This is a two-year proposal. The main focus of this proposal is to develop and compare the random access algorithms of uplink traffic for Internet of Things (IoT) in 4G and 5G networks. The target wireless networks include 4G NB-IoT, and 5G URLLC and NOMA-SCMA. Among them, 4G NB-IoT is grant based access while 5G is grant free access scheme. For the algorithm development, both of rule-based and machine learning-based approaches are applied. For the performance comparisons, three different traffic models, include applications of disaster prevention (aperiodic real time burst traffic and non-real time periodic data), intelligent factory (periodic and aperiodic real time data), and healthcare (real time aperiodic and periodic data), will be applied for detail examination during simulation process. And the applicability of different access networks and algorithms for different IoT services will be proposed. The main research contents of each year are provided as follows:The first year: we will develop the rule based and machine learning based algorithms for NB-IoT and URLLC access networks. Thus 4 algorithms will be proposed and compared in different application scenarios.The second year: Comparing to traditional OFDMA, SCMA applies non orthogonal multiplexing access, which provides more Contention Transmission Unit (CTU) for UE access. We will focus on the CTU numbers allocation of different classes and its mapping rule so that the uplink performance can be improved in huge UEs environment. And the applicability for different IoT services of the proposed algorithms in these two years will be completely analyzed.
|Effective start/end date||1/08/21 → 31/07/22|
UN Sustainable Development Goals
In 2015, UN member states agreed to 17 global Sustainable Development Goals (SDGs) to end poverty, protect the planet and ensure prosperity for all. This project contributes towards the following SDG(s):
- Random Access
- Narrow band internet of things (NB-IoT)
- Ultra Reliable Low Latency Communications (URLLC)
- Non orthogonal multiplexing access (NOMA)
- Machine learning
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