20 Gb/s QPSK transmission using an electro-optically modulated vertical-cavity surface-emitting laser

20 Gb/s QPSK transmission over 100 m of OM3 fibre using an EOM VCSEL under QPSK modulation is reported. Bit-error-ratio measurements are carried out to express the quality of the transmission scheme. © 2011 OSA.

Mode division multiplexing exploring hollow-core photonic bandgap fibers

We review our recent exploratory investigations on mode division multiplexing using hollow-core photonic bandgap fibers (HC-PBGFs). Compared with traditional multimode fibers, HC-PBGFs have several attractive features such as ultra-low nonlinearities, low-loss transmission window around 2 μm etc. After having discussed the potential and challenges of using HC-PBGFs as transmission fibers for mode multiplexing applications, we will report a number of recent proof-of-concept results obtained in our group using direct detection receivers. The first one is the transmission of two 10.7 Gbit/s non-return to zero (NRZ) data signals over a 30 m 7-cell HC-PBGF using the offset mode launching method. In another experiment, a short piece of 19-cell HC-PBGF was used to transmit two 20 Gbit/s NRZ channels using a spatial light modulator for precise mode excitation. Bit-error-ratio (BER) performances below the forward-error-correction (FEC) threshold limit (3.3×10-3) are confirmed for both data channels when they propagate simultaneously. © 2013 IEEE.

20 Gb/s QPSK transmission using an electro-optically modulated vertical-cavity surface-emitting laser

20 Gb/s QPSK transmission over 100 m of OM3 fibre using an EOM VCSEL under QPSK modulation is reported. Bit-error-ratio measurements are carried out to express the quality of the transmission scheme. © 2011 Optical Society of America.

Sequential decision making in general state space models

Many problems in control and signal processing can be formulated as sequential decision problems for general state space models. However, except for some simple models one cannot obtain analytical solutions and has to resort to approximation. In this thesis, we have investigated problems where Sequential Monte Carlo (SMC) methods can be combined with a gradient based search to provide solutions to online optimisation problems. We summarise the main contributions of the thesis as follows. Chapter 4 focuses on solving the sensor scheduling problem when cast as a controlled Hidden Markov Model. We consider the case in which the state, observation and action spaces are continuous. This general case is important as it is the natural framework for many applications. In sensor scheduling, our aim is to minimise the variance of the estimation error of the hidden state with respect to the action sequence. We present a novel SMC method that uses a stochastic gradient algorithm to find optimal actions. This is in contrast to existing works in the literature that only solve approximations to the original problem. In Chapter 5 we presented how an SMC can be used to solve a risk sensitive control problem. We adopt the use of the Feynman-Kac representation of a controlled Markov chain flow and exploit the properties of the logarithmic Lyapunov exponent, which lead to a policy gradient solution for the parameterised problem. The resulting SMC algorithm follows a similar structure with the Recursive Maximum Likelihood(RML) algorithm for online parameter estimation. In Chapters 6, 7 and 8, dynamic Graphical models were combined with with state space models for the purpose of online decentralised inference. We have concentrated more on the distributed parameter estimation problem using two Maximum Likelihood techniques, namely Recursive Maximum Likelihood (RML) and Expectation Maximization (EM). The resulting algorithms can be interpreted as an extension of the Belief Propagation (BP) algorithm to compute likelihood gradients. In order to design an SMC algorithm, in Chapter 8 uses a nonparametric approximations for Belief Propagation. The algorithms were successfully applied to solve the sensor localisation problem for sensor networks of small and medium size.

Packet error rate and bit error rate non-deterministic relationship in optical network applications

The non-deterministic relationship between Bit Error Rate and Packet Error Rate is demonstrated for an optical media access layer in common use. We show that frequency components of coded, non-random data can cause this relationship. © 2005 Optical Society of America.

Fixed-lag blind equalization and sequence estimation in digital communications systems using sequential importance sampling

We present methods for fixed-lag smoothing using Sequential Importance sampling (SIS) on a discrete non-linear, non-Gaussian state space system with unknown parameters. Our particular application is in the field of digital communication systems. Each input data point is taken from a finite set of symbols. We represent transmission media as a fixed filter with a finite impulse response (FIR), hence a discrete state-space system is formed. Conventional Markov chain Monte Carlo (MCMC) techniques such as the Gibbs sampler are unsuitable for this task because they can only perform processing on a batch of data. Data arrives sequentially, so it would seem sensible to process it in this way. In addition, many communication systems are interactive, so there is a maximum level of latency that can be tolerated before a symbol is decoded. We will demonstrate this method by simulation and compare its performance to existing techniques.

Forward Smoothing Using Sequential Monte Carlo

Sequential Monte Carlo (SMC) methods are a widely used set of computational tools for inference in non-linear non-Gaussian state-space models. We propose a new SMC algorithm to compute the expectation of additive functionals recursively. Essentially, it is an on-line or "forward only" implementation of a forward filtering backward smoothing SMC algorithm proposed by Doucet, Godsill and Andrieu (2000). Compared to the standard \emph{path space} SMC estimator whose asymptotic variance increases quadratically with time even under favorable mixing assumptions, the non asymptotic variance of the proposed SMC estimator only increases linearly with time. We show how this allows us to perform recursive parameter estimation using an SMC implementation of an on-line version of the Expectation-Maximization algorithm which does not suffer from the particle path degeneracy problem.