Ergodic spectrum management systems and methods

Patent metadata

Field	Value
Publication number	US-11696155-B2
Application number	US-202016804000-A
Country	US
Kind code	B2
Filing date	Feb 27, 2020
Priority date	Feb 28, 2019
Publication date	Jul 4, 2023
Grant date	Jul 4, 2023

Abstract

Presented are Ergodic Spectrum Management (ESM) systems and methods that take advantage of the presence of statistical consistencies (“ergodicity”) and correlations, such as a wireless network's average dimensional consistencies of probability distributions (in time, space, and frequency) of channel gains, to adaptively learn qualitative and quantitative network/user behavior; estimate or predict network performance; and guide locally implemented radio resource management (RRM) decisions of wireless multi-user transmissions in a manner such as to reduce interference and improve latency; connection stability; efficiency; and overall wireless performance. ESM also enhances end-users' Quality of Experience (QoE) by allowing movement across bands and regions as users/devices roam. A remote-cloud-based resource management implementation of ESM's Learn-ed Resource Managers (LRMs) removes the need for heavy edge-computing close to radio cells.

First claim

What is claimed is:

1. A system for improving quality of experience (QoE) with a wireless communication system, the system comprising:
a first access node within a plurality of access nodes, the first access node collects data comprising a probability distribution of channel gains that have been obtained for one or more channels in the wireless communication system; and
a processor coupled to the first access node, the processor identifies user or network behavior by performing steps comprising:
receiving the collected data at a management interface, wherein the collected data comprises information to assess QoE including user satisfaction;
performing an ergodic analysis such that consistent use patterns within the collected data are exploited to determine a policy that satisfies one or more constraints; and
providing the policy to at least one access node within the plurality of access nodes to cause the at least one access node to adapt one or more parameters to improve (QoE) with the wireless communication system.

2. The system according to claim 1 wherein the processor is a learn-ed resource manager (LRM).

3. The system according to claim 1 wherein the ergodic analysis is performed based on extracted consistent patterns within a channel along with collected feedback data.

4. The system according to claim 3 wherein the collected feedback data is provided from at least one of a user, a network operator, a consumer device, and network equipment or system.

5. The system according to claim 1 wherein the collected data comprises at least one of a geometric average channel gain, reference signal received power, reference signal received quality, interference data, and noise data.

6. The system according to claim 1 wherein determining the policy comprises using the collected data to predict a set of transmission parameters.

7. The system according to claim 6 wherein the set of transmission parameters comprises at least one of a modulation and coding-system (MCS) parameter, an energy parameter, a beamform parameter, a precoder parameter, a transmission's duration in symbol periods, a channel-frequency index, and a code-rate parameter.

8. The system according to claim 1 wherein the processor determines the policy by using at least one of an ergodic water filling method, an ergodic iterative water filling method, an ergodic spectrum management (ESM) Stage 1 iterative water filling, an ESM Stage 2 optimum spectrum balancing, an ESM Stage 2 orthogonal dimension division (ODD) method, an ESM Stage 3 method, a gradient descent method, and any other form of an iterative optimization method.

9. The system according to claim 1 wherein the processor uses at least one of a Quality of Service (QoS) data and the collected feedback data to estimate QoE parameters, the QoS data being related to line conditions.

10. The system according to claim 1 wherein the processor discretizes the probability distribution of channel gains into observation intervals that correspond to different MCS parameters.

11. A method for improving quality of experience (QoE) with a wireless communication system, the method comprising:
receiving collected data comprising a probability distribution of channel gains that have been obtained by one or more channels in the wireless communication system, wherein the collected data comprises information to assess QoE including user satisfaction;
performing an ergodic analysis such that consistent use patterns within the collected data are exploited to determine a policy that satisfies one or more constraints; and
providing the policy to at least one access node within a plurality of access nodes within the wireless communication system, the policy causes the at least one access node to adapt one or more parameters to improve (QoE) with the wireless communication system.

12. The method according to claim 11 wherein the step of performing an ergodic analysis is performed by a learn-ed resource manager.

13. The method according to claim 11 wherein the ergodic analysis is performed based on extracted consistent patterns within a channel along with collected feedback data.

14. The method according to claim 13 wherein the collected feedback data is provided from at least one of a user, a network operator, a consumer device, and network equipment or system.

15. The method according to claim 11 wherein the collected data comprises at least one of a geometric average channel gain, reference signal received power, reference signal received quality, interference data, and noise data.

16. The method according to claim 11 wherein the step of determining the policy comprises using the collected data to predict a set of transmission parameters.

17. The method of claim 16 wherein the set of transmission parameters comprises at least one of a modulation and coding-system (MCS) parameter, an energy parameter, a beamform parameter, a precoder parameter, a transmission's duration in symbol periods, a channel-frequency index, and a code-rate parameter.

18. The method according to claim 11 wherein the step of determining the policy uses at least one of an ergodic water filling method, an ergodic iterative water filling method, an ergodic spectrum management (ESM) Stage 1 iterative water filling, an ESM Stage 2 optimum spectrum balancing, an ESM Stage 2 orthogonal dimension division (ODD) method, an ESM Stage 3 method, a gradient descent method, and any other form of an iterative optimization method.

19. The method according to claim 11 wherein the step of determining the policy uses at least one of a Quality of Service (QoS) data and collected feedback data to estimate QoE parameters, the QoS data being related to line conditions.

20. A non-transitory computer-readable medium or media comprising one or more sequences of instructions which, when executed by at least one processor, causes steps to be performed comprising:
receiving collected data comprising a probability distribution of channel gains that have been obtained by one or more channels in a wireless communication system, wherein the collected data comprises information to assess QoE including user satisfaction;
performing an ergodic analysis such that consistent use patterns within the collected data are exploited to determine a policy that satisfies one or more constraints; and
providing the policy to at least one access node within a plurality of access nodes within the wireless communication system, the policy causes the at least one access node to adapt one or more parameters to improve (QoE) with the wireless communication system.

Assignees

• Assia Spe Llc

Inventors

• Cioffi John M
• Hwang Chan-Soo
• Kerpez Kenneth J
• Oh Jisung
• Kanellakopoulos Ioannis
• Chow Peter

Classifications

• H04W28/16 Primary

  Central resource management; Negotiation of resources or communication parameters, e.g. negotiating bandwidth or QoS [Quality of Service] · CPC title

• H04W24/10

  Scheduling measurement reports {; Arrangements for measurement reports} · CPC title

• H04W24/02 Primary

  Arrangements for optimising operational condition · CPC title

• H04W16/10

  Dynamic resource partitioning · CPC title

• G06N20/00

  Machine learning · CPC title