Beihang University

About: Beihang University is a education organization based out in Beijing, China. It is known for research contribution in the topics: Computer science & Control theory. The organization has 67002 authors who have published 73507 publications receiving 975691 citations. The organization is also known as: Beijing University of Aeronautics and Astronautics.

Optimal User Pairing for Downlink Non-Orthogonal Multiple Access (NOMA)

Abstract: In this letter, we explore user pairing in a downlink non-orthogonal multiple access (NOMA) network. As power allocation inherently intertwines with user pairing, a joint user pairing and power allocation problem is considered to optimize the achievable sum rate (ASR) with minimum rate constraint for each user, which is a mixed integer programming problem. To solve this non-convex problem, we first obtain the optimal power allocation in an NOMA system with only 2 users; then analyze the user pairing problem in a simplified situation, i.e., an NOMA system with four users. Finally, we obtain the closed-form globally optimal solution in a general NOMA system. Extensive performance evaluations are conducted to compare the ASRs of the NOMA and OMA systems. Results show that the performance of the NOMA system with the proposed optimal user pairing is significantly better than that of the OMA system, as well as the performance of the NOMA system with random user pairing.

Emerging Implantable Energy Harvesters and Self-Powered Implantable Medical Electronics

Abstract: Implantable energy harvesters (IEHs) are the crucial component for self-powered devices. By harvesting energy from organisms such as heartbeat, respiration, and chemical energy from the redox reaction of glucose, IEHs are utilized as the power source of implantable medical electronics. In this review, we summarize the IEHs and self-powered implantable medical electronics (SIMEs). The typical IEHs are nanogenerators, biofuel cells, electromagnetic generators, and transcutaneous energy harvesting devices that are based on ultrasonic or optical energy. A benefit from these technologies of energy harvesting in vivo, SIMEs emerged, including cardiac pacemakers, nerve/muscle stimulators, and physiological sensors. We provide perspectives on the challenges and potential solutions associated with IEHs and SIMEs. Beyond the energy issue, we highlight the implanted devices that show the therapeutic function in vivo.

Spreading dynamics in complex networks

Abstract: Searching for influential spreaders in complex networks is an issue of great significance for applications across various domains, ranging from epidemic control, innovation diffusion, viral marketing, and social movement to idea propagation. In this paper, we first display some of the most important theoretical models that describe spreading processes, and then discuss the problem of locating both the individual and multiple influential spreaders respectively. Recent approaches in these two topics are presented. For the identification of privileged single spreaders, we summarize several widely used centralities, such as degree, betweenness centrality, PageRank, k-shell, etc. We investigate the empirical diffusion data in a large scale online social community?LiveJournal. With this extensive dataset, we find that various measures can convey very distinct information of nodes. Of all the users in the LiveJournal social network, only a small fraction of them are involved in spreading. For the spreading processes in LiveJournal, while degree can locate nodes participating in information diffusion with higher probability, k-shell is more effective in finding nodes with a large influence. Our results should provide useful information for designing efficient spreading strategies in reality.

Robust Iterative Learning Control for Nonrepetitive Uncertain Systems

Abstract: This technical note proposes a robust iterative learning control (ILC) strategy to regulate iteratively-operated, finite-duration nonrepetitive systems characterized by iteration-varying uncertainties in initial states, external disturbances, plant model matrices and desired reference trajectories. Our convergence analysis exploits results on input-to-state stability of discrete parameterized systems. For a class of multiple-input, multiple-output discrete-time linear systems, with all iteration-varying uncertainties bounded, we give one condition that ensures boundedness of all system trajectories and an additional, second condition that ensures convergence of tracking error. Notably, we do not require the standard ILC contraction mapping requirement to hold at each iteration. Moreover, we show that it is possible to achieve perfect output tracking if the iteration-varying uncertainties all converge with increasing iteration.