H. Zarini, N. Gholipoor, M. R. Mili, M. Rasti, H. Tabassum and E. Hossain, "Resource Management for Multiplexing eMBB and URLLC Services Over RIS-Aided THz Communication," in IEEE Transactions on Communications, vol. 71, no. 2, pp. 1207-1225, Feb. 2023, doi: 10.1109/TCOMM.2023.3233988
Resource management for multiplexing eMBB and URLLC services over RIS-aided THz communication
|Author:||Zarini, Hosein1; Gholipoor, Narges2; Mili, Mohammad Robat3;|
1Department of Computer Engineering, Sharif University of Technology, Tehran, Iran
2Department of Electrical and Computer Engineering, Tarbiat Modares University, Tehran, Iran
3Pasargad Institute for Advanced Innovative Solutions (PIAIS), Tehran, Iran
4Center for Wireless Communications, University of Oulu, Oulu, Finland
5Department of Electrical Engineering and Computer Science (EECS), York University, Toronto, Canada
6University of Manitoba, Winnipeg, Canada
|Online Access:||PDF Full Text (PDF, 3.4 MB)|
|Persistent link:|| http://urn.fi/urn:nbn:fi-fe2023032032402
Institute of Electrical and Electronics Engineers,
|Publish Date:|| 2023-03-20
Integrating the multitude of emerging internet of things (IoT) applications with diverse requirements in beyond fifth generation (B5G) networks necessitates the coexistence of enhanced mobile broadband (eMBB) and ultra-reliable low-latency communication (URLLC) services. However, bandwidth limited and congested sub-6GHz bands are incapable of fulfilling this coexistence. In this paper, we consider a reconfigurable intelligent surface (RIS)-aided wideband terahertz (THz) communication system to this end. In specific, we formulate a resource management problem, aiming at jointly optimizing the reflection coefficient of the RIS elements and the transmit power of the base station, as well as the wideband THz resource block allocation. To solve this problem, we adopt a supervised learning approach relying on optimization, deep learning and ensemble learning methods. Simulation results show that for an RIS of size 11×11, up to 49% spectral efficiency gain is achieved for the eMBB service compared to the counterparts, while ensuring the reliability and latency requirements of the URLLC service. Further, the ensemble learning model can perform real-time resource management at the expense of up to 1% performance loss, compared to the optimization approach.
IEEE transactions on communications
|Pages:||1207 - 1225|
|Type of Publication:||
A1 Journal article – refereed
|Field of Science:||
213 Electronic, automation and communications engineering, electronics
The work of Mehdi Rasti was supported by the University of Oulu and the Academy of Finland Profi6 336449.
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