Low-Complexity Equalisers for Offset Constellations in Massive MIMO Schemes

This work was supported in part by the European Regional Development Fund (FEDER), through the Competitiveness and Internationalization Operational Program of the Portugal 2020 Framework, in part by the Regional OP Centro under Grant POCI-01-0145-FEDER-030588, in part by the Regional OP Lisboa under Grant Lisboa-01-0145-FEDER-03058, in part by the FCT/MEC through national funds of MASSIVE5G Project under Grant SAICT-45-2017-02 and PES3N Project under Grant 2018-SAICT-45-2017-POCI-01-0145-FEDER-030629, in part by the UID/EEE/50008/2019 Project, and in part by the FCT Ph.D. under Grant SFRH/BD/108522/2015.Massive multi-input-multi-output (m-MIMO) schemes require low-complexity implementations at both the transmitter and the receiver side, especially for systems operation at millimeter wave (mmWave) bands. In this paper, we consider the use of offset constellations in m-MIMO systems operating at mmWave frequencies. These signals are designed to have either an almost constant envelope or be decomposed as the sum of constant-envelope signals, making them compatible with strongly nonlinear power amplifiers, which can have low-implementation complexity and high amplification efficient, making them particularly interesting for mmWave communications. We design and evaluate low-complexity frequency-domain receivers for offset signals. It is shown that the proposed receivers can have excellent performance/complexity trade-offs in m-MIMO scenarios, making them particularly interesting for future wireless systems operating at mmWave bands.publishersversionpublishe

Digital Signal Processing for Optical Communications and Coherent LiDAR

Internet data traffic within data centre, access and metro networks is experiencing unprecedented growth driven by many data-intensive applications. Significant efforts have been devoted to the design and implementation of low-complexity digital signal processing (DSP) algorithms that are suitable for these short-reach optical links. In this thesis, a novel low-complexity frequency-domain (FD) multiple-input multiple-output (MIMO) equaliser with momentum-based gradient descent algorithm is proposed, capable of mitigating both static and dynamic impairments arising from the optical fibre. The proposed frequency-domain equaliser (FDE) also improves the robustness of the adaptive equaliser against feedback latencies which is the main disadvantage of FD adaptive equalisers under rapid channel variations. The development and maturity of optical fibre communication techniques over the past few decades have also been beneficial to many other fields, especially coherent light detection and ranging (LiDAR) techniques. Many applications of coherent LiDAR are also cost-sensitive, e.g., autonomous vehicles (AVs). Therefore, in this thesis, a low-cost and low-complexity single-photodiode-based coherent LiDAR system is investigated. The receiver sensitivity performance of this receiver architecture is assessed through both simulations and experiments, using two ranging waveforms known as double-sideband (DSB) amplitude-modulated chirp signal and single-sideband (SSB) frequency-modulated continuous-wave (FMCW) signals. Besides, the impact of laser phase noise on the ranging precision when operating within and beyond the laser coherence length is studied. Achievable ranging precision beyond the laser coherence length is quantified

Single-Frequency Network Terrestrial Broadcasting with 5GNR Numerology

L'abstract è presente nell'allegato / the abstract is in the attachmen

Design of neural network-based nonlinear equalisers for coherent optical communication systems

Recent advancements in beyond 5G (B5G) networks and future communications demand ultra-high capacity and, therefore, impose stringent requirements on fibre-optic transmission infrastructures. Optical fibre channel impairments such as nonlinearities induced by the Kerr effect and their interactions with chromatic dispersion pose a significant challenge in achieving desirable transmission capacity in current coherent optical communication systems. Digital signal processing techniques, such as electronic dispersion compensation and digital backpropagation, may achieve suboptimal performance or require impractical high computing resources. Machine learning (ML) techniques offer a promising solution to the optical fibre nonlinearity equalisation problem due to their ability to exploit underlying features among the vast volume of digital information available in modern society. This thesis focuses on the design of nonlinear equalisers using machine learning, specifically neural networks (NNs), for coherent optical communication systems. A comprehensive study of machine learning algorithms, including unsupervised and supervised learning algorithms, applied in nonlinear equalisation is conducted, demonstrating that the bit error rate performance of these algorithms is superior to conventional symbol detection using mean square error. The results demonstrate the potential of machine learning algorithms, particularly NNs, for nonlinearity equalisation in coherent optical systems and provide a motivation for further exploration. The ML- and recurrent neural network (RNN)-based nonlinear equalisers with sequential symbol input are investigated. The results suggest the input sequences can provide relevant residual channel memory information for these equalisers to enhance the system performance after training, offering confidence in the design of low-complexity NN-based equalisers. Furthermore, an attention-aided partial bidirectional recurrent neural network (BRNN)-based nonlinear equaliser is proposed, successfully reducing complexity of ∼56.2% with the assistance of the attention mechanism, which also provides evidence of symbol-wise nonlinear memory. The contributions presented in this thesis demonstrate the potential of machine learning algorithms and NN-based equalisers, investigate and validate the feasibility of sequential input for them, and provide an effective evidence-based pruning process for the design of NN-based equalisers for optical transmission systems. xv

