Pioneering Telecom Innovations
Key Research Areas
Core domains of technological investigation at ATCNN, including 5G/6G, mmWave, massive MIMO, and SDN/NFV.
Ganpat Vidyanagar, Mehsana-Gozaria Highway, PO – 384012, Gujarat
Core domains of technological investigation at ATCNN, including 5G/6G, mmWave, massive MIMO, and SDN/NFV.
ATCNN — Advanced Technology Centre of Next Generation Network (5G & Beyond)
14 Problem Statements with Research Abstracts
1. AI-Native Telecom Networks
Problem Statement:
Develop AI/ML-based solutions for autonomous network optimization, predictive maintenance, fault detection, traffic forecasting, and energy-efficient operation of 5G/6G-Advanced networks.
Abstract:
The emergence of 5G-Advanced and the anticipation of sixth-generation networks have fundamentally shifted the design philosophy of wireless infrastructure from static, rule-based configuration toward intelligent, self-managing systems. Contemporary networks generate telecommunications time series that are high-dimensional, multi-layered, and too voluminous for human operators to manage effectively, making autonomous AI-driven management not merely desirable but operationally necessary. This research proposes the design and validation of an AI-native network management framework using the Ganpat University ATCNN Private 5G testbed as the primary experimental platform. Specifically, the research will develop lightweight spiking neural network-based xApps deployed on the O-RAN near-real-time RAN Intelligent Controller for autonomous radio resource management, predictive fault localization across correlated KPI streams, and energy-aware sleep scheduling aligned with traffic predictions. The framework will be evaluated against 3GPP Release 18 AI/ML for NG-RAN benchmarks and validated under simulated electronic warfare interference conditions. Expected outcomes include a patent-pending xApp architecture achieving fault prediction accuracy exceeding 93%, end-to-end latency optimization of at least 35%, and publication in an IEEE Transactions-class journal. The research directly leverages ATCNN’s live 5G SA Core, Open RAN gNB, and source code access for real-world experimental validation.
2. Smart Healthcare over 5G
Problem Statement:
Design 5G-enabled healthcare applications for remote patient monitoring, telemedicine, AI-assisted diagnostics, connected ambulances, wearable health devices, and emergency healthcare services.
Abstract:
India’s rural healthcare deficit, with fewer than one physician per 1,000 citizens in many districts, creates an urgent need for scalable remote care infrastructure. Fifth-generation networks, with URLLC slices supporting sub-millisecond latency and eMBB enabling high-definition video, provide the communication backbone for connected healthcare. This research proposes a 5G-enabled Smart Healthcare Platform for rural and semi-urban Gujarat, integrating wearable IoT health devices for continuous patient monitoring, an AI-assisted diagnostic engine, and a connected ambulance system using real-time 5G-to-hospital telemetry. The platform will be validated using Ganpat University’s Private 5G infrastructure, with a URLLC slice for critical medical telemetry and an eMBB slice for telemedicine video. The AI diagnostic module will employ federated learning to train models across hospital nodes without centralizing sensitive patient data, ensuring DPDP Act compliance. Additionally, 5G RedCap devices will be evaluated as cost-effective wearable health monitor connectivity, addressing the battery-life and cost constraints of rural deployment. A pilot deployment will be conducted in collaboration with a North Gujarat healthcare institution. Expected outcomes include a functional prototype, two patent applications, and publications in biomedical engineering and health informatics journals.
3. Smart Agriculture
Problem Statement:
Develop IoT and AI-based precision agriculture solutions using 5G connectivity for crop monitoring, irrigation automation, livestock management, pest detection, and smart farming.
