Communication Engineering

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Enhancing Spectrum Prediction and Awareness with Deep Learning Approaches
(Addis Ababa University, 2024-05) Bethelhem Seifu; Dereje Hailemariam (PhD); Pollin, Sofie (Prof.)
The growing demand for ubiquitous connectivity and real-time applications has placed significant strain on the finite radio spectrum, making efficient utilization crucial. To address this, static allocation methods must be replaced with real-time spectrum management that ensures optimal sharing, utilization, and management of spectrum bands among multiple users. By building comprehensive Spectrum Awareness (SA) through continuous monitoring and situational understanding of the radio environment and network conditions, and by intelligently predicting spectrum availability and usage through accurate Spectrum Prediction (SP), new opportunities for wireless services can be unlocked, paving the way for the next generation of connected technologies. In that regard, this PhD thesis aims to enhance SP and SA tasks using Deep Learning (DL) techniques, addressing three primary challenges: achieving accurate long-term spectrum predictions with limited data, understanding spectrum usage across multiple dimensions, and developing robust prediction models to improve accuracy. These challenges are tackled across three network perspectives: Cognitive Radio Networks (CRNs), Mobile Network Operators (MNOs), and Unmanned Aerial Vehicle (UAVs)-assisted networks. First, long-term spectrum predictability and multi-dimensional analysis are explored for CRNs using a distance-dependent central data fusion center and predictors based on Long Short- Term Memory (LSTM) and Convolutional Long Short-Term Memory (ConvLSTM) networks. These models analyze temporal and spatial dependencies to generate single- and multi-location interpolated spectrum data. Evaluation of prediction performance showed, LSTM models performing best for lower-frequency bands with deterministic Primary Users (PU) patterns, achieving an improvement by 9.7% over the baseline model and a prediction error below 5 dBm for 2.5 hours, using only 75 minutes of past monitoring data. Conversely, ConvLSTM models excel at higher-frequency bands by processing interpolated spectrum maps, yielding approximately a 14% improvement in prediction accuracy. With limited PU knowledge and sparse sensor deployment, increasing the cooperative region provides minimal accuracy gains, with a maximum error probability reduction by 0.15 at 1500 meters coverage radius. Second, the SP problem for MNOs is tackled using voice and data traffic from Global System for Mobile (GSM) and Long-Term Evolution (LTE) networks as proxies for spectrum utilization. By mapping traffic data to channel utilization and employing DL-based models, spectrum utilization was predicted accurately without direct monitoring. Voice traffic, with its deterministic nature, allowed for pre-mapping before prediction, while LTE data traffic required a post-prediction mapping approach to prevent error propagation. For this prediction task, different DL models based on Convolutional Neural Networks (CNNs) and LSTMwere proposed, and their prediction accuracy was assessed using Root Mean Squared Error (RMSE) and Mean Absolute Error (MAE) voice channel utilization, improving performance by 26.9% at the cluster level and 36% at the base station level due to its ability to capture temporal patterns. On the other hand, for LTE traffic prediction, the CNN-LSTM hybrid model achieved a 40% improvement in RMSE at the cluster level, effectively managing multivariate data and detecting peak-hour irregularities. Lastly, for wireless networks with 3D architectures, such as UAV-assisted systems, Spectrum Situational awareness (SSA) is achieved with Volumetric Radio Environment Maps (VREMs) that represent propagation loss across both areal and altitude dimensions. This work introduces two novel DL approaches for constructing the VREMs: the Volume-to-Volume (Vol2Vol) method and the Sliced-VREM method. The Vol2Vol approach directly models 3D data using a 3D-GAN, achieving high accuracy at greater altitudes, with a Structural Similarity Index (SSIM) of 0.9447. On the other hand, the Sliced-VREM method leverages stacked 2D environmental maps and transmitters with an altitude-aware Spider-UNet to efficiently capture altitude dependencies, making it more computationally practical for lower-altitude scenarios. Both approaches demonstrate significant performance improvements over traditional 2D baselines, with MAE reductions of up to 62.9%. Furthermore, preliminary spectrum occupancy maps generated from these VREMs reveal spatial and altitudinal variations, even at low heights of 9 m or less, showcasing their potential to provide actionable insights for dynamic spectrum sharing without relying on extensive monitoring infrastructure.
