Node Synchronization for the Viterbi Decoder

Motivated by the needs of NASA's Voyager 2 mission, in this paper we describe an algorithm which detects and corrects losses of node synchronization in convolutionally encoded data. This algorithm, which would be implemented as a hardware device external to a Viterbi decoder, makes statistical decisions about node synch based on the hard-quantized undecoded data stream. We will show that in a worst-case Voyager environment, our method will detect and correct a true loss of synch (thought to be a very rare event) within several hundred bits; many of the resulting outages will be corrected by the outer Reed-Solomon code. At the same time, the mean time between false alarms is on the order of several years, independent of the signal-to-noise ratio

A survey of the state-of-the-art and focused research in range systems

In this one-year renewal of NASA Contract No. 2-304, basic research, development, and implementation in the areas of modern estimation algorithms and digital communication systems have been performed. In the first area, basic study on the conversion of general classes of practical signal processing algorithms into systolic array algorithms is considered, producing four publications. Also studied were the finite word length effects and convergence rates of lattice algorithms, producing two publications. In the second area of study, the use of efficient importance sampling simulation technique for the evaluation of digital communication system performances were studied, producing two publications

Efficient Importance sampling Simulations for Digital Communication Systems

Importance sampling is a- modified. Monte Carlo simulation technique which can dramatically reduce the computational cost of the Monte Carlo method. A complete development is presented for its use in the estimation of bit error rates /V for digital communication systems with small Gaussian noise inputs. Emphasis is on the optimal mean-translation Gaussian simulation density function design and the event simulation method as applied to systems which employ quasi-regular trellis codes. These codes include the convolutional codes and many TCM (Ungerboeck) codes. Euclidean distance information of a code is utilized to facilitate the simulation. Also, the conditional importance sampling technique is presented which can handle many non-Gaussian system inputs. Theories as well as numerical examples are given. In particular, we study the simulations of an uncoded MSK and a trellis-coded 8- PSK transmissions over a general bandlimited nonlinear satellite channel model. Our algorithms are shown to be very efficient at low Pb compared to the ordinary Monte Carlo method. Many techniques we have developed are applicable to other system simulations as building blocks for their particular system configurations and channels

TCM Decoding Using Neural Networks

This paper presents a neural decoder for trellis coded modulation (TCM) schemes. Decoding is performed with Radial Basis Function Networks and Multi-Layer Perceptrons. The neural decoder effectively implements an adaptive Viterbi algorithm for TCM which learns communication channel imperfections. The implementation and performance of the neural decoder for trellis encoded 16-QAM with amplitude imbalance are analyzed

Study of information transfer optimization for communication satellites

The results are presented of a study of source coding, modulation/channel coding, and systems techniques for application to teleconferencing over high data rate digital communication satellite links. Simultaneous transmission of video, voice, data, and/or graphics is possible in various teleconferencing modes and one-way, two-way, and broadcast modes are considered. A satellite channel model including filters, limiter, a TWT, detectors, and an optimized equalizer is treated in detail. A complete analysis is presented for one set of system assumptions which exclude nonlinear gain and phase distortion in the TWT. Modulation, demodulation, and channel coding are considered, based on an additive white Gaussian noise channel model which is an idealization of an equalized channel. Source coding with emphasis on video data compression is reviewed, and the experimental facility utilized to test promising techniques is fully described

Importance Sampling Simulation of the Stack Algorithm with Application to Sequential Decoding

