US-12621809-B2 - Dynamic machine learning decision threshold for resource allocation

Abstract

Methods and apparatuses are provided for dynamic machine learning decision threshold for resource allocation/de-allocation. In one embodiment, a network node includes processing circuitry configured to cause the network node to dynamically adjust an allocation decision threshold; and determine whether to allocate at least one radio resource based at least in part on the allocation decision threshold. In one embodiment, a network node includes processing circuitry configured to cause the network node to dynamically adjust a de-allocation decision threshold; and determine whether to de-allocate at least one radio resource based at least in part on the de-allocation decision threshold.

Inventors

• Akram Bin Sediq
• Peiliang CHANG
• Mats Zachrison

Assignees

• TELEFONAKTIEBOLAGET LM ERICSSON (PUBL)

Dates

Publication Date

20260505

Application Date

20200312

Claims (20)

1. 1 . A method implemented in a network node, the method comprising: dynamically adjusting an allocation decision threshold; and determining whether to allocate at least one radio resource based at least in part on the allocation decision threshold, comprising: estimating a benefit of allocating the at least one radio resource to a wireless device, wherein estimating the benefit to the wireless device is based at least in part on using a machine learning algorithm; comparing the estimated benefit to the allocation decision threshold; and one of allocating and not allocating the at least one radio resource to the wireless device based at least in part on the comparison of the estimated benefit to the allocation decision threshold, wherein dynamically adjusting the allocation decision threshold comprises: determining whether there is at least one radio resource that is available for the allocation to a wireless device; increasing the allocation decision threshold by at least one step up parameter when there is at least one radio resource that is available for the allocation to the wireless device and the estimated benefit to the wireless device is greater than or equal to the allocation decision threshold; and decreasing the allocation decision threshold by at least one step down parameter when there is an unavailability of at least one radio resource for the allocation to the wireless device, wherein a size of at least one of the step up parameter and the step down parameter is based at least in part on a target allocation, wherein the size of the step down parameter is described according to a formula: downStep=AllocTarget/1−allocTarget×upStep where downStep is the step down parameter, upStep is the step up parameter, and allocTarget is the target allocation.
2. 2 . The method of claim 1 , wherein determining whether to allocate the at least one radio resource further comprises: determining whether to allocate the at least one radio resource for a sounding reference signal, SRS, based at least in part on the allocation decision threshold.
3. 3 . The method of claim 1 , wherein dynamically adjusting the allocation decision threshold comprises: dynamically adjusting the allocation decision threshold to achieve the target allocation.
4. 4 . The method of claim 1 , wherein the target allocation comprises a target probability that an event will occur.
5. 5 . The method of claim 4 , wherein the target allocation is based at least in part on: a target allocation error.
6. 6 . The method of claim 4 , wherein the target probability is a predetermined target probability that the event will occur, the event being one of: that a radio resource is unavailable for allocation to a wireless device; and that the radio resource is unavailable for allocation to the wireless device while the estimated benefit to the wireless device is greater than or equal to the allocation decision threshold.
7. 7 . The method of claim 1 , wherein estimating the benefit to the wireless device is further based at least in part on a total of data transmitted in a downlink channel to the wireless device.
8. 8 . A method implemented in a network node, the method comprising: dynamically adjusting a de-allocation decision threshold; and determining whether to de-allocate at least one radio resource based at least in part on the de-allocation decision threshold, comprising: estimating a non-benefit of de-allocating the at least one radio resource to a wireless device, wherein estimating the non-benefit to the wireless device is based at least in part on using a machine learning algorithm; comparing the estimated non-benefit to the de-allocation decision threshold; and one of de-allocating and not de-allocating the at least one radio resource to the wireless device based at least in part on the comparison of the estimated non-benefit to the de-allocation decision threshold, wherein dynamically adjusting the de-allocation decision threshold further comprises: increasing the de-allocation decision threshold by at least one step up parameter when the one of the de-allocating and not de-allocating is de-allocating; and decreasing the de-allocation decision threshold by at least one step down parameter when the one of the de-allocating and not de-allocating is not de-allocating, wherein a size of at least one of the step up parameter and the step down parameter is based at least in part on a target de-allocation, wherein the size of the step down parameter is described according to a formula: downStepDealloc=deallocTarget/1−deallocTarget×upStepDealloc where downStepDealloc is the step down parameter, upStepDealloc is the step up parameter, and deallocTarget is the target de-allocation.
