Network node and method performed therein in a communication network

Abstract

Embodiments herein disclose, for example, a method performed by a network node (12) in a communications network for handling operation, wherein the network node comprises at least one power supply unit and one or more additional power supply units for powering the network node. The network node (12) obtains an output from a computational model; and sets an output voltage from the at least one power supply unit based on the obtained output.

Description

TECHNICAL FIELD

[0001] Embodiments herein relate to a network node and a method performed therein in relation to operation of the network node. Furthermore, a computer program product and a computer-readable storage medium are also provided herein. In particular, embodiments herein relate to handling operation of a network node in a communication network, e.g. selection and control of voltage etc. during operation. BACKGROUND

[0002] In a typical communication network, wireless communication devices (also known as communication devices, user equipments, UEs, stations, STAs, and / or wireless devices) communicate via a radio access network (RAN) to one or more core networks (CNs). 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 (RATs) can be referred to as, e.g., a NodeB, an evolved NodeB (eNodeB), and gNode B (gNB). A 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 and communicates with wireless devices within its range. The radio network node communicates via the downlink (DL) to the wireless devices and the wireless devices communicate via the uplink (UL) to the radio network node. The radio network node can be a distributed node comprising a remote radio unit and a separate baseband unit.

[0003] The Universal Mobile Telecommunication System (UMTS) is a third generation (3G) telecommunication network that 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) to communicate with user equipments (UE). In a forum known as the Third Generation Partnership Project (3GPP), telecommunications suppliers propose and agree on standards for the current and future generation of cellular networks and UTRANs. The 3GPP also studies and develops optimizations and enhancements to the standards. In some RANs, e.g., as in UMTS, several radio network nodes can be connected to a controller node (e.g., a radio network controller (RNC) or base station controller (BSC)), e.g., by landline or microwave, that supervises and coordinates various activities of the plural radio network nodes connected thereto. The RNC is typically connected to one or more core networks.

[0004] The specifications for Evolved Packet System (EPS) have been finalized within the 3rd Generation Partnership Project (3GPP), and this work continues in upcoming 3GPP releases such as 4G and 5G networks. EPS comprises the Evolved Universal Terrestrial Radio Access Network (E-UTRAN) (also known as Long Term Evolution (LTE) Radio Access Network) and the Evolved Packet Core (EPC) (also known as the System Architecture Evolution (SAE) Core Network). E-UTRAN / LTE is a 3GPP radio access technology where radio network nodes are directly connected to the EPC core network. Therefore, the EPS radio access network (RAN) has a essentially "flat" architecture, consisting of radio network nodes directly connected to one or more core networks.

[0005] With the emergence of 5G technology (also known as New Radio (NR)), the use of numerous transmit and receive antenna elements is of great interest because it can utilize beamforming, such as transmit-side and receive-side beamforming. Transmit-side beamforming means that the transmitter can amplify the transmitted signal in one or more selected directions while suppressing the transmitted signal in other directions. Similarly, on the receive side, the receiver can amplify signals from one or more selected directions while suppressing unwanted signals from other directions.

[0006] Beamforming allows signals to be stronger for individual connections. On the transmitting side, this is achieved by concentrating transmitted power in the desired direction, while on the receiving side, it is achieved by increasing receiver sensitivity in the desired direction. This beamforming enhances the throughput and coverage of the connection. It also allows for reduced interference from unwanted signals, enabling multiple simultaneous transmissions over multiple individual connections using the same resources in the time-frequency grid, a phenomenon known as Multiple-User Multiple-Input Multiple-Output (MIMO).

[0007] Network operators need to improve their total cost of ownership (TCO), which includes capital expenditures (CAPEX) and operating expenditures (OPEX). One of the key challenges and priorities is to improve network operational efficiency across the entire site, and especially at network nodes such as radio network nodes like base stations.

[0008] To add and expand network nodes with one or more 5G radio units, operators need to consider total power consumption and simultaneously reduce the total power consumption of each network node. In other cases, adding only 5G radio units to existing GSM, WCDMA, and LTE networks will increase total power consumption. Therefore, it is necessary to reduce power requirements.

[0009] A drawback of current deployments of network nodes, such as radio network nodes, and likewise in other fields, such as computer servers, etc., is that the power architecture itself is static. The total end-to-end (e2e) power consumption from AC input to output units, such as remote radio unit outputs, needs to be related and controlled in terms of energy efficiency, and needs to be more dynamic.

[0010] Another problem with current deployments of network nodes is the use of batteries, such as Valve Regulated Lead Acid (VRLA) batteries, which today are used as backup batteries for network nodes, such as radio network nodes in GSM, WCDMA and LTE. The VRLA battery characteristics do not enable efficient operation of the power distribution system voltage, mainly because the VRLA battery needs to be set to a threshold, such as -54.5 Volts Direct Current (VDC), in order to preserve the battery life.

[0011] When an operator of a network node wants to add additional features, such as adding a 5G radio, the operator needs to upgrade the architecture of the network node, the power supply cable and / or the AC input fuse to be able to support the newly added 5G radio. However, there is not enough power in the current infrastructure of the network node to feed the additional features, such as 5G radio, see Figure 1 which shows the structure of an old site.

[0012] Another deployment used by network node operators is to use a Direct Current / Direct Current (DC / DC) booster for the system voltage to increase the power distribution voltage and gain efficiency. But this approach reduces the reliability of the network node by adding power units in the field, and also introduces a single point of failure Figure 2 with additional cost and the price of reducing the overall Mean Time Between Failures (MTBF) of the network node. SUMMARY

[0013] It is an object of embodiments herein to provide a mechanism for improving the operation of a network node in a communications network.

[0014] According to one aspect, the object is achieved by providing a method, performed by a network node in a communications network, for handling operation of the network node. The network node comprises at least one power supply unit and one or more additional power supply units for powering the network node. The network node: obtains an output from a computational model; and sets an output voltage from the at least one power supply unit based on the obtained output.

[0015] According to yet another aspect, the object is achieved by providing a network node in a communications network for handling operation of the network node. The network node comprises at least one power supply unit and one or more additional power supply units for powering the network node. The network node is configured to: obtain an output from a computational model; and based on the obtained output, set an output voltage from the at least one power supply unit.

