Reconfigurable Viterbi decoder on mesh connected multiprocessor architecture

In modern wireline and wireless communication systems, Viterbi decoder is one of the most compute intensive and essential elements. Each standard requires a different configuration of Viterbi decoder. Hence there is a need to design a flexible reconfigurable Viterbi decoder to support different configurations on a single platform. In this paper we present a reconfigurable Viterbi decoder which can be reconfigured for standards such as WCDMA, CDMA2000, IEEE 802.11, DAB, DVB, and GSM. Different parameters like code rate, constraint length, polynomials and truncation length can be configured to map any of the above mentioned standards. Our design provides higher throughput and scalable power consumption in various configuration of the reconfigurable Viterbi decoder. The power and throughput can also be optimized for different standards.

High-Throughput Flexible Constraint Length Viterbi Decoders on De Bruijn, Shuffle-Exchange and Butterfly Connected Architectures

Flexible constraint length channel decoders are required for software defined radios. This paper presents a novel scalable scheme for realizing flexible constraint length Viterbi decoders on a de Bruijn interconnection network. Architectures for flexible decoders using the flattened butterfly and shuffle-exchange networks are also described. It is shown that these networks provide favourable substrates for realizing flexible convolutional decoders. Synthesis results for the three networks are provided and a comparison is performed. An architecture based on a 2D-mesh, which is a topology having a nominally lesser silicon area requirement, is also considered as a fourth point for comparison. It is found that of all the networks considered, the de Bruijn network offers the best tradeoff in terms of area versus throughput.

Multi-Pattern Viterbi Algorithm for joint decoding of multiple speech patterns

Joint decoding of multiple speech patterns so as to improve speech recognition performance is important, especially in the presence of noise. In this paper, we propose a Multi-Pattern Viterbi algorithm (MPVA) to jointly decode and recognize multiple speech patterns for automatic speech recognition (ASR). The MPVA is a generalization of the Viterbi Algorithm to jointly decode multiple patterns given a Hidden Markov Model (HMM). Unlike the previously proposed two stage Constrained Multi-Pattern Viterbi Algorithm (CMPVA),the MPVA is a single stage algorithm. MPVA has the advantage that it cart be extended to connected word recognition (CWR) and continuous speech recognition (CSR) problems. MPVA is shown to provide better speech recognition performance than the earlier techniques: using only two repetitions of noisy speech patterns (-5 dB SNR, 10% burst noise), the word error rate using MPVA decreased by 28.5%, when compared to using individual decoding. (C) 2010 Elsevier B.V. All rights reserved.

Realizing a flexible constraint length Viterbi decoder for software radio on a de Bruijn interconnection network

Building flexible constraint length Viterbi decoders requires us to be able to realize de Bruijn networks of various sizes on the physically provided interconnection network. This paper considers the case when the physical network is itself a de Bruijn network and presents a scalable technique for realizing any n-node de Bruijn network on an N-node de Bruijn network, where n < N. The technique ensures that the length of the longest path realized on the network is minimized and that each physical connection is utilized to send only one data item, both of which are desirable in order to reduce the hardware complexity of the network and to obtain the best possible performance.

Synthesizable high performance adaptive equaliser and viterbi decoder for the class-IV PRML channel

The design and VLSI implementation of two key components of the class-IV partial response maximum likelihood channel (PR-IV) the adaptive filter and the Viterbi decoder are described. These blocks are implemented using parameterised VHDL modules, from a library of common digital signal processing (DSP) and arithmetic functions. Design studies, based on 0.6 micron 3.3V standard cell processes, indicate that worst case sampling rates of 49 mega-samples per second are achievable for this system, with proportionally high sampling rates for full custom designs and smaller dimension processes. Significant increases in the sampling rate, from 49 MHz to approximately 180 MHz, can be achieved by operating four filter modules in parallel, and this implementation has 50% lower power consumption than a pipelined filter operating at the same speed.

