Joint Sum Rate and Blocklength Optimization in RIS-Aided Short Packet URLLC Systems

被引：32

作者：

Hashemi, Ramin ^{[1
]}

Ali, Samad ^{[1
]}

Mahmood, Nurul Huda ^{[1
]}

Latva-Aho, Matti ^{[1
]}

机构：

[1] Univ Oulu, Ctr Wireless Commun CWC, Oulu 90014, Finland

来源：

IEEE COMMUNICATIONS LETTERS | 2022年 / 26卷 / 08期

基金：

芬兰科学院;

关键词：

Ultra reliable low latency communication; Error probability; Optimization; Azimuth; Array signal processing; Wireless sensor networks; Resource management; Multi-objective optimization; reconfigurable intelligent surface (RIS); short packet communication;

D O I：

10.1109/LCOMM.2022.3180396

中图分类号：

TN [电子技术、通信技术];

学科分类号：

0809 ;

摘要：

In this letter, a multi-objective optimization problem (MOOP) is proposed for maximizing the achievable finite blocklength (FBL) rate while minimizing the utilized channel blocklengths (CBLs) in a reconfigurable intelligent surface (RIS)-assisted short packet communication system. The formulated MOOP has two objective functions namely maximizing the total FBL rate with a target error probability, and minimizing the total utilized CBLs which is directly proportional to the transmission duration. The considered MOOP variables are the base station (BS) transmit power, number of CBLs, and passive beamforming at the RIS. Since the proposed non-convex problem is intractable to solve, the Tchebyshev method is invoked to transform it into a single-objective OP, then the alternating optimization (AO) technique is employed to iteratively obtain optimized parameters in three main sub-problems. The numerical results show a fundamental trade-off between maximizing the achievable rate in the FBL regime and reducing the transmission duration. Also, the applicability of RIS technology is emphasized in reducing the utilized CBLs while increasing the achievable rate significantly.

引用

页码：1838 / 1842

页数：5

共 18 条

[1] A Survey on Information and Communication Technologies for Industry 4.0: State-of-the-Art, Taxonomies, Perspectives, and Challenges [J].

Aceto, Giuseppe ;

Persico, Valerio ;

Pescape, Antonio .

IEEE COMMUNICATIONS SURVEYS AND TUTORIALS, 2019, 21 (04) :3467-3501

[2]

Boyd S., 2004, Convex optimization, DOI [10.1017/CBO9780511804441, DOI 10.1017/CBO9780511804441]

[3] Smart Radio Environments Empowered by Reconfigurable Intelligent Surfaces: How It Works, State of Research, and The Road Ahead [J].

Di Renzo, Marco ;

Zappone, Alessio ;

Debbah, Merouane ;

Alouini, Mohamed-Slim ;

Yuen, Chau ;

de Rosny, Julien ;

Tretyakov, Sergei .

IEEE JOURNAL ON SELECTED AREAS IN COMMUNICATIONS, 2020, 38 (11) :2450-2525

[4]

Grant M., 2009, CVX: Matlab software for disciplined convex programming

[5] Weighted Sum-Rate Maximization for Reconfigurable Intelligent Surface Aided Wireless Networks [J].

Guo, Huayan ;

Liang, Ying-Chang ;

Chen, Jie ;

Larsson, Erik G. .

IEEE TRANSACTIONS ON WIRELESS COMMUNICATIONS, 2020, 19 (05) :3064-3076

[6] Joint Sum Rate and Error Probability Optimization: Finite Blocklength Analysis [J].

Haghifam, Mandi ;

Mili, Mohammad Robat ;

Makki, Behrooz ;

Nasiri-Kenari, Masoumeh ;

Svensson, Tommy .

IEEE WIRELESS COMMUNICATIONS LETTERS, 2017, 6 (06) :726-729

[7] Average Rate and Error Probability Analysis in Short Packet Communications Over RIS-Aided URLLC Systems [J].

Hashemi, Ramin ;

Ali, Samad ;

Mahmood, Nurul Huda ;

Latva-aho, Matti .

IEEE TRANSACTIONS ON VEHICULAR TECHNOLOGY, 2021, 70 (10) :10320-10334

[8]

Mahmood N. H., 2020, White Paper on Critical and Massive Machine Type Communication Towards 6G. 6G Research Visions, V11

[9] Survey of multi-objective optimization methods for engineering [J].

Marler, RT ;

Arora, JS .

STRUCTURAL AND MULTIDISCIPLINARY OPTIMIZATION, 2004, 26 (06) :369-395

[10]

Pan C., 2021, ARXIV211205989, V2021

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