[0016] Further, there is provided herein a computer program product comprising instructions, which when executed on at least one processor, cause the at least one processor to carry out any of the above-mentioned methods performed by the network node. Further, there is provided herein a computer-readable storage medium having stored thereon a computer program product comprising instructions, which when executed on at least one processor, cause the at least one processor to carry out a method according to any of the above-mentioned methods performed by the network node.

[0017] There is disclosed herein a network node for improving and increasing overall operational efficiency by setting an output voltage based on an output from a computational model. This can be done by controlling in advance, e.g. deactivating VRLA batteries, increasing the system voltage to e.g. -57.5 VDC. By using a computational model, e.g. a machine learning (ML) model, to predict when a power outage, e.g. a grid failure, occurs, VRLA batteries can be connected in time (before needed). The PSU AC input voltage can also be measured to detect a power outage and reconnect VRLA batteries.

[0018] The method can be applicable for operators in e.g. Western countries if the grid is stable and VRLA batteries are only used in backup mode. The method can be initiated as a service by initial tuning for radio and power. The functionality can only require software (SW) changes and no additional hardware (HW) units. The energy distribution savings achieved from -54.5 to -57.5 VDC output voltage corresponds to an improvement of saving 5 A = 10% or an improvement of saving 8 A = 4.6%. In embodiments herein, no DC / DC booster is needed which can increase the failure rate. Embodiments herein thus improve operation of a network node in a communications network. BRIEF DESCRIPTION OF DRAWINGS

[0019] Embodiments will now be described in more detail in relation to the enclosed drawings, which are:

[0020] Figure 1 is a schematic overview illustrating a network node according to prior art;

[0021] Figure 2 is a schematic overview illustrating a network node according to prior art; is a schematic overview illustrating a network node according to prior art;

[0022] Figure 3 is a schematic overview illustrating a communication network according to embodiments herein;

[0023] Figure 4 is a combined signaling scheme and flow chart according to embodiments herein;

[0024] Figure 5 is a combined signaling scheme and flow chart according to embodiments herein;

[0025] Figure 6 is a block diagram illustrating a network node according to embodiments herein;

[0026] Figure 7 is a signaling scheme according to embodiments herein;

[0027] Figure 8 is a signaling scheme according to embodiments herein;

[0028] Figure 9 is a schematic flow chart illustrating a method performed by a network node according to embodiments herein;

[0029] Figure 10 is a block diagram illustrating a network node according to embodiments herein;

[0030] Figure 11 a telecommunication network connected via an intermediate network to a host computer is schematically illustrated;

[0031] Figure 12 is a general block diagram of a host computer communicating via a base station with a user equipment over a partially wireless connection; and

[0032] Figures 13-16 is a flow chart illustrating a method implemented in a communication system including a host computer, a base station and a user equipment. DETAILED DESCRIPTION

[0033] Embodiments herein can be described in relation to network nodes within the context of 3GPP NR radio technology (3GPP TS 38.300 V15.2.0 (2018-06)), e.g. using gNB as radio network nodes. It should be understood that the problems and solutions described herein are equally applicable to wireless access networks and network nodes implementing other access technologies and standards. NR is used as an example technology to which the embodiments are applicable, and hence, in the description, using NR is particularly useful for understanding the problems and the solutions thereto. In particular, the embodiments are also applicable to 3GPP LTE or 3GPP LTE and NR integration (also denoted as non-standalone NR).

[0034] Embodiments herein relate generally to communication networks.Figure 3 is a schematic overview illustrating a communication network 1. The communication network 1 comprises e.g. one or more RANs and one or more CNs. The communication network 1 can use one or more different technologies, e.g. Wi-Fi, Long Term Evolution (LTE), LTE-Advanced, Fifth Generation (5G), Wideband Code-Division Multiple Access (WCDMA), Global System for Mobile Communications / Enhanced Data rate for GSM Evolution (GSM / EDGE), Worldwide Interoperability for Microwave Access (WiMax), or Ultra Mobile Broadband (UMB), just to mention a few possible implementations. Embodiments herein relate to recent technology trends of particular interest in 5G systems, but the embodiments are also applicable to further development of existing communication systems, e.g. WCDMA and LTE.

[0035] In the communication network 1, wireless devices (e.g. UEs 10, like mobile stations, non-access point (non-AP) stations (STAs), STAs, user equipments, and / or wireless terminals) communicate via one or more access networks (AN) (e.g. RANs) to one or more core networks (CNs). The skilled person should understand that “UE” is a non-limiting term which refers to any terminal, wireless communication terminal, user equipment, Machine-Type Communication (MTC) device, Device-to-Device (D2D) terminal, IoT operable device or node, e.g. a smartphone, laptop, mobile phone, sensor, relay, mobile tablet, or even a small cell site capable of communicating with a network node within an area served by the network node using radio communication.

[0036] The communication network 1 comprises network nodes 12 providing radio coverage, e.g. over a geographical area (a service area 11) of a radio access technology (RAT), e.g. NR, LTE, Wi-Fi, WiMAX, etc. The network nodes 12 can be transmission and reception points, computing servers, databases, servers in communication with other servers, servers in a server farm, base stations, e.g. network nodes such as satellites, wireless local area network (WLAN) access points or access point stations (AP STAs), access nodes, access controllers, radio base stations, e.g. NodeBs, evolved Node Bs (eNBs, eNodeBs), gNodeBs (gNBs), base transceiver stations, base band units, access point base stations, base station routers, transmission devices of radio base stations, stand-alone access points, or any other network units or nodes, depending e.g. on the radio access technology and terminology used. Alternatively or additionally, the network nodes 12 can be controller nodes or packet processing nodes, etc. The network nodes 12 can be referred to as serving network nodes, where the service area 11 can be referred to as a serving cell or a primary cell, while the serving network nodes communicate with the UEs 10 in the form of DL transmissions to the UEs 10 and UL transmissions from the UEs 10. The network nodes 12 can be distributed nodes comprising a base band unit and one or more remote radio units.

