METHOD FOR MANAGING THE STANDBY POSITION OF A RADIO RESOURCE OF A WIRELESS ACCESS POINT DEVICE, STANDBY MANAGEMENT DEVICE, AND WIRELESS ACCESS POINT DEVICE.

Abstract

The invention relates to a method for predicting, using machine learning, time periods (T2) during which a radio interface (R1) of a wireless access point (11) in a communication network (1) can be switched off or put into standby mode for energy-saving purposes. Figure to be published with the abstract: Fig. 3

Description

Title of the invention: METHOD FOR MANAGING THE STANDBY MODE OF A RADIO RESOURCE OF A DEVICE WIRELESS ACCESS POINT, SLEEP MANAGEMENT DEVICE, AND WIRELESS ACCESS POINT DEVICE. technical field

[0001] The present invention relates to the field of communication networks comprising one or more wireless access point devices, particularly in home or professional environments. More specifically, the invention relates to an optimized method for switching off or putting into standby mode radio resources equipping one or more wireless access point devices of a communication network. PRIOR TECHNOLOGY

[0002] Wireless telecommunication networks use electromagnetic wave communication interfaces, which are commonly called radio interfaces, radios, or radio resources. For energy-saving purposes, radio resources, distributed across devices or equipment such as wireless access points, are not constantly powered but can be switched off or put into standby mode. Various techniques exist for determining the times when radio resources are switched off and switched back on, but they present a significant risk of a radio interface being switched off at a time when a user is attempting to connect a station to it to access the communication network. Therefore, there is a need to minimize this risk. The situation can be improved. Description of the invention

[0003] An object of the present invention is to provide a method for switching off or putting into standby mode the radio resources of wireless access point devices in a communication network, with the aim of saving electrical energy while reducing the risk of causing outages or service interruptions at inopportune times. Thus, a method is proposed for determining, for each access point in a communication network, or more precisely for each radio resource in a communication network, the most appropriate time periods during which this radio resource can be put into standby mode.

[0004] To this end, a method for managing the standby mode of a radio resource of a wireless access point device in a communication network is proposed, the method comprising: - i) obtaining initial information indicating a lack of use by a station of said radio resource in relation to an initial reference period known as the learning period, - ii) a determination of one or more second periods, called extinction periods, from all or part of said first information and in relation to third periods, called reference periods, each of the extinction periods being of a duration less than or equal to said learning period, and each of the reference periods being less than said learning period and less than or equal to any one of the extinction periods, - iii) a standby mode for said radio resource during the said shutdown period(s),

[0005] the process being such that said determination of one or more extinction periods includes training an automatic (or automated) learning model implemented by a neural network.

[0006] According to one embodiment, said determination of one or more second periods is carried out from a first subset of said first information and a determination of a confidence index of an absence of connection, for each of said third periods, is carried out from a second subset of said first information, different from said first subset.

[0007] According to one embodiment, said machine learning model is a 2-class classification model according to which a first class is defined by the absence of a connection of any station to said radio resource during a considered reference period and a second class is defined by a connection of at least one station connected to said radio resource during a considered reference period.

[0008] The invention also relates to a device or circuit, called a "standby module" for a radio resource of a wireless access point device of a communication network, the standby module comprising electronic circuitry configured to operate: - i) obtaining initial information indicating a lack of use by a station of said radio resource in relation to an initial reference period known as the learning period, - ii) a determination of one or more second periods, called extinction periods, based on all or part of the said first information and in relation to third periods, called reference periods, each of the extinction periods being of a duration less than or equal to said period learning period, and each of the reference periods being shorter than said learning period and shorter than or equal to any one of the extinction periods, - iii) a standby mode for said radio resource during the said shutdown period(s),

[0009] the standby module further comprising electronic circuitry configured to operate said determination of one or more shutdown periods from a training of a machine learning model.

[0010] According to one embodiment, the radio resource standby module further includes electronic circuitry configured to perform said determination of one or more second periods from a first subset of said first information and a determination of a confidence index of no connection, for each of said third periods, from a second subset of said first information, different from said first subset.

