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1. A Comprehensive Survey on Moving Networks

Author:Jaffry, S;Hussain, R;Gui, X;Hasan, SF

Source:IEEE COMMUNICATIONS SURVEYS AND TUTORIALS,2021,Vol.23

Abstract:The unprecedented increase in the demand for mobile data, fuelled by new emerging applications and use-cases such as high-definition video streaming and heightened online activities has caused massive strain on the existing cellular networks. As a solution, the fifth generation (5G) of cellular technology has been introduced to improve network performance through various innovative features such as millimeter-wave spectrum and Heterogeneous Networks (HetNets). In essence, HetNets include several small cells underlaid within macro-cell to serve densely populated regions like stadiums, shopping malls, and so on. Recently, a mobile layer of HetNet has been under consideration by the research community and is often referred to as moving networks. Moving networks comprise of mobile cells that are primarily introduced to improve Quality of Service (QoS) for the commuting users inside public transport because QoS is deteriorated due to vehicular penetration losses and high Doppler shift. Furthermore, the users inside fast moving public transport also exert excessive load on the core network due to large group handovers. To this end, mobile cells will play a crucial role in reducing the overall handover count and will help in alleviating these problems by decoupling in-vehicle users from the core network. This decoupling is achieved by introducing separate in-vehicle access link, and out-of-vehicle back-haul links with the core network. Additionally side-haul links will connect mobile cells with their neighbors. To date, remarkable research results have been achieved by the research community in addressing challenges linked to moving networks. However, to the best of our knowledge, a discussion on moving networks and mobile cells in a holistic way is still missing in the current literature. To fill the gap, in this article, we comprehensively survey moving networks and mobile cells. We cover the technological aspects of moving cells and their applications in the futuristic applications. We also discuss the use-cases and value additions that moving networks may bring to future cellular architecture and identify the challenges associated with them. Based on the identified challenges, we discuss the future research directions.
2. Cellular Traffic Prediction using Recurrent Neural Networks

Author:Jaffry, S;Hasan, SF

Source:2020 IEEE 5TH INTERNATIONAL SYMPOSIUM ON TELECOMMUNICATION TECHNOLOGIES (ISTT),2020,Vol.

Abstract:Autonomous network traffic prediction will be a key feature in beyond 5G networks. In the past, researchers have used statistical methods such as Auto Regressive Integrated Moving Average (ARIMA) to provide traffic prediction. However ARIMA based models fail to provide accurate predictions in highly dynamic cellular environment. Hence, researchers are exploring deep learning techniques such as Recurrent Neural Networks (RNN) and Long-Short-Term-Memory (LSTM) to develop autonomous cellular traffic prediction models.This paper proposes a LSTM based cellular traffic prediction model using real world call data record. We have compared the LSTM based prediction with ARIMA model and vanilla Feed-Forward Neural Network (FFNN). The results show that LSTM and FFNN can accurately predict cellular traffic. However, it has been found that LSTM models converged more quickly in terms of training the model for prediction.
3. Comparison of Deep Reinforcement Learning Algorithms in Data Center Cooling Management: A Case Study

Author:Hua,Tianyang;Wan,Jianxiong;Jaffry,Shan;Rasheed,Zeeshan;Li,Leixiao;Ma,Zhiqiang

Source:Conference Proceedings - IEEE International Conference on Systems, Man and Cybernetics,2021,Vol.

Abstract:The growth in scale and power density of Data Centers (DC) poses serious challenges to the cooling management. Recently, there are many studies using machine learning to solve the cooling management problems. However, a comprehensive comparative study is still missing. In this work, we compare the performance of various Deep Reinforcement Learning (DRL) algorithms, including Deep-Q Networks (DQN), Deep Deterministic Policy Gradient (DDPG), and Branching Dueling Q-Network (BDQ), using the Active Ventilation Tiles (AVTs) control problem in raised-floor DC as an example. In particular, we design two multiagent algorithms based on DQN and three critic architectures for DDPG. Simulations based on real world workload show that DDPG provides the best performance over the considered algorithms.
Total 3 results found
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