Estimation of phase noise in oscillators with colored noise sources

In this letter we study the design of algorithms for estimation of phase noise (PN) with colored noise sources. A soft-input maximum a posteriori PN estimator and a modified soft-input extended Kalman smoother are proposed. The performance of the proposed algorithms are compared against those studied in the literature, in terms of mean square error of PN estimation, and symbol error rate of the considered communication system. The comparisons show that considerable performance gains can be achieved by designing estimators that employ correct knowledge of the PN statistics

Data detection algorithms for perpendicular magnetic recording in the presence of strong media noise

As the throughput and density requirements increase for perpendicular magnetic recording channels, the presence of strong media noise degrades performance. Detection algorithms have been developed that increase performance in channels with strong media noise through the use of data dependent detectors. However optimal data dependent detectors are exponentially more complex than data independent detectors, and therefore cannot be fully exploited. In this thesis we shall discuss the existing detection algorithms, comparing the performance against the complexity. We then introduce a new sub-optimal detection algorithm, which employs a simple pre-detector that supplies estimates to a main detector. Numerical simulations are performed which show near optimal performance, but without the exponential increase in complexity. We will also show how detector implementations can exploit structure in the trellis to further reduce complexity, through loops and path invariants. An analytical means of measuring bit error rate from only the statistics of noise is presented, and this is utilised to optimally determine the equaliser and ISI target coefficients for a white noise Viterbi detector. Finally, we introduce a new class of VLSI binary addition algorithms which can be utilised to increase the throughput of a Viterbi detector, but which also has a wider application in hardware design

GPU Utilization: Predictive SARIMAX Time Series Analysis

This work explores collecting performance metrics and leveraging the output for prediction on a memory-intensive parallel image classification algorithm - Inception v3 (or Inception3 ). Experimental results were collected by nvidia-smi on a computational node DGX-1, equipped with eight Tesla V100 Graphic Processing Units (GPUs). Time series analysis was performed on the GPU utilization data taken, for multiple runs, of Inception3’s image classification algorithm (see Figure 1). The time series model applied was Seasonal Autoregressive Integrated Moving Average Exogenous (SARIMAX)

Empirical modeling of end-to-end delay dynamics in best-effort networks

Quality of Service (QoS) is the ability to guarantee that data sent across a network will be recieved by the desination within some constraints. For many advanced applications, such as real-time multimedia QoS is determined by four parameters--end-to-end delay, delay jitter, available bandwidth or throughput, and packet drop or loss rate. It is interesting to study and be able to predict the behavior of end-to-end packet delays in a Wide area network (WAN) because it directly a??ects the QoS of real-time distributed applications. In the current work a time-series representation of end-to-end packet delay dynamics transported over standard IP networks has been considered. As it is of interest to model the open loop delay dynamics of an IP WAN, the UDP is used for transport purposes. This research aims at developing models for single-step-ahead and multi-step-ahead prediction of moving average, one-way end-to-end delays in standard IP WAN??s. The data used in this research has been obtained from simulations performed using the widely used simulator ns-2. Simulation conditions have been tuned to enable some matching of the end-to-end delay profiles with real traffic data. This has been accomplished through the use of delay autocorrelation profiles. The linear system identification models Auto-Regressive eXogenous (AR) and Auto-Regressive Moving Average with eXtra / eXternal (ARMA) and non-linear models like the Feedforwad Multi-layer Perceptron (FMLP) have been found to perform accurate single-step-ahead predictions under varying conditions of cross-traffic flow and source send rates. However as expected, as the multi-step-ahead prediction horizon is increased, the models do not perform as accurately as the single-step-ahead prediction models. Acceptable multi-step-ahead predictions for up to 500 msec horizon have been obtained

Markov-modulated model for landing flow dynamics: An ordinal analysis validation

Air transportation is a complex system characterised by a plethora of interactions at multiple temporal and spatial scales; as a consequence, even simple dynamics like sequencing aircraft for landing can lead to the appearance of emergent behaviours, which are both difficult to control and detrimental to operational efficiency. We propose a model, based on a modulated Markov jitter, to represent ordinal pattern properties of real landing operations in European airports. The parameters of the model are tuned by minimising the distance between the probability distributions of ordinal patterns generated by the real and synthetic sequences, as estimated by the Permutation Jensen-Shannon Distance. We show that the correlation between consecutive hours in the landing flow changes between airports, and that it can be interpreted as a metric of efficiency. We further compare the dynamics pre and post COVID-19, showing how this has changed beyond what can be attributed to a simple reduction of traffic. We finally draw some operational conclusions, and discuss the applicability of these findings in a real operational environment.Comment: 12 pages, 11 figures, 2 table