Sequential decoding on intersymbol interference channels with application to magnetic recording

Ankara : Department of Electrical and Electronics Engineering and the Institute of Engineering and Sciences of Bilkent University, 1990.Thesis (Master's) -- Bilkent University, 1990.Includes bibliographical references leaves 27-28In this work we treat sequential decoding in the problem of sequence estimation on intersymbol interference ( ISI ) channels. We consider the magnetic recording channel as the particular ISI channel and investigate the coding gains that can be achieved with sequential decoding for different information densities. Since the cutoff rate determines this quantity , we find lower bounds to the cutoff rate. The symmetric cutoff rate is computed as a theoretical lower bound and practical lower bounds are found through simulations. Since the optimum decoding metric is impractical, a sub-optimum metric has been used in the simulations. The results show that this metric can not achieve the cutoff rate in general, but still its performance is not far from that of the optimum metric. We compare the results to those of Immink[9] and see that one can achieve positive coding gains at information densities of practical interest where other practical codes used in magnetic recording show coding loss.Alanyalı, MuratM.S

Equalization Methods in Digital Communication Systems

Tato práce je psaná v angličtině a je zaměřená na problematiku ekvalizace v digitálních komunikačních systémech. Teoretická část zahrnuje stručné pozorování různých způsobů návrhu ekvalizérů. Praktická část se zabývá implementací nejčastěji používaných ekvalizérů a s jejich adaptačními algoritmy. Cílem praktické části je porovnat jejich charakteristiky a odhalit činitele, které ovlivňují kvalitu ekvalizace. V rámci problematiky ekvalizace jsou prozkoumány tři typy ekvalizérů. Lineární ekvalizér, ekvalizér se zpětnou vazbou a ML (Maximum likelihood) ekvalizér. Každý ekvalizér byl testován na modelu, který simuloval reálnou přenosovou soustavu s komplexním zkreslením, která je složena z útlumu, mezisymbolové interference a aditivního šumu. Na základě implenentace byli určeny charakteristiky ekvalizérů a stanoveno že optimální výkon má ML ekvalizér. Adaptační algoritmy hrají významnou roli ve výkonnosti všech zmíněných ekvalizérů. V práci je nastudována skupina stochastických algoritmů jako algoritmus nejmenších čtverců(LMS), Normalizovaný LMS, Variable step-size LMS a algoritmus RLS jako zástupce deterministického přístupu. Bylo zjištěno, že RLS konverguje mnohem rychleji, než algoritmy založené na LMS. Byly nastudovány činitele, které ovlivnili výkon popisovaných algoritmů. Jedním z důležitých činitelů, který ovlivňuje rychlost konvergence a stabilitu algoritmů LMS je parametr velikosti kroku. Dalším velmi důležitým faktorem je výběr trénovací sekvence. Bylo zjištěno, že velkou nevýhodou algoritmů založených na LMS v porovnání s RLS algoritmy je, že kvalita ekvalizace je velmi závislá na spektrální výkonové hustotě a a trénovací sekvenci.The thesis is focused on the problem of equalization in digital communication systems. Theoretical part includes brief observation of different approaches of equalizer designing. The practical part deals with implementation of the most often used equalizers and their adaptation algorithms. The aim of practical part is to make a comparison characteristic of different type of equalizers and reveal factors that influence the quality of equalization. Within a framework of the problem of equalization three types of equalizers were researched: linear equalizers, decision feedback equalizers (DFE) and maximum likelihood equalizers (ML). Each equalizer was tested on the model which approximates the real transmission system with complex distortion consisted of attenuation, intersymbol interference and additive noise. The comparison characteristics of equalizers were revealed on the basis of implementation. It was ascertained that ML equalizer has the optimum performance among three equalizers. The adaptation algorithm play significant role in performance of mentioned equalizers. Two groups of algorithms were studied: stochastic and deterministic. The first one includes following algorithms: least-mean-square algorithm (LMS), normalized LMS algorithm (NLMS) and variable step-size LMS algorithm (VSLMS). The second one is represented by RLS algorithm. It was determined that RLS algorithm converges much faster than LMS-based algorithms. The several factors that influenced the performance of all algorithms were studied. One of the most important factors that influences the speed of convergence and stability of the LMS algorithm is step-size parameter. Another very important factor is selecting the training sequence. The big disadvantage of LMS-based algorithms compare to RLS-based algorithms was found: the quality of equalization is highly dependent on the power spectral density of the training sequence.

