The existence of high carrier frequency offset (CFO) and fading channels degrades the performance of communication systems in high mobility environments significantly. To address these challenges, in this paper, we propose an attention guided multi-task network for joint CFO and channel estimation in orthogonal frequency division multiplexing (OFDM) systems. Specifically, considering the correlation between CFO estimation and channel estimation, we construct a multi-task neural network framework which consists of a shared branch (SB), a CFO estimation branch (CFOEB) and a channel estimation branch (CEB). Among them, the SB extracts common features of two tasks, and the CFOEB and CEB make more detailed CFO and channel estimation, respectively. To further suppress the influence of CFO estimation error on channel estimation, we introduce an attention guided module (AGM) which completes the re-calibration of the channel dimension on the basis of different CFO characteristics. We train the whole network by an end-to-end paradigm and construct a suitable multi-task loss function to balance the losses between different tasks. Simulation results demonstrate that the proposed attention guided multi-task learning based joint CFO and channel estimation scheme outperforms the conventional methods and has greater robustness under various conditions.

Vehicular communication in high mobility environments has been widely explored in recent years. However, due to the existence of carrier frequency offset (CFO) and dynamic channel, the performance of vehicular communication systems over fast time-varying scenes drops severely. To address this problem, in this paper, we propose a simple and effective CRS-based joint CFO and channel estimation baseline method using deep learning (DL) for orthogonal frequency division multiplexing (OFDM) systems. Concretely, we construct a joint neural network (NN) architecture consisting of a CFO estimation network (CFOENet) based on fully connected layers and a channel estimation network (CENet) composed of convolutional layers. The proposed NN architecture can fully exploit the correlation of the cell reference signal (CRS), while learning the CFO characteristics and channel state information (CSI) changes simultaneously, which highly improves the pilot usage efficiency. We conduct adequate simulation experiments, and the results demonstrate that the proposed DL-based scheme can achieve better performance in terms of CFO estimation, channel estimation and overall system performance than conventional methods, while our method has stronger robustness and generalization ability under various channel conditions. The proposed joint CFO and channel estimation scheme has great potential in the field of the Internet of Vehicles.

The uplink timing synchronization is indispensable for establishing a reliable link between the base station and the user equipment (UE). To tackle this problem, a new random access preamble (RAP) waveform is designed for cellular communication systems operating in high-mobility scenarios for orthogonal time frequency space systems. Meanwhile, we also propose a scheme to detect access UEs and estimate the corresponding round-trip propagation delay (i.e., timing advance (TA)). Concretely, the RAP root sequence consists of chirp sequences in the delay-Doppler domain, and the set of available RAPs is generated by cyclic shifting the root sequence. Then, the cross-correlation in the fractional Fourier domain is carried out to achieve the RAP detection and TA estimation of the access UEs. Exhaustive numerical simulation results corroborate that the proposed scheme has a superior RAP detection and TA estimation performance and exhibits sufficient robustness regarding Doppler shifts and the number of UEs.