US-12628006-B2 - Network node and a method performed in a wireless communication network for handling configuration of radio network nodes using reinforcement learning

Abstract

A method is herein provided, performed by a network node for handling configuration of radio network nodes in a wireless communication network. The network node such as a O&M node or similar calculates a configuration for one or more radio network nodes by using a machine learning model with a search space of parameters, wherein the search space is reduced based on an importance factor for parameters of the radio network nodes and/or the wireless communication network.

Inventors

• Rafia Inam
• Kaushik Dey

Assignees

• TELEFONAKTIEBOLAGET LM ERICSSON (PUBL)

Dates

Publication Date

20260512

Application Date

20200923

Claims (18)

1. 1 . A method performed by a network node for handling configuration of radio network nodes in a wireless communication network, the method comprising: calculating a configuration for radio network nodes using a machine learning (ML) model with a search space of parameters, wherein the search space is reduced based on an importance factor for parameters of the radio network nodes and/or the wireless communication network, wherein the importance factor is defined as a ratio defining an interest and emphasis of a parameter.
2. 2 . The method according to claim 1 , wherein the interest and emphasis is for a plurality of radio network nodes and based on a business intent.
3. 3 . The method according to claim 1 , wherein the search space is further reduced, before reducing the search space using the importance factor for parameters, by using a similarity matrix for clustering radio network nodes of similar parameters.
4. 4 . The method according to claim 3 , wherein the ML model is using the output of similarity matrix and the output of the reduction based on the importance factor for parameters.
5. 5 . The method according to claim 3 , wherein the similarity matrix is generated by hierarchical clustering of similar sites of radio network nodes, wherein the clustering is based on similarity of load and resource factors.
6. 6 . The method according to claim 1 , wherein input to the ML model is a combination of network and demand parameters for radio network nodes selected in the wireless communication network, and wherein output of the ML model is a set of parameter values for each radio network node attaining a state of low energy consumption while maximizing demand requirements.
7. 7 . The method according to claim 6 , wherein the selected radio network nodes are based on a similarity matrix for clustering radio network nodes of similar parameters.
8. 8 . The method according to claim 1 , wherein an output of the ML model is weighted based on an average of all quality of experiences (QoE) which can be predicted from network parameters and a penalty factor for each unit of added energy consumption.
9. 9 . The method according to claim 1 , further comprising detecting a mobility pattern in the wireless communication network; and wherein the ML model used in calculating the configuration is selected based on the detected mobility pattern.
10. 10 . The method according to claim 1 , further comprising sending the calculated configuration to the radio network nodes.
11. 11 . A network node for handling configuration of radio network nodes in a wireless communication network, the network node comprising: processing circuitry; and memory coupled with the processing circuitry, wherein the memory includes instructions that when executed by the processing circuitry causes the network node to perform operations comprising: calculate a configuration for radio network nodes using a machine learning (ML) model with a search space of parameters, wherein the search space is reduced based on an importance factor for parameters of the radio network nodes and/or the wireless communication network, wherein the importance factor is defined as a ratio defining an interest and emphasis of a parameter.
12. 12 . The network node according to claim 11 , wherein the interest and emphasis is for a plurality of radio network nodes and based on a business intent.
13. 13 . The network node according to claim 11 , wherein the search space is further reduced, before reducing the search space using the importance factor for parameters, by using a similarity matrix for clustering radio network nodes of similar parameters.
14. 14 . The network node according to claim 13 , wherein the ML model is using the output of the similarity matrix and the output of the reduction based on the importance factor for parameters.
15. 15 . The network node according to claim 13 , wherein the similarity matrix is generated by hierarchical clustering of similar sites of radio network nodes, wherein the clustering is based on similarity of load and resource factors.
16. 16 . The network node according to claim 11 , wherein input to the ML model is a combination of network and demand parameters for radio network nodes selected in the wireless communication network, and wherein output of the ML model is a set of parameter values for each radio network node attaining a state of low energy consumption while maximizing demand requirements.
17. 17 . The network node according to claim 11 , wherein the operations further comprise: detect a mobility pattern in the wireless communication network; and select the ML model used in calculating the configuration based on the detected mobility pattern.
18. 18 . The network node according to claim 11 , wherein the operations further comprise: send the calculated configuration to the radio network nodes.

