Towards Low Block Error Rate in Cognitive Radio Short-Packet Communication

Short-packet communication (SPC) is a key enabling technology for ultra-reliable and low-latency communication, which is required by current 5G and emerging 6G networks. In a recent study, researchers from Vietnam analytically and computationally investigated the block error rate (BLER) performance of intelligent reflecting surface (IRS)–assisted cognitive radio SPC networks. Their findings show that the coherent utilization of both direct and IRS-reflecting links can significantly enhance BLER performance under stringent reliability and interference constraints.

• Image title: IRS-Aided Cognitive Radio Short-Packet Communication Networks for uRLLC
• Image caption: Researchers evaluate the block error rate (BLER) performance of intelligent reflecting surface (IRS)–assisted cognitive radio (CR) short-packet communication (SPC) networks under interference temperature constraints, using closed-form mathematical analysis and deep neural network–based evaluation, with accuracy verified through Monte Carlo simulations.
• Image credit: The authors.
• License type: Original Content
• Usage restrictions: Cannot be reused without permission.

Ultra-reliable and low-latency communication (uRLLC) is indispensable for current 5G and emerging 6G communication systems. uRLLC services are expected to deliver stringent performance requirements, including a maximum end-to-end delay of just 1 ms and an exceptionally high reliability of 99.99999%. To meet these demands, short-packet communication (SPC) has recently emerged as a promising enabling technology for uRLLC in Internet of Everything–based applications. By leveraging finite-block-length coding, SPC significantly reduces end-to-end transmission delay compared with conventional long-block-length schemes. This paradigm shift has opened new avenues for system design and performance analysis, prompting researchers to explore novel metrics such as the maximal achievable rate and block error rate (BLER) in SPC-aided wireless networks.

Building on these advances, a team of researchers from Vietnam National University Ho Chi Minh City, Vietnam, has comprehensively investigated intelligent reflecting surface (IRS)–assisted cognitive radio (CR) SPC networks. The study was conducted by Ms. Tu-Trinh Thi Nguyen from the University of Science, in collaboration with Mr. Xuan-Xinh Nguyen from Ho Chi Minh City University of Technology (HCMUT). In the proposed framework, a base station communicates with a user in the secondary network with support from an IRS, operating under a Nakagami-m fading environment.

The findings were published in the ETRI Journal and made available online on September 30, 2025.

In their study, the researchers evaluated the BLER performance of the secondary network under a limited interference temperature, considering scenarios both with and without a direct base station–user link. Two complementary approaches were employed: conventional mathematical analysis and data-driven performance evaluation.

Using the analytical approach, the team derived closed-form expressions for both the average BLER and the asymptotic average BLER of the secondary user. In parallel, they adopted a machine learning–based strategy in which BLER was treated as a regression problem using a deep neural network (DNN) model. The accuracy of both the analytical results and the DNN-based BLER predictions was verified through extensive Monte Carlo simulations.

“We determined that coherently utilizing both direct and IRS-reflecting links can significantly enhance the BLER performance of IRS-assisted cognitive radio short-packet communication networks,” remarks Ms. Nguyen.

This innovation has the potential for widespread implementation. “The proposed IRS-assisted CR SPC network is well-suited for low-latency, low-overhead, high-reliability, and spectrum-efficient scenarios. Consequently, its potential real-world applications include the massive Internet of Things and smart cities, the Industrial Internet of Things and Industry 4.0, as well as vehicular and intelligent transportation systems. In these applications, a large number of sensors are deployed, and a massive volume of small-sized data packets is exchanged with stringent requirements on latency and reliability to enable intelligent communication networks,” highlights Mr. Nguyen.

Overall, this work provides a comprehensive framework, including mathematical analysis and learning-based evaluation, to efficiently assess system performance. The developed framework is essential for further research in both the industrial and academic communities.

Reference

Title of original paper: IRS-aided cognitive radio short-packet communications over Nakagami-m fading channels: BLER analysis and deep learning evaluation

Journal: ETRI Journal

DOI: https://doi.org/10.4218/etrij.2024-0576

About the institute

Established in 1976, the Electronics and Telecommunications Research Institute (ETRI) is a non-profit government-funded research institute and is one of the leading research institutes in the wireless communications domain. It has more than 2500 patents filed. Equipped with state-of-the-art labs, this institute strives for social and economic development through technology research.

About the authors

Ms. Tu-Trinh Thi Nguyen is a lecturer at Ho Chi Minh City University of Science, VNU-HCM, Vietnam. She is interested in intelligent reflecting surface (IRS)-assisted wireless systems and short-packet communications. She holds an M.Sc. degree in Telecommunications Engineering and is actively involved in research on IRS-aided wireless systems.

Mr. Xuan-Xinh Nguyen is a lecturer in the Department of Telecommunications at Ho Chi Minh City University of Technology (HCMUT), VNU-HCM, Vietnam. He works in areas including MIMO communications, full-duplex radio systems, convex optimization for wireless communications, and IRS-aided wireless systems.