Performance Analysis of Co-OFDM Integrated with WDM for Long Haul Optical Communication
(Addis Ababa University, 2025-03) Yeroma Genet; Yalemzewd Negash (PhD)
The current internet demand requires high spectral efficiency. The integration of CO-OFDM with a WDM system has high spectral efficiency by dividing subcarriers orthogonally. To transmit within a single optical fiber, multiple data streams come together into one by a WDM system. This paper aims to investigate the effect of parametric value difference on the performance of the WDM system as well as CO-OFDM integrated with WDM. The power of the CW laser, the power of the EDFA, the length of DCF, and the EDF are the parameters used. The performance of the system is measured by BER, Q-factor, Eye Opening Height, and OSNR. The system is designed and analyzed using the ”Opti-System” simulation tool. The initial focus is to design WDM without a CO-OFDM system to study the parameters that influence the system’s performance. CW laser power is accomplished better at 0 dBm instead of at 10 dBm because when the intensity of light increases, the nonlinearity effects are high. At 100mW, EDFA power dispatches better than if it were at 200mW. When the EDFA amplifies the signal power simultaneously, the noise power is amplified thus, the system’s performance is minimized. DCF length is proportional to the length of single-mode fiber, resulting in improved performance. If the above parameter values are not within the normal range, the performance of the system becomes highly distracted. The subsequent task involves understanding and analyzing which factors limit the transmission capacity and the reachability of CO-OFDM with WDM. The effect of the transmitted data and length on the system’s performance is observed.
Bandwidth Enhancement, Analysis, and Comparison of Microstrip Patch Antennas for Millimeter Wave
(Addis Ababa University, 2025-05) Rukiya Mohammed; Murad Ridwan (PhD)
In the rapidly advancing landscape of wireless communication technology, there is a pressing need for antennas that are compact, lightweight, cost-effective, and have a low profile. Microstrip patch antennas have emerged as popular choices in numerous wireless communication applications due to their inherent benefits such as lightweight, compact size, affordability, ease of fabrication, and high reliability. However, these antennas encounter challenges, notably in the form of narrow bandwidth and low gain. Various design factors influence the radiation properties of microstrip antennas, encompassing feeding techniques, substrate materials, patch configuration, and ground structures. This thesis is devoted to addressing the limitations of narrow bandwidth and low gain in microstrip patch antennas, specifically tailored for 5G mobile communication. In this study, we enhanced a microstrip patch antenna’s design to boost performance for 5G applications. By carefully adjusting its dimensions, the antenna achieved a bandwidth of 7.64% (2140 MHz), a return loss of -39.388 dB, and a VSWR of 1.021. These results show that our method significantly enhances the antenna’s performance compared to previous designs, making it more effective for 5G communication.
Comparative Analysis of Fronthaul Delay in 5G C-RAN: The Case of Ethio Telecom Legehar Area
(Addis Ababa University, 2025) Ebisa Regasa; Tsegamlak Terefe (PhD)
This thesis investigates fronthaul delay in 5G Centralized-Radio Access Network (C-RAN) architecture, focusing on Ethio Telecom’s Legehar area. Fronthaul (FH) is the communication link between the Radio Unit (RU) and Distributed Unit (DU). This research compares D-RAN and C-RAN architecture to evaluate their delay performance. The increasing demand for low latency has emphasized the need to analyze the fronthaul delays in diverse scenarios, including Financial hubs, major event venues such as Stadium, and Africa Union (AU) summits. C-RAN is a promising solution for meeting the growing demand for high-speed, low-latency, and reliable wireless connectivity. Legehar and its surroundings are key areas where delay affects users’ demands. The impact of delay on FH was demonstrated by comparing and analyzing various network architectures and FH configurations. Simulation and analytical techniques were used to analyze the delay characteristics. The findings demonstrate that the C-RAN exhibits superior delay performance under specific conditions, offering insights for optimizing 5G deployments. The simulation results show that the eCPRI protocol performed better than the current Ethio Telecom used, known as CPRI. Because the FH data rate impacts delay, CPRI uses the maximum data rate of 24.33 Gbps. The delay for 24.33 Gbps is 0.493 μs. eCPRI at 50 Gbps rate can achieve a delay of 0.24 μs. Additionally, there is a user plane delay that achieved to 0.48 μs by dividing plane.
