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EP-4132120-B1 - OPTIMIZING USAGE OF POWER USING SWITCH OFF OF CELLS

EP4132120B1EP 4132120 B1EP4132120 B1EP 4132120B1EP-4132120-B1

Inventors

  • MAGGI, Lorenzo
  • VALCARCE RIAL, Alvaro
  • MIHAILESCU, CLAUDIU
  • HOLMA, Maunu Elias
  • Feki, Afef

Dates

Publication Date
20260513
Application Date
20220613

Claims (13)

  1. An apparatus (700) comprising at least one processor (710), and at least one memory (720) including a computer program code, wherein the at least one memory (720) and the computer program code are configured, with the at least one processor (710), to cause the apparatus (700) to: obtain historical data from a plurality of access nodes (104) comprised in a network; determine, for at least one of the plurality of access nodes (104), a region comprising a set of values for threshold pairs that comprise minimum and maximum threshold pairs, wherein the region is a subset of all admissible thresholds, and the subset comprises elements sortable such that minimum and maximum thresholds are both non-decreasing; determine, from the region, one threshold pair, wherein the threshold pair defines pre-determined thresholds for determining if a cell is to be switched on or switched off; determine the one threshold pair by estimating a value for a function that determines values with which a probability that an average user throughput is higher than one of the pre-determined thresholds is equal to a pre-determined value and the value for the function is estimated using a Bayesian approach; provide the threshold pair to the network for deployment; and collect data regarding at least one key performance indicator from the plurality of access nodes (104).
  2. The apparatus (700) according to claim 1, wherein the historical data comprises one or more of the following: average throughput, physical resource block utilization, carrier frequency, average channel quality indicator and time stamps of a time window over which the data is averaged.
  3. The apparatus (700) according to any previous claim, wherein the apparatus (700) is further caused to obtain the historical data from the plurality of access nodes and determine the region comprising the set of values for threshold pairs that comprise the minimum and maximum threshold pairs again after a pre-determined time period has passed and/or based on a triggering event.
  4. The apparatus (700) according to any previous claim, wherein the apparatus (700) is caused to obtain the historical data from the plurality of access nodes (104) and determine the region, that is a search region, comprising the set of values for threshold pairs that comprise the minimum and maximum threshold pairs offline.
  5. The apparatus (700) according to any previous claim, wherein the apparatus (700) is further caused to determine the region individually for each access node (104) comprised in the plurality of access nodes (104).
  6. The apparatus according to any previous claim, wherein the apparatus (700) is comprised in or connected to an over the top node.
  7. A method comprising: obtaining historical data from a plurality of access nodes comprised in a network; determining, for at least one of the plurality of access nodes (104), a region comprising a set of values for threshold pairs that comprise minimum and maximum threshold pairs, wherein the region is a subset of all admissible thresholds, wherein the subset comprises elements sortable such that minimum and maximum thresholds are both non-decreasing; determining, from the region, one threshold pair, wherein the threshold pair defines pre-determined thresholds for determining if a cell is to be switched on or switched off; determining the one threshold pair by estimating a value for a function that determines values with which a probability that an average user throughput is higher than one of the pre-determined threshold is equal to a pre-determined value and the value for the function is estimated using a Bayesian approach; providing the threshold pair to the network for deployment; and collecting data regarding at least one key performance indicator from the plurality of access nodes (104).
  8. The method according to claim 8, wherein the method further comprises obtaining the historical data from the plurality of access nodes (104) and determining the region comprising the set of values for threshold pairs that comprise the minimum and maximum threshold pairs again after a pre-determined time period has passed and/or based on a triggering event.
  9. The method according to any of claim 7 to 8, wherein the method further comprises obtaining the historical data from the plurality of access nodes (104) and determining the region, that is a search region, comprising the set of values for threshold pairs that comprise the minimum and maximum threshold pairs offline.
  10. The method according to any of claims 7 to 9, wherein the method further comprises determining the region individually for each access node (104) comprised in the plurality of access nodes (104).
  11. The method according to any of claims 7 to 10, wherein the historical data comprises one or more of the following: average throughput, physical resource block utilization, carrier frequency, average channel quality indicator and time stamps of a time window over which the data is averaged.
  12. A non-transitory computer readable medium comprising program instructions for causing an apparatus (700) to perform at least the following: obtain historical data from a plurality of access nodes (104) comprised in a network; determine, for at least one of the plurality of access nodes (104), a region comprising a set of values for threshold pairs that comprise minimum and maximum threshold pairs, wherein the region is a subset of all admissible thresholds, wherein the subset comprises elements sortable such that minimum and maximum thresholds are both non-decreasing; determine, from the region, one threshold pair, wherein the threshold pair defines pre-determined thresholds for determining if a cell is to be switched on or switched off; determine the one threshold pair by estimating a value for a function that determines values with which a probability that an average user throughput is higher than one of the pre-determined thresholds is equal to a pre-determined value and the value for the function is estimated using a Bayesian approach; provide the threshold pair to the network for deployment; and collect data regarding at least one key performance indicator from the plurality of access nodes (104).
  13. The non-transitory computer readable medium according to claim 12, wherein the apparatus (700) is further caused to obtain the historical data from the plurality of access nodes and determine the region comprising the set of values for threshold pairs that comprise minimum and maximum threshold pairs again after a pre-determined time period has passed and/or based on a triggering event.

