DETAILED ACTION
This office action is in response to the amendment filed on 7/16/2026 in which claims 1-20 are pending.
Notice of Pre-AIA or AIA Status
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
Response to Arguments
Applicant's arguments filed on 7/16/2026 have been fully considered but they are not persuasive.
Handling of UEs that operate in different modes (SA or NSA modes)
Chen ([0047] FIG. 2 illustrates an example system framework, or architecture 200 of the present disclosure. As illustrated in FIG. 2, the system architecture 200 includes a radio access network (RAN) 270, which may include one or more cells and may comprise equipment for one or more types of radio access technologies, e.g., 3G, 4G/LTE, 5G-SA, 5G-NSA, etc. In one example, components of RAN 270 may collect raw operational data of the RAN, such as base station equipment operating temperatures (e.g., BBU temperatures, operational states (e.g., active/inactive, on/off, etc.), device voltages, raw call trace records (CTRs), information regarding mobile devices detected by a base station, such as IMEI/IMSI, the numbers of active mobile devices, the number of active sessions per sector, the number of active sessions per mobile device, transmit power, receive power, RSRP measurement data obtained from mobile devices, information regarding a currently active scheduling algorithm, a currently active handover setting, etc.). In accordance with the present disclosure, any operational data that may be generated by and/or collected by components of RAN 270 and which may be made accessible by equipment vendors may be utilized as raw operational data.)
The UEs in Chen can operate in SA or NSA modes.
Separate predictions of SA traffic parameters and NSA traffic parameters, resource/allocations based on the SA and NSA demand, or the prediction of UE transitions between SA and NSA modes Chen ([0047] FIG. 2 illustrates an example system framework, or architecture 200 of the present disclosure. As illustrated in FIG. 2, the system architecture 200 includes a radio access network (RAN) 270, which may include one or more cells and may comprise equipment for one or more types of radio access technologies, e.g., 3G, 4G/LTE, 5G-SA, 5G-NSA, etc. In one example, components of RAN 270 may collect raw operational data of the RAN, such as base station equipment operating temperatures (e.g., BBU temperatures, operational states (e.g., active/inactive, on/off, etc.), device voltages, raw call trace records (CTRs), information regarding mobile devices detected by a base station, such as IMEI/IMSI, the numbers of active mobile devices, the number of active sessions per sector, the number of active sessions per mobile device, transmit power, receive power, RSRP measurement data obtained from mobile devices, information regarding a currently active scheduling algorithm, a currently active handover setting, etc.). In accordance with the present disclosure, any operational data that may be generated by and/or collected by components of RAN 270 and which may be made accessible by equipment vendors may be utilized as raw operational data.)
([0049] KPI composer/publisher module 216 may derive performance indicators (e.g., KPIs) based on the raw operational data (or at least a portion thereof, as provided by data digestion module 214). In one example, KPI composer/publisher module 216 may also reformat the KPIs into state information 220 and reward (objective) information 230 as inputs for the RLA 240.)
Fig. 3 shows the use cases for the collected raw data
It is clear that any data including the SA and NSA access technologies would be collected for resource allocation and a prediction is determined.
Claims 1 and 13 of the instant application do not state or recite that “the predictions of the traffic parameters are separate” and do not state or recite that “the prediction of UE transitions between SA and NSA” and therefore the rejection to the claims is maintained.
Claim Rejections - 35 USC § 102
The following is a quotation of the appropriate paragraphs of 35 U.S.C. 102 that form the basis for the rejections under this section made in this Office action:
A person shall be entitled to a patent unless –
(a)(1) the claimed invention was patented, described in a printed publication, or in public use, on sale, or otherwise available to the public before the effective filing date of the claimed invention.
Claim(s) 1-4, 9-11, 13-15, 18, and 19, is/are rejected under 35 U.S.C. 102(a)(1) as being anticipated by Chen et al. (US20210241090A1).
As to claims 1 and 13, Chen teaches a system, comprising: ([0003] FIG. 1 illustrates an example of a system including a telecommunications service provider network, according to the present disclosure;)
at least one baseband unit (BBU) entity; ([0026] For instance, cell site 113 may include RRH and BBU components.)
a first radio unit communicatively coupled to the at least one BBU entity via a fronthaul network; ([0026] With regard to cell sites 111 and 112, the “base stations” may comprise RRHs at cell sites 111 and 112 coupled with respective baseband units of BBU pool 114.)
one or more antennas communicatively coupled to the first radio unit, wherein the first radio unit is communicatively coupled to a respective subset of the one or more antennas; (In accordance with the present disclosure, any one or more of cell sites 111-113 may be deployed with antenna and radio infrastructures, including multiple input multiple output (MIMO) and millimeter wave antennas)
wherein the at least one BBU entity, the first radio unit, and the one or more antennas are configured to implement a base station for wirelessly communicating with user equipment in a first cell; and ([0026] For instance, cell site 113 may include RRH and BBU components. Thus, cell site 113 may comprise a self-contained “base station.”)