Abstract:
Gujarat’s agricultural sector, contributing approximately 20% to the state’s GDP and employing over 50% of its rural population, faces mounting challenges from erratic monsoons, groundwater depletion, and rising input costs that demand a transition from intuition-based to data-driven farming practices. This research proposes the development of a 5G-connected precision agriculture platform that integrates a dense IoT sensor network for soil moisture, temperature, humidity, and crop health monitoring with an AI engine for real-time agronomic decision support. The platform will use Ganpat University’s Private 5G network and 5G drone to provide aerial multispectral imaging for crop disease and pest detection, with edge AI inference enabling sub-second alerts to farmers via a mobile application. Automated drip irrigation actuation based on soil moisture thresholds and weather prediction models will reduce water consumption by an estimated 30–40% compared to conventional methods. Livestock management modules will use 5G-connected biometric tags for real-time animal health and location monitoring. The research will be piloted with a Krishi Vigyan Kendra in the Mehsana district, ensuring domain-specific validation and farmer-centric design. Outcomes include a deployable prototype, field trial data, patent filings on the AI-irrigation algorithm and the drone-based pest detection system, and publications in precision agriculture and IoT journals.
4. Industry 4.0 & Smart Manufacturing
Problem Statement:
Build intelligent industrial automation solutions leveraging Private 5G, AI, robotics, digital twins, predictive maintenance, and Industrial IoT.
Abstract:
Gujarat’s manufacturing sector, encompassing pharmaceuticals, chemicals, textiles, and engineering goods, faces increasing pressure to adopt Industry 4.0 practices to remain globally competitive. Private 5G networks, with deterministic latency and network slicing, are emerging as the preferred wireless infrastructure for smart factory environments where real-time industrial control is critical. This research proposes the design and demonstration of a Private 5G-enabled Industry 4.0 platform using Ganpat University’s ATCNN testbed as the development and validation environment. The platform will integrate Industrial IoT sensors for machine health monitoring, an AI-driven predictive maintenance engine reducing unplanned downtime through anomaly detection, a real-time digital twin mirroring the physical production line for simulation and optimization, and a robotic arm controller operating over a 5G URLLC slice with sub-10-millisecond command latency. The system will be benchmarked against the OPC-UA industrial communication standard and validated with an MSME manufacturing partner in the Mehsana industrial corridor. Expected outcomes include a quantifiable reduction in machine downtime of at least 25%, a digital twin patent application, and publications in IEEE Transactions on Industrial Informatics.
5. Disaster Management & Public Safety
Problem Statement:
Develop resilient communication systems using 5G/6G-Advanced for emergency response, disaster recovery, search and rescue, early warning systems, and public safety communications.
Abstract:
Gujarat’s geography exposes the state to earthquakes, cyclones, and floods that severely degrade conventional communication infrastructure precisely when reliable communications are most critical. The 2001 Bhuj earthquake and repeated cyclone events demonstrated that commercial networks fail systematically during large-scale disasters, creating dangerous communications blackouts for first responders. This research proposes a resilient 5G-based Disaster Communication and Early Warning System operating independently of fixed infrastructure through deployable self-organizing network nodes, satellite backhaul integration, and mesh relay capabilities using Software Defined Radio platforms available at Ganpat University’s ATCNN lab. The system will incorporate AI-powered early warning analytics processing multi-source sensor data from seismic, meteorological, and flood monitoring networks, generating geo-targeted public alerts within two minutes of threshold events. A dedicated first-responder network slice will prioritize emergency communications with guaranteed QoS. The USRP and SDR devices in ATCNN will be used to prototype the deployable mesh relay nodes. The research will coordinate with Gujarat SDMA for policy alignment and field trial planning. Outcomes include a prototype deployable communication node, an AI early warning engine, patents on the mesh relay architecture, and publications in disaster management and communications journals.
6. Fixed Wireless Access (FWA)
Problem Statement:
Develop innovative FWA solutions for affordable broadband in rural, remote, and underserved areas using 5G technologies.