Description

Field The following exemplary embodiments relate to wireless communication and saving energy consumed within a cellular communication network. Background Cellular communication networks comprise capacity that is capable of handling communication during peak hours. Yet, the resources required to handle peak hours may not be needed all the time. Thus, to optimize the operating hours of the resources may help to reduce the energy required by the network. Publication Y. Gao, J. Chen, Z. Liu, B. Zhang, Y. Ke and R. Liu, "Machine Learning based Energy Saving Scheme in Wireless Access Networks," 2020 International Wireless Communications and Mobile Computing (IWCMC), Limassol, Cyprus, 2020, pp. 1573-1578, doi: 10.1109/IWCMC48107.2020.9148536 discusses that with the deployment of the 5G, lower latency and higher frequency brings not only the users' satisfaction but also the dense deployment of base stations which causes large energy consumption. Thus, energy saving is a challenging issue. Machine learning techniques, which could extract features from raw data in the wireless access network and predict the future state by the previous features, are useful to energy saving. This publication proposes an energy saving scheme based on machine learning techniques to achieve the balance between system performance and energy efficiency. Cell switching-off by load prediction using different algorithms, and dynamic threshold adjustment improve the energy efficiency and thereby reduces the operation cost. Publication CN109803285A discloses a cell processing method, a cell processing device and network equipment, and the method comprises the steps: predicting the business volume of a user located in a target cell according to the current position of the user and the state of the business currently used by the user, and switching the target cell to an energy-saving state according to the business volume of the user located in the target cell. According to the scheme of the invention, Cell energy-saving processing can be carried out based on user analysis. According to the method and the device, when the target cell is subjected to energy-saving processing, the influence of entering, residing and leaving of a user on the cell business volume under the current actual condition is considered, so that the future network load of the target cell is more accurately known, the cell energy-saving accuracy is improved, the probability of deviation is reduced, and the network performance and the user experience are improved. Brief Description The scope of protection sought for various embodiments of the invention is set out by the independent claims. Dependent claims define further embodiments included in the scope of protection. The exemplary embodiments and features, if any, described in this specification that do not fall under the scope of the independent claims are to be interpreted as examples useful for understanding various embodiments of the invention. List of Drawings In the following, the invention will be described in greater detail with reference to the embodiments and the accompanying drawings, in which FIG. 1 illustrates an exemplary embodiment of a radio access network.FIG. 2 illustrates an example of tracking traffic load and applying pre-configured thresholds.FIG. 3 illustrates an exemplary embodiment of a network architecture in which the optimization takes place.FIG. 4 illustrates a flow chart according to an exemplary embodiment for optimizing threshold values.FIG. 5 illustrates an exemplary network-level view of apparatuses for and with which the optimization process described above may be performed.FIG. 6A-6E illustrates graphs regarding simulation results.FIG. 7 illustrates an exemplary embodiment of an apparatus. Description of Embodiments The following embodiments are exemplifying. Although the specification may refer to "an", "one", or "some" embodiment(s) in several locations of the text, this does not necessarily mean that each reference is made to the same embodiment(s), or that a particular feature only applies to a single embodiment. Single features of different embodiments may also be combined to provide other embodiments. As used in this application, the term 'circuitry' refers to all of the following: (a) hardware-only circuit implementations, such as implementations in only analog and/or digital circuitry, and (b) combinations of circuits and software (and/or firmware), such as (as applicable): (i) a combination of processor(s) or (ii) portions of processor(s)/software including digital signal processor(s), software, and memory(ies) that work together to cause an apparatus to perform various functions, and (c) circuits, such as a microprocessor(s) or a portion of a microprocessor(s), that require software or firmware for operation, even if the software or firmware is not physically present. This definition of 'circuitry' applies to all uses of this term in this application. As a further example, as used