[0037] It should be noted that a service area can be denoted as a cell, a beam, a beam group, etc. to define a radio coverage area.

[0038] According to embodiments herein, when deployed on e.g. a current network node, the radio unit of the network node 12 can increase the total power consumption on the network node 12. In order to obtain energy efficiency improvements in the network node 12, there is a need to reduce the power loss in the power distribution infrastructure, especially the current, since P = R * I 2 where P is the power, R is the resistance, and I is the current.

[0039] The network node 12 comprises at least one power supply unit (PSU), e.g. connected to a power grid, and one or more additional power supply units (PUs), e.g. movable power storage / energy storage, for supplying power to the network node 12. The additional power supply units can comprise VRLA batteries, e.g. the VRLA batteries described above. The operating voltage of the VRLA batteries is between -57.5 VDC and -40.0 VDC, where the nominal voltage of the VRLA batteries is -54.5 VDC. The VRLA batteries can only withstand 8 hours of continuous operation at -57.5 VDC, otherwise the VRLA batteries will degrade and affect the battery life.

[0040] According to embodiments herein, the network node 12 obtains an output from a computational model, such as a machine learning (ML) model, and based on the obtained output, sets the output voltage from at least one power supply unit, i.e. sets the operating voltage. That is, according to the output of the computational model, the network node 12 can set the operating voltage of one or more PSUs, e.g. to -57.5 VDC, since the input voltage of the PSU indicates, e.g. that no disturbance to the grid will occur. Thus, the computational model is able to provide an automatic dynamic control of the one or more power supply units, and is able to increase the network efficiency by introducing an automatic dynamic control of active PSUs. Additionally, the network node 12 can also use the output from the computational model as a central component of the control logic of the network node 12 to activate or not activate additional PUs by, e.g. activating a battery switch, such as a battery fuse unit (BFU). For example, the control can be such that when the system voltage is increased to -57.5 VDC, the additional PU, e.g. a VRLA battery, is disconnected, see Figure 6 .

[0041] Embodiments herein are based on a control of two input parameters of the probability of no disturbance or outage on the grid or AC voltage, i.e. the reliability of the power supply to one or more PSUs, to set the output voltage and determine whether to disconnect an additional PU. Thus, the two parameters can be a ML sensing of the PSU voltage alternating current (VAC) and a ML prediction model of the outage. If the probability of an outage, as indicated by the output from the computational model, is high, i.e. above a set threshold, e.g. a likelihood of 25%, the additional PU is immediately reconnected.

[0042] Figure 4 is a schematic combined signaling scheme and flow chart illustrating embodiments herein.

[0043] Action 401. The network node 12, or any network node, collects data, which is denoted previous data to be fed to the computational model. The previous data can comprise the operating state of the power feed to one or more PSUs.

[0044] Action 402. The network node 12 can then send the collected previous data to another network node or server that trains the computational model.

[0045] Action 403. The other network node 13 can then use the collected previous data to train the computational model.

[0046] Action 404. The network node 12 can also collect current data indicating a specific operating state. For example, the current data can comprise the PSU input voltage, such as VAC, and / or the output from the ML prediction model of the outage.

[0047] Action 405. The network node 12 can send the collected current data to another network node 13.

[0048] Action 406. The other network node 13 can then execute the computational model using the received collected data as input to the computational model. An output is generated from the computational model. For example, the output can indicate an operational state to one or more PSU feeds.

[0049] Action 407. The other network node 13 can then send the output to the network node 12.

[0050] Action 408. The network node 12 can then set an output voltage of one or more active PSUs based on the received output. For example, the output indicates a stable feed to one or more PSUs and thus the network node 12 can set the output voltage to a higher voltage as additional PUs can not be used or activated.

[0051] Figure 5 is a schematic combined signaling scheme and flowchart illustrating embodiments herein.

[0052] Action 501. The network node 12 or any network node can obtain or collect data to be fed to a computational model. The data can include an operational state to one or more PSU feeds.

[0053] Action 502. The network node 12 can then send the collected data to another network node or server that trains the computational model. It should be noted that the data can be taken or obtained from one or more network nodes including or not including the network node 12.

[0054] Action 503. The other network node 13 can then train the computational model using the collected data.

[0055] Action 504. The other network node 13 can then send the trained computational model or a part thereof to the network node 12.

[0056] Action 505. The network node 12 can then execute or run the computational model using current data as input to the computational model. For example, the current data can include PSU input voltages such as VAC and / or output from a ML prediction model of a power outage. An output is generated from the computational model. For example, the output can indicate an operational state to one or more PSU feeds, e.g. whether it is a stable operational state.

[0057] Action 506. The network node 12 can then disconnect additional PUs such as batteries based on the output, e.g. in case of a stable operational state.

[0058] Action 507. The network node 12 can then increase the output voltage of one or more PSU, i.e. increase the operating voltage, based on the output.

[0059] Action 508. Additionally, the network node 12 can then connect additional power supply units according to additional execution of the computational model and can be based on additional output. It should be noted that the current power architecture of the network node also contains a power filter unit (PFU), which is a unit that collects or obtains charge during operation, so that the PFU can act as an intermediate backup unit and can be used during connection of additional power supply units, since the PFU is supplying power to the radio unit until the additional PU is connected.

[0060] Action 509. The network node 12 can also decrease the voltage to avoid damaging the additional power supply units based on the additional output. Thus, the network node 12 can set the output voltage of one or more PSU to a different level, e.g. decrease the output voltage. For example, the output indicates a power outage of one or more PSU, and thus the network node 12 can set the output voltage to a lower voltage, since additional PU can be used or activated.

[0061] As mentioned above, the current power architecture of the network node can also contain a PFU, which acts as an intermediate backup unit between switching intervals of the output voltage, thus storing power during intervals between inactive batteries. The stored voltage depends on the number of radio units installed for the network node 12 acting as a filter. Furthermore, in the power architecture of the network node, the PSU can also include a hold up, which can also be used as an intermediate backup unit acting as a capacitive storage device.

[0062] The computational model can suggest to the local controller, e.g. local processor, to adapt the suggested changes from the computational model and generate observations in combination with different training sets, including the cloud.

