Applications of Non-Orthogonal Waveforms and Artificial Neural Networks in Wireless Vehicular Communications

Ph. D. ThesisWe live in an ever increasing world of connectivity. The need for highly robust, highly efficient wireless communication has never been greater. As we seek to squeeze better and better performance from our systems, we must remember; even though our computing devices are increasing in power and efficiency, our wireless spectrum remains limited. Recently there has been an increasing trend towards the implementation of machine learning based systems in wireless communications. By taking advantage of a neural networks powerful non-linear computational capability, communication systems have been shown to achieve reliable error free transmission over even the most dispersive of channels. Furthermore, in an attempt to make better use of the available spectrum, more spectrally efficient physical layer waveforms are gathering attention that trade increased interference for lower bandwidth requirements. In this thesis, the performance of neural networks that utilise spectrally efficient waveforms within harsh transmission environments are assessed. Firstly, we investigate and generate a novel neural network for use within a standards compliant vehicular network for vehicle-to-vehicle communication, and assess its performance practically in several of the harshest recorded empirical channel models using a hardware-in-the-loop testing methodology. The results demonstrate the strength of the proposed receiver, achieving a bit-error rate below 10−3 at a signal-to-noise ratio (SNR) of 6dB. Secondly, this is then further extended to utilise spectrally efficient frequency division multiplexing (SEFDM), where we note a break away from the 802.11p vehicular communication standard in exchange for a more efficient use of the available spectrum that can then be utilised to service more users or achieve a higher data throughput. It is demonstrated that the proposed neural network system is able to act as a joint channel equaliser and symbol receiver with bandwidth compression of up to 60% when compared to orthogonal frequency division multiplexing (OFDM). The effect of overfitting to the training environment is also tested, and the proposed system is shown to generalise well to unseen vehicular environments with no notable impact on the bit-error rate performance. Thirdly, methods for generating inputs and outputs of neural networks from complex constellation points are investigated, and it is reasoned that creating ‘split complex’ neural networks should not be preferred over ‘contatenated complex’ neural networks in most settings. A new and novel loss function, namely error vector magnitude (EVM) loss, is then created for the purposes of training neural networks in a communications setting that tightly couples the objective function of a neural network during training to the performance metrics of transmission when deployed practically. This loss function is used to train neural networks in complex environments and is then compared to popular methods from the literature where it is demonstrated that EVM loss translates better into practical applications. It achieved the lowest EVM error, thus bit-error rate, across all experiments by a margin of 3dB when compared to its closest achieving alternative. The results continue and show how in the experiment EVM loss was able to improve spectral efficiency by 67% over the baseline without affecting performance. Finally, neural networks combined with the new EVM loss function are further tested in wider communication settings such as visible light communication (VLC) to validate the efficacy and flexibility of the proposed system. The results show that neural networks are capable of overcoming significant challenges in wireless environments, and when paired with efficient physical layer waveforms like SEFDM and an appropriate loss function such as EVM loss are able to make good use of a congested spectrum. The authors demonstrated for the first time in practical experimentation with SEFDM that spectral efficiency gains of up to 50% are achievable, and that previous SEFDM limitations from the literature with regards to number of subcarriers and size of the transmit constellation are alleviated via the use of neural networksEPSRC, Newcastle Universit

ELLIPSIS AS A MARKER OF INTERACTION IN SPOKEN DISCOURSE

In this article, we discuss strategies for interaction in spoken discourse, focusing on ellipsis phenomena in English. The data comes from the VOICE corpus of English as a Lingua Franca, and we analyse education data in the form of seminar and workshop discussions, working group meetings, interviews and conversations. The functions ellipsis carries in the data are Intersubjectivity, where participants develop and maintain an understanding in discourse; Continuers, which are examples of back channel support; Correction, both self- and other-initiated; Repetition; and Comments, which are similar to Continuers but do not have a back channel support function. We see that the first of these, Intersubjectivity, is by far the most popular, followed by Repetitions and Comments. These results are explained as consequences of the nature of the texts themselves, as some are discussions of presentations and so can be expected to contain many Repetitions, for example. The speech event is also an important factor, as events with asymmetrical power relations like interviews do not contain so many Continuers. Our clear conclusion is that the use of ellipsis is a strong marker of interaction in spoken discourse

Psychological approaches to understanding and promoting recovery in psychosis and bipolar disorder:a mixed-methods approach

BackgroundRecovery in mental health is a relatively new concept, but it is becoming more accepted that people can recover from psychosis. Recovery-orientated services are recommended for adult mental health, but with little evidence base to support this. ObjectivesTo facilitate understanding and promotion of recovery in psychosis and bipolar disorder (BD), in a manner that is empowering and acceptable to service users. MethodThere were six linked projects using qualitative and quantitative methodologies: (1) developing and piloting a service user-defined measure of recovery; (2) a Delphi study to determine levels of consensus around the concept of recovery; (3) examination of the psychological factors associated with recovery and how these fluctuate over time; (4) development and evaluation of cognitive–behavioural approaches to guided self-help including a patient preference trial (PPT); (5) development and evaluation of cognitive–behavioural therapy (CBT) for understanding and preventing suicide in psychosis including a randomised controlled trial (RCT); and (6) development and evaluation of a cognitive–behavioural approach to recovery in recent onset BD, including a RCT of recovery-focused cognitive–behavioural therapy (RfCBT). Service user involvement was central to the programme. ResultsMeasurement of service user-defined recovery from psychosis (using the Subjective Experience of Psychosis Scale) and BD (using the Bipolar Recovery Questionnaire) was shown to be feasible and valid. The consensus study revealed a high level of agreement among service users for defining recovery, factors that help or hinder recovery and items which demonstrate recovery. Negative emotions, self-esteem and hope predicted recovery judgements, both cross-sectionally and longitudinally, whereas positive symptoms had an indirect effect. In the PPT, 89 participants entered the study, three were randomised, 57 were retained in the trial until 15-month follow-up (64%). At follow-up there was no overall treatment effect on the primary outcome (Questionnaire about the Process of Recovery total; p = 0.82). In the suicide prevention RCT, 49 were randomised and 35 were retained at 6-month follow-up (71%). There were significant improvements in suicidal ideation [Adult Suicidal Ideation Questionnaire; treatment effect = –12.3, 95% confidence interval (CI) –24.3 to –0.14], Suicide Probability Scale (SPS; treatment effect = –7.0, 95% CI –15.5 to 0) and hopelessness (subscale of the SPS; treatment effect = –3.8, 95% CI –7.3 to –0.5) at follow-up. In the RCT for BD, 67 participants were randomised and 45 were retained at the 12-month follow-up (67%). Recovery score significantly improved in comparison with treatment as usual (TAU) at follow-up (310.87, 95% CI 75.00 to 546.74). At 15-month follow-up, 32 participants had experienced a relapse of either depression or mania (20 TAU vs. 12 RfCBT). The difference in time to recurrence was significant (estimated hazard ratio 0.38, 95% CI 0.18 to 0.78; p < 0.006). ConclusionsThis research programme has improved our understanding of recovery in psychosis and BD. Key findings indicate that measurement of recovery is feasible and valid. It would be feasible to scale up the RCTs to assess effectiveness of our therapeutic approaches in larger full trials, and two of the studies (CBT for suicide prevention in psychosis and recovery in BD) found significant benefits on their primary outcomes despite limited statistical power, suggesting definitive trials are warranted. FundingThe National Institute for Health Research Programme Grants for Applied Research programme