Pub Date : 2021-09-13DOI: 10.1109/africon51333.2021.9570868
Emmanuel M. Migabo, Karim D Djouani, A. Kurien
The Narrowband Internet of Things (NB-IoT) has gained significant attention in the areas of real-time critical IoT applications such as industrial control. This comes with more demand in terms of the NB-IoT data rate performance. The NB-IoT data rate can be enhanced at various levels among which its modulation type, its channel coding design and even its wireless radio antenna diversity. According to the 3GPP NB-IoT standard, the NB-IoT, in its current standardized state, is limited to only handle up to the QPSK modulation. This, in terms of the modulation perspective, limits its data rate enhancement ability to respond to the growing demand of time-critical applications. Several research works have proposed an enhanced version of the NB-IoT approaches such as the spectral efficient frequency division multiplexing (SEFDM) which uses higher modulation formats. However, most of these techniques remain energy expensive. This paper proposes a 2-D channel-aware adaptive selection of the Modulation coding scheme and the transmission repetition number capable to enhance the overall data rate performance of the network while maintaining its energy efficiency. The proposed approach is simulated using the PHY layer of the MATLAB LTE toolbox. The obtained results show that as more NB-IoT nodes join the network, the proposed approach outperforms the SEFDM and the traditional fixed MCS and repetition number selection schemes, in terms of its data rate and energy efficiency.
{"title":"Energy Efficient Data Rate Enhancement Channel Coding Technique for Narrowband Internet of Things (NB-IoT)","authors":"Emmanuel M. Migabo, Karim D Djouani, A. Kurien","doi":"10.1109/africon51333.2021.9570868","DOIUrl":"https://doi.org/10.1109/africon51333.2021.9570868","url":null,"abstract":"The Narrowband Internet of Things (NB-IoT) has gained significant attention in the areas of real-time critical IoT applications such as industrial control. This comes with more demand in terms of the NB-IoT data rate performance. The NB-IoT data rate can be enhanced at various levels among which its modulation type, its channel coding design and even its wireless radio antenna diversity. According to the 3GPP NB-IoT standard, the NB-IoT, in its current standardized state, is limited to only handle up to the QPSK modulation. This, in terms of the modulation perspective, limits its data rate enhancement ability to respond to the growing demand of time-critical applications. Several research works have proposed an enhanced version of the NB-IoT approaches such as the spectral efficient frequency division multiplexing (SEFDM) which uses higher modulation formats. However, most of these techniques remain energy expensive. This paper proposes a 2-D channel-aware adaptive selection of the Modulation coding scheme and the transmission repetition number capable to enhance the overall data rate performance of the network while maintaining its energy efficiency. The proposed approach is simulated using the PHY layer of the MATLAB LTE toolbox. The obtained results show that as more NB-IoT nodes join the network, the proposed approach outperforms the SEFDM and the traditional fixed MCS and repetition number selection schemes, in terms of its data rate and energy efficiency.","PeriodicalId":170342,"journal":{"name":"2021 IEEE AFRICON","volume":"23 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-09-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130021100","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2021-09-13DOI: 10.1109/africon51333.2021.9570875
Matthew Dicks, Josiah Chavula
Community networks are infrastructures that are run by the citizens for the citizens. These networks are often run with limited resources compared to traditional Internet Service Providers. For such networks, careful traffic classification can play an important role in improving quality of service. Deep learning techniques have been shown to be effective for this classification task, especially since classical approaches struggle to deal with encrypted traffic. However, deep learning models often tend to be computationally expensive, which limits their suitability for low-resource community networks. This paper explores the computational efficiency and accuracy of Long Short-Term Memory (LSTM) and Multi-Layer Perceptron (MLP) deep learning models for packet-based classification of traffic in a community network. We find that LSTM models attain higher out-of-sample accuracy than traditional support vector machines classifiers and the simpler multi-layer perceptron neural networks, given the same computational resource constraints. The improvement in accuracy offered by the LSTM has a tradeoff of slower prediction speed, which weakens their relative suitability for use in real-time applications. However, we observe that by reducing the size of the input supplied to the LSTMs, we can improve their prediction speed whilst maintaining higher accuracy than other simpler models.