[0063] The input to the computational model can be one or more of the following:

[0064] Feeding related data, e.g. PSU AC voltage disturbance time tracking (10 milliseconds (ms) per half cycle), which is continuously observed from the PSU and can produce a failure probability number, e.g. all measurements made less than 10 ms are considered as disturbances in the computational model that makes predictions and are quantified.

[0065] The probability of “no” power outage, which the network node 12 can use as input to control the voltage settings. Note: the probability of “no” power outage for a typical operator in e.g. western countries is almost 99.99%.

[0066] Thus, the computational model can be used in this approach to predict the operational state of the network node 12, the operational state of AC voltage disturbance outages and fault outages, and base the operational state on probabilistic calculations.

[0067] In some cases, the computational model can suggest to reconnect an additional power unit for a critical event, e.g. where the additional power unit is a Sealed Lead Acid (SLA) battery. These cases are e.g.:

[0068] - SLA based connection where important customers are in the cell.

[0069] - Event based battery connection.

[0070] - In case of high traffic demand (e.g. above a threshold), adopt a connection with a backup battery to reduce the risk of disconnecting the battery.

[0071] - In critical Machine Type Communication (MTC), an additional power unit can be connected to improve reliability.

[0072] - Policy based connection.

[0073] - Or, when performing a battery self-test.

[0074] Figure 6 A block diagram is shown depicting an apparatus of a baseband unit in e.g. the network node 12 connected to a remote radio unit via a 60 meter (m) cable. The network node 12 can comprise a plurality of PSUs and a VRLA connected to a PDU e.g. via a BFU. A computational model such as ML senses VAC drop trends and / or disturbance trends on current running data. The ML control is used to predict the frequency of battery usage. The ML control can adjust the voltage Vadj. The apparatus can also disconnect the battery (e.g. the BFU is disconnected) at a stable operating voltage (voltage is set to -57.5 VDC) and connect the battery at a non-stable operating state (set the output voltage to e.g. 54.5 VDC).

[0075] In geographical areas where outages are frequent and typically follow seasonal patterns, it is simpler to predict outages well in advance. However, in areas where outages are very rare, it is necessary to train over a much longer time scale to capture the patterns in the readings from the PSU just before an outage or when the battery is used.

[0076] Additionally, observations and context information from the cells in the area (town or place) can be used as input to decide whether a battery should be connected or not.

[0077] Figure 7The sequence diagram in FIG. 6 contains all the active actor network nodes that depict the training process of the ML component. The inputs are the time-stamped historical voltage readings from the PSU, a signal of whether the site is running using the battery or AC power from the battery, and additional inputs from the global controller in the cloud (which sends observations from neighboring sites at every time stamp), such as PSU, battery, important customers, or event information. The PSU can send aggregated periodic voltage readings of the PSU to the global controller, while the battery can send periodic on / off signals to the global controller.

[0078] As shown in FIG. 7, once the training process is complete using a statistically significant number of training samples that constitute a blackout, the local controller / baseband can dynamically connect and disconnect the battery based on the blackout prediction from the ML component (see FIG. 8). Figure 7 Figure 8 The blackout probability and context determine whether the local controller signals a battery “connect” or “disconnect” signal. The ML component with a feedback loop can retrain after every batch of data samples (and in particular, after a blackout).

[0079] With the presence of the global controller, sites can share their patterns with each other. For example, voltage fluctuations in neighboring sites can indicate a potential problem in the area. Similarly, the global controller can explicitly signal to keep the battery connected due to an important event or high criticality service usage in the area with the help of human expert input.

[0080] PSU AC = input voltage sensing for half-sine period to reconnect battery

[0081] BFU = connect and disconnect when blackout probability is very low

[0082] PSU = when battery is disconnected => increase system voltage from -54.5 VDC to -57.5 VDC

[0083] Cloud: can be used for training dataset

[0084] The computational model can include the following actions:

[0085] 1. The computational model can compute the probability of no blackout (i.e., a stable power grid).

[0086] 2. The computational model can also continue to observe and track AC voltage at the input of the network node.

[0087] 3. The computational model can also compute a half-sine wave disconnect by observing PSU AC voltage disturbances, such as 10 ms.

[0088] 4. The computational model can also compute the probability of “no” power disturbance and a blackout probability.​

[0089] 5. Automatically connect and reconnect batteries based on power disturbance probability and / or outage probability and AC voltage tracking input.

[0090] 6. The computational model can also generate a distributed training set of disturbances and controls of BFU and PSU voltages in a cloud network.

[0091] 7. The computational model can also automatically connect batteries under "priority" conditions, i.e. conditions can be based on one or more of:

[0092] a) SLA based connection (important customers in the cell).

[0093] b) Event based battery connection (e.g. predicted upcoming storm).

[0094] c) Traffic demand, connection (please do not risk disconnecting battery.

[0095] d) Connect battery in critical MTC to improve reliability.

[0096] e) Policy based connection.

[0097] f) Or, when performing battery self-test.

[0098] The method actions for handling operations (e.g. selecting output voltage from one or more PSUs) performed by a network node 12 in a communication network according to embodiments will now be described with reference to the flowchart shown in Figure 9 These actions do not have to be taken in the order stated below, but can be taken in any suitable order. Actions performed in some embodiments are marked with dashed boxes. As mentioned above, the network node 12 comprises at least one power supply unit and one or more additional power supply units for powering the network node 12 (e.g. powering a radio unit connected to the network node 12). The one or more additional power supply units can comprise one or more rechargeable units, energy storage devices and / or batteries. It should be noted that the network node can be a distributed radio network node comprising at least one remote radio unit, one baseband unit together with the at least one power supply unit, and one or more additional power supply units. The network node can be a base station, an access node, a server or a communication node.

[0099] Action 900. The network node 12 can internally or externally provide data of the network node 12 to train a computational model. The data can comprise: an indication of one or more power failures, one or more set voltages, a condition of one or more additional power supply units, and an indication of an amount of usage of the one or more additional power supply units at the one or more set voltages. The computational model can be trained at the network node 12 or at another network node 13. The computational model can be a machine learning model, e.g. a neural network or the like.