Portado del algoritmo de Viterbi a una arquitectura multiprocesador

Cada vez es más frecuente que los sistemas de comunicaciones realicen buena parte de sus funciones (modulación y demodulación, codificación y decodificación...) mediante software en lugar de utilizar hardware dedicado. Esta técnica se denomina “Radio software”. El objetivo de este PFC es estudiar un algoritmo implementado en C empleado en sistemas de comunicaciones modernos, en concreto la decodificación de Viterbi, el cual se encarga de corregir los posibles errores producidos a lo largo de la comunicación, para poder trasladarlo a sistemas empotrados multiprocesador. Partiendo de un código en C para el decodificador que realiza todas sus operaciones en serie, en este Proyecto fin de carrera se ha paralelizado dicho código, es decir, que el trabajo que realizaba un solo hilo para el caso del código serie, es procesado por un número de hilos configurables por el usuario, persiguiendo que el tiempo de ejecución se reduzca, es decir, que el programa paralelizado se ejecute de una manera más rápida. El trabajo se ha realizado en un PC con sistema operativo Linux, pero la versión paralelizada del código puede ser empleada en un sistema empotrado multiprocesador en el cual cada procesador ejecuta el código correspondiente a uno de los hilos de la versión de PC. ABSTRACT It is increasingly common for communications systems to perform most of its functions (modulation and demodulation, coding and decoding) by software instead of than using dedicated hardware. This technique is called: “Software Radio”. The aim of the PFC is to study an implemented algorithm in C language used in modern communications systems, particularly Viterbi decoding, which amends any possible error produced during the communication, in order to be able to move multiprocessor embedded systems. Starting from a C code of the decoder that performs every single operation in serial, in this final project, this code has been parallelized, which means that the work used to be done by just a single thread in the case of serial code, is processed by a number of threads configured by the user, in order to decrease the execution time, meaning that the parallelized program is executed faster. The work has been carried out on a PC using Linux operating system, but the parallelized version of the code could also be used in an embedded multiprocessor system in which each processor executes the corresponding code to every single one of the threads of the PC version.

Study of EEPN mitigation using modified RF pilot and Viterbi-Viterbi based phase noise compensation

We propose - as a modification of the optical (RF) pilot scheme -a balanced phase modulation between two polarizations of the optical signal in order to generate correlated equalization enhanced phase noise (EEPN) contributions in the two polarizations. The method is applicable for n-level PSK system. The EEPN can be compensated, the carrier phase extracted and the nPSK signal regenerated by complex conjugation and multiplication in the receiver. The method is tested by system simulations in a single channel QPSK system at 56 Gb/s system rate. It is found that the conjugation and multiplication scheme in the Rx can mitigate the EEPN to within 1/2 orders of magnitude. Results are compared to using the Viterbi-Viterbi algorithm to mitigate the EEPN. The latter method improves the sensitivity more than two orders of magnitude. Important novel insight into the statistical properties of EEPN is identified and discussed in the paper. © 2013 Optical Society of America.

Phase noise tolerance study in coherent optical circular QAM transmissions with Viterbi- Viterbi carrier phase estimation

We present a performance evaluation of a non-conventional approach to implement phase noise tolerant optical systems with multilevel modulation formats. The performance of normalized Viterbi-Viterbi carrier phase estimation (V-V CPE) is investigated in detail for circular m-level quadrature amplitude modulation (C-mQAM) signals. The intrinsic property of C-mQAM constellation points with a uniform phase separation allows a straightforward employment of V-V CPE without the need to adapt constellation. Compared with conventional feed-forward CPE for square QAM signals, the simulated results show an enhanced tolerance of linewidth symbol duration product (ΔvTs) at a low sensitivity penalty by using feed-forward CPE structure with C-mQAM. This scheme can be easily upgraded to higher order modulations without inducing considerable complexity.

