Beijing University of Posts and Telecommunications

About: Beijing University of Posts and Telecommunications is a education organization based out in Beijing, Beijing, China. It is known for research contribution in the topics: MIMO & Quality of service. The organization has 39576 authors who have published 41525 publications receiving 403759 citations. The organization is also known as: BUPT.

A Distributed Computation Offloading Strategy in Small-Cell Networks Integrated With Mobile Edge Computing

Abstract: Mobile edge computing is conceived as an appealing technology to enhance cloud computing capability of mobile devices (MDs) at the edge of the networks. Although some researchers use the technology to address the intensive tasks’ high computation needs of MDs in small-cell networks (SCNs), most of them ignore considering the interests interaction between small cells and MDs. In this paper, we study a distributed computation offloading strategy for a multi-device and multi-server system based on orthogonal frequency-division multiple access in SCNs. First, to satisfy the interest requirements of different MDs and analyze the interactions among multiple small cells, we formulate a distributed overhead minimization problem, aiming at jointly optimizing energy consumption and latency of each MD. Second, to ensure the individuals of different MDs, we formulate the proposed overhead minimization problem as a strategy game. Then, we prove the strategy game is a potential game by the feat of potential game theory. Moreover, the potential game-based offloading algorithm is proposed to reach a Nash equilibrium. In addition, to guarantee the performance of the designed algorithm, we consider the lower bound of iteration times to derive the worst case performance guarantee. Finally, the simulation results corroborate that the proposed algorithm can effectively minimize the overhead of each MD compared with different other existing algorithms.

Joint Patch and Multi-label Learning for Facial Action Unit and Holistic Expression Recognition

Abstract: Most action unit (AU) detection methods use one-versus-all classifiers without considering dependences between features or AUs. In this paper, we introduce a joint patch and multi-label learning (JPML) framework that models the structured joint dependence behind features, AUs, and their interplay. In particular, JPML leverages group sparsity to identify important facial patches, and learns a multi-label classifier constrained by the likelihood of co-occurring AUs. To describe such likelihood, we derive two AU relations, positive correlation and negative competition , by statistically analyzing more than 350,000 video frames annotated with multiple AUs. To the best of our knowledge, this is the first work that jointly addresses patch learning and multi-label learning for AU detection. In addition, we show that JPML can be extended to recognize holistic expressions by learning common and specific patches, which afford a more compact representation than the standard expression recognition methods. We evaluate JPML on three benchmark datasets CK+, BP4D, and GFT, using within-and cross-dataset scenarios. In four of five experiments, JPML achieved the highest averaged F1 scores in comparison with baseline and alternative methods that use either patch learning or multi-label learning alone.

Downlink resource allocation for Device-to-Device communication underlaying cellular networks

Abstract: Device-to-Device (D2D) communication underlaying cellular networks can enhance the network capacity and spectrum efficiency when sharing cellular resources. However, the severe interference between D2D and cellular networks may lead to performance decrease of D2D and cellular communication. In this paper, we concentrate on suppressing the interference between D2D users and cellular networks when D2D communication reuses the cellular resources in downlink. An efficient resource allocation scheme is proposed to manage the interference between D2D and cellular networks. First, the mutual interference is restricted under the constraints by adopting the interference limited area control method. After that, the resources are assigned to D2D users to improve the sum rate of cellular communication and D2D communication. Besides, the simulation results are presented and analyzed. Finally, we conclude that the proposed scheme can significantly improve the total capacity of cellular and D2D communication, while at the same time suppressing the mutual interference.

Federated-Learning-Enabled Intelligent Fog Radio Access Networks: Fundamental Theory, Key Techniques, and Future Trends

Abstract: The rise of big data and AI boosts the development of future wireless networks. However, due to the high cost of data offloading and model training, it is challenging to implement network intelligence based on the existing centralized learning strategies, especially at the edge of networks. To provide a feasible solution, a paradigm of federated learning- enabled intelligent F-RANs is proposed, which can take full advantage of fog computing and AI. The fundamental theory with respect to the accuracy loss correction and the model compression is studied, which can provide some insights into the design of federated learning in F-RANs. To support the implementation of federated learning, some key techniques are introduced to fully integrate the communication, computation, and storage capability of F-RANs. Moreover, future trends of federated learning-enabled intelligent F-RANs, such as potential applications and open issues, are discussed.