# 3 GPP 5G V2X Error Correction Coding for Various Propagation Environments: A QoS Approach
## 1 Introduction and Literature Review  
Intelligent transportation systems (ITSs) are advanced solutions for providing traffic safety and sustainability, including infrastructure, vehicles, users, traffic and mobility management, as well as interfaces with other modes of transport [1]. The key enablers of ITSs are V2X communications, which include vehicle-to-vehicle (V2V), vehicle-to-network (V2N) or vehicle-to-infrastructure (V2I), vehicle-to-roadside units (V2R) and vehicle-to-pedestrian (V2P) communications [2]. ITSs were introduced in 1999 by the US Federal Communication Commission, and they resulted in great research work in the USA [3] and Europe [4], which resulted in the first standards for V2X in the USA [5] and in Europe [6], respectively. Briefly,3 GPP Release 12 [7] was the first standard that introduced V2X communication services, enabling direct communication and data exchange between vehicles. This standard was the base for the 4G LTE V2X standard under Releases 14 [8] and 15 [9], while 3 GPP Release 16 was the first to include sidelink communications based on the 5G NR-air interface [10]. The success of 5G NR [11] led to 5G NR V2X, which was not only designed to complement LTE V2X on sidelink communications, but it also mainly targets four advanced use cases groups, namely, vehicle platooning, advanced driving, sensors and remote driving [12]. In [13], 3 GPP specified the payload, data rate, maximum end-to-end latency, reliability and required communication range as the key requirements for the above 3 GPP use case groups and defined the range of acceptable values for them. These key requirements and their typical values are the QoS requirements of a V2X system depending on the service it implements and are the most important quality and efficiency measures for a 5G NR V2X communication system. Briefly, 5 GAA combines and extends the 3 GPP requirements, and, apart from the 3 GPP requirements, it introduces vehicle velocity and vehicle density as important QoS requirements [14]. On the other hand, channel coding is a crucial and vital process in a communication system since it is the key process of reliable communication. Turbo codes were introduced in 1993 [15; 16], and they were the first class in coding/decoding techniques to achieve near Shannon performance [17]. This class consists of two coding/decoding algorithms, namely, the MAP algorithm (BCJR) [18], and the soft-output Viterbi algorithm (SOVA) [19]. Being complex, it was difficult to implement BCJR in real systems, so the less complex Max-Log-MAP (maxlogBCJR) implementation [20; 21] as well as the Log-MAP (logBCJR) implementation of the algorithm [22] were proposed, although they were suboptimal. Turbo codes had outstanding error-correcting behavior, so they became part of 4G-LTE [23; 24]. The drawbacks of turbo coding were latency and complexity in large frame sizes [25], and as a result, LDPC [26; 27; 28] was introduced as a simpler [29] coding scheme for 5G NR [30]. The latest coding scheme is polar [31], which is a low-complexity capacity-achieving coding scheme, and due to its superior performance, it became part of the 3 GPP 5G NR [32]. In [33], the authors proved that turbo codes can be used as an efficient coding solution for short-frame 5G V2V and V2I services and flat Rayleigh fading channels. In [34; 35], the authors studied the effect of turbo codes on QoS and BER performance in a 3 GPP 5G NR V2X geometry-based propagation environment, where small data frames are transmitted. In [36], the authors studied the behavior of turbo codes under low latency and high-performance use cases, while in [37], the authors performed extensive research on the BER and FER performance of turbo, LDPC and polar codes, introducing a stochastic V2X communication model, based on the 3 GPP 5G V2X specifications, and they concluded that turbo schemes are more suitable for small-frame 5G V2X services in terms of BER and FER performance.