A cluster-voting approach for speaker diarization and linking of Australian broadcast news recordings

We present a clustering-only approach to the problem of speaker diarization to eliminate the need for the commonly employed and computationally expensive Viterbi segmentation and realignment stage. We use multiple linear segmentations of a recording and carry out complete-linkage clustering within each segmentation scenario to obtain a set of clustering decisions for each case. We then collect all clustering decisions, across all cases, to compute a pairwise vote between the segments and conduct complete-linkage clustering to cluster them at a resolution equal to the minimum segment length used in the linear segmentations. We use our proposed cluster-voting approach to carry out speaker diarization and linking across the SAIVT-BNEWS corpus of Australian broadcast news data. We compare our technique to an equivalent baseline system with Viterbi realignment and show that our approach can outperform the baseline technique with respect to the diarization error rate (DER) and attribution error rate (AER).

Multi Pattern Dynamic Time Warping For Automatic Speech Recognition

We are addressing the problem of jointly using multiple noisy speech patterns for automatic speech recognition (ASR), given that they come from the same class. If the user utters a word K times, the ASR system should try to use the information content in all the K patterns of the word simultaneously and improve its speech recognition accuracy compared to that of the single pattern based speech recognition. T address this problem, recently we proposed a Multi Pattern Dynamic Time Warping (MPDTW) algorithm to align the K patterns by finding the least distortion path between them. A Constrained Multi Pattern Viterbi algorithm was used on this aligned path for isolated word recognition (IWR). In this paper, we explore the possibility of using only the MPDTW algorithm for IWR. We also study the properties of the MPDTW algorithm. We show that using only 2 noisy test patterns (10 percent burst noise at -5 dB SNR) reduces the noisy speech recognition error rate by 37.66 percent when compared to the single pattern recognition using the Dynamic Time Warping algorithm.

A conditional random field-based downscaling method for assessment of climate change impact on multisite daily precipitation in the Mahanadi basin

Downscaling to station-scale hydrologic variables from large-scale atmospheric variables simulated by general circulation models (GCMs) is usually necessary to assess the hydrologic impact of climate change. This work presents CRF-downscaling, a new probabilistic downscaling method that represents the daily precipitation sequence as a conditional random field (CRF). The conditional distribution of the precipitation sequence at a site, given the daily atmospheric (large-scale) variable sequence, is modeled as a linear chain CRF. CRFs do not make assumptions on independence of observations, which gives them flexibility in using high-dimensional feature vectors. Maximum likelihood parameter estimation for the model is performed using limited memory Broyden-Fletcher-Goldfarb-Shanno (L-BFGS) optimization. Maximum a posteriori estimation is used to determine the most likely precipitation sequence for a given set of atmospheric input variables using the Viterbi algorithm. Direct classification of dry/wet days as well as precipitation amount is achieved within a single modeling framework. The model is used to project the future cumulative distribution function of precipitation. Uncertainty in precipitation prediction is addressed through a modified Viterbi algorithm that predicts the n most likely sequences. The model is applied for downscaling monsoon (June-September) daily precipitation at eight sites in the Mahanadi basin in Orissa, India, using the MIROC3.2 medium-resolution GCM. The predicted distributions at all sites show an increase in the number of wet days, and also an increase in wet day precipitation amounts. A comparison of current and future predicted probability density functions for daily precipitation shows a change in shape of the density function with decreasing probability of lower precipitation and increasing probability of higher precipitation.

Joint decoding of multiple speech patterns for robust speech recognition

We are addressing a new problem of improving automatic speech recognition performance, given multiple utterances of patterns from the same class. We have formulated the problem of jointly decoding K multiple patterns given a single Hidden Markov Model. It is shown that such a solution is possible by aligning the K patterns using the proposed Multi Pattern Dynamic Time Warping algorithm followed by the Constrained Multi Pattern Viterbi Algorithm The new formulation is tested in the context of speaker independent isolated word recognition for both clean and noisy patterns. When 10 percent of speech is affected by a burst noise at -5 dB Signal to Noise Ratio (local), it is shown that joint decoding using only two noisy patterns reduces the noisy speech recognition error rate to about 51 percent, when compared to the single pattern decoding using the Viterbi Algorithm. In contrast a simple maximization of individual pattern likelihoods, provides only about 7 percent reduction in error rate.