Qualcomm Patent | Dynamic patterns for active-inactive states for wireless communications

Patent: Dynamic patterns for active-inactive states for wireless communications

Publication Number: 20250310889

Publication Date: 2025-10-02

Assignee: Qualcomm Incorporated

Abstract

Disclosed are systems and techniques for wireless communications. For instance, a process can include receiving an indication of one or more parameters for input to a reinforcement learning machine learning (ML) model; determining a power schedule for a wireless communications chipset based on the one or more parameters using the reinforcement learning ML model; and determining to switch the wireless communications chipset into a low power state or a higher power state based on the determined power schedule.

Claims

1. A device for wireless communications, comprising:at least one memory comprising instructions;a wireless communications chipset; andat least one processor coupled to wireless communications chipset and the at least one memory and configured to:receive an indication of one or more parameters for input to a reinforcement learning machine learning (ML) model;determine a power schedule for the wireless communications chipset based on the one or more parameters using the reinforcement learning ML model;update the power schedule based on a performance metric, wherein the performance metric is measured based on at least one of a human perceptible delay or a human perceptible performance change; anddetermine to switch the wireless communications chipset into a low power state or a higher power state based on the determined power schedule.

2. The device of claim 1, wherein the one or more parameters include at least one of a primary frequency of operations, a latency budget, or a power budget.

3. The device of claim 2, wherein the indication of the primary frequency of operations is based on a rate at which images are received for display.

4. The device of claim 2, wherein the latency budget is based on a maximum allowed time for motion to be displayed.

5. The device of claim 2, wherein the at least one processor is further configured to:receive a performance metric based on the power schedule; andupdate the power schedule based on the performance metric.

6. The device of claim 5, wherein the performance metric is based on an amount of time used by the device to receive an input and output an image for display based on the received input.

7. The device of claim 5, wherein, to update the power schedule, the at least one processor is configured to:determine an amount of power used based on the power schedule;determine a penalty value based on the amount of power used and the performance metric; andcompare the determined penalty value to a stored penalty value associated with the power schedule.

8. The device of claim 1, wherein the reinforcement learning ML model includes a set of predetermined penalty scores.

9. The device of claim 8, wherein the set of predetermined penalty scores are determined based on a training process.

10. The device of claim 9, wherein the power schedule comprises a target wake time (TWT), and wherein the set of predetermined penalty scores are predetermined for a range of TWT service period start offsets and a range of lengths of time for the higher power state, and wherein the training process determines penalty scores across the range of TWT service period start offsets and range of lengths of time.

11. The device of claim 1, wherein the at least one processor is further configured to:transmit an indication to block requests for the wireless communications chipset while the wireless communications chipset is in the low power state; andtransmit an indication to unblock requests for the wireless communications chipset while the wireless communications chipset is in the higher power state.

12. The device of claim 11, wherein the indication to block requests and the indication to unblock requests are transmitted to a driver for the wireless communications chipset.

13. The device of claim 1, wherein a driver for the wireless communications chipset is configured to block requests for the wireless communications chipset and unblock requests for the wireless communications chipset based on the power schedule.

14. A method for wireless communications, comprising:receiving an indication of one or more parameters for input to a reinforcement learning machine learning (ML) model;determining a power schedule for a wireless communications chipset based on the one or more parameters using the reinforcement learning ML model;updating the power schedule based on a performance metric, wherein the performance metric is measured based on at least one of a human perceptible delay or a human perceptible performance change; anddetermining to switch the wireless communications chipset into a low power state or a higher power state based on the determined power schedule.

15. The method of claim 14, wherein the one or more parameters include at least one of a primary frequency of operations, a latency budget, or a power budget.

16. The method of claim 15, wherein the indication of the primary frequency of operations is based on a rate at which images are received for display.

17. The method of claim 15, wherein the latency budget is based on a maximum allowed time for motion to be displayed.

18. The method of claim 15, further comprising:receiving a performance metric based on the power schedule; andupdating the power schedule based on the performance metric.

19. The method of claim 18, wherein the performance metric is based on an amount of time used by a device to receive an input and output an image for display based on the received input.

20. The method of claim 18, wherein updating the power schedule comprises:determining an amount of power used based on the power schedule;determining a penalty value based on the amount of power used and the performance metric; andcomparing the determined penalty value to a stored penalty value associated with the power schedule.

21. The device of claim 1, wherein the at least one processor is further configured to apply an adjustable balancing factor to the performance metric.

