Fractional fourier based sparse channel estimation for multicarrier underwater acoustic communication system

This paper presents a hybrid sparse channel estimation based on Fractional Fourier Transform (FrFT) for orthogonal frequency division multiplex (OFDM) scenario to exploit channel sparsity of underwater acoustic (UWA) channel. A novel channel dictionary matrix based on chirp signals is constructed and mutual coherence is adopted to evaluate its preservation of sparse information. In addition, Compressive Sampling Matching Pursuit (CoSaMP) is implemented to estimate the sparse channel coefficients. Simulation results demonstrate a significant Normalized Mean Square Error (NMSE) improvement of 10dB over Basis Expansion Model (BEM) with less complexity

Channel Estimation and ICI Cancelation in Vehicular Channels of OFDM Wireless Communication Systems

Orthogonal frequency division multiplexing (OFDM) scheme increases bandwidth efficiency (BE) of data transmission and eliminates inter symbol interference (ISI). As a result, it has been widely used for wideband communication systems that have been developed during the past two decades and it can be a good candidate for the emerging communication systems such as fifth generation (5G) cellular networks with high carrier frequency and communication systems of high speed vehicles such as high speed trains (HSTs) and supersonic unmanned aircraft vehicles (UAVs). However, the employment of OFDM for those upcoming systems is challenging because of high Doppler shifts. High Doppler shift makes the wideband communication channel to be both frequency selective and time selective, doubly selective (DS), causes inter carrier interference (ICI) and destroys the orthogonality between the subcarriers of OFDM signal. In order to demodulate the signal in OFDM systems and mitigate ICIs, channel state information (CSI) is required. In this work, we deal with channel estimation (CE) and ICI cancellation in DS vehicular channels. The digitized model of the DS channels can be short and dense, or long and sparse. CE methods that perform well for short and dense channels are highly inefficient for long and sparse channels. As a result, for the latter type of channels, we proposed the employment of compressed sensing (CS) based schemes for estimating the channel. In addition, we extended our CE methods for multiple input multiple output (MIMO) scenarios. We evaluated the CE accuracy and data demodulation fidelity, along with the BE and computational complexity of our methods and compared the results with the previous CE procedures in different environments. The simulation results indicate that our proposed CE methods perform considerably better than the conventional CE schemes

Cooperative Wideband Spectrum Sensing Based on Joint Sparsity

COOPERATIVE WIDEBAND SPECTRUM SENSING BASED ON JOINT SPARSITY By Ghazaleh Jowkar, Master of Science A thesis submitted in partial fulfillment of the requirements for the degree of Master of Science at Virginia Commonwealth University Virginia Commonwealth University 2017 Major Director: Dr. Ruixin Niu, Associate Professor of Department of Electrical and Computer Engineering In this thesis, the problem of wideband spectrum sensing in cognitive radio (CR) networks using sub-Nyquist sampling and sparse signal processing techniques is investigated. To mitigate multi-path fading, it is assumed that a group of spatially dispersed SUs collaborate for wideband spectrum sensing, to determine whether or not a channel is occupied by a primary user (PU). Due to the underutilization of the spectrum by the PUs, the spectrum matrix has only a small number of non-zero rows. In existing state-of-the-art approaches, the spectrum sensing problem was solved using the low-rank matrix completion technique involving matrix nuclear-norm minimization. Motivated by the fact that the spectrum matrix is not only low-rank, but also sparse, a spectrum sensing approach is proposed based on minimizing a mixed-norm of the spectrum matrix instead of low-rank matrix completion to promote the joint sparsity among the column vectors of the spectrum matrix. Simulation results are obtained, which demonstrate that the proposed mixed-norm minimization approach outperforms the low-rank matrix completion based approach, in terms of the PU detection performance. Further we used mixed-norm minimization model in multi time frame detection. Simulation results shows that increasing the number of time frames will increase the detection performance, however, by increasing the number of time frames after a number of times the performance decrease dramatically

Compressive channel estimation

In dieser Arbeit untersuchen wir die kompressive Kanalschätzung (KKS), also die Anwendung der Theorie und Methodologie des Compressed Sensing (CS) auf das Problem der Kanalschätzung doppelt selektiver Kanäle in Multicarrier-Systemen. Nach einer kurzen Einführung in die kabellose Kommunikation und einem kleinen Überblick über CS und einigen seiner Varianten betrachten wir die in [1] präsentierte elementare kompressive Kanalschätzmethode. Wir analysieren ihre Leistungsfähigkeit sowie ihre Komplexität, und wir untersuchen die ihr zugrundeliegende Annahme, nämlich die "delay-Doppler sparsity" typischer Kanäle, genauer. Aufbauend auf dieser Analyse stellen wir einige Varianten und Erweiterungen der kompressiven Kanalschätzmethode vor. Zuerst nutzen wir die Tatsache dass typische Kanäle auch als "group sparse" angesehen werden können. Dies ist eine Folge des sogenannten Leck Effekts, welcher die Leistung einer jeden kompressiven Kanalschätzmethode beeinträchtigt und daher eine enorme Herausforderungen für die KKS darstellt. Weiters betrachten wir die Erweiterung der kompressiven Schätzmethode auf Mehrantennensysteme (MIMO). Wir zeigen, dass die einzelnen Querkanäle eines solchen MIMO Systems (in etwa) als "jointly sparse", sogar als "jointly group sparse" angesehen, und daher Methoden des Multichannel CS (MCS) verwendet werden können. Letztens nutzen wir - unter Verwendung der Konzepte des Modified CS (MOD-CS) - die approximative "sequential sparsity" des Kanals zum Kanal-Tracking über mehrere aufeinanderfolgende Symbolblöcke hinweg. Diese Vorgehensweise kann die Leistung zusätzlich steigern, viel wichtiger jedoch, sie kann die Komplexität der Methode reduzieren. Darüber hinaus adaptieren wir die Technik der Basis-Optimierung, welche in [2, 3] vorgestellt wurde, für die verschiedenen Szenarien, und wir präsentieren Simulationsergebnisse, welche die verbesserte Leistung all jener Kanalschätzmethoden demonstrieren, die in dieser Arbeit erklärt werden.In this thesis we investigate compressive channel estimation (CCE), i.e. the application of the theory and methodology of Compressed Sensing (CS) to the problem of estimating doubly selective channels in multicarrier systems. After a brief introduction to wireless communications and a short survey of CS and some of its variations, we review the basic compressive channel estimator that was introduced in [1]. We analyze its performance as well as its computational complexity, and we explore the basic assumption underlying the compressive estimator, namely the delay-Doppler sparsity of typical channels, in more detail. Based on this analysis, we propose several variations and extensions of the conventional compressive channel estimator. First, we make use of the fact that typical channels can be considered group sparse as well. This is due to the so-called leakage effect, which actually impairs the performance of any channel estimator utilizing CS techniques and therefore is one of the main challenges in CCE. Then, we investigate the extension of the compressive estimators to the multi-antenna (MIMO) case. We show that the various cross-channels of a MIMO system can (approximately) be considered jointly sparse, even jointly group sparse, and that therefore the methodology of multichannel CS can be utilized. Last, by using the recently introduced concept of modified CS, we exploit the approximate sequential sparsity of the channel in order to track it over a period of several consecutive symbol blocks. This approach can yield an additional performance gain, but more importantly it can substantially reduce the computational complexity of the method. Additionally, we adapt the basis optimization techniques introduced in [2, 3] to the various settings, and we present simulation results that demonstrate the performance gains that can be achieved by using each of the compressive estimators presented in this thesis