[0100] Action 901. The network node 12 obtains an output from the computational model.

[0101] Action 902. The network node 12 can activate usage or non-usage of the one or more additional power supply units / energy storage devices based on the set voltage, e.g. switching on / off a BFU unit connected to the one or more VRLA batteries. The network node 12 can activate usage or non-usage by connecting or disconnecting a fuse unit connected to the one or more additional power supply units, also based on the obtained output of the computational model.

[0102] Action 903. The network node 12 can use a power filter unit to power until the one or more additional power supply units are connected for power (e.g. a set time interval) when activating usage of the one or more additional power supply units. The network node 12 can be a radio network node and the power filter unit can be an intermediate backup unit for bridging power to one or more radio units of the radio network node.

[0103] Action 904. The network node 12 sets an output voltage from the at least one power supply unit also based on the obtained output. For example, when the obtained output of the computational model indicates a stable grid to the at least one power supply unit, the network node 12 can set the output voltage by: increasing the output voltage above a threshold value, e.g. setting the output voltage to -57.5 VDC; and deactivating one or more additional power supply units, e.g. disconnecting VRLA batteries, e.g. dynamically.

[0104] Figure 10 is a block diagram illustrating a network node 12 for handling operations, e.g. setting an operating voltage, in a communication network. According to embodiments herein, the network node comprises at least one power supply unit 1010 and one or more additional power supply units / energy storage units 1011 for powering the network node 12.

[0105] The network node 12 can comprise processing circuitry 1001, e.g. one or more processors, configured to perform the methods herein.

[0106] The network node 12 can comprise an obtaining unit 1002, e.g. a receiver or a transceiver. The network node 12, the processing circuitry 1001 and / or the obtaining unit 1002 is configured to obtain an output from a computational model.

[0107] The network node 12 can comprise an operating unit 1003. The network node 12, the processing circuitry 1001 and / or the operating unit 1003 is configured to set an output voltage from at least one power supply unit 1010 based on the obtained output. For example, when the obtained output of the computational model indicates a stable power grid to the at least one power supply unit 1010, the network node 12, the processing circuitry 1001 and / or the operating unit 1003 can be configured to set the output voltage by: increasing the output voltage above a threshold value; and dynamically deactivating one or more additional power supply units / energy storage devices 1011.

[0108] The network node 12 can comprise an activating unit 1004, e.g. a fuse unit. The network node 12, the processing circuitry 1001 and / or the activating unit 1004 can be configured to activate the use or non-use of one or more additional power supply units 1011, e.g. switch on / switch off a BFU unit connected to a VRLA battery, based on the set voltage. The network node 12, the processing circuitry 1001 and / or the activating unit 1004 can be configured to use a power filter unit (PFU) 1012 for power supply when activating the use of one or more additional power supply units 1011 until the one or more additional power supply units are connected for power supply. The network node can e.g. be a radio network node and the power filter unit can be an intermediate backup unit for bridging power supply to one or more radio units of the radio network node. The network node 12, the processing circuitry 1001 and / or the activating unit 1004 can be configured to activate the use or non-use by also connecting or disconnecting a fuse unit 1012 connected to the one or more additional power supply units 1011 based on the obtained output of the computational model. The one or more additional power supply units 1011 can comprise one or more chargeable units, energy storage devices and / or batteries.

[0109] The network node 12 can comprise a providing unit 1005, e.g. a transmitter or a transceiver. The network node 12, the processing circuitry 1001 and / or the providing unit 1005 can be configured to provide data of the network node 12 to train the computational model. The data can comprise: an indication of one or more power failures, one or more set voltages, a status of one or more additional power supply units, and an indication of an amount of use of the one or more additional power supply units at the one or more set voltages. The computational model can be trained at the network node 12 or at another network node 13.

[0110] The network node can be a distributed radio network node comprising at least one remote radio unit, one baseband unit located together with at least one power supply unit 1010, and one or more additional power supply units 1011.

[0111] The computational model can be a machine learning model, such as a neural network or a computational tree model.

[0112] The network node can be a base station, an access node, a server, or a communication node.

[0113] The network node 12 also comprises a memory 1006. The memory comprises one or more units to be used for storing data, such as output voltage, outage, operational data, applications that when executed perform the methods disclosed herein, etc. The network node 12 comprises a communication interface comprising, e.g., one or more antennas.

[0114] The methods for the network node 12 according to embodiments described herein are respectively implemented by means of, e.g., a computer program product 1007 or a computer program comprising instructions, i.e., software code portions, which, when executed on at least one processor, cause the at least one processor to carry out the actions described herein as being performed by the network node 12. The computer program product 1007 can be stored on a computer-readable storage medium 1008, e.g., a Universal Serial Bus (USB) stick, an optical disc, etc. The computer-readable storage medium 1008 having stored thereon the computer program product can comprise instructions which, when executed on at least one processor, cause the at least one processor to carry out the actions described herein as being performed by the network node 12. In some embodiments, the computer-readable storage medium can be a non-transitory or a transitory computer-readable storage medium.

[0115] In some embodiments, the more general term "radio network node" is used, and it can correspond to any type of radio network node or any network node that communicates with a wireless device and / or another network node. Examples of network nodes are NodeB, master eNB, secondary eNB, network node belonging to a master cell group (MCG) or secondary cell group (SCG), base station (BS), multi-standard radio (MSR) radio node such as a MSR BS, eNodeB, network controller, radio network controller (RNC), base station controller (BSC), relay, donor node controlling relay, base transceiver station (BTS), access point (AP), transmission points, transmission nodes, remote radio unit (RRU), nodes in distributed antenna system (DAS), core network node (e.g., mobile switching center (MSC), mobile management entity (MME), etc.), operations and maintenance (O&M), operations support system (OSS), self-organizing network (SON), positioning node (e.g., evolved serving mobile location center (E-SMLC), minimize drive test (MDT), etc.

