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Conference Papers Year : 2023

Connection Throughput Maximization for Grant-Based NOMA Massive IoT with Graph Matching

Abstract

We propose a framework for maximizing the number of machine-type devices connected in the uplink of a Narrowband Internet of Things (NB-IoT) network using non-orthogonal multiple access (NOMA). The system is based on the fast-uplink grant (FUG), where the base station (BS) schedules the access for active devices requesting connection. This problem is a mixedinteger non-convex problem and real-time solutions using general solvers are computationally prohibitive. The proposed scheduling solution comprises efficient device clustering and optimum power allocation using a bipartite graph matching approach, termed connection throughput maximizing full matching with pruning (CTMBM). Different from the other solutions of state-of-the-art, our proposed scheme considers scheduling over multiple transmission time intervals while considering the transmission deadlines and quality of service (QoS) for the devices. Additionally, we provide a method for priority scheduling of a subset of devices. We compare our solution to the state-of-the-art schemes and analyze the achieved gains through Monte-Carlo computer simulations.
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Dates and versions

hal-04360320 , version 1 (21-12-2023)

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Identifiers

  • HAL Id : hal-04360320 , version 1

Cite

Shashwat Mishra, Lou Salaün, Jean-Marie Gorce, Chung Shue Chen. Connection Throughput Maximization for Grant-Based NOMA Massive IoT with Graph Matching. IEEE Global Communications Conference (GLOBECOM), IEEE, Dec 2023, Kuala Lumpur (Malaysia), Malaysia. ⟨hal-04360320⟩
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