Dynamic Rate Adaptation for Improved Throughput and Delay in Wireless Network Coded Broadcast

In this paper we provide theoretical and simulation-based study of the delivery delay performance of a number of existing throughput optimal coding schemes and use the results to design a new dynamic rate adaptation scheme that achieves improved overall throughput-delay performance. Under a baseline rate control scheme, the receivers' delay performance is examined. Based on their Markov states, the knowledge difference between the sender and receiver, three distinct methods for packet delivery are identified: zero state, leader state and coefficient-based delivery. We provide analyses of each of these and show that, in many cases, zero state delivery alone presents a tractable approximation of the expected packet delivery behaviour. Interestingly, while coefficient-based delivery has so far been treated as a secondary effect in the literature, we find that the choice of coefficients is extremely important in determining the delay, and a well chosen encoding scheme can, in fact, contribute a significant improvement to the delivery delay. Based on our delivery delay model, we develop a dynamic rate adaptation scheme which uses performance prediction models to determine the sender transmission rate. Surprisingly, taking this approach leads us to the simple conclusion that the sender should regulate its addition rate based on the total number of undelivered packets stored at the receivers. We show that despite its simplicity, our proposed dynamic rate adaptation scheme results in noticeably improved throughput-delay performance over existing schemes in the literature.Comment: 14 pages, 15 figure

Performance modelling of a multiple threshold RED mechanism for bursty and correlated Internet traffic with MMPP arrival process

Access to the large web content hosted all over the world by users of the Internet engage many hosts, routers/switches and faster links. They challenge the internet backbone to operate at its capacity to assure e±cient content access. This may result in congestion and raises concerns over various Quality of Service (QoS) issues like high delays, high packet loss and low throughput of the system for various Internet applications. Thus, there is a need to develop effective congestion control mechanisms in order to meet various Quality of Service (QoS) related performance parameters. In this paper, our emphasis is on the Active Queue Management (AQM) mechanisms, particularly Random Early Detection (RED). We propose a threshold based novel analytical model based on standard RED mechanism. Various numerical examples are presented for Internet traffic scenarios containing both the burstiness and correlation properties of the network traffic

Empirical exploration of air traffic and human dynamics in terminal airspaces

Air traffic is widely known as a complex, task-critical techno-social system, with numerous interactions between airspace, procedures, aircraft and air traffic controllers. In order to develop and deploy high-level operational concepts and automation systems scientifically and effectively, it is essential to conduct an in-depth investigation on the intrinsic traffic-human dynamics and characteristics, which is not widely seen in the literature. To fill this gap, we propose a multi-layer network to model and analyze air traffic systems. A Route-based Airspace Network (RAN) and Flight Trajectory Network (FTN) encapsulate critical physical and operational characteristics; an Integrated Flow-Driven Network (IFDN) and Interrelated Conflict-Communication Network (ICCN) are formulated to represent air traffic flow transmissions and intervention from air traffic controllers, respectively. Furthermore, a set of analytical metrics including network variables, complex network attributes, controllers' cognitive complexity, and chaotic metrics are introduced and applied in a case study of Guangzhou terminal airspace. Empirical results show the existence of fundamental diagram and macroscopic fundamental diagram at the route, sector and terminal levels. Moreover, the dynamics and underlying mechanisms of "ATCOs-flow" interactions are revealed and interpreted by adaptive meta-cognition strategies based on network analysis of the ICCN. Finally, at the system level, chaos is identified in conflict system and human behavioral system when traffic switch to the semi-stable or congested phase. This study offers analytical tools for understanding the complex human-flow interactions at potentially a broad range of air traffic systems, and underpins future developments and automation of intelligent air traffic management systems.Comment: 30 pages, 28 figures, currently under revie

An Empirical Air-to-Ground Channel Model Based on Passive Measurements in LTE

In this paper, a recently conducted measurement campaign for unmanned-aerial-vehicle (UAV) channels is introduced. The downlink signals of an in-service long-time-evolution (LTE) network which is deployed in a suburban scenario were acquired. Five horizontal and five vertical flight routes were considered. The channel impulse responses (CIRs) are extracted from the received data by exploiting the cell specific signals (CRSs). Based on the CIRs, the parameters of multipath components (MPCs) are estimated by using a high-resolution algorithm derived according to the space-alternating generalized expectation-maximization (SAGE) principle. Based on the SAGE results, channel characteristics including the path loss, shadow fading, fast fading, delay spread and Doppler frequency spread are thoroughly investigated for different heights and horizontal distances, which constitute a stochastic model.Comment: 15 pages, submitted version to IEEE Transactions on Vehicular Technology. Current status: Early acces

ASIdE: Using Autocorrelation-Based Size Estimation for Scheduling Bursty Workloads.

Temporal dependence in workloads creates peak congestion that can make service unavailable and reduce system performance. To improve system performability under conditions of temporal dependence, a server should quickly process bursts of requests that may need large service demands. In this paper, we propose and evaluateASIdE, an Autocorrelation-based SIze Estimation, that selectively delays requests which contribute to the workload temporal dependence. ASIdE implicitly approximates the shortest job first (SJF) scheduling policy but without any prior knowledge of job service times. Extensive experiments show that (1) ASIdE achieves good service time estimates from the temporal dependence structure of the workload to implicitly approximate the behavior of SJF; and (2) ASIdE successfully counteracts peak congestion in the workload and improves system performability under a wide variety of settings. Specifically, we show that system capacity under ASIdE is largely increased compared to the first-come first-served (FCFS) scheduling policy and is highly-competitive with SJF. © 2012 IEEE

Data based identification and prediction of nonlinear and complex dynamical systems

We thank Dr. R. Yang (formerly at ASU), Dr. R.-Q. Su (formerly at ASU), and Mr. Zhesi Shen for their contributions to a number of original papers on which this Review is partly based. This work was supported by ARO under Grant No. W911NF-14-1-0504. W.-X. Wang was also supported by NSFC under Grants No. 61573064 and No. 61074116, as well as by the Fundamental Research Funds for the Central Universities, Beijing Nova Programme.Peer reviewedPostprin