[0011] According to one embodiment, the radio resource standby module further includes electronic circuitry configured to operate said automated learning by means of a two-class classification model according to which a first class is defined by the absence of a connection of at least one station to said radio resource during a considered reference period and a second class is defined by a connection of at least one station connected to said radio resource during a considered reference period.

[0012] Another object of the invention is a wireless access point device comprising at least one radio resource and a sleep management module as described above.

[0013] Another object of the invention is a communication network comprising at least one access point device as mentioned above.

[0014] The invention also relates to a computer program product comprising program code instructions to execute steps of the process as previously described, when this program is executed by a processor of a radio resource sleep management module, as well as a storage medium comprising such a computer program product. Brief description of the drawings

[0015] The features of the invention mentioned above, as well as others, will become clearer upon reading the following description of at least one exemplary embodiment, said description being made in relation to the accompanying drawings, among which:

[0016] [Fig.1] illustrates a LAN-type communication network comprising wireless access point devices, according to one embodiment;

[0017] [Fig.2] illustrates reference periods used to operate a learning automated use of a radio resource of a wireless access point device and to operate periods of standby of this radio resource, according to an embodiment;

[0018] [Fig.3] is a flowchart illustrating steps in a standby procedure of a radio resource, according to one embodiment; and,

[0019] [Fig.4] is a diagram illustrating an example of the internal architecture of a module of management of standby mode of a radio resource, according to an embodiment;

[0020] DETAILED DESCRIPTION OF IMPROVEMENTS

[0021] Figure 1 illustrates a LAN (Local Area Network) communication network 1. The communication network 1 is connected to a WAN (Wide Area Network) via a home gateway GW 10 and a communication link 10a that connects the gateway GW 10 to the WAN 1000. According to the described embodiment, the communication network 1 comprises three wireless access point devices API 11, AP2 12 and AP3 13, each configured to operate wireless connections between one or more stations (not shown in Figure 1) and the communication network 1. According to the described embodiment in Figure 1, the communication network 1 comprises three wireless access point devices API 11, AP2 12 and AP3 13, each configured to operate wireless connections between one or more stations (not shown in Figure 1) and the communication network 1.[l] The wireless access point device 11 is connected to the communication network 1 via the wireless access point device 12, through a communication link 11' established between the wireless access point device 11 and the wireless access point device 12. Also according to the described embodiment, the wireless access point device 12 is connected to the communication network 1 via the home gateway GW 10, through a communication link 12'. Finally, and still according to the described embodiment, the wireless access point device 13 is also connected to the communication network 1 via the home gateway GW 10, through a communication link 13'.The term "station" here refers to any electronic and / or computer device configured to be connected to at least one LAN communication network, such as, for example, a desktop computer, a laptop computer, a connected tablet, a connected smart TV, a smartphone, a smartwatch, a connected household appliance, an alarm or personal assistance device, a radio receiver, a data storage device, etc., these examples being obviously not exhaustive. Of course, other various electronic and / or computer devices can be connected to communication network 1, but for the sake of simplicity, these are not shown in [Fig. 1]. The . Wireless access point device 11 includes one radio resource RI; wireless access point device 12 includes one radio resource R2; and wireless access point device 13 includes two radio resources R3 and R4. The terms "radio resource" or "radio" here refer to an electronic interface configured to operate bidirectional wireless communications between a compatible remote device and the communication network 1, for example, according to a protocol from the 802.11 family of standards of the Institute of Electrical and Electronics Engineers (IEEE), or so-called 'Wi-Fi' networks. Examples of implementations can be found, for example, in the context of the IEEE 802.11-2020 standard or the 802.11ax-2021 amendment or the IEEE 802.11be amendment, in its D4.0 or D5.0 version, or in its later or final versions.Other implementation examples can also be found, for example, in the context of an IEEE 802.11 standard version or an amendment to this 802.11 standard incorporating the IEEE 802.1 Ibe amendment, such as the IEEE 802.1 Ibf D3.0 amendment or the IEEE 802.11bn amendment. They concern both home wireless networks and enterprise networks.

