Multi-antenna spectrum sensing by exploiting spatio-temporal correlation

Abstract

In this paper, we propose a novel mechanism for spectrum sensing that leads us to exploit the spatio-temporal correlation present in the received signal at a multi-antenna receiver. For the proposed mechanism, we formulate the spectrum sensing scheme by adopting the generalized likelihood ratio test (GLRT). However, the GLRT degenerates in the case of limited sample support. To circumvent this problem, several extensions are proposed that bring robustness to the GLRT in the case of high dimensionality and small sample size. In order to achieve these sample-efficient detection schemes, we modify the GLRT-based detector by exploiting the covariance structure and factoring the large spatio-temporal covariance matrix into spatial and temporal covariance matrices. The performance of the proposed detectors is evaluated by means of numerical simulations, showing important advantages over existing detectors.

Fading correlation modelling for troposcatter microwave propagation in array antenna detection applications

“…There are two ways to deal with the temporal correlation: one is to extract the temporal uncorrelated data with an interval longer than the correlated delay length according to the results in Section 3.4, and therefore eliminate the effect of temporal correlation. Another is to extract temporal correlated blocks of array snapshots and obtain independent realisations [31][32][33]; enough intervals of blocks are indispensable to ensure the independence between adjacent realisations. The first extraction method results in temporal uncorrelated data and the second extraction method obtains independent temporal correlated realisations.…”

“…K × K denotes the K × K temporal correlation matrix with element at the ith row and the jth column as ρ Aτ i, j , ρ Aτ denotes the fading correlation amplitude coefficient along time delay as in Section 3.4, time delay τ = i − j / f s , f s denotes the sample rate. Detection of spatial, temporal correlated signals has been well studied in array signal processing, especially in spectrum sensing, and are significantly benefited from a priori signal correlation knowledge [31][32][33][34][35]. Additionally, received signal can be handled in frequency domain.…”

Target detection for a kind of troposcatter over‐the‐horizon passive radar based on channel fading information

“…However, troposcatter channel fading occurs in both the spatial and temporal domains; hence, the temporal correlation properties of captured array signals must be considered. Several array signal sensing techniques have been proposed in [14–17] that consider the temporal correlation in received multi‐channel signals. In [14], two reduced‐complexity methods were proposed to deal with temporally correlated signals over multipath fading channels, where a priori information on the channel gain and phase was exploited to build the detectors.…”

“…In [16], several detectors were developed exploiting spatial and temporal correlations of target signals with different combination methods, while the correlations were considered as second‐order features of the target signals without any assumptions for the channel responses. In [17], the spatial–temporal correlations between the received signals of a multi‐antenna system were exploited, and several modified GLRT detectors were proposed to deal with the contradiction between limited sample support and high dimensionality. The notion of factoring the large spatial–temporal covariance matrix into independent spatial and temporal covariance matrices was considered, but only the persymmetric features of the spatial–temporal covariance matrix were used.…”

Kronecker-Based Fusion Rule for Cooperative Spectrum Sensingwith Multi-Antenna Receivers

“…In these techniques we propose the decomposition of a large covariance matrix into small matrices by exploiting the inter-receiver and inter-antenna spatial structures in the received observations. To be more specific, the two detectors use single-pair Kronecker product (SPKP) [18] and multi-pairs Kronecker product (MKPK) [19] of the inter-receiver and inter-antenna covariance matrices. By doing so, the demand for large sample size reduces, and hence, this results in an enhanced robustness against the small sample support.…”