H
Hafizal Mohamad
Researcher at Universiti Sains Islam Malaysia
Publications - 95
Citations - 792
Hafizal Mohamad is an academic researcher from Universiti Sains Islam Malaysia. The author has contributed to research in topics: Cognitive radio & Handover. The author has an hindex of 11, co-authored 87 publications receiving 524 citations. Previous affiliations of Hafizal Mohamad include MIMOS.
Papers
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A Comprehensive Survey on Mobility Management in 5G Heterogeneous Networks: Architectures, Challenges and Solutions
TL;DR: This paper provides a comprehensive study on the mobility management in 5G HetNet in terms of radio resource control, the initial access and registration procedure of the user equipment to the network, the paging procedure that provides the location of the UE within thenetwork, connected mode mobility management schemes, beam level mobility and beam management.
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Clustering algorithms for Cognitive Radio networks
TL;DR: Of particular focus is clustering metrics and how these metrics have been applied to form clusters in Cognitive Radio networks, including clustering objectives, characteristics, performance enhancements, complexity analysis, and open issues are presented.
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Clustering and Reinforcement-Learning-Based Routing for Cognitive Radio Networks
Yasir Saleem,Kok-Lim Alvin Yau,Hafizal Mohamad,Nordin Ramli,Mubashir Husain Rehmani,Qiang Ni +5 more
TL;DR: This tutorial presents SMART, which is a cluster-based routing scheme designed for the CRN, and evaluates its performance via simulations in order to show the effectiveness of cluster- based routing in CRNs using RL.
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SMART: A SpectruM-Aware ClusteR-based rouTing scheme for distributed cognitive radio networks
Yasir Saleem,Yasir Saleem,Kok-Lim Alvin Yau,Hafizal Mohamad,Nordin Ramli,Mubashir Husain Rehmani +5 more
TL;DR: A SpectruM-Aware clusteR-based rouTing (SMART) scheme that enables SUs to form clusters in a cognitive radio network (CRN) and enables each SU source node to search for a route to its destination node on the clustered network.
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Route Selection for Multi-Hop Cognitive Radio Networks Using Reinforcement Learning: An Experimental Study
TL;DR: Three route selection schemes to enhance the network performance of CR networks are proposed, and experimental results show that RL approaches achieve a better performance in comparison with the HC approach, and also achieve close to the performance achieved by the SL approach.