# US 2023/0276293 A1 · Method and base station for managing scheduling performance of ngbr bearers

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- Number: US 2023/0276293 A1
- Document ID: US20230276293A1
- Status: Published application
- Kind: A1
- Application number: 17/740,841
- Filed: 2022-05-10
- Published: 2023-08-31
- Earliest claimed: 2022-02-25
- Assignees: Samsung Electronics Co., Ltd.
- Applicants: Samsung Electronics Co., Ltd.
- Inventors: Amarpreet Singh SETHI; Jitender Singh SHEKHAWAT; Sahil VAID; Anshuman NIGAM
- Web page: https://patentgrep.com/patent/US20230276293A1

## CPC

- H04W 28/0268: {using specific QoS parameters for wireless networks, e.g. QoS class identifier [QCI] or guaranteed bit rate [GBR] (negotiating SLA or negotiating QoS H04W28/24)}
- H04W 72/54: based on quality criteria
- H04L 1/0003: {by switching between different modulation schemes}
- H04L 47/30: in combination with information about buffer occupancy at either end or at transit nodes
- H04W 28/02: Traffic management, e.g. flow control or congestion control
- H04W 28/0252: {per individual bearer or channel (dynamic wireless traffic scheduling H04W72/12)}
- H04W 28/0257: {the individual bearer or channel having a maximum bit rate or a bit rate guarantee}
- H04W 28/20: Negotiating bandwidth
- H04W 72/1221: {based on age of data to be sent}
- H04W 28/16: Central resource management; Negotiation of resources or communication parameters, e.g. negotiating bandwidth or QoS [Quality of Service]
- H04W: WIRELESS COMMUNICATION NETWORKS (broadcast communication H04H; communication systems using wireless links for non-selective communication, e.g. wireless extensions H04M1/72)
- H04L: TRANSMISSION OF DIGITAL INFORMATION, e.g. TELEGRAPHIC COMMUNICATION (arrangements common to telegraphic and telephonic communication H04M)

## Abstract

A method for managing scheduling performance of bearers by a base station in a wireless network is provided. The method includes determining a configured packet delay budget (PDB) of non-guaranteed bit rate (NGBR) bearers and a configured PDB of guaranteed bit rate (GBR) bearers; monitoring a PDB of the NGBR bearers and a PDB of the GBR bearers during data communication; determining an average of quality-of-service class identifier (QCI) divergence of the NGBR bearers and an average of QCI divergence of the GBR bearers, wherein the QCI divergence of a bearer is a percentage variation between a monitored PDB and a configured PDB of the bearer; and controlling resource allocation parameters for minimizing the average of QCI divergence of the NGBR bearers and the average of QCI divergence of the GBR bearers.

## Claims

### Claim 1

A method for managing scheduling performance of bearers by a base station in a wireless network, the method comprising:
determining a configured packet delay budget (PDB) of non-guaranteed bit rate (NGBR) bearers and a configured PDB of guaranteed bit rate (GBR) bearers;
monitoring a PDB of the NGBR bearers and a PDB of the GBR bearers during data communication;
determining an average of quality-of-service class identifier (QCI) divergence of the NGBR bearers and an average of QCI divergence of the GBR bearers, wherein the QCI divergence of a bearer is a percentage variation between a monitored PDB and a configured PDB of the bearer; and
controlling resource allocation parameters for minimizing the average of QCI divergence of the NGBR bearers and the average of QCI divergence of the GBR bearers.

### Claim 2 (depends on 1)

The method of claim 1, wherein the average of QCI divergence of the GBR bearers is minimized by maintaining the monitored PDB of each GBR bearer to be lesser than the configured PDB of the GBR bearer.

### Claim 3 (depends on 1)

The method of claim 1, wherein controlling the resource allocation parameters comprises:
determining the resource allocation parameters comprising a number of the GBR bearers to be served before the NGBR bearers, a maximum amount of allocable pending data of the GBR bearers, and a maximum amount of allocable pending data of the NGBR bearers using an artificial intelligence (AI) engine; and
allocating resource blocks (RBs) to the GBR bearers and the NGBR bearers based on the resource allocation parameters for the data communication.

### Claim 4 (depends on 3)

The method of claim 3, wherein allocating the RBs to the GBR bearers and the NGBR bearers based on the resource allocation parameters for the data communication, comprises:
receiving a list of prioritized GBR bearers and a list of prioritized NGBR bearers;
determining that the RBs are available to allocate;
determining whether served GBR bearers are less than the number of the GBR bearers to be served before the NGBR bearers;
allocating the RBs to next prioritized GBR bearers based on the maximum amount of allocable pending data of the GBR bearers, in response to determining that the served GBR bearers are less than the number of the GBR bearers to be served before the NGBR bearers; and
allocating the RBs to next prioritized NGBR bearers based on the maximum amount of allocable pending data of the NGBR bearers, in response to determining that the served GBR bearers are not less than the number of the GBR bearers to be served before the NGBR bearers.

