Abstract Iterative receiver techniques, multiple-input – multiple-output (MIMO) processing, and orthogonal frequency division multiplexing (OFDM) are amongst the key physical layer technologies when aiming at higher spectral efficiency for a wireless communication system. Special focus is put on iterative detection, decoding, and channel estimation for a MIMO-OFDM system. After designing separately efficient algorithms for the detection, channel decoding, and channel estimation, the objective is to optimize them to work together through optimizing the activation schedules for soft-in soft-out (SfISfO) components. A list parallel interference cancellation (PIC) detector is derived to approximate an a posteriori probability (APP) algorithm with reduced complexity and minimal loss of performance. It is shown that the list PIC detector with good initialization outperforms the K-best list sphere detector (LSD) in the case of small list sizes, whereas the complexities of the algorithms are of the same order. The convergence of the iterative detection and decoding is improved by using a priori information to also recalculate the candidate list, aside from the log-likelihood ratios (LLRs) of the coded bits. Unlike in pilot based channel estimation, the least-squares (LS) channel estimator based on symbol decisions requires a matrix inversion in MIMO-OFDM. The frequency domain (FD) space-alternating generalized expectation-maximization (SAGE) channel estimator calculates the LS estimate iteratively, avoiding the matrix inversion with constant envelope modulation. The performance and computational complexity of the FD-SAGE channel estimator are compared to those of pilot based LS channel estimation with minimum mean square error (MMSE) post-processing exploiting the time correlation of the channel. A time domain (TD) SAGE channel estimator is derived to avoid the matrix inversion in channel estimation based on symbol decisions for MIMO-OFDM systems also with non-constant envelope modulation. An obvious problem, with more than two blocks in an iterative receiver, is to find the optimal activation schedule of the different blocks. It is proposed to use extrinsic information transfer (EXIT) charts to characterize the behavior of the receiver blocks and to find out the optimal activation schedule for them. A semi-analytical expression of the EXIT function is derived for the LS channel estimator. An algorithm is proposed to generate the EXIT function of the APP algorithm as a function of the channel estimate’s mutual information (MI). Surface fitting is used to get closed form expressions for the EXIT functions of the APP algorithm and the channel decoder. Trellis search algorithms are shown to find the convergence with the lowest possible complexity using the EXIT functions. With the proposed concept, the activation scheduling can be adapted to prevailing channel circumstances and unnecessary iterations will be avoided.

