SRM University AP-Andhra Pradesh has collaborated with the Indian Institute of Remote Sensing (IIRS) -Indian Space Research Organization (ISRO)’s outreach programme. As part of the association, the students of SRM AP have the excellent opportunity to register for annual courses as well as live and interactive programmes being organized by IIRS-ISRO.

Department of Electronics and Communication Engineering, SRM AP, have participated and successfully completed the programmes offered IIRS-ISRO. Lehitha Paturi, Bharadwaj Kadiyala, and Sai Yasaswini Metla have been awarded the certificate from the IIRS-ISRO on completing the online course on “Satellite Photogrammetry and its Application”. Also, Annapragada Sai Mounika has been certified for taking up the course on “Application of Geoinformatics in Ecological Studies”. Further, Tamatam Sravani Ratna has been accredited for undertaking both the aforementioned programmes organized by IIRS-ISRO.

SRM AP encourages the students to proactively avail the interactive distance learning courses and webinars that will enable them to achieve their career goals in aerospace and geospatial technologies and make a mark globally.

Simulating time-variant channel impulse response for mmWave I2I channels using Doppler spread information

Dr Anirban Ghosh

Dr Anirban Ghosh, Assistant Professor, Electronics and Communication Engineering, has recently published a paper titled “Time Variance of 60 GHz VI2I channel”. The paper is published in the renowned journal Elsevier-Vehicular Communication with an Impact Factor of 4.7. This paper explores the implementation challenges in unlicensed 60 GHz frequency band for autonomous vehicle infrastructure. This work has been implemented in collaboration with his colleagues from NIT Durgapur and collaborators from Brno University under the aegis of Prof. Ales Prokes. This work is also funded by the Science Foundation grant (Czech) and National Sustainability Program grant (Czech) and DST-Core Research Grant(India).

Fig 1: Field test setup at the measurement site

Smart cities are cities on the move; having a mission of delivering people and goods with zero congestion, zero fatality and zero energy wastage. For realising this mission, a smart city needs an intelligent transport system (ITS). As far as the communication aspect of ITS is concerned, historically, the urban ITS planners were more concerned about vehicle-to-vehicle (V2V) and vehicle-to-infrastructure (V2I) modes of communication. This is because the V2V and V2I channels are essentially wireless, and establishing reliable low-latency links over these channels is challenging. Infrastructure-to-infrastructure (I2I) communication is also an integral part of the ITS initiative, and due to their rapid, dynamic and non-invasive nature of the installation, wireless I2I links are preferred over wired links in several smart-road based ITS applications. In order to implement wireless I2I communication, transceivers may be fitted with different kinds of roadside units (RSUs), e.g., short height traffic signposts, overhead gantries, and cantilever sign supports. The traditional role of these RSUs is to support the basic ITS goals, i.e. driver assistance or traffic management. As the vehicular networks scale-up, ITS computations are being shifted to the edge, and the RSUs are going to play more prominent roles in the form of edge computing devices. For example, RSUs can form the cloudlet layer of a vehicular fog computing (VFC) architecture for the internet of vehicles (IoV).

Vehicular wireless I2I channels realise fixed-to-fixed (F2F) radio communication between two RSUs. For any F2F radio channel with stationary transmitter (TX) and receiver (RX), the time-variance is caused by the mobility of the scatterers. These mobile scatterers, say passersby for an indoor scenario, or passing vehicles for an outdoor scenario, cause changes in path lengths in a fading multipath channel. When viewed in frequency-domain, this yields a change in carrier frequency termed as Doppler shift which is proportional to the velocity of the scatterer, and the spectral broadening caused by the time rate of change of the channel is measured with the Doppler spread. Information about Doppler spread is critical for V2V applications such as platooning. In the context of V2I applications, accurate estimation of the Doppler spread is required for designing adaptive transceivers, smart antennas and for determining cellular handoffs. Doppler spread information can also be used for I2I applications like speed estimation. The radar-based solution has a lower latency compared to a camera-based solution, owing to the video acquisition and processing time of the later. As the Doppler shift is a linear function of the carrier frequency, it is of greater concern while moving up in the frequency ladder to the millimetre wave (mmWave) regime. The Doppler spread in the unlicensed 60 GHz mmWave band would be 10-30 times that in the current sub-6 GHz band, with a range spanning from 10 Hz to 20 kHz depending on the velocity of the scatterers. Moreover, compared to V2V links, the effect of moving scatterers is much more pronounced in F2F links; walking pedestrians with a velocity order of ∼ 1 m/s or even the tree leaves fluttering in the wind are important for F2F channel modelling. For highways, the moving vehicles have a velocity an order higher (> 10 m/s) and contribute significantly to the time-variance of the roadside wireless I2I links.

