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EP-4740544-A1 - NETWORK SLICE FEASIBILITY ASSESSMENT FOR SLICE ORCHESTRATION IN A WIRELESS NETWORK

EP4740544A1EP 4740544 A1EP4740544 A1EP 4740544A1EP-4740544-A1

Abstract

This disclosure provides systems, methods and apparatus, including computer programs encoded on computer storage media, for network slice feasibility assessment for slice orchestration in a wireless network. Some aspects relate to providing on-demand approval or rejection of a requested network slice at a device associated with service management. The device may select respective resource allocations of the requested network slice for each cell of a set of cells in a wireless network, add the respective predicted resource allocations to respective current resource utilizations at each of the cells, and output a recommendation associated with the requested network slice in accordance with the summations. The device may select the respective resource allocations of the requested network slice in accordance with a service level agreement (SLA) of the requested network slice and, in some implementations, observed network conditions at each of the cells in the wireless network.

Inventors

  • IZHAKI, GAL
  • CASTANEDA, German David
  • YELLIN, DANIEL

Assignees

  • QUALCOMM INCORPORATED

Dates

Publication Date
20260513
Application Date
20240605

Claims (20)

  1. 1. A device associated with service management of a wireless network, comprising: a processing system that includes processor circuitry and memory circuitry that stores code, the processing system configured to cause the device to: receive a request associated with a network slice in the wireless network, the request indicating one or more parameters associated with a service level agreement of the network slice; select, in accordance with the one or more parameters associated with the sendee level agreement, a respective physical resource block (PRB) allocation of the network slice for each cell of a set of cells of the wireless network; and output, to a netw ork slice management function and in accordance w ith a respective PRB utilization at each cell of the set of cells and the respective PRB allocation of the network slice for each cell of the set of cells, a recommendation associated with the network slice.
  2. 2. The device of claim 1, wherein, to select the respective PRB allocation of the network slice for each cell of the set of cells, the processing system is further configured to cause the device to: predict the respective PRB allocation of the network slice for each cell of the set of cells in accordance with the one or more parameters associated w ith the service level agreement.
  3. 3. The device of claim 2, wherein, to predict the respective PRB allocation of the network slice for each cell of the set of cells, the processing system is further configured to cause the device to: predict the respective PRB allocation of the network slice for each cell of the set of cells in accordance with both the one or more parameters associated w ith the sen ice level agreement and observed netw ork conditions at the set of cells.
  4. 4. The device of claim 2, wherein the processing system is further configured to cause the device to: scan the set of cells of the wireless network in accordance with respective summations of respective PRB utilizations at the set of cells and respective PRB allocations of the network slice for the set of cells, wherein outputting the recommendation associated with the network slice is in accordance with scanning the set of cells.
  5. 5. The device of claim 4, wherein, to output the recommendation associated with the network slice, the processing system is further configured to cause the device to: output, in accordance with greater than or equal to a threshold quantity of cells of the set of cells be able to accommodate the respective summations, an approval of the network slice; or output, in accordance with fewer than the threshold quantity of cells of the set of cells be able to accommodate the respective summations, a rejection of the network slice.
  6. 6. The device of claim 4, wherein, to output the recommendation associated with the network slice, the processing system is further configured to cause the device to: output, in accordance with cells of the set of cells that be able to accommodate the respective summations serving greater than or equal to a threshold quantity of intended users, an approval of the network slice; or output, in accordance with the cells of the set of cells that be able to accommodate the respective summations serving fewer than the threshold quantity of intended users, a rejection of the network slice.
  7. 7. The device of claim 2, wherein, to predict the respective PRB allocation of the network slice for each cell of the set of cells, the processing system is further configured to cause the device to: predict a first PRB allocation of the network slice for a first cell of the set of cells in accordance with the one or more parameters associated with the sendee level agreement and first observed network conditions at the first cell; and predict a second PRB allocation of the network slice for a second cell of the set of cells in accordance with the one or more parameters associated with the service level agreement and second observed network conditions at the second cell.
