Browsing by Author "Mollel, Michael"

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Comparison of Empirical Propagation Path Loss Models for Mobile Communication
(Computer Engineering and Intelligent Systems, 2014) Mollel, Michael; Michael, Kisangiri
Empirical propagation models have found favor in both research and industrial communities owing to their speed of execution and their limited reliance on detailed knowledge of the terrain. In mobile communication the accuracy prediction of path losses is a crucial element during network planning and optimization. However, the existence of multiple propagation models means that there is no propagation model which is precisely and accurate in prediction of path loss fit for every environs other than in which they were designed. This paper presents few empirical models suitable for path loss prediction in mobile communication. Experimental measurements of received power for the 900 MHz GSM system are made in urban, suburban, and rural areas of Dar es Salaam, Tanzania. Measured data are compared with those obtained by five prediction models: Stanford University Interim (SUI) models [1], the COST-231 Hata model [2], the ECC-33 model [3], the ERICSSON model [4], and the HATA-OKUMURA model [5]. The results show that in general the SUI, COST-231, ERICSSON, and Hata-Okumura under-predict the path loss in all environments, while the ECC-33 model shows the best results, especially in suburban and over-predict pathloss in urban area.
Deep Reinforcement Learning based Handover Management for Millimeter Wave Communication
(International Journal of Advanced Computer Science and Applications,, 2021) Mollel, Michael; Kaijage, Shubi; Michael, Kisangiri
The Millimeter Wave (mm-wave) band has a broad-spectrum capable of transmitting multi-gigabit per-second date-rate. However, the band suffers seriously from obstruction and high path loss, resulting in line-of-sight (LOS) and non-line-of-sight (NLOS) transmissions. All these lead to significant fluctu-ation in the signal received at the user end. Signal fluctuations present an unprecedented challenge in implementing the fifth gen-eration (5G) use-cases of the mm-wave spectrum. It also increases the user’s chances of changing the serving Base Station (BS) in the process, commonly known as Handover (HO). HO events become frequent for an ultra-dense dense network scenario, and HO management becomes increasingly challenging as the number of BS increases. HOs reduce network throughput, and hence the significance of mm-wave to 5G wireless system is diminished without adequate HO control. In this study, we propose a model for HO control based on the offline reinforcement learning (RL) algorithm that autonomously and smartly optimizes HO decisions taking into account prolonged user connectivity and throughput. We conclude by presenting the proposed model’s performance and comparing it with the state-of-art model, rate based HO scheme. The results reveal that the proposed model decreases excess HO by 70%, thus achieving a higher throughput relative to the rates based HO scheme.
The effect of real ground on dual band Yagi-Uda antenna
(IJERT, 2013-10) Mollel, Michael; Kisangiri, Michael
This paper aims to analyse radiation pattern for horizontally polarised dual band Yagi-Uda antenna (900/1800 MHz) with the presence of real ground. Simulations were performed at 920 MHz and 1780 MHz and antenna were placed in different distance from the ground. Simulation results of radiation pattern are calculated in the H-plane of the antenna. A simulation without the ground plane are compared with the result from the one with the real ground. The result shows that the ground plane greatly influences the radiation pattern and increases the gain of H plane significantly.
Handover Management in Dense Networks with Coverage Prediction from Sparse Networks
(IEEE, 2019) Mollel, Michael; Ozturk, Metin; Kisangiri, Michael; Kaijage, Shubi; Onireti, Oluwakayode; Imran, Muhammad; Abbasi, Qammer
Millimeter Wave (mm-Wave) provides high bandwidth and is expected to increase the capacity of the network thousand-fold in the future generations of mobile communications. However, since mm-Wave is sensitive to blockage and incurs in a high penetration loss, it has increased complexity and bottleneck in the realization of substantial gain. Network densification, as a solution for sensitivity and blockage, increases handover (HO) rate, unnecessary and ping-pong HO’s, which in turn reduces the throughput of the network. On the other hand, to minimize the effect of increased HO rate, Time to Trigger (TTT) and Hysteresis factor (H) have been used in Long Term Evolution (LTE). In this paper, we primarily present two different networks based on Evolved NodeB (eNB) density: sparse and dense. As their name also suggests, the eNB density in the dense network is higher than the sparse network. Hence, we proposed an optimal eNB selection mechanism for 5G intra-mobility HO based on spatial information of the sparse eNB network. In this approach, User Equipment (UE) in the dense network is connected only to a few selected eNBs, which are delivered from the sparse network, in the first place. HO event occurs only when the serving eNB can no longer satisfy the minimum Signal-to-Noise Ratio (SNR) threshold. For the eNBs, which are deployed in the dense network, follow the conventional HO procedure. Results reveal that the HO rate is decreased significantly with the proposed approach for the TTT values between 0 ms to 256 ms while keeping the radio link failure (RLF) at an acceptable level; less than 2% for the TTT values between 0 ms to 160 ms. This study paves a way for HO management in the future 5G network.
