Introducing a Knowledge Graph (KG) to facilitate a recommender system has become a tendency in recent years. Many existing methods leverage KGs to obtain side information of items to promote item representation learning for enhancing recommendation performance. However, they ignore that KGs also may contribute to better user representation learning. To solve this issue, we propose a novel algorithm, the KIGR ( K nowledge-aware I nteraction G raph for R ecommendation), to mine user–item interactions via KGs for assisting user representation learning. Specifically, a user–item interaction is encoded by attentively summing up the relation embedding about the item in the KG. Then, an unsupervised learning method is used to group the user–item interactions into different latent types. Further, a user–item interaction graph is divided into several subgraphs, which is referred to as a Knowledge-aware Interaction Graph, making each subgraph only contain one latent type of interaction. Finally, user representation is the fusion of user interest embedding, which is learned on the knowledge-aware interaction graph, whereas item representation is learned on the KG. Experimental results on MovieLens, LastFM and Amazon-Book validate that the proposed KIGR has a superior performance compared with the state-of-the-art algorithms.

With the rapid development of Integrated Sensing and Communication in 6G, Channel State Information (CSI)-based fingerprint indoor localization technology is becoming crucial. However, during the offline phase, the fingerprint database update process using crowdsourcing techniques is prone to noise interference and incomplete coverage, and fitting Gaussian regression models requires extensive computational resources. In this paper, we propose a fine-grained CSI fingerprint database update method based on Deep-Broad Learning system (DeepBLS) and Generative Adversarial Networks (GAN), termed DBLG. Firstly, we employ the combined DeepBLS network for the initial construction of the global CSI fingerprint database. Subsequently, we utilize GAN to extract features from the raw data, predict and update the global CSI fingerprint database, and construct a high-precision fingerprint database using confidence coefficients. Finally, We implement the proposed algorithm in two real-world environments and conduct extensive experiments to verify its performance. Compared to several existing methods, our approach shows superior performance in updating the CSI finger-print database, achieving a 46.78 % improvement in localization accuracy.

Background:Multi-source cross-project defect prediction (MSCPDP) attempts to transfer defect knowledge learned from multiple source projects to the target project. MSCPDP has drawn increasing attention from academic and industry communities owing to its advantages compared with single-source cross-project defect prediction (SSCPDP). However, two main problems, which are how to effectively extract the transferable knowledge from each source dataset and how to measure the amount of knowledge transferred from each source dataset to the target dataset, seriously restrict the performance of existing MSCPDP models.Objective:In this paper, we propose a novel multi-source transfer weighted ensemble learning (MASTER) method for MSCPDP.Method:MASTER measures the weight of each source dataset based on feature importance and distribution difference and then extracts the transferable knowledge based on the proposed feature-weighted transfer learning algorithm. Experiments are performed on 30 software projects. We compare MASTER with the latest state-of-the-art MSCPDP methods with statistical test in terms of famous effort-unaware measures (i.e., PD, PF, AUC, and MCC) and two widely used effort-aware measures (Popt20% and IFA).Result:The experiment results show that: 1) MASTER can substantially improve the prediction performance compared with the baselines, e.g., an improvement of at least 49.1% in MCC, 48.1% in IFA; 2) MASTER significantly outperforms each baseline on most datasets in terms of AUC, MCC,Popt20% and IFA; 3) MSCPDP model significantly performs better than the mean case of SSCPDP model on most datasets and even outperforms the best case of SSCPDP on some datasets.Conclusion:It can be concluded that 1) it is very necessary to conduct MSCPDP, and 2) the proposed MASTER is a more promising alternative for MSCPDP.

Incorporating knowledge graph as side information has become a new trend in recommendation systems. Recent studies regard items as entities of a knowledge graph and leverage graph neural networks to assist item encoding, yet by considering each relation type independently. However, relation types are often too many and sometimes one relation type involves too few entities. We argue that there may exist some latent relevance among relations in KG. It may not necessary nor effective to consider all relation types for item encoding. In this paper, we propose a VRKG4Rec model (Virtual Relational Knowledge Graphs for Recommendation), which clusters relations with latent relevance to generates virtual relations. Specifically, we first construct virtual relational graphs (VRKGs) by an unsupervised learning scheme. We also design a local weighted smoothing (LWS) mechanism for node encoding on VRKGs, which iteratively updates a node embedding only depending on the node itself and its neighbors, but involve no additional training parameters. LWS mechanism is also employed on a user-item bipartite graph for user representation learning, which utilizes item encodings with virtual relational knowledge to help train user representations. Experiment results on two public datasets validate that our VRKG4Rec model outperforms the state-of-the-art methods. The implementations are available at https://github.com/lulu0913/VRKG4Rec.

High-speed train delay prediction has always been one of the important research issues in the railway dispatching. Accurate and interpretable delay prediction can enable staff to implement preventive measures and scheduling decisions in advance, and guide relevant departments to cooperate in completing complex transportation tasks, so as to improve rail transit operations, service quality, and the efficiency of train operation. This article proposes a new interpretable model based on graph community neural network and time-series fuzzy decision tree. This model can well capture the influence of spatiotemporal characteristics, train community structure, and multifactor in high-speed train station delay prediction. Besides, the time series fuzzy decision tree based on multiobjective optimization and reduced error pruning can mine potential decision rules to improve the model's interpretability, transparency, and high reliability. Finally, we prove that the prediction effect of the proposed model is superior than the other seven state-of-the-art models and our model is interpretable.

