Abstract
Ergodic Spectrum Management (ESM)’s basic features are introduced as a cloud-based management of wireless connectivity that targets improvement of internet-user’s quality of experience. Ergodic Spectrum Management (or ESM) learns and exploits near-ergodicity, or time-consistency, to improve a communication-link connection’s stable and efficient use in time, space, and frequency; while using consumer quality of experience as the target metric. ESM methods can also improve existing radio resource management, particularly advancing unlicensed spectrum-use efficiency to levels at or exceeding those associated with licensed spectra, as shown herein. ESM’s use of learned probability distributions’ dimensional (time, space, and frequency) consistencies enables latency-insensitive remote-cloud-based resource management to be applied to wireless multi-user transmission. ESM methods are developed for 3 increasingly more effective stages that correspondingly increasingly rely on data collection and functional-profile (policy) guidance of physical-layer design choices. ESM application to either and both of existing and future unlicensed- and licensed-spectra networks is suggested as a means to improve overall wireless performance. Examples and field data are provided to show the potential of very large improvements in wireless system connectivity, throughput, and quality of experience.
Highlights
E RGODIC Spectrum Management (ESM) methods target cloud-based remote management of wireless transmission links’ efficient and stable operation as learned from link users’ quality of experience (QoE)
ESM’s learned exploitation of wireless-network’s statistical consistencies can help reduce costs of existing resource management (RRM) industry drives towards concentrated edge computing/reaction
ESM can remove the need to have the as much computation for RRM at the edge because part of the computational responsibility moves to the cloud
Summary
E RGODIC Spectrum Management (ESM) methods target cloud-based remote management of wireless transmission links’ efficient and stable operation as learned from link users’ quality of experience (QoE) Both the transmission link’s characteristics, as well as link user’s experience, have certain statistical consistencies, or ergodicities; an ESM-empowered cloud server learns and exploits these ergodicities to manage (ideally optimize) time, space, and frequency use, and inter-user contention. This section prepares for alteration of original single-user ergodic approaches to Section III’s multi-user cases
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