Fig 2: Comparison of the measured and
proposed models of ACF and Doppler
Spectrum with existing analytical models

In this paper, Dr Ghosh studied the time-variance of a roadside infrastructure to infrastructure (I2I) channel operating at 60 GHz millimetre wave (mmWave) band, where the time-variance is caused by moving vehicles acting as scatterers. At first, measurement data is obtained by placing the transmitter (TX) and the receiver (RX) at different heights to emulate a link between two nonidentical roadside units (RSUs), and time-domain channel sounding is performed by sending complementary Golay sequences from the TX to the RX. A linear piecewise interpolation of the corresponding temporal auto-correlation function (ACF) is used to find the Doppler spread of the I2I channel, where their interpolation method compensates for a slower sampling rate. Next, a framework is presented for the time-variant channel impulse response (CIR) simulation, which focuses on moving scatterers only and validates the linear piecewise ACF model. The framework is useful for time-variant vehicular I2I channel simulation and in speed estimation related vehicular applications. Finally, a double-slope curve-fitted analytical model for ACF is proposed as a generalisation to the linear piecewise model. The proposed model and its Doppler spectrum are found to be in agreement with the analytical results for fixed-to-fixed (F2F) channels with moving scatterers and matches perfectly with the measured data. “Our research has explained a framework for simulating time-variant channel impulse response (CIR) for mmWave I2I channels with moving scatterers using Doppler spread information – which provides means to study the various characteristics of an I2I channel even without carrying out any expensive channel sounding campaign”, said Dr Ghosh.

Dr Ghosh and his collaborators are currently exploring further challenges in communication between vehicles (V2V) in the same frequency range (60 GHz).

To know more about the paper, please visit-

Monochromatic painting of Mahatma Gandhi won hearts and the first prize at the inter-department competition.

Painting of GandhiThe Department of Electronics and Communication Engineering is very proud of its student Ms Vyshnavi Tanikonda for securing the first prize in the Independence Day Painting Competition. Ms Vyshnavi painted a beautiful monochromatic painting of Mahatma Gandhi, the ‘Father of the Nation’ who fought for the independence and sovereignty of the country till his last breath. Ms Vyshnavi, a 2nd-year student, successfully captured the true essence of the great patriot in her painting which brought her the top prize in the inter-departmental competition. The Department of ECE heartily congratulates its daughter for bringing this honour to the department.

Department of Electronics and Communication Engineering, SRM University-AP, Andhra Pradesh organised an insightful workshop on “Artificial Intelligence (AI) for Healthcare” in collaboration with IEEE-SRM AP Student Branch on 25th July. Prof. D. Narayana Rao, Pro-Vice-Chancellor, inaugurated the session. The workshop was divided into two sessions.
1. Technical presentation
2. Panel discussion

Dr Muralikrishna Voonna, Managing Director, Mahatma Gandhi Cancer Hospital & Research Institute, and Dr Rajiv Janardhanan, Director, Amity Institute of Public Health, Professor ACVB participated in the technical presentation. Dr Voona delivered a lecture on “Impact of AI in Clinical Healthcare” whereas Dr Rajiv Janardhanan spoke on “Sustainable Healthcare: AI-enabled platforms for Affordable and Accessible Healthcare Solutions”.

Eminent panellists such as Padma Shree Awardee Prof. Upendra Kaul, Dr Pooja Ramakant, Dr Sunil Taneja, Dr Muralikrishna Voonna, and Dr Rajiv Janardhanan interacted on “Impact of Covid-19, AI on Healthcare” during the second half of the session. Prof. Priya Ranjan, Department of ECE, SRM-AP, acted as the moderator of the session. Dr Udaya Shankar V, Assistant Professor, Department of ECE, was the coordinator of the workshop.