{"title":"Deep Learning Traffic Classification in Resource-Constrained Community Networks","authors":"Matthew Dicks, Josiah Chavula","doi":"10.1109/africon51333.2021.9570875","DOIUrl":"https://doi.org/10.1109/africon51333.2021.9570875","url":null,"abstract":"Community networks are infrastructures that are run by the citizens for the citizens. These networks are often run with limited resources compared to traditional Internet Service Providers. For such networks, careful traffic classification can play an important role in improving quality of service. Deep learning techniques have been shown to be effective for this classification task, especially since classical approaches struggle to deal with encrypted traffic. However, deep learning models often tend to be computationally expensive, which limits their suitability for low-resource community networks. This paper explores the computational efficiency and accuracy of Long Short-Term Memory (LSTM) and Multi-Layer Perceptron (MLP) deep learning models for packet-based classification of traffic in a community network. We find that LSTM models attain higher out-of-sample accuracy than traditional support vector machines classifiers and the simpler multi-layer perceptron neural networks, given the same computational resource constraints. The improvement in accuracy offered by the LSTM has a tradeoff of slower prediction speed, which weakens their relative suitability for use in real-time applications. However, we observe that by reducing the size of the input supplied to the LSTMs, we can improve their prediction speed whilst maintaining higher accuracy than other simpler models.","PeriodicalId":170342,"journal":{"name":"2021 IEEE AFRICON","volume":"2002 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-09-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129572056","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2021-09-13DOI: 10.1109/africon51333.2021.9570861
Lebohang Caswel Tlake, E. Markus, A. Abu-Mahfouz
Technology is expanding at a higher rate and because of this expansion there is a global transition from 4th Generation (4G) to 5th Generation (5G) due to the steep rise in the demand of users and challenges which are not addressed effectively by the 4G. The 3GPP standards which in full is 3rd Generation Partnership Project have given 5G the name New Radio (NR) in which it reuses the spectrum allocated for both (NB-IoT) Narrowband Internet of Things and 4G. There are different elements which contribute to an interference named Narrowband Interference (NBI) which is caused by spectrum reuse of NR, NB-IoT and (LTE) Long Term Evolution, thus leads to mismatch of sampling rate and performance degradation. This paper presents a survey of these interference challenges. The main contribution of this paper is to comprehensively analyse different classifications to possibly assist the form of coexistence of 5GNR NB-IoT and LTE. Different approaches were analysed, their strength and limitations in various literatures, however the interference mitigation scheme is still an open gap determined.
{"title":"A Review of Interference Challenges on Integrated 5GNR and NB-IoT Networks","authors":"Lebohang Caswel Tlake, E. Markus, A. Abu-Mahfouz","doi":"10.1109/africon51333.2021.9570861","DOIUrl":"https://doi.org/10.1109/africon51333.2021.9570861","url":null,"abstract":"Technology is expanding at a higher rate and because of this expansion there is a global transition from 4th Generation (4G) to 5th Generation (5G) due to the steep rise in the demand of users and challenges which are not addressed effectively by the 4G. The 3GPP standards which in full is 3rd Generation Partnership Project have given 5G the name New Radio (NR) in which it reuses the spectrum allocated for both (NB-IoT) Narrowband Internet of Things and 4G. There are different elements which contribute to an interference named Narrowband Interference (NBI) which is caused by spectrum reuse of NR, NB-IoT and (LTE) Long Term Evolution, thus leads to mismatch of sampling rate and performance degradation. This paper presents a survey of these interference challenges. The main contribution of this paper is to comprehensively analyse different classifications to possibly assist the form of coexistence of 5GNR NB-IoT and LTE. Different approaches were analysed, their strength and limitations in various literatures, however the interference mitigation scheme is still an open gap determined.","PeriodicalId":170342,"journal":{"name":"2021 IEEE AFRICON","volume":"12 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-09-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128353682","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2021-09-13DOI: 10.1109/africon51333.2021.9571003
Jack O. Nyanjom, P. Akuon, V. Oduol
A signal transmission technique is proposed where different ranks of frequency subcarriers are used to convey information to the receiver i.e. Frequency-coded Multiple Rank Modulation (FMRM). In FMRM, the number of the subcarriers is determined by the input stream of information bits. The input information bit stream, which is random, is sent into the ranking modulator. Instead of detecting the channel index, the receiver detects the number of frequency subcarriers of an Orthogonal Frequency Division Multiplexing (OFDM) scheme. The pattern of frequency subcarriers is pre-set and known at the receiver using the system mapper. Only the rank information is used to decode the bits in the frequency domain. Analytical upper bounds on Bit Error Probability (BEP) of the FMRM system are derived and then compared with simulation results for different transmission schemes at similar data rates, which show tractable performance. In addition to improved data rates, for better information security operations, the FMRM system has the additional benefit that the wireless channel does not completely modulate the information sent, but the information is also modulated by the number of frequency chains.