Techno-economic competitiveness alternative of 5G FWA in Suburban Areas: The Case of Bishoftu, Ethiopia
(Addis Ababa University, 2025) Natnael Yekoye; Yalemzewd Negash (PhD)
Telecommunication services generation popularity is so fast and plays a vital role in enabling Reliable Communication. Furthermore, these services have also been instrumental in accelerating digital transformations across various industries and sectors. It’s amazing to see how technology has evolved over the years to make communication faster, easier, and more efficient than ever before. In today’s fast-paced world, it’s essential to have reliable and high-speed telecommunications services to stay connected and get things done efficiently. With the increase in remote work and online activities, the demand for high broadband speed and affordable telecom services has become even more critical. Service providers need to keep up with this demand and offer reliable and cost-effective solutions to their customers. Due to these trends, fixed wireless broadband access(FWA) is one of the right solution services for businesses that need high-speed internet connectivity at affordable prices. 5G Fixed Wireless Access (FWA) networks have the potential to provide ultra-high-speed broadband at an affordable price for suburban and rural areas, making them a potential competitor for other technologies like FTTH and copper. The main purpose of this thesis is to conduct a research investigation on the Techno-Economic Competitiveness of 5G FWA in sub-urban areas, with a specific focus on Bishoftu Town as an example.
Techno-Economic Analysis of Cloud-Enabled Small Cell Networks for Video Service Delivery: A Case Study in Addis Ababa
(Addis Ababa University, 2025-02) Altaseb Desalegn; Yihenew Wondie (PhD)
The demand for immersive video services, such as 360-degree live streaming, augmented reality, and virtual reality has rapidly increased, especially at crowded events, while traditional macrocellular networks often struggle when there is excessive traffic. Insufficient capacity and latency have a major impact on the capacity of a radio access network (RAN). To meet this growing demand, a robust and scalable network infrastructure is required. Cloud-enabled small cell networks hold significant promise for improving mobile network capacity and coverage challenges, at the same time improving cost, energy usage, deployment flexibility, and network management. Combining cloud infrastructure and small-cell networks, along with virtualized execution of computing resources, provides a solution. However, a full evaluation of the networks’ economic viability is required prior to their implementation, as this includes a precise computation of the resources required and a thorough assessment of the expected outcomes. This article provides a paradigm for evaluating the economic feasibility of cloudenabled small-cell networks to deliver immersive video services during packed events. We select Meskel Square in Addis Ababa as a forecasting research location, which serves as an urban setting with concentrated hotspots. For selected scenario of implementing RAN, forecasting the required compute, storage, and radio resources and associated costs with implementing new software and hardware architecture. IST-TONIC and CELTICECOSYS in MATLAB and Microsoft Excel are utilized as part of a framework that comprises marketing forecasts, network dimensioning, revenue modeling, and economic analysis. We use a 10% discount rate and a 10-year study period for a breakdown of investments value analysis. The net present value (NPV), internal rate of return (IRR), payback period (PP), operational expenditures (OpeEx), capital expenditures (CapEx) and total costs of ownership (TCO) are evaluated. According to the results obtained, the deployment scenario with the highest return economic advantages that affect the rate of return is Open radio access network. Virtualized radio access networks come in second, centralized radio access network in third, and distributed RAN in fourth. Compared to distributed radio access networks, other architecture exhibits a better cost position. The payback periods for distributed RAN, centralized RAN, virtualized RAN, and open RAN in this scenario are 2.34, 2.048, 2.039, and 2.034 years respectively for the deployment scenario. Every architecture has a positive net present value (NPV) during the study periods and a significantly higher IRR value than the specified discounted rate. According to the findings, all scenarios are deployable and open RAN has significantly highest economic return with reduced total cost of ownership.
A Comparative Analysis of Power Amplifier Types in Massive MIMO Systems for 5G Networks.
(Addis Ababa University, 2025-03) Gamachu Tafari; Yihenew Wondie (PhD)
Class AB, Doherty, and Envelope Tracking Power Amplifiers (ETPA) are the three types of power amplifiers that are thoroughly compared in this thesis in the context of 5G networks. Current communication standards require power amplifiers to achieve high efficiency over increasingly larger dynamic ranges and bandwidths while maintaining strict linearity criteria. The study uses key criteria like power-added efficiency, linearity, gain, and output power to evaluate their performance. Simulations were conducted using MATLAB and the Advanced Design System to evaluate amplifier properties under various settings, including the use of Digital Pre-Distortion to improve nonlinearity. The results showed that Class AB amplifiers achieved PAE of 51.090 % and improved AMAM Conversion. Excellent back-off efficiency and significant advantages in high-power scenarios are exhibited by Doherty amplifiers, especially when DPD integration is employed for enhanced linearity. Despite achieving 70% PAE and demonstrating superior efficiency over a broad dynamic range, envelope tracking amplifiers require complex design due to their reliance on dynamic voltage control. By balancing the trade-offs between linearity and efficiency, this study determines the optimal amplifier topologies for 5G networks that are both high-performance and energy-efficient. These discoveries contribute to the advancement of the technology by revealing the future applications of power amplifiers in wireless communication systems.