In his opening remarks, Prof. D. Narayana Rao asserted that healthcare in India faces significant challenges on quality, accessibility and affordability for a large section of the Indian population. Many healthcare experts are aware that in India, 70% of healthcare infrastructures are in the cities which caters only to 30% of India’s Population. “The shortage of doctors, lack of infrastructures, different mindset about the urban and rural population of the country and low government spending on the healthcare are very much motivating India towards innovative and sustainable and affordable technology to improve the quality of life,” said Prof Rao. He further mentioned that AI in Indian healthcare is expanding at a significant rate of 40% approximately. The AI-enabled health services such as automated analysis of medical tests, predictive healthcare diagnosis with the help of monitoring equipment and wearable sensor-based medical devices are expected to revolutionise medical treatment process in the country. AI is being extensively used in six healthcare segments such as hospital administration, pharmaceuticals, diagnostics, medical equipment and supplies, medical insurance and telemedicine. “Moreover, hospitals can implement patient-centric plans with the help of AI to avoid unnecessary hospital procedures and making healthcare services faster in India. The Government of India also shows its keen inclination as in the past week, the Hon’ble prime minister Sri Narendra Modi had a discussion with the CEO of IBM regarding the development of AI-enabled medical instruments for healthcare in the country. Needless to say, there are a plethora of opportunities for new engineers and doctors,” opined Prof Rao.

In his talk, Dr Murali Krishna Voonna confirmed that Artificial intelligence has a huge potential to become a transformational force in healthcare. It allows humans to gain unprecedented insights into diagnosis, care process, treatment variability and patient outcomes. Nowadays, machine learning uses statistical technics to give computing systems to learn with incoming data to identify patterns and make decisions which will help the doctors to assess risks, make a correct diagnosis, and offer more effective treatments. Robot-assisted surgery is now being widely popular for the precision and accuracy in performing a complicated surgery. AI is world’s one of the highest growth industries projected to reach $150 billion by 2026. It can compensate for physician biases and be used in personalised therapy by studying genome. Dr Voonna further informed that there is a huge scope for development in the segments such as brain-computer interface, next-generation radiology tools, creation of precise analytics for pathology images, expanding access to treat underserved regions, clinical decision support etc. to name a few. “Several reputed companies have invested in India to improve the AI in healthcare. NITI Aayog is working with Google to develop artificial intelligence ecosystems in India both in training and research. They have started a National Research Strategy for Artificial Intelligence recently. National Digital Health Authority has been formed as a regulatory body which is going to make the regulation in the Artificial Intelligence in start-ups and business and commercialisation of the products. Still, AI in healthcare is a decision support tool but not a decision-making tool,” explained Dr Voonna.

In the opinion of Dr Rajiv Janardhanan, the healthcare disparity continued because the investments of the government in the years after independence is less than 2 %. COVID-19 has turned out to be a blessing for health care as it made people especially policy-makers to realise that the healthcare infrastructure needs to be improved drastically. The AI-enabled intelligent support system is required because of budget constraints, rising costs of advanced medical treatments, increased complexity and cost of delivering healthcare, as well as increased expectations and demand for quality patient-centred healthcare. India comprises of a healthcare ecosystem where 80% of the healthcare is expensive, and 70% of the population is living in rural areas with marginalised and inaccessible healthcare. This makes the rationale to develop tools which are community empowering. “Knowledge dissemination of health literacy is required necessarily to increase the productive hours of the nation. When we empower the stakeholders with health literacy it directly aligns with the health promotion efforts, provides a rationale with community-empowering policy decision making, where the weakest stakeholder in the healthcare system can be an active participant,” asserted Dr Janardhanan. AI-enabled tagging of data can convert precision medicine to a community-centric new system which is called precision public health. This conversion has to be technology-enabled with support from the engineering fraternity to make it affordable and accessible.

During the second half of the session, the panel discussed the impact of COVID-19 on health immunity system. Padma Shree Awardee Prof Kaul explained in detail how COVID-19 disease affects the human body and the process of vaccination. He gave a detailed overview of how vaccines are prepared and the process of a clinical trial. He further shared the updated information on the COVID-19 vaccine with particular focus on India’s progress in making the vaccine. Dr Taneja and Dr Ramakanth expressed their views on COVID situation based on their daily experience at the hospitals. The panel agreed that the adoption of artificial intelligence (AI) in the healthcare system enables healthcare services, delivered at a lower cost with increased efficiency and emphasis on the diagnostics. AI should be encouraged to be used in machines to predict, comprehend, learn and act. It has the ability to play the role of game-changer in the areas of wellness, early detection, diagnosis, decision making, treatment, end of life, research and training. However, some concerns related to the safety, data quality, accountability, transparency, legal aspects etc. still persists.

