Design and Analysis of GFDM-Based Wireless Communication Systems

Le multiplexage généralisé par répartition en fréquence (GFDM), une méthode de traitement par blocs de modulation multiporteuses non orthogonales, est une candidate prometteuse pour les technologies de forme d'onde pour les systèmes sans fil au-delà de la cinquième génération (5G). La capacité du GFDM à ajuster de manière flexible la taille du bloc et le type de filtres de mise en forme des impulsions en fait une méthode appropriée pour répondre à plusieurs exigences importantes, comme une faible latence, un faible rayonnement hors bande (OOB) et des débits de données élevés. En appliquant aux systèmes GFDM la technique des systèmes à entrées multiples et sorties multiples (MIMO), la technique de MIMO massif ou des codes de contrôle de parité à faible densité (LDPC), il est possible d'améliorer leurs performances. Par conséquent, l'étude de ces systèmes combinés sont d'une grande importance théorique et pratique. Dans cette thèse, nous étudions les systèmes de communication sans fil basés sur le GFDM en considérant trois aspects. Tout d'abord, nous dérivons une borne d'union sur le taux d'erreur sur les bits (BER) pour les systèmes MIMO-GFDM, technique qui est basée sur des probabilités d'erreur par paires exactes (PEP). La PEP exacte est calculée en utilisant la fonction génératrice de moments(MGF) pour les détecteurs à maximum de vraisemblance (ML). La corrélation spatiale entre les antennes et les erreurs d'estimation de canal sont prises en compte dans l'environnement de canal étudié. Deuxièmement, les estimateurs et les précodeurs de canal de faible complexité basés sur une expansion polynomiale sont proposés pour les systèmes MIMO-GFDM massifs. Des pilotes sans interférence sont utilisés pour l'estimation du canal basée sur l'erreur quadratique moyenne minimale(MMSE) pour lutter contre l'influence de la non-orthogonalité entre les sous-porteuses dans le GFDM. La complexité de calcul cubique peut être réduite à une complexité d'ordre au carré en utilisant la technique d'expansion polynomiale pour approximer les inverses de matrices dans l'estimation MMSE conventionnelle et le précodage. De plus, nous calculons les limites de performance en termes d'erreur quadratique moyenne (MSE) pour les estimateurs proposés, ce qui peut être un outil utile pour prédire la performance des estimateurs dans la région de Eₛ/N₀ élevé. Une borne inférieure de Cramér-Rao(CRLB) est dérivée pour notre modèle de système et agit comme une référence pour les estimateurs. La complexité de calcul des estimateurs de canal proposés et des précodeurs et les impacts du degré du polynôme sont également étudiés. Enfin, nous analysons les performances de la probabilité d'erreur des systèmes GFDM combinés aux codes LDPC. Nous dérivons d'abord les expressions du ratio de vraisemblance logarithmique (LLR) initiale qui sont utilisées dans le décodeur de l'algorithme de somme de produits (SPA). Ensuite, basé sur le seuil de décodage, nous estimons le taux d'erreur de trame (FER) dans la région de bas E[indice b]/N₀ en utilisant le BER observé pour modéliser les variations du canal. De plus, une borne inférieure du FER du système est également proposée basée sur des ensembles absorbants. Cette borne inférieure peut agir comme une estimation du FER dans la région de E[indice b]/N₀ élevé si l'ensemble absorbant utilisé est dominant et que sa multiplicité est connue. La quantification a également un impact important sur les performances du FER et du BER. Des codes LDPC basés sur un tableau et construit aléatoirement sont utilisés pour supporter les analyses de performances. Pour ces trois aspects, des simulations et des calculs informatiques sont effectués pour obtenir des résultats numériques connexes, qui vérifient les méthodes proposées.8 372162\u a Generalized frequency division multiplexing (GFDM) is a block-processing based non-orthogonal multi-carrier modulation scheme, which is a promising candidate waveform technology for beyond fifth-generation (5G) wireless systems. The ability of GFDM to flexibly adjust the block size and the type of pulse-shaping filters makes it a suitable scheme to meet several important requirements, such as low latency, low out-of-band (OOB) radiation and high data rates. Applying the multiple-input multiple-output (MIMO) technique, the massive MIMO technique, or low-density parity-check (LDPC) codes to GFDM systems can further improve the systems performance. Therefore, the investigation of such combined systems is of great theoretical and practical importance. This thesis investigates GFDM-based wireless communication systems from the following three aspects. First, we derive a union bound on the bit error rate (BER) for MIMO-GFDM systems, which is based on exact pairwise error probabilities (PEPs). The exact PEP is calculated using the moment-generating function (MGF) for maximum likelihood (ML) detectors. Both the spatial correlation between antennas and the channel estimation errors are considered in the investigated channel environment. Second, polynomial expansion-based low-complexity channel estimators and precoders are proposed for massive MIMO-GFDM systems. Interference-free pilots are used in the minimum mean square error (MMSE) channel estimation to combat the influence of non-orthogonality between subcarriers in GFDM. The cubic computational complexity can be reduced to square order by using the polynomial expansion technique to approximate the matrix inverses in the conventional MMSE estimation and precoding. In addition, we derive performance limits in terms of the mean square error (MSE) for the proposed estimators, which can be a useful tool to predict estimators performance in the high Eₛ/N₀ region. A Cramér-Rao lower bound (CRLB) is derived for our system model and acts as a benchmark for the estimators. The computational complexity of the proposed channel estimators and precoders, and the impacts of the polynomial degree are also investigated. Finally, we analyze the error probability performance of LDPC coded GFDM systems. We first derive the initial log-likelihood ratio (LLR) expressions that are used in the sum-product algorithm (SPA) decoder. Then, based on the decoding threshold, we estimate the frame error rate (FER) in the low E[subscript b]/N₀ region by using the observed BER to model the channel variations. In addition, a lower bound on the FER of the system is also proposed based on absorbing sets. This lower bound can act as an estimate of the FER in the high E[subscript b]/N₀ region if the absorbing set used is dominant and its multiplicity is known. The quantization scheme also has an important impact on the FER and BER performances. Randomly constructed and array-based LDPC codes are used to support the performance analyses. For all these three aspects, software-based simulations and calculations are carried out to obtain related numerical results, which verify our proposed methods