{"title":"BER Performance of Frequency-coded Multiple Rank Modulation","authors":"Jack O. Nyanjom, P. Akuon, V. Oduol","doi":"10.1109/africon51333.2021.9571003","DOIUrl":"https://doi.org/10.1109/africon51333.2021.9571003","url":null,"abstract":"A signal transmission technique is proposed where different ranks of frequency subcarriers are used to convey information to the receiver i.e. Frequency-coded Multiple Rank Modulation (FMRM). In FMRM, the number of the subcarriers is determined by the input stream of information bits. The input information bit stream, which is random, is sent into the ranking modulator. Instead of detecting the channel index, the receiver detects the number of frequency subcarriers of an Orthogonal Frequency Division Multiplexing (OFDM) scheme. The pattern of frequency subcarriers is pre-set and known at the receiver using the system mapper. Only the rank information is used to decode the bits in the frequency domain. Analytical upper bounds on Bit Error Probability (BEP) of the FMRM system are derived and then compared with simulation results for different transmission schemes at similar data rates, which show tractable performance. In addition to improved data rates, for better information security operations, the FMRM system has the additional benefit that the wireless channel does not completely modulate the information sent, but the information is also modulated by the number of frequency chains.","PeriodicalId":170342,"journal":{"name":"2021 IEEE AFRICON","volume":"49 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-09-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123406990","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2021-09-13DOI: 10.1109/africon51333.2021.9571018
K. Kalegele
This article presents preliminary results from a study to establish practicability of learning analytics in Open and Distance Learning (ODL) environment. Undesirably, a significant number of ODL students take longer to complete studies, exhibiting what can be referred to as learning recession. Meanwhile, learning institutions lack timely means to predict if a student’s learning is receding. In the study, attributes for potential use in developing prediction models are being engineered. In a preliminary stage, results of learning analytics have confirmed some facts and depicted interesting patterns. These preliminary results are expected to enable further studies on development of relevant predictive models.
{"title":"Analysis of ODL Logs for Predicting Recession","authors":"K. Kalegele","doi":"10.1109/africon51333.2021.9571018","DOIUrl":"https://doi.org/10.1109/africon51333.2021.9571018","url":null,"abstract":"This article presents preliminary results from a study to establish practicability of learning analytics in Open and Distance Learning (ODL) environment. Undesirably, a significant number of ODL students take longer to complete studies, exhibiting what can be referred to as learning recession. Meanwhile, learning institutions lack timely means to predict if a student’s learning is receding. In the study, attributes for potential use in developing prediction models are being engineered. In a preliminary stage, results of learning analytics have confirmed some facts and depicted interesting patterns. These preliminary results are expected to enable further studies on development of relevant predictive models.","PeriodicalId":170342,"journal":{"name":"2021 IEEE AFRICON","volume":"242 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-09-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123072870","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2021-09-13DOI: 10.1109/africon51333.2021.9571004
S. O. Awino, T. Afullo
This paper proposes Parsimonious Periodic Autoregressive (PPAR) models for modelling the bursty impulsive noise present in low-voltage power line communication (PLC) networks in the frequency range of 1 – 30 MHz. The acquired impulsive noise time series is seasonal and exhibit an autocorrelation structure that depends not only on the time lag between observations but also the season of the window length period of measurements. Assuming the seasons are grouped into groups of one or more seasons with similar autoregressive (AR) characteristics, individual AR models for various seasons are combined to obtain a single model for all seasons in a given group. Consequently after grouping, the parameters of the more PPAR models are estimated and diagnostically checked, validated through measurement data acquired from the University of KwaZulu-Natal and compared to other periodic time series models.
