/pdf/convex-analysisnoer-san-nojin-zhan-nituite-5dl2rbmoyr.pdf

Convex Analysisの二,三の進展について

A key challenge of future mobile communication research is to strike an attractive compromise between wireless network's area spectral efficiency and energy efficiency. This necessitates a clean-slate approach to wireless system design, embracing the rich body of existing knowledge, especially on multiple-input-multiple-ouput (MIMO) technologies. This motivates the proposal of an emerging wireless communications concept conceived for single-radio-frequency (RF) large-scale MIMO communications, which is termed as SM. The concept of SM has established itself as a beneficial transmission paradigm, subsuming numerous members of the MIMO system family. The research of SM has reached sufficient maturity to motivate its comparison to state-of-the-art MIMO communications, as well as to inspire its application to other emerging wireless systems such as relay-aided, cooperative, small-cell, optical wireless, and power-efficient communications. Furthermore, it has received sufficient research attention to be implemented in testbeds, and it holds the promise of stimulating further vigorous interdisciplinary research in the years to come. This tutorial paper is intended to offer a comprehensive state-of-the-art survey on SM-MIMO research, to provide a critical appraisal of its potential advantages, and to promote the discussion of its beneficial application areas and their research challenges leading to the analysis of the technological issues associated with the implementation of SM-MIMO. The paper is concluded with the description of the world's first experimental activities in this vibrant research field.

https://hal-supelec.archives-ouvertes.fr/hal-00840278/document

Spatial Modulation for Generalized MIMO: Challenges, Opportunities, and Implementation

What is index modulation (IM)? This is an interesting question that we have started to hear more and more frequently over the past few years. The aim of this paper is to answer this question in a comprehensive manner by covering not only the basic principles and emerging variants of IM, but also reviewing the most recent as well as promising advances in this field toward the application scenarios foreseen in next-generation wireless networks. More specifically, we investigate three forms of IM: spatial modulation, channel modulation and orthogonal frequency division multiplexing (OFDM) with IM, which consider the transmit antennas of a multiple-input multiple-output system, the radio frequency mirrors (parasitic elements) mounted at a transmit antenna and the subcarriers of an OFDM system for IM techniques, respectively. We present the up-to-date advances in these three promising frontiers and discuss possible future research directions for IM-based schemes toward low-complexity, spectrum- and energy-efficient next-generation wireless networks.

Index Modulation Techniques for Next-Generation Wireless Networks

A key challenge of future mobile communication research is to strike an attractive compromise between wire- less network's area spectral efficiency and energy efficiency. This necessitates a clean-slate approach to wireless system design, embracing the rich body of existing knowledge, espe- cially on multiple-input-multiple-ouput (MIMO) technologies. This motivates the proposal of an emerging wireless commu- nications concept conceived for single-radio-frequency (RF) large-scale MIMO communications, which is termed as SM. The concept of SM has established itself as a beneficial transmission paradigm, subsuming numerous members of the MIMO system family. The research of SM has reached sufficient maturity to motivate its comparison to state-of-the-art MIMO communica- tions, as well as to inspire its application to other emerging wireless systems such as relay-aided, cooperative, small-cell, optical wireless, and power-efficient communications. Further- more, it has received sufficient research attention to be im- plemented in testbeds, and it holds the promise of stimulating further vigorous interdisciplinary research in the years to come. This tutorial paper is intended to offer a comprehensive state-of-the-art survey on SM-MIMO research, to provide a critical appraisal of its potential advantages, and to promote the discussion of its beneficial application areas and their research challenges leading to the analysis of the technological issues associated with the implementation of SM-MIMO. The paper is concluded with the description of the world's first experimental activities in this vibrant research field.

Spatial Modulation for Generalized MIMO: Challenges, Opportunities, and Implementation This tutorial paper offers a comprehensive overview of the state of the art in spatial modulation for generalized multiple-input-multiple-output (MIMO) technologies.

A new class of low-complexity, yet energy-efficient Multiple-Input Multiple-Output (MIMO) transmission techniques, namely, the family of Spatial Modulation (SM) aided MIMOs (SM-MIMO), has emerged. These systems are capable of exploiting the spatial dimensions (i.e., the antenna indices) as an additional dimension invoked for transmitting information, apart from the traditional Amplitude and Phase Modulation (APM). SM is capable of efficiently operating in diverse MIMO configurations in the context of future communication systems. It constitutes a promising transmission candidate for large-scale MIMO design and for the indoor optical wireless communication while relying on a single-Radio Frequency (RF) chain. Moreover, SM may be also viewed as an entirely new hybrid modulation scheme, which is still in its infancy. This paper aims for providing a general survey of the SM design framework as well as of its intrinsic limits. In particular, we focus our attention on the associated transceiver design, on spatial constellation optimization, on link adaptation techniques, on distributed/cooperative protocol design issues, and on their meritorious variants.