Index Modulation Techniques for Energy-efficient Transmission in Large-scale MIMO Systems

This thesis exploits index modulation techniques to design energy- and spectrum-efficient system models to operate in future wireless networks. In this respect, index modulation techniques are studied considering two different media: mapping the information onto the frequency indices of multicarrier systems, and onto the antenna array indices of a platform that comprises multiple antennas. The index modulation techniques in wideband communication scenarios considering orthogonal and generalized frequency division multiplexing systems are studied first. Single cell multiuser networks are considered while developing the system models that exploit the index modulation on the subcarriers of the multicarrier systems. Instead of actively modulating all the subcarriers, a subset is selected according to the index modulation bits. As a result, there are subcarriers that remain idle during the data transmission phase and the activation pattern of the subcarriers convey additional information. The transceivers for the orthogonal and generalized frequency division multiplexing systems with index modulation are both designed considering the uplink and downlink transmission phases with a linear combiner and precoder in order to reduce the system complexity. In the developed system models, channel state information is required only at the base station. The linear combiner is designed adopting minimum mean square error method to mitigate the inter-user-interference. The proposed system models offer a flexible design as the parameters are independent of each other. The parameters can be adjusted to design the system in favor of the energy efficiency, spectrum efficiency, peak-to-average power ratio, or error performance. Then, the index modulation techniques are studied for large-scale multiple-input multiple-output systems that operate in millimeter wave bands. In order to overcome the drawbacks of transmission in millimeter wave frequencies, channel properties should be taken in to account while envisaging the wireless communication network. The large-scale multiple-input multiple-output systems increase the degrees of freedom in the spatial domain. This feature can be exploited to focus the transmit power directly onto the intended receiver terminal to cope with the severe path-loss. However, scaling up the number of hardware elements results in excessive power consumption. Hybrid architectures provide a remedy by shifting a part of the signal processing to the analog domain. In this way, the number of bulky and high power consuming hardware elements can be reduced. However, there will be a performance degradation as a consequence of renouncing the fully digital signal processing. Index modulation techniques can be combined with the hybrid system architecture to compensate the loss in spectrum efficiency to further increase the data rates. A user terminal architecture is designed that employs analog beamforming together with spatial modulation where a part of the information bits is mapped onto the indices of the antenna arrays. The system is comprised a switching stage that allocates the user terminal antennas on the phase shifter groups to minimize the spatial correlation, and a phase shifting stage that maximizes the beamforming gain to combat the path-loss. A computationally efficient optimization algorithm is developed to configure the system. The flexibility of the architecture enables optimization of the hybrid transceiver at any signal-to-noise ratio values. A base station is designed in which hybrid beamforming together with spatial modulation is employed. The analog beamformer is designed to point the transmit beam only in the direction of the intended user terminal to mitigate leakage of the transmit power to other directions. The analog beamformer to transmit the signal is chosen based on the spatial modulation bits. The digital precoder is designed to eliminate the inter-user-interference by exploiting the zero-forcing method. The base station computes the hybrid beamformers and the digital combiners, and only feeds back the digital combiners of each antenna array-user pair to the related user terminals. Thus, a low complexity user architecture is sufficient to achieve a higher performance. The developed optimization framework for the energy efficiency jointly optimizes the number of served users and the total transmit power by utilizing the derived upper bound of the achievable rate. The proposed transceiver architectures provide a more energy-efficient system model compared to the hybrid systems in which the spatial modulation technique is not exploited. This thesis develops low-complexity system models that operate in narrowband and wideband channel environments to meet the energy and spectrum efficiency demands of future wireless networks. It is corroborated in the thesis that adopting index modulation techniques both in the systems improves the system performance in various aspects.:1 Introduction 1 1.1 Motivation 1 1.2 Overview and Contribution 2 1.3 Outline 9 2 Preliminaries and Fundamentals 13 2.1 Multicarrier Systems 13 2.2 Large-scale Multiple Input Multiple Output Systems 17 2.3 Index Modulation Techniques 19 2.4 Single Cell Multiuser Networks 22 3 Multicarrier Systems with Index Modulation 27 3.1 Orthogonal Frequency Division Multiplexing 28 3.2 Generalized Frequency Division Multiplexing 40 3.3 Summary 52 4 Hybrid Beamforming with Spatial Modulation 55 4.1 Uplink Transmission 56 4.2 Downlink Transmission 74 4.3 Summary 106 5 Conclusion and Outlook 109 5.1 Conclusion 109 5.2 Outlook 111 A Quantization Error Derivations 113 B On the Achievable Rate of Gaussian Mixtures 115 B.1 The Conditional Density Function 115 B.2 Tight Bounds on the Differential Entropy 116 B.3 A Bound on the Achievable Rate 118 C Multiuser MIMO Downlink without Spatial Modulation 121 Bibliograph