Importance sampling is a Monte Carlo variance reduction technique which in many applications has resulted in a significant reduction in computational cost required to obtain accurate Monte Carlo estimates. The basic idea is to generate the random inputs using a biased simulation distribution. That is, one that differs from the true underlying probability model. Simulation data is then weighted by an appropriate likelihood ratio in order to obtain an unbiased estimate of the desired parameter. This thesis presents new importance sampling techniques for the simulation of systems that employ the stack algorithm. The stack algorithm is primarily used in digital communications to decode convolutional codes, but there are also other applications. For example, sequential edge linking is a method of finding edges in images that employs the stack algorithm. In brief, the stack algorithm is an algorithm that attempts to find the maximum metric path through a large decision tree. There are two quantities that characterize its performance. First there is the probability of a branching error. The second quantity is the distribution of computation. It turns out that the number of tree nodes examined in order to make a specific branching decision is a random variable. The distribution of computation is the distribution of this random variable. The estimation of the distribution of computation, and parameters derived from this distribution, is the main goal of this work. We present two new importance sampling schemes (including some variations) for estimating the distribution of computation of the stack algorithm. The first general method is called the reference path method. This method biases noise inputs using the weight distribution of the associated convolutional code. The second method is the partitioning method. This method uses a stationary biasing of noise inputs that alters the drift of the node metric process in an ensemble average sense. The biasing is applied only up to a certain point in time; the point where the correct path node metric minimum occurs. This method is inspired by both information theory and large deviations theory. This thesis also presents another two importance sampling techniques. The first is called the error events simulation method. This scheme will be used to estimate the error probabilities of stack algorithm decoders. The second method that we shall present is a new importance sampling technique for simulating the sequential edge linking algorithm. The main goal of this presentation will be the development of the basic theory that is relevant to this simulation problem, and to discuss some of the key issues that are related to the sequential edge linking simulation

Development of Simulation Components for Wireless Communication

abstract: This thesis work present the simulation of Bluetooth and Wi-Fi radios in real life interference environments. When information is transmitted via communication channels, data may get corrupted due to noise and other channel discrepancies. In order to receive the information safely and correctly, error correction coding schemes are generally employed during the design of communication systems. Usually the simulations of wireless communication systems are done in such a way that they focus on some aspect of communications and neglect the others. The simulators available currently will either do network layer simulations or physical layer level simulations. In many situations, simulations are required which show inter-layer aspects of communication systems. For all such scenarios, a simulation environment, WiscaComm which is based on time-domain samples is built. WiscaComm allows the study of network and physical layer interactions in detail. The advantage of time domain sampling is that it allows the simulation of different radios together which is better than the complex baseband representation of symbols. The environment also supports study of multiple protocols operating simultaneously, which is of increasing importance in today's environment.Dissertation/ThesisMasters Thesis Electrical Engineering 201

Improving the Performance of Viterbi Decoder using Window System

An efficient Viterbi decoder is introduced in this paper; it is called Viterbi decoder with window system. The simulation results, over Gaussian channels, are performed from rate 1/2, 1/3 and 2/3 joined to TCM encoder with memory in order of 2, 3. These results show that the proposed scheme outperforms the classical Viterbi by a gain of 1 dB. On the other hand, we propose a function called RSCPOLY2TRELLIS, for recursive systematic convolutional (RSC) encoder which creates the trellis structure of a recursive systematic convolutional encoder from the matrix “H”. Moreover, we present a comparison between the decoding algorithms of the TCM encoder like Viterbi soft and hard, and the variants of the MAP decoder known as BCJR or forward-backward algorithm which is very performant in decoding TCM, but depends on the size of the code, the memory, and the CPU requirements of the application

The Composite Analytic and Simulation Package or RFI (CASPR) on a coded channel

CASPR is an analysis package which determines the performance of a coded signal in the presence of Radio Frequency Interference (RFI) and Additive White Gaussian Noise (AWGN). It can analyze a system with convolutional coding, Reed-Solomon (RS) coding, or a concatenation of the two. The signals can either be interleaved or non-interleaved. The model measures the system performance in terms of either the E(sub b)/N(sub 0) required to achieve a given Bit Error Rate (BER) or the BER needed for a constant E(sub b)/N(sub 0)

A study of data coding technology developments in the 1980-1985 time frame, volume 2

The source parameters of digitized analog data are discussed. Different data compression schemes are outlined and analysis of their implementation are presented. Finally, bandwidth compression techniques are given for video signals