Adaptive equalisation for fading digital communication channels

This thesis considers the design of new adaptive equalisers for fading digital communication channels. The role of equalisation is discussed in the context of the functions of a digital radio communication system and both conventional and more recent novel equaliser designs are described. The application of recurrent neural networks to the problem of equalisation is developed from a theoretical study of a single node structure to the design of multinode structures. These neural networks are shown to cancel intersymbol interference in a manner mimicking conventional techniques and simulations demonstrate their sensitivity to symbol estimation errors. In addition the error mechanisms of conventional maximum likelihood equalisers operating on rapidly time-varying channels are investigated and highlight the problems of channel estimation using delayed and often incorrect symbol estimates. The relative sensitivity of Bayesian equalisation techniques to errors in the channel estimate is studied and demonstrates that the structure's equalisation capability is also susceptible to such errors. Applications of multiple channel estimator methods are developed, leading to reduced complexity structures which trade performance for a smaller computational load. These novel structures are shown to provide an improvement over the conventional techniques, especially for rapidly time-varying channels, by reducing the time delay in the channel estimation process. Finally, the use of confidence measures of the equaliser's symbol estimates in order to improve channel estimation is studied and isolates the critical areas in the development of the technique — the production of reliable confidence measures by the equalisers and the statistics of symbol estimation error bursts

Neural networks for optical channel equalization in high speed communication systems

La demande future de bande passante pour les données dépassera les capacités des systèmes de communication optique actuels, qui approchent de leurs limites en raison des limitations de la bande passante électrique des composants de l’émetteur. L’interférence intersymbole (ISI) due à cette limitation de bande est le principal facteur de dégradation pour atteindre des débits de données élevés. Dans ce mémoire, nous étudions plusieurs techniques de réseaux neuronaux (NN) pour combattre les limites physiques des composants de l’émetteur pilotés à des débits de données élevés et exploitant les formats de modulation avancés avec une détection cohérente. Notre objectif principal avec les NN comme égaliseurs de canaux ISI est de surmonter les limites des récepteurs optimaux conventionnels, en fournissant une complexité évolutive moindre et une solution quasi optimale. Nous proposons une nouvelle architecture bidirectionnelle profonde de mémoire à long terme (BiLSTM), qui est efficace pour atténuer les graves problèmes d’ISI causés par les composants à bande limitée. Pour la première fois, nous démontrons par simulation que notre BiLSTM profonde proposée atteint le même taux d’erreur sur les bits(TEB) qu’un estimateur de séquence à maximum de vraisemblance (MLSE) optimal pour la modulation MDPQ. Les NN étant des modèles pilotés par les données, leurs performances dépendent fortement de la qualité des données d’entrée. Nous démontrons comment les performances du BiLSTM profond réalisable se dégradent avec l’augmentation de l’ordre de modulation. Nous examinons également l’impact de la sévérité de l’ISI et de la longueur de la mémoire du canal sur les performances de la BiLSTM profonde. Nous étudions les performances de divers canaux synthétiques à bande limitée ainsi qu’un canal optique mesuré à 100 Gbaud en utilisant un modulateur photonique au silicium (SiP) de 35 GHz. La gravité ISI de ces canaux est quantifiée grâce à une nouvelle vue graphique des performances basée sur les écarts de performance de base entre les solutions optimales linéaires et non linéaires classiques. Aux ordres QAM supérieurs à la QPSK, nous quantifions l’écart de performance BiLSTM profond par rapport à la MLSE optimale à mesure que la sévérité ISI augmente. Alors qu’elle s’approche des performances optimales de la MLSE à 8QAM et 16QAM avec une pénalité, elle est capable de dépasser largement la solution optimale linéaire à 32QAM. Plus important encore, l’avantage de l’utilisation de modèles d’auto-apprentissage comme les NN est leur capacité à apprendre le canal pendant la formation, alors que la MLSE optimale nécessite des informations précises sur l’état du canal.The future demand for the data bandwidth will surpass the capabilities of current optical communication systems, which are approaching their limits due to the electrical bandwidth limitations of the transmitter components. Inter-symbol interference (ISI) due to this band limitation is the major degradation factor to achieve high data rates. In this thesis, we investigate several neural network (NN) techniques to combat the physical limits of the transmitter components driven at high data rates and exploiting the advanced modulation formats with coherent detection. Our main focus with NNs as ISI channel equalizers is to overcome the limitations of conventional optimal receivers, by providing lower scalable complexity and near optimal solution. We propose a novel deep bidirectional long short-term memory (BiLSTM) architecture, that is effective in mitigating severe ISI caused by bandlimited components. For the first time, we demonstrate via simulation that our proposed deep BiLSTM achieves the same bit error rate (BER) performance as an optimal maximum likelihood sequence estimator (MLSE) for QPSK modulation. The NNs being data-driven models, their performance acutely depends on input data quality. We demonstrate how the achievable deep BiLSTM performance degrades with the increase in modulation order. We also examine the impact of ISI severity and channel memory length on deep BiLSTM performance. We investigate the performances of various synthetic band-limited channels along with a measured optical channel at 100 Gbaud using a 35 GHz silicon photonic(SiP) modulator. The ISI severity of these channels is quantified with a new graphical view of performance based on the baseline performance gaps between conventional linear and nonlinear optimal solutions. At QAM orders above QPSK, we quantify deep BiLSTM performance deviation from the optimal MLSE as ISI severity increases. While deep BiLSTM approaches the optimal MLSE performance at 8QAM and 16QAM with a penalty, it is able to greatly surpass the linear optimal solution at 32QAM. More importantly, the advantage of using self learning models like NNs is their ability to learn the channel during the training, while the optimal MLSE requires accurate channel state information