[0116] In some embodiments, the non-limiting term "wireless device" or "user equipment (UE)" is used, and it refers to any type of wireless device communicating with a network node and / or another UE in a cellular or mobile communication system. Examples of UEs are target device, device-to-device (D2D) UE, proximity capable UE (also known as ProSe UE), machine type UE or UE with machine-to-machine (M2M) communication capability, PDA, PAD, Tablet, mobile terminal, smart phone, laptop embedded equipped (LEE), laptop mounted equipment (LME), USB dongle, etc.

[0117] The embodiments are described for 5G. However, the embodiments are applicable to any RAT or multi-RAT systems where a UE receives and / or transmits signals (e.g., data), such as LTE, LTE FDD / TDD, WCDMA / HSPA, GSM / GERAN, Wi Fi, WLAN, CDMA2000, etc.

[0118] As will be readily appreciated by those familiar with communications design, the functional devices or circuits can be implemented using digital logic and / or one or more microcontrollers, microprocessors or other digital hardware. In some embodiments, multiple or all of the various functions can be implemented together, such as in a single application-specific integrated circuit (ASIC), or in two or more separate devices with appropriate hardware and / or software interfaces between them. For example, multiple functions can be implemented on a processor shared by other functional components of a wireless device or network node.

[0119] Alternatively, various functional elements of the processing devices discussed can be provided through the use of dedicated hardware, as would be understood by one skilled in the art. Thus, the term "processor" or "controller" as used can encompass one or more hardware processors, and the term "software" can encompass one or more software modules that can be executed by one or more hardware processors. Additionally, the term "processor" or "controller" as used can explicitly encompass the above combinations of hardware and software. The term "software" can encompass routines, programs, applications, data, and / or other logic that can be executed by one or more hardware processors. The term "non-volatile storage" as used can encompass one or more types of storage devices such as, for example, one or more types of disk storage, semiconductor memory, or other non-volatile storage elements. The term "non-transitory" is used herein to describe a computer-readable medium, device, or system that does not include a transitory signal. Thus, the term "non-transitory” should be understood to encompass the above combinations of hardware and software. The above combinations of hardware and software should be considered as being encompassed within the scope of the present disclosure.

[0120] Reference Figure 11 With reference to

[0121] The telecommunications network 3210 is itself connected to a host computer 3230, which can embody a server, a cloud-implemented server, a distributed server, or a combination of servers, or be embodied as processing resources in a server farm, server arrangement, or server centre. The host computer 3230 can be under the ownership or control of a service provider, or can be operated by the service provider or on behalf of the service provider. The connections 3221, 3222 between the telecommunications network 3210 and the host computer 3230 can extend directly from the core network 3214 to the host computer 3230 or can go via an optional intermediate network 3220. The intermediate network 3220 can be one of, or a combination of more than one of, the Internet, a public land mobile network, a private land mobile network, a local area network, a wide area network, a metropolitan area network, a cable network, a fiber optic network, or any other suitable wired or wireless network, including networks that are not yet in existence. The intermediate network 3220 can be a partially or fully managed network.

[0122] Overall, Figure 11 The communication system enables connectivity between the connected UEs 3291, 3292 and the host computer 3230. The connectivity can be described as an over-the-top (OTT) connection 3250. The host computer 3230 and the connected UEs 3291, 3292 are configured to communicate data and / or signaling over the OTT connection 3250 using the access network 3211, the core network 3214, any intermediate network 3220, and possible further infrastructure (not shown) as intermediaries. The OTT connection 3250 can be transparent in the sense that the participating communication devices through which the OTT connection 3250 passes are unaware of the application, session or other communication that gives rise to the data being transmitted over the OTT connection 3250. For example, a base station 3212 can not be informed or need not know that incoming downlink communications with which it is involved were originally addressed to the host computer 3230 and were only prepped to the connected UE 3291 because the data originated from the host computer 3230. Similarly, the base station 3212 does not need to be aware that of the fate of outgoing uplink communications from the UE 3291 to the host computer 3230 that pass through the base station 3212; the base station 3212 can not be informed or need not know that the outgoing communications were addressed to the host computer 3230.

[0123] According to an embodiment, example implementations of the UE, base station, and host computer discussed in the previous paragraphs will now be described with reference to Figure 12 In the communication system 3300, host computer 3310 comprises hardware 3315 including communication interface 3316 configured to set up and maintain a wired or wireless connection with an interface of a different communication device of the communication system 3300. The host computer 3310 further comprises processing circuitry 3318, which can have storage and / or processing capabilities. In particular, the processing circuitry 3318 can comprise one or more programmable processors, application-specific integrated circuits, field programmable gate arrays or combinations of these (not shown) adapted to execute instructions. The host computer 3310 further comprises software 3311, which is stored in or accessible by the host computer 3310 and executable by the processing circuitry 3318. The software 3311 includes a host application 3312. The host application 3312 can be operable to provide a service to a remote user, such as a UE 3330 connecting via an OTT connection 3350 terminating at the UE 3330 and the host computer 3310. In providing the service to the remote user, the host application 3312 can provide user data which is transmitted using the OTT connection 3350.

[0124] The communication system 3300 further includes the base station 3320 provided in a telecommunication system and the base station 3320 comprises hardware 3325 enabling it to communicate with the host computer 3310 and the UE 3330. The hardware 3325 of the base station 3320 can include a communication interface 3326 for Figure 12 establishing and maintaining a wired or wireless connection with an interface of a different communication device of the communication system 3300 as well as a radio interface 3327 for establishing and maintaining at least a wireless connection 3370 with the UE 3330 located in a coverage area 3361 (not shown in FIG. 13) served by the base station 3320. The communication interface 3326 can be configured to facilitate a connection 3360 to the host computer 3310. The connection 3360 can be direct or it can pass through the core network (not shown in FIG. 13) of the telecommunication system and / or through one or more intermediate networks outside the telecommunication system. In the embodiment shown, the hardware 3325 of the base station 3320 further includes processing circuitry 3328 which can comprise one or more programmable processors, application-specific integrated circuits, field programmable gate arrays or combinations of these (not shown) adapted to execute instructions. The base station 3320 further has software 3321 stored internally or accessible via an external connection. Figure 12

