Schedulers for next generation wireless networks : realizing QoE trade-offs for heterogeneous traffic mixes

dc.contributor.advisorDe Veciana, Gustavo
dc.contributor.committeeMemberBaccelli, Francois
dc.contributor.committeeMemberShakkottai, Sanjay
dc.contributor.committeeMemberHasenbein, John
dc.contributor.committeeMemberVikalo, Haris
dc.creatorAnand, Arjun
dc.creator.orcid0000-0002-5693-4748 2018
dc.description.abstractIn this thesis we will focus on the design of schedulers for next generation wireless networks which support application mixes, characterized by different, possibly complex, application/user Quality of Experience (QoE) metrics. The central problem underlying resource allocation for such systems is realizing QoE trade-offs among various applications/users given the dynamic loads and capacity variability they would typically see. In the first part of the thesis our focus is on applications where QoE depends on flow-level delay-based metrics. We consider system-wide metrics which directly capture both users' QoE metrics and appropriate QoE trade-offs among various applications for a wide range of system loads. This approach is different from the traditional wireless scheduler designs which have been driven by rate-based criteria, e.g., utility maximizing/proportionally fair, and/or queue-based packet schedulers which do not directly reflect the link between flow-level delays and users' QoE. In the second part of this thesis we address the key design challenges in networks supporting Ultra Reliable Low Latency Communications (URLLC) traffic which requires extremely high reliability (99.999%) and very low delays (1 msec). We will explore three different types flow delay-based metrics in this proposal, based on 1) overall mean delay; 2) functions of mean delays; and, 3) mean of functions of delays. We begin by considering minimization of mean flow delay for an M/GI/1 queuing model for a wireless Base Station (BS) where the flow size distributions are of the New Better than Used in Expectation + Decreasing Hazard Rate (NBUE +DHZ) type. Such a flow size distribution have been observed in real systems and we too validate this model based on collected data. Using a combination of analysis and simulation we show that our scheduler achieves good performance for users that might correspond to interactive applications like web browsing and/or stored video streaming and is robust to variations in system loads. Next we consider a generalization of this approach where we minimize a metric based on cost functions of the mean flow delays in a multi-class system where users/flows are classified based on their respective QoE requirements and each class's QoE requirement is modeled by its respective cost function. This approach helps us model QoE more accurately and gives us more flexibility in considering QoE trade-offs among heterogeneous user classes. We optimize two different metrics based on how we average the cost functions of delays, namely, functions of mean delays; and mean of functions of delays. The former can be used when users' experiences are sensitive to mean delays and while the latter can be used when user's experience is also sensitive to higher moments of delays, e.g., variance or soft thresholds on delay. Extensive simulations confirm the effectiveness of our proposed approaches at realizing various QoE trade-offs and performance. In 5G wireless networks URLLC traffic is expected to support many applications like industrial automation, mission critical traffic, virtual traffic etc, where the wireless network has to reliability transport small packets with very high reliability and low delays. We address the following aspects related to the system design for URLLC traffic, 1) quantifying the impact of various system parameters like system bandwidth, link SINR, delay and latency constraints on URLLC 'capacity'; 2) provisioning wireless system appropriately to meet URLLC Quality of Service (QoS) requirements; and, 3) designing efficient Hybrid Automatic Repeat Request (HARQ) schemes for transmitting small packets. Further, due the heterogeneity in delay requirements between URLLC and other types of traffic, sharing radio resources between them creates its own unique challenges. We develop efficient multiplexing schemes between URLLC traffic and other mobile broadband traffic based on preemptive puncturing/superposition of the mobile broadband transmissions by URLLC transmissions.
dc.description.departmentElectrical and Computer Engineering
dc.subjectWireless networks
dc.subjectResource allocation
dc.titleSchedulers for next generation wireless networks : realizing QoE trade-offs for heterogeneous traffic mixes
dc.type.materialtext and Computer Engineering and Computer Engineering University of Texas at Austin of Philosophy

Access full-text files

Original bundle

Now showing 1 - 1 of 1
Thumbnail Image
1.83 MB
Adobe Portable Document Format

License bundle

Now showing 1 - 2 of 2
No Thumbnail Available
4.45 KB
Plain Text
No Thumbnail Available
1.84 KB
Plain Text