A Survey on FEC Techniques for Industrial Wireless Communications

• Published in: IEEE open journal of the Industrial Electronics Society Vol. 3; pp. 1 - 27
• Main Authors: Fanari, Lorenzo, Iradier, Eneko, Bilbao, Inigo, Cabrera, Rufino, Montalban, Jon, Angueira, Pablo, Seijo, Oscar, Val, Inaki
• Format: Journal Article
• Language: English
• Published: New York IEEE 2022; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• Subjects: Algorithms; Complexity; Convolutional codes; Decoding; Error correction; factory automation (FA); FEC; Forward error correction; forward error correction (FEC); Industrial applications; Industrial electronics; Industrial Wireless Communications; Industry 4.0; Latency; Network latency; Packet transmission; PHY; Reliability; Reliability analysis; Short Packet Transmission; Ultra reliable low latency communication; Wireless communication; Wireless Communications
• ISSN: 2644-1284; 2644-1284
• DOI: 10.1109/OJIES.2022.3219607

Industry 4.0 aims to digitize industrial processes entirely, and wireless technologies represent one of the enablers for scalable and flexible communications. However, the current standards and proprietary solutions do not meet the industry's tight requirements in fundamental use cases such as Factory Automation (FA). One of the key research challenges towards replacing wired fieldbuses with wireless links is the design of techniques that enable real-time and deterministic behavior when transmitting short packets. Forward Error Correction (FEC) techniques are critical to this objective, and coding/decoding algorithms must comply with reliability and low latency specifications. This paper surveys existing FEC techniques for short packet transmissions. Compared to other survey papers in the field, we propose several FEC candidate techniques specifically suitable for FA wireless systems. We explore four of these techniques, also examining hardware architecture proposals. The paper proposes a methodology to evaluate their latency and reliability performance. We finally discuss the lessons learned and challenges for future research.