Feedforward data-aided phase noise estimation from a DCT basis expansion

This contribution deals with phase noise estimation from pilot symbols. The phase noise process is approximated by an expansion of discrete cosine transform (DCT) basis functions containing only a few terms. We propose a feedforward algorithm that estimates the DCT coefficients without requiring detailed knowledge about the phase noise statistics. We demonstrate that the resulting (linearized) mean-square phase estimation error consists of two contributions: a contribution from the additive noise, that equals the Cramer-Rao lower bound, and a noise independent contribution, that results front the phase noise modeling error. We investigate the effect of the symbol sequence length, the pilot symbol positions, the number of pilot symbols, and the number of estimated DCT coefficients it the estimation accuracy and on the corresponding bit error rate (PER). We propose a pilot symbol configuration allowing to estimate any number of DCT coefficients not exceeding the number of pilot Symbols, providing a considerable Performance improvement as compared to other pilot symbol configurations. For large block sizes, the DCT-based estimation algorithm substantially outperforms algorithms that estimate only the time-average or the linear trend of the carrier phase. Copyright (C) 2009 J. Bhatti and M. Moeneclaey

Feedforward pilot-aided carrier synchronization using a DCT basis expansion

This contribution deals with phase noise estimation from pilot symbols. The phase noise process is approximated by an expansion of Discrete Cosine-Transform (DCT) basis functions containing only a few terms. We propose a feedforward algorithm that estimates the DCT coefficients without requiring detailed knowledge about the phase noise statistics. We demonstrate that the resulting (linearized) mean-square phase estimation error consists of two contributions: a contribution from the additive noise, that equals the Cramer-Rao lower bound, and a noise-independent contribution that results from the phase noise modeling error. We investigate the effect of the symbol sequence length and the number of estimated DCT coefficients on the estimation accuracy and on the corresponding bit error rate (BER). We propose a pilot symbol configuration allowing to estimate any number of DCT coefficients not exceeding the number of pilot symbols. For large block sizes, the DCT-based estimation algorithm substantially outperforms algorithms that estimate only the time-average or the linear trend of the carrier phase

Pilot-aided carrier synchronization using an approximate DCT-based phase noise model

This contribution deals with phase noise estimation from pilot symbols. The phase noise process is approximated by an expansion of DCT basis functions containing only a few terms. We propose an algorithm that estimates the DCT coefficients without requiring detailed knowledge about the phase noise statistics. We demonstrate that the resulting (linearized) mean-square estimation error consists of two contributions: a contribution from the additive noise, that equals the Cramer-Rao lower bound, and a noise-independent contribution that results from the phase noise modeling error. Performance can be optimized by a proper selection of the symbol block length and of the number of DCT coefficients to be estimated. For large block sizes, considerable performance improvement is found as compared to the case where only the time-average of the carrier phase is estimated

Optimization of pilot-aided DCT-based phase noise estimation

The presented work addresses the issue of phase noise estimation for pilot-aided burst-mode transmission in digital communication systems. We propose to estimate the phase noise from a truncated discrete-cosine transform (DCT) expansion model. The key idea is to reconstruct the low-pass phase noise process via only a small number N of DCT coefficients of the phase expansion. An evident question that arises is how to choose N. Based on a few valid approximations, we derive an analytical expression of the bit-error rate (BER) degradation in the presence of residual phase noise, which allows us to determine the value of N that yields the minimum BER degradation

Performance analysis of iterative decision-directed phase noise estimation

This contribution deals with estimation and compensation of phase noise in single-carrier digital communications. We present an iterative feedforward decision-directed phase noise estimation algorithm, that is based on approximating the phase noise process by an expansion of DCT basis functions containing only a few terms. An extension to the estimation algorithm is proposed, improving the performance in terms of the mean-square error. We demonstrate that the resulting (linearized) mean-square estimation error consists of two contributions: a contribution from the additive noise, that equals the Cramer-Rao lower bound, and a noise-independent contribution that results from the phase noise modeling error. The phase estimate that yields the lowest possible mean-square error is obtained, assuming knowledge of the phase noise statistics at the receiver

Low-complexity frequency offset and phase noise estimation for burst-mode digital transmission

The presence of a frequency offset (FO) and phase noise can cause severe performance degradation in digital communication systems. This work combines a simple FO estimation technique with a low-complexity phase noise estimation method, inspired by the space-alternating generalized expectation-maximization algorithm. Using a truncated discrete-cosine transform (DCT) expansion, the phase noise estimate is derived from the estimated DCT coefficients of the phase. A number of implementations of the proposed algorithm are discussed. Numerical results indicate that when estimating the FO from pilot symbols only, comparable performance can be reached as the computationally more complex case where the FO is updated iteratively, with small convergence time. The phase noise estimation step is well capable of compensating for the residual FO. For the considered scenario, performing FO compensation before iterative phase noise estimation yields a bit-error rate performance degradation close to the case where the FO is known

Computationally-efficient iterative demodulation of coded PSK signals affected by phase noise

This paper considers two recently-proposed receivers, Tikh and DCT. Both receivers are computationally-efficient, iterative and designed to be robust against phase noise on the local oscillators of digital bandpass communication systems. The presented results build on our prior research. We discuss the initialization of the DCT receiver, explore reducing the computational complexity by simplifying the receiver scheduling and study the effect of a small frequency offset. Coded PSK signaling and additive white Gaussian noise are assumed

Improving OWL RL reasoning in N3 by using specialized rules

Semantic Web reasoning can be a complex task: depending on the amount of data and the ontologies involved, traditional OWL DL reasoners can be too slow to face problems in real time. An alternative is to use a rule-based reasoner together with the OWL RL/RDF rules as stated in the specification of the OWL 2 language profiles. In most cases this approach actually improves reasoning times, but due to the complexity of the rules, not as much as it could. In this paper we present an improved strategy: based on the TBoxes of the ontologies involved in a reasoning task, we create more specific rules which then can be used for further reasoning. We make use of the EYE reasoner and its logic Notation3. In this logic, rules can be employed to derive new rules which makes the rule creation a reasoning step on its own. We evaluate our implementation on a semantic nurse call system. Our results show that adding a pre-reasoning step to produce specialized rules improves reasoning times by around 75 %

Ontology reasoning using rules in an eHealth context

Traditionally, nurse call systems in hospitals are rather simple: patients have a button next to their bed to call a nurse. Which specific nurse is called cannot be controlled, as there is no extra information available. This is different for solutions based on semantic knowledge: if the state of care givers (busy or free), their current position, and for example their skills are known, a system can always choose the best suitable nurse for a call. In this paper we describe such a semantic nurse call system implemented using the EYE reasoner and Notation3 rules. The system is able to perform OWL-RL reasoning. Additionally, we use rules to implement complex decision trees. We compare our solution to an implementation using OWL-DL, the Pellet reasoner, and SPARQL queries. We show that our purely rule-based approach gives promising results. Further improvements will lead to a mature product which will significantly change the organization of modern hospitals