Tools
ToolsMalik, Hassan, Shah, Syed Tariq, Haider, Syed Kamran, Shawky, Mahmoud A. and Ullah, Insaf (2026) A Sustainable and Intelligent Unified LPWAN-as-a-Service Framework for 6G IoT Using RedCap. In: IEEE Wireless Communications and Networking Conference 2026. The Institute of Electrical and Electronics Engineers (IEEE), Kuala Lumpur. (In Press)
Full text not available from this repository. (Request a copy)Abstract
5G Reduced Capability (RedCap) is a promising technology introduced to bridge the gap between legacy LPWAN technologies (i.e., NB-IoT, LTE-M, URLLC) and full 5G NR. However, 5G RedCap operates with fixed radio parameters and cannot dynamically adapt to changing service requirements. This paper proposes a Unified LPWAN-as-a-Service (ULaaS) framework that leverages existing provision of 5G NR to optimize the RRC parameters, such as bandwidth part, power class, antenna configuration, and modulation scheme, to support legacy LPWAN technologies-inspired operational behaviours within a Single 5G RedCap device and policy-driven network architecture. The framework enables multi-persona operation by dynamically changing configurations through standard 5G management entities—specifically the Network Exposure Function (NEF), Policy Control Function (PCF), and Session Management Function (SMF). This software-driven approach aligns well with the 6G design principles of intelligent control, hardware reuse, and resource-efficient networking, enabling the sustainable and intelligent Green Internet of Things. The implementation of ULaaS is realized on MATLAB-based waveform-assisted evaluation to demonstrate that the adaptive RedCap parameter tuning can effectively approximate the latency, throughput, and energy efficiency behavior of legacy LPWAN technologies. The results show that ULaaS can serve as a unified layer that enhances RedCap’s flexibility for heterogeneous IoT deployments. Index Terms—5G RedCap, LPWAN-as-a-Service, NB-IoT, LTE-M, URLLC, 6G-IoT connectivity.
| Item Type: | Book Section |
|---|---|
| Faculty \ School: | Faculty of Science > School of Computing Sciences |
| UEA Research Groups: | Faculty of Science > Research Groups > Cyber Intelligence and Networks Faculty of Science > Research Groups > Data Science and AI |
| Depositing User: | LivePure Connector |
| Date Deposited: | 09 Mar 2026 12:30 |
| Last Modified: | 09 Mar 2026 12:30 |
| URI: | https://ueaeprints.uea.ac.uk/id/eprint/102268 |
| DOI: |
Actions (login required)
 |
View Item |