44 research outputs found

    Trellis-Based Equalization for Sparse ISI Channels Revisited

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    Sparse intersymbol-interference (ISI) channels are encountered in a variety of high-data-rate communication systems. Such channels have a large channel memory length, but only a small number of significant channel coefficients. In this paper, trellis-based equalization of sparse ISI channels is revisited. Due to the large channel memory length, the complexity of maximum-likelihood detection, e.g., by means of the Viterbi algorithm (VA), is normally prohibitive. In the first part of the paper, a unified framework based on factor graphs is presented for complexity reduction without loss of optimality. In this new context, two known reduced-complexity algorithms for sparse ISI channels are recapitulated: The multi-trellis VA (M-VA) and the parallel-trellis VA (P-VA). It is shown that the M-VA, although claimed, does not lead to a reduced computational complexity. The P-VA, on the other hand, leads to a significant complexity reduction, but can only be applied for a certain class of sparse channels. In the second part of the paper, a unified approach is investigated to tackle general sparse channels: It is shown that the use of a linear filter at the receiver renders the application of standard reduced-state trellis-based equalizer algorithms feasible, without significant loss of optimality. Numerical results verify the efficiency of the proposed receiver structure.Comment: To be presented at the 2005 IEEE Int. Symp. Inform. Theory (ISIT 2005), September 4-9, 2005, Adelaide, Australi

    Blind Estimation of Linear and Nonlinear Sparse Channels, Journal of Telecommunications and Information Technology, 2013, nr 1

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    This paper presents a Clustering Based Blind Channel Estimator for a special case of sparse channels – the zero pad channels. The proposed algorithm uses an unsupervised clustering technique for the estimation of data clusters. Clusters labelling is performed by a Hidden Markov Model of the observation sequence appropriately modified to exploit channel sparsity. The algorithm achieves a substantial complexity reduction compared to the fully evaluated technique. The proposed algorithm is used in conjunction with a Parallel Trellis Viterbi Algorithm for data detection and simulation results show that the overall scheme exhibits the reduced complexity benefits without performance reduction

    State–of–the–art report on nonlinear representation of sources and channels

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    This report consists of two complementary parts, related to the modeling of two important sources of nonlinearities in a communications system. In the first part, an overview of important past work related to the estimation, compression and processing of sparse data through the use of nonlinear models is provided. In the second part, the current state of the art on the representation of wireless channels in the presence of nonlinearities is summarized. In addition to the characteristics of the nonlinear wireless fading channel, some information is also provided on recent approaches to the sparse representation of such channels

    Advanced receivers and waveforms for UAV/Aircraft aeronautical communications

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    Nowadays, several studies are launched for the design of reliable and safe communications systems that introduce Unmanned Aerial Vehicle (UAV), this paves the way for UAV communication systems to play an important role in a lot of applications for non-segregated military and civil airspaces. Until today, rules for integrating commercial UAVs in airspace still need to be defined, the design of secure, highly reliable and cost effective communications systems still a challenging task. This thesis is part of this communication context. Motivated by the rapid growth of UAV quantities and by the new generations of UAVs controlled by satellite, the thesis aims to study the various possible UAV links which connect UAV/aircraft to other communication system components (satellite, terrestrial networks, etc.). Three main links are considered: the Forward link, the Return link and the Mission link. Due to spectrum scarcity and higher concentration in aircraft density, spectral efficiency becomes a crucial parameter for largescale deployment of UAVs. In order to set up a spectrally efficient UAV communication system, a good understanding of transmission channel for each link is indispensable, as well as a judicious choice of the waveform. This thesis begins to study propagation channels for each link: a mutipath channels through radio Line-of-Sight (LOS) links, in a context of using Meduim Altitude Long drones Endurance (MALE) UAVs. The objective of this thesis is to maximize the solutions and the algorithms used for signal reception such as channel estimation and channel equalization. These algorithms will be used to estimate and to equalize the existing muti-path propagation channels. Furthermore, the proposed methods depend on the choosen waveform. Because of the presence of satellite link, in this thesis, we consider two low-papr linear waveforms: classical Single-Carrier (SC) waveform and Extented Weighted Single-Carrier Orthogonal Frequency-Division Multiplexing (EW-SC-OFDM) waveform. channel estimation and channel equalization are performed in the time-domain (SC) or in the frequency-domain (EW-SC-OFDM). UAV architecture envisages the implantation of two antennas placed at wings. These two antennas can be used to increase diversity gain (channel matrix gain). In order to reduce channel equalization complexity, the EWSC- OFDM waveform is proposed and studied in a muti-antennas context, also for the purpose of enhancing UAV endurance and also increasing spectral efficiency, a new modulation technique is considered: Spatial Modulation (SM). In SM, transmit antennas are activated in an alternating manner. The use of EW-SC-OFDM waveform combined to SM technique allows us to propose new modified structures which exploit exces bandwidth to improve antenna bit protection and thus enhancing system performances