Abstract Future wireless systems will require high data rate with low transmit and processing power consumption. A combination of multiple-input multiple-output (MIMO) transmission with orthogonal frequency division multiplexing (OFDM) is a promising approach for offering better performance in terms of the capacity and quality of service (QoS). The detector in the wireless receiver is one of the highest power consuming parts. In order to minimize the power consumption, it is desirable for the detector to be able to change the detection algorithm to suit the channel conditions. In this thesis work, we study the suitability of different MIMO detection algorithms for adaptive operation. The selective spanning with fast enumeration (SSFE), K-best list sphere detector (LSD), linear minimum mean square error (LMMSE), and successive interference cancellation (SIC) detectors are compared to each other in terms of communications performance in the 4 × 4 and 8 × 8 MIMO–OFDM systems. The impact of least squares (LS) and minimum mean square error (MMSE) channel estimation methods, mobile speed, and transmit precoding at the base station on detector algorithm selection is also considered. The SIC detector is shown to suffer from error propagation in poor channel conditions. The SSFE detector is unable to outperform the K-best LSD and is occasionally outperformed by the LMMSE detector. The LMMSE detector is able to outperform the K-best LSD on the low signal-to-noise (SNR) regime when the mobile speed is high and the spatial channel correlation is low or moderate; it is also found to be more robust against channel estimation errors. Because a realistic adaptive detector is expected to support only two detection algorithms, the K-best LSD and LMMSE are selected based on the performance results for application specific integrated circuit (ASIC) architecture design and further comparison. The chosen algorithms are evaluated by considering the performance and implementation results. The K-best LSD provides good performance under challenging channel conditions with the cost of high complexity and power consumption. The LMMSE detector is energy efficient but performs poorly in correlated channels. However, exceptions exist, and detailed results on when to use a simple detector and when to use a complex detector are provided. / Tiivistelmä Tulevaisuuden langattomat tietoliikennejärjestelmät edellyttävät suurta datanopeutta ja vähäistä tehonkulutusta datan lähetyksessä ja käsittelyssä. Monitulo-monilähtötekniikan (MIMO) ja monikantoaaltomoduloinnin (OFDM) yhdistelmä (MIMO–OFDM) on lupaava lähestymistapa hyvän suorituskyvyn saavuttamiseksi, sekä kapasiteetin että luotettavuuden kannalta. Yksi langattoman vastaanottimen eniten tehoa kuluttavista osista on ilmaisin. Tehonkulutuksen minimoimiseksi tulisi ilmaisimen pystyä vaihtamaan ilmaisinalgoritmia radiokanavan olosuhteisiin sopivaksi. Tässä väitöskirjatyössä tarkastellaan erilaisten MIMO-ilmaisinalgoritmien sopivuutta mukautuvaan ilmaisuun. Listapalloilmaisimen (list sphere detector, LSD), valikoivan laajennuksen listailmaisimen (selective spanning with fast enumeration, SSFE), lineaarisen pienimmän keskineliövirheen ilmaisimen (linear minimum mean square error, LMMSE) ja peräkkäisen häiriönpoistoilmaisimen (successive interference cancellation, SIC) suorituskykyjä verrataan toisiinsa sekä 4 × 4 että 8 × 8 MIMO–OFDM järjestelmissä. Pienimmän neliösumman (LS) ja pienimmän keskineliövirheen (MMSE) kanavaestimointialgoritmien, vastaanottimen nopeuden ja lähetyksen esikoodauksen vaikutus ilmaisinalgoritmin valintaan otetaan huomioon vertailussa. Haastavissa kanavaolosuhteissa SIC-ilmaisin kärsii virheen etenemisestä. SSFE-ilmaisimen suorituskyky on huonompi kuin K-best LSD-ilmaisimen, ja joissakin tilanteissa huonompi kuin LMMSE-ilmaisimen. LMMSE-ilmaisin pystyy parempaan suorituskykyyn kuin K-best LSD-ilmaisin kun signaali-kohinasuhde (SNR) on pieni, vastaanottimen nopeus on suuri ja radiokanavan korrelaatio on matala tai kohtalainen. LMMSE-ilmaisin myös kestää epätarkat kanavaestimaatit paremmin kuin LSD-ilmaisin. Realistisessa vastaanottimessa mukautuva ilmaisin tukee vain kahta ilmaisinalgoritmia, ja sen takia K-best LSD and LMMSE-ilmaisimet valittiin suorituskykytulosten perusteella toteutettaviksi ASIC-teknologialla. Valittuja ilmaisinalgoritmeja arvioidaan sekä suorituskyvyn että toteutustulosten perusteella. K-best LSD-ilmaisimella on hyvä suorituskyky haastavissa kanavaolosuhteissa, mutta toteutus on monimutkainen ja tehonkulutus korkea. LMMSE-ilmaisin on energiatehokas, mutta suorituskyky on huono korreloivissa kanavissa. Poikkeuksia näihin tilanteisiin kuitenkin esiintyy, ja työssä esitetään suositus milloin yksinkertaista ilmaisinta voidaan käyttää tehonkulutuksen minimoimiseksi ja milloin taas monimutkainen ilmaisin on välttämätön luotettavan tiedonsiirron takaamiseksi.