{"title":"On the Application of Parsimonious Periodic Autoregressive Models to Bursty Impulsive Noise in Low-Voltage PLC Networks","authors":"S. O. Awino, T. Afullo","doi":"10.1109/africon51333.2021.9571004","DOIUrl":"https://doi.org/10.1109/africon51333.2021.9571004","url":null,"abstract":"This paper proposes Parsimonious Periodic Autoregressive (PPAR) models for modelling the bursty impulsive noise present in low-voltage power line communication (PLC) networks in the frequency range of 1 – 30 MHz. The acquired impulsive noise time series is seasonal and exhibit an autocorrelation structure that depends not only on the time lag between observations but also the season of the window length period of measurements. Assuming the seasons are grouped into groups of one or more seasons with similar autoregressive (AR) characteristics, individual AR models for various seasons are combined to obtain a single model for all seasons in a given group. Consequently after grouping, the parameters of the more PPAR models are estimated and diagnostically checked, validated through measurement data acquired from the University of KwaZulu-Natal and compared to other periodic time series models.","PeriodicalId":170342,"journal":{"name":"2021 IEEE AFRICON","volume":"33 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-09-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115669951","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2021-09-13DOI: 10.1109/africon51333.2021.9570926
Abdulrahman A. Faris, P. Akuon, Vitalis Oduol Kalecha
Space shift keying antenna sequence modulation (SSK-ASM) is proposed for conveying information bits through unique antenna sequence patterns. The information is conveyed in spatial domain only via mixed Gaussian channel such Rayleigh Flat Fading. In this paper we, present analytical formulation of bit error rate performance of SSK-ASM, by utilizing Sample variance (SV) and Mutual information (MI) criterion in single input Multiple output (SIMO) configuration. Moreover, the exact Average bit error probability (ABEP) over Rayleigh Flat fading channel is developed in close-form. Monte Carlo simulations is shown to be tightly bound to the theoretical framework for SV criterion method.
{"title":"BER Performance of SSK Sequence Modulation","authors":"Abdulrahman A. Faris, P. Akuon, Vitalis Oduol Kalecha","doi":"10.1109/africon51333.2021.9570926","DOIUrl":"https://doi.org/10.1109/africon51333.2021.9570926","url":null,"abstract":"Space shift keying antenna sequence modulation (SSK-ASM) is proposed for conveying information bits through unique antenna sequence patterns. The information is conveyed in spatial domain only via mixed Gaussian channel such Rayleigh Flat Fading. In this paper we, present analytical formulation of bit error rate performance of SSK-ASM, by utilizing Sample variance (SV) and Mutual information (MI) criterion in single input Multiple output (SIMO) configuration. Moreover, the exact Average bit error probability (ABEP) over Rayleigh Flat fading channel is developed in close-form. Monte Carlo simulations is shown to be tightly bound to the theoretical framework for SV criterion method.","PeriodicalId":170342,"journal":{"name":"2021 IEEE AFRICON","volume":"272 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-09-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114945488","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2021-09-13DOI: 10.1109/africon51333.2021.9570886
Abdoulaye Tapsoba, F. Ouédraogo
Information System security is becoming a critical issue today, given the large-scale use of the Internet, the diversity of storage and different means of exchanging information. Solutions developed based on signatures are necessary but ineffective nowadays. The introduction of artificial intelligence has brought new life to the field of network intrusion detection. In this context, through this work, we aim to perform a binary and multi-class classification model using supervised learning algorithms for the prediction of new threats. The proposed approach has been tested on the NSL-KDD dataset. We achieved an accuracy of 80.4% for binary classification and 77.5% for multi-class prediction. These very encouraging prediction rates were obtained with the Support Vector Vachine (SVM) and the Multi-Layer Perceptron (MLP).