The enthralling session ended with the vote of thanks. The insightful event certainly helped the participants to understand the approach in which AI is reinventing and improving modern healthcare through technologies that can estimate, comprehend, explore, and perform effectively.

Dr Om Jee Pandey Two research papers of Dr Om Jee Pandey, Assistant Professor, Department of Electronics and Communication Engineering, have been accepted in reputed IEEE Journals. The papers titled “Multiuser Full-Duplex IoT Networks with Wireless-Powered Relaying: Performance Analysis and Energy Efficiency Optimization,” and “Time Synchronized Node Localization Using Optimal H-Node Allocation in a Small World WSN” are going to be published in IEEE Transactions on Green Communications and Networking and IEEE Communications Letters, respectively. The paper “Multiuser Full-Duplex IoT Networks with Wireless-Powered Relaying: Performance Analysis and Energy Efficiency Optimization,” was pursued in collaborations with Prof. Ha H. Nguyen, Department of Electrical and Computer Engineering, University of Saskatchewan, Saskatoon, Canada, and Dr Mahendra K. Shukla, who is associated with Macau University of Science and Technology as a Post Doctoral Fellow.

Block diagram of the full-duplex
wireless-powered relay based on power splitting
There has been a growing interest in improving energy efficiency for next-generation Internet of Things (IoT) networks due to the increasing demand for green communications. Motivated by this demand, energy efficiency maximization has been investigated for several networks. Specifically, without considering simultaneous wireless information and power transfer networks, various works have extensively studied energy efficiency maximization for half-duplex networks and full-duplex networks. Further, by considering simultaneous wireless information and power transfer networks, several works have examined energy efficiency maximization in half-duplex networks. Moreover, energy efficiency for next-generation IoT networks with simultaneous wireless information and power transfer has also been studied by considering half-duplex transceivers. It appears that energy efficiency problems for multiuser full-duplex IoT networks have not been studied in the open literature.

Multiuser FD-IoT network with
wireless-powered TWR
Motivated by the above discussion, in this work we consider a full-duplex IoT network in which a multi-antenna access point and several single antenna IoT devices exchange information bidirectionally with the aid of a wireless-powered single-antenna relay, under generalized Nakagami-m fading channels. In particular, we consider the power splitting protocol at the full-duplex relay terminal, and each communication terminal operates in the full-duplex mode. For such a network, we first formulate the instantaneous end-to-end signal-to-interference-plus-noise ratios (SINRs) by employing maximum-ratio transmission (MRT) beamforming and maximum-ratio combining (MRC) at the access point A, as well as IoD scheduling. Then, based on the obtained SINRs, we evaluate the overall outage probability and ergodic sum-rate of the network. Furthermore, we develop an optimal power allocation to maximize the network’s EE.

Integral area for the
overall outage probability
Recently, with many technological advancements in wireless data transmission, IoT is expected to affect all aspects of our daily lives with its ever-growing and emerging applications. Traditional wireless communication networks are usually limited by the operational time of energy-constrained devices, and therefore energy consumption minimization is a crucial factor in IoT networks. Simultaneous wireless information and power transfer have emerged as a sustainable solution to the scenarios where replacing or recharging batteries is very costly, and may even be impossible. The basic idea of simultaneous wireless information and power transfer is that since radio frequency signals can carry information and energy at the same time, the received radio frequency signals can be utilized for energy harvesting to keep the energy-constrained devices operational. Existing studies adopt two different protocols, namely time switching and power switching, to implement simultaneous wireless information and power transfer receiver architecture. With these protocols, either the power or time of the received signal is split so that one part is used for information processing, and the other is used for energy harvesting.

The proposed research can play a crucial role in societal development as it can be applied in developing many state-of-the-art applications in the areas related to green healthcare systems, energy-efficient agriculture systems, green transportation, low power home automation, green manufacturing, low power smart utilities, and energy-efficient industries etc.

His other paper “Time Synchronized Node Localization Using Optimal H-Node Allocation in a Small World WSN”, DOI (identifier) 10.1109/LCOMM.2020.3008086, has been done in collaborations with Prof. Rajesh M Hegde, Department of Electrical Engineering, IIT Kanpur and Dr Mahendra K. Shukla, Post Doctoral Fellow at Macau University of Science and Technology. To know more about Dr Pandey’s work in the domain of Small-World Wireless Sensor Network, visit-

Dr Pandey is now not only working for the commercial applications of his current research but also has devoted himself to his future projects, that include-
Full-Duplex Network Development for Green Healthcare Systems.
Energy-Efficient Cyber-Physical Systems and Internet of Things.
Development of Energy-Efficient Agriculture Systems.
Green and Intelligent Transportation over Full-Duplex Context-Aware Pervasive Systems.

