In energy harvesting communications, the transmitters have to adapt transmission to the availability of energy harvested during communication. The performance of the transmission depends on the channel conditions which vary randomly due to mobility and environmental changes. During this work, we consider the problem of power allocation taking into account the energy arrivals over time and the quality of channel state information (CSI) available at the transmitter, in order to maximize the throughput. Differently from previous work, the CSI at the transmitter is not perfect and may include estimation errors. We solve this problem with respect to the energy harvesting constraints. Assuming a perfect knowledge of the CSI at the receiver, we determine the optimal power policy for different models of the energy arrival process (offline and online model). Indeed, we obtain the power allocation scheme when the transmitter has either perfect CSI or no CSI. We also investigate of utmost interest the case of fading channels with imperfect CSI. Moreover, a study of the asymptotic behavior of the communication system is proposed. Specifically, we analyze of the average throughput in a system where the average recharge rate goes asymptotically to zero and when it is very high.

京都大学 / 新制・課程博士 / 博士(情報学) / 甲第23549号 / 情博第779号 / 新制||情||133(附属図書館) / 京都大学大学院情報学研究科通信情報システム専攻 / (主査)教授 原田 博司, 教授 大木 英司, 准教授 山本 高至 / 学位規則第4条第1項該当 / Doctor of Informatics / Kyoto University / DGAM

Wireless underwater acoustic (UWA) communications is a developing field with various applications. The underwater acoustic communication channel is very special and its behavior is environment-dependent. The UWA channel is characterized by low available bandwidth, and severe motion-introduced Doppler effect compared to wireless radio communication. Recent literature suggests that machine learning (ML)-based channel estimation and equalization offer benefits over traditional techniques (a decision feedback equalizer), in UWA communications. ML can be advantageous due to the difficultly in designing algorithms for UWA communication, as finding general channel models have proven to be difficult. This study aims to explore if ML-based channel estimation and equalization as a part of a sophisticated physical layer structure can offer improved performance. In the study, supervised ML using a deep neural network and a recurrent neural network will be utilized to improve the bit error rate. A channel simulator with environment-specific input is used to study a wide range of channels. The simulations are utilized to study in which environments ML should be tested. It is shown that in highly time-varying channels, ML outperforms traditional techniques if trained with prior information of the channel. However, utilizing ML without prior information of the channel yielded no improvement of the performance.

The master's thesis briefly analyses the principle of OFDM modulation, possibilities of the synchronization and channel estimation in OFDM. The simply model of OFDM system is made in MATLAB. Because of the implementation in FPGA is generated the behavioral description of the OFDM demodulator through the combination of the schematics description and the description in the VHDL language. The ISE development environment is used.

Massive MU-MIMO (Multiuser-Multiple Input and Multple Output) is a promising technology for 5G wireless communications because of its spectrum and energy efficiency. To combat the distortion from multipath fading channel, the acquisition of channel state information is essential, which generally requires the training signal that lowers the data rate. In addition, coarse quantization can reduce the high computational energy and cost, yet results in the loss of information. In this thesis, an iterative decision feedback receiver, including iterative Channel Estimation (CE) and equalization, is constructed for a Massive MU-MIMO uplink system. The impact of multipath distortion and coarse quantization can be gradually reduced due to the iterative structure that exploits extrinsic feedback to improve the CE and data detection, so that the data rate is improved by reducing training signals for CE and by using low precision quantization. To observe and evaluate the convergence behaviour, an Extrinsic Information Transfer (EXIT) chart method is utilized to visualize the performance of the iterative receiver. / Graduate