  8. 8. The device of claim 1, wherein the processing system is further configured to cause the device to: train a machine learning model to output the recommendation associated with the network slice in accordance with one or more of a prediction of the respective PRB allocation of the network slice for each cell of the set of cells, the respective PRB utilization at each cell of the set of cells, one or more radio frequency metrics associated with the set of cells, or a morphology associated with the set of cells, wherein the prediction is associated with observed network conditions at the set of cells of the wireless network.
  9. 9. The device of claim 8, wherein, to train the machine learning model, the processing system is further configured to cause the device to: provide, as a training set associated with the machine learning model, a plurality of network snapshots, wherein each network snapshot of the plurality of network snapshots corresponds to a suitable PRB allocation to a requested network slice and is associated with a unique permutation of one or more cell types, one or more cluster sizes, one or more cell physical characteristics, one or more cell load conditions, or one or more cell channel quality distributions, one or more interference levels, or any combination thereof.
  10. 10. The device of claim 8, wherein, to train the machine learning model, the processing system is further configured to cause the device to: receive, at the device associated with the service management of the wireless network and in accordance with deployment of the network slice in the wireless network, information indicative of one or more performance indicators associated with the network slice; and update the machine learning model in accordance with the one or more performance indicators.
  11. 11. (Original) The device of claim 10, wherein the one or more performance indicators include an actual PRB usage by the network slice at each of the set of cells, and wherein, to update the machine learning model, the processing system is further configured to cause the device to: update the machine learning model in accordance with a delta between the respective PRB allocation of the network slice for each cell of the set of cells and the actual PRB usage by the network slice for each cell of the set of cells.
  12. 12. The device of claim 8, wherein the observed network conditions at the set of cells include one or more of a channel quality distribution for each cell of the set of cells, a morphology associated with the set of cells, a traffic behavior at each cell of the set of cells, or a frequency band associated with the set of cells.
  13. 13. The device of claim 1, wherein the processing system is further configured to cause the device to: trigger a resource estimation application of the device in accordance with receiving the request, the resource estimation application configured to output the respective PRB allocation of the network slice for each cell of the set of cells; store information indicative of the respective PRB allocation of the network slice for each cell of the set of cells at the device; and trigger a feasibility application of the device in accordance with storing the information indicative of the respective PRB allocation of the network slice for each cell of the set of cells, the feasibility application configured to output the recommendation associated with the network slice.
  14. 14. The device of claim 13. wherein a set of inputs to the feasibility application include one or more of a set of radio frequency performance metrics, a capacity assessment associated with the set of cells, the respective PRB allocation of the network slice for each cell of the set of cells, an output of a machine learning model trained to assist in a determination of the recommendation, a traffic forecast associated with the set of cells, a coverage evaluation associated with the set of cells, or a slice admission policy.
  15. 15. The device of claim 1, wherein the processing system is further configured to cause the device to: predict a future respective PRB utilization at each cell of the set of cells in accordance with a traffic forecast, wherein the recommendation associated with the network slice is in accordance with the respective PRB utilization at each cell of the set of cells, a prediction of the future respective PRB utilization at each cell of the set of cells, and a prediction of the respective PRB allocation of the network slice for each cell of the set of cells.
  16. 16. The device of claim 1, wherein the one or more parameters associated with the service level agreement are indicative of one or more of a throughput expectation, a latency constraint, a bit error rate, or a quantity of intended users at each cell of the set of cells.
  17. 17. The device of claim 1, wherein the one or more parameters associated wi th the service level agreement are indicative of a slice admission policy associated with the network slice, and wherein the recommendation is in accordance with the slice admission policy.
  18. 18. The device of claim 1, wherein the set of cells are located within a geographic coverage area associated with the network slice.
  19. 19. The device of claim 1, wherein the set of cells, in accordance with which the recommendation associated with the network slice is output, includes one or more cells of a larger set of cells.
  20. 20. The device of claim 19, wherein the processing system is further configured to cause the device to: train a machine learning model to output the recommendation associated with the network slice in accordance with the larger set of cells, wherein outputting the recommendation associated with the network slice is in accordance with training the machine learning model.