{"title":"Evaluation of supervised learning algorithms in binary and multi-class network anomalies detection","authors":"Abdoulaye Tapsoba, F. Ouédraogo","doi":"10.1109/africon51333.2021.9570886","DOIUrl":"https://doi.org/10.1109/africon51333.2021.9570886","url":null,"abstract":"Information System security is becoming a critical issue today, given the large-scale use of the Internet, the diversity of storage and different means of exchanging information. Solutions developed based on signatures are necessary but ineffective nowadays. The introduction of artificial intelligence has brought new life to the field of network intrusion detection. In this context, through this work, we aim to perform a binary and multi-class classification model using supervised learning algorithms for the prediction of new threats. The proposed approach has been tested on the NSL-KDD dataset. We achieved an accuracy of 80.4% for binary classification and 77.5% for multi-class prediction. These very encouraging prediction rates were obtained with the Support Vector Vachine (SVM) and the Multi-Layer Perceptron (MLP).","PeriodicalId":170342,"journal":{"name":"2021 IEEE AFRICON","volume":"38 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-09-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127819217","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2021-09-13DOI: 10.1109/africon51333.2021.9571011
A. A. Jhumka, R. King, C. Ramasawmy
The increasing use of renewable energy as an alternative source of energy is getting more attention. However, with this increasing trend, it is more likely to have a major impact on the stability of the grid due to the reduction in inertia. The integration of variable renewable energy generation, converter connected generation in particular leads to synchronous conventional generation being displaced hence reducing the total inertia of the system. This in turns affects the system dynamics, modal frequency and damping of the electromechanical modes of rotor oscillations. Furthermore, during a system disturbance, generation and demand become unbalanced, resulting in a change of system frequency. Inertia will be an important parameter to be considered for the grid stability. With the increasing use of power electronics devices connecting renewable energy sources, virtual/synthetic inertia is the alternative to restore the frequency balance. However attention should be drawn that inducing inertia to the system will affect the rotor angle, which will be critical in the transient stability analysis of the power system grid. The aim of this paper is to provide an innovative method of establishing the impact of renewable energy like solar on the stability of the power grid, through modelling of the rotor angle and inertia analysis. The proposed system is adapted from IEEE 9-bus system with an adjusted frequency of 50 Hz. The proposed methodology is to compare the dynamics of the conventional system with synchronous generators to that of similar arrangement in presence of renewable energy sources. Transient stability was analysed with a 3-phase fault on the system and the variation of rotor angle was simulated on MATLAB in both case studies.
