CROSSTALK-RESILIANT CODING FOR HIGH DENSITY DIGITAL RECORDING

Increasing the track density in magnetic systems is very difficult due to inter-track interference (ITI) caused by the magnetic field of adjacent tracks. This work presents a two-track partial response class 4 magnetic channel with linear and symmetrical ITI; and explores modulation codes, signal processing methods and error correction codes in order to mitigate the effects of ITI. Recording codes were investigated, and a new class of two-dimensional run-length limited recording codes is described. The new class of codes controls the type of ITI and has been found to be about 10% more resilient to ITI compared to conventional run-length limited codes. A new adaptive trellis has also been described that adaptively solves for the effect of ITI. This has been found to give gains up to 5dB in signal to noise ratio (SNR) at 40% ITI. It was also found that the new class of codes were about 10% more resilient to ITI compared to conventional recording codes when decoded with the new trellis. Error correction coding methods were applied, and the use of Low Density Parity Check (LDPC) codes was investigated. It was found that at high SNR, conventional codes could perform as well as the new modulation codes in a combined modulation and error correction coding scheme. Results suggest that high rate LDPC codes can mitigate the effect of ITI, however the decoders have convergence problems beyond 30% ITI

Adaptive power control in CDMA cellular communication systems

Power control is an essential radio resource management method in CDMA cellular communication systems, where co-channel interference is the primary capacity-limiting factor. Power control aims to control the transmission power levels in such a way that acceptable quality of service for the users is guaranteed with lowest possible transmission powers. All users benefit from the minimized interference and the preserved signal qualities. In this thesis new closed loop power control algorithms for CDMA cellular communication systems are proposed. To cope with the random changes of the radio channel and interference, adaptive algorithms are considered that utilize ideas from self-tuning control systems. The inherent loop delay associated with closed loop power control can be included in the design process, and thus alleviated with the proposed methods. Another problem in closed-loop power control is that extensive control signaling consumes radio resources, and thus the control feedback bandwidth must be limited. A new approach to enhance the performance of closed-loop power control in limited-feedback-case is presented, and power control algorithms based on the new approach are proposed. The performances of the proposed algorithms are evaluated through both analysis and computer simulations, and compared with well-known algorithms from the literature. The results indicate that significant performance improvements are achievable with the proposed algorithms.reviewe

Proceedings of the Second International Mobile Satellite Conference (IMSC 1990)

Presented here are the proceedings of the Second International Mobile Satellite Conference (IMSC), held June 17-20, 1990 in Ottawa, Canada. Topics covered include future mobile satellite communications concepts, aeronautical applications, modulation and coding, propagation and experimental systems, mobile terminal equipment, network architecture and control, regulatory and policy considerations, vehicle antennas, and speech compression

