Research Advances on Analog-to-Information Converter Based on Union of Subspaces Model

依据Shannon采样定理的模拟-数字转换器(Analog-to-Digital Convertor,ADC)越来越难以满足对高频、宽频信号的采样需求,为实现低速率采样同时缓解数据传输、存储及处理的压力,基于亚Nyquist采样的模拟信息转换器(Analog-to-Information Convertor,AIC)成为研究热点。本文首先概述了压缩感知(Compressed Sensing,CS)理论、单向量空间(Single Vector Space,SVS)和联合子空间(Union of Subspaces,UoS)采样理论,着重总结和对比了几种符合UoS模型信号的AIC采样架构及恢复算法,进一步提出了一种多天线采样系统架构及基于子空间分解的增强型重构方法,最后展望了AIC未来研究方向。</p

Supervised and Unsupervised Subband Adaptive Denoising Frameworks with Polynomial Threshold Function

Unlike inflexible structure of soft and hard threshold function, a unified linear matrix form with flexible structure for threshold function is proposed. Based on the unified linear flexible structure threshold function, both supervised and unsupervised subband adaptive denoising frameworks are established. To determine flexible coefficients, a direct mean-square error (MSE) minimization is conducted in supervised denoising while Stein&#39;s unbiased risk estimate as a MSE estimate is minimized in unsupervised denoising. The SURE rule requires no hypotheses or a priori knowledge about clean signals. Furthermore, we discuss conditions to obtain optimal coefficients for both supervised and unsupervised subband adaptive denoising frameworks. Applying an Odd-Term Reserving Polynomial (OTRP) function as concrete threshold function, simulations for polynomial order, denoising performance, and noise effect are conducted. Proper polynomial order and noise effect are analyzed. Both proposed methods are compared with soft and hard based denoising technologies - VisuShrink, SureShrink, MiniMaxShrink, and BayesShrink - in denoising performance simulation. Results show that the proposed approaches perform better in both MSE and signal-to-noise ratio (SNR) sense

A Multiple-Input Nyquist Folding Receiver Architecture for Low SNR Wideband Spectrum Sensing

The ever increasing demand of communication makes the spectrum more crowded, and thus the Cognitive Radio (CR) emerged as a potential effective scheme to increase the utilization of spectrum. Sub-Nyquist sampling based CR can greatly reduce the sampling rate than those Nyquist sampling based schemes for wideband spectrum sensing. But existing single node sub-Nyquist sampling schmes seldom use spatial diversity and show poor recovery and detection performance in low Signal to Noise Ratio (SNR) condition. NYquist Folding Receiver (NYFR) as one of effective sub-Nyquist sampling schemes exhibits a lower system working frequency than most other schemes, but it is greatly effectted by noise. In this paper, we first investigate the property that the Additive White Gaussian Noise (AWGN) after sub-Nyquist sampling by NYFR is still the AWGN, then, we propose a Multiple Input NYquist Folding Receiver (MI-NYFR) scheme by using the noise white-keeping property and spatial diversity. The proposed scheme is tested respectively in denoising and detection respects in AWGN channel. Simulation results show that the proposed MI-NYFR exhibits good noise robustness and high detection probability very well in low SNR condition.</p

RF Chain Reduction for MIMO Systems: A Hardware Prototype

Radio frequency (RF) chain circuits play a major role in digital receiver architectures, allowing passband communication signals to be processed in baseband. When operating at high frequencies, these circuits tend to be costly. This increased cost imposes a major limitation on future multiple-input&ndash;multiple-output (MIMO) communication technologies. A common approach to mitigate the increased cost is to utilize hybrid architectures, in which the received signal is combined in analog into a lower dimension, thus reducing the number of RF chains. In this article we study the design and hardware implementation of hybrid architectures via minimizing channel estimation error. We first derive the optimal solution for complex-gain combiners and propose an alternating optimization algorithm for phase-shifter combiners. We then present a hardware prototype implementing analog combining for RF chain reduction. The prototype consists of a specially designed configurable combining board as well as a dedicated experimental setup. Our hardware prototype allows us evaluating the effect of analog combining in MIMO systems using actual communication signals. The experimental study, which focuses on channel estimation accuracy in MIMO channels, demonstrates that using the proposed prototype, the achievable channel estimation performance is within a small gap in a statistical sense from that obtained using a costly receiver in which each antenna is connected to a dedicated RF chain.</p

An Improved Upper Bound on the Maximum Eigenvalue of Exponential Model Based Spatial Correlation Matrices in Massive MIMO Systems

Massive Multiple-Input, Multiple-Output (MIMO) communications are considered as one of the most promising directions for the next generation communications and have attracted extensive research interests. The using of exponential model is recognized as an effective manner to describe the spatial correlation matrix in a spatially correlated MIMO channel for its simple form and accurate fitting on the experimental measurements. Based on this useful exponential model, an improved upper bound on the maximum eigenvalue of the spatial correlation matrix is given in this paper. Complying with the existing results, the proposed upper bound is also a function of the number of antennas and the absolute value of correlation coefficient. However, the proposed upper bound is obtained using a trace-based derivation, which is tighter than existing results in high spatial correlation massive MIMO systems. Simulations show that the proposed upper bound is closer to the true maximum eigenvalues for both uniform linear array and uniform planar array scenarios, which is expected to be widely used in massive MIMO systems

Compressive Subspace Learning With Antenna Cross-Correlations for Wideband Spectrum Sensing

Compressive subspace learning (CSL) with the exploitation of space diversity has found a potential performance improvement for wideband spectrum sensing (WBSS). However, previous works mainly focus on either exploiting antenna auto-correlations or adopting a multiple-input multiple-output (MIMO) channel without considering the spatial correlations, which will degrade their performances. In this paper, we consider a spatially correlated MIMO channel and propose two CSL algorithms (i.e., mCSLSACC and vCSLACC) which exploit antenna cross-correlations, where the mCSLSACC utilizes an antenna averaging temporal decomposition, and the vCSLACC uses a spatial-temporal joint decomposition. For both algorithms, the conditions of statistical covariance matrices (SCMs) without noise corruption are derived. Through establishing the singular value relation of SCMs in statistical sense between the proposed and traditional CSL algorithms, we show the superiority of the proposed CSL algorithms. By further depicting the receiving correlation matrix of MIMO channel with the exponential correlation model, we give important closed-form expressions for the proposed CSL algorithms in terms of the amplification of singular values over traditional CSL algorithms. Such expressions provide a possibility to determine optimal algorithm parameters for high system performances in an analytical way. Simulations validate the correctness of this work and its performance improvement over existing works in terms of WBSS performance.</p