OFDM Modulated PNC in V2X Communications: An ICI-Aware Approach Against CFOs and Time-Frequency-Selective Channels

• Published in: IEEE access 2019, Vol.7, p.4880-4897
• Main Authors: Situ, Zhenhui, Ho, Ivan Wang-Hei, Wang, Taotao, Liew, Soung Chang, Chau, Sid Chi-Kin
• Format: Article
• Language: English
• Subjects: Algorithms; Automobiles; Bit error rate; Carrier frequencies; Channel estimation; Channels; Decoding; Equalization; High speed; inter-carrier interference; Interference; Iterative decoding; Mobile ad hoc networks; Network latency; OFDM; Offsets; Optimization; Orthogonal Frequency Division Multiplexing; physical-layer network coding; Random noise; Relays; Signal detection; time-frequency-selective channel; V2X communication; Wireless networks
• ISSN: 2169-3536; 2169-3536
• DOI: 10.1109/ACCESS.2018.2889219

This paper studies the application of physical-layer network coding (PNC) in vehicle-to-everything communications to accommodate the time-critical nature of vehicular ad-hoc networks (VANETs). The idea can theoretically reduce the transmission latency by 50%, thus alleviating the short contact time issues caused by high-speed vehicle motion. Conventional studies of PNC primarily considered static networks. In highly mobile networks like VANETs, the carrier frequency offsets (CFOs) due to high-speed motion will lead to inter-carrier interference (ICI) in orthogonal frequency division multiplexing (OFDM) systems. Moreover, the vehicular environment with time-frequency-selective channels further undermines accurate channel estimation for multiple users. It is also worth noting that the CFO that exists in OFDM modulated PNC cannot be completely eliminated through CFO tracking and equalization as in conventional point-to-point transmissions. These critical issues can significantly increase the bit error rate at the receiver. To address these challenges, this paper proposes an ICI-aware approach that jointly achieves accurate channel estimation, signal detection, and channel decoding. We express the channel estimation and detection and decoding as two optimization problems and resolve them with the expectation-maximization algorithm and the belief propagation algorithm, respectively. The proposed approach can efficiently mitigate the negative effect of ICI by exploiting both pilot and data tones in channel estimation, detection, and decoding. Both simulation and experiment are conducted to evaluate the proposed approach, and the results reveal that the proposed algorithm outperforms the benchmark that simply treats ICI as Gaussian noise.