Browsing by Author "Deng, Lei"

Now showing 1 - 4 of 4

Delay-Constrained Input-Queued Switch
(2018-11-01)
Deng, Lei
;
Wong, Wing Shing
;
Chen, Po-Ning
;
Han, Yunghsiang S.
;
Hou, Hanxu
In this paper, we study the delay-constrained input-queued switch, where each packet has a deadline and it will expire if it is not delivered before its deadline. Such new scenario is motivated by the proliferation of real-time applications in multimedia communication systems, tactile Internet, networked controlled systems, and cyber-physical systems. The delay-constrained input-queued switch is completely different from the well-understood delay-unconstrained one and thus poses new challenges. We focus on three fundamental problems centering around the performance metric of timely throughput: (i) how to characterize the capacity region? (ii) how to design a feasibility/throughput-optimal scheduling policy? and (iii) how to design a network-utility-maximization scheduling policy? We use three different approaches to solve these three fundamental problems. The first approach is based on Markov Decision Process (MDP) theory, which can solve all three problems. However, it suffers from the curse of dimensionality. The second approach breaks the curse of dimensionality by exploiting the combinatorial features of the problem. It gives a new capacity region characterization with only a polynomial number of linear constraints. The third approach is based on the framework of Lyapunov optimization, where we design a polynomial-time maximum-weight T-disjoint-matching scheduling policy which is proved to he feasibility/throughput-optimal. Our three approaches apply to the frame-synchronized traffic pattern but our MDP-based approach can be extended to more general traffic patterns.
Demonstration of high precision 3D indoor positioning system based on two-layer ANN machine learning technique
(2019-01-01)
He, Jiale
;
Hsu, Chin-Wei
;
Zhou, Qi
;
Tang, Ming
;
Fu, Songnian
We experimentally demonstrate a high precision 3D indoor visible light positioning system utilizing two-layer machine learning technique. The measured average positioning resolution of <1cm in an unit volume of 0.9 x 1 x 0.4 m(3).
On the Asymptotic Performance of Delay-Constrained Slotted ALOHA
(2018-01-01)
Deng, Lei
;
Deng, Jing
;
Chen, Po-Ning
;
Han, Yunghsiang S.
;
電機工程學系
Motivated by the proliferation of real-time applications in multimedia communication systems, tactile Internet, networked controlled systems, and cyber-physical systems, supporting delay-constrained traffic become critical for the communication system. In delay-constrained traffic, each packet has a hard deadline and if it cannot be delivered before its deadline, it becomes useless and will be removed from the system. In this work, we consider a slotted ALOHA system where multiple stations need to deliver delay-constrained traffic to a common receiver by accessing a shared channel. We prove that, under the frame-synchronized traffic pattern, the maximum system timely throughput converges to 1/e = 36.8% as the number of stations goes to infinity, which is the same as the asymptotic maximum system throughput for delay-unconstrained slotted ALOHA system with saturate traffic. While this is not completely surprising, we further investigate the speed of such a maximum system throughput approaching 1/e under borderline traffic.
Poster: Delay-Constrained Input-Queued Switch
(2018-01-01)
Deng, Lei
;
Wong, Wing Shing
;
Chen, Po-Ning
;
Han, Yunghsiang S.
;
交大名義發表
We study delay-constrained input-queued switches where each packet has a deadline that will expire if it is not delivered before its deadline. Such a new scenario is motivated by the proliferation of real-time applications in multimedia communication systems, tactile Internet, networked controlled systems, and cyber-physical systems. One fundamental problem centering around the performance metric of timely throughput is how to characterize the capacity region. In this work, for the frame-synchronized traffic pattern, we characterize the capacity region by a polynomial number of linear constraints.