Flexible numerology of the physical layer has been introduced in the latest release of 5G new radio (NR) and the baseline waveform generation is chosen to be cyclic-prefix based orthogonal frequency division multiplexing (CP-OFDM). Thanks to the narrow subcarrier spacing and low complexity one tap equalization (EQ) of OFDM, it suits well to time-dispersive channels. For the upcoming 5G and beyond use-case scenarios, it is foreseen that the users might experience high mobility conditions. While the frame structure of the 5G NR is designed for long coherence times, the synchronization and channel estimation (CE) procedures are not fully and reliably covered for diverse applications. The research on alternative multi-carrier waveforms has brought up valuable results in terms of spectral efficiency, applications coexistence and flexibility. Nevertheless, the receiver design becomes more challenging for multiple-input multiple-output (MIMO) non-orthogonal multi-carriers because the receiver must deal with multiple dimensions of interference. This thesis aims to deliver accurate pilot-aided estimations of the wireless channel for coherent detection. Considering a MIMO non-orthogonal multi-carrier, e.g. generalized frequency division multiplexing (GFDM), we initially derive the classical and Bayesian estimators for rich multi-path fading channels, where we theoretically assess the choice of pilot design. Moreover, the well time- and frequency-localization of the pilots in non-orthogonal multi-carriers allows to reuse their energy from cyclic-prefix (CP). Taking advantage of this feature, we derive an iterative approach for joint CE and EQ of MIMO systems. Furthermore, exploiting the block-circularity of GFDM, we comprehensively analyze the complexity aspects, and propose a solution for low complexity implementation. Assuming very high mobility use-cases where the channel varies within the symbol duration, further considerations, particularly the channel coherence time must be taken into account. A promising candidate that is fully independent of the multi-carrier choice is unique word (UW) transmission, where the CP of random nature is replaced by a deterministic sequence. This feature, allows per-block synchronization and channel estimation for robust transmission over extremely doubly-dispersive channels. In this thesis, we propose a novel approach to extend the UW-based physical layer design to MIMO systems and we provide an in-depth study of their out-of-band emission, synchronization, CE and EQ procedures. Via theoretical derivations and simulation results, and comparisons with respect to the state-of-the-art CP-OFDM systems, we show that the proposed UW-based frame design facilitates robust transmission over extremely doubly-dispersive channels.:1 Introduction 1 1.1 Multi-Carrier Waveforms 1 1.2 MIMO Systems 3 1.3 Contributions and Thesis Structure 4 1.4 Notations 6 2 State-of-the-art and Fundamentals 9 2.1 Linear Systems and Problem Statement 9 2.2 GFDM Modulation 11 2.3 MIMO Wireless Channel 12 2.4 Classical and Bayesian Channel Estimation in MIMO OFDM Systems 15 2.5 UW-Based Transmission in SISO Systems 17 2.6 Summary 19 3 Channel Estimation for MIMO Non-Orthogonal Waveforms 21 3.1 Classical and Bayesian Channel Estimation in MIMO GFDM Systems 22 3.1.1 MIMO LS Channel Estimation 23 3.1.2 MIMO LMMSE Channel Estimation 24 3.1.3 Simulation Results 25 3.2 Basic Pilot Designs for GFDM Channel Estimation 29 3.2.1 LS/HM Channel Estimation 31 3.2.2 LMMSE Channel Estimation for GFDM 32 3.2.3 Error Characterization 33 3.2.4 Simulation Results 36 3.3 Interference-Free Pilot Insertion for MIMO GFDM Channel Estimation 39 3.3.1 Interference-Free Pilot Insertion 39 3.3.2 Pilot Observation 40 3.3.3 Complexity 41 3.3.4 Simulation Results 41 3.4 Bayesian Pilot- and CP-aided Channel Estimation in MIMO NonOrthogonal Multi-Carriers 45 3.4.1 Review on System Model 46 3.4.2 Single-Input-Single-Output Systems 47 3.4.3 Extension to MIMO 50 3.4.4 Application to GFDM 51 3.4.5 Joint Channel Estimation and Equalization via LMMSE Parallel Interference Cancellation 57 3.4.6 Complexity Analysis 61 3.4.7 Simulation Results 61 3.5 Pilot- and CP-aided Channel Estimation in Time-Varying Scenarios 67 3.5.1 Adaptive Filtering based on Wiener-Hopf Approac 68 3.5.2 Simulation Results 69 3.6 Summary 72 4 Design of UW-Based Transmission for MIMO Multi-Carriers 73 4.1 Frame Design, Efficiency and Overhead Analysis 74 4.1.1 Illustrative Scenario 74 4.1.2 CP vs. UW Efficiency Analysis 76 4.1.3 Numerical Results 77 4.2 Sequences for UW and OOB Radiation 78 4.2.1 Orthogonal Polyphase Sequences 79 4.2.2 Waveform Engineering for UW Sequences combined with GFDM 79 4.2.3 Simulation Results for OOB Emission of UW-GFDM 81 4.3 Synchronization 82 4.3.1 Transmission over a Centralized MIMO Wireless Channel 82 4.3.2 Coarse Time Acquisition 83 4.3.3 CFO Estimation and Removal 85 4.3.4 Fine Time Acquisition 86 4.3.5 Simulation Results 88 4.4 Channel Estimation 92 4.4.1 MIMO UW-based LMMSE CE 92 4.4.2 Adaptive Filtering 93 4.4.3 Circular UW Transmission 94 4.4.4 Simulation Results 95 4.5 Equalization with Imperfect Channel Knowledge 96 4.5.1 UW-Free Equalization 97 4.5.2 Simulation Results 99 4.6 Summary 102 5 Conclusions and Perspectives 103 5.1 Main Outcomes in Short 103 5.2 Open Challenges 105 A Complementary Materials 107 A.1 Linear Algebra Identities 107 A.2 Proof of lower triangular Toeplitz channel matrix being defective 108 A.3 Calculation of noise-plus-interference covariance matrix for Pilot- and CPaided CE 108 A.4 Bock diagonalization of the effective channel for GFDM 109 A.5 Detailed complexity analysis of Sec. 3.4 109 A.6 CRLB derivations for the pdf (4.24) 113 A.7 Proof that (4.45) emulates a circular CIR at the receiver 114