[0022] The wireless access point 11 further includes a sleep management module 111. Similarly, the wireless access points 12 and 13 include a sleep management module 112 and a sleep management module 113, respectively. For the sake of simplicity, only the sleep management module 111 of the wireless access point 11 is shown in [Fig. 1]. The following description describes sleep management of the radio resource RI of the wireless access point 11 only, which applies similarly to the sleep management modules of the wireless access points 12 and 13. A remote server SRV 1001 is further connected to the WAN 1000 via a communication link 1001'.The remote server is configured to remotely perform operations, including data processing, and to communicate with equipment or devices on communication network 1 according to predefined communication protocols, including an IP-type communication protocol.

[0023] Cleverly, and according to at least one embodiment, the sleep management module 111 of the radio resource RI of the wireless access point device 11 is configured to operate, and does operate, an automated learning process of the use of the radio resource RI over time. The term "use of the radio resource RI" here refers, in relation to the radio resource RI, to whether or not the radio resource RI is used at a given time to establish a connection between the wireless access point device 11 and one or more stations (i.e., one or more third-party devices that are connected to the communication network 1 via the radio resource RI). This The concept of usage also includes, more broadly, the regularity or irregularity of a connection, the frequency of the connection (for example, the number of connections per hour, per day, per week, per month, etc.) as well as the number of stations connected over time (no stations, one station, two stations, three and more, etc.).

[0024] The automated learning performed by the sleep management module 111 of the wireless access point device 11 is carried out using recorded (stored) and time-stamped connection information. According to one embodiment, all information relating to the connections and disconnections of one or more stations that have occurred recently, i.e. for example during the last few weeks or months, is stored in a non-volatile memory of the wireless access point device, with a reference to each of the stations (for example a MAC address serving as a unique identifier) ​​and time-stamped references.According to one variant, this information, which constitutes a form of connection and disconnection log for the RI radio resource and more broadly the wireless access point device 11, is recorded in a memory of the remote server SRV 1001, in order to allow the accumulation of a large amount of data, but also a shared centralization for all the wireless access point devices of the communication network 1. Advantageously, this information, called here "first information" is representative of a use or a lack of use by any station of the RI radio resource in relation to a first reference period called the learning period.The sleep management module 111 is configured to analyze this initial information for a reference training period T1 that is of particular interest in terms of usage, for example, a one-week period of repeated, cyclical use. Indeed, it is very common for (human) users of a communication network to exhibit recurring behaviors on a weekly scale, as daily life is often organized according to such a weekly timescale. For example, in the domestic context of a household, or even in a professional business context, usage patterns may be such that it is possible to identify, for one or more of the wireless access point devices in a communication network, periods during which no station is typically connected.For example, one can imagine a home where no one is present on Tuesday afternoons, except in exceptional circumstances. Another example is a business whose offices are closed on Friday afternoons. Yet another example is a shop whose physical storefront is closed on Mondays, but whose office is occupied and used for administrative tasks. Thus, it is possible to determine, through machine learning, periods during which... of which the RI radio resource of the wireless access point 11 device is never used, in relation to a reference period such as the week running from Monday at 00:00 until the following Sunday at 23:59. Like any temporal analysis, such an analysis requires a certain level of precision, commonly referred to as "granularity," which means that other reference periods must be predefined to avoid processing too much information while ensuring that the data stored for the analysis is meaningful.Thus, with regard to the analysis of the time and the moments of connection and disconnection of stations to a communication network, it is possible to consider that a level of precision (and therefore a unit of time) on the order of a minute is too high, and that a level of precision on the order of half a day is far too low, the ultimate goal being to save electrical energy. This is why the process of putting a radio resource into standby mode, which is the subject of one or more embodiments, is designed with a temporality referring to several reference periods: . - an initial reference period which is a "learning period" "Tl, used repeatedly to detect regularities in the use of a radio resource, which can be translated into second periods T2 during which one or more radio resources of a communication network 1 can be put into standby mode or switched off; the reference learning period Tl is preferably one week, for the reasons mentioned above, and" - third reference periods T3 which define the precision or granularity of the analysis, for example, periods of a duration of a quarter of an hour, half an hour or one hour.

