The differential chaos shift keying with code index modulation (CIM-DCSK) using Walsh codes can increase the data rate, but error rate performance degrades over practical multipath channel with high delay spread. To tackle this drawback, this paper proposes a parallel CIM-DCSK (PT-CIM-DCSK), where the first time slot is used to transmit the chaotic reference sequence, and the second slot is used to transmit the multiple sequences carrying data bits. In particular, multiple $q$ chaotic sequences are added for data transmission in parallel, and each sequence is modulated by CIM-DCSK with $m_{c}$ bits. By exploiting the quasi-orthogonal characteristics of permuted chaotic signals, the interference between $q$ sequences can be remarkably suppressed. In this way, given the data rate, PT-CIM-DCSK can achieve a larger length of Walsh segment than the CIM-DCSK, since its parallel transmission compensates the rate. In addition, the error performance of the proposed scheme over the multipath Rayleigh fading channel is theoretically analyzed. The theoretical and consistent simulation results demonstrate that the PT-CIM-DCSK can obtain significant performance gains over the conventional CIM-DCSK, and the gain can be up to 5 dB in multipath fading channel with high delay. This is because the larger Walsh segment of PT-CIM-DCSK provides the enhanced robustness against multipath effect. The performance superiority of the proposed scheme is further verified in practical ultra-wideband (UWB) wireless industrial scenarios, which suggests promising for low-cost and low-complexity wireless communication applications.

As one of the most promising solutions, edge caching is emerging to reduce the content retrieval latency and relieve the huge burden on the backhaul links in the 6th generation (6G) millimeter wave (mmWave) intelligent vehicular networks. In this paper, we first formulate the optimization problem in terms of request hit probability and investigate two crucial factors, which impact the effectiveness of cache placement in device-to-device (D2D) enabled mmWave vehicular networks. Then, the general mathematical expressions for optimization of the request hit probability with the constraints of caching capacity are derived. Moreover, a group caching scheme (GCS) is proposed to obtain the sub-optimal results by utilizing relaxation method and Karush-Kuhn-Tucker (KKT) conditions. Inspired from the Matern hard-core processes and by exploiting the spatially correlated characteristics of users distribution, hard-cored based caching algorithm (HCCA) is proposed to avoid the simultaneous caching for a particular file. In addition, a joint caching and scheduling strategy is investigated which maximizes the number of concurrent transmissions by D2D enabled vehicle pairs matching and antennas adjustment. Comprehensive simulations validate our theoretical analysis and demonstrate that the proposed scheme can achieve higher performance in terms of request hit probability and the number of concurrent transmissions compared to the existing methods. Consequently, the proposed caching scheme has great potential in future intelligent vehicular applications.

Detection-based tracking is one of the main methods of multi-object tracking. It can achieve good tracking performance when using excellent detectors but it may associate wrong targets when facing overlapping and low-confidence detections. To address this issue, this paper proposes a novel multi-object tracker (SCTracker) by exploiting shape constraint and confidence. In the data association stage, an Intersection of Union distance with shape constraints is developed to calculate the cost matrix between tracks and detections, which can reduce the track of the wrong target with the similar position but inconsistent shape. Moreover, the detection confidence is calculated in the update stage of the Kalman Filter to improve the track performance with the inaccurate detection result. Experimental results on MOT 17 dataset show that the proposed SCTracker can improve the tracking performance of multi-object tracking as compared to the state-of-the-art methods.

With the rapid development of mobile networks and devices, real-time video transmission has become increasingly important worldwide. Constrained by the bandwidth limitations of single networks, extensive research has shifted towards video transmission in multi-network environments. However, differences in bandwidth and latency in heterogeneous networks (such as LTE and Wi-Fi) lead to high latency and packet loss issues, severely affecting video quality and user experience. This paper proposes a Forward Error Correction (FEC)-based Low-Delay Multipath Scheduling algorithm (LDMP-FEC). This algorithm combines the Gilbert model with a continuous Markov chain to adaptively adjust FEC redundancy, thereby enhancing data integrity. Through the FEC Recovery Priority Scheduling (FEC-RPS) algorithm, it dynamically optimizes the transmission order of data packets, reducing the number of out-of-order packets (OFO-packets) and end-to-end latency. Experimental results show that LDMP-FEC significantly reduces the number of out-of-order packets in heterogeneous network environments, improving performance by 50% compared to the round-robin and MinRtt algorithms, while maintaining end-to-end latency within 150 ms. Under various packet loss conditions, LDMP-FEC sustains a playable frame rate (PFR) above 90% and a Peak Signal-to-Noise Ratio (PSNR) exceeding 35 dB, providing an efficient and reliable solution for real-time video and other low-latency applications.

The safety of autonomous driving is closely linked to accurate depth perception. With the continuous development of autonomous driving, depth completion has become one of the crucial methods in this field. However, current depth completion methods have major shortcomings in small objects. To solve this problem, this paper proposes an end-to-end architecture with adaptive spatial feature fusion by encoder–decoder (ASFF-ED) module for depth completion. The architecture is built on the basis of the network architecture proposed in this paper, and is able to extract depth information adaptively with different weights on the specified feature map, which effectively solves the problem of insufficient depth accuracy of small objects. At the same time, this paper also proposes a depth map visualization method with a semi-quantitative visualization, which makes the depth information more intuitive to display. Compared with the currently available depth map visualization methods, this method has stronger quantitative analysis and horizontal comparison ability. Through experiments of ablation study and comparison, the results show that the method proposed in this paper exhibits a lower root-mean-squared error (RMSE) and better small object detection performance on the KITTI dataset.