The next-generation wireless technologies are currently being researched to address the ever-increasing demands for higher data rates, massive connectivity, improved reliability, and extended coverage. Recently, massive multiple-input multiple-output (MIMO) has gained significant attention as a new physical-layer transmission technology that can achieve unprecedented spectral and energy efficiency gains via aggressive spatial multiplexing. Thus, massive MIMO has been one of the key enabling technologies for the fifth-generation and subsequent wireless standards. This dissertation thus focuses on developing a system, channel, and signal models by considering the practical wireless transmission impairments for massive MIMO systems, and ascertaining the viability of massive MIMO in fulfilling massive access, improved spectrum, enhanced security, and energy efficiency requirements. Specifically, new system and channel models, pilot sequence designs and channel estimation techniques, secure transmit/receive beamforming techniques, transmit power allocation schemes with enhanced security provisions, energy efficiency, and user fairness, and comprehensive performance analysis frameworks are developed for massive MIMO-aided non-orthogonal multiple access (NOMA), cognitive spectrum-sharing, and wireless relaying architectures.Our first work focuses on developing physical-layer transmission schemes for NOMA-aided massive MIMO systems. A spatial signature-based user-clustering and pilot allocation scheme is first formulated, and thereby, a hybrid orthogonal multiple access (OMA)/NOMA transmission scheme is proposed to boost the number of simultaneous connections. In our second work, the viability of invoking downlink pilots to boost the achievable rate of NOMA-aided massive MIMO is investigated. The third research contribution investigates the performance of underlay spectrum-sharing massive MIMO systems for reverse time division duplexing based transmission strategies, in which primary and secondary systems concurrently operate in opposite directions. Thereby, we show that the secondary system can be operated with its maximum average transmit power independent of the primary system in the limit of infinity many primary/secondary base-station antennas. In our fourth work, signal processing techniques, power allocation, and relay selection schemes are designed and analyzed for massive MIMO relay networks to optimize the trade-off among the achievable user rates, coverage, and wireless resource usage. Finally, the cooperative jamming and artificial noise-based secure transmission strategies are developed for massive MIMO relay networks with imperfect legitimate user channel information and with no channel knowledge of the eavesdropper. The key design criterion of the aforementioned transmission strategies is to efficiently combine the spatial multiplexing gains and favorable propagation conditions of massive MIMO with properties of NOMA, underlay spectrum-sharing, and wireless relay networks via efficient signal processing.