Designing and Optimizing Repeater Using Telecom Microwave Antenna case of Fana Broadcasting Corporate (FBC) and Ethiotelecom
(Addis Ababa University, 2024-03) Efnan Merga; Murad Ridwan (PhD)
Microwave antennas are widely utilized in telecommunications, radar systems, satellite communications, and television broadcasting to transmit programs—such as relaying an outside broadcast to a main studio—as well as in other applications requiring the transmission of high-frequency electromagnetic waves over long distances. Fana Broadcasting Corporation (FBC) currently utilizes microwave links to transmit public and governmental programs from temporary outside broadcast (OB) studios across Addis Ababa to its main headquarters studio. However, the city’s complex terrain and the rapid rise of tall buildings frequently obstruct the line of sight between transmitters and receivers, leading to an increased occurrence of failures during live television broadcasts. This thesis addresses the identified challenges by designing and optimizing an EthioTelecom microwave antenna to function as a repeater for broadcasting. It evaluates the impact of substituting ITU-R parameters with Localized values, such as rain fading, to enhance performance.
Performance Analysis of a Microstrip Antenna For 5g Applications Using Metamaterial
(Addis Ababa University, 2024-03) Moti Indalew; Murad Ridwan (PhD)
Because of its low profile, low cost, and ease of fabrication in the circuit boards to construct smart cities using the internet of things (IOT), microstrip antennas have grown to be an important component of today’s wireless communication world. The majority of 5th generation applications are incompatible with poor performance characteristics such narrow bandwidth, low power handling capability, low gain, and huge antenna footprint. Different design optimization technique studies have been conducted to improve the bandwidth, gain, and size of microstrip antennas for recent generation networks, but the performance of these antennas is still need the enhancement of bandwidth for fifth generation applications and above. The primary goal of this research is to determine how metamaterials affect the bandwidth performance of microstrip antennas. CST Microwave Studio was used to design and simulate the antenna’s structure, which works in the 28 GHz range. The Rogers RT Duroid 5880 material, which has a dielectric coefficient of 2.2, was used as the substrate for the antenna construction. With an increase in turns, the metamaterial-based microstrip antenna’s bandwidth expanded for every split ring resonator segmentation. Notably, it changed quickly from single turn to two turn, but only marginally after that. Compared to circular and triangular split ring resonator; rectangular split ring resonaror exhibit superior bandwidth augmentation. By etching the rectangular, circular and triangular split reing resonator, respectively, with N=3, the bandwidth is increased by 53.06%, 46.7% and 26.89% compared to the conventional microstrip antenna. The bandwidth is enhanced by using more split ring resonators than by using just one.The bandwidth is increased by 101.81% when utilizing 2x2 split ring resonator elements compared to a conventional microstrip antenna without a split ring resonator. The radiation efficiencies of conventional microstrip antennas, CSRR-based microstrip antennas, rectangular split ring resonator-based microstrip antennas, triangular split ring resonator-based microstrip antennas, and pentagonal split ring resonator-based microstrip antennas are 86 %, 87.9%, 85.3%, 80.3 % and 92.4 %, respectively.
Quality Assessment for 5G Enhanced Mobile Broadband Service in Addis Ababa
(Addis Ababa university, 2024-10) Mubarik Ahmed; Beneyam Berehanu (PhD)
The worldwide telecom industry is moving toward 5G network to address high bandwidth, low latency, and massive connectivity requirements. The recent deployment of 5G NR technology in Ethiopia promised to transform the speed and quality of MBB services in majority of the business districts and selected residential areas of Addis Ababa. To achieve expected outcomes from this new 5G network, its quality and perception of customers should be evaluated in its early stage. This will enable timely and informed measures by subscribers, mobile network operators, mobile device manufacturers, the regulatory authority, ECA, and other stakeholders. In this thesis, spatiotemporal evaluation of QoS metrics-download throughput, upload throughput and latency-is conducted using crowdsourcing application, Ookla SpeedTest, Network performance reporting system, PRS and drive test tool, PHU. A subjective survey is used to assess user experience. Despite the significant disparity in 5G coverage, result from all four sources indicate that 5G eMBB service performance in AA is very good as compared to average global 5G performance and IMT-2020 minimum technical requirements. Overall average download throughput obtained is 364 Mbps from SpeedTest, 311 Mbps from PRS and 414 Mbps from PHU. And average upload throughput is 58 Mbps from SpeedTest, 20 Mbps from PRS and 89 Mbps from PHU. From subjective survey, MOS on the overall performance of 5G eMBB service in AA is 4.1. The results also show a consistent trend regarding the impact of spatiotemporal and device variations on the performance of 5G network.
