Digital Signal Processing Techniques Applied to Radio over Fiber Systems

The dissertation aims to analyze different Radio over Fiber systems for the front-haul applications. Particularly, analog radio over fiber (A-RoF) are simplest and suffer from nonlinearities, therefore, mitigating such nonlinearities through digital predistortion are studied. In particular for the long haul A-RoF links, direct digital predistortion technique (DPDT) is proposed which can be applied to reduce the impairments of A-RoF systems due to the combined effects of frequency chirp of the laser source and chromatic dispersion of the optical channel. Then, indirect learning architecture (ILA) based structures namely memory polynomial (MP), generalized memory polynomial (GMP) and decomposed vector rotation (DVR) models are employed to perform adaptive digital predistortion with low complexities. Distributed feedback (DFB) laser and vertical capacity surface emitting lasers (VCSELs) in combination with single mode/multi-mode fibers have been linearized with different quadrature amplitude modulation (QAM) formats for single and multichannel cases. Finally, a feedback adaptive DPD compensation is proposed. Then, there is still a possibility to exploit the other realizations of RoF namely digital radio over fiber (D-RoF) system where signal is digitized and transmits the digitized bit streams via digital optical communication links. The proposed solution is robust and immune to nonlinearities up-to 70 km of link length. Lastly, in light of disadvantages coming from A-RoF and D-RoF, it is still possible to take only the advantages from both methods and implement a more recent form knows as Sigma Delta Radio over Fiber (S-DRoF) system. Second Order Sigma Delta Modulator and Multi-stAge-noise-SHaping (MASH) based Sigma Delta Modulator are proposed. The workbench has been evaluated for 20 MHz LTE signal with 256 QAM modulation. Finally, The 6x2 GSa/s sigma delta modulators are realized on FPGA to show a real time demonstration of S-DRoF system. The demonstration shows that S-DRoF is a competitive competitor for 5G sub-6GHz band applications

MAD-STORM:Maneuverable Autonomous Drone with Sensing Technologies for Observing Rainfall and Meteorology in Northern Ireland

The Maneuverable Autonomous Drone with Sensing Technologies for Observing Rainfall and Meteorology (MAD-STORM) is an in-house Internet of Things (IoT)-driven Unmanned Aerial Vehicle (UAV) targeting wide array of applications, encompassing search and rescue, surveillance, crowd monitoring, and environmental sensing. This paper focuses on the design and implementation of MAD-STORM, highlighting its environmental sensing for temperature, humidity, and rain detection. Leveraging internet connectivity, MAD-STORM facilitates real-time data streaming and analysis from a ground station, while also aligning with IoT principles to establish cloud connectivity. The inclusion of global positioning system modules aids in determining navigation coordinates, ensuring the precise execution of MAD-STORM activities. This paper offers a comprehensive overview of MAD-STORM's development stages, system design, hardware, software components, intelligent capabilities and challenges. The real-time demonstration video and detals for this work is available at https://www.ulster.ac.uk/research/topic/engineering/afmm/projects/madni.</p

Enhancing Signal Detection in 6G Networks through LSTM-based MIMO Technology

Artificial intelligence (AI) transforms the multiple input multiple output (MIMO) technology into a promising candidate for beyond-fifth-generation (B5G) and upcoming sixth-generation (6G) networks. However, due to the large number of antennas in the MIMO systems, the detection process becomes very complex and also shows high computational complexity. To address this issue, this paper introduces an optimized AI-based signal detection method based on Long short-term memory (LSTM) called LSTM-based signal detection for MIMO systems. The proposed model works more efficiently in signal detection as compared to the conventional signal detection methods in terms of symbol error rate (SER) at different signal-to-noise ratios (SNR). The optimized simple LSTM architecture provides significant advantages in detecting patterns from input data. This paper goes through the various aspects of signal detection using LSTM, such as system architecture design, data preparation and training process of the neural network, performance evaluation, and future scope. Overall, this paper provides a comprehensive resource for the deployment of an LSTM neural network for signal detection in the upcoming 6G wireless networks.</p