22. The device of claim 7, wherein the at least one processor is further configured to determine whether the determined penalty value diverges from the stored penalty value based on comparing the determined penalty value to the stored penalty value.

Description

FIELD

The present disclosure generally relates to wireless communications. For example, aspects of the present disclosure relate to systems and techniques for dynamic pattern for active-inactive states for wireless communications.

BACKGROUND

Wireless communications systems are deployed to provide various telecommunications and data services, including telephony, video, data, messaging, and broadcasts. Broadband wireless communications systems have developed through various generations, including a first-generation analog wireless phone service (1G), a second-generation (2G) digital wireless phone service (including interim 2.5G networks), a third-generation (3G) high speed data, Internet-capable wireless device, and a fourth-generation (4G) service (e.g., Long-Term Evolution (LTE), WiMax). Examples of wireless communications systems include code division multiple access (CDMA) systems, time division multiple access (TDMA) systems, frequency division multiple access (FDMA) systems, orthogonal frequency division multiple access (OFDMA) systems, Global System for Mobile communication (GSM) systems, etc. Other wireless communications technologies include 802.11 Wi-Fi, Bluetooth, among others.

A fifth-generation (5G) mobile standard calls for higher data transfer speeds, greater number of connections, and better coverage, among other improvements. The 5G standard (also referred to as “New Radio” or “NR”), according to Next Generation Mobile Networks Alliance, is designed to provide data rates of several tens of megabits per second to each of tens of thousands of users, with 1 gigabit per second to tens of workers on an office floor. Several hundreds of thousands of simultaneous connections should be supported in order to support large sensor deployments.

Although wireless communication systems have made great technological advancements over many years, challenges still exist. For example, certain devices, such as extended reality (XR) devices (e.g., virtual reality (VR) and/or augmented reality (AR) device), may include many different applications which are regularly performing many different uplink and/or downlink transmissions. While these transmissions together may utilize less bandwidth than available bandwidth, the timing of these transmissions may make it difficult for the wireless communications systems to enter a power saving mode (e.g., sleep mode) to reduce power consumption of the wireless communications system as compared to an active mode. Consequently, techniques to improve power savings for such devices may be enhanced.

SUMMARY

The following presents a simplified summary relating to one or more aspects disclosed herein. Thus, the following summary should not be considered an extensive overview relating to all contemplated aspects, nor should the following summary be considered to identify key or critical elements relating to all contemplated aspects or to delineate the scope associated with any particular aspect. Accordingly, the following summary presents certain concepts relating to one or more aspects relating to the mechanisms disclosed herein in a simplified form to precede the detailed description presented below.

Disclosed are systems, methods, apparatuses, and computer-readable media for performing wireless communications. In one illustrative example, a wireless node for wireless communications is provided. The first device includes at least one memory comprising instructions and at least one processor coupled to the at least one memory and configured to: receive an indication of one or more parameters for input to a reinforcement learning machine learning (ML) model; determine a power schedule for the wireless communications chipset based on the one or more parameters using the reinforcement learning ML model; and determine to switch the wireless communications chipset into a low power state or a higher power state based on the determined power schedule.

As another example, a method for wireless communications is provided. The method includes: receiving an indication of one or more parameters for input to a reinforcement learning machine learning (ML) model; determining a power schedule for a wireless communications chipset based on the one or more parameters using the reinforcement learning ML model; and determining to switch the wireless communications chipset into a low power state or a higher power state based on the determined power schedule.

In another example, a non-transitory computer-readable medium having stored thereon instructions that, when executed by at least one processor, cause the at least one processor to: receive an indication of one or more parameters for input to a reinforcement learning machine learning (ML) model; determine a power schedule for a wireless communications chipset based on the one or more parameters using the reinforcement learning ML model; and determine to switch the wireless communications chipset into a low power state or a higher power state based on the determined power schedule.

As another example, an apparatus for wireless communications is provided. The apparatus includes: means for receiving an indication of one or more parameters for input to a reinforcement learning machine learning (ML) model; means for determining a power schedule for a wireless communications chipset based on the one or more parameters using the reinforcement learning ML model; and means for determining to switch the wireless communications chipset into a low power state or a higher power state based on the determined power schedule.

Aspects generally include a method, apparatus, system, computer program product, non-transitory computer-readable medium, user equipment, base station, wireless communication device, and/or processing system as substantially described herein with reference to and as illustrated by the drawings and specification.