Online video is the most popular Internet application. As the throughput would frequently change under different network conditions, it is important to adaptively select the proper bitrate and improve user’s quality of experience. In this paper, we propose a new DRL-based rate adaption algorithm for video streaming, which holistically captures user’s preference of video contents, network throughput and buffer occupancy, and select the proper bitrate for video to improve the QoE. Specifically, we use 3D Convolutional neural (C3D) network to learn the spatio-temporal features, and implement the semantic analysis of videos. We also apply the Term Frequency-Inverse Document Frequency (TF-IDF) method to analyze the user’s preference of different scene types, according to its viewing history. The dynamic adaptive streaming is formulated as a Markov Decision Process (MDP) problem, and use the Actor-Critic (A3C) algorithm to dynamically choose the optimal bitrate. As corroborated by simulations, our algorithm can accurately obtain the user’s preference, keep the bitrate allocation consistent with the user’s preference, and maintain video quality. Compared with the state-of-the-art Pensieve algorithm, our algorithm improves the average QoE by at least 12.5%. It also has a significant improvement over other baseline methods.

On-demand deployment of small cells plays a key role in augmenting macro-cell coverage for outdoor hotspots, where user devices are brought together and intensively upload self-generated data. In this paper, we study spectrum sharing among rapidly deployable small cells in the uplink, even without a priori global knowledge. We propose a hybrid multi-agent approach, which allows a leading macro-cell base station (MBS) and multiple following small base stations (SBSs) to take part in a user-centric, online joint optimization of small cell deployment and uplink resource allocation. Specifically, we propose a centralized mechanism for the MBS to solve the first subproblem of small cell deployment stage by stage, based on an adversarial bandit model. Furthermore, we propose a distributed mechanism for the group of SBSs to collectively solve the second subproblem of uplink resource allocation stage by stage, based on a stochastic game model. We prove that our approach is guaranteed to produce a joint strategy, which is built upon a mixed strategy with bounded regret on the first tier and an equilibrium solution on the second tier. Our approach is validated by simulations on the aspects of convergence behavior, strategy correctness, power consumption, and spectral efficiency.

In a vehicular platoon, the lead vehicle that is responsible for managing the platoon's moving directions and velocity periodically disseminates messages to the following automated vehicles in a multi-hop vehicular network. However, due to the broadcast nature of wireless channels, vehicle-to-vehicle (V2V) communications are vulnerable to eavesdropping and message modification. Generating secret keys by extracting the shared randomness in a wireless fading channel is a promising way for V2V communication security. We study a security scheme for platoon-based V2V communications, where the platooning vehicles generate a shared secret key based on the quantized fading channel randomness. To improve conformity of the generated key, the probability of secret key agreement is formulated, and a novel secret key agreement algorithm is proposed to recursively optimize the channel quantization intervals, maximizing the key agreement probability. Numerical evaluations demonstrate that the key agreement probability achieved by our security protocol given different platoon size, channel quality, and number of quantization intervals. Furthermore, by applying our security protocol, it is shown that the probability that the encrypted data being cracked by an eavesdropper is less than 5%.

Cloud has yet to be applied to computationally intensive radio signal processing, due to closely coupled computing tasks resulting from interference. This paper presents a new cloud-assisted joint beamforming architecture, where computations are decoupled for individual wireless users and pipelined for cloud execution, using Difference of Convex (DC), ℓ1-norm approximations, and dual decompositions. User-specific tasks are constructed and aligned with the cloud to leverage computation reuses and minimize overhead. The time-complexity is dramatically improved to support networks with tens to hundreds of base stations and users, without compromising the sum rate and quality-of-service. Further, the superiority of DC to the state-of-the-art Weighted Minimum Mean Square Error (WMMSE) in terms of convex relaxation is observed and discussed. Corroborated by simulations, the reason is revealed as WMMSE aggressively increases the data rate at interim stages, hence adversely interacting with ℓ1-norm approximation and reducing the feasible solution regions at later stages.

This paper presents a suboptimal approach for resource allocation of massive MIMO‐OFDMA systems for high‐speed train (HST) applications. An optimization problem is formulated to alleviate the severe Doppler effect and maximize the energy efficiency (EE) of the system. We propose to decouple the problem between the allocations of antennas, subcarriers, and transmit powers and solve the problem by carrying out the allocations separately and iteratively in an alternating manner. Fast convergence can be achieved for the proposed approach within only several iterations. Simulation results show that the proposed algorithm is superior to existing techniques in terms of system EE and throughput in different system configurations of HST applications.

Although differentiated services (DiffServ) networks have been well discussed in the past several years, a conventional Active Queue Management (AQM) algorithm still cannot provide low-complexity and cost-effective differentiated bandwidth allocation in DiffServ. In this paper, a novel AQM scheme called CHOKeR is designed to protect TCP flows effectively. We adopt a method from CHOKeW to draw multiple packets randomly from the output buffer. CHOKeR enhances the drawing factor by using a multistep increase and single-step decrease (MISD) mechanism. In order to explain the features of CHOKeR, an analytical model is used, followed by extensive simulations to evaluate the performance of CHOKeR. The analytical model and simulation results demonstrate that CHOKeR achieves proportional bandwidth allocation between different priority levels, fairness guarantee among equal priority flows, and protection of TCP against high-speed unresponsive flows when network congestion occurs.