Mohsen Guizani

Bio: Mohsen Guizani is an academic researcher from Qatar University. The author has contributed to research in topics: Computer science & Cloud computing. The author has an hindex of 79, co-authored 1110 publications receiving 31282 citations. Previous affiliations of Mohsen Guizani include Jaypee Institute of Information Technology & University College for Women.

A Reinforcement Learning Method for Joint Mode Selection and Power Adaptation in the V2V Communication Network in 5G

Abstract: A 5G network is the key driving factor in the development of vehicle-to-vehicle (V2V) communication technology, and V2V communication in 5G has recently attracted great interest. In the V2V communication network, users can choose different transmission modes and power levels for communication, to guarantee their quality-of-service (QoS), high capacity of vehicle-to-infrastructure (V2I) links and ultra-reliability of V2Vlinks. Aiming atV2V communication mode selection and power adaptation in 5G communication networks, a reinforcement learning (RL) framework based on slow fading parameters and statistical information is proposed. In this paper, our objective is to maximize the total capacity of V2I links while guaranteeing the strict transmission delay and reliability constraints of V2V links. Considering the fast channel variations and the continuous-valued state in a high mobility vehicular environment, we use a multi-agent double deep Q-learning (DDQN) algorithm. Each V2V link is considered as an agent, learning the optimal policy with the updated Q-network by interacting with the environment. Experiments verify the convergence of our algorithm. The simulation results show that the proposed scheme can significantly optimize the total capacity of the V2I links and ensure the latency and reliability requirements of the V2V links.

Online Parallelized Service Function Chain Orchestration in Data Center Networks

Abstract: In recent years, much attention has been focused on deploying service function chains (SFCs), each of which is composed of a set of virtual network functions (VNFs) in a specified order. This is a promising approach for enabling cloud service providers to deploy user service requests more flexibly while saving costs. However, less effort has been directed toward meeting heterogeneous needs, such as high throughput or low latency of user service requests with heterogeneous bandwidth demands, especially in data center networks (DCNs). In this paper, we propose an efficient orchestration algorithm for online SFC requests. It first splits a large flow into a number of subflows and replicates the same number of sub-SFCs. Each subflow is redirected to one of these “parallelized” sub-SFCs, which is termed a sub-user request. Then, each sub-user request is deployed based on a worst-fit strategy, and VNFs in the same SFC are instantiated on the same server to the greatest possible extent. Our algorithm is expected to enable network load balancing, reducing the delay experienced by small flows while improving the acceptance ratio for user requests. Finally, the simulation results show that the proposed algorithm outperforms other comparable algorithms.

Joint Resource Allocation and Trajectory Optimization With QoS in UAV-Based NOMA Wireless Networks

Abstract: Replacing base stations with unmanned aerial vehicles (UAVs) to serve the communication of ground users has attracted a lot of attention recently. In this paper, we study the joint resource allocation and UAV trajectory optimization for maximizing the total energy efficiency in UAV-based non-orthogonal multiple access (NOMA) downlink wireless networks with the quality of service (QoS) requirements. To handle the user scheduling problem, a heuristic algorithm based on matching and swapping theory is proposed first to allocate users that access UAV in each subperiod, then the transmit power allocation problem which considers the maximum transmit power and minimum user date rate is transformed to a convex optimization problem using logarithmic approximation. Meanwhile, the successive convex optimization is used in UAV trajectory optimization problem and a joint optimization algorithm is presented with the algorithm’s convergence and computational complexity. Finally, numerical results are provided to support the rationality of the proposed algorithm.

Unlocking 5G Spectrum Potential for Intelligent IoT: Opportunities, Challenges, and Solutions

Abstract: The Internet of Things (IoT), one of the hottest trends in technology, is transforming our future by interconnecting everything; humans, vehicles, appliances, utilities, infrastructures, street lights, etc., through intelligent connections. For deploying the realization of IoT by 2020, Fifth Generation (5G) wireless communication networks are considered as an essential unifying fabric that will connect billions of devices in some of the fastest, most reliable and most effi cient ways possible, whose impact will be revolutionary, reshaping industries and transforming our world. Therefore, 5G is currently attracting extensive research interest from both industry and academia. It is widely agreed that in contrast to 4G, 5G should achieve 1000 times system throughput, 10 times spectral effi ciency, higher data rates (i.e., the peak data rate of 20 Gb/s and the user experienced rate of 1Gb/s), 25 times average cell throughput, less than 1 ms in end-to-end (E2E) latency, and 100 times higher connectivity density. Among those requirements, the 1000-fold increase in system capacity becomes the most important and maybe the most challenging for 5G systems.

Machine learning

Abstract: Machine Learning is the study of methods for programming computers to learn. Computers are applied to a wide range of tasks, and for most of these it is relatively easy for programmers to design and implement the necessary software. However, there are many tasks for which this is difficult or impossible. These can be divided into four general categories. First, there are problems for which there exist no human experts. For example, in modern automated manufacturing facilities, there is a need to predict machine failures before they occur by analyzing sensor readings. Because the machines are new, there are no human experts who can be interviewed by a programmer to provide the knowledge necessary to build a computer system. A machine learning system can study recorded data and subsequent machine failures and learn prediction rules. Second, there are problems where human experts exist, but where they are unable to explain their expertise. This is the case in many perceptual tasks, such as speech recognition, hand-writing recognition, and natural language understanding. Virtually all humans exhibit expert-level abilities on these tasks, but none of them can describe the detailed steps that they follow as they perform them. Fortunately, humans can provide machines with examples of the inputs and correct outputs for these tasks, so machine learning algorithms can learn to map the inputs to the outputs. Third, there are problems where phenomena are changing rapidly. In finance, for example, people would like to predict the future behavior of the stock market, of consumer purchases, or of exchange rates. These behaviors change frequently, so that even if a programmer could construct a good predictive computer program, it would need to be rewritten frequently. A learning program can relieve the programmer of this burden by constantly modifying and tuning a set of learned prediction rules. Fourth, there are applications that need to be customized for each computer user separately. Consider, for example, a program to filter unwanted electronic mail messages. Different users will need different filters. It is unreasonable to expect each user to program his or her own rules, and it is infeasible to provide every user with a software engineer to keep the rules up-to-date. A machine learning system can learn which mail messages the user rejects and maintain the filtering rules automatically. Machine learning addresses many of the same research questions as the fields of statistics, data mining, and psychology, but with differences of emphasis. Statistics focuses on understanding the phenomena that have generated the data, often with the goal of testing different hypotheses about those phenomena. Data mining seeks to find patterns in the data that are understandable by people. Psychological studies of human learning aspire to understand the mechanisms underlying the various learning behaviors exhibited by people (concept learning, skill acquisition, strategy change, etc.).

Pattern Recognition and Machine Learning

Abstract: Probability Distributions.- Linear Models for Regression.- Linear Models for Classification.- Neural Networks.- Kernel Methods.- Sparse Kernel Machines.- Graphical Models.- Mixture Models and EM.- Approximate Inference.- Sampling Methods.- Continuous Latent Variables.- Sequential Data.- Combining Models.