Semantic Communication for B5G Wireless Networks
Dr. Neetesh Purohit is a Professor and Dean (Academics) at Indian Institute of Information Technology Allahabad (IIITA), Prayagraj UP Bharat. As an author as well as a reviewer he has contributed in many reputed publications like IEEE Transactions, IEEE Journals, IEEE Signal Processing letters, Elsevier and Springer Journals, IET Communications. He is a co-author of the book 'Covid War, UP Model: Strategies, Tactics, Impact'. Dr. Purohit has delivered more than 60 invited talks in various FDP/conferences/workshops including a few motivational talks to meritorious school children under INSPIRE and NASI fellowships. As Principal investigators/Co-investigator he is contributing in many government funded and consultancy projects; transferring 3D Printer Technology to Indian Telecom Industry (ITI) Mankapur (its commercial production has been started); AVIRAL, FAPIS, STI Hub on ICT, are some of his well-recognized projects. As National Coordinator of the Flexible Academic Program (FAP) he is working towards implementation of National Education Policy 2020. Further, he is an external member of 'Academic Ethics Committee' IIT Kanpur. He is a Senior Member of IEEE.
It is a well-known fact that Shannon’s Communication system model is serving as the backbone of all modern communication systems including the latest fifth generation (5G) cellular networks. However, efficient implementation of a reliable beyond 5G (B5G) wireless networks for all three communication modalities, namely, Human-to-Human (H2H), Human-to-Machine (H2M), and Machine-to-Machine (M2M), has a larger set of requirements which is indicating the need of upgrading the Shannon’s Model itself. Introducing goal oriented design, utilizing the information residing in semantic contents of the message, creating and utilizing knowledge banks at the source and destination, unification of information generation, transmission and usage, emphasizing semantic error correction, etc. are some complimentary provisions envisioned over and above the Shannon’s model, which is collectively referred as ‘Semantic Communications’. The advancements in the technologies like Artificial Intelligence and Internet of Things, availability of significant storage and computational capabilities even in the handheld wireless devices, and many other similar developments can speed up the development of Semantic Communication. Observing the potential of the Semantic Communication system model further research is needed towards defining a robust mathematical framework for end to end semantic communication including the microscopic and macroscopic assessment of semantic value of the messages, identifying the characteristics of involved semantic noise along with the ways of nullifying its impact, efficient implementation models for goal oriented communication, upgrading the Machine Learning techniques from traditional pattern matching to the level of understanding and reasoning, Developing suitable Semantic Metrics, and Semantic Aware Multiple access, etc.
Integrated Access and Backhaul with Full Duplex Communication
Özgür Gürbüz received her B.S. and M.S degrees in Electrical and Electronics Engineering at Middle East Technical University, in 1992 and 1995, respectively. She received her Ph.D. degree in Electrical and Computer Engineering from Georgia Institute of Technology in 2000. From 2000 until 2002 she worked as a researcher and systems/algorithms engineer for Cisco Systems, in Wireless Access and Wireless Networking Business Units. As of September 2002, Dr. Gurbuz joined the Faculty of Engineering and Natural Sciences at Sabanci University, where she is now a Professor. Her research interests are in the field of wireless communications and networks, specifically design of link and higher layer network algorithms/protocols for emerging physical layer techniques including full-duplex communication, cooperative communication, MIMO, smart antennas. Recently, she has been working on full-duplex communication, digital self-interference cancellation and applications of machine learning in wireless communications/networks and THz communications. She is a member of IEEE and IEEE Communications Society.
Despite the large available bandwidth and data rates provided, mmWave suffers from limited coverage and capacity due to the severe path loss and penetration loss experienced. One approach is living with the limited coverage via network densification by deploying more cellular base stations with smaller coverage in a given area. The large number of base stations require huge amount of CAPEX/OPEX cost for establishing their backhaul connections, especially if fiber backhauls are considered. Integrated access and backhaul (IAB) technology has emerged as a cost-effective alternative, where the base stations relay the backhaul traffic wirelessly. Sharing the radio resources between backhaul and access links orthogonally, time or frequency domain duplexing can be performed. In order to fully exploit the radio resources and double spectral efficiency, hence double the rates of backhaul and access links, full duplex (FD) communication can be applied via self-interference (SI) cancellation techniques, employing antenna suppression in propagation domain, analog cancellation at the RF level and digital SI cancellation at the baseband. In this talk, we present our digital SI cancellation solution for OFDM with a switched FD radio architecture along with neural network based frequency-domain non-linear estimation algorithm guiding time and/or frequency domain linear cancellation techniques. The proposed architecture can be implemented by only simple modification on an OFDM transceiver, allowing non-linear SI estimation to be performed separately from and prior to linear SI cancellation, so that the estimated non-linear SI signal is provided as a reference to linear cancellation. Experimental results obtained on OFDM based software defined radio set-up demonstrate that, with the proposed solution, non-linearity problem is alleviated and SI is reduced to the noise level for almost all transmit power levels. Moreover, in the proposed solution, the non-linear model needs to be trained once at power-up; hence the performance is not affected by changes in the multi-path environment, unlike existing schemes, which require re-optimization of model parameters and re-estimation per setting. With the same reasoning, the estimation overhead is totally diminished to zero and complexity is significantly reduced, to that of linear estimation, making our solution promising for digital SIC for FD IAB basestation.
