ABSTRACT Advances in microelectronic components and high‐speed networks have enabled the widespread application of virtual reality (VR) technology in education. However, insufficient attention to knowledge visualization in VR learning has resulted in disorganized knowledge structures, comprehension difficulties, and mismatches between user experience and learning achievement. Therefore, we propose a Knowledge Cube (KC)‐based visualization method to standardize knowledge encoding in VR learning. During courseware development, the instructor defines discrete knowledge as Events, organizes them into Event Groups, and populates data into a KC model to generate VR courseware. In subsequent VR learning, when learners search for task‐relevant knowledge using the provided retrieval method, the KC model presents the corresponding events within interactive scenarios according to its predefined structure. Comparative experiments on different knowledge visualization methods revealed that, in VR learning, the KC method outperforms other VR approaches in both learning performance and efficiency. This method effectively guided learners to focus on the learning content and optimized the knowledge encoding in VR learning. This provides an operational framework for knowledge encoding in VR courseware design and emphasizes the importance of supporting effective learning behaviors over merely pursuing immersion, presenting a new perspective for refining the design approach of VR courseware.

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.

The delay-sensitive and resource-limited natures of multi-access edge computing (MEC) applications make it necessary to offload local tasks to nearby MEC servers. Large amounts of offloading data are transferred among MEC servers within the stringent deadline constraints. The offloading system hence needs to deliver the data over end-to-end (E2E) connections as soon as they arrive. However, traffic fluctuations make the offloading system difficult to provision the required bandwidth over the E2E connections and guarantee the deadline. In this paper, we present a storage-enabled adaptive multi-path scheduling method (SAM) for offloading transfers across the optical metro network. Instead of delivering the whole data across a single path, SAM can adaptively split the data into small segments and route them through multiple paths. This not only improves the throughput, but also enables large time windows to schedule segments on their own paths within the given deadline. With the large time window, SAM can leverage MEC storage to temporarily store the segment at the intermediate servers when provisioning the entire E2E path fails. Studies show that compared with the state-of-the-art scheduling methods, SAM can accommodate more transfer requests, shorten delivery time and accelerate offloading transfers efficiently. SAM is beneficial to task offloading.

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.

In the past few years, data traffic generated by video applications has become the dominant traffic on the internet due to the rapid development of wireless network and mobile terminal. However, using a single wireless network for video transmission with limited bandwidth is facing great challenges under the increasing video quality requirement. Fortunately, mobile terminal equipped with multiple network interfaces can transmit data over heterogeneous wireless networks concurrently to achieve higher bandwidth. In order to make effective use of heterogeneous access network resources, this paper develops a heuristic flow splitting algorithm based on ACO (Ant Colony Optimization) algorithm. Firstly, a system model with the objective that minimizes the multipath video transmission distortion is proposed under the end-to-end delay constraint. Secondly, to solve the problem based on heuristic ACO, the relevant models such as pheromone model and reliability model are presented, and a heuristic ACO algorithm is proposed. Finally, the proposed algorithm is tested and evaluated in the real-world platform. The evaluation results show that the proposed algorithm performs better than the referenced traditional algorithm in terms of PSNR and end-to-end frame delay.