### Claim 5 (depends on 3)

The method of claim 3, wherein determining the resource allocation parameters using the AI engine, comprises:
determining a state of the AI engine comprising a modulation and coding scheme-buffer occupancy (MCS-BO) distribution for the GBR bearers, an MCS-BO distribution for the NGBR bearers, and a throughput of the base station; and
determining the resource allocation parameters using the AI engine based on the state for obtaining a reward of the AI engine comprising minimization of the average QCI divergence and maximization of the throughput.

### Claim 6

A method for managing scheduling performance of bearers by a base station in a wireless network, the method comprising:
determining a configured packet delay budget (PDB) of bearers of each quality-of-service class identifier (QCI) class;
monitoring the PDB of the bearers of each QCI class during data communication;
determining an average of QCI divergence of the bearers of each QCI class, wherein the QCI divergence of a bearer is a percentage variation between a monitored PDB and a configured PDB of the bearer; and
controlling resource allocation parameters, priority metric parameters of the bearers of each QCI class for minimizing the average QCI divergence of the bearers of each QCI class.

### Claim 7 (depends on 6)

The method of claim 6, wherein controlling the resource allocation parameters, and the priority metric parameters of the bearers of each QCI class, comprises:
determining the resource allocation parameters comprising a number of guaranteed bit rate (GBR) bearers to be served before non-guaranteed bit rate (NGBR) bearers, a maximum amount of allocable pending data of the GBR bearers, a maximum amount of allocable pending data of the NGBR bearers, and the priority metric parameters of the bearer comprising a QCI priority weightage of the NGBR bearers and a QCI priority weightage of the GBR bearers using an artificial intelligence (AI) engine; and
allocating resource blocks (RBs) to the GBR bearers and the NGBR bearers based on the resource allocation parameters and the priority metric parameters for the data communication.

### Claim 8 (depends on 7)

The method of claim 7, wherein allocating the RBs to the GBR bearers and the NGBR bearers based on the resource allocation parameters and the priority metric parameters for the data communication, comprises:
receiving a list of prioritized GBR bearers and a list of prioritized NGBR bearers adjusted based on the priority metric parameters;
determining that the RBs are available to allocate;
determining whether served GBR bearers are less than the number of the GBR bearers to be served before the NGBR bearers; and
allocating the RBs to next prioritized GBR bearers based on the maximum amount of allocable pending data of the GBR bearers, in response to determining that the served GBR bearers are less than the number of the GBR bearers to be served before the NGBR bearers; and
allocating the RBs to next prioritized NGBR bearers based on the maximum amount of allocable pending data of the NGBR bearers, in response to determining that the served GBR bearers are not less than the number of the GBR bearers to be served before the NGBR bearers.

### Claim 9 (depends on 7)

The method of claim 7, wherein determining the resource allocation parameters and the priority metric parameters using the AI engine, comprises:
determining a state of the AI engine comprising a modulation and coding scheme-buffer occupancy (MCS-BO) distribution for the GBR bearers, an MCS-BO distribution for the NGBR bearers, and a throughput of the base station; and
determining the resource allocation parameters and the priority metric parameters using the AI engine based on the state for obtaining a reward of the AI engine comprising minimization of the average QCI divergence per QCI class and maximization of the throughput, wherein the QCI class is a 5th generation quality-of-service identifier (5QI) value.

### Claim 10 (depends on 7)

The method of claim 7,
wherein the maximum amount of allocable pending data of the GBR bearers is determined based a configured GBR of the GBR bearers, and
wherein the maximum amount of allocable pending data of the NGBR bearers is determined based on configured aggregate maximum bit rate (AMBR) and configured QCI priority of the NGBR bearers.

### Claim 11

A base station for managing scheduling performance of bearers in a wireless network, comprising:
a memory; and
at least one processor coupled to the memory and configured to:
determine a configured packet delay budget (PDB) of the non-guaranteed bit rate (NGBR) bearers and a configured PDB of guaranteed bit rate (GBR) bearers,
monitor a PDB of the NGBR bearers and a PDB of the GBR bearers during data communication,
determine an average of quality-of-service class identifier (QCI) divergence of the NGBR bearers and an average of QCI divergence of the GBR bearers, wherein the QCI divergence of a bearer is a percentage variation between a monitored PDB and a configured PDB of the bearer, and
control resource allocation parameters for minimizing the average of QCI divergence of the NGBR bearers and the average of QCI divergence of the GBR bearers.

### Claim 12 (depends on 11)

The base station of claim 11, wherein the at least one processor is configured to maintain the PDB of each GBR bearer to be lesser than the configured PDB of the GBR bearer for minimizing the average of QCI divergence of the GBR bearers includes minimizing of the average of QCI divergence of the GBR bearers.

