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    Öğe
    Data transmission enhancement using optimal coding technique over ın vivo channel for ınterbody communication
    (2022) Mezher, Mohanad Ahmed; Din, Sadia; Ilyas, Muhammad; Bayat, Oğuz; Abbasi, Qammer Hussain; Ashraf, Imran
    Wireless in vivo actuators and sensors are examples of sophisticated technologies. Another breakthrough is the use of in vivo wireless medical devices, which provide scalable and cost-effective solutions for wearable device integration. In vivo wireless body area networks devices reduce surgery invasiveness and provide continuous health monitoring. Also, patient data may be collected over a long period of time. Given the large fading in in vivo channels due to the signal path going through flesh, bones, skins, and blood, channel coding is considered a solution for increasing the efficiency and overcoming inter-symbol interference in wireless communications. Simulations are performed by using 50 MHz bandwidth at Ultra-Wideband frequencies (3.10–10.60 GHz). Optimal channel coding (Turbo codes, Convolutional codes, with the help of polar codes) improves data transmission performance over the in vivo channel in this research. Moreover, the results reveal that turbo codes outperform polar and convolutional codes in terms of bit error rate. Other approaches perform similarly when the information block length is increased. The simulation in this work indicates that the in vivo channel shows less performance than the Rayleigh channel due to the dense structure of the human body (flesh, skins, blood, bones, muscles, and fat).
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    Öğe
    Resolving energy consumption issues and spectrum allocation for future broadband networks
    (IEEE Access, 2021) Din, Sadia; Ilyas, Muhammad; Ashraf, Imran; Choi, And Gyu Sang
    With the fast and rapid pace of developments in wireless technology, energy consumption has become a problem of great significance for future networks. Over the past few years, several energy monitoring policies have been initiated to promote energy efficiency emphasizing the active and important role of consumers to realize this goal. This study resolves the energy consumption issues of broadband networks and determines the energy usage associated with high spectrum allocation in future broadband networks by leveraging clustering from the data mining domain. For analyzing the overall patterns of energy consumption in broadband networks, this study segments the broadband networks based on the similarities of their electrical load profiles and the proportion of energy usage per hour (%) as a common framework and divides the users into different groups. The prime objective for the segmentation is to provide personalized recommendations to each group to reduce the energy consumption and associated costs thereby fostering energy efficiency measures and improving consumer engagement. The segmentation is obtained by an iterative process based on computational clusters calculation which is finalized by a post clustering analysis. Post clustering analysis involves visualization and statistical data mining techniques to analyze the energy consumption patterns and reallocating to a more appropriate group. The K-Means clustering technique is utilized for this purpose which provides the best prediction accuracy of 98.46% for energy load profiles at the spectrum of 100GHz. The energy consumption segmentation of the consumers provides knowledge and a better understanding of the consumer for optimizing energy consumption for future broadband.