The performance of multichannel random demodulator for multiband signals

Abstract

This paper propose multichannel random demodulator (MCRD) system, which is block structured sparsity based of multiband signals. It is a kind of compressive sampling system. Each channel of the system firstly multiplies analog signal by a band of random sequence, the product is put into integrator and sampled at a low rate. ADC of this system only need to generate one sample which greatly reduce ADC design complexity. Perfect recovery from the proposed samples is achieved by processing all measurements jointly with orthogonal matching pursuit (OMP) algorithm. The simulation analyzes the compression rate, the number of effective sub-carriers and conversion rate of pseudorandom number generator impact on this system. The results show that MCRD system ensure perfect reconstruction performance for multiband signal that is sampled below the Nyquist rate. Specially, conversion rate impact on system performance would be greatly for higher sampling rate, select the appropriate conversion rates can improve the system performace while reducing system complexity.

Analog Compressed Sensing for Sparse Frequency Shift Keying Modulation Schemes

“…The random demodulator used a chipping sequence, which are signals that are composed of rectangular pulses of length less than or equal to the Nyquist rate, to recover noisy and noiseless analog signals in band-limited systems by sampling the output multiple times with an ADC [23]- [27]. The multichannel random demodulator, which contains a bank of chipping sequences, was used to recover noisy OFDM signals, which reduced the ADC requirements but increased the overall amount of hardware [28]. The modulated wideband converter also used multiple periodic chipping sequences to reconstruct noisy multiband analog signals [29].…”

Application of Compressive Sensing in Cognitive Radio Communications: A Survey

“…The CR user has to decide if the primary signal is present (H 1 ) or not (H 0 ) from the observations y(n) collected over the sensing duration. Applicable to many types of compressible signals Number of filter taps must be known [50], [51] Measurement operator can be stored and applied efficiently Nonlinear reconstruction algorithm Easy implementation Random Convolution (RC) Available implicit algorithms based on the FFT Not applicable for all sparse/compressible signals [131], [132] Utilization of the known pulse in many physical systems The pulse structure may not be known Random Demodulator (RD) No need for a high-rate ADC Slow reconstruction process and high sampling delay [59], [60], [62] Robust against noise and quantization errors Only suitable for signals having a finite set of pure sinusoids Modulated Wideband Converter (MWC) Suitable for analog multiband signals Requires ideal low pass filters for reconstruction [61], [63] Parameter choice is insensitive to the exact bandwidth Imperfections of non-ideal lowpass filters Flexible control of sampling rate at each channel Limited number of bands and bandwidth Fast reconstruction process and low sampling delay Compressive Multiplexer (CMUX) It requires only one ADC rather than one per channel Undersampling factor is more restricted [53], [54] Flexibility to increase the total bandwidth Inherent non-idealities in the RF tuner Simpler calibration…”