• Anil Kumar Budati Institute of Computer Science and Innovation, UCSI University, Malaysia
  • Mohammad Kamrul Hasan Center for Cyber Security, Faculty of Information Science and Technology, Universiti Kebangsaan Malaysia
  • Rengaraj Arthi Department of Electronics and Communication Engineering, SRMIST, Ramapuram Campus, Chennai, India
  • Karthikeyan Thangavel Department of Electronics and Telecommunication Engineering, University of Technology and Applied Sciences, Muscat, Oman


Mutual/single information theory has been the theoretical background of old and advanced communication systems. Information theory has shown innovative directions for future breakthroughs at each critical evolution stage of mobile communication generation. Modern wireless communications revolve around infinitely more complicated topologies, which often include multiple users, fluctuating channel strengths, and nodes that cooperate or compete. The network’s size increases proportionately, and the information transmission becomes more complex. The applications of AI and ML technologies in wireless communications have drawn significant attention recently in information theory. AI has demonstrated tangible success in speech understanding, image identification, and natural language processing domains, thus exhibiting its great potential in solving problems that cannot be easily modelled. AI techniques have become an enabler in wireless communications to fulfil the increasing and diverse requirements across an extensive range of application scenarios in modern 5G/6G networks. There are several typical wireless scenarios, such as channel modelling, channel decoding and signal detection, and channel coding design, in which AI, ML and deep neural networks play an important role in wireless communications.
This special issue aims to encourage researchers to present original and recent developments inn information theory to analyze what lies in 5G/6G networks. It focuses on the fundamental theory, performance limits, design, and management issues in 5G/6G communication systems. For this purpose, comprehensive overviews and surveys for future networks and original papers related to these techniques are proposed. This issue is composed of 6 outstanding contributions.



1. Sudarshan S. Sonawane, Satish R. Kolhe, Handling dimensionality of ambiguity using ensemble classification in social networks to detect multi-label sentiment polarity, Computer Assisted Methods in Engineering and Science, 30(1): 7–26, 2023, doi: 10.24423/cames.471.
2. Vamsee Krishna, P. Sudhakara Reddy, S. Chandra Mohan Reddy, Rapid design exploration of low pass highly efficient single loop single bit sigma delta () modulators, Computer Assisted Methods in Engineering and Science, 30(1): 27–39, 2023, doi: 10.24423/cames.511.
3. M.T. Thirthe Gowda, J. Chandrika, Hybrid texture and gradient modeling for dynamic background subtraction identification system in tobacco plant using 5G data service, Computer Assisted Methods in Engineering and Science, 30(1): 41–54, 2023, doi: 10.24423/cames.455.
4. K. Neelima, S. Vasundra, Machine learning-based business rule engine data transformation over high-speed networks, Computer Assisted Methods in Engineering and Science, 30(1): 55-74, 2023, doi: 10.24423/cames.472.
5. Venkatasivanagaraju S., M. Venkateswara Rao, Loadability maximisation in bilateral network for real-time forecasting system using cuckoo search algorithm, Computer Assisted Methods in Engineering and Science, 30(1): 73–88, 2023, doi: 10.24423/cames.475.
6. Ch. Pratyusha Chowdari, J. Beatrice Seventline, Realization of multiplexer logic-based 2-D block FIR filter using distributed arithmetic, Computer Assisted Methods in Engineering and Science, 30(1): 89–103, 2023, doi: 10.24423/cames.538.
Feb 28, 2023
How to Cite
BUDATI, Anil Kumar et al. Preface. Computer Assisted Methods in Engineering and Science, [S.l.], v. 30, n. 1, p. 3–6, feb. 2023. ISSN 2956-5839. Available at: <>. Date accessed: 21 mar. 2023.