Matching Items (2)
Description
A unique feature, yet a challenge, in cognitive radio (CR) networks is the user hierarchy: secondary users (SU) wishing for data transmission must defer in the presence of active primary users (PUs), whose priority to channel access is strictly higher.Under a common thread of characterizing and improving Quality of Service (QoS) for the SUs, this dissertation is progressively organized under two main thrusts: the first thrust focuses on SU's throughput by exploiting the underlying properties of the PU spectrum to perform effective scheduling algorithms; and the second thrust aims at another important QoS performance of the SUs, namely delay, subject to the impact of PUs' activities, and proposes enhancement and control mechanisms. More specifically, in the first thrust, opportunistic spectrum scheduling for SU is first considered by jointly exploiting the memory in PU's occupancy and channel fading. In particular, the underexplored scenario where PU occupancy presents a {long} temporal memory is taken into consideration. By casting the problem as a partially observable Markov decision process, a set of {multi-tier} tradeoffs are quantified and illustrated. Next, a spectrum shaping framework is proposed by leveraging network coding as a {spectrum shaper} on the PU's traffic. Such shaping effect brings in predictability of the primary spectrum, which is utilized by the SUs to carry out adaptive channel sensing by prioritizing channel access order, and hence significantly improve their throughput. On the other hand, such predictability can make wireless channels more susceptible to jamming attacks. As a result, caution must be taken in designing wireless systems to balance the throughput and the jamming-resistant capability. The second thrust turns attention to an equally important performance metric, i.e., delay performance. Specifically, queueing delay analysis is conducted for SUs employing random access over the PU channels. Fluid approximation is taken and Poisson driven stochastic differential equations are applied to characterize the moments of the SUs' steady-state queueing delay. Then, dynamic packet generation control mechanisms are developed to meet the given delay requirements for SUs.
ContributorsWang, Shanshan (Author) / Zhang, Junshan (Thesis advisor) / Xue, Guoliang (Committee member) / Hui, Joseph (Committee member) / Duman, Tolga (Committee member) / Arizona State University (Publisher)
Created2012
Description
In today's global market, companies are facing unprecedented levels of uncertainties in supply, demand and in the economic environment. A critical issue for companies to survive increasing competition is to monitor the changing business environment and manage disturbances and changes in real time. In this dissertation, an integrated framework is proposed using simulation and online calibration methods to enable the adaptive management of large-scale complex supply chain systems. The design, implementation and verification of the integrated approach are studied in this dissertation. The research contributions are two-fold. First, this work enriches symbiotic simulation methodology by proposing a framework of simulation and advanced data fusion methods to improve simulation accuracy. Data fusion techniques optimally calibrate the simulation state/parameters by considering errors in both the simulation models and in measurements of the real-world system. Data fusion methods - Kalman Filtering, Extended Kalman Filtering, and Ensemble Kalman Filtering - are examined and discussed under varied conditions of system chaotic levels, data quality and data availability. Second, the proposed framework is developed, validated and demonstrated in `proof-of-concept' case studies on representative supply chain problems. In the case study of a simplified supply chain system, Kalman Filtering is applied to fuse simulation data and emulation data to effectively improve the accuracy of the detection of abnormalities. In the case study of the `beer game' supply chain model, the system's chaotic level is identified as a key factor to influence simulation performance and the choice of data fusion method. Ensemble Kalman Filtering is found more robust than Extended Kalman Filtering in a highly chaotic system. With appropriate tuning, the improvement of simulation accuracy is up to 80% in a chaotic system, and 60% in a stable system. In the last study, the integrated framework is applied to adaptive inventory control of a multi-echelon supply chain with non-stationary demand. It is worth pointing out that the framework proposed in this dissertation is not only useful in supply chain management, but also suitable to model other complex dynamic systems, such as healthcare delivery systems and energy consumption networks.
ContributorsWang, Shanshan (Author) / Wu, Teresa (Thesis advisor) / Fowler, John (Thesis advisor) / Pfund, Michele (Committee member) / Li, Jing (Committee member) / Pavlicek, William (Committee member) / Arizona State University (Publisher)
Created2010