Face Drawing GAN by Channel Attention and Matrix Product Attention

In this work we present a semi-blind algorithm for the estimation of a flat-fading MIMO channel matrix H. The algorithm is based on a decomposition of the channel matrix H as the product of a whitening matrix W and a unitary matrix Q. The whitening matrix can be estimated blindly from all received data. Several techniques are then suggested to estimate the optimum rotation matrix from training samples. Since it uses both blind and training data, the algorithm is semi-blind in nature. Theoretical results show that estimation of the channel matrix based on estimating only the Q matrix from pilot data can perform more efficiently than estimating H directly from the pilot data. However, performance of the technique depends on the accuracy with which W is estimated and is found to typically perform well in low SNR and fading environments.

This paper proposes a whitening-rotation (WR)-based algorithm for semi-blind estimation of a complex flat-fading multi-input multi-output (MIMO) channel matrix H. The proposed algorithm is based on decomposition of H as the matrix product H=WQ/sup H/, where W is a whitening matrix and Q is unitary rotation matrix. The whitening matrix W can be estimated blind using only received data while Q is estimated exclusively from pilot symbols. Employing the results for the complex-constrained Cramer-Rao Bound (CC-CRB), it is shown that the lower bound on the mean-square error (MSE) in the estimate of H is directly proportional to its number of unconstrained parameters. Utilizing the bounds, the semi-blind scheme is shown to be very efficient when the number of receive antennas is greater than or equal to the number of transmit antennas. Closed-form expressions for the CRB of the semi-blind technique are presented. Algorithms for channel estimation based on the decomposition are also developed and analyzed. In particular, the properties of the constrained maximum-likelihood (ML) estimator of Q for an orthogonal pilot sequence is examined, and the constrained estimator for a general pilot sequence is derived. In addition, a Gaussian likelihood function is considered for the joint optimization of W and Q, and its performance is studied. Simulation results are presented to support the algorithms and analysis, and they demonstrate improved performance compared to exclusively training-based estimation.

In this letter, a pre-processed lattice reduction (PLR) scheme is developed for the lattice reduction aided (LRA) detection of multiple input multiple-output (MIMO) systems in spatially correlated channel. The PLR computes the LLL-reduced matrix of the equivalent matrix, which is the product of the present channel matrix and unimodular transformation matrix for LR of spatial correlation matrix, rather than the present channel matrix itself. In conjunction with PLR followed by recursive lattice reduction (RLR) scheme [7], pre-processed RLR (PRLR) is shown to efficiently carry out the LR of the channel matrix, especially for the burst packet message in spatially and temporally correlated channel while matching the performance of conventional LRA detection.

The generalized frequency division multiplexing with index modulation (GFDM-IM) is a recently developed multi-carrier technique, which has the signal feature that only part of subcarriers are activated. In this paper, the message passing (MP)-based signal detection of GFDM-IM is studied, and two MP detectors are presented. In the first MP detector, MP algorithm is performed directly in the factor graph constructed by the product of GFDM modulation matrix and channel matrix. In the second MP detector, the received signal is first frequency-domain equalized, and then MP algorithm is performed based on a sparse factor graph by utilizing the structured sparsity of the modulation matrix. In both MP detectors, an additional pattern node is introduced to leverage the relation in the variable nodes belonging to the same IM block introduced by activation pattern constraint. Simulation results show that, the proposed MP detectors show some superiority over conventional linear detectors in terms of error performance and/or complexity.

In this paper, a low-complexity channel estimation method is proposed for single-user millimeter-wave MIMO systems, which is applicable to both uniform linear array and uniform planar array structures. Through applying the discrete Fourier transform (DFT) basis to jointly represent the channel matrix and design the pilot beams such that the product of the pilot beam matrix with the inverse DFT is a unitary matrix, the received pilot signal can be approximately expressed as a scaled version of channel representation coefficients. Thus, the channel matrix can be estimated directly by two times of matrix multiplication. More specifically, multiply the received pilot signal with DFT matrix on the left and inverse DFT in the right. Analytical and simulation results show that the proposed method has lower computational complexity and better estimation accuracy than the least square and compressed channel sensing using orthogonal matching pursuit.