[0025] Figure 2 illustrates the time references T1, T2, and T3, which are the first periods T1, second periods T2, and third periods T3 described above, in a graphical representation showing the progression of time t on the x-axis. The illustration shows in the upper part of Figure 2 a period T1 of one week, determined in relation to the radio resource RI, divided into seven days T1-1, T1-2, T1-3, T1-4, T1-5, T1-6, and T1-7. The lower part of [Fig.2] shows as an example details of day Tl-2 (i.e. a Tuesday), namely 48 reference periods T3, referenced here as T3-2-1 to T3-2-48, in a format T3-ij where i is the day number in the first reference period Tl and j is the number of the third reference period T3 in the day in question of the first reference period Tl (of a duration of one week).Since there are 48 T3 periods according to the illustrated implementation example, each of the third reference T3 periods lasts 30 minutes. These third reference time periods are . therefore called T3-2-1, T3-2-2, T3-2-3, etc. It follows that a detailed analysis by the sleep management module 111 of the wireless access point device 11, operating cyclically over several reference periods Tl, that is, based on data collected representative of the use and therefore the lack of use of the radio resource RI over several successive weeks, allows the determination of periods T2 of the week during which it is likely that no station will be connected to the radio resource RI. The T3 periods are therefore 336 in number (48 half-hours x 7 days) for a reference period Tl equal to one week. According to the example described, these periods are Tl 1 on Monday (i.e., during Tl-1); T21 and T22 on Tuesday (i.e., during Tl-2); T31 on Wednesday (i.e., during Tl-3); T41 and T42 on Thursday (i.e. during Tl-4); T51 on Friday (i.e. during Tl-5) and T71 on Sunday (i.e. during Tl-7).Based on the example shown, it can be observed that no standby (or shutdown) period for the RI radio resource is foreseeable on Saturday (i.e., during T1-6). For example, this could be the case if at least one station is consistently operational and connected to communication network 1, via the RI radio resource, throughout Saturday, for example, if one or more smartphones are continuously connected to communication network 1 via the RI radio resource.

[0026] Advantageously, and according to one embodiment, the sleep management module 111 of the wireless access point device 11 comprises a neural network having, in one example, an input layer that processes initial information, including at least the day of the week T1-i and the time slot T3-ij; a hidden layer composed of 64 neurons and configured to perform machine learning; and an output layer having a sigmoid activation function, an Adam optimizer, and a binary cross-entropy loss function. This example is not limiting, and the sleep management module can be implemented in another form, such as, for example, a decision tree modeling possible outcomes of a sequence of interconnected choices.This structure allows the monitoring management module 111 to implement a two-class classification model: class 0 if no station is connected to the radio resource during the considered reference period T3, and class 1 if at least one station is connected to the radio resource RI during this reference period T3. The neural network of the monitoring management module 111 therefore processes information from the first time-stamped data collected (connection and disconnection information from stations to the radio resource RI), which indicates, for each half-hour reference period T3, whether at least one station is connected to the radio resource RI or not. Ideally, the training phase is carried out over an overall training period of several weeks (i.e., several successive periods T1). The training is run iteratively for several reference periods T1. As a result, it is possible to predict, for each half-hour reference period T3 during a coming or current week, the probability that a station will or will not be connected to the radio resource RI.

[0027] The same principle is applied to all wireless access point devices of the communication network 1. Automated learning is carried out for each of the wireless access point devices 11, 12 and 13, by its internal sleep management module, which module includes a neural network configured for this purpose.

[0028] According to one embodiment, the initial information representing the use of each of the radio resources of the communication network 1 is collected by the remote server SRV 1001, which performs automated learning for each module and then sends it the predetermined periods T2 during which radio resources can be put into standby or switched off. The term "standby" here refers to any method of reducing electrical power that substantially limits the power consumption of a radio resource. This can be simple standby, deep standby, or even complete shutdown of the radio resource in question. For example, the radio resources can be powered by supply lines controlled by electronic switches operated from the various standby management modules, such as the standby management module 111.

