Cost-reduction using non-uniform traffic in optical networks

Next-generation networks are likely to be non-uniform in all their aspects, including number of lightpaths carried per link, number of wavelengths per link, number of fibres per link, asymmetry of the links, and traffic flows. Routing and wavelength allocation models generally assume that the optical network is uniform and that the number of wavelengths per link is a constant. In practice however, some nodes and links carry heavy traffic and additional wavelengths are needed in those links. We study a wavelength-routed optical network based on the UK JANET topology where traffic demands between nodes are assumed to be non-uniform. We investigate how network capacity can be increased by locating congested links and suggesting cost-effective upgrades. Different traffic demands patterns, hop distances, number of wavelengths per link, and routing algorithms are considered. Numerical results show that a 95% increase in network capacity is possible by overlaying fibre on just 5% of existing links. We conclude that non-uniform traffic allocation can be beneficial to localize traffic in nodes and links deep in the network core and provisioning of additional resources there can efficiently and cost-effectively increase network capacity. © 2013 IEEE.

Weakly-constrained codes for suppression of patterning effects in digital communications

We propose weakly-constrained stream and block codes with tunable pattern-dependent statistics and demonstrate that the block code capacity at large block sizes is close to the the prediction obtained from a simple Markov model published earlier. We demonstrate the feasibility of the code by presenting original encoding and decoding algorithms with a complexity log-linear in the block size and with modest table memory requirements. We also show that when such codes are used for mitigation of patterning effects in optical fibre communications, a gain of about 0.5dB is possible under realistic conditions, at the expense of small redundancy 10%). © 2006 IEEE.

Improved data visualisation through multiple dissimilarity modelling

Popular dimension reduction and visualisation algorithms rely on the assumption that input dissimilarities are typically Euclidean, for instance Metric Multidimensional Scaling, t-distributed Stochastic Neighbour Embedding and the Gaussian Process Latent Variable Model. It is well known that this assumption does not hold for most datasets and often high-dimensional data sits upon a manifold of unknown global geometry. We present a method for improving the manifold charting process, coupled with Elastic MDS, such that we no longer assume that the manifold is Euclidean, or of any particular structure. We draw on the benefits of different dissimilarity measures allowing for the relative responsibilities, under a linear combination, to drive the visualisation process.

Weakly-constrained codes for suppression of patterning effects in digital communications

We propose weakly-constrained stream and block codes with tunable pattern-dependent statistics and demonstrate that the block code capacity at large block sizes is close to the the prediction obtained from a simple Markov model published earlier. We demonstrate the feasibility of the code by presenting original encoding and decoding algorithms with a complexity log-linear in the block size and with modest table memory requirements. We also show that when such codes are used for mitigation of patterning effects in optical fibre communications, a gain of about 0.5dB is possible under realistic conditions, at the expense of small redundancy (≈10%). © 2010 IEEE