This paper considers a general linear vector Gaussian channel with arbitrary signaling and pursues two closely related goals: i) closed-form expressions for the gradient of the mutual information with respect to arbitrary parameters of the system, and ii) fundamental connections between information theory and estimation theory. Generalizing the fundamental relationship recently unveiled by Guo, Shamai, and Verdu/spl acute/, we show that the gradient of the mutual information with respect to the channel matrix is equal to the product of the channel matrix and the error covariance matrix of the best estimate of the input given the output. Gradients and derivatives with respect to other parameters are then found via the differentiation chain rule.

This paper considers a general linear vector Gaussian channel with arbitrary signaling and pursues two closely related goals: i) closed-form expressions for the gradient of the mutual information with respect to arbitrary parameters of the system, and ii) fundamental connections between information theory and estimation theory. Generalizing the fundamental relationship recently unveiled by Guo, Shamai, and Verdu, we show that the gradient of the mutual information with respect to the channel matrix is equal to the product of the channel matrix and the error covariance matrix of the estimate of the input given the output

ABSTRACTChondrocytes are the uniquely resident cells found in all types of cartilage and key to their function is the ability to respond to mechanical loads with changes of metabolic activity. This mechanotransduction property is, in part, mediated through the activity of a range of expressed transmembrane channels; ion channels, gap junction proteins, and porins. Appropriate expression of ion channels has been shown essential for production of extracellular matrix and differential expression of transmembrane channels is correlated to musculoskeletal diseases such as osteoarthritis and Albers-Schönberg. In this study we analyzed the consistency of gene expression between channelomes of chondrocytes from human articular and costal (teenage and fetal origin) cartilages. Notably, we found 14 ion channel genes commonly expressed between articular and both types of costal cartilage chondrocytes. There were several other ion channel genes expressed only in articular (6 genes) or costal chondrocytes (5 genes). Significant differences in expression of BEST1 and KCNJ2 (Kir2.1) were observed between fetal and teenage costal cartilage. Interestingly, the large Ca2+ activated potassium channel (BKα, or KCNMA1) was very highly expressed in all chondrocytes examined. Expression of the gap junction genes for Panx1, GJA1 (Cx43) and GJC1 (Cx45) was also observed in chondrocytes from all cartilage samples. Together, this data highlights similarities between chondrocyte membrane channel gene expressions in cells derived from different anatomical sites, and may imply that common electrophysiological signaling pathways underlie cellular control. The high expression of a range of mechanically and metabolically sensitive membrane channels suggest that chondrocyte mechanotransduction may be more complex than previously thought.

Orthogonal time frequency space (OTFS) modulation is a delay-Doppler (DD) domain modulation scheme that can adapt to the fast time-varying channels. Since OTFS is a two-dimensional modulation scheme in DD domain, effective channel in DD domain can be represented by a few delay and Doppler parameters. Due to the sparsity of effective channel in DD domain, we model the output signal matrix as the product of pilot symbol matrix and sparse channel vector and introduce the Doppler gird segmentation factor to subdivide the Doppler taps, which solves the issue of fractional Doppler. Then, we model the channel estimation problem as a sparse signal recovery problem, and propose a channel estimation method based on an efficient sparse Bayesian learning (ESBL) in this paper. Specifically, we design a pilot pattern with no guard interval, and the pilot power is equal to the symbol power, and then use a low-complexity sparse Bayesian learning based on Gaussian-scale mixtures to directly estimate the effective channel. Compared to traditional channel estimation based on sparse Bayesian learning (SBL), our proposed ESBL-based algorithm has superior normalized mean squared error (NMSE) and bit error rate (BER) performance.

Accurate and Efficient Single Image Super-Resolution with Matrix Channel Attention Network

We propose and study algorithms for constrained maximum-likelihood (ML) estimation of a unitary matrix in the context of semi-blind multi-input multi-output (MIMO) channel estimation. The flat-fading r/spl times/t MIMO channel matrix, H, for r/spl ges/t can be decomposed as the matrix product H = WQ/sup H/, where W is a whitening matrix and Q is a unitary rotation matrix. Exclusive estimation of Q from pilot symbols has been shown potentially to achieve a 3 dB or greater improvement in terms of channel estimation accuracy. We develop and present the OPML, IGML and ROML algorithms for the constrained estimation of the unitary matrix Q; they are appropriate for a variety of scenarios, e.g., orthogonal pilots, low complexity, etc. Simulation results are provided to demonstrate the efficacy of the algorithms.