Decision-feedback equalization for digital communication over dispersive channels.

ESD-TR-67-466.Bibliography: p.85.Contract AF 19(628)-5167

Sequential decoding of trellis codes through ISI channels

Thesis (M.S.)--Massachusetts Institute of Technology, Dept. of Electrical Engineering and Computer Science, 1996.Includes bibliographical references (leaves 56-57).by Patrick M. Maurer.M.S

Available Techniques for Magnetic Hard Disk Drive Read Channel Equalization

This paper presents an extensive, non-exhaustive, study of available hard disk drive read channel equalization techniques used in the storage and readback of magnetically stored information. The physical elements and basic principles of the storage processes are introduced together with the basic theoretical definitions and models. Both read and write processes in magnetic storage are explained along with the definition of simple key concepts such as user bit density, intersymbol interference, linear and areal density, read head pulse response models, and coding algorithm

Reduced Receivers for Faster-than-Nyquist Signaling and General Linear Channels

Fast and reliable data transmission together with high bandwidth efficiency are important design aspects in a modern digital communication system. Many different approaches exist but in this thesis bandwidth efficiency is obtained by increasing the data transmission rate with the faster-than-Nyquist (FTN) framework while keeping a fixed power spectral density (PSD). In FTN consecutive information carrying symbols can overlap in time and in that way introduce a controlled amount of intentional intersymbol interference (ISI). This technique was introduced already in 1975 by Mazo and has since then been extended in many directions. Since the ISI stemming from practical FTN signaling can be of significant duration, optimum detection with traditional methods is often prohibitively complex, and alternative equalization methods with acceptable complexity-performance tradeoffs are needed. The key objective of this thesis is therefore to design reduced-complexity receivers for FTN and general linear channels that achieve optimal or near-optimal performance. Although the performance of a detector can be measured by several means, this thesis is restricted to bit error rate (BER) and mutual information results. FTN signaling is applied in two ways: As a separate uncoded narrowband communication system or in a coded scenario consisting of a convolutional encoder, interleaver and the inner ISI mechanism in serial concatenation. Turbo equalization where soft information in the form of log likelihood ratios (LLRs) is exchanged between the equalizer and the decoder is a commonly used decoding technique for coded FTN signals. The first part of the thesis considers receivers and arising stability problems when working within the white noise constraint. New M-BCJR algorithms for turbo equalization are proposed and compared to reduced-trellis VA and BCJR benchmarks based on an offset label idea. By adding a third low-complexity M-BCJR recursion, LLR quality is improved for practical values of M. M here measures the reduced number of BCJR computations for each data symbol. An improvement of the minimum phase conversion that sharpens the focus of the ISI model energy is proposed. When combined with a delayed and slightly mismatched receiver, the decoding allows a smaller M without significant loss in BER. The second part analyzes the effect of the internal metric calculations on the performance of Forney- and Ungerboeck-based reduced-complexity equalizers of the M-algorithm type for both ISI and multiple-input multiple-output (MIMO) channels. Even though the final output of a full-complexity equalizer is identical for both models, the internal metric calculations are in general different. Hence, suboptimum methods need not produce the same final output. Additionally, new models working in between the two extremes are proposed and evaluated. Note that the choice of observation model does not impact the detection complexity as the underlying algorithm is unaltered. The last part of the thesis is devoted to a different complexity reducing approach. Optimal channel shortening detectors for linear channels are optimized from an information theoretical perspective. The achievable information rates of the shortened models as well as closed form expressions for all components of the optimal detector of the class are derived. The framework used in this thesis is more general than what has been previously used within the area