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

Multiple-Antenna Systems: From Generic to Hardware-Informed Precoding Designs

5G-and-beyond communication systems are expected to be in a heterogeneous form of multiple-antenna cellular base stations (BSs) overlaid with small cells. The fully-digital BS structures can incur significant power consumption and hardware complexity. Moreover, the wireless BSs for small cells usually have strict size constraints, which incur additional hardware effects such as mutual coupling (MC). Consequently, the transmission techniques designed for future wireless communication systems should respect the hardware structures at the BSs. For this reason, in this thesis we extend generic downlink precoding to more advanced hardware-informed transmission techniques for a variety of BS structures. This thesis firstly extends the vector perturbation (VP) precoding to multiple-modulation scenarios, where existing VP-based techniques are sub-optimal. Subsequently, this thesis focuses on the downlink transmission designs for hardware effects in the form of MC, limited number of radio frequency (RF) chains, and low-precision digital-to-analog converters (DACs). For these scenarios, existing precoding techniques are either sub-optimal or not directly applicable due to the specific hardware constraints. In this context, this thesis first proposes analog-digital (AD) precoding methods for MC exploitation in compact single-user multiple-antenna systems with the concept of constructive interference, and further extends the idea of MC exploitation to multi-user scenarios with a joint optimisation on the precoding matrix and the mutual coupling effect. We further consider precoding for wireless BSs with a limited number of RF chains, in the form of compact parasitic antenna array as well as hybrid analog-digital structures designed for large-scale multiple-antenna systems. In addition, with a reformulation of the constructive interference, this thesis also considers the low-complexity precoding design for the use of low-resolution DACs for a massive-antenna array at the BSs. Analytical and numerical results reveal an improved performance of the proposed techniques compared to the state-of-the-art approaches, which validates the effectiveness of the introduced methods

High speed energy efficient incoherent optical wireless communications

The growing demand for wireless communication capacity and the overutilisation of the conventional radio frequency (RF) spectrum have inspired research into using alternative spectrum regions for communication. Using optical wireless communications (OWC), for example, offers significant advantages over RF communication in terms of higher bandwidth, lower implementation costs and energy savings. In OWC systems, the information signal has to be real and non-negative. Therefore, modifications to the conventional communication algorithms are required. Multicarrier modulation schemes like orthogonal frequency division multiplexing (OFDM) promise to deliver a more efficient use of the communication capacity through adaptive bit and energy loading techniques. Three OFDM-based schemes – direct-current-biased OFDM (DCO-OFDM), asymmetrically clipped optical OFDM(ACO-OFDM), and pulse-amplitude modulated discrete multitone (PAM-DMT) – have been introduced in the literature. The current work investigates the recently introduced scheme subcarrier-index modulation OFDM as a potential energy-efficient modulation technique with reduced peak-to-average power ratio (PAPR) suitable for applications in OWC. A theoretical model for the analysis of SIM-OFDMin a linear additive white Gaussian noise (AWGN) channel is provided. A closed-form solution for the PAPR in SIM-OFDM is also proposed. Following the work on SIM-OFDM, a novel inherently unipolar modulation scheme, unipolar orthogonal frequency division multiplexing (U-OFDM), is proposed as an alternative to the existing similar schemes: ACO-OFDMand PAM-DMT. Furthermore, an enhanced U-OFDMsignal generation algorithm is introduced which allows the spectral efficiency gap between the inherently unipolar modulation schemes – U-OFDM, ACO-OFDM, PAM-DMT – and the conventionally used DCO-OFDM to be closed. This results in an OFDM-based modulation approach which is electrically and optically more efficient than any other OFDM-based technique proposed so far for intensity modulation and direct detection (IM/DD) communication systems. Non-linear distortion in the optical front-end elements is one of the major limitations for high-speed communication in OWC. This work presents a generalised approach for analysing nonlinear distortion in OFDM-based modulation schemes. The presented technique leads to a closed-form analytical solution for an arbitrary memoryless distortion of the information signal and has been proven to work for the majority of the known unipolar OFDM-based modulation techniques - DCO-OFDM, ACO-OFDM, PAM-DMT and U-OFDM. The high-speed communication capabilities of novel Gallium Nitride based μm-sized light emitting diodes (μLEDs) are investigated, and a record-setting result of 3.5Gb/s using a single 50-μm device is demonstrated. The capabilities of using such devices at practical transmission distances are also investigated, and a 1 Gb/s link using a single device is demonstrated at a distance of up to 10m. Furthermore, a proof-of-concept experiment is realised where a 50-μm LED is successfully modulated using U-OFDM and enhanced U-OFDM to achieve notable energy savings in comparison to DCO-OFDM