[0029] According to one embodiment, the standby management module is configured to operate from data relating to all radio resources applied to its inputs and presenting as output data indicating standby prospects for each of the radio resources of the communication network 1.

[0030] Figure 3 is a flowchart illustrating the steps of a process executed by the sleep management module 111 of the wireless access point device 11, configured to determine sleep periods T2 of the radio resource RI based on predictions P of radio resource RI usage by one or more stations at a given time during the current week or a future week. A first step S0 is an initialization step at the end of which all devices in the communication network 1 are powered on and initialized to operate nominally. A step SI is a step for collecting initial information, including connection and disconnection information observed from one or more stations to the communication network 1 during one or more (but at least one) learning periods T1, including timestamp information.This information is stored, for example in the wireless access point device 1 or in the remote server SRV 1001. An S2 step conditionally includes an automated learning phase (or training or «. machine learning (in English) during which the internal neural network of the sleep management module 111 is trained to determine periods T2 for which a certain probability level P of non-use of the radio resource RI is determined, based on the initial information collected and then formatted. If no machine learning phase has yet been executed, then a machine learning phase is necessary and is executed. Conversely, if a machine learning phase has already been executed, then a new machine learning phase is optional. It should be noted, however, that the more machine learning phases there are, the more accurate and reliable the predictions delivered by the trained model will be.The neural network is trained by applying initial collected and formatted information to its inputs and outputs. This information consists of times during the training period T1 with reference to successive reference periods T3 as inputs, and information regarding the connection of at least one station to the radio resource RI or the absence of any connection of any station to the radio resource RI as outputs. At the end of this training, the neural network is configured to provide a prediction P of the absence of any connection of any station to the radio resource RI for one or more (K) successive reference periods T3. This prediction P is accompanied, for each reference period T3, by a confidence index C(P) representing a level of confidence assigned to the prediction.Thus, a probability level is then determined during an exploitation phase of the trained model, again using initial information obtained, for each of the reference periods T3, leading to the determination of standby periods T2. According to one embodiment, this is done by analyzing, during a sequence of K reference periods T3, whether at least N successive reference periods T3 present a prediction P according to which no station will be connected to the radio resource RI. This information is stored for later access. Once these possible standby periods T2 have been determined, a step S3 consists of a step of using these determined periods, during which the standby management module 111 commands the radio resource RI to go into standby and, if necessary, to come out of standby, according to the determined periods T2.

[0031] According to one embodiment, the S3 step of controlling the standby mode of the radio resource RI is performed by sending a shutdown command to the radio resource RI according to a predefined, generic or proprietary protocol, under the control of a dedicated controller (a module comprising electronic circuitry or a microprocessor, for example). According to one embodiment, the commands are sent according to a protocol conforming to a standard called "EasyMesh", according to which a controller of the communication network concerned sends an Easy message The Mesh AP AutoConfiguration Renew message is sent to an EasyMesh Agent responsible for shutting down the radio resource at an access point in the communication network. The EasyMesh Agent then responds to this message with an EasyMesh AP Autoconfiguration WSC M1 message for the radio in question, and more generally for all the radio resources it manages. For each EasyMesh AP Autoconfiguration WSC M1 message, the controller then responds with an EasyMesh AP Autoconfiguration WSC M2 message containing the list of Basic Service Sets (BSS) to configure for the radio resource in question. In the example described here, the controller advantageously and cleverly avoids including any configuration for that radio resource in the EasyMesh AP Autoconfiguration WSC M2 message dedicated to that radio resource when shutting down a given radio resource.Conversely, for a command to turn on a radio resource following a period of shutdown, the controller includes a configuration for the radio resource concerned in the dedicated EasyMesh AP Autoconfiguration WSC M2 message.

[0032] According to one embodiment, the training dataset (the initial information) is divided into two subsets of information (here, the initial information). The first subset is used to perform the predictions using the neural network, and the second subset is used to determine a confidence level Cs associated with each of the determined prediction pieces of information. Thus, predicted data are compared to actual data for predefined reference periods T3, and the value of the confidence level Cs is calculated such that X% of predictions P whose associated confidence level C(P) is greater than or equal to Cs are correct (in other words, the predicted value is equal to the actual value). In one example embodiment, X is equal to 100%.