Several heterogeneous, intelligent and distributed devices can be connected to interact with one another over the internet in what is known as the internet of things (IoT). Also, the concept of IoT can be exploited in the industrial environment for increasing the production output of goods and services and for mitigating the risk of disaster occurrences. This application of IoT for enhancing industrial production is known as industrial IoT (IIoT). More so, the benefits of IoT technology can be particularly exploited across the maritime industry in what is termed the internet of maritime things (IoMT) where sensors and actuator devices are implanted on marine equipment in order to foster the communication efficacy of nautical radio networks. Marine explorations may suffer from unwanted situations such as transactional delays, environmental degradation, insecurity, seaport congestions, accidents and collisions etc, which could arise from severe environmental conditions. As a result, there is a need to develop proper communication techniques that will improve the overall quality of service (QoS) and quality of experience (QoE) of marine users. To address these, the merits of contemporaneous technologies such as ubiquitous computing, software-defined networking (SDN) and network functions virtualization (NFV) in addition to salubrious communication techniques including emergent configurations (EC), channel estimation (CE) and communication routing protocols etc, can be utilized for sustaining optimal operation of pelagic networks. Emergent configuration (EC) is a technology that can be adapted into maritime radio networks to support the operation and collaboration of IoT connected devices in order to improve the efficiency of the connected IoT systems for maximum user satisfaction. To meet user goals, the connected devices are required to cooperate with one another in an adaptive, interoperable, and homogeneous manner. In this thesis, a survey on the concept of IoT is presented in addition to a review of IIoT systems. The applications of ubiquitous computing and SDN technology are employed to design a newfangled network architecture which is specifically propounded for enhancing the throughput of oil and gas production in the maritime ecosystem. The components of this architecture work in collaboration with one another by attempting to manage and control the exploration process of deep ocean activities especially during emergencies involving anthropogenic oil and gas spillages. Several heterogeneous, intelligent and distributed devices can be connected to interact with one another over the internet in what is known as the internet of things (IoT). Also, the concept of IoT can be exploited in the industrial environment for increasing the production output of goods and services and for mitigating the risk of disaster occurrences. This application of IoT for enhancing industrial production is known as industrial IoT (IIoT). More so, the benefits of IoT technology can be particularly exploited across the maritime industry in what is termed the internet of maritime things (IoMT) where sensors and actuator devices are implanted on marine equipment in order to foster the communication efficacy of nautical radio networks. Marine explorations may suffer from unwanted situations such as transactional delays, environmental degradation, insecurity, seaport congestions, accidents and collisions etc, which could arise from severe environmental conditions. As a result, there is a need to develop proper communication techniques that will improve the overall quality of service (QoS) and quality of experience (QoE) of marine users. To address these, the merits of contemporaneous technologies such as ubiquitous computing, software-defined networking (SDN) and network functions virtualization (NFV) in addition to salubrious communication techniques including emergent configurations (EC), channel estimation (CE) and communication routing protocols etc, can be utilized for sustaining optimal operation of pelagic networks. Emergent configuration (EC) is a technology that can be adapted into maritime radio networks to support the operation and collaboration of IoT connected devices in order to improve the efficiency of the connected IoT systems for maximum user satisfaction. To meet user goals, the connected devices are required to cooperate with one another in an adaptive, interoperable, and homogeneous manner. In this thesis, a survey on the concept of IoT is presented in addition to a review of IIoT systems. The applications of ubiquitous computing and SDN technology are employed to design a newfangled network architecture which is specifically propounded for enhancing the throughput of oil and gas production in the maritime ecosystem. The components of this architecture work in collaboration with one another by attempting to manage and control the exploration process of deep ocean activities especially during emergencies involving anthropogenic oil and gas spillages. On the other hand, CE is a utilitarian communication technique that can be exploited during maritime exploration processes which offer additional reinforcement to the capacities of the nautical radio network. This technique enables the receivers of deep-sea networks to efficiently approximate the channel impulse response (CIR) of the wireless communication channel so that the effects of the communication channel on the transmitting aggregated cluster head information can be proficiently understood and predicted for useful decision-making procedures. Two CE schemes named inter-symbol interference/ average noise reduction (ISI/ANR) and reweighted error-reducing (RER) are designed in this study for estimating maritime channels for supporting the communication performances of nautical radio networks in both severe and light-fading environmental conditions. In the proposed RER method, the Manhattan distance of the CIR of an orthodox adaptive estimator is taken, which is subsequently normalised by a stability constant ɛ whose responsibility is for correcting any potential numerical system instability that may arise during the updating stages of the estimation process. To decrease the received signal error, a log-sum penalty function is eventually multiplied by an adjustable leakage (ɛ ) ̈that provides additional stability to the oscillating channel behaviour. The performance of the proposed RER method is further strengthened and made resilient against channel effects by the introduction of a reweighting attractor that further contracts the mean square error of this proposed estimator. In the ISI/ANR technique, the effects of possible ISI that may arise from maritime transmissions is considered and transformed using a low-pass filter that is incorporated for eliminating the effects of channel noise possible effects of multipath propagation. The RER scheme offered superior CE performances in comparison to other customary techniques such as the adaptive recursive least squares and normalised least mean square method in addition to conventional linear approaches such as least squares, linear minimum mean square error and maximum-likelihood estimation method. The proposed ISI/ANR technique offered an improved MSE performance in comparison to all considered linear methods. Finally, from this study, we were able to establish that accurate CE methods can improve the QoS and QoE of nautical radio networks in terms of network data rate and system outage probability. / Thesis (PhD (Computer Engineering))--University of Pretoria, 2021. / University of Pretoria Doctoral research grant, South African National Research Foundation/Research and Innovation Support and Advancement (NRF/RISA) research grant. Center for Connected Intelligence, Advanced Sensor Networks research group, University of Pretoria. / Electrical, Electronic and Computer Engineering / PhD (Computer Engineering) / Unrestricted

