In view of super-large scale access and dynamic connectivity requirements in 6G, the number of users and data is increasing exponentially, which makes it difficult to achieve sustainable development of communications. Meanwhile, with the development of cross-modal processing, semantic information is highly considered accurate and intelligent, which is expected to bring new ideas for 6G. Therefore, in this study, we propose a novel framework of cross-modal semantic communications to improve the efficiency of the multimodal processing, especially the tactile modal. Meanwhile, we have redesigned the significant aspects of artificial intelligence (AI), such as encoding, transmission, and processing. As federated learning (FL) inherently supports multiple privacy-preserving and security measures, we also introduce FL to assist AI training in cross-modal semantic communications, including the expansion of multimodal data, cross-modal semantic extraction, comprehensive decision-making, and privacy protection. First, in encoding aspects, we propose a hybrid coding for haptic signal coding by deep learning (DL) according to the semantic association between material identification and tactile code optimization. Second, in transmission aspects, we propose a modal-aware resource allocation for the fairness optimization between transmission requirements and network resources with deep reinforcement learning (DRL). Third, in signal processing, low-rank signal reconstruction and immersive quality of experience (QoE) evaluation by DL and machine learning (ML) are provided to improve and quantify users’ experience. In addition, the computational experiments of those key technologies are shown individually and the specification of their probability is discussed separately. Finally, future research topics related to the above issues are suggested.

Eddy-current testing technique has been extensively explored for estimating the electromagnetic property of steel plates in various industrial applications. In this article, a physics-guided deep learning (DL) method is proposed to estimate the permeability of plate in high thickness with probe liftoff. A simplified analytical model is derived, which realises the single to multiple frequency inductance transformation and calculates the related physical properties of the measurement configuration. A constant is found, which is a fundamental coefficient describing the first-order nature of the sensor response to a plate and it is insensitive to plate properties and probe dimensions. The nonlinear mapping from physical information, derived from the simplified analytical model, to plate permeability is constructed by the DL model based on the modified ResNet18-1D. Numerical simulations and experiments have been performed to evaluate the proposed method for permeability estimation with various plate materials and probe liftoff. The method achieves real-time accurate estimation of plate permeability with a relative error lower than 3%.

To meet the demanding need for global connectivity, non-terrestrial networks can provide essential support to complement and extend the terrestrial infrastructure. However, the presence of non-terrestrial networks comes up with new demands. For example, a critical problem is satisfying the latency constraints of delay-sensitive applications while reducing the signaling consumption to save valuable channel resources in the random access stage. To address this issue, we propose a distributed access class barring (ACB) factor determination algorithm in this paper to satisfy the specific latency constraints of delay-sensitive applications and reduce the signaling exchange between user equipments (UEs) and the base station simultaneously. With this algorithm, UEs can estimate the required ACB factor only by their previous experiences in an estimation period rather than relying on the base station. Simulations show that the proposed distributive ACB factor determination algorithm can satisfy the requirements of delay-sensitive applications well when the delay constraint is not too strict. Besides, the influence of the variation of UEs and the estimation period is also discussed. It is found that the estimation period plays an important role in the accurate estimation of the ACB factor and needs to adapt to the changes in the number of UEs.

With the widespread adoption of smartphones in our daily life, mobile games experienced increasing demand over the past years. Meanwhile, the quality of mobile games has been continuously drawing more and more attention, which can greatly affect the player experience. For better quality assurance, general-purpose testing has been extensively studied for mobile apps. However, due to the unique characteristic of mobile games, existing mobile testing techniques may not be directly suitable and applicable. To better understand the challenges in mobile game testing, in this paper, we first initiate an early step to conduct an empirical study towards understanding the challenges and pain points of mobile game testing process at our industrial partner NetEase Games. Specifically, we first conduct a survey from the mobile test development team at NetEase Games via both scrum interviews and questionnaires. We found that accurate and effective GUI widget detection for mobile games could be the pillar to boost the automation of mobile game testing and other downstream analysis tasks in practice.

User Quality of Experience (QoE) has been brought to service providers' attention with the boom of multimedia services, such as Internet Protocol Television (IPTV). In this paper, we propose a QoE prediction model based on improved probabilistic neural network (PNN) to study the mapping relationship between IPTV viewing records and the user QoE. Specifically, we combine the particle swarm optimization (PSO) with PNN, utilizing PSO to search the spread parameter in PNN, thus this parameter can be automatically obtained, saving much time and effort. Experimental results show that the PNN-PSO can achieve the highest G-mean in comparison with other models. Moreover, it finds the best spread automatically and consequently increases prediction accuracy by 13% compared with the PNN.

With the popularity of IPTV in television industry, IPTV operators pay more attention on user's experience so that they could gain more revenue under fierce competition. Quality of Experience (QoE) is used to describe the user's degree of satisfaction and the prediction of QoE is gradually to be the interest for IPTV operators. To solve this problem, we propose a QoE prediction algorithm. Specifically, we preprocess the raw data at first and select the important attributes influencing QoE. This work introduces a new attribute called viewing ratio. It can effectively reflect users' subjective feelings. Subsequently, we combine the BP neural network with Adaboosting, proposing the BP_Ada algorithm to predict the QoE. Experimental results show that the proposed algorithm has better performance comparing with the competing algorithms.

IPTV is a new multimedia service over the Internet. The rapid development of IPTV makes the assessment of quality of experience in IPTV a hot topic to the service providers. In this paper, we study the relationship between the record of some viewing parameters from the IPTV set-top box and the users' Quality of Experience (QoE). Firstly, we analyze the data and choose some important attributions and then map the trouble tickets table to QoE representing acceptable or unacceptable. According to the imbalanced feature of the dataset, we proposed the multi-layer neural network using BP algorithm based on SGD for prediction of QoE. To avoid overfitting, we apply dropout method to the model when training the dataset. At last, we compare the proposed model to SVM and Decision Tree. Experimental results show that the proposed methods can indeed improve the accuracy of QoE prediction.

Good Quality of Experience is critical to the success of IPTV business development and promotion. To this end, the paper combines status data from the set-top box with the data of user's complaints and then selects the appropriate model to predict user's QoE. Firstly, we clean and conduct some statistical analysis for the dataset. Then, random under-sampling and synthetic over-sampling are applied to the dataset after these procedures. In order to get better performance, this paper improves the Synthetic Minority Over-sampling Technique (SMOTE) algorithm. In addition, we compare the decision tree model and k-Nearest Neighbors (k-NN) model in user's complaint dataset. Through rigorous modeling and prediction, extensive experimental results show that k-NN model performs better than the decision tree in terms of predicting the user's complaint.