Improved handover decision scheme for 5g mm-wave communication: optimum base station selection using machine learning approach.
(NM-AIST, 2022-09) Mollel, Michael
The rapid growth in mobile and wireless devices has led to an exponential demand for data traf fic and exacerbated the burden on conventional wireless networks. Fifth generation (5G) and beyond networks are expected to not only accommodate this growth in data demand but also provide additional services beyond the capability of existing wireless networks, while main taining a high quality-of-experience (QoE) for users. The need for several orders of magnitude increase in system capacity has necessitated the use of millimetre wave (mm-wave) frequencies as well as the proliferation of low-power small cells overlaying the existing macro-cell layer. These approaches offer a potential increase in throughput in magnitudes of several gigabits per second and a reduction in transmission latency, but they also present new challenges. For exam ple, mm-wave frequencies have higher propagation losses and a limited coverage area, thereby escalating mobility challenges such as more frequent handovers (HOs). In addition, the ad vent of low-power small cells with smaller footprints also causes signal fluctuations across the network, resulting in repeated HOs (ping-pong) from one small cell (SC) to another. Therefore, efficient HO management is very critical in future cellular networks since frequent HOs pose multiple threats to the quality-of-service (QoS), such as a reduction in the system throughput as well as service interruptions, which results in a poor QoE for the user. How ever, HO management is a significant challenge in 5G networks due to the use of mm-wave frequencies which have much smaller footprints. To address these challenges, this work in vestigates the HO performance of 5G mm-wave networks and proposes a novel method for achieving seamless user mobility in dense networks. The proposed model is based on a double deep reinforcement learning (DDRL) algorithm. To test the performance of the model, a com parative study was made between the proposed approach and benchmark solutions, including a benchmark developed as part of this thesis. The evaluation metrics considered include system throughput, execution time, ping-pong, and the scalability of the solutions. The results reveal that the developed DDRL-based solution vastly outperforms not only conventional methods but also other machine-learning-based benchmark techniques. The main contribution of this thesis is to provide an intelligent framework for mobility man agement in the connected state (i.e HO management) in 5G. Though primarily developed for mm-wave links between UEs and BSs in ultra-dense heterogeneous networks (UDHNs), the proposed framework can also be applied to sub-6 GHz frequencies.
Intelligent handover decision scheme using double deep reinforcement learning
(Elsevier B.V., 2020-10) Mollel, Michael; Abubakar, Attai Ibrahim; Ozturk, Metin; Kaijage, Shubi; Michael, Kisangiri,; Zoha, Ahmed; Imran, Muhammad Ali; Abbasi, Qammer
Handovers (HOs) have been envisioned to be more challenging in 5G networks due to the inclusion of millimetre wave (mm-wave) frequencies, resulting in more intense base station (BS) deployments. This, by its turn, increases the number of HOs taken due to smaller footprints of mm-wave BSs thereby making HO management a more crucial task as reduced quality of service (QoS) and quality of experience (QoE) along with higher signalling overhead are more likely with the growing number of HOs. In this paper, we propose an offline scheme based on double deep reinforcement learning (DDRL) to minimize the frequency of HOs in mm-wave networks, which subsequently mitigates the adverse QoS. Due to continuous and substantial state spaces arising from the inherent characteristics of the considered 5G environment, DDRL is preferred over conventional -learning algorithm. Furthermore, in order to alleviate the negative impacts of online learning policies in terms of computational costs, an offline learning framework is adopted in this study, a known trajectory is considered in a simulation environment while ray-tracing is used to estimate channel characteristics. The number of HO occurrence during the trajectory and the system throughput are taken as performance metrics. The results obtained reveal that the proposed method largely outperform conventional and other artificial intelligence (AI)-based models.