    Spatial diversity in MIMO communication systems with distributed or co-located antennas

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    The use of multiple antennas in wireless communication systems has gained much attention during the last decade. It was shown that such multiple-input multiple-output (MIMO) systems offer huge advantages over single-antenna systems. Typically, quite restrictive assumptions are made concerning the spacing of the individual antenna elements. On the one hand, it is typically assumed that the antenna elements at transmitter and receiver are co-located, i.e., they belong to some sort of antenna array. On the other hand, it is often assumed that the antenna spacings are sufficiently large, so as to justify the assumption of independent fading. In this thesis, the above assumptions are relaxed. In the first part, it is shown that MIMO systems with distributed antennas and MIMO systems with co-located antennas can be treated in a single, unifying framework. In the second part this fact is utilized, in order to develop appropriate transmit power allocation strategies for co-located and distributed MIMO systems. Finally, the third part focuses on specific synchronization problems that are of interest for distributed MIMO systems

    Channel detection on two-dimensional magnetic recording

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    Two-dimensional magnetic recording (TDMR) coupled with shingled-magnetic recording (SMR) is one of next generation techniques for increasing the hard disk drive (HDD) capacity up to 10 Tbit/in2 in order to meet the growing demand of mass storage.We focus on solving the tough problems and challenges on the detection end of TDMR. Since the reader works on the overlapped tracks, which are even narrower than the read head, the channel detector works in an environment of low signal-to-noise ratio (SNR), two-dimensional (2-D) inter-symbol interference (ISI) and colored noise, therefore it requires sophisticated detection techniques to provide reliable data recovery. Given that the complexity of optimal 2-D symbol detection is exponential on the data width, we had to choose suboptimal solutions.To build our research environment, we use an innovative Voronoi grain based channel model which captures the important features of SMR, such as squeezed tracks, tilted bit cells, 2-D ISI, electronic and media noise, etc. Then we take an in-depth exploration of channel detection techniques on the TDMR channel model. Our approaches extend the conventional 1-D detection techniques, by using a joint-track equalizer to optimize the 2-D partial-response (PR) target followed by the multi-track detector (MTD) for joint detection, or using the inter-track interference (ITI) canceller to estimate and cancel the ITI from side tracks, followed by a standard BCJR detector. We used the single-track detector (STD) for pre-detecting the side tracks to lower the overall complexity. Then we use pattern-dependent noise prediction (PDNP) techniques to linearly predict the noise sample, so as to improve the detection performance under colored media noise, and especially the data dependent jitter noise. The results show that our 2-D detectors provide significant performance gains against the conventional detectors with manageable complexity

    Sparse Nonlinear MIMO Filtering and Identification

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    In this chapter system identification algorithms for sparse nonlinear multi input multi output (MIMO) systems are developed. These algorithms are potentially useful in a variety of application areas including digital transmission systems incorporating power amplifier(s) along with multiple antennas, cognitive processing, adaptive control of nonlinear multivariable systems, and multivariable biological systems. Sparsity is a key constraint imposed on the model. The presence of sparsity is often dictated by physical considerations as in wireless fading channel-estimation. In other cases it appears as a pragmatic modelling approach that seeks to cope with the curse of dimensionality, particularly acute in nonlinear systems like Volterra type series. Three dentification approaches are discussed: conventional identification based on both input and output samples, semi–blind identification placing emphasis on minimal input resources and blind identification whereby only output samples are available plus a–priori information on input characteristics. Based on this taxonomy a variety of algorithms, existing and new, are studied and evaluated by simulation

