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.

Markov Decision-Based Pilot Optimization for 5G V2X Vehicular Communications

Abstract: This paper proposes a Markov decision process (MDP)-based pilot placement optimization approach for the radio access in 5G vehicle to everything communications to support Internet of Vehicles applications The optimal placement problem of pilot symbols is based on a typical pilot-assisted frequency-division multiplexing transmission and simplified to a finite state-space representation We propose and formulate a finite MDP so as to determine an appropriate pilot pattern from a set of candidate pilot configurations Also, an enhanced pilot placement scheme is developed to reduce the complexity for solving the formulated MDP problems Furthermore, we derive analytical expressions of the mutual information, which to some extent allow us to jointly evaluate the dynamics of the channel state in time and frequency domains Numerical results generated by Monte Carlo simulations show that the proposed pilot optimization policy is capable of improving the channel estimation in fast time-varying vehicular channels, and the mutual information-based measurement criteria can yield more accurate evaluations in fast time-varying vehicular channels than other conventional schemes

Security Analysis of a Space-Based Wireless Network

Abstract: With the gradual deployment of the spacebased wireless network, security risks in the data communication between satellites and even the internal structure of a satellite become extremely important. In this article, we use the satellite's internal communications security as an example to illustrate the space network security threats. We first provide a summary of the security requirements of space-based wireless networks. Then three typical attack approaches for satellite platforms based on MIL-STD-1553B bus are described. Subsequently, we present some attack simulation results and suggest some protective mechanisms.

Present landscape of quantum computing

Abstract: Quantum computing is currently a topic of interest that harnesses the phenomena of quantum mechanics. It can address several scientific challenges and generate new business opportunities. Recently, for the first time in the history of quantum computing, the authors are starting to see practical applications. Keeping this in mind, this article is designed to explore the field without any required prerequisites. The authors start with a brief overview of the fundamentals of quantum computing and also outline several applications. The timeline for widespread adoption cannot be predicted, but quite a few organisations have built the first generation of quantum computers using various hardware technologies. The authors have briefly covered the wide landscape of hardware technologies. The first generation of quantum computers can be programmed using available software development kits and accessed using online cloud services. Furthermore, the growing trend in investments and patents in the field of quantum computing is also presented. A major reason for this trend is the threat that quantum computers pose against cryptography.

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.