Spectral Efficiency of One-Bit Sigma-Delta Massive MIMO

We examine the uplink spectral efficiency of a massive MIMO base station employing a one-bit Sigma-Delta ( \Sigma \Delta ) sampling scheme implemented in the spatial rather than the temporal domain. Using spatial rather than temporal oversampling, and feedback of the quantization error between adjacent antennas, the method shapes the spatial spectrum of the quantization noise away from an angular sector where the signals of interest are assumed to lie. It is shown that, while a direct Bussgang analysis of the \Sigma \Delta approach is not suitable, an alternative equivalent linear model can be formulated to facilitate an analysis of the system performance. The theoretical properties of the spatial quantization noise power spectrum are derived for the \Sigma \Delta array, as well as an expression for the spectral efficiency of maximum ratio combining (MRC). Simulations verify the theoretical results and illustrate the significant performance gains offered by the \Sigma \Delta approach for both MRC and zero-forcing receivers

DNN-based Detectors for Massive MIMO Systems with Low-Resolution ADCs

Low-resolution analog-to-digital converters (ADCs) have been considered as a practical and promising solution for reducing cost and power consumption in massive Multiple-Input-Multiple-Output (MIMO) systems. Unfortunately, low-resolution ADCs significantly distort the received signals, and thus make data detection much more challenging. In this paper, we develop a new deep neural network (DNN) framework for efficient and low-complexity data detection in low-resolution massive MIMO systems. Based on reformulated maximum likelihood detection problems, we propose two model-driven DNN-based detectors, namely OBMNet and FBMNet, for one-bit and few-bit massive MIMO systems, respectively. The proposed OBMNet and FBMNet detectors have unique and simple structures designed for low-resolution MIMO receivers and thus can be efficiently trained and implemented. Numerical results also show that OBMNet and FBMNet significantly outperform existing detection methods.Comment: 6 pages, 8 figures, submitted for publication. arXiv admin note: text overlap with arXiv:2008.0375

Linear and Deep Neural Network-based Receivers for Massive MIMO Systems with One-Bit ADCs

The use of one-bit analog-to-digital converters (ADCs) is a practical solution for reducing cost and power consumption in massive Multiple-Input-Multiple-Output (MIMO) systems. However, the distortion caused by one-bit ADCs makes the data detection task much more challenging. In this paper, we propose a two-stage detection method for massive MIMO systems with one-bit ADCs. In the first stage, we propose several linear receivers based on the Bussgang decomposition, that show significant performance gain over existing linear receivers. Next, we reformulate the maximum-likelihood (ML) detection problem to address its non-robustness. Based on the reformulated ML detection problem, we propose a model-driven deep neural network-based (DNN-based) receiver, whose performance is comparable with an existing support vector machine-based receiver, albeit with a much lower computational complexity. A nearest-neighbor search method is then proposed for the second stage to refine the first stage solution. Unlike existing search methods that typically perform the search over a large candidate set, the proposed search method generates a limited number of most likely candidates and thus limits the search complexity. Numerical results confirm the low complexity, efficiency, and robustness of the proposed two-stage detection method.Comment: 12 pages, 10 figure

Linear and Dirty-Paper Techniques for the Multiuser MIMO Downlink

Multi-input, multi-output (MIMO) communications systems have attracted considerable attention over the past decade, mostly for single-user, point-to-point scenarios. The multiple-user MIMO case has attracted less attention, and most of the research on this problem has focused on uplink communications. Only recently has the multi-user MIMO downlink been addressed, beginning with information-theoretic capacity results [1–5], and followed by prac-tical implementations, including those based on linear techniques [6, 7] and non-linear pre-coding [8–11]. In this chapter we review these techniques and discuss some important open problems

Nutraceutical augmentation of circulating endothelial progenitor cells and hematopoietic stem cells in human subjects

The medical significance of circulating endothelial or hematopoietic progenitors is becoming increasing recognized. While therapeutic augmentation of circulating progenitor cells using G-CSF has resulted in promising preclinical and early clinical data for several degenerative conditions, this approach is limited by cost and inability to perform chronic administration. Stem-Kine is a food supplement that was previously reported to augment circulating EPC in a pilot study. Here we report a trial in 18 healthy volunteers administered Stem-Kine twice daily for a 2 week period. Significant increases in circulating CD133 and CD34 cells were observed at days 1, 2, 7, and 14 subsequent to initiation of administration, which correlated with increased hematopoietic progenitors as detected by the HALO assay. Augmentation of EPC numbers in circulation was detected by KDR-1/CD34 staining and colony forming assays. These data suggest Stem-Kine supplementation may be useful as a stimulator of reparative processes associated with mobilization of hematopoietic and endothelial progenitors

Reconfigurable Intelligent Surfaces for 6G Systems: Principles, Applications, and Research Directions

This is the submitted version of the paper accepted by IEEE Communications Magazine made available on arXiv under a Creative Commons (CC BY) Attribution License). In this paper, we have answered four critical questions in RIS/IRS. More importantly, we have pointed out several promising research directionsThis work was supported in part by the National Key Research and Development Project under Grant 2019YFE0123600, in part by the Research Fund of National Mobile Communications Research Laboratory, Southeast University (No.2021B01) and the Fundamental Research Funds for the Central Universities. M. Di Renzo’s work was supported in part by the EU-H2020 projects ARIADNE (871464) and RISE-6G (101017011). The work of M. Chen is supported by the National Natural Science Foundation of China under Grant 61871128. The work of Y. Hao is supported by EPSRC EP/R035393/1. The work of A. L. Swindlehurst is supported by U.S. National Science Foundation grant ECCS2030029 L. Hanzo would like to acknowledge the financial support of the European Research Council’s Advanced Fellow Grant QuantCom (Grant No. 789028)