[0125] The communication system 3300 further includes the UE 3330 already referred to. The hardware 3335 of the UE 3330 can include a radio interface 3337 configured to set up and maintain a wireless connection 3370 with a base station serving a coverage area in which the UE 3330 currently is located. The hardware 3335 of the UE 3330 further includes processing circuitry 3338 which can comprise one or more programmable processors, application-specific integrated circuits, field programmable gate arrays or combinations of these (not shown) adapted to execute instructions. The UE 3330 further comprises software 3331 stored internally or accessible via an external connection (not shown) and executable by the processing circuitry 3338. The software 3331 includes a client application 3332. The client application 3332 can be operable to provide a service to a human or non-human user via the UE 3330 with the support of the host computer 3310. In the host computer 3310, an executing host application 3312 can communicate with the executing client application 3332 via the OTT connection 3350 terminating at the UE 3330 and the host computer 3310. In providing the service to the user, the client application 3332 can receive request data from the host application 3312 and provide user data in response to the request data. The OTT connection 3350 can carry both the request data and the user data. The client application 3332 can interact with the user to generate the user data.

[0126] Note that Figure 12 ​The host computer 3310, base station 3320, and UE 3330 shown can be respectively connected to... Figure 11 The host computer 3230, one of the base stations 3212a, 3212b, and 3212c, and one of the UEs 3291 and 3292 are identical. That is to say, the internal working principles of these entities can be as follows: Figure 12 As shown, and independently, the surrounding network topology can be Figure 11 The surrounding network topology.

[0127] exist Figure 12 The OTT connection 3350 has been abstractly depicted to illustrate communication between the host computer 3310 and the user equipment 3330 via the base station 3320, without explicitly referencing any intermediate devices or the precise routing of messages via these devices. The network infrastructure can determine the routing, and can be configured to hide the routing from the UE 3330, the service provider operating the host computer 3310, or both. When the OTT connection 3350 is active, the network infrastructure can further make decisions, dynamically changing the routing accordingly (e.g., based on load balancing considerations or network reconfiguration).

[0128] The wireless connection 3370 between UE 3330 and base station 3320 is based on the teachings of embodiments described throughout this disclosure. One or more of the various embodiments improve the performance of OTT services provided to UE 3330 using OTT connection 3350 (where wireless connection 3370 forms the final segment). More precisely, the teachings of these embodiments enable improved operating voltage to enhance the performance of network nodes, thereby providing benefits such as improved battery life and better responsiveness.

[0129] A measurement procedure can be implemented to monitor the data rate, latency, and other factors on which the one or more embodiments improve. In response to a change in a measurement result, there can also be an optional network functionality to reconfigure the OTT connection 3350 between the host computer 3310 and the UE 3330. The measurement procedure and / or the network functionality to reconfigure the OTT connection 3350 can be implemented in the software 3311 of the host computer 3310 or in the software 3331 of the UE 3330, or both. In embodiments, sensors (not shown) can be deployed in or in association with the communication devices through which the OTT connection 3350 passes; the sensors can participate in the measurement procedure by supplying values of the monitored quantities exemplified above, or supplying values of other physical quantities from which software 3311, 3331 can compute or estimate the monitored quantities. The reconfiguring of the OTT connection 3350 can include message format, retransmission settings, preferred routing, etc. The reconfiguring need not affect the base station 3320, and it can be unknown or invisible to the base station 3320. Procedures and functionalities of this kind are known, specified or customary in the art. In certain embodiments, measurements can involve proprietary UE signaling facilitating the host computer's 3310 measurements of throughput, propagation times, error rates, etc. The measurements can be implemented in that the software 3311, 3331 causes messages to be transmitted, in particular empty or 'dummy' messages, using the OTT connection 3350 while it monitors propagation times, errors, etc.

[0130] Figure 13 is a flow chart illustrating a method implemented in a communication system, in accordance with one embodiment. The communication system includes a host computer, a base station and a UE which can be those described with reference to Figure 11 and Figure 12 Figures 15 and 16. In the interest of brevity only reference to new Figure 13 features will be included in the description of this section. In a first step 3410 of the method, the host computer provides user data. In an optional substep 3411 of the first step 3410, the host computer provides the user data by executing a host application. In a second step 3420, the host computer initiates a transmission carrying the user data to the UE. In an optional third step 3430, the base station transmits to the UE the user data carried in the transmission that the host computer initiated. In an optional fourth step 3440, the UE executes a client application associated with the host application executed by the host computer.

[0131] Figure 14 is a flow chart illustrating a method implemented in a communication system, in accordance with one embodiment. The communication system includes a host computer, a base station and a UE which can be those described with reference to Figure 11 and Figure 12The host computer, base station, and UE described. To simplify the present disclosure, only references to the accompanying Figure 14 Figures are included in this section. In a first step 3510 of the method, the host computer provides user data. In an optional substep (not shown) the host computer provides the user data by executing a host application. In a second step 3520, the host computer initiates a transmission carrying the user data to the UE. The transmission can pass via the base station, in accordance with the teachings of the embodiments described throughout this disclosure. In an optional third step 3530, the UE receives the user data carried in the transmission.

[0132] Figure 15 is a flow chart illustrating a method implemented in a communication system, in accordance with one embodiment. The communication system includes a host computer, a base station and a UE which can be those described with reference to Figure 11 and Figure 12 The host computer, base station, and UE described. To simplify the present disclosure, only references to the accompanying Figure 15 Figures are included in this section. In an optional first step 3610 of the method, the UE receives input data provided by the host computer. Additionally or alternatively, in an optional second step 3620, the UE provides user data. In an optional substep 3621 of the second step 3620, the UE provides the user data by executing a client application. In another optional substep 3611 of the first step 3610, the UE executes a client application which provides the user data in reaction to the received input data provided by the host computer. In providing the user data, the executed client application can further take into account user input received from the user. Regardless of the specific manner in which the user data is provided, the UE initiates, in an optional third substep 3630, transmission of the user data to the host computer. In a fourth step 3640 of the method, the host computer receives the user data transmitted from the UE, in accordance with the teachings of the embodiments described throughout this disclosure.

