Constant modulus blind equalisation algorithms under soft constraint satisfaction

New constant modulus (CM) algorithms are presented that are based on soft constraint satisfaction. The stationary points of an algorithm in this family are studied for an AR(p) channel and it is shown that Ding-type undesirable local solutions (ULS) do not exist. This is due to the normalisation of the gradient vector and the soft nonlinearity used in these algorithms. Error performance surfaces (EPS) and convergence trajectories from arbitrary initialisations are presented for various channels that support the analytical finding

A Novel Approach for Adaptive Signal Processing

Adaptive linear predictors have been used extensively in practice in a wide variety of forms. In the main, their theoretical development is based upon the assumption of stationarity of the signals involved, particularly with respect to the second order statistics. On this basis, the well-known normal equations can be formulated. If high- order statistical stationarity is assumed, then the equivalent normal equations involve high-order signal moments. In either case, the cross moments (second or higher) are needed. This renders the adaptive prediction procedure non-blind. A novel procedure for blind adaptive prediction has been proposed and considerable implementation has been made in our contributions in the past year. The approach is based upon a suitable interpretation of blind equalization methods that satisfy the constant modulus property and offers significant deviations from the standard prediction methods. These blind adaptive algorithms are derived by formulating Lagrange equivalents from mechanisms of constrained optimization. In this report, other new update algorithms are derived from the fundamental concepts of advanced system identification to carry out the proposed blind adaptive prediction. The results of the work can be extended to a number of control-related problems, such as disturbance identification. The basic principles are outlined in this report and differences from other existing methods are discussed. The applications implemented are speech processing, such as coding and synthesis. Simulations are included to verify the novel modelling method

Adaptive soft-constraint satisfaction (SCS) algorithms for fractionally-spaced blind equalizers

Constant modulus algorithms based on a deterministic error criterion are presented. Soft-constraint satisfaction methods yield a general family of blind equalization algorithms employing nonlinear functions of the equalizer output which must satisfy certain conditions. The algorithms are also extended to cover fractionally-spaced blind equalization. A normalization factor which appears as a result of the deterministic formulation of the problem helps the blind equalizer improve its performance. Also, the family supports a wide range of nonlinear functions. Extensive simulations are presented to reveal convergence characteristics which also include signals from the signal processing information base (SPIB

New block-based blind equalization algorithms

New block-based blind equalization algorithms are introduced based upon the cost function underlying the recently proposed soft constraint satisfaction blind equalization algorithm. The derivation of these .algorithms is based on mapping the original constrained optimization problem in CN into a much simpler optimization problem in W2. Versions of the new algorithms are also developed for fractionally-spaced equalizers. Simulations on a baud-spaced and a fractionally-spaced channel support the potential of the resulting block-based techniques

Optics for AI and AI for Optics

Artificial intelligence is deeply involved in our daily lives via reinforcing the digital transformation of modern economies and infrastructure. It relies on powerful computing clusters, which face bottlenecks of power consumption for both data transmission and intensive computing. Meanwhile, optics (especially optical communications, which underpin today’s telecommunications) is penetrating short-reach connections down to the chip level, thus meeting with AI technology and creating numerous opportunities. This book is about the marriage of optics and AI and how each part can benefit from the other. Optics facilitates on-chip neural networks based on fast optical computing and energy-efficient interconnects and communications. On the other hand, AI enables efficient tools to address the challenges of today’s optical communication networks, which behave in an increasingly complex manner. The book collects contributions from pioneering researchers from both academy and industry to discuss the challenges and solutions in each of the respective fields