90 research outputs found

    Single-Frequency Network Terrestrial Broadcasting with 5GNR Numerology

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    Signal Processing and Learning for Next Generation Multiple Access in 6G

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    Wireless communication systems to date primarily rely on the orthogonality of resources to facilitate the design and implementation, from user access to data transmission. Emerging applications and scenarios in the sixth generation (6G) wireless systems will require massive connectivity and transmission of a deluge of data, which calls for more flexibility in the design concept that goes beyond orthogonality. Furthermore, recent advances in signal processing and learning have attracted considerable attention, as they provide promising approaches to various complex and previously intractable problems of signal processing in many fields. This article provides an overview of research efforts to date in the field of signal processing and learning for next-generation multiple access, with an emphasis on massive random access and non-orthogonal multiple access. The promising interplay with new technologies and the challenges in learning-based NGMA are discussed

    Enhanced energy and spectrum efficiency in visible light communications

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    In recent years, there has been a surge in data traffic, leading to the investigation of using optical frequencies in conjunction with radio frequency (RF) wireless communication systems. One such technology is visible light communication (VLC), which uses light-emitting diodes (LEDs) in the visible light spectrum to transmit data. VLC has gained popularity for short-range wireless connections due to its energy efficiency, low-cost, and wide availability of front-end devices. However, one of the main challenges in designing a VLC system is improving its energy and spectral efficiency. This thesis aims to investigate techniques and determine the most effective methods for enhancing the energy and spectral efficiency of VLC systems. The thesis examined methods for optimising the bias point of an LED to benefit from increasing bandwidth at higher driving current while minimising the resulting signal distortion. The approaches are based on allowing for some nonlinear distortion or reducing signal swing/signal-to-noise ratio (SNR) while benefiting from higher bandwidth at higher driving currents. A framework is presented to estimate the attainable capacity under both conditions. Simulation results showed that the optimal bias point does not lie in the middle of the dynamic range. This was verified through a PAM-based VLC experiment, which showed that the transmission rate can be increased by choosing the optimal bias current instead of the midpoint of the linear range. Subsequently, VLC with probabilistic shaping (PS) is studied to optimise the distribution of source symbols and improve system performance. In this study, the error performance of PS is analysed, and closed-form analytical expressions are provided. The results show that PS outperforms the conventional uniform distribution and significantly reduces the required SNR to achieve a certain error probability. To demonstrate the practical application of PS in VLC, it was implemented in conjunction with optical orthogonal frequency-division multiplexing (OFDM) modulation. This allowed for continuous and adaptive loading of information bits to the channel response, resulting in an efficient use of available modulation bandwidth and transmission rates close to the channel capacity limits. In the two experimental demonstrations, a single low-power LED and a wavelength-division multiplexing (WDM) system using three off-the-shelf LEDs were used to achieve bit rates of 1.13~Gbps and 10.81~Gbps, respectively, representing increases of 27.13\% and 25.7\% over the traditional bit-power loading technique. Finally, an alternative approach towards enhancing the energy of VLC systems is introduced using frequency shift chirp modulation (FSCM). The error performance of FSCM was analysed in different types of channels, and a proof-of-concept experiment was conducted to demonstrate its potential use in VLC systems. FSCM offers improved robustness in band-limited, frequency-selective channels compared to other modulation techniques. This makes it a promising choice for integrating into VLC systems, particularly in low-power and low-rate application scenarios

    Spectrally and Energy Efficient Wireless Communications: Signal and System Design, Mathematical Modelling and Optimisation

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    This thesis explores engineering studies and designs aiming to meeting the requirements of enhancing capacity and energy efficiency for next generation communication networks. Challenges of spectrum scarcity and energy constraints are addressed and new technologies are proposed, analytically investigated and examined. The thesis commences by reviewing studies on spectrally and energy-efficient techniques, with a special focus on non-orthogonal multicarrier modulation, particularly spectrally efficient frequency division multiplexing (SEFDM). Rigorous theoretical and mathematical modelling studies of SEFDM are presented. Moreover, to address the potential application of SEFDM under the 5th generation new radio (5G NR) heterogeneous numerologies, simulation-based studies of SEFDM coexisting with orthogonal frequency division multiplexing (OFDM) are conducted. New signal formats and corresponding transceiver structure are designed, using a Hilbert transform filter pair for shaping pulses. Detailed modelling and numerical investigations show that the proposed signal doubles spectral efficiency without performance degradation, with studies of two signal formats; uncoded narrow-band internet of things (NB-IoT) signals and unframed turbo coded multi-carrier signals. The thesis also considers using constellation shaping techniques and SEFDM for capacity enhancement in 5G system. Probabilistic shaping for SEFDM is proposed and modelled to show both transmission energy reduction and bandwidth saving with advantageous flexibility for data rate adaptation. Expanding on constellation shaping to improve performance further, a comparative study of multidimensional modulation techniques is carried out. A four-dimensional signal, with better noise immunity is investigated, for which metaheuristic optimisation algorithms are studied, developed, and conducted to optimise bit-to-symbol mapping. Finally, a specially designed machine learning technique for signal and system design in physical layer communications is proposed, utilising the application of autoencoder-based end-to-end learning. Multidimensional signal modulation with multidimensional constellation shaping is proposed and optimised by using machine learning techniques, demonstrating significant improvement in spectral and energy efficiencies