Root Cause Analysis of Optical Transport Network Channel Failure
(Addis Ababa University, 2024-08) Aziza Ahmed; Yalemzewd Negash (PhD)
As demands for high bandwidth surge due to 4K/8K streaming, 5G networks, and cloud-based applications, robust optical transport networks (OTNs) are critical. OTN failures can significantly impact service quality, network availability, and service level agreements (SLAs). While research has addressed fault prediction and localization, a gap exists in root cause analysis (RCA) for OTN channel failures. This study proposes a novel approach utilizing machine learning (ML) to pinpoint the root cause of these failures efficiently. This study compared four ML classifier models to analyze real data from ethio telecom’s network. The data included eight key features that influence OTN channel performance. Extreme Gradient Boosting (XGBoost) emerged as the superior performer, achieving an impressive 99.91% accuracy and a high F1-score of 97.5%. Furthermore, it excelled in efficiency, with training times of just 5.42 seconds and testing times of 0.2 seconds. Interestingly, the model identified minimum input optical power (Min IOP) as the most critical factor, suggesting that extrinsic loss within the fiber optic cables is a major cause of OTN channel failures. This study explores a novel ML system for OTN RCA, enabling faster and more precise root cause identification. This empowers network operators to proactively address issues and ensure optimal performance, significantly boosting network reliability and efficiency in the high-bandwidth age.
LTE PRB Utilization Prediction for Load Balancing Between Frequency Layers
(Addis Ababa University, 2024-05) Fetihya Mohammed; Fetihya Mohammed (PhD)
The ever-increasing number of smart devices and services strains Long-Term Evolution( LTE) network capacity, impacting Key Performance Indicators(KPIs) like user experience. Accurate prediction of LTE resource utilization is crucial for network optimization and improving user experience. Physical Resource Block (PRB) utilization prediction plays a vital role in analyzing resource allocation within the network. This thesis investigates the application of machine learning models for predicting LTE PRB utilization to facilitate load balancing between frequency layers. Three prominent models – Prophet, long short term memory (LSTM), and eXtreme Gradient Boosting(XGBoost) were evaluated and compared. The results demonstrate that Prophet significantly outperforms LSTM and XGBoost in terms of prediction accuracy. Prophet achieved an R-squared value of 0.95 and a Mean Absolute Error(MAE) of 4.98, indicating a highly accurate fit. Conversely, LSTM and XGBoost obtained R-squared values of approximately 0.63 with respective MAE values of around 17. These findings suggest Prophet’s superior accuracy makes it a promising choice for predicting PRB utilization and enabling effective load balancing in LTE networks. This thesis contributes to the field of LTE network optimization by demonstrating the effectiveness of machine learning, particularly Prophet, for PRB utilization prediction. This capability can be leveraged to develop efficient load balancing algorithms that improve network performance and user experience.
Quality of Experience Modeling for Fixed Broadband Internet Using Machine Learning Algorithms
(Addis Ababa University, 2024-04) Abayneh Mekonnen; Dereje Hailemariam (PhD)
As the demand for dependable fixed broadband internet services continues to grow, ensuring an excellent Quality of Experience (QoE) for end-users is essential. This thesis centers on QoE modeling, employing advanced machine learning techniques, specifically Support Vector Machine (SVM) and Random Forest algorithms. The study utilizes subjective assessments and Quality of Service (QoS) metrics, including latency, upload speed, download speed, uptime, packet loss, and jitter, to comprehensively comprehend and model the factors influencing user satisfaction. The research incorporates an exhaustive feature selector to extract pertinent features from the dataset, enhancing the precision of the models. Hyperparameter optimization is carried out through a Grid Search approach to fine-tune the models for optimal performance. To assess the models, a robust cross-validation methodology is implemented. The results indicate that SVM surpasses Random Forest in QoE modeling for Virtual Internet Service Providers (vISPs) like Websprix and ZERGAW Cloud with average accuracy score of 92% and 70% respectively. Conversely, Random Forest proves to be the more suitable model for predicting QoE in the case of the national ISP, ethio telecom with average accuracy value of 88%. This comparative performance analysis offers valuable insights into the distinct strengths of each model for different service providers. The research findings also indicate that employing both subjective and QoS metrics in combination to model the user QoE yields superior model performance and predictive outcomes compared to relying solely on subjective assessments and QoS metrics. These findings contribute to the ongoing discussion on QoE enhancement in fixed broadband internet services, providing practical recommendations for service providers based on observed model performances. The application of machine learning, feature selection, and hyperparameter optimization techniques underscores the importance of these methodologies in customizing QoE models to specific service contexts, ultimately enhancing user satisfaction in diverse fixed broadband Internet environments.