Abstract:
Despite India’s progress in 5G urban deployment, rural broadband penetration remains critically low, with over 600 million rural citizens lacking reliable high-speed internet. Fixed Wireless Access using 5G mid-band and sub-6 GHz spectrum presents a viable alternative to fibre rollout in geographically challenging areas, enabling last-mile connectivity without capital-intensive civil infrastructure. This research proposes the design, optimization, and field validation of a 5G FWA system tailored for rural Gujarat deployment, addressing three specific challenges: interference management in spectrum-congested multi-cell environments, adaptive beamforming for irregular rural terrain, and cost-effective CPE design meeting BIS standards. Ganpat University’s Private 5G network will simulate rural cell configurations and validate link budgets using the USRP testbed. A pilot deployment in a Mehsana district village will benchmark actual throughput, coverage, and latency against TRAI’s 25 Mbps Broadband India standard. The research will also develop an AI-powered capacity planning tool optimizing tower placement and beam configuration for rural FWA deployments. Outcomes include a validated FWA system design, pilot deployment data, contributions to India’s indigenous 5G ecosystem, and publications in IEEE Communications and rural connectivity journals.
7. RedCap Devices
Problem Statement:
Design cost-effective Reduced Capability (RedCap) devices and applications for wearables, healthcare sensors, Industrial IoT, smart meters, and logistics.
Abstract:
The global 5G device ecosystem has historically been bifurcated between high-cost smartphones and ultra-low-capability LPWAN devices, leaving a substantial mid-tier IoT segment inadequately served. The 3GPP Release 17 Reduced Capability standard, commercially deployed by 34 operators across 24 countries as of 2026, addresses this gap by delivering up to 220 Mbps downlink at 50–70% lower module cost than full 5G NR, while maintaining native 5G network slicing and low-latency capabilities. This research proposes the design and prototyping of RedCap-based device solutions targeting four high-value Indian application segments: wearable health monitors for remote patient care, smart energy meters for Gujarat DISCOMs, Industrial IoT condition monitoring sensors for MSME factories, and logistics asset tracking for GIFT City and Mundra Port supply chains. Ganpat University’s 5G SA Core and USRP platform will emulate a RedCap-capable network environment for prototype firmware development. A cost-performance analysis benchmarking RedCap against LTE-M, NB-IoT, and full 5G NR will provide quantitative guidance for deployment decision-making. Outcomes include prototype devices, a cost-performance framework, standards contributions to 3GPP eRedCap, and publications in IEEE Internet of Things Journal.
8. Open RAN & Private 5G
Problem Statement:
Develop Open RAN and Private 5G solutions for enterprises, campuses, smart factories, ports, mines, airports, and educational institutions.
Abstract:
The disaggregation of the Radio Access Network through O-RAN Alliance open interface specifications enables multi-vendor interoperability, software-defined intelligence, and the democratization of network deployment beyond traditional telecom operators. Private 5G, powered by O-RAN architecture and campus spectrum, is rapidly becoming the connectivity platform of choice for enterprises requiring deterministic performance, data sovereignty, and application-specific network slicing. This research leverages Ganpat University’s fully operational O-RAN compatible Private 5G infrastructure — including 5G SA Core, UERANSIM gNB, Open RAN support, and source code access — to develop and validate Private 5G deployment models for five vertical sectors: educational campuses, smart ports referencing Mundra and Kandla, mining operations in Kutch and Banaskantha, airport ground operations, and SME industrial parks. The research will design a modular Private 5G deployment framework with plug-and-play O-RAN components, AI-driven network slicing policies adapted to each vertical’s QoS requirements, and automated enterprise onboarding. Security isolation between operational technology and information technology slices will be validated against O-RAN WG11 security threat models. A reference deployment on the Ganpat University campus serves as the primary validation environment. Outcomes include a deployment framework, an O-RAN xApp portfolio, and industry-ready technical guidelines.
9. Telecom Security
Problem Statement:
Design AI-enabled cybersecurity solutions for telecom infrastructure, secure network access, threat detection, fraud prevention, and privacy protection.