Description

NETWORK SLICE FEASIBILITY ASSESSMENT FOR SLICE ORCHESTRATION IN A WIRELESS NETWORK CROSS REFERENCE TO RELATED APPLICATIONS [0001] The present Application for Patent claims priority to U.S. Patent Application No. 18/470,268 by IZHAKI et al., entitled ‘ NETWORK SLICE FEASIBILITY ASSESSMENT FOR SLICE ORCHESTRATION IN A WIRELESS NETWORK,” filed September 19, 2023 and U.S. Provisional Patent Application No. 63/511.817 by IZHAKI et al., entitled “NETWORK SLICE FEASIBILITY ASSESSMENT FOR SLICE ORCHESTRATION IN A WIRELESS NETWORK,” filed July 3, 2023; each of which is assigned to the assignee hereof, and each of which is expressly incorporated by reference herein. TECHNICAL FIELD [0002] This disclosure relates to wireless communications, including network slice feasibility' assessment for slice orchestration in a wireless network. DESCRIPTION OF THE RELATED TECHNOLOGY [0003] Wireless communication systems are widely deployed to provide various types of communication content such as voice, video, packet data, messaging, broadcast, and so on. These systems may be capable of supporting communication with multiple users by sharing the available system resources (such as time, frequency, and power). Examples of such multiple-access systems include fourth generation (4G) systems such as Long Term Evolution (LTE) systems, LTE- Advanced (LTE- A) systems, or LTE-A Pro systems, and fifth generation (5G) systems which may be referred to as New Radio (NR) systems. These systems may employ technologies such as code division multiple access (CDMA), time division multiple access (TDMA), frequency division multiple access (FDMA), orthogonal FDMA (OFDMA), or discrete Fourier transform spread orthogonal frequency division multiplexing (DFT-s-OFDM). A wireless multiple-access communications system may include one or more base stations (BSs) or one or more network access nodes. each simultaneously supporting communication for multiple communication devices, which may be otherwise known as user equipment (UE). SUMMARY [0004] The systems, methods, and devices of this disclosure each have several innovative aspects, no single one of which is solely responsible for the desirable attributes disclosed herein. [0005] One innovative aspect of the subject matter described in this disclosure can be implemented in a device associated with service management of a wireless network. The device may include a processing system that includes processor circuitry and memory circuitry that stores code. The processing system may be configured to cause the device to receive, at a device associated with service management of the wireless network, a request associated with a netw ork slice in the wireless network, the request indicating one or more parameters associated with a service level agreement (SLA) of the netw ork slice, select, in accordance with the one or more parameters associated with the SLA, a respective physical resource block (PRB) allocation of the network slice for each cell of a set of cells of the wireless network, and outputting, to a network slice management function and in accordance with a respective PRB utilization at each cell of the set of cells and the respective PRB allocation of the netw ork slice for each cell of the set of cells, a recommendation associated with the network slice. [0006] Another innovative aspect of the subject matter described in this disclosure can be implemented in a method for network slice management in a wireless network. The method may include receiving, at a device associated with service management of the wireless network, a request associated with a network slice in the wireless netw ork, the request indicating one or more parameters associated with an SLA of the network slice, selecting, in accordance with the one or more parameters associated with the SLA, a respective PRB allocation of the netw ork slice for each cell of a set of cells of the wireless netw ork, and outputting, to a network slice management function and in accordance with a respective PRB utilization at each cell of the set of cells and the respective PRB allocation of the network slice for each cell of the set of cells, a recommendation associated with the network slice. [0007] Another innovative aspect of the subject matter described in this disclosure can be implemented in a device associated with service management of a wireless network. The device may include means for receiving a request associated with a network slice in the wireless network, the request indicating one or more parameters associated wi th an SLA of the network slice, means for selecting, in accordance with the one or more parameters associated with the SLA, a respective PRB allocation of the network slice for each cell of a set of cells of the wireless network, and means for outputting, to a network slice management function and in accordance with a respective PRB utilization at each cell of the set of cells and the respective PRB allocation of the netwo