Abstract
In the ever-growing field of 5G technology, Massive Multiple Input Multiple Output (MIMO) systems hold the promise of substantial improvements in network capacity and efficiency. However, the demand for low latency, imposed by real-time Internet of Things (IoT) applications presents some significant obstacles. This study delves into novel approaches to reducing latency within 5G MIMO setups, focusing particularly on the integration of shortened Transmission Time Intervals (TTIs) and preemptive scheduling tactics. Through simulations involving a small-scale MIMO configuration servicing multiple IoT devices, this study thoroughly evaluates the effects of these methods on transmission latency. The desired result is to demonstrate that the adoption of reduced TTIs notably mitigates overall latency, thereby augmenting the efficacy of time-sensitive IoT tasks such as industrial automation and healthcare monitoring systems. This research not only contributes to the theoretical progression of 5G communications but also furnishes practical insights for network operators and system designers trying to fine-tune 5G networks for evolving IoT environments. The simulation results indicate that the proposed techniques significantly reduce latency by approximately 30% compared to standard configurations, particularly benefiting high-priority IoT applications. These findings highlight the effectiveness of shortened TTIs and preemptive scheduling in enhancing the performance of real-time IoT tasks within 5G MIMO networks.
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Acknowledgements
The research project was supported by the Hellenic Foundation for Research and Innovation (H.F.R.I.) under the “2nd Call for H.F.R.I. Research Projects to support Faculty Members & Researchers” (Project Number: 02440).
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Katsigiannis, CA., Tsachrelias, K., Kokkinos, V., Bouras, C., Gkamas, A., Pouyioutas, P. (2025). Enhancing Real-Time IoT Applications: Latency Reduction Techniques in 5G MIMO Networks. In: Barolli, L. (eds) Advances on Broad-Band Wireless Computing, Communication and Applications. BWCCA 2024. Lecture Notes on Data Engineering and Communications Technologies, vol 231. Springer, Cham. https://doi.org/10.1007/978-3-031-76452-3_2
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DOI: https://doi.org/10.1007/978-3-031-76452-3_2
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