Reconfigurable Intelligent Surface in URLLC Wireless Systems

Thesis event information

Date and time of the thesis defence

Place of the thesis defence

IT 115

Topic of the dissertation

Reconfigurable Intelligent Surface in URLLC Wireless Systems

Doctoral candidate

Master of Science (Tech.) Ramin Hashemi

Faculty and unit

University of Oulu Graduate School, Faculty of Information Technology and Electrical Engineering, Centre for Wireless Communications

Subject of study

Communications engineering


Professor Risto Wichman, Department of Information and Communications Engineering, Aalto University


Professor Matti Latva-aho, University of Oulu

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Reconfigurable Intelligent Surface in URLLC Wireless Systems

The aim of this thesis is to devise novel promising frameworks, e.g., statistical analysis, performing optimization algorithms, and applying novel machine learning (ML) methods to realize stringent ultra-reliable and low-latency communication (URLLC) requirements, e.g., in factories and mission-critical applications with smart wireless networks assisted by reconfigurable intelligent surface (RIS) technologies.

First, we study the average achievable finite blocklength (FBL) rate and error probability of a RIS-aided system. The distribution of the received signal-to-noise ratio (SNR) is matched to a Gamma random variable whose parameters depend on the total number of RIS elements. The performance loss due to the presence of phase errors arising from limited quantization levels at the RIS elements is discussed. The required number of RIS elements to achieve a desired error probability in the FBL regime is derived and the advantages of RIS technology for URLLC systems are highlighted.

In the second part of the thesis, we study a multi-objective optimization problem for maximizing the achievable FBL rate while minimizing the transmission time in a RIS-assisted short packet system. The two objective functions are the total FBL rate with a target error probability and minimizing the total used channel blocklengths (CBLs). A fundamental trade-off between maximizing the achievable rate in the FBL regime and reducing the transmission duration was shown. Also, the applicability of RIS in reducing the used CBLs while increasing the achievable rate is emphasized.

Finally, a joint active/passive beamforming and CBL optimization in a non-ideal RIS-aided URLLC system is analyzed with novel ML techniques. Specifically, we leverage an actor-critic policy gradient deep reinforcement learning algorithm named twin-delayed deep deterministic policy gradient (TD3). We show that optimizing the RIS phase shifts, base station beamforming, and CBL variables via the TD3 method with deterministic policy outperforms conventional methods and it is highly beneficial for improving the network total FBL rate considering finite CBL size.
Last updated: 23.1.2024