Optimization of Hata Model based on Measurements Data using Least Square Method: A Case Study in Dar-es-Salaam – Tanzania
(International Journal of Computer Applications, 2014-09) Mollel, Michael; Michael, Kisangiri
ABSTRACT In this study, we present a measurement-based model for path loss prediction in three GSM service areas at 900 MHz .Modified Hata model for rural, suburban, and urban environments were derived in this study on the basis of experimental path loss measurements with the use of least square method. The models developed predicted with reasonable accuracy the path loss of radio networks investigated with a root mean square error of < 6 dB .The developed model can be useful in network planning and optimization for the environments taken as case study for the investigation, as well as any macro- and microcell environment, which is similar to the environment considered in this research.
An Overview of Various Propagation Model for Mobile Communication
(Pan African International Conference on Information Science, Computing and Telecommunications, 2014) Mollel, Michael; Michael, Kisangiri
System’s propagation characteristics through a medium is one of the important task to be done before planning and optimization of any network in order to estimate the signal parameter accurately for mobile system. Propagation analysis provides a good initial estimate of the signal characteristics .The ability to accurately predict radio-propagation behaviour for wireless personal communication system, such as cellular mobile radio, is becoming crucial to system design. This paper aim to address various propagation model used in planning and optimization of the network and much emphasize on empirical (statistical) models it address models limitation and its applicability. To validate the models, field measurements were carried out at different locations within Dar es Salaam and its environs. The measurements system consist of live radio base stations transmitting at 900/ 1800/2100 MHz Downlink signal strength level data were collected using drive test exercise. The respective path loss values were estimated and compared with existing model for rural, suburban and urban areas. The result indicated an appreciable inconsistency with the empirical models for all terrain areas.
A Survey of Machine Learning Applications to Handover Management in 5G and Beyond
(IEEE, 2021-03-19) Mollel, Michael; Abubakar, Attai Ibrahim; Ozturk, Metin; Kaijage, Shubi; Michael, Kisangiri; Hussain, Sajjad; Imran, Muhammad Ali; Abbasi, Qammer
Handover (HO) is one of the key aspects of next-generation (NG) cellular communication networks that need to be properly managed since it poses multiple threats to quality-of-service (QoS) such as the reduction in the average throughput as well as service interruptions. With the introduction of new enablers for fifth-generation (5G) networks, such as millimetre wave (mm-wave) communications, network densification, Internet of things (IoT), etc., HO management is provisioned to be more challenging as the number of base stations (BSs) per unit area, and the number of connections has been dramatically rising. Considering the stringent requirements that have been newly released in the standards of 5G networks, the level of the challenge is multiplied. To this end, intelligent HO management schemes have been proposed and tested in the literature, paving the way for tackling these challenges more efficiently and effectively. In this survey, we aim at revealing the current status of cellular networks and discussing mobility and HO management in 5G alongside the general characteristics of 5G networks. We provide an extensive tutorial on HO management in 5G networks accompanied by a discussion on machine learning (ML) applications to HO management. A novel taxonomy in terms of the source of data to be utilized in training ML algorithms is produced, where two broad categories are considered; namely, visual data and network data. The state-of-the-art on ML-aided HO management in cellular networks under each category is extensively reviewed with the most recent studies, and the challenges, as well as future research directions, are detailed.
A Survey of Machine Learning Applications to Handover Management in 5G and Beyond
(IEEE, 2021-03-14) Mollel, Michael; Abubakar, Attai; Ozturk, Metin; Kaijage, Shubi; Kisangiri, Michael; Imran, Muhammad; Abbasi, Qammer
Handover (HO) is one of the key aspects of next-generation (NG) cellular communication networks that need to be properly managed since it poses multiple threats to quality-of-service (QoS) such as the reduction in the average throughput as well as service interruptions. With the introduction of new enablers for fifth-generation (5G) networks, such as millimetre wave (mm-wave) communications, network densification, Internet of things (IoT), etc., HO management is provisioned to be more challenging as the number of base stations (BSs) per unit area, and the number of connections has been dramatically rising. Considering the stringent requirements that have been newly released in the standards of 5G networks, the level of the challenge is multiplied. To this end, intelligent HO management schemes have been proposed and tested in the literature, paving the way for tackling these challenges more efficiently and effectively. In this survey, we aim at revealing the current status of cellular networks and discussing mobility and HO management in 5G alongside the general characteristics of 5G networks. We provide an extensive tutorial on HO management in 5G networks accompanied by a discussion on machine learning (ML) applications to HO management. A novel taxonomy in terms of the source of data to be utilized in training ML algorithms is produced, where two broad categories are considered; namely, visual data and network data. The state-of- the-art on ML-aided HO management in cellular networks under each category is extensively reviewed with the most recent studies, and the challenges, as well as future research directions, are detailed