Tujuan Pendidikan Islam Menurut Al-Ghazali Ditinjau dari Perspektif Hadis

The purpose of education is a component that we must know and understand in life. This has been discussed by scholars, including Imam al-Ghazali. For this reason, this study discusses the goals of Islamic education according to al-Ghazali from the perspective of Hadith. This research is a library research, namely research that uses data sources from library searches in the form of books and articles, namely the books of Hadith and their syarah, as well as the Nabawiyah sirah. The approach used is, by using qualitative data analysis methods. While the data collection technique in this study is a documentation technique, namely by collecting hadiths related to the theme, using several key words. The result of this research is that the purpose of education according to Al-Ghazali emphasizes religious and moral education. According to him, the meaning and purpose of Islamic education is education that seeks and aims in the process of forming a complete human being. As for making a curriculum, Al Ghazali has two tendencies, namely a tendency towards religion and a pragmatic tendency. As for the material aspects of Islamic education according to Al Ghazali's thoughts include: faith, morals, reason, social and physical education. The purpose of education according to the hadith of the Prophet Muhammad is to create humans who have noble character, to create humans who have a balanced life between the world and the hereafter, to create people who are useful and efficient for themselves, their families, communities and nations and even benefit the world. who are trustworthy and responsible, Realizing humans who are integrated with the progress of the world without leaving the noble values ​​contained in Islamic morals. According to him, a good teacher, apart from being intelligent and perfect in mind, must also have commendable qualities. The characteristics that must be possessed by a student are humility, purify oneself from all evil, obey and istiqamah. Meanwhile, the evaluation of education is all forms of activities related to their respective responsibilities in the educational process. The purpose of education in the perspective of hadith can be classified as follows; tarbiyah jismiyah (physical education), tarbiyah ruhiyah, tarbiyah aqliyyah (aqal education), tarbiyah wijdaniyyah (emotional education), tarbiyyah al-khuluqiyyah (moral education), and tarbiyah ijtima'iyyah (educational education). social).Keywords : Educational Goals, Al-Ghazali, Hadith Perspective

A Reinforcement Learning Control and Fault Detection Method for the MADNI Drone

Amidst the tumultuous storms and challenging weather conditions that have engulfed Northern Ireland at the end of 2023 into 2024, highlights the demand of Unmanned Aerial Vehicles (UAVs) equipped with Search and Rescue (SAR) capabilities to revolutionise emergency response efforts and bolstering resilience in the face of adversity. This paper explores the application of Reinforcement Learning (RL) techniques, specifically Deep Deterministic Policy Gradient (DDPG) methods, for enhancing control and fault detection capabilities in the real-life Manoeuvrable Autonomous Drone for Navigation and Intelligence (MADNI). We investigate the performance of DDPG agents trained with different optimisers, including Adaptive Moment Estimation (ADAM), Root Mean Square Propagation (RMSprop), Stochastic Gradient Descent (SGD), Adaptive Gradient Algorithm (AdaGrad), and Stochastic Gradient Descent with Momentum (SGDM). Our study aims to assess the effectiveness of these optimisation methods by improving the stability, convergence speed, and fault detection accuracy of the MADNI model. By conducting comprehensive simulations and experiments, we evaluate the ability of DDPG-based RL agents to navigate and detect faults in dynamic and uncertain environments. The findings of this research contribute to advancing autonomous systems' reliability and adaptability by identifying optimal strategies for training RL agents in UAV control and fault detection tasks.</p

Optimizing MIMO Detection with DM-Detnet in 6G Networks

Artificial intelligence (AI) has transformed multiple inputs multiple output (MIMO) technology into a key enabling technology for beyond fifth-generation (B5G) technology such as sixth-generation (6G) networks. However, MIMO technology faces some crucial research challenges, among which signal detection is a significant problem. This paper presents an optimized deep learning-based MIMO detection method called deep MIMO detection network (DM-Detnet) for MIMO detection. The light network architecture of DM-Detnet allows signal detection to be performed in a layer-by-layer manner. This work primarily concentrates on the effect of signal-to-noise ratio (SNR) points on network training and testing. We conducted a detailed simulation study to analyze the performance of our model based on specific low and high SNR points. With intensive training and optimization, the DM-Detnet model achieves better performance in MIMO scenarios. Simulation results show that the optimized DM-Detnet achieves better symbol error rate (SER) performance and is also able to achieve lower computational complexity than benchmark conventional MIMO detectors.</p

Experimental Analysis of A-RoF Based Optical Communication System for 6G O-RAN Downlink

This paper explores recent advancements in optical communication for sixth generation (6G) networks, focusing on the proposed architecture, Open Radio Access Network (O-RAN) specifications, and Radio over Fiber (RoF) systems. Experimental evaluation of 6G Analog RoF, utilizing 60 GHz and 28 GHz carriers over 10 km single mode fiber, demonstrates the efficacy of Digital Pre-Distortion (DPD) linearization in reducing Error Vector Magnitude (EVM). Despite the observed rise in EVM with increased bandwidth, slight performance improvements are facilitated by DPD. This underscores the significance of ongoing advancements in mitigating challenges and harnessing the full potential of 6G Analog RoF (A-RoF) technology for upcoming O-RAN. These developments are poised to transform communication networks, ensuring enhanced speed, reliability, and efficiency to meet the dynamic demands of the digital landscape in the upcoming 6G era and beyond.</p