/pdf/design-guidelines-for-spatial-modulation-4di7fcwiu9.pdf

Design Guidelines for Spatial Modulation

Space shift keying (SSK) modulation and its extension, the generalized SSK (GSSK), present an attractive framework for the emerging large-scale MIMO systems in reducing hardware costs. In SSK, the maximum likelihood (ML) detector incurs considerable computational complexities. We propose a compressed sensing based detector, NCS, by formulating the SSK-type detection criterion as a convex optimization problem. The proposed NCS requires only O(ntNrNt) complexity, outperforming the O(NrNtnt) complexity in the ML detector, at the cost of slight fidelity degradation. Simulations are conducted to substantiate the analytical derivation and the detection accuracy.

/pdf/compressed-sensing-detector-design-for-space-shift-keying-in-23ppo4fmxq.pdf

Compressed Sensing Detector Design for Space Shift Keying in MIMO Systems

Sparse Fast Fourier Transform (sFFT) [1][2], has been recently proposed to outperform FFT in reducing computational complexity. Assume that an input signal of length N in the frequency domain is K-sparse, where K ≤ N. sFFT costs O(K logN) instead of O(N logN) in FFT. In this paper, a new fast sFFT algorithm is proposed and costs O(K logK) averagely without any operations being related to N. The idea is to downsample the original input signal at the beginning. Subsequent processing operates under downsampled signals, which length is proportional to O(K). However, downsampling possibly leads to “aliasing.” By shift theorem of DFT, the aliasing problem can be formulated as the “Moment-preserving problem.” In addition, a top-down iterative strategy combined with different downsampling factors further saves computational costs. Complexity analysis and experimental results show that our method outperforms FFT and sFFT.

/pdf/sparse-fast-fourier-transform-by-downsampling-3nmes7okik.pdf

Sparse Fast Fourier Transform by downsampling

The existing dictionary learning methods mostly focus on ID signals, leading to the disadvantage of incurring overload of memory and computation if the size of training samples is large enough. Recently, 2D dictionary learning paradigm has been validated to save massive memory usage, especially for large-scale problems. To address this issue, we propose novel 2D dictionary learning algorithms based on tensors in this paper. Our learning problem is efficiently solved by CANDECOMP/PARAFAC (CP) decomposition. In addition, our algorithms guarantee sparsity constraint, which makes that sparse representation of the learned dictionary is equivalent to the ground truth. Experimental results confirm the effectness of our methods.

/pdf/2d-sparse-dictionary-learning-via-tensor-decomposition-46ldfmfvoy.pdf

2D sparse dictionary learning via tensor decomposition

Compressive sensing (CS) is proposed for signal sampling below the Nyquist rate based on the assumption that the signal is sparse in some transformed domain. Most sensing matrices (e.g., Gaussian random matrix) in CS, however, usually suffer from unfriendly hardware implementation, high computation cost, and huge memory storage. In this letter, we propose a deterministic sensing matrix for collecting measurements fed into sparse fast Fourier transform (sFFT) as the decoder. Compared with the conventional paradigm with Gaussian random matrix at encoder and convex programming or greedy method at decoders, sFFT can reconstruct sparse signals with very low computation cost under the comparable number of measurements. But, the limitation is that the signal must be sparse in the frequency domain. We further show how to relax this limitation into any domains with the transformation matrix or dictionary being circulant. Experimental and theoretical results validate that the proposed method achieves fast sensing, fast recovery, and low memory cost.

Compressive Sensing Matrix Design for Fast Encoding and Decoding via Sparse FFT

Orthogonal matching pursuit (OMP) is a powerful greedy algorithm in compressed sensing for recovering sparse signals despite its high computational cost for solving large scale problems. Moreover, its theoretic performance analysis based on mutual incoherence property (MIP) is still not accurate enough. To overcome these difficulties, this paper proposes a fast OMP (FOMP) algorithm by reformulating OMP in terms of refining l 2 -norm solutions in a greedy manner. l 2 -norm solutions are known for being non-sparse, but we show that the l 2 -norm solution associated with a greedy structure actually solves the sparse signal reconstruction problem well. We analyze exact recovery of FOMP via an order statistics probabilistic model and provide practical performance bounds.

Sung-Hsien Hsieh

Papers

Compressed Sensing Detector Design for Space Shift Keying in MIMO Systems

Sparse Fast Fourier Transform by downsampling

2D sparse dictionary learning via tensor decomposition

Compressive Sensing Matrix Design for Fast Encoding and Decoding via Sparse FFT

Fast OMP: Reformulating OMP via iteratively refining ℓ 2 -norm solutions