9. 9 . The method of claim 8 , wherein determining whether to deallocate the at least one radio resource further comprises: determining whether to de-allocate the at least one radio resource for a sounding reference signal, SRS, based at least in part on the de-allocation decision threshold.
10. 10 . The method of claim 8 , wherein dynamically adjusting the de-allocation decision threshold comprises: dynamically adjusting the de-allocation decision threshold to achieve the target de-allocation.
11. 11 . The method of claim 8 , wherein the target de-allocation comprises a target probability that an event will occur.
12. 12 . The method of claim 11 , wherein the target de-allocation is based at least in part on: a target de-allocation error.
13. 13 . The method of claim 11 , wherein the target probability is a predetermined target probability associated with at least one of: a probability of de-allocating resources to a wireless device; and a probability of de-allocating resources to the wireless device and a same wireless device is subsequently allocated resources.
14. 14 . The method of claim 8 , wherein dynamically adjusting the de-allocation decision threshold further comprises: decreasing the de-allocation decision threshold by at least one step down parameter when the one of the de-allocating and not de-allocating is de-allocating; and increasing the de-allocation decision threshold by at least one step up parameter and at least one step down parameter when the one of the de-allocating and not de-allocating is not deallocating and is further a re-allocation.
15. 15 . The method of claim 8 , wherein estimating the non-benefit to the wireless device is further based at least in part on a total of data transmitted in a downlink channel to the wireless device.
16. 16 . A network node comprising processing circuitry, the processing circuitry configured to cause the network node to: dynamically adjust an allocation decision threshold; and determine whether to allocate at least one radio resource based at least in part on the allocation decision threshold, comprising: estimating a benefit of allocating the at least one radio resource to a wireless device, wherein estimating the benefit to the wireless device is based at least in part on using a machine learning algorithm; comparing the estimated benefit to the allocation decision threshold; and one of allocating and not allocating the at least one radio resource to the wireless device based at least in part on the comparison of the estimated benefit to the allocation decision threshold, wherein dynamically adjusting the allocation decision threshold comprises: determining whether there is at least one radio resource that is available for the allocation to a wireless device; increasing the allocation decision threshold by at least one step up parameter when there is at least one radio resource that is available for the allocation to the wireless device and the estimated benefit to the wireless device is greater than or equal to the allocation decision threshold; and decreasing the allocation decision threshold by at least one step down parameter when there is an unavailability of at least one radio resource for the allocation to the wireless device, wherein a size of at least one of the step up parameter and the step down parameter is based at least in part on a target allocation, wherein the size of the step down parameter is described according to a formula: downStep=allocTarget/1−allocTarget×upStep where downStep is the step down parameter, upStep is the step up parameter, and allocTarget is the target allocation.
17. 17 . A network node comprising processing circuitry, the processing circuitry configured to cause the network node to: dynamically adjust a de-allocation decision threshold; and determine whether to de-allocate at least one radio resource based at least in part on the de-allocation decision threshold, comprising: estimating a non-benefit of de-allocating the at least one radio resource to a wireless device, wherein estimating the non-benefit to the wireless device is based at least in part on using a machine learning algorithm; comparing the estimated non-benefit to the de-allocation decision threshold; and one of de-allocating and not de-allocating the at least one radio resource to the wireless device based at least in part on the comparison of the estimated non-benefit to the de-allocation decision threshold, wherein dynamically adjusting the de-allocation decision threshold further comprises: increasing the de-allocation decision threshold by at least one step up parameter when the one of the de-allocating and not de-allocating is de-allocating; and decreasing the de-allocation decision threshold by at least one step down parameter when the one of the de-allocating and not de-allocating is not de-allocating, wherein a size of at least one of the step up parameter and the step down parameter is based at least in part on a target de-allocation, wherein the size of the step down parameter is described according to a formula: downStepDealloc=deallocTarget/1−deallocTarget×upStepDealloc where downStepDealloc is the step down parameter, upStepDealloc is the step up parameter, and deallocTarget is the target de-allocation.
18. 18 . The method of claim 1 , wherein estimating the benefit to the wireless device is further based at least in part on a total of time that the wireless device has been active in a system of the network node.
19. 19 . The method of claim 1 , wherein estimating the benefit to the wireless device is further based at least in part on a downlink inactivity time.
20. 20 . The method of claim 8 , wherein estimating the non-benefit to the wireless device is further based at least in part on a total of time that the wireless device has been active in a system of the network node.