A channel-eigenvector invariant space-time constellation (CEI-STC) is a set of matrices such that the product of any pairwise difference matrix and its complex conjugate transpose is a scalar matrix. The pairwise error probability of a multi-antenna system equipped with the CEI-STC does not depend on the eigenvectors of the channel matrix. It may, however, depend on the eigenvalues of the channel. The maximum cardinality of a CEI-STC whose entries are restricted to a finite set is studied. A lower bound and an upper bound on the maximum cardinality are obtained.

This paper considers an uplink multiuser hybrid beamforming system where a base station (BS) communicates with multiple users simultaneously. The performance of the uplink multiuser hybrid beamforming system depends on the effective channel which is given by the product of channel matrix and the analog beams. Therefore, to maximize the performance, we need to acquire information of the channels and select the appropriate analog beams from the set of predefined analog beams. In this paper, we propose the channel estimation methods and analog beam selection algorithm for the uplink multiuser hybrid beamforming system. First, we design the estimation methods to exploit the channel information of the users by considering Rayleigh fading and millimeter wave (mmWave) channel models. Then, using the estimated channel information, we propose a low-complexity analog beam selection algorithm for the uplink multiuser hybrid beamforming system. We compare the complexity to show that the proposed analog beam selection algorithm has much less complexity than the exhaustive search-based optimum analog beam selection while the performance loss of the proposed analog beam selection algorithm is not significant compared to the optimum analog beam selection, which is shown by the numerical results.

We consider channel estimation (CE) against jamming attack (JA) in a single cell massive multiple-input–multiple-output (MIMO) system, wherein malicious users (MUs) want to beguile the base station (BS) into obtaining false channel state information. The MUs perform JA in uplink by sending jamming symbols to the BS through pilot and data transmission of the legitimate users (LUs). Especially, its employed active JA strategies include 1) sending the jamming symbols according to arbitrary distributions that are unknown to the BS; and 2) sending the jamming symbols that are correlative to those of the LUs. We analyze the empirical distribution of the uplink received signals (ED-RS), and prove that its characteristic function (CF) asymptotically approaches to the product of the CFs of the <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">desired signal</i> (DS) and the noise, where the DS is the product of the channel matrix and the signal sequences sent by the LUs/MUs. Motivated by the proof, our proposed extractor first estimates the CF of DS from the ED-RS and then extracts the alphabet of the DS. The extracted alphabet includes linear combinations of channel vectors of LUs and MUs, which leads to CE from the alphabet decomposition. Both analysis and simulation confirm that the proposed CE achieves good performance.

본 논문에서는 하나의 AP가 다수의 사용자를 지원하는 상향링크 다중사용자 하이브리드 빔포밍 시스템을 고려한다. 상향링크 다중사용자 하이브리드 빔포밍 시스템의 성능은 채널에 아날로그 빔이 결합된 형태의 유효 채널에 의해서 결정된다. 따라서 시스템의 성능을 최대화하기 위해서는 채널의 정보를 획득하고 획득된 채널 정보를 이용해서 아날로그 빔을 적절히 선택해야 한다. 본 논문에서는 상향링크 다중사용자 하이브리드 빔포밍 시스템에 적합한 채널 추정 방법과 저복잡도 아날로그 빔 선택 알고리즘을 제안한다. 또한 수학적으로 계산 복잡도 분석을 통해서 제안하는 저복잡도 아날로그 빔 선택 알고리즘이 최적의 아날로그 빔 선택 알고리즘에 비해서 복잡도가 훨씬 작은 것을 보여준다. 모의 실험 결과를 통하여 동일한 조건 하에서 제안된 저복잡도 아날로그 빔 선택 알고리즘이 최적의 아날로그 빔 선택 알고리즘에 비해 줄어든 계산 복잡도에 비하여 성능 면에서 열화가 거의 없는 것을 확인한다. In this paper, we consider an uplink multiuser hybrid beamforming system where an access point (AP) communicates with multiple users simultaneously. The performance of the uplink multiuser hybrid beamforming system depends on the effective channel which is given by the product of the channel matrix and the analog beams. Therefore, to maximize the performance, we need to obtain the channel information and then select the analog beams appropriately by using the acquired channel information. In this paper, we propose the channel estimation method and low complexity analog beam selection algorithm for the uplink multiuser hybrid beamforming system. Additionally, our analysis shows that the proposed low complexity analog beam selection algorithm provides much less complexity than the optimum analog beam selection algorithm. From the numerical results, we confirm that the proposed low complexity analog beam selection algorithm has little performance degradation in spite of much less complexity than the optimum analog beam selection algorithm under the equal system configuration.