This thesis investigates the use of Maximum Likelihood Sequence Estimation (MLSE) in the presence of unknown channel parameters. MLSE is a fundamental problem that is closely related to many modern research areas like Space-Time Coding, Overloaded Array Processing and Multi-User Detection. Per-Survivor Processing (PSP) is a technique for approximating MLSE for unknown channels by embedding channel estimation into the structure of the Viterbi Algorithm (VA). In the case of successful acquisition, the convergence rate of PSP is comparable to that of the pilot-aided RLS algorithm. However, the performance of PSP degrades when certain sequences are transmitted. In this thesis, the blind acquisition characteristics of PSP are discussed. The problematic sequences for any joint ML data and channel estimator are discussed from an analytic perspective. Based on the theory of indistinguishable sequences, modifications to conventional PSP are suggested that improve its acquisition performance significantly. The effect of tree search and list-based algorithms on PSP is also discussed. Proposed improvement techniques are compared for different channels. For higher order channels, complexity issues dominate the choice of algorithms, so PSP with state reduction techniques is considered. Typical misacquisition conditions, transients, and initialization issues are reported. / Master of Science

Orthogonal Frequency Division Multiplexing (OFDM) is a multi-carrier modulation scheme that provides efficient bandwidth utilization and robustness against time dispersive channels. This thesis deals with self-interference, or the corruption of desired signal by itself, in OFDM systems. Inter-symbol Interference (ISI) and Inter-carrier Interference (ICI) are two types of self-interference in OFDM systems. Cyclic prefix is one method to prevent the ISI which is the interference of the echoes of a transmitted signal with the original transmitted signal. The length of cyclic prefix required to remove ISI depends on the channel conditions, and usually it is chosen according to the worst case channel scenario. Methods to find the required parameters to adapt the length of the cyclic prefix to the instantaneous channel conditions are investigated. Frequency selectivity of the channel is extracted from the instantaneous channel frequency estimates and methods to estimate related parameters, e.g. coherence bandwidth and Root-mean-squared (RMS) delay spread, are given. These parameters can also be used to better utilize the available resources in wireless systems through transmitter and receiver adaptation. Another common self-interference in OFDM systems is the ICI which is the power leakage among different sub-carriers that degrades the performance of both symbol detection and channel estimation. Two new methods are proposed to reduce the effect of ICI in symbol detection and in channel estimation. The first method uses the colored nature of ICI to cancel it in order to decrease the error rate in the detection of transmitted symbols, and the second method reduces the effect of ICI in channel estimation by jointly estimating the channel and frequency offset, a major source of ICI.