Deep Learning-based Cell Performance Degradation Prediction
(Addis Ababa University, 2022-07) Betelehem Dagnaw; Dereje Hailemariam (PhD)
In light of rapid developments in the telecommunications sector, there is a growing volume of generated data as well as high customer expectations regarding both cost and Quality of Service (QoS). For Mobile Network Operators (MNOs), the changing dynamics of radio networks pose challenges in coping with the increased number of network faults and outages, which both lead to performance degradation and increased operational expenditures (OPEX). Human expertise is required to diagnose, identify and x faults and outages. However, the increasing density of mobile cells and diversi cation of cell types are making this approach less feasible, both nancially and technically. In this paper, relying on the power of deep learning and the availability of large radio network data at MNOs, we propose a system that predicts the performance degradation of cells using key performance indicators (KPIs). Data collected from the Universal Mobile Telecommunications Service (UMTS) network of an operator located in Addis Ababa, Ethiopia, is used to build models in the system. The proposed system consists of a multivariate time series forecasting model, which forecasts KPIs in advance. In addition, a cell performance degradation detection model, which detects anomalous records in the KPI data based on the forecasting model outputs. Convolutional Long Short-Term Memory (ConvLSTM) and LSTM Autoencoders are cascaded for prediction and degradation detection. The results show that the system is capable of predicting KPIs with a Root Mean Square Error (RMSE) of 0.896 and a Mean Absolute Error (MAE) of 0.771, and detecting degradation with 98% accuracy. This research can therefore contribute signi cantly to improving network failure management systems by predicting the impact of upcoming cell performance degradations on network service before they occur. This research can therefore contribute signi cantly to improving network failure management systems by predicting the impact of upcoming cell performance degradations on network service before they occur.
Real-time Feature Extraction in a Distributed Acoustic Sensor Based on Phase Demodulation With Fast Hilbert Transform
(Addis Ababa University, 2024-03) Semira Mohammed; Yonas Seifu (PhD); Bisrat Derebssa (PhD)
Phase-sensitive optical Time Domain Reflectometry (Φ-OTDR) is the most common implementation of a Distributed Acoustic Sensor (DAS) system. It employs the observation of speckles resulting from Rayleigh Back-scattering from coherent pulses in an optical fiber[1]. Since they are sensitive to local disturbances altering the intensity and phase of light, perturbations induced by events cause changes in the speckle pattern whose precise measurement provides information on the amplitude and frequency of vibrations distributed along the fiber. Demodulation of the local phase change is key to the precise measurement of events since it is more linearly related to the strain applied to the fiber. One of the key issues in distributed sensing is that phase demodulation schemes usually require additional post-processing algorithm runs for each spatial location, which introduces delays, and hence reductions in dynamic sensing capability when scaled along the whole sensing distance. In this research, we analyze the impact of the post-processing in different phase demodulation techniques employing Phase-Generated Carrier (PGC) on the bandwidth of distributed feature extraction in a typical DAS system by quantifying the total computation time needed for a benchmark, 10-km sensing range at meterscale and sub-meter-scale spatial resolutions. We then design, implement, and analyze a signal processing scheme for phase extraction in Φ -OTDR enabling real-time dynamic measurements based on a Fast Hilbert Transform (FHT). Particular focus is given to the choice of this demodulation scheme for optimizing the bandwidth of distributed feature extraction as it enables the use of parallel processing of adjacent blocks in such a way that the overall throughput of spatially resolved concurrent demodulation allows dynamic vibration sensing at speeds relevant to most distributed monitoring applications. Our analysis shows that that on average 3 orders magnitude reductions in computation times are achieved when employing the Fast Hilbert transform for demodulation compared to the commonly used PGC-arctan algorithm, while there is a three-fold reduction compared to PG-DCM and PG-DMS algorithms.