[0033] According to one embodiment, the prediction of the presence or absence of at least one station connected to the radio resource RI is determined for K upcoming reference periods T3. The number K of successive reference periods for which a prediction P is determined is predefined. This is an input to the implemented algorithm, which can be adjusted according to a compromise between the performance level and the amount of resources required to implement the method and used by the algorithm. This number K is necessarily greater than an integer N, which designates a block of N consecutive reference periods T3 during which it is predicted that the radio resource RI will be inactive. The integer N here provides a low-pass filter function, guaranteeing a certain level of continuity or stability in the switching on or off of a radio resource to avoid excessively rapid variations.N is also an adjustable parameter of the algorithm, which can be modified remotely or by reconfiguring low-level software (or "firmware").

[0034] Thus, in one embodiment, each prediction P of the presence or absence of a station connected to the radio resource RI is accompanied by a confidence index C(P), for example, in the form of a decimal value in the range [0, 1]. For example, a prediction P determined for a period T3 equal to 1 (there will probably be a connected station) and associated with a confidence index C(P) = 0.97 means that this probability P is estimated to be 97% reliable. Associating a confidence index C(P) with a prediction P makes it possible to determine, for the radio resource RI, whether it is possible to put it into standby mode for one or more reference periods T3, thereby determining a standby or shutdown period T2 to be used during operation.

[0035] According to one embodiment, a radio resource Rn is intended to be switched off if for N consecutive reference periods T3 (N<=K), the prediction P indicates that no station will be connected to this radio resource Rn and if the confidence index C(P) assigned to this prediction P is greater than a threshold value Cs of confidence index.

[0036] Thus, for each reference period T3, if the determined confidence index C(P) is greater than a predefined threshold value Cs, and the prediction P is equal to 0, the radio resource RI is considered to be inactive during that reference period T3. The predefined threshold value Cs is configurable and depends on a desired efficiency coefficient (also called the aggressiveness coefficient). The lower the threshold value, the more often the radio resource RI will be switched off, which allows for a significant saving in electrical energy but considerably increases the risk of negatively impacting the user experience (by putting a radio resource into standby mode for a given period when a user ultimately wishes to connect to it during the same period).Conversely, a higher threshold value will result in greater availability of the radio resource RI, which will have a positive impact on the user experience but will reduce the desired electrical energy savings. Consecutive reference periods T3 for which the radio resource is predicted to be inactive then constitute the determined standby periods T2.

[0037] According to one embodiment, steps SI, S2 and S3 are operated iteratively.

[0038] Figure 4 schematically illustrates an example of the internal architecture of a sleep management module 111 of the wireless access point 11. It should be noted that Figure 4 could also represent the internal architecture of a wireless access point such as the wireless access point device 12 or the wireless access point device 13, or even the internal architecture of a gateway device such as the home gateway GW 10. According to the hardware architecture example shown in Figure 4, the sleep management module 111 of the wireless access point device 11 comprises, connected by a communication bus 120: a processor or CPU (Central Processing Unit) 101; and RAM (Random Access Memory). Access Memory » in English) 102; a ROM (Read Only Memory) 103; a storage unit such as a hard disk drive (or a storage media reader, such as an SD card reader (“Secure Digital”) 104; at least one communication interface 105 enabling the sleep management module 111 of the wireless access point 111 to communicate with other devices to which it is connected, such as radio resources whose switching off and on it it controls, or external devices, such as the home gateway GW 10 or the remote server SRV 1001.

[0039] The processor 101 is capable of executing instructions loaded into RAM 102 from ROM 103, external memory (not shown), storage media (such as an SD card), or a communication network. When the sleep management module 111 of the wireless access point device 11 is powered on, the processor 101 is capable of reading instructions from RAM 102 and executing them. These instructions form a computer program causing the processor 101 to implement all or part of a process described in relation to [Fig. 3] or described variants of that process.

