Secret key extraction is a crucial issue in physical layer security and a less complex and, at the same time, a more robust scheme for the next generation of 5G and beyond. Unlike previous works on this topic, in which Orthogonal Frequency Division Multiplexing (OFDM) sub-channels were considered to be independent, the effect of correlation between sub-channels on the secret key rate is address...

In this paper, a novel contrast enhancement technique for contrast enhancement of a low-contrast satellite image has been proposed based on the singular value decomposition (SVD) and discrete cosine transform (DCT). The singular value matrix represents the intensity information of the given image and any change on the singular values change the intensity of the input image. The proposed techniq...

This paper proposes a multiresolution form of the singular value decomposition (SVD) and shows how it may be used for signal analysis and approximation. It is well-known that the SVD has optimal decorrelation and subrank approximation properties. The multiresolution form of SVD proposed here retains those properties, and moreover, has linear computational complexity. By using the multiresolutio...

Hybrid analog-digital beamforming design based on the channel state information (CSI) for wideband millimeter wave channel models receives attention in recent years. However, without the CSI, most previously proposed beamforming methods, such as singular value decomposition (SVD)-based ones, are almost infeasible due to the complexity of both the channel estimation and SVD for large antenna arr...

In this paper, signal vector based detection (SVD) algorithm for spatial modulation (SM) is modified to achieve a near maximum-likelihood (ML) performance and reduces the complexity compared to ML. First, the proposed low-complexity SVD (LC-SVD) algorithm orders the antenna index list based on the angle between the received vector y and the channel vector j h , and then it estimates symbol by c...

In this paper, a modified adaptive sparse channel estimator based on singular value decomposition (SVD) for OFDM systems is proposed. The conventional adaptive sparsity matching pursuit (ASMP) based compressive channel estimation has bad anti-noise performance, although not needing the information of sparsity. Because using the SVD to modify the measurement matrix of CS can improve the robustne...

We derive coupled on-line learning rules for the singular value decomposition (SVD) of a cross-covariance matrix. In coupled SVD rules, the singular value is estimated alongside the singular vectors, and the effective learning rates for the singular vector rules are influenced by the singular value estimates. In addition, we use a first-order approximation of Gram-Schmidt orthonormalization as ...

Singular-value decomposition (SVD)-based multiple-input multiple-output (MIMO) systems have attracted a lot of attention in the wireless community. However, applying SVD to frequency-selective MIMO channels results in unequally weighted single-input single-output (SISO) channels requiring complex resource allocation techniques for optimizing the channel performance. Therefore, a different appro...

We investigate a multiple input multiple output (MIMO) relay broadcast channel (RBC) with full cooperation between users. A beamforming and combining design is proposed based on singular value decomposition (SVD) of the channel matrix between users. Then, users can simultaneously relay each other’s information on the same frequency band with zero interference from each antenna’s transmit signal...

We investigate if well-known LGN ion channel properties can facilitate information-theoretic optimal coding through temporal decorrelation; and if so, whether the degree of temporal decorrelation can be adapted dynamically to ensure such optimization at longer time scales. Signi cant temporal decorrelation for time lags above 50 ms is achievable in a LGN cell model with inputs generated from na...