Performance Analysis of Downlink Linear Precoding for Multi-Cell Massive MIMO under Correlated Rayleigh Fading Channels
(Addis Ababa University, 2022-09) Habte Aregawi; Murad Ridwan
The Fifth Generation (5G) networks have performance targets of high Spectral Efficiency (SE), decreased latency, energy savings, cost reduction, high system capacity, and huge device connections. To increase the SE of networks, researchers deal with increasing the transmit power, obtaining the array gain, using Space Division Multiple Access (SDMA), and deploying massive numbers of antennas at the Base Station (BS). A Multi User- Multiple Input Multiple Output (MU-MIMO) technology that combines SDMA with Time Division Duplex (TDD) to limit the Channel State Information (CSI) acquisition overhead and a massive number of antennas at the BS is known as Massive-Multiple Input Multiple Output (M-MIMO). For efficient use of massive antennas at the BS, the channel characteristics between User Equipments (UEs) and the BS must be known. Practical channels are known to be spatially correlated due to sampling at the BS, environmental orientations, and polarization effects. Estimation of spatially correlated channels in a multi-cell M-MIMO system degrades due to reuse of pilot signals among UEs, which cannot be addressed by increasing the number of BS antennas. Alleviating the impact of pilot contamination in multi-cell cellular systems is conducted in various research. However, describing pilot contamination effects based on UEs position on the channel estimation is not addressed in most of the researches. In this research, the effect of UEs position on channel estimation and the ability to get favourable channels is investigated under correlated Rayleigh fading channels. Using blind estimation of precoded channels, the performance of different linear precoding schemes is examined using MATLAB simulation platform. The pilot contamination effect is negligible under more correlated channels if the angle of arrival (position of UEs) is slightly different. The Minimum Mean Square Error (MMSE) precoding schemes have better performance than Regularized Zero Forcing (RZF), Zero Forcing (ZF), and Maximum Ratio Transmission (MRT). RZF has better performance than ZF when the effective Signal to Noise Ratio (SNR) is low or the number of antennas at the BS is small, unless they have the same level of performance.
Performance Comparison of Multi-Mode Modulation Techniques for SDR Using FPGA
(Addis Ababa University, 2023-11) Sisay Bogale; Yihenew Wondie (PhD)
Radio devices that were previously built in hardware have been replaced in recent years by reconfigurable software defined radio (SDR) systems. Conventional hardware-based radios have restricted multi-functionality and are physically changeable only. This leads to an increase in production expenses and a reduction in the number of waveform standards that can be supported. A rapid and affordable answer to this issue is provided by software-defined radio technology, which enables software upgrades for multi-mode, multi-band, and multi-functional wireless devices. In SDR, different modulation techniques are used to achieve efficient communication over a radio channel. Multi-mode modulation is an approach that allows the use of multiple modulation schemes in a single system, which can enhance the flexibility and resilience of communication systems. This paper presented a design and implementation of multi-mode modulation techniques for SDR using FPGA and analyze the performance based on the FPGA resource utilization. It combines six modulation schemes: QASK, QPSK, QAM, AM, PM and FM to create multi-mode modulation system. The performance of this multi-mode modulation system is evaluated in terms of FPGA resource utilization such as total computational power, total number of Look Table (LUT) or memory used, Flip Flops (FF) and Input/Output (IO) port usage. Xilinx Vivado system generator for DSP with MATLAB/Simulink is used to design, simulate and verify the multi-mode modulator, which would then be implemented on a Xilinx Zedboard FPGA hardware. A total of 0.225W power, 844 number of LUT and 1 IO port is utilized by the implemented design. The biggest thing we achieved in this research is that we saved computational power. 1.572W and 1.134W amount of power is saved by our design as compared to previous two studies.
Design and Performance Evaluation of Power-aware Routing Protocols for Wireless Sensor Networks – GAICH and GCH
(Addis Ababa University, 2011-10) Seifemichael Bekele; Dereje Hailemariam (PhD)
In recent years, the advancements in wireless communications and electronics have enabled the development of low-cost, low-power and multifunctional wireless sensor networks (WSNs). As nodes in sensor networks are equipped with a limited power source, efficient utilization of power is a very important issue in order to extend the network lifetime. It is for these reasons that researchers are currently focusing on the design of power-aware protocols and algorithms for sensor networks. In this thesis, two routing protocols that provide efficient energy management for WSNs are proposed. The first protocol, GAICH (Genetic Algorithm Inspired Clustering Hierarchy), makes use of genetic algorithm to create optimum clusters in terms of energy consumption. The other one, GCH (Grid Clustering Hierarchy), creates clusters by forming virtual girds, where nodes share the role of cluster head in a round-robin fashion. These protocols have been implemented in MATLAB using a standard radio energy dissipation model that is used for the simulation of WSNs. Performance comparison has been made with two of the existing routing protocols: LEACH and Direct Transmission, on different performance metrics. Simulation results show that GAICH and GCH are better than LEACH in the total packets sent to the base station and network lifetime. Moreover, different techniques for optimizing energy consumption in WSNs are suggested.