The foregoing has outlined rather broadly the features and technical advantages of examples according to the disclosure in order that the detailed description that follows may be better understood. Additional features and advantages will be described hereinafter. The conception and specific examples disclosed may be readily utilized as a basis for modifying or designing other structures for carrying out the same purposes of the present disclosure. Such equivalent constructions do not depart from the scope of the appended claims. Characteristics of the concepts disclosed herein, both their organization and method of operation, together with associated advantages, will be better understood from the following description when considered in connection with the accompanying figures. Each of the figures is provided for the purposes of illustration and description, and not as a definition of the limits of the claims.

While aspects are described in the present disclosure by illustration to some examples, those skilled in the art will understand that such aspects may be implemented in many different arrangements and scenarios. Techniques described herein may be implemented using different platform types, devices, systems, shapes, sizes, and/or packaging arrangements. For example, some aspects may be implemented via integrated chip implementations or other non-module-component based devices (e.g., end-user devices, vehicles, communication devices, computing devices, industrial equipment, retail/purchasing devices, medical devices, and/or artificial intelligence devices). Aspects may be implemented in chip-level components, modular components, non-modular components, non-chip-level components, device-level components, and/or system-level components. Devices incorporating described aspects and features may include additional components and features for implementation and practice of claimed and described aspects. For example, transmission and reception of wireless signals may include one or more components for analog and digital purposes (e.g., hardware components including antennas, radio frequency (RF) chains, power amplifiers, modulators, buffers, processors, interleavers, adders, and/or summers). In some aspects, one or more of the apparatuses described herein comprises a mobile device (e.g., a mobile telephone or so-called “smart phone”, a tablet computer, or other type of mobile device), a wearable device, an extended reality device (e.g., a virtual reality (VR) device, an augmented reality (AR) device, or a mixed reality (MR) device), a personal computer, a laptop computer, a video server, a television (e.g., a network-connected television), a vehicle (or a computing device of a vehicle), or other device. In some aspects, the apparatus(es) includes at least one camera for capturing one or more images or video frames. For example, the apparatus(es) can include a camera (e.g., an RGB camera) or multiple cameras for capturing one or more images and/or one or more videos including video frames. In some aspects, the apparatus(es) includes at least one display for displaying one or more images, videos, notifications, or other displayable data. In some aspects, the apparatus(es) includes at least one transmitter configured to transmit one or more video frame and/or syntax data over a transmission medium to at least one device. In some aspects, the at least one processor includes a neural processing unit (NPU), a neural signal processor (NSP), a central processing unit (CPU), a graphics processing unit (GPU), any combination thereof, and/or other processing device or component. It is intended that aspects described herein may be practiced in a wide variety of devices, components, systems, distributed arrangements, and/or end-user devices of varying size, shape, and constitution.

Other objects and advantages associated with the aspects disclosed herein will be apparent to those skilled in the art based on the accompanying drawings and detailed description.

BRIEF DESCRIPTION OF THE DRAWINGS

Examples of various implementations are described in detail below with reference to the following figures:

FIG. 1A is a block diagram illustrating an example of a wireless communication network, in accordance with some examples;

FIG. 1B is a diagram illustrating another example of a wireless network, in accordance with the present disclosure;

FIG. 1C illustrates a wireless communication system (also known as a wireless local area network (WLAN) or a Wi-Fi network) configured in accordance with the present disclosure;

FIG. 2 is a diagram illustrating a design of a base station and a User Equipment (UE) device that enable transmission and processing of signals exchanged between the UE and the base station, in accordance with some examples;

FIG. 3 is a diagram illustrating an example of a disaggregated base station, in accordance with some examples;

FIG. 4 is a block diagram illustrating components of a user equipment, in accordance with some examples;

FIG. 5 is a diagram illustrating an architecture of an example extended reality (XR) system, in accordance with some aspects of the disclosure;

FIG. 6A is a timeline illustrating power schedules, in accordance with aspects of the present disclosure;

FIG. 6B illustrates a table of penalty scores, in accordance with aspects of the present disclosure;

FIG. 7 is a flow diagram illustrating a technique for updating a TWT schedule of a wireless device, in accordance with aspects of the present disclosure;

FIG. 8A is a block diagram illustrating a RAT of a wireless device configured to use a dynamic pattern for active-inactive states for wireless communications, in accordance with aspects of the present disclosure;

FIG. 8B is a timeline illustrating blocking of out of turn data, in accordance with aspects of the present disclosure;

FIG. 9 is a flow diagram illustrating another example of a process for wireless communications, in accordance with aspects of the present disclosure; and

FIG. 10 is a diagram illustrating an example of a computing system, according to aspects of the present disclosure.