{"title":"Rotor Angle and Inertia Analysis in Presence of Renewable Energy Source","authors":"A. A. Jhumka, R. King, C. Ramasawmy","doi":"10.1109/africon51333.2021.9571011","DOIUrl":"https://doi.org/10.1109/africon51333.2021.9571011","url":null,"abstract":"The increasing use of renewable energy as an alternative source of energy is getting more attention. However, with this increasing trend, it is more likely to have a major impact on the stability of the grid due to the reduction in inertia. The integration of variable renewable energy generation, converter connected generation in particular leads to synchronous conventional generation being displaced hence reducing the total inertia of the system. This in turns affects the system dynamics, modal frequency and damping of the electromechanical modes of rotor oscillations. Furthermore, during a system disturbance, generation and demand become unbalanced, resulting in a change of system frequency. Inertia will be an important parameter to be considered for the grid stability. With the increasing use of power electronics devices connecting renewable energy sources, virtual/synthetic inertia is the alternative to restore the frequency balance. However attention should be drawn that inducing inertia to the system will affect the rotor angle, which will be critical in the transient stability analysis of the power system grid. The aim of this paper is to provide an innovative method of establishing the impact of renewable energy like solar on the stability of the power grid, through modelling of the rotor angle and inertia analysis. The proposed system is adapted from IEEE 9-bus system with an adjusted frequency of 50 Hz. The proposed methodology is to compare the dynamics of the conventional system with synchronous generators to that of similar arrangement in presence of renewable energy sources. Transient stability was analysed with a 3-phase fault on the system and the variation of rotor angle was simulated on MATLAB in both case studies.","PeriodicalId":170342,"journal":{"name":"2021 IEEE AFRICON","volume":"35 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-09-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124717320","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2021-09-13DOI: 10.1109/africon51333.2021.9570865
A. T. Ajibare, Daniel T. Ramotsoela, L. Akinyemi, S. Oladejo
This paper presents the impact of radiofrequency (RF) electromagnetic fields (EMF) radiation exposure induced by wireless networks, most importantly 5G cellular networks for both the uplink and downlink radio emissions using exposure index-based power control algorithm (EIPCA), a novel simulation method that quantifies the realistic electromagnetic exposure of the human user. The exposure index (EI) is used to characterize the EMF exposure taking in account the power density, specific absorption rate (SAR); using both the electric field strength and magnetic field strength as well as considering the variability of other factors such as environment, the conductivity and the mass density of tissue. This work aims at simulating the radiations emitted from access points (APs) and mobile devices, analyzing and comparing them with the threshold set by the International Commission on Non-Ionizing Radiation Protection (ICNIRP) for the understanding of radiation impact. The numerical results reveal that the maximum radiation exposure emitted is far lower than the ICNIRP standard. It is shown that the exposure from mobile devices (uplink) has more EI compared with the ones due with the transmitting stations (downlink) radio emissions, and both can be minimized when there is an optimal power control scheme in the network as revealed in the power density received from the APs. Moreover, it compares the exposure level in both the fourth-generation (4G) and the fifth-generation (5G) networks, this will increase the health awareness concerning radiation exposure of 5G networks to the general public.
{"title":"RF EMF Radiation Exposure Assessment of 5G Networks: Analysis, Computation and Mitigation Methods","authors":"A. T. Ajibare, Daniel T. Ramotsoela, L. Akinyemi, S. Oladejo","doi":"10.1109/africon51333.2021.9570865","DOIUrl":"https://doi.org/10.1109/africon51333.2021.9570865","url":null,"abstract":"This paper presents the impact of radiofrequency (RF) electromagnetic fields (EMF) radiation exposure induced by wireless networks, most importantly 5G cellular networks for both the uplink and downlink radio emissions using exposure index-based power control algorithm (EIPCA), a novel simulation method that quantifies the realistic electromagnetic exposure of the human user. The exposure index (EI) is used to characterize the EMF exposure taking in account the power density, specific absorption rate (SAR); using both the electric field strength and magnetic field strength as well as considering the variability of other factors such as environment, the conductivity and the mass density of tissue. This work aims at simulating the radiations emitted from access points (APs) and mobile devices, analyzing and comparing them with the threshold set by the International Commission on Non-Ionizing Radiation Protection (ICNIRP) for the understanding of radiation impact. The numerical results reveal that the maximum radiation exposure emitted is far lower than the ICNIRP standard. It is shown that the exposure from mobile devices (uplink) has more EI compared with the ones due with the transmitting stations (downlink) radio emissions, and both can be minimized when there is an optimal power control scheme in the network as revealed in the power density received from the APs. Moreover, it compares the exposure level in both the fourth-generation (4G) and the fifth-generation (5G) networks, this will increase the health awareness concerning radiation exposure of 5G networks to the general public.","PeriodicalId":170342,"journal":{"name":"2021 IEEE AFRICON","volume":"44 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-09-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129664987","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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