Prediction of Base Transceiver Station Power Supply System Failure Indicators using Deep Neural Networks for Multi-Time Variant Time Series
(Addis Ababa University, 2023-11) Jalene Bekuma; Dereje Hailemariam (PhD)
The uninterrupted operation of wireless communication services relies heavily on the stability of power supply systems for Base Transceiver Stations (BTS). This study is dedicated to predicting potential failure indicators in BTS power systems using deep neural network architectures, such as recurrent and convolutional neural networks. The study integrates principal component analysis (PCA) for data dimensionality reduction and addresses challenges related to power system failures caused by environmental factors, power fluctuations, and equipment malfunctions within the Ethio telecom BTS system. The dataset utilized in this study spans four weeks of data from multiple sites, with observations sampled at 5-minute intervals, obtained from the ET NetEcho power monitoring system. The study meticulously explores the data preprocessing steps for time series analysis, encompassing consolidation, cleaning, scaling, and dimensionality reduction using PCA. Furthermore, it delves into the detailed implementation of CNN, LSTM, and CNN-LSTM models for time series prediction, thoroughly evaluating their performance and convergence. The experimental results clearly indicate that CNN-LSTM model surpasses both LSTM and CNN models in predicting BTS power system failure indicators, achieving the lowest loss values of 0.036 MSE, 0.189 RMSE and 0.112 MAE using CNN_LSTM model. These findings shows the potential of deep neural network architectures, particularly CNN_LSTM model in accurately predicting BTS power system failure indicators for the next thirty minutes. The significance of accurate prediction models in proactively detecting failures and minimizing their impact is highlighted, contributing to the reliability and stability of BTS power supply systems for wireless communication services.
Optimization of Millimeter Wave Microstrip Antenna for Wireless Application Using Genetic Algorithm
(Addis Ababa University, 2023-12) Arebu Dejene
In the telecommunications industry, wireless communications have progressed very rapidly in the last two decades. The requirement for high data rates and the paucity of spectrum in existing wireless communication drive next-generation communication technology to mm-wave frequencies, which also require adequate and efficient antenna technology for successful operation. These signals, however, have a high path loss and are susceptible to blocking. These mm-wave signal propagation challenges can be overcome by using high-directivity, wide-band, and multi-band antennas. Nonetheless, creating such a high-performance antenna in every way is a challenging endeavor. This dissertation discourses on the modeling, optimizing, and synthesizing of a rectangular microstrip patch antenna with dual-band and multi-band service for mm-wave communication using a binary-coded genetic algorithm to improve the directivity and bandwidth. The algorithm iteratively creates new models of patch surfaces by employing an iterative combination of HFSS and MATLAB software, and then returns the best antenna model. Accordingly, the dissertation exhibits improvements in the directivity, bandwidth, and multi-functionality of a single microstrip antenna. With patch geometry optimization, a dual-band antenna was optimized and resonated at 28.0 GHz and 46.6 GHz with acceptable performance. Another optimization was carried out on a single microstrip antenna for triple band operation and directivity improvement. The optimized antenna resonated at three distinct frequency bands centered at 28.0 GHz, 40.0 GHz, and 47.0 GHz, and demonstrates broadside radiation patterns with peak directivities of 7.7 dB, 12.1 dB, and 8.2 dB, respectively. On the other hand, bandwidth melioration was achieved by a genetically optimized quad-band antenna, which was resonated at four frequencies centered at 28.3 GHz, 38.1 GHz, 46.6 GHz, and 60.0 GHz, and a total operating bandwidth of 11.5 GHz. The dissertation also presents a penta-band mm-wave antenna for wearable applications. The proposed antenna designed on PTFE fabric substrate and resonates at five distinct frequencies: 27.8 GHz, 30.3 GHz, 40.1 GHz, 47.2 GHz, and 56.7 GHz. In free space, the antenna achieves a wide bandwidth of 0.69, 2.32, 2.22, 1.76, and 8.11 GHz and an improved broadside directivity of 10.3, 8.5, 7.8, 9.6, and 8.9 dB, respectively. Overall, the optimized antennas performances were suitable for multi-functional mm-wave applications.

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