### Claim 13 (depends on 11)

The base station of claim 11, wherein for minimizing the average of QCI divergence of the GBR bearers includes minimizing of the average of QCI divergence of the GBR bearers, the at least one processor is configured to:
determine the resource allocation parameters comprising a number of the GBR bearers to be served before the NGBR bearers, a maximum amount of allocable pending data of the GBR bearers, and a maximum amount of allocable pending data of the NGBR bearers using an artificial intelligence (AI) engine, and
allocate resource blocks (RBs) to the GBR bearers and the NGBR bearers based on the resource allocation parameters for the data communication.

### Claim 14 (depends on 13)

The base station of claim 13, wherein for allocating the RBs to the GBR bearers and the NGBR bearers based on the resource allocation parameters for the data communication, the at least one processor is configured to:
receive a list of prioritized GBR bearers and a list of prioritized NGBR bearers,
determine that the RBs are available to allocate,
determine whether served GBR bearers are less than the number of the GBR bearers to be served before the NGBR bearers,
allocate the RBs to next prioritized GBR bearers based on the maximum amount of allocable pending data of the GBR bearers, in response to determining that the served GBR bearers are less than the number of the GBR bearers to be served before the NGBR bearers, and
allocate the RBs to next prioritized NGBR bearers based on the maximum amount of allocable pending data of the NGBR bearers, in response to determining that the served GBR bearers are not less than the number of the GBR bearers to be served before the NGBR bearers.

### Claim 15 (depends on 13)

The base station of claim 13, wherein for determining the resource allocation parameters using the AI engine, the at least one processor is configured to:
determine a state of the AI engine comprising a modulation and coding scheme-buffer occupancy (MCS-BO) distribution for the GBR bearers, an MCS-BO distribution for the NGBR bearers, and a throughput of the base station, and
determine the resource allocation parameters using the AI engine based on the state for obtaining a reward of the AI engine comprising minimization of the average QCI divergence and maximization of the throughput.

### Claim 16

A base station for enhancing relative scheduling performance of non-guaranteed bit rate (NGBR) bearers in a wireless network, comprises:
a memory; and
at least one processor coupled to the memory and configured to:
determine a configured packet delay budget (PDB) of bearers of each quality-of-service class identifier (QCI) class,
monitor the PDB of the bearers of each QCI class during data communication,
determine an average of QCI divergence of the bearers of each QCI class, wherein the QCI divergence of a bearer is a percentage variation between a monitored PDB and a configured PDB of the bearer, and
control resource allocation parameters, and priority metric parameters of the bearers of each QCI class for minimizing the average QCI divergence of the bearers of each QCI class.

### Claim 17 (depends on 16)

The base station of claim 16, wherein for minimizing the average QCI divergence of the bearers of each QCI class, the at least one processor is configured to:
determine the resource allocation parameters comprising a number of guaranteed bit rate (GBR) bearers to be served before non-guaranteed bit rate (NGBR) bearers, a maximum amount of allocable pending data of the GBR bearers, a maximum amount of allocable pending data of the NGBR bearers, and the priority metric parameters of the bearer comprising a QCI priority weightage of the NGBR bearers and a QCI priority weightage of the GBR bearers using an artificial intelligence (AI) engine, and
allocating resource blocks (RBs) to the GBR bearers and the NGBR bearers based on the resource allocation parameters and the priority metric parameters for the data communication.

### Claim 18 (depends on 17)

The base station of claim 17, wherein for allocating of the RBs to the GBR bearers and the NGBR bearers based on the resource allocation parameters and the priority metric parameters for the data communication, the at least one processor is configured to:
receive a list of prioritized GBR bearers and a list of prioritized NGBR bearers adjusted based on the priority metric parameters,
determine that the RBs are available to allocate,
determine whether served GBR bearers are less than the number of the GBR bearers to be served before the NGBR bearers,
allocate the RBs to next prioritized GBR bearers based on the maximum amount of allocable pending data of the GBR bearers, in response to determining that the served GBR bearers are less than the number of the GBR bearers to be served before the NGBR bearers, and
allocate the RBs to next prioritized NGBR bearers based on the maximum amount of allocable pending data of the NGBR bearers, in response to determining that the served GBR bearers are not less than the number of the GBR bearers to be served before the NGBR bearers.

### Claim 19 (depends on 17)

The base station of claim 17, wherein for determining the resource allocation parameters and the priority metric parameters using the AI engine, the at least one processor is configured to:
determine a state of the AI engine comprising a modulation and coding scheme-buffer occupancy (MCS-BO) distribution for the GBR bearers, an MCS-BO distribution for the NGBR bearers, and a throughput of the base station, and
determine the resource allocation parameters and the priority metric parameters using the AI engine based on the state for obtaining a reward of the AI engine comprising minimization of the average QCI divergence per QCI class and maximization of the throughput, wherein the QCI class is a 5th generation quality-of-service identifier (5QI) value.

### Claim 20 (depends on 17)

The base station of claim 17,
wherein the maximum amount of allocable pending data of the GBR bearers is determined based a configured GBR of the GBR bearers, and
wherein the maximum amount of allocable pending data of the NGBR bearers is determined based on a configured aggregate maximum bit rate (AMBR) and a configured QCI priority of the NGBR bearers.