Design and Software Validation of Coded Communication Schemes using Multidimensional Signal Sets without Constellation Expansion Penalty in Band-Limited Gaussian and Fading Channels

It has been well reported that the use of multidimensional constellation signals can help to reduce the bit error rate in Additive Gaussian channels by using the hyperspace geometry more efficiently. Similarly, in fading channels, dimensionality provides an inherent signal space diversity (distinct components between two constellations points), so the amplitude degradation of the signal are combated significantly better. Moreover, the set of n-dimensional signals also provides great compatibility with various Trellis Coded modulation schemes: N-dimensional signaling joined with a convolutional encoder uses fewer redundant bits for each 2D signaling interval, and increases intra-subset minimum squared Euclidean distance (MSED) to approach the ultimate capacity limit predicted by Shannon\u27s theory. The multidimensional signals perform better for the same complexity than two-dimensional schemes. The inherent constellation expansion penalty factor paid for using classical mapping structures can be decreased by enlarging the constellation\u27s dimension. In this thesis, a multidimensional signal set construction paradigm that completely avoids the constellation expansion penalty is used in Band-limited channels and in fading channels. As such, theoretical work on performance analysis and computer simulations for Quadrature-Quadrature Phase Shift Keying (Q2PSK), Constant Envelope (CE) Q2PSK, and trellis-coded 16D CEQ2PSK in ideal band-limited channels of various bandwidths is presented along with a novel discussion on visualization techniques for 4D Quadrature-Quadrature Phase Shift Keying (Q2PSK), Saha\u27s Constant Envelope (CE) Q2PSK, and Cartwright\u27s CEQ2PSK in ideal band-limited channels. Furthermore, a metric designed to be used in fading channels, with Hamming Distance (HD) as a primary concern and Euclidean distance (ED) as secondary is also introduced. Simulation results show that the 16D TCM CEQ2PSK system performs well in channels with AWGN and fading, even with the simplest convolutional encoder tested; achievable coding gains using 16-D CEQ2PSK Expanded TCM schemes under various conditions are finally reported

Spatial diversity in MIMO communication systems with distributed or co-located antennas

The use of multiple antennas in wireless communication systems has gained much attention during the last decade. It was shown that such multiple-input multiple-output (MIMO) systems offer huge advantages over single-antenna systems. Typically, quite restrictive assumptions are made concerning the spacing of the individual antenna elements. On the one hand, it is typically assumed that the antenna elements at transmitter and receiver are co-located, i.e., they belong to some sort of antenna array. On the other hand, it is often assumed that the antenna spacings are sufficiently large, so as to justify the assumption of independent fading. In this thesis, the above assumptions are relaxed. In the first part, it is shown that MIMO systems with distributed antennas and MIMO systems with co-located antennas can be treated in a single, unifying framework. In the second part this fact is utilized, in order to develop appropriate transmit power allocation strategies for co-located and distributed MIMO systems. Finally, the third part focuses on specific synchronization problems that are of interest for distributed MIMO systems

Performance analysis of the HARQ dynamic decode-and-forward protocol.

The explosive growth of data trafficc in wireless communication systems comes together with the urgent need to minimize its environmental and financial impact.Therefore, the main objective in the field of green radio communication is to improve the energy efficiency of wireless communication systems with respect to the future performance demands on the wireless communication infrastructure. In this context, recent research in cooperative and cognitive communication techniques attracts particular attention.While cognitive radio improves spectral efficiency by enhanced spectrum utilization, cooperative communication techniques achieve remarkable gains in spectral efficiency by enabling the terminals to share their resources. In particular, creating virtual multi-antenna arrays by antenna sharing enables exploitation of spatial diversity gains and multiplexing gains within a network of single antenna terminals. This technique is particularly attractive for mobile wireless networks, since power and space constraints often prohibit the integration of multiple antennas into mobile terminals.This work studies the performance of the hybrid automatic repeat-request (HARQ) dynamic decode-and-forward (DDF) protocol in the half-duplex relay channel. The reason behind exploration of the HARQ-DDF protocol is that it achieves the optimal performance in terms of the diversity-multiplexing tradeoff (DMT) and the diversity-multiplexing-delay tradeoff(DMDT). However, DMT and DMDT are evaluated as the signal-to-noise ratio (SNR) approaches infinity.In practice, key performance measures are the fixed-rate outage probability and delay-limited throughput achieved at the SNR expected during operation. To this end, it is common practice to give the performance of the DDF protocol as a function of the source-to-destination channel SNR (SD-SNR). In this dissertation the focus is to study the performance of the HARQ-DDF protocol measured as a function of the SNR as seen at the destination (D-SNR).This approach enables the performance comparison with the HARQ-SISO and the HARQ-MISO protocol from an energy efficiency perspective on the system level. Furthermore, a novel variant of the HARQ-MISO protocol, the hybrid repeat-with-diversity-request (HARDQ) MISO protocol, is introduced.Considering outage probability as measure of reliability, closed-form solutions and simulation results show that the HARDQ-MISO and the HARQ-DDF protocol outperform the HARQ-MISO protocol from an energy efficiency point ofview. From a delay-limited throughput point of view the HARQ-MISO protocol is beneficial. It is demonstrated that code-rate assignment allows to achieve significant performance gains in terms of delay-limited throughput. Furthermore, reducing the decoding cost using code-rate assignment techniques comes together with only negligible performance loss