Description

CROSS REFERENCE TO RELATED APPLICATIONS This application is a 35 U.S.C. § 371 national stage application of PCT International Application No. PCT/SE2020/050893 filed on Sep. 23, 2020, the disclosure and content of which is incorporated by reference herein in its entirety. TECHNICAL FIELD Embodiments herein relate to a network node and a method performed therein regarding communication in a wireless communication network. Furthermore, a computer program product and a computer-readable storage medium are also provided herein. Especially, embodiments herein relate to handling or enabling communication in an energy efficient manner, e.g. handling configuration of radio network nodes, in the wireless communication network. BACKGROUND In a typical wireless communication network, UEs, also known as wireless communication devices, mobile stations, stations (STA) and/or wireless devices, communicate via a Radio Access Network (RAN) to one or more core networks (CN). The RAN covers a geographical area which is divided into service areas or cell areas, with each service area or cell area being served by a radio network node such as an access node e.g. a Wi-Fi access point or a radio base station (RBS), which in some radio access technologies (RAT) may also be called, for example, a NodeB, an evolved NodeB (eNodeB) and a gNodeB (gNB). The service area or cell area is a geographical area where radio coverage is provided by the radio network node. The radio network node operates on radio frequencies to communicate over an air interface with the UEs within range of the access node. The radio network node communicates over a downlink (DL) to the UE and the UE communicates over an uplink (UL) to the radio network node. The radio network node may be a distributed node comprising a remote radio unit and a separated baseband unit. A Universal Mobile Telecommunications System (UMTS) is a third generation telecommunication network, which evolved from the second generation (2G) Global System for Mobile Communications (GSM). The UMTS terrestrial radio access network (UTRAN) is essentially a RAN using wideband code division multiple access (WCDMA) and/or High-Speed Packet Access (HSPA) for communication with UEs. In a forum known as the Third Generation Partnership Project (3GPP), telecommunications suppliers propose and agree upon standards for present and future generation networks, and investigate, amongst others, enhanced data rate and radio capacity. In some RANs, e.g. as in UMTS, several radio network nodes may be connected, e.g., by landlines or microwave, to a controller node, such as a radio network controller (RNC) or a base station controller (BSC) in 2G, which supervises and coordinates various activities of the plural radio network nodes connected thereto. The RNCs are typically connected to one or more core networks. Specifications for the Evolved Packet System (EPS) have been completed within the 3GPP and work continues in the coming 3GPP releases regarding 5G and 6G networks. The EPS comprises the Evolved Universal Terrestrial Radio Access Network (E-UTRAN), also known as the Long-Term Evolution (LTE) radio access network, and the Evolved Packet Core (EPC), also known as System Architecture Evolution (SAE) core network. E-UTRAN/LTE is a 3GPP radio access technology wherein the radio network nodes are directly connected to the EPC core network. As such, the Radio Access Network (RAN) of an EPS has an essentially “flat” architecture comprising radio network nodes connected directly to one or more core networks. 5G is the fifth generation of cellular technology and was introduced in Release 15 of the 3GPP standard. It is designed to increase speed, reduce latency, and improve flexibility of wireless services. The 5G system (5GS) includes both a new radio access network (NG-RAN) and a new core network i.e. 5G core (5GC). Similar to E-UTRAN in 4G, the NG-RAN uses a flat architecture and consists of base stations, e.g. gNBs and/or ng-eNBs, which may be interconnected with each other by means of the Xn-interface. The gNBs are also connected by means of the N2 and N3 interface to the 5GC, more specifically to the Access and Mobility Function (AMF) by the N2 interface and to the User Plane Function (UPF) by means of the N3 interface. The gNB in turn supports one or more cells which provides the radio access to the UE. The radio access technology, called New Radio (NR) is orthogonal frequency-division multiplexing (OFDM) based like in LTE and offers high data transfer speeds and low latency. Note that NR is sometimes used to refer to the whole 5G system although it is strictly speaking only the 5G radio access technology. It is expected that NR will be rolled out gradually on top of the legacy LTE network starting in areas where high data traffic is expected. This means that NR coverage will be limited in the beginning and users must move between NR and LTE as they go in and out of coverage. To support fast mobility between N