Abstract:
The cloudification of 5G Core infrastructure, open API architecture of O-RAN, and proliferation of network slicing have dramatically expanded the cyberattack surface of modern telecommunications networks. The O-RAN Alliance’s WG11 Security Threat Model identifies the near-RT RIC, xApp execution environment, and E2/A1/O1 interfaces as critical attack surfaces requiring novel defense mechanisms. Simultaneously, the advent of cryptographically relevant quantum computers creates an urgent harvest-now-decrypt-later threat to 5G authentication credentials secured by classical ECDH algorithms. This research proposes a comprehensive Telecom Security Framework encompassing three integrated components: an AI-driven anomaly detection engine for real-time threat identification across 5G Core network functions using federated learning; a post-quantum cryptographic migration module integrating NIST FIPS 203 (CRYSTALS-Kyber) and FIPS 204 (CRYSTALS-Dilithium) into 5G NAS authentication without violating URLLC latency budgets; and an xApp integrity verification mechanism preventing malicious xApps from issuing unauthorized RAN control actions. All three components will be developed and tested on Ganpat University’s ATCNN Private 5G infrastructure. Outcomes include two patent applications, a quantum-safe 5G authentication implementation, and publications in IEEE Security and Privacy.
10. Smart Cities
Problem Statement:
Develop 5G-enabled smart city solutions for intelligent transportation, traffic management, waste management, smart lighting, surveillance, and public utilities.
Abstract:
India’s Smart Cities Mission encompasses 100 cities including Gandhinagar, Surat, Rajkot, Vadodara, and Ahmedabad in Gujarat, but faces a persistent connectivity bottleneck: IoT sensors, cameras, and edge nodes require wireless connectivity that is simultaneously high-bandwidth, low-latency, massively scalable, and cost-effective at the per-device level. Fifth-generation networks, through their mMTC capability supporting up to one million devices per square kilometer, provide the communication substrate that smart city infrastructure genuinely requires. This research proposes a 5G-enabled Smart City Platform encompassing five integrated subsystems: an AI-powered adaptive traffic signal control system; a 5G-connected smart waste management system using fill-level sensors and optimized collection routing; an intelligent public lighting network with occupancy-based dimming reducing energy consumption by 40%; an AI video analytics surveillance system for incident detection; and a smart utility monitoring system for real-time water and electricity tracking. The platform will be prototyped using Ganpat University’s Private 5G network with mMTC slicing and edge AI inference. A pilot deployment in Mehsana Municipality will provide field validation data. Outcomes include a multi-module smart city platform, IEEE Access publications, and patent filings.
11. Edge AI & MEC
Problem Statement:
Develop edge computing and Multi-access Edge Computing (MEC) applications for ultra-low latency AI services in healthcare, manufacturing, transportation, and smart infrastructure.
Abstract:
The centralized cloud computing model is fundamentally unsuited to applications requiring sub-10-millisecond response times, high data volumes that would saturate backhaul links, or data sovereignty constraints preventing transmission to remote data centers. Multi-access Edge Computing, standardized by ETSI and integrated into 5G through the User Plane Function, addresses these limitations by moving computation within one or two network hops of the end device. This research proposes the design and deployment of a 5G MEC Platform for four high-value application domains validated on Ganpat University’s Private 5G and edge computing infrastructure. In healthcare, an edge-deployed AI model will perform real-time ECG anomaly detection with inference latency under 5 milliseconds. In manufacturing, an edge vision system will perform product quality inspection at production-line speeds. In transportation, an edge inference engine will process vehicle sensor data for real-time congestion prediction. In smart infrastructure, an edge AI module will perform predictive fault detection for municipal utility equipment. The research will design a containerized MEC application orchestration framework enabling seamless migration between edge nodes as users move. Outcomes include a validated MEC platform, edge AI models for four verticals, and IEEE Communications Society publications.
12. Green Telecom
Problem Statement:
Develop sustainable and energy-efficient telecom technologies to reduce power consumption, optimize network resources, and promote green communication infrastructure.
