition of check bits, which allows the encoding throughput to be increased. Hardware architecture implementation on Xilinx XC7K325t field programmable gate array (FPGA) shows that the encoding throughput rate of the proposed FEC encoder core can reach 1.19 Gb/s. And the overall data throughput is improved by 30.9% compared with the constant coding modulation (CCM) schemes.

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An Efficient FEC Encoder Core for VCM LEO Satellite-Ground Communications

We present a composite Compressed Sensing system for the acquisition and recovery of compressible signals, where a sparse Binary Sensing Matrix aids Sparsity Order Estimation, and a Gaussian Sensing Matrix aids reconstruction. The Binary Sensing Matrix is deterministic and is adapted according to the varying nature of the sparsity order. We estimate the sparsity order by exploiting the sparse structure of the Binary Sensing Matrix and the statistics of the obtained measurements. We refine the estimates of the sparsity order using a Kalman filter with a discrete Markov model that characterizes the sparsity order variation. A Binary Sensing Matrix-Aided Orthogonal Matching Pursuit is developed for faster recovery of compressible signals. Simulation results on real-world and synthetic data demonstrate the merits of the proposed sparsity order estimation and recovery methods compared to other existing methods. Our proposed methods are practical and recover compressible signals at least 25% faster than the existing methods.

https://ieeexplore.ieee.org/ielx7/6287639/9668973/09739658.pdf

Sparsity Order Estimation for Compressed Sensing System using sparse Binary Sensing Matrix

Some bands in the frequency spectrum have become overloaded and others underutilized due to the considerable increase in demand and user allocation policy. Cognitive radio applies detection techniques to dynamically allocate unlicensed users. Cooperative spectrum sensing is currently showing promising results. Therefore, in this work, we propose a cooperative spectrum detection system based on a residual neural network architecture combined with feature extractor and random forest classifier. The objective of this paper is to propose a cooperative spectrum sensing approach that can achieve high accuracy in higher levels of noise power density with less unlicensed users cooperating in the system. Therefore, we propose to extract features of the sensing information of each unlicensed user, then we use a random forest to classify if there is a presence of a licensed user in each band analyzed by the unlicensed user. Then, information from several unlicensed users are shared to a fusion center, where the decision about the presence or absence of a licensed user is accomplished by a model trained by a residual neural network. In our work, we achieved a high level of accuracy even when the noise power density is high, which means that our proposed approach is able to recognize the presence of a licensed user in 98% of the cases when the evaluated channel suffers a high level of noise power density (−134 dBm/Hz). This result was achieved with the cooperation of 10 unlicensed users.

Deep Cooperative Spectrum Sensing Based on Residual Neural Network Using Feature Extraction and Random Forest Classifier.

Low Density Parity Check (LDPC) codes have gained lot of importance in the channel coding arena, because these can provide excellent performance close to Shannon limit & can easily beat the best known turbo codes for large block lengths. This paper explains generic algorithms for hardware efficient implementation of Encoder & Decoder for LDPC codes adopted by Digital Video Broadcast-Satellite-Second Generation (DVB-S2) standard. Low complexity & high throughput architectures have been proposed for LDPC Encoder & Sum Product Algorithm based LDPC Decoder. MATLAB & HDL simulation results are presented for proposed encoder & decoder architectures. Satellite link test results of a scaled down FPGA based implementation with IESS satellite modem are also presented.

Efficient implementation of Low Density Parity Check codes for satellite ground terminals

Beacon receiver with better power estimation accuracy is the need for the propagation model development for Ka Band propagation studies experiment. This paper proposes the design philosophy of Digital Receiver, signal processing steps & design of window to achieve the beacon power estimation accuracy of +/- 0.5 dB with measurement rate of 1 Hz up to 30 dBHz C/N0 of a single channel Ka-band beacon receiver working with IPSTAR Ka-band on-board beacon. A custom window with intelligent signal processing steps resulted into improved beacon power measurement accuracy of +/- 0.5 dB over a large programmable bandwidth which is better than the available beacon receiver developed so far under similar signal conditions. A performance comparison with different window functions is also presented. The present receiver design works in closed loop with L-band downconverter to achieve better power estimation accuracy and suitable for unmanned operation. The designed receiver is tested within lab setup with simulated input and also tested in field with 20.199827 GHz beacon onboard IPSTAR.

Digital receiver based Ka Band beacon receiver for improved beacon power estimation

— This paper proposes a Compressive Signal Processing Receiver based on Modulated Wideband Converter (MWC) sensing architecture and power spectrum estimation method in compressive domain for interference detection and filtering. The proposed receiver uses the signal processing method capable of reconstructing the unknown power spectrum of a signal from the MWC output samples in compressive domain. Interference detection achieved with respect to ITU or service provider defined PFD limits for operating frequency band on per Hz basis for a given receive system. Interference filtering by a novel method of compressive domain filtering which exploits the conjugate symmetry of DFT matrix for modifying the sensing matrix to achieve the sub-band filtering in compressive domain for interference removal is proposed. Proposed receiver is suitable for next generation of wideband satellite transponders. Matlab simulation to verify the proposed algorithm and results are presented.

Interference detection & filtering in satellite transponder

This paper proposes a compressive signal processing (CSP) receiver for detection and filtering of narrowband jamming signal within a wideband satellite transponder. The proposed receiver utilizes Modulated Wideband Converter (MWC) sensing architecture to detect the presence of the jamming signal using sub-Nyquist sampling. An Automatic Frequency Control (AFC) technique is used to estimate and track the center frequency of interfering signal. The interfering signal is removed using an on-board notch filter with programmable center frequency and bandwidth. The MWC technique combined with the adaptive notch filter is used to limit the impact of jamming signal without affecting the ongoing communication within the filtered sub band. The proposed scheme provides for a computationally efficient implementation of the next-generation wideband satellite transponders that are robust to narrowband jamming.

Chandra Prakash

Papers

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

Efficient implementation of Low Density Parity Check codes for satellite ground terminals

Digital receiver based Ka Band beacon receiver for improved beacon power estimation

Interference detection & filtering in satellite transponder

Narrow Band Jamming Detection & Filtering in Satellite Transponder