    Advances in Modeling and Signal Processing for Bit-Patterned Magnetic Recording Channels with Written-In Errors

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    In the past perpendicular magnetic recording on continuous media has served as the storage mechanism for the hard-disk drive (HDD) industry, allowing for growth in areal densities approaching 0.5 Tb/in2. Under the current system design, further increases are limited by the superparamagnetic effect where the medium's thermal energy destabilizes the individual bit domains used for storage. In order to provide for future growth in the area of magnetic recording for disk drives, a number of various technology shifts have been proposed and are currently undergoing considerable research. One promising option involves switching to a discrete medium in the form of individual bit islands, termed bit-patterned magnetic recording (BPMR).When switching from a continuous to a discrete media, the problems encountered become substantial for every aspect of the hard-disk drive design. In this dissertation the complications in modeling and signal processing for bit-patterned magnetic recording are investigated where the write and read processes along with the channel characteristics present considerable challenges. For a target areal density of 4 Tb/in2, the storage process is hindered by media noise, two-dimensional (2D) intersymbol interference (ISI), electronics noise and written-in errors introduced during the write process. Thus there is a strong possibility that BPMR may prove intractable as a future HDD technology at high areal densities because the combined negative effects of the many error sources produces an environment where current signal processing techniques cannot accurately recover the stored data. The purpose here is to exploit advanced methods of detection and error correction to show that data can be effectively recovered from a BPMR channel in the presence of multiple error sources at high areal densities.First a practical model for the readback response of an individual island is established that is capable of representing its 2D nature with a Gaussian pulse. Various characteristics of the readback pulse are shown to emerge as it is subjected to the degradation of 2D media noise. The writing of the bits within a track is also investigated with an emphasis on the write process's ability to inject written-in errors in the data stream resulting from both a loss of synchronization of the write clock and the interaction of the local-scale magnetic fields under the influence of the applied write field.To facilitate data recovery in the presence of BPMR's major degradations, various detection and error-correction methods are utilized. For single-track equalization of the channel output, noise prediction is incorporated to assist detection with increased levels of media noise. With large detrimental amounts of 2D ISI and media noise present in the channel at high areal densities, a 2D approach known as multi-track detection is investigated where multiple tracks are sensed by the read heads and then used to extract information on the target track. For BPMR the output of the detector still possesses the uncorrected written-in errors. Powerful error-correction codes based on finite geometries are employed to help recover the original data stream. Increased error-correction is sought by utilizing two-fold EG codes in combination with a form of automorphism decoding known as auto-diversity. Modifications to the parity-check matrices of the error-correction codes are also investigated for the purpose of attempting more practical applications of the decoding algorithms based on belief propagation. Under the proposed techniques it is shown that effective data recovery is possible at an areal density of 4 Tb/in2 in the presence of all significant error sources except for insertions and deletions. Data recovery from the BPMR channel with insertions and deletions remains an open problem

    Low-density parity-check coding for high-density magnetic recording systems.

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    Our strategy is to combine advanced signal processing techniques, the core of which is soft-decision iterative channel detection, with powerful low-density parity-check (LDPC) coding techniques.Magnetic recording channels (MRCs), including both longitudinal and perpendicular ones, are subject to a number of physical impairments, such as electronic/media noise, intersymbol interference (ISI), erasure, and intertrack interference (ITI). These impairments, if not appropriately handled, are barriers to achieving ultra-high densities. The goal of this dissertation is to study the impact of these multiple impairments on system performance, and to develop techniques to mitigate this impact such that the performance is as close to the theoretical limit of the channel as can be achieved by practical and implementable means.Specifically, the performance of regular LDPC codes on MRCs is first evaluated. Both randomly and structurally constructed codes are considered. Secondly, density evolution is used to analyze and design LDPC codes for MRCs. Results show that better irregular codes can be obtained. Afterwards, this algorithm is modified to include erasures, and erasure detection algorithms are studied. Fourthly, an improved algorithm for LDPC decoding, called signal-to-noise ratio (SNR) mismatch is unveiled. This algorithm may be useful for future practical applications. Finally, a channel detection algorithm for handling ITI in perpendicular recording is optimized, the eventual goal of which is to maximize the attainable track density
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