Abstract:
Telecommunications networks globally consume approximately 200–250 terawatt-hours annually, growing at 15–20% per year driven by 5G densification and active antenna systems. India’s telecom sector accounts for approximately 1.8% of national electricity consumption, with tower-level diesel generators contributing significantly to the sector’s carbon footprint. The 3GPP Release 18 energy efficiency framework and O-RAN Alliance’s AI-powered sleep mode specifications provide a foundation for reducing per-bit energy consumption, but their effectiveness depends on AI traffic prediction model quality. This research proposes a Green Telecom Optimization Framework comprising four integrated components on Ganpat University’s Private 5G testbed: an AI traffic prediction engine using LSTM networks for proactive base station sleep scheduling with less than 0.5% service quality impact; an intelligent massive MIMO beam management system reducing total radiated power; a renewable energy integration module optimizing grid, solar, and battery power using predicted solar generation and traffic demand; and a carbon footprint monitoring dashboard with real-time per-cell energy intensity metrics. The research targets 30–40% reduction in active energy consumption per unit of network capacity. Outcomes include an open-source Green Telecom Optimization toolkit, two patent applications, and publications in IEEE Green Communications.
13. Connected Mobility
Problem Statement:
Develop intelligent transportation and connected mobility solutions using 5G, V2X communication, AI, and edge computing for safer and more efficient transportation.
Abstract:
India records over 150,000 road fatalities annually, with a significant proportion attributable to delayed emergency response, driver inattention, and absence of real-time hazard communication between vehicles and infrastructure. Vehicle-to-Everything communication, standardized under 3GPP Release 16 and evolving through Release 18 C-V2X enhancements, enables direct communication between vehicles, pedestrians, road infrastructure, and traffic management systems with sub-20-millisecond latency over 5G URLLC slices. This research proposes a 5G-enabled Connected Mobility Platform integrating four subsystems: a V2X communication module enabling real-time collision avoidance alerts and emergency vehicle priority signaling; an AI-powered traffic flow optimization engine dynamically adjusting signal timing; an edge-deployed incident detection system processing roadside camera feeds to identify accidents within two seconds; and a connected public transit management platform enabling real-time bus tracking and capacity-based routing. The platform will be prototyped using Ganpat University’s 5G URLLC slice and edge computing resources, with simulation validation against SUMO and CARLA traffic platforms. Coordination with Gujarat Transport Department will be sought for field trial authorization. Outcomes include a connected mobility prototype, simulation results, patent filings, and publications in IEEE Intelligent Transportation Systems.
14. Digital Inclusion
Problem Statement:
Develop affordable and accessible digital solutions using 5G technologies to bridge the digital divide in education, healthcare, financial inclusion, and rural development.
Abstract:
India’s digital divide remains a profound structural inequality: while urban India has high smartphone penetration, rural communities — particularly tribal populations, persons with disabilities, elderly citizens, and low-income women — remain systematically excluded from digital participation. Bridging this divide requires designing solutions that are affordable at the device and data level, accessible in local languages, and relevant to the actual needs of excluded communities. This research proposes a 5G-powered Digital Inclusion Platform targeting four dimensions of exclusion: education, through a 5G FWA-connected learning hub delivering AI-personalized curriculum content to tribal schools in North Gujarat using low-cost tablets; healthcare, through a community health worker platform providing AI-assisted diagnostic support over 5G in villages beyond primary health centre reach; financial inclusion, through a biometric-authenticated digital payment interface operable over 5G network slicing’s QoS differentiation; and agricultural market access, through a voice-interface commodity price and e-market platform for smallholder farmers. Each module will be co-designed with target communities through participatory design workshops. The platform will be piloted in three Mehsana and Banaskantha district villages. Outcomes include a digital inclusion toolkit, community impact assessment, and publications in ICT for Development and IEEE Access.