To meet the urgent demand of high-speed data rate and to support large number of users, the massive multiple-input multiple-output (MIMO) technology is becoming one of the most promising candidates for the next generation wireless communications, namely the 5G. To realize the full potential of massive MIMO, it is necessary to have the channel state information (CSI) (partially) available at the transmitter. Hence, an efficient channel estimation is one of the key enablers and also critical challenges for 5G communications. Dealing with such problems, this dissertation investigates the design of efficient pilot-data transmission pattern and channel estimation in massive MIMO for both multipair relaying and peer-to-peer systems. Firstly, this dissertation proposes a pilot-data transmission overlay scheme for multipair MIMO relaying systems employing either half- or full-duplex (HD or FD) communications at the relay station (RS). In the proposed scheme, pilots are transmitted in partial overlap with data to decrease the channel estimation overhead. The RS can detect the source data by exploiting the asymptotic orthogonality of massive MIMO channels. Due to the transmission overlay, the effective data period is extended, hence improving system throughput. Both theoretical and simulation results verify that the proposed pilot-data overlay scheme outperforms the conventional separate pilot-data design in the limited coherence interval scenario. Moreover, a power allocation problem is formulated to properly adjust the transmission power of source data transmission and relay data forwarding which further improves the system performance. Additionally, this dissertation proposes and analyzes an efficient HD decode-and-forward (DF) scheme, named sum decode-and-forward (SDF), with the physical layer network coding (PNC) in the multipair massive MIMO two-way relaying system. As comparison, a joint decode-and-forward (JDF) scheme applied to the multipair massive MIMO relaying is also proposed and investigated. In the SDF scheme, a half number of pilots are saved compared to the JDF scheme which in turn increases the spectral efficiency of the system. Both the theoretical analyses and numerical results verifies such superiority of the SDF scheme. Further, the power efficiency of the proposed schemes is also investigated. Simulation results show that the signal transmission power can be rapidly reduced if the massive antenna arrays are equipped on the RS and the required data transmission power can further decrease if the training power is fixed. Finally, this dissertation investigates the general channel estimation problem in the massive MIMO system which employs the hybrid analog/digital precoding structure with limited radio-frequency (RF) chains. By properly designing RF combiners and performing multiple trainings, the performance of the proposed channel estimation can approach that of full-chain estimations depending on the degree of channel spatial correlation and the number of RF chains which is verified by simulation results in terms of both mean square error (MSE) and spectral efficiency. Moreover, a covariance matching method is proposed to obtain channel correlation in practice and the simulation verifies its effectiveness by evaluating the spectral efficiency performance in parametric channel models. / Graduate / 0537 / 0544 / leyuanpan@gmail.com

Faced with imminent spectrum scarcity largely due to inflexible licensed band arrangements, cognitive radio (CR) has been proposed to facilitate higher spectrum utilization by allowing cognitive users (CUs) to access the licensed bands without causing harmful interference to primary users (PUs). To achieve this without the aid of PUs, the CUs have to perform spectrum sensing reliably detecting the presence or absence of PU signals. Without reliable spectrum sensing, the discovery of spectrum opportunities will be inefficient, resulting in limited utilization enhancement. This dissertation examines three major techniques for spectrum sensing, which are matched filter, energy detection, and cyclostationary feature detection. After evaluating the advantages and disadvantages of these techniques, we narrow down our research to a focus on cyclostationary feature detection (CFD). Our first contribution is to boost performance of an existing and prevailing CFD method. This boost is achieved by our proposed optimal and sub-optimal schemes for identifying best hypothesis test points. The optimal scheme incorporates prior knowledge of the PU signals into test point selection, while the sub-optimal scheme circumvents the need for this knowledge. The results show that our proposed can significantly outperform other existing schemes. Secondly, in view of multi-antenna deployment in CR networks, we generalize the CFD method to include the multi-antenna case. This requires effort to justify the joint asymptotic normality of vector-valued statistics and show the consistency of covariance estimates. Meanwhile, to effectively integrate the received multi-antenna signals, a novel cyclostationary feature based channel estimation is devised to obtain channel side information. The simulation results demonstrate that the errors of channel estimates can diminish sharply by increasing the sample size or the average signal-to-noise ratio. In addition, no research has been found that analytically assessed CFD performance over fading channels. We make a contribution to such analysis by providing tight bounds on the average detection probability over Nakagami fading channels and tight approximations of diversity reception performance subject to independent and identically distributed Rayleigh fading. For successful coexistence with the primary system, interference management in cognitive radio networks plays a prominent part. Normally certain average or peak transmission power constraints have to be placed on the CR system. Depending on available channel side information and fading types (fast or slow fading) experienced by the PU receiver, we derive the corresponding constraints that should be imposed. These constraints indicate that the second moment of interference channel gain is an important parameter for CUs allocating transmission power. Hence, we develop a cooperative estimation procedure which provides robust estimate of this parameter based on geolocation information. With less aid from the primary system, the success of this procedure relies on statistically correlated channel measurements from cooperative CUs. The robustness of our proposed procedure to the uncertainty of geolocation information is analytically presented. Simulation results show that this procedure can lead to better mean-square error performance than other existing estimates, and the effects of using inaccurate geolocation information diminish steadily with the increasing number of cooperative cognitive users.

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.

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

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