Dr Om Jee Pandey, Assistant Professor, Department of Electronics and Communication Engineering, has recently published two papers in renowned IEEE journals. His paper “Secrecy Performance Analysis of Two-way Relay Non-Orthogonal Multiple Access Systems” was published in IEEE Access Journal, having an impact factor: 4.06. Whereas, “Fault-Resilient Distributed Detection and Estimation over an SW-WSN Using LCMV Beamforming” was published in IEEE Transactions in Network and Service Management (Impact factor: 4.682). The study was done in associations with Prof. Rajesh M Hegde, Department of Electrical Engineering, IIT Kanpur and Prof. Ha H. Nguyen, Department of Electrical and Computer Engineering, University of Saskatchewan, Saskatoon, Canada. His research was focused on developing efficient Wireless Sensor Network services in small-world models. The proposed research can be applied in developing many state-of-the-art applications in the areas related to healthcare systems, climate-smart agriculture, intelligent transportation, home automation, manufacturing, smart utilities, and industries etc.

In order to develop efficient Wireless Sensor Network (WSN) services, several methods have been proposed for node localization, clustering of sensor nodes, new routing algorithms, adaptive duty cycles, usage of mixed-transmission models, placement of intelligent gateways, usage of mobile elements, and bypassing holes. However, the introduction of small-world characteristics in a WSN has hitherto not been used in this context. A small-world network is typically characterized by a low average path length and high average clustering coefficient and has been widely used to model social networks. However, small world models have not been used in the development of WSN. The primary objective of this research is to develop small world models for WSN services such as sensor node localization, data gathering, data fusion, network lifetime maximization, energy-balancing, reduced data transmission delay, time synchronization, energy-harvesting, and robust distributed detection and estimation. A small world WSN reduces the number of hops required for data transmission, and maximal utilization of sensor nodes closer to the sink. Other advantages of small world WSN include efficient bandwidth utilization, increased network lifetime, and fast convergence of event detection and parameter estimation. Small world WSN developed in tandem with novel routing strategies leads to improved WSN services for various applications.

In WSNs, sensor devices transfer the data cooperatively using multiple hops over a network. Multiple hops required for data transmission over a WSN leads to reduced network performance in the context of device localization, energy efficiency, energy balancing, data latency, speed of event detection and parameter estimation, and network robustness. This is because of poor distance estimation between sensor device pairs, maximum utilization of sensor devices closer to the sink, and a short radio range of sensor devices. Hence, the primary focus of this research work is to reduce the number of hops required for data transmission. In this context, Small World Characteristics (SWC) can be introduced in a WSN, leading to a small world WSN (SW-WSN). An SW-WSN is a well-connected network with reduced hop counts between node pairs.

Small world phenomena were first observed by Stanely Milgram in social connectivity of people. Later, it came out as a theory of “six degrees of separation”. Six degrees of separation claims that all people are six, or fewer, social connections away from each other. This theory is also known as the 6 Handshakes rule. Further, small-world phenomena have been investigated in graphs. Subsequently, SWC is observed in wireless networks. In general, small world WSNs (SW-WSNs) are characterized by low Average Path Length (APL) and high Average Clustering Coefficient (ACC). For an undirected WSN with N nodes, representing a regular network, the APL increases linearly with the number of nodes present in the network. On the other hand, in a WSN having SWC the APL, between two randomly selected nodes grows proportionally to the logarithm of the number of nodes in the network. “WSNs are spatial graphs, where links are created using radio connectivity. There is a limit on the radio range of a sensor node, hence in such networks, the long-range connections are generally absent. Thus, these networks are clustered, but they do not experience small-world phenomena. Hence, the primary focus of this work is to develop SW-WSN using various methods leading to efficient WSN services” explained Dr Pandey.

Dr Pandey is now busy to take the studies further. His future projects include-
• Small World WSN (or SW-WSN) Development for Smart Healthcare.
• Cognitive SW-WSN for Energy-Efficient CPS and IoT applications.
• Climate Smart Agriculture using Cognitive SW Characteristics.
• Low-Latency and Intelligent Transportation over small world CPS.
• Multi Sensor Fusion over SW-WSN for CPS/IoT Applications.
• Information and Context Quality in WSN, IoT and CPS Networks.