[0040] All or part of the method described in relation to [Fig. 3] or its described variants can be implemented in software form by executing a set of instructions by a programmable machine, for example a DSP (Digital Signal Processor) or a microcontroller, or be implemented in hardware form by a dedicated machine or component, for example a FPGA (Field-Programmable Gate Array) or an ASIC (Application-Specific Integrated Circuit). In general, the sleep management module 111 of the wireless access point 11 includes electronic circuitry configured to implement the methods described in relation to itself.Obviously, the sleep management module 111 of the wireless access point 11 also includes all the elements usually present in a system comprising a control unit and its peripherals, such as, a power supply circuit, a power monitoring circuit, one or more clock circuits, a reset circuit, input / output ports, interrupt inputs, bus drivers, this list being non-exhaustive.

Claims

Demands

1. A method for putting a radio resource (RI) of a wireless access point device (11) of a communication network (1) into standby mode, the method comprising: - i) obtaining (SI) initial information representing a lack of use by a station of said radio resource (RI) in relation to a first reference period (T1) called the learning period, - ii) determining (S2) one or more second periods (T2), called the extinction periods, from all or part of said initial information and in relation to third periods (T3), called the reference periods, each of the extinction periods (T2) being of a duration less than or equal to said learning period (T1), and each of the reference periods (T3) being less than said learning period (T1) and less than or equal to an extinction period (T2),- iii) a standby mode for said radio resource (RI) during the said extinction period(s) (T2), the method being characterized in that said determination (S2) of one or more extinction periods (T2) comprises a phase of training an automated learning model.

2. A standby method according to claim 1, wherein said determination of one or more second periods (T2) is carried out from a first subset of said first information and a determination of a confidence index (C) for each of said third periods (T3) is carried out from a second subset of said first information, different from said first subset.

3. A standby method according to claim 1 and 2, wherein said machine learning model is a two-class classification model whereby a first class is defined by the absence of a connection of any station to said radio resource (RI) during a considered reference period (T3) and a second class is defined by a connection of less one station to said radio resource (RI) during a reference period (T3) considered.

4. Sleep management module (111) of a radio resource (RI) of a wireless access point device (11) of a communication network (1), the sleep management module comprising electronic circuitry configured to operate: - i) obtaining (SI) initial information representing a lack of use by a station of said radio resource (RI) in relation to a first reference period (T1) called the learning period, - ii) determining (S2) one or more second periods (T2), called extinction periods (T2), from all or part of said initial information and in relation to third periods (T3), called reference periods, each of the extinction periods (T2) being of a duration less than or equal to said learning period (T1),and each of the reference periods (T3) being less than said learning period (T1) and less than or equal to an extinction period (T2), - iii) a standby mode of said radio resource (RI) during said extinction period(s) (T2), the standby management module (111) being characterized in that it further comprises electronic circuitry for operating said determination (S2) of one or more extinction periods (T2) from a training of an automated learning model.

5. Radio resource (RI) standby management module (111) according to claim 4, further comprising electronic circuitry for operating said determination (S2) of one or more second periods (T2) from a first subset of said first information and a determination of a confidence index (C) of said third periods (T3) from a second subset of said first information, different from said first subset.

6. A sleep management module (111) for a radio resource (RI) according to any one of claims 4 and 5, further comprising electronic circuitry for performing said automated learning

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10. thanks to a two-class classification model according to which a first class is defined by the absence of a connection of at least one station to said radio resource (RI) during a reference period (T3) considered and a second class is defined by a connection of at least one station to said radio resource (RI) during a reference period (T3) considered. Access point device (11) comprising at least one radio resource (RI) and a sleep management module (111) for said radio resource (RI), according to any one of claims 4 to 6. Communication network (1) comprising at least one wireless access point device (11) according to claim 7. A computer program product characterized in that it comprises program code instructions for executing the steps of the process according to any one of claims 1 to 3, when said program is executed by a processor of a sleep management module (111) of a radio resource (RI). Information storage medium comprising a computer program product according to the preceding claim.

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