Low-Complexity Near-Optimal Detection Algorithms for MIMO Systems

As the number of subscribers in wireless networks and their demanding data rate are exponentially increasing, multiple-input multiple-output (MIMO) systems have been scaled up in the 5G where tens to hundreds of antennas are deployed at base stations (BSs). However, by scaling up the MIMO systems, designing detectors with low computational complexity and close to the optimal error performance becomes challenging. In this dissertation, we study the problem of efficient detector designs for MIMO systems. In Chapter 2, we propose efficient detection algorithms for small and moderate MIMO systems by using lattice reduction and subspace (or conditional) detection techniques. The proposed algorithms exhibit full receive diversity and approach the bit error rate (BER) of the optimal maximum likelihood (ML) solution. For quasi-static channels, the complexity of the proposed schemes is cubic in the system dimension and is only linear in the size of the QAM modulation used. However, the computational complexity of lattice reduction algorithms imposes a large burden on the proposed detectors for large MIMO systems or fast fading channels. In Chapter 3, we propose detectors for large MIMO systems based on the combination of minimum mean square error decision feedback equalization (MMSE-DFE) and subspace detection tailored to an appropriate channel ordering. Although the achieved diversity order of the proposed detectors does not necessarily equal the full receive diversity for some MIMO systems, the coding gain allows for close to ML error performance at practical values of signal-to-noise ratio (SNR) at the cost of a small computational complexity increase over the classical MMSE- DFE detection. The receive diversity deficiency is addressed by proposing another algorithm in which a partial lattice reduction (PLR) technique is deployed to improve the diversity order. Massive multiuser MIMO (MU-MIMO) is another technology where the BS is equipped with hundreds of antennas and serves tens of single-antenna user terminals (UTs). For the uplink of massive MIMO systems, linear detectors, such as zero-forcing (ZF) and minimum mean square error (MMSE), approach the error performances of sophisticated nonlinear detectors. However, the exact solutions of ZF and MMSE involve matrix-matrix multiplication and matrix inversion operations which are expensive for massive MIMO systems. In Chapter 4, we propose efficient truncated polynomial expansion (TPE)-based detectors that achieve the error performance of the exact solutions with a computational complexity proportional to the system dimensions. The millimeter wave (mmWave) massive MIMO is another key technology for 5G cellular networks. By using hybrid beamforming techniques in which a few numbers of radio frequency (RF) chains are deployed at the BSs and the UTs, the fully-digital precoder (combiner) is approximated as a product of analog and digital precoders (combiners). In Chapter 5, we consider a signal detection scheme using the equivalent channel consisting of the precoder, mmWave channel, and combiner. The available structure in the equivalent channel enables us to achieve the BER of the optimal ML solution with a significant reduction in the computational complexity

D 3. 3 Final performance results and consolidated view on the most promising multi -node/multi -antenna transmission technologies

This document provides the most recent updates on the technical contributions and research challenges focused in WP3. Each Technology Component (TeC) has been evaluated under possible uniform assessment framework of WP3 which is based on the simulation guidelines of WP6. The performance assessment is supported by the simulation results which are in their mature and stable state. An update on the Most Promising Technology Approaches (MPTAs) and their associated TeCs is the main focus of this document. Based on the input of all the TeCs in WP3, a consolidated view of WP3 on the role of multinode/multi-antenna transmission technologies in 5G systems has also been provided. This consolidated view is further supported in this document by the presentation of the impact of MPTAs on METIS scenarios and the addressed METIS goals.Aziz, D.; Baracca, P.; De Carvalho, E.; Fantini, R.; Rajatheva, N.; Popovski, P.; Sørensen, JH.... (2015). D 3. 3 Final performance results and consolidated view on the most promising multi -node/multi -antenna transmission technologies. http://hdl.handle.net/10251/7675

Deep Learning Designs for Physical Layer Communications

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