Spectrum Sensing Using Co-Prime Array Based Modulated Wideband Converter

Abstract

As known to us all, it is challenging to monitor wideband signals in frequency domain due to the restriction of hardware. Several practical sampling schemes, such as multicoset sampling and the modulated wideband converter (MWC), have been proposed. In this work, a co-prime array (CA) based modulated wideband converter (MWC) spectrum sensing method is suggested. Our proposed method has the same sampling principle as the MWC but has some advantages compared to MWC. Firstly, CA-based MWC is an array-based MWC system. Each sensor is usually corrupted by independent noise for an array system which can be used for noise averaging, while all channels in conventional MWC have the same receiving noise. Secondly, by incorporating the co-prime array, we can estimate the power spectrum of signal directly employing its second-order statistical properties. Moreover, the system minimal sampling rate can be reduced further because of the reduction of sampling channels. Simulation results show that our method has better performance than traditional methods.

Detection and Frequency Estimation of Frequency Hopping Spread Spectrum Signals Based on Channelized Modulated Wideband Converters

“…where x ir (n) and h r (n) are defined in (12) and (13). Based on (18), the source signal and low-pass filter is decimated. This structure can be reused by all m channels through an interleaver and a summer, which is shown in Figure 5.…”

“…If these data conform to the restricted isometry property (RIP) principle, the original signals can be recovered perfectly with an overwhelmingly high probability. Some methods have been presented to apply this theory in practice, such as the Random Demodulation (RD) method [12,13], Multi-coset method [14], Modulated Wideband Converter (MWC) [15][16][17][18][19] and so on. Since the FHSS signals are obviously sparse in the time-frequency domain, using the CS theory for the recovery, estimation and detection of FHSS signals is a hot spot of recent research.…”

A Sub-Nyquist Sampling Digital Receiver System Based on Array Compression

“…In 2010, the MWC structure was proposed by Mishali and Eldar to achieve sub-Nyquist sampling [7]. However, most research on MWC structures has focused on signal reconstruction, and few studies have considered the direct processing of the CS data [8][9][10]. In [11], we extended the MWC to the discretetime domain and proposed a new CS-based wideband digital receiver, where the CS data were processed directly to reduce the complexity.…”

Joint carrier frequency and DOA estimation using a modified ULA based MWC discrete compressed sampling receiver

“…The newly introduced CS theory brings us a new concept on the low‐rate data acquisition [7–9]. Due to the sparsity of the radar signals in frequency domain [10], CS based analogue‐to‐information converter techniques such as random demodulator [11], quadrature compressed sampling (QuadCS) [12, 13] and modulated wideband converter (MWC) [14–16] are proposed to alleviate the pressure of signal acquisition. QuadCS system is aiming at the acquisition of band‐pass signals, which needs to know the intermediate frequency and bandwidth of the radar signals in prior [12, 13].…”

“…QuadCS system is aiming at the acquisition of band‐pass signals, which needs to know the intermediate frequency and bandwidth of the radar signals in prior [12, 13]. However, MWC is proposed to acquire the sparse multi‐band signals which are the same as what transmitting in the passive radar and electronic reconnaissance environment [14–16]. Inspired by MWC structure, we propose a new MWC discrete compressed sampling (CS) receiver in [17] and propose an energy detection method to complete the sparse signal detection task [18].…”