    Deep Learning Designs for Physical Layer Communications

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    Wireless communication systems and their underlying technologies have undergone unprecedented advances over the last two decades to assuage the ever-increasing demands for various applications and emerging technologies. However, the traditional signal processing schemes and algorithms for wireless communications cannot handle the upsurging complexity associated with fifth-generation (5G) and beyond communication systems due to network expansion, new emerging technologies, high data rate, and the ever-increasing demands for low latency. This thesis extends the traditional downlink transmission schemes to deep learning-based precoding and detection techniques that are hardware-efficient and of lower complexity than the current state-of-the-art. The thesis focuses on: precoding/beamforming in massive multiple-inputs-multiple-outputs (MIMO), signal detection and lightweight neural network (NN) architectures for precoder and decoder designs. We introduce a learning-based precoder design via constructive interference (CI) that performs the precoding on a symbol-by-symbol basis. Instead of conventionally training a NN without considering the specifics of the optimisation objective, we unfold a power minimisation symbol level precoding (SLP) formulation based on the interior-point-method (IPM) proximal ‘log’ barrier function. Furthermore, we propose a concept of NN compression, where the weights are quantised to lower numerical precision formats based on binary and ternary quantisations. We further introduce a stochastic quantisation technique, where parts of the NN weight matrix are quantised while the remaining is not. Finally, we propose a systematic complexity scaling of deep neural network (DNN) based MIMO detectors. The model uses a fraction of the DNN inputs by scaling their values through weights that follow monotonically non-increasing functions. Furthermore, we investigate performance complexity tradeoffs via regularisation constraints on the layer weights such that, at inference, parts of network layers can be removed with minimal impact on the detection accuracy. Simulation results show that our proposed learning-based techniques offer better complexity-vs-BER (bit-error-rate) and complexity-vs-transmit power performances compared to the state-of-the-art MIMO detection and precoding techniques

    LiFi Transceiver Designs for 6G Wireless Networks

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    Due to the dramatic increase in high data rate services, and in order to meet the demands of the sixth-generation (6G) wireless networks, researchers from both academia and industry have been exploring advanced transmission techniques, new network archi- tectures and new frequency bands, such as the millimeter wave (mmWave), the infrared, and the visible light bands. Light-fdelity (LiFi) particularly is an emerging, novel, bidirectional, high-speed and fully networked optical wireless communication (OWC) technology that has been introduced as a promising solution for 6G networks, especially for indoor connectivity, owing to the large unexploited spectrum that translates to signifcantly high data rates. Although there has been a big leap in the maturity of the LiFi technology, there is still a considerable gap between the available LiFi technology and the required demands of 6G networks. Motivated by this, this dissertation aims to bridge between the current research literature of LiFi and the expected demands of 6G networks. Specifcally, the key goal of this dissertation is to fll some shortcomings in the LiFi technology, such as channel modeling, transceiver designs, channel state information (CSI) acquisition, localization, quality-of-service (QoS), and performance optimization. Our work is devoted to address and solve some of these limitations. Towards achieving this goal, this dissertation makes signifcant contributions to several areas of LiFi. First, it develops novel and measurements-based channel models for LiFi systems that are required for performance analysis and handover management. Second, it proposes a novel design for LiFi devices that is capable of alleviating the real behaviour of users and the impurities of indoor propagation environments. Third, it proposes intelligent, accurate and fast joint position and orientation techniques for LiFi devices, which improve the CSI estimation process and boost the indoor location-based and navigation-based services. Then, it proposes novel proactive optimization technique that can provide near-optimal and real-time service for indoor mobile LiFi users that are running some services with high data rates, such as extended reality, video conferencing, and real-time video monitoring. Finally, it proposes advanced multiple access techniques that are capable of cancelling the efects of interference in indoor multi-user settings. The studied problems are tackled using various tools from probability and statistic theory, system design and integration theory, optimization theory, and deep learning. The Results demonstrate the efectiveness of the proposed designs, solutions, and techniques. Nevertheless, the fndings in this dissertation highlight key guidelines for the efective design of LiFi while considering their unique propagation features

    Cooperative Radio Communications for Green Smart Environments

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    The demand for mobile connectivity is continuously increasing, and by 2020 Mobile and Wireless Communications will serve not only very dense populations of mobile phones and nomadic computers, but also the expected multiplicity of devices and sensors located in machines, vehicles, health systems and city infrastructures. Future Mobile Networks are then faced with many new scenarios and use cases, which will load the networks with different data traffic patterns, in new or shared spectrum bands, creating new specific requirements. This book addresses both the techniques to model, analyse and optimise the radio links and transmission systems in such scenarios, together with the most advanced radio access, resource management and mobile networking technologies. This text summarises the work performed by more than 500 researchers from more than 120 institutions in Europe, America and Asia, from both academia and industries, within the framework of the COST IC1004 Action on "Cooperative Radio Communications for Green and Smart Environments". The book will have appeal to graduates and researchers in the Radio Communications area, and also to engineers working in the Wireless industry. Topics discussed in this book include: • Radio waves propagation phenomena in diverse urban, indoor, vehicular and body environments• Measurements, characterization, and modelling of radio channels beyond 4G networks• Key issues in Vehicle (V2X) communication• Wireless Body Area Networks, including specific Radio Channel Models for WBANs• Energy efficiency and resource management enhancements in Radio Access Networks• Definitions and models for the virtualised and cloud RAN architectures• Advances on feasible indoor localization and tracking techniques• Recent findings and innovations in antenna systems for communications• Physical Layer Network Coding for next generation wireless systems• Methods and techniques for MIMO Over the Air (OTA) testin
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