A QoS-based charging and resource allocation framework for next generation wireless networks

Wireless networks are evolving to include Internet access to interactive multimedia and video conferencing as well as traditional services such as voice, email and web access. These new applications can demand large amounts of network resources, such as bandwidth, to achieve the highest levels of quality (e.g. picture quality). In conjunction with this trend, charging and resource allocation systems must evolve to explicitly consider the trade-off between resource consumption and the Quality of Service (QoS) provided. This paper proposes a novel QoS-based charging and resource allocation framework. The framework allocates resources to customers based on their QoS perceptions and requirements, thereby charging fairly while improving resource allocation efficiency. It also allows the network operators to pursue a wide variety of policy options, including maximizing revenue or using auction or utility-based pricing. Copyrigh

Latency versus transmission power trade-off in free-space optical (FSO) satellite networks with multiple inter-continental connections

ABSTRACT: In free-space optical satellite networks (FSOSNs), satellites connected via laser inter-satellite links (LISLs), latency is a critical factor, especially for long-distance inter-continental connections. Since satellites depend on solar panels for power supply, power consumption is also a vital factor. We investigate the minimization of total network latency (i.e., the sum of the network latencies of all inter-continental connections in a time slot) in a realistic model of a FSOSN, the latest version of the Starlink Phase 1 Version 3 constellation. We develop mathematical formulations of the total network latency over different LISL ranges and different satellite transmission power constraints for multiple simultaneous inter-continental connections. We use practical system models for calculating network latency and satellite optical link transmission power, and we formulate the problem as a binary integer linear program. The results reveal that, for satellite transmission power limits set at 0.5 W, 0.3 W, and 0.1 W, the average total network latency for all five inter-continental connections studied in this work levels off at 339 ms, 361 ms, and 542 ms, respectively. Furthermore, the corresponding LISL ranges required to achieve these average total network latency values are 4500 km, 3000 km, and 1731 km, respectively. Different limitations on satellite transmission power exhibit varying effects on average total network latency (over 100 time slots), and they also induce differing changes in the corresponding LISL ranges. In the absence of satellite transmission power constraints, as the LISL range extends from the minimum feasible range of 1575 km to the maximum feasible range of 5016 km, the average total network latency decreases from 589 ms to 311 ms

Discovering the Characteristics of Mathematical Programs via Sampling

Complex models, large scale: – Unexpected results, bad performance, solver failure… Limited information returned by (e.g. NLP) solvers: – Feasible, KKT conditions satisfied – No improvement in many iterations: stopping. – Unable to find feasible point. – Too many iterations. – Various specific failure messages… Questions: – Why do I have this problem? – How do I make the solver run better on this model? Needed: tools to discover the characteristics of models Discovering Characteristics of Math Programs 2Model Characteristics Some characteristics (e.g. for NLPs): • Shapes of the constraints and objective (convex, concave, both, almost linear, etc.) • Shape of the feasible region (convex, non-convex) • Redundancy of constraints • Location of feasible region Insights gained: • Better understanding of outcomes and behaviour • Functions that can be approximated (e.g. linear) • Constraints that can be ignored • Best type of solution algorithm to apply • Good starting poin