Description

CROSS-REFERENCE TO RELATED APPLICATION This application is a Submission Under 35 U.S.C. § 371 for U.S. National Stage Patent Application of International Application Number: PCT/IB2020/052244, filed Mar. 12, 2020 entitled “DYNAMIC MACHINE LEARNING DECISION THRESHOLD FOR RESOURCE ALLOCATION,” the entirety of which is incorporated herein by reference. TECHNICAL FIELD The present disclosure relates wireless communications and, in particular, to dynamic machine learning decision threshold for resource allocation/de-allocation. BACKGROUND In a wireless network, there is a general problem of assigning a limited number of radio resources in a given system to user devices (e.g., wireless devices and/or user equipments). Techniques for more efficiently allocating such radio resources to user devices are being considered. SUMMARY Some embodiments of the present disclosure advantageously provide methods, apparatuses and systems related to using one or more dynamic machine learning decision thresholds for resource allocation/de-allocation. According to one aspect of the present disclosure, a method implemented in a network node is provided. The method includes dynamically adjusting an allocation decision threshold; and determining whether to allocate at least one radio resource based at least in part on the allocation decision threshold. In some embodiments of this aspect, determining whether to allocate the at least one radio resource further includes determining whether to allocate the at least one radio resource for a sounding reference signal, SRS, based at least in part on the allocation decision threshold. In some embodiments of this aspect, dynamically adjusting the allocation decision threshold includes dynamically adjusting the allocation decision threshold to achieve a target allocation. In some embodiments of this aspect, the target allocation comprises a target probability that an event will occur. In some embodiments of this aspect, the target allocation is based at least in part on at least one of: a target allocation error; a cost associated with allocating the at least one radio resource; and a number of radio resource control, RRC, reconfigurations associated with allocating the at least one radio resource. In some embodiments of this aspect, the target probability is a predetermined target probability that the event will occur. In some embodiments of this aspect, the event is one of: that a radio resource is unavailable for allocation to a wireless device; and that the radio resource is unavailable for allocation to the wireless device while an estimated benefit to the wireless device is greater than or equal to the allocation decision threshold. In some embodiments of this aspect, dynamically adjusting the allocation decision threshold includes determining whether there is at least one radio resource that is available for the allocation to a wireless device; increasing the allocation decision threshold by at least one step up parameter when there is at least one radio resource that is available for the allocation to the wireless device; and decreasing the allocation decision threshold by at least one step down parameter when there is an unavailability of at least one radio resource for the allocation to the wireless device. In some embodiments of this aspect, dynamically adjusting the allocation decision threshold includes determining whether there is at least one radio resource that is available for the allocation to a wireless device; increasing the allocation decision threshold by at least one step up parameter when there is at least one radio resource that is available for the allocation to the wireless device and an estimated benefit to the wireless device is greater than or equal to the allocation decision threshold; and decreasing the allocation decision threshold by at least one step down parameter when there is an unavailability of at least one radio resource for the allocation to the wireless device. In some embodiments of this aspect, a size of at least one of the step up parameter and the step down parameter is based at least in part on a target allocation. In some embodiments of this aspect, determining whether to allocate the at least one radio resource based at least in part on the allocation decision threshold includes estimating a benefit of allocating the at least one radio resource to the wireless device; comparing the estimated benefit to the allocation decision threshold; and one of allocating and not allocating the at least one radio resource to the wireless device based at least in part on the comparison of the estimated benefit to the allocation decision threshold. In some embodiments of this aspect, estimating the benefit of allocating the at least one radio resource to the wireless device further includes estimating the benefit to the wireless device based at least in part on at least one of: a total of data transmitted in a downlink channel to the wireless device; a total o