Arumugam Nallanathan

Current Research Projects

  • UAV Enabled Wireless Networks
  • Recently, unmanned aerial vehicle (UAV) networks have kindled strong attention of both academia and industry, owing to their low cost, fast mobility, and adjustable altitude. In contrast to conventional terrestrial base stations (BSs), the maneuverability of UAVs provides multifarious potential benefits for various wireless systems. On the one hand, UAVs are capable of providing ubiquitous coverage efficiently in several special applications, e.g. firefighting, reconnaissance, remote sensing, disaster rescue, etc. On the other hand, by optimizing moving trajectories and serving positions, these mobile terminals are able to enhance the capacity of traditional cellular networks, especially for hotspot regions and ad hoc communications. Considering the limited spectrum resource at cellular carrier frequencies below 6 GHz, millimetre-wave (mmWave) becomes an ideal candidate for UAV networks due to its huge free bandwidth. MmWave allows larger bandwidth allocations than sub-6 GHz. In particular, at 30 GHz and 60 GHz, the allocated band-width for each user can be over 1 GHz, which boosts the data rate up to Gigabit-per-second. Moreover, benefited by the small wavelength of mmWave signals, large-scale antenna arrays can be integrated on UAVs. This improvement enables new spatial processing techniques, such as mmWave massive multiple in-put multiple output (MIMO) and array signal processing in UAV-aided networks. The aim of this project is to develop channel Estimation and tracking strategy for MmWave UAV Systems as well as to develop hybrid precoding schemes.

  • Massive Internet of Things (mIoT)
  • Massive Internet of Things (mIoT) is deemed to connect billions of miscellaneous mobile devices or IoT devices that empowers individuals and industries to achieve their full potential. A plethora of new applications, such as autonomous driving, remote health care, smart-homes, smart-grids etc., are being innovated via mIoT, in which ubiquitous connectivity among massive IoT devices are fully automated without human intervention. The successful operation of these IoT applications faces various challenges, among them providing wireless access for the tremendous number of IoT devices has been considered to be the main problem. Different connectivity technologies such as cellular based NB-IoT, LTE-M, EC-GSM-IoT and unlicensed spectrum technologies such as LoRa and Sigfox are emerging as cheaper connectivity options optimized for range and energy. Cellular-based network is deemed as a potential solution to provide last mile connectivity for massive number of IoT devices, due to its advantages in high scalability, diversity, and security, as well as low cost of additional infrastructure deployments. One of the key challenges in mIoT is the random access of the channel. The aim of this project is to develop robust random access protocols for mIoT under the bursty traffic scenarios.

  • Machine Learning in Wireless Networks
  • With an ever increasing density of mobile broadband users, next generation wireless networks (5G) need to support a higher density of users compared to today’s networks. One approach for meeting this need is to more effectively share network resources through femtocells. However, lack of guidelines for providing fairness to users and significant interference caused by unplanned deployment of femtocells are important issues that have to be resolved to make heterogeneous networks (HetNets) viable. However, the interference caused by femtocells to neighboring cells is a limiting performance factor in dense HetNets. This interference is being managed via distributed resource allocation methods. However, as the density of the network increases so does the complexity of such resource allocation methods. Yet, unplanned deployment of femtocells requires an adaptable and self-organizing algorithm to make HetNets viable. As such, in this project a machine learning approach based on Q-learning will be developed to solve the resource allocation problem in such complex networks. By defining each base station as an agent, a cellular network will be modeled as a multi-agent network. Subsequently, cooperative Q-learning can be applied as an efficient approach to manage the resources of a multi-agent network. Furthermore, we can consider the quality of service (QoS) for each user and fairness in the network.

  • Fog Radio Access for 5G and Beyond
  • Compared to 4G wireless communication system, the 5G wireless communication system is expected to achieve system capacity growth by a factor of at least 1000, and energy efficiency growth by a factor of at least 10. To realize these goals, the cloud radio access network (C-RAN) was proposed as a combination of emerging technologies from both the wireless communication and information technology by incorporating cloud computing into radio access networks (RANs). In C-RANs, the baseband processing is centralized in the base band unit (BBU) pool and the densely deployed remote radio heads (RRHs) connected to BBU pool via fronthaul to fulfil the user-centric capability. Unfortunately, the practical fronthaul is often capacity and time-delay constrained, leading to significantly degraded spectral efficiency and energy efficiency performance. Also, heavy burdens are brought to the computing capability in the BBU pool by the full-centralized architecture of C-RANs. Fog radio access networks (F-RANs) harnesses the benefits of edge caching and C-RANs. The aim of this project is to develop a novel theoretical and practical framework for the design and operation of next-generation networks with edge caching and processing capabilities.

  • Molecular Communications: Unleashing the Internet of Nano Things (IoNT)
  • Communication and information theory underpins coordination across the fabric of modern civilization. As enabling technologies allow device dimensions to shrink, a new frontier of connected embedded devices has emerged. We now live in an age where nano-devices have the potential to sense and coordinate microscopic operations ranging from targeted in vivo drug delivery to precision material self-healing. The Internet-of-Nano-Things (IoNT) paradigm has the potential to dramatically transform society and is recognised as one of the top 10 emerging technologies by the World Economic Forum. Looking 10-20 years ahead, swarms of nano-devices will need to communicate and coordinate, leading to unparalleled transformations in healthcare and other industrial sectors. The aim of this project is to address practical issues concerning the design and implementation of a new generation of nano-scale networked systems, especially in electromagnetically denied biological environments and in complex industrial settings. More specifically, the objectives of this project are: (1) obtain fundamental understanding to how information can propagate in a variety of in-body diffusion channels by developing multi-scale biophysical models, (2) develop capacity achieving communication protocols with low-complexity down-scaling potential, and (3) design and build functional systems and demonstrate their communication capability in complex macro- and micro-scale diffusion-advection environments.

    Current Research Fundings

  • Principal Investigator, ‘Communications Signal Processing Based Solutions for Massive Machine-to-Machine Networks (M3NETs)’ March 2018 – February 2021
  • Principal Investigator, ‘Enabling High-Speed Microwave and Millimetre Wave Links (MiMiWaveS)’ September 2016 – August 2019
  • Principal Investigator, ‘Simultaneously Wireless InFormation and energy Transfer (SWIFT)’ February 2016 – January 2019
  • Principal Investigator, ‘Massive MIMO Wireless Networks: Theory and Methods’ May 2015 – October 2018
  • Co-Investigator, ‘Scalable Full Duplex Dense Wireless Networks (SENSE)’ November 2016-October 2019
  • Co-Investigator, ‘Sensing and Security for Smart IoT’ UKIERI with Indian Institute of Technology (IIT), Delhi, March 2018-March 2020