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 Joint Learning and Communications Framework for Federated Learning over Wireless Networks

Abstract: In this paper, the problem of training federated learning (FL) algorithms over a realistic wireless network is studied. In particular, in the considered model, wireless users execute an FL algorithm while training their local FL models using their own data and transmitting the trained local FL models to a base station (BS) that will generate a global FL model and send it back to the users. Since all training parameters are transmitted over wireless links, the quality of the training will be affected by wireless factors such as packet errors and the availability of wireless resources. Meanwhile, due to the limited wireless bandwidth, the BS must select an appropriate subset of users to execute the FL algorithm so as to build a global FL model accurately. This joint learning, wireless resource allocation, and user selection problem is formulated as an optimization problem whose goal is to minimize an FL loss function that captures the performance of the FL algorithm. To address this problem, a closed-form expression for the expected convergence rate of the FL algorithm is first derived to quantify the impact of wireless factors on FL. Then, based on the expected convergence rate of the FL algorithm, the optimal transmit power for each user is derived, under a given user selection and uplink resource block (RB) allocation scheme. Finally, the user selection and uplink RB allocation is optimized so as to minimize the FL loss function. Simulation results show that the proposed joint federated learning and communication framework can reduce the FL loss function value by up to 10% and 16%, respectively, compared to: 1) An optimal user selection algorithm with random resource allocation and 2) a standard FL algorithm with random user selection and resource allocation.

A Survey of Non-Orthogonal Multiple Access for 5G

Abstract: Compared to the traditional orthogonal multiple access (MA), non-orthogonal multiple access (NOMA) technology can achieve higher capacity gain and higher spectrum efficiency, support larger massive connectivity In this article, according to signal transmission characteristic, the NOMA schemes are classified into four categories: scrambling based NOMA, interleaving based NOMA, spreading based NOMA and coding based NOMA Furthermore, the process and characteristics of different schemes are summarized The performance of the NOMA schemes is evaluated According to the evaluation results, the scheme with superior performance is given By analyzing and comparing features of these technologies, a research guiding is given for future 5G multiple access

Large-scale vehicle re-identification in urban surveillance videos

Abstract: Vehicle, as a significant object class in urban surveillance, attracts massive focuses in computer vision field, such as detection, tracking, and classification. Among them, vehicle re-identification (Re-Id) is an important yet frontier topic, which not only faces the challenges of enormous intra-class and subtle inter-class differences of vehicles in multicameras, but also suffers from the complicated environments in urban surveillance scenarios. Besides, the existing vehicle related datasets all neglect the requirements of vehicle Re-Id: 1) massive vehicles captured in real-world traffic environment; and 2) applicable recurrence rate to give cross-camera vehicle search for vehicle Re-Id. To facilitate vehicle Re-Id research, we propose a large-scale benchmark dataset for vehicle Re-Id in the real-world urban surveillance scenario, named “VeRi”. It contains over 40,000 bounding boxes of 619 vehicles captured by 20 cameras in unconstrained traffic scene. Moreover, each vehicle is captured by 2∼18 cameras in different viewpoints, illuminations, and resolutions to provide high recurrence rate for vehicle Re-Id. Finally, we evaluate six competitive vehicle Re-Id methods on VeRi and propose a baseline which combines the color, texture, and highlevel semantic information extracted by deep neural network.

The Seventh Visual Object Tracking VOT2019 Challenge Results

Abstract: The Visual Object Tracking challenge VOT2019 is the seventh annual tracker benchmarking activity organized by the VOT initiative. Results of 81 trackers are presented; many are state-of-the-art trackers published at major computer vision conferences or in journals in the recent years. The evaluation included the standard VOT and other popular methodologies for short-term tracking analysis as well as the standard VOT methodology for long-term tracking analysis. The VOT2019 challenge was composed of five challenges focusing on different tracking domains: (i) VOTST2019 challenge focused on short-term tracking in RGB, (ii) VOT-RT2019 challenge focused on "real-time" shortterm tracking in RGB, (iii) VOT-LT2019 focused on longterm tracking namely coping with target disappearance and reappearance. Two new challenges have been introduced: (iv) VOT-RGBT2019 challenge focused on short-term tracking in RGB and thermal imagery and (v) VOT-RGBD2019 challenge focused on long-term tracking in RGB and depth imagery. The VOT-ST2019, VOT-RT2019 and VOT-LT2019 datasets were refreshed while new datasets were introduced for VOT-RGBT2019 and VOT-RGBD2019. The VOT toolkit has been updated to support both standard shortterm, long-term tracking and tracking with multi-channel imagery. Performance of the tested trackers typically by far exceeds standard baselines. The source code for most of the trackers is publicly available from the VOT page. The dataset, the evaluation kit and the results are publicly available at the challenge website.

Deep reinforcement learning based computation offloading and resource allocation for MEC

Abstract: Mobile edge computing (MEC) has the potential to enable computation-intensive applications in 5G networks. MEC can extend the computational capacity at the edge of wireless networks by migrating the computation-intensive tasks to the MEC server. In this paper, we consider a multi-user MEC system, where multiple user equipments (UEs) can perform computation offloading via wireless channels to an MEC server. We formulate the sum cost of delay and energy consumptions for all UEs as our optimization objective. In order to minimize the sum cost of the considered MEC system, we jointly optimize the offloading decision and computational resource allocation. However, it is challenging to obtain an optimal policy in such a dynamic system. Besides immediate reward, Reinforcement Learning (RL) also takes a long-term goal into consideration, which is very important to a time-variant dynamic systems, such as our considered multi-user wireless MEC system. To this end, we propose RL-based optimization framework to tackle the resource allocation in wireless MEC. Specifically, the Q-learning based and Deep Reinforcement Learning (DRL) based schemes are proposed, respectively. Simulation results show that the proposed scheme achieves significant reduction on the sum cost compared to other baselines.