Vijay Kumar Chaurasiya
Research Issues in Internet of Things (IoT) Deployments
Dr. Vijay Kumar Chaurasiya is working as Associate Professor in the Department of Information Technology at Indian Institute of Information Technology, Allahabad. He has more than 18 years of experience in teaching and research. He received Ph.D. Degree in Information Technology (Wireless Sensor Networks) from IIIT, Allahabad in 2010, Master's Degree in Wireless Communication and Computing from IIIT, Allahabad in 2004 and Bachelor’s Degree in Computer Science and Engineering from MIET, Meerut in 2001. He has published more than 50 papers in reputed International Journals and Conferences in the area of Network Protocols, Cloud Computing, IoT, Routing Techniques, Clustering, Localization, Machine Learning etc. His current research interests include IoT Protocols and Application, Resource Management in Wireless Sensor Networks, Network Security, and Data Analytics. He has supervised more than 50 Master students and 11 Ph.D. scholars. He is a senior member of Association for Computing Machinery (ACM), Institute of Electrical and Electronics Engineers (IEEE) and Vijnana Bharati (VIBHA), India.
Wireless and mobile communication, sensor networks and Internet of Things (IoT) would form the backbone to create pervasive and ubiquitous environments that would profoundly influence society and thus are important to society. The wireless technologies such as IEEE standards, and Internet of Things (IoT) would encompass a wide range of domains such as hardware devices, routing algorithms, heterogeneous network elements, machine learning techniques, cloud computing and blockchain technology, etc. Although we have seen a significant deployment of IoT networks these days, there are many domains which require further research to reap the full potential of IoT based applications. There is an increasing demand in the industry to comply to Industry 4.0 (also known as "Industrie 4.0"), after the introduction of Industrial IoT (IIoT). Therefore, a lot of research is required at different layers of TCP/IP reference model. Further, existing IoT protocols, machine learning techniques, cloud computing, data storage and blockchain require newer standards and protocols for application specific implementations. Therefore, in this talk, I will discuss the pressing research issues in the IoT ecosystem and how we can overcome these problems for wide deployment of IoT networks in various application domains.
The 5G NR MBS Trial IN Nanjing
Pan Changyong, Professor of Tsinghua University, Deputy Director of National Engineering Laboratory of Digital Television (Beijing), Fellow of IET, Fellow of Chinese Institute of Electronics, has made innovative achievements in the fields of digital television, remote sensing satellite data reception, visible light, power line convergence communication and other broadband transmission. He has made important contributions in the fields of digital television, visible light, power line convergence communication and other broadband transmission.
He is mainly engaged in scientific research and teaching in the field of broadband digital transmission. He is responsible for completing the revision of 19 international standards and has participated in the development of 11 national standards. He has applied for 85 invention patents and received 53 authorized patents. He has published 170 academic papers and edited 6 books related to digital TV transmission technology. He has won 1 National Science and Technology Progress Award, 2 National Technical Invention Awards and other 32 Society/Association Awards.
The way in which content and services of Public Service Media (PSM) organizations are delivered is evolving, driven in particular by the popularity of personal devices (smartphones, tablets) for accessing audio-visual media (AV). The new technology called 5G NR Multicast and Broadcast Services (shortened to “5G NR MBS” in this document for brevity), developed and specified as part of the general mobile communication technology of 3GPP, is a broadcast mode of operation that seems to be a promising candidate for finally allowing all PSM services, both linear and nonlinear, to reach smartphones and tablets. During the Sept.2022 meeting of ITU-R SG6, 5G NR MBS was included as system S In ITU-R BT 2016 Error-correction, data framing, modulation and emission methods for terrestrial multimedia broadcasting for mobile reception using handheld receivers in VHF/UHF bands. China Broadnet (CBN) is promoting 5G NR Multicast and Broadcast Service (MBS) in China. In Oct.2021, CBN conducted a 5G NR MBS trial in Nanjing, Jiangsu Province of China. This paper introduced the field trial setup and test result of Nanjing field trial. This paper introduces the information about this field trial, which include general network architecture, Base station configuration, Service streams, field trial process and field trial result. The test includes Coverage test, mobile reception test and inter-site switching test. During the coverage test, coverage test selects two bandwidth option:5MHz and 10MHz bandwidth at the end of this contribution, some initial conclusions were proposed.
Kapyrin Nikolai Igorevich
Application of model-based development and machine learning in designing software components for radars systems
Engineer at ETCM Exponenta, specializing in communication in the fields of machine learning and control systems. In 2009 – master in "Architecture of Complex Software/Hardware Systems" at ENSTA (France), in 2010 – master in "Avionics and Data Acquisition and Processing Systems" of the Moscow Aviation Institute (state technical university), stayed for R&D and full-time teaching until 2018, then worked on L&D at Samsung Research Russia and at the Corporate University of Sberbank.
The report presents an account of combining machine learning and model-based development to design an algorithm for groups of targets classification, used in a radar system. The development of this component in an extremely short timespan was made possible by modeling an environment used for data synthesis and by using a graphical modeling tool to train a number of different neural networks. The report also lists other tasks, where machine learning has been tested in developing software for radar systems.