Development of an acoustic communication link for micro underwater vehicles

PhD ThesisIn recent years there has been an increasing trend towards the use of Micro Remotely Operated Vehicles (μROVs), such as the Videoray and Seabotix LBV products, for a range of subsea applications, including environmental monitoring, harbour security, military surveillance and offshore inspection. A major operational limitation is the umbilical cable, which is traditionally used to supply power and communications to the vehicle. This tether has often been found to significantly restrict the agility of the vehicle or in extreme cases, result in entanglement with subsea structures. This thesis addresses the challenges associated with developing a reliable full-duplex wireless communications link aimed at tetherless operation of a μROV. Previous research has demonstrated the ability to support highly compressed video transmissions over several kilometres through shallow water channels with large range-depth ratios. However, the physical constraints of these platforms paired with the system cost requirements pose significant additional challenges. Firstly, the physical size/weight of transducers for the LF (8-16kHz) and MF (16-32kHz) bands would significantly affect the dynamics of the vehicle measuring less than 0.5m long. Therefore, this thesis explores the challenges associated with moving the operating frequency up to around 50kHz centre, along with the opportunities for increased data rate and tracking due to higher bandwidth. The typical operating radius of μROVs is less than 200m, in water < 100m deep, which gives rise to multipath channels characterised by long timespread and relatively sparse arrivals. Hence, the system must be optimised for performance in these conditions. The hardware costs of large multi-element receiver arrays are prohibitive when compared to the cost of the μROV platform. Additionally, the physical size of such arrays complicates deployment from small surface vessels. Although some recent developments in iterative equalisation and decoding structures have enhanced the performance of single element receivers, they are not found to be adequate in such channels. This work explores the optimum cost/performance trade-off in a combination of a micro beamforming array using a Bit Interleaved Coded Modulation with Iterative Decoding (BICM-ID) receiver structure. The highly dynamic nature of μROVs, with rapid acceleration/deceleration and complex thruster/wake effects, are also a significant challenge to reliable continuous communications. The thesis also explores how these effects can best be mitigated via advanced Doppler correction techniques, and adaptive coding and modulation via a simultaneous frequency multiplexed down link. In order to fully explore continuous adaptation of the transmitted signals, a real-time full-duplex communication system was constructed in hardware, utilising low cost components and a highly optimised PC based receiver structure. Rigorous testing, both in laboratory conditions and through extensive field trials, have enabled the author to explore the performance of the communication link on a vehicle carrying out typical operations and presenting a wide range of channel, noise, Doppler and transmission latency conditions. This has led to a comprehensive set of design recommendations for a reliable and cost effective link capable of continuous throughputs of >30 kbits/s

Constructing Neural Network-Based Models for Simulating Dynamical Systems

Dynamical systems see widespread use in natural sciences like physics, biology, chemistry, as well as engineering disciplines such as circuit analysis, computational fluid dynamics, and control. For simple systems, the differential equations governing the dynamics can be derived by applying fundamental physical laws. However, for more complex systems, this approach becomes exceedingly difficult. Data-driven modeling is an alternative paradigm that seeks to learn an approximation of the dynamics of a system using observations of the true system. In recent years, there has been an increased interest in data-driven modeling techniques, in particular neural networks have proven to provide an effective framework for solving a wide range of tasks. This paper provides a survey of the different ways to construct models of dynamical systems using neural networks. In addition to the basic overview, we review the related literature and outline the most significant challenges from numerical simulations that this modeling paradigm must overcome. Based on the reviewed literature and identified challenges, we provide a discussion on promising research areas

The Roles of Language Models and Hierarchical Models in Neural Sequence-to-Sequence Prediction

With the advent of deep learning, research in many areas of machine learning is converging towards the same set of methods and models. For example, long short-term memory networks are not only popular for various tasks in natural language processing (NLP) such as speech recognition, machine translation, handwriting recognition, syntactic parsing, etc., but they are also applicable to seemingly unrelated fields such as robot control, time series prediction, and bioinformatics. Recent advances in contextual word embeddings like BERT boast with achieving state-of-the-art results on 11 NLP tasks with the same model. Before deep learning, a speech recognizer and a syntactic parser used to have little in common as systems were much more tailored towards the task at hand. At the core of this development is the tendency to view each task as yet another data mapping problem, neglecting the particular characteristics and (soft) requirements tasks often have in practice. This often goes along with a sharp break of deep learning methods with previous research in the specific area. This work can be understood as an antithesis to this paradigm. We show how traditional symbolic statistical machine translation models can still improve neural machine translation (NMT) while reducing the risk for common pathologies of NMT such as hallucinations and neologisms. Other external symbolic models such as spell checkers and morphology databases help neural grammatical error correction. We also focus on language models that often do not play a role in vanilla end-to-end approaches and apply them in different ways to word reordering, grammatical error correction, low-resource NMT, and document-level NMT. Finally, we demonstrate the benefit of hierarchical models in sequence-to-sequence prediction. Hand-engineered covering grammars are effective in preventing catastrophic errors in neural text normalization systems. Our operation sequence model for interpretable NMT represents translation as a series of actions that modify the translation state, and can also be seen as derivation in a formal grammar.EPSRC grant EP/L027623/1 EPSRC Tier-2 capital grant EP/P020259/