[0133] Figure 16 is a flow chart illustrating a method implemented in a communication system, in accordance with one embodiment. The communication system includes a host computer, a base station and a UE which can be those described with reference to Figure 11 and Figure 12 The host computer, base station, and UE described. To simplify the present disclosure, only references to the accompanying Figure 16 Figures are included in this section. In an optional first step 3710 of the method, the base station receives user data from the UE, in accordance with the teachings of the embodiments described throughout this disclosure. In an optional second step 3720, the base station initiates a transmission of the received user data to the host computer. In a third step 3730, the host computer receives the user data carried in the transmission initiated by the base station.

[0134] It will be understood that the foregoing description and drawings represent non-limiting examples of the methods and apparatus taught herein. Thus, the apparatus and techniques taught herein are not limited by the foregoing description and drawings. Rather, the embodiments herein are limited only by the following claims and their legal equivalents.

[0135] Abbreviations

[0136] A Ampere

[0137] AC Alternating Current

[0138] BFU Battery Fuse Unit

[0139] CAPEX Capital Expenditure

[0140] CMTC Critical Machine Type Communication

[0141] DC / DC Direct Current Converter

[0142] I Current

[0143] ML Machine Learning

[0144] OPEX Operational Expenditure

[0145] P Power

[0146] PFU Power Filter Unit

[0147] PDU Power Distribution Unit

[0148] PM Performance Manager

[0149] PSU Power Supply Unit

[0150] R Resistance

[0151] TCO Total Cost of Ownership

[0152] VDC Voltage Direct Current

[0153] VRLA Valve Regulated Lead Acid

Claims

1. A method for processing operations of a network node (12) in a communication network, performed by the node itself, wherein... The network node includes at least one power supply unit and one or more additional power supply units for supplying power to the network node, and the method includes: - Obtain (901) outputs from the computational model; - Based on the obtained output, set (904) the output voltage from the at least one power supply unit; and -Based on the set voltage, activate (902) the use or non-use of the one or more additional power supply units. The output of the obtained calculation model indicates a stable power grid to the at least one power supply unit. Then, setting the output voltage includes: increasing the output voltage above a threshold; and deactivating the one or more additional power supply units.

2. The method according to claim 1, further comprising: When activating the use of the one or more additional power supply units, power is supplied using the (903) power filter unit until the one or more additional power supply units are connected to supply power.

3. The method according to claim 2, wherein, The network node is a radio network node, and the power filter unit is an intermediate backup unit for bridging power to one or more radio units of the radio network node.

4. The method according to any one of claims 1-3, wherein, Activating or deactivating includes: also based on the output of the obtained computational model, connecting or disconnecting the fuse units connected to the one or more additional power supply units.

5. The method according to any one of claims 1-3, further comprising: - Provide data from the network nodes (900) to train the computational model.

6. The method according to claim 5, wherein, The data includes: indications of one or more power failures, one or more set voltages, the status of the one or more auxiliary power supply units, and indications of the usage of the one or more auxiliary power supply units at the one or more set voltages.

7. The method according to claim 5, wherein, The computational model is trained at the network node (12) or at another network node (13).

8. The method according to any one of claims 1-3 and 6-7, wherein, The one or more additional power units include: one or more rechargeable units, energy storage devices, batteries, or power storage devices.

9. The method according to any one of claims 1-3 and 6-7, wherein, The network node is a distributed radio network node, which includes: at least one remote radio unit, a baseband unit located together with the at least one power supply unit, and the one or more auxiliary power supply units.

10. The method according to any one of claims 1-3 and 6-7, wherein, The computational model is a machine learning model.

11. The method according to any one of claims 1-3 and 6-7, wherein, The network node is a base station, access node, server, or communication node.

12. A network node (12) in a communication network, for processing operations of the network node, wherein, The network node (12) includes at least one power supply unit (1010) and one or more additional power supply units (1011) for supplying power to the network node (12), and wherein the network node (12) is configured to: Obtain the output from the computational model; Based on the obtained output, the output voltage from the at least one power supply unit (1010) is set; and Based on the set voltage, the use or non-use of the one or more additional power supply units (1011) is activated. The output of the obtained computational model indicates a stable power grid to the at least one power supply unit, and then the network node is configured to set the output voltage by increasing the output voltage above a threshold and dynamically deactivating the one or more additional power supply units.

13. The network node (12) according to claim 12, wherein, The network node (12) is configured to use a power filter unit to supply power when the use of the one or more additional power units is activated, until the one or more additional power units are connected to supply power.

14. The network node (12) according to claim 13, wherein, The network node (12) is a radio network node, and the power filter unit is an intermediate backup unit for bridging power to one or more radio units of the radio network node.

15. The network node (12) according to any one of claims 12-14, wherein, The network node (12) is configured to activate or deactivate by: connecting or disconnecting fuse units connected to the one or more additional power supply units based on the output of the obtained computational model.

16. The network node (12) according to any one of claims 12-14, wherein, The network node (12) is also configured to provide data from the network node to train the computational model.

17. The network node (12) according to claim 16, wherein, The data includes: indications of one or more power failures, one or more set voltages, the status of the one or more auxiliary power supply units, and indications of the usage of the one or more auxiliary power supply units at the one or more set voltages.

18. The network node (12) according to claim 16, wherein, The computational model is trained at the network node (12) or at another network node (13).

19. The network node (12) according to any one of claims 12-14 and 17-18, wherein, The one or more additional power units include: one or more rechargeable units, energy storage devices, batteries, or power storage devices.

20. The network node (12) according to any one of claims 12-14 and 17-18, wherein, The network node is a distributed radio network node, which includes: at least one remote radio unit, a baseband unit located together with the at least one power supply unit, and the one or more auxiliary power supply units.

21. The network node (12) according to any one of claims 12-14 and 17-18, wherein, The computational model is a machine learning model.

22. The network node (12) according to any one of claims 12-14 and 17-18, wherein, The network node is a base station, access node, server, or communication node.

23. A computer program product comprising instructions that, when executed on at least one processor, cause the at least one processor to perform the method according to any one of claims 1-11 executed by the network node.

24. A computer-readable storage medium having stored thereon a computer program product comprising instructions that, when executed on at least one processor, cause the at least one processor to perform the method according to any one of claims 1-11 executed by the network node.

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