Acquisition Of Pseudonoise Sequences Research Articles

Cognitive Computation

Probabilistic programming allows artificial systems to better operate with uncertainty, and stochastic arithmetic provides a way to carry out approximate computations with few resources. As such, both are plausible models for natural cognition. The authors' work on the automatic design of probabilistic machines computing soft inferences, with an arithmetic based on stochastic bitstreams, allowed to develop the following compilation toolchain: given a high-level description of some general problem, formalized as a Bayesian Program, the toolchain automatically builds a low-level description of an electronic circuit computing the corresponding probabilistic inference. This circuit can then be implemented and tested on reconfigurable logic. This paper describes two circuits as validating examples. The first one implements a Bayesian filter solving the problem of Pseudo Noise sequence acquisition in telecommunications. The second one implements decision making in a sensorimotor system: it allows a simple robot to avoid obstacles using Bayesian sensor fusion.

A Simplified Detection Method for PN Sequence Acquisition and its Performance Analysis

Some OFDM signal systems use PN (Pseudo-noise) sequence in time domain to synchronize. During the acquisition progress, the ground noise power has to be calculated, and the determination of the detection threshold is important. This paper introduced a simplified detection method for PN sequence, and analyzed the threshold to optimize the performance.

Rapid Acquisition of PN Sequences With a New Decision Logic

In this paper, a fast acquisition scheme of pseudonoise (PN) sequences for direct-sequence spread-spectrum systems is proposed. The scheme exploits a new decision logic not only to estimate chip values, but also to check the reliability of the chip estimates. As a result, the internal state of a PN sequence generator is estimated more accurately. We derive the probability of finding a correct state estimate and show that the proposed scheme can reduce the average number of chips for acquisition when compared with the conventional scheme. It is also shown that the performance improvement is more noticeable in the moderate signal-to-noise-ratio range.

Seed accumulating sequential estimation for PN sequence acquisition at low signal-to-noise ratio

The sequential estimation (SE) proposed by Ward for rapid acquisition of pseudo-noise (PN) sequences performs well only at moderate chip signal-to-noise ratios (SNRs). In this paper, a seed accumulating sequential estimation (SASE) method based on accumulated seeds of the received PN sequence is proposed, which performs well at low chip SNR also. The mean acquisition time performance of the SASE method is investigated. Numerical results show that the SASE performs dramatically better than the SE at low chip SNR and the improvement becomes larger as the period of PN sequence increases.

A coherent acquisition method for a PN sequence using binary search and an auxiliary sequence

Direct sequence spread spectrum systems require a local replica of the pseudonoise (PN) sequence to be synchronized with the received signal's spreading code. A method is proposed to improve the PN sequence acquisition time and decrease the acquisition time variability. The proposed method is based on a binary search algorithm. At each stage of the algorithm, an auxiliary signal is used to formulate a new search region within the PN sequence, and at each stage the search region is reduced by half. The region containing the incoming PN phase is therefore obtained within O(log/sub 2/ (N+1)) steps. A closed loop coherent PN acquisition algorithm presented by Salih and Tantaratana (1996) motivated the use of the auxiliary signal, and the algorithm of Salih et al. is also used for comparison. Results indicate the algorithm presented in this paper can achieve a factor of two to three times improvement in the mean acquisition time with a significant decrease in the acquisition time variability.

Adaptive acquisition of PN sequences for DSSS communications

Since the received signal levels in mobile communications are unknown and the location is varying, acquisition schemes for pseudonoise (PN) sequences with fixed thresholds cannot provide satisfactory performance. This fixed-threshold scheme may cause too many false alarms or result in a low detection probability for a selected threshold value. We present an adaptive acquisition scheme for PN sequences which estimate the background power level, multiply it with a threshold coefficient to keep the false alarms constant, and use it as a threshold.

Pseudo noise sequences for engineers

Pseudo random binary sequences (PRBSs), also known as pseudo noise (PN), linear feedback shift register (LFSR) sequences or maximal length binary sequences (m-sequences) are widely used in digital communications and the theory involved has been treated extensively in the literature. However, a practising engineer is interested in the fundamentals and the applications of PN sequences, and the methods of generating them with hardware. This paper presents, without the mathematical rigours, some of the interesting characteristics and the use of these characteristics in the generation and acquisition of PN sequences. The series-parallel method of generating PN sequences at high speeds with low-speed devices, which is of interest to hardware designers, is discussed. Some applications of PN sequences in communications and instrumentation are discussed.

Acquisition of PN sequences in chip synchronous DS/SS systems using a random sequence model and the SPRT

The use of a sequential probability ratio test (SPRT) for the acquisition of pseudonoise (PN) sequences in chip syn- chronous direct-sequence spread-spectrum (DS/SS) systems is considered. The out-of-phase sequence is modeled as a random sequence and the probabilities of error and expected sample sizes for the corresponding test are derived. A different (and very commonly used) test is obtained if the out-of-phase sequence is modeled as a zero sequence. The probabilities of error and the expected sample sizes of both SPRT's are compared, and it is shown that the latter test has a significantly larger probability of type I error. Numerical evaluation of the performance of both tests applied to a PN sequence of period 21° - 1 gives results in agreement with the analytical results. We conclude that a random sequence is an excellent model for a PN sequence, and that significant degradation in performance can be expected if the test design is based on the zero sequence model rather than on the random sequence model.

Sequential acquisition of PN sequences for DS/SS communications: design and performance

The performance of sequential acquisition of m sequences, based on the sequential probability ratio test (SPRT), is studied. The authors consider a sliding correlator-type structure for the acquisition scheme, which the mean acquisition time is proportional to the average number of samples used for a synchronization test. Sequential acquisition requires the knowledge of the partial correlation of the pseudonoise (PN) sequence, however, as the partial correlation of an m sequence is difficult to model the authors propose an approximate upper bound. This is then piecewise linearized and used for designing the SPRT which is then compared to the fixed-dwell time technique, a scheme using the fixed sample size test. Numerical results show that the saving of acquisition time by the SPRT increases as the desired probabilities of errors and/or the input SNR decrease. Analytic results are verified by computer simulation.< <ETX xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">&gt;</ETX>