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.

Resource Allocation for Multi-Cell IRS-Aided NOMA Networks

Abstract: This article proposes a novel framework of resource allocation in multi-cell intelligent reflecting surface (IRS) aided non-orthogonal multiple access (NOMA) networks, where an IRS is deployed to enhance the wireless service. The problem of joint user association, subchannel assignment, power allocation, phase shifts design, and decoding order determination is formulated for maximizing the achievable sum rate. The challenging mixed-integer non-linear problem is decomposed into an optimization subproblem (P1) with continuous variables and a matching subproblem (P2) with integer variables. In an effort to tackle the non-convex optimization problem (P1), iterative algorithms are proposed for allocating transmission power, designing reflection matrix, and determining decoding order by invoking relaxation methods such as convex upper bound substitution, successive convex approximation, and semidefinite relaxation. In terms of the combinational problem (P2), swap matching-based algorithms are developed for achieving a two-sided exchange-stable state among users, BSs and subchannels. Numerical results demonstrate that: i) the sum rate of multi-cell NOMA networks is capable of being increased by 35% with the aid of the IRS; ii) the proposed algorithms for multi-cell IRS-aided NOMA networks can enjoy 22% higher energy efficiency than conventional NOMA counterparts; iii) the trade-off between spectrum efficiency and coverage area can be tuned by judiciously selecting the location of the IRS.

Decentralized Networked Control System Design Using T–S Fuzzy Approach

Abstract: The robust control problem is studied for a class of large-scale networked control systems. The subsystems are in the nonlinear form, and they exchange information through the communication networks. The interconnections considered are nonlinear, and not the traditional linear form, which brings a challenging issue for the decentralized control design. We develop a new memoryless control scheme with the use of the decomposition for each subsystem that is based on the input matrix. By Takagi-Sugeno (T-S) fuzzyfication for each subsystem, the interconnected T-S fuzzy subsystems are obtained. When the upper bound functions of uncertain interconnections are known, we design a decentralized memoryless state feedback controller. When the parameters of bound functions are not available, the adaptive method is used, and the decentralized memoryless adaptive controller is developed. By the construction of a new Lyapunov-Krasovskii functional, we prove the stability of the resultant closed-loop system for the both cases. Finally, we apply the theoretic results to the decentralized controller design of networked interconnected chemical reactor systems. The simulations are performed, and the effectiveness of the proposed method is demonstrated.

FedLoc: Federated Learning Framework for Data-Driven Cooperative Localization and Location Data Processing

Abstract: In this overview paper, data-driven learning model-based cooperative localization and location data processing are considered, in line with the emerging machine learning and big data methods. We first review (1) state-of-the-art algorithms in the context of federated learning, (2) two widely used learning models, namely the deep neural network model and the Gaussian process model, and (3) various distributed model hyper-parameter optimization schemes. Then, we demonstrate various practical use cases that are summarized from a mixture of standard, newly published, and unpublished works, which cover a broad range of location services, including collaborative static localization/fingerprinting, indoor target tracking, outdoor navigation using low-sampling GPS, and spatio-temporal wireless traffic data modeling and prediction. Experimental results show that near centralized data fitting- and prediction performance can be achieved by a set of collaborative mobile users running distributed algorithms. All the surveyed use cases fall under our newly proposed Federated Localization (FedLoc) framework, which targets on collaboratively building accurate location services without sacrificing user privacy, in particular, sensitive information related to their geographical trajectories. Future research directions are also discussed at the end of this paper.

An Energy-Efficient n-Epidemic Routing Protocol for Delay Tolerant Networks

Abstract: In Delay Tolerant Networks (DTN), as disconnections between nodes are frequent, establishing routing path from the source node to the destination node may not be possible. However, if a node transmits packets to all its encounters, its batteries will be used up quickly. Many researches have been done on routing and forwarding algorithms in DTN, but few of them have explicitly address the energy issue. In this paper, we propose n-epidemic routing protocol, an energy-efficient routing protocol for DTN. The n-epidemic routing protocol is based on the reasoning that in order to reach a large audiences with low number of transmissions, it is better to transmit only when the number of neighbors reaching a certain threshold. We compare the delivery performance of n-epidemic routing protocol with basic epidemic routing protocol using both analytical approach and empirical approach with real experimental dataset. The experiment shows that n-epidemic routing protocol can increase the delivery performance of basic epidemic-routing by 434% averagely.

Flexible and Stretchable Capacitive Sensors with Different Microstructures

Abstract: Recently, sensors that can imitate human skin have received extensive attention. Capacitive sensors have a simple structure, low loss, no temperature drift, and other excellent properties, and can be applied in the fields of robotics, human-machine interactions, medical care, and health monitoring. Polymer matrices are commonly employed in flexible capacitive sensors because of their high flexibility. However, their volume is almost unchanged when pressure is applied, and they are inherently viscoelastic. These shortcomings severely lead to high hysteresis and limit the improvement in sensitivity. Therefore, considerable efforts have been applied to improve the sensing performance by designing different microstructures of materials. Herein, two types of sensors based on the applied forces are discussed, including pressure sensors and strain sensors. Currently, five types of microstructures are commonly used in pressure sensors, while four are used in strain sensors. The advantages, disadvantages, and practical values of the different structures are systematically elaborated. Finally, future perspectives of microstructures for capacitive sensors are discussed, with the aim of providing a guide for designing advanced flexible and stretchable capacitive sensors via ingenious human-made microstructures.