a machine learning computing system communicatively coupled to the at least one BBU entity, wherein the machine learning computing system is configured to: ([0045] For example, SON/SDN controller 102 may include a reinforcement learning agent (RLA) and/or data pre-processing unit, e.g., addition to other functions of SON/SDN controller 102.)
receive one or more performance indicators for standalone user equipment and one or more performance indicators for non-standalone user equipment from the at least one BBU entity; ([0047] FIG. 2 illustrates an example system framework, or architecture 200 of the present disclosure. As illustrated in FIG. 2, the system architecture 200 includes a radio access network (RAN) 270, which may include one or more cells and may comprise equipment for one or more types of radio access technologies, e.g., 3G, 4G/LTE, 5G-SA, 5G-NSA, etc. In one example, components of RAN 270 may collect raw operational data of the RAN, such as base station equipment operating temperatures (e.g., BBU temperatures, operational states (e.g., active/inactive, on/off, etc.), device voltages, raw call trace records (CTRs), information regarding mobile devices detected by a base station, such as IMEI/IMSI, the numbers of active mobile devices, the number of active sessions per sector, the number of active sessions per mobile device, transmit power, receive power, RSRP measurement data obtained from mobile devices, information regarding a currently active scheduling algorithm, a currently active handover setting, etc.). In accordance with the present disclosure, any operational data that may be generated by and/or collected by components of RAN 270 and which may be made accessible by equipment vendors may be utilized as raw operational data. [0049] KPI composer/publisher module 216 may derive performance indicators (e.g., KPIs) based on the raw operational data (or at least a portion thereof, as provided by data digestion module 214). In one example, KPI composer/publisher module 216 may also reformat the KPIs into state information 220 and reward (objective) information 230 as inputs for the RLA 240.)
determine one or more predicted traffic parameters for standalone user equipment based on the received one or more performance indicators for standalone user equipment from the at least one BBU entity; and ([0049] KPI composer/publisher module 216 may derive performance indicators (e.g., KPIs) based on the raw operational data (or at least a portion thereof, as provided by data digestion module 214). In one example, KPI composer/publisher module 216 may also reformat the KPIs into state information 220 and reward (objective) information 230 as inputs for the RLA 240. The state information 220 may comprise KPIs reflecting status of the RAN 270, such as PRB utilization, RF distribution, etc. The reward information 230 may comprise KPIs indicating the network performance, such as harmonic UE throughput, etc. [0050] In other words, each of the plurality of neural networks 247-249 is to encode (and learn through RL) a respective policy for selecting at least one setting of at least one parameter of the radio access network (an “action”) to increase (e.g., raise or maximize) a respective predicted reward in accordance with the state information.)
determine one or more predicted traffic parameters for non-standalone user equipment based on the received one or more performance indicators for non- standalone user equipment from the at least one BBU entity; ([0049] KPI composer/publisher module 216 may derive performance indicators (e.g., KPIs) based on the raw operational data (or at least a portion thereof, as provided by data digestion module 214). In one example, KPI composer/publisher module 216 may also reformat the KPIs into state information 220 and reward (objective) information 230 as inputs for the RLA 240. The state information 220 may comprise KPIs reflecting status of the RAN 270, such as PRB utilization, RF distribution, etc. The reward information 230 may comprise KPIs indicating the network performance, such as harmonic UE throughput, etc. [0050] In other words, each of the plurality of neural networks 247-249 is to encode (and learn through RL) a respective policy for selecting at least one setting of at least one parameter of the radio access network (an “action”) to increase (e.g., raise or maximize) a respective predicted reward in accordance with the state information.)
wherein one or more components of the system are configured to allocate resources for standalone user equipment and/or non-standalone user equipment based on the one or more predicted traffic parameters for standalone user equipment, the one or more predicted traffic parameters for non-standalone user equipment, and service requirements for the base station. ([0054] FIG. 3 illustrates a chart 300 with example use cases that may be associated with different sub-agents of a reinforcement learning agent and the respective state information, reward information, and actions that may relate to the different use cases. For instance, as illustrated in FIG. 3, chart 300 includes various performance indicators (e.g., KPIs) that may comprise state information for RL agents (sub-agents), e.g., one for each respective use case. The state information may include cell-level and mobile device/UE-level KPIs. Each of the use cases may be associated with a respective agent (or sub-agent) with a neural network approximating the Q-function and having different reward-action sets. For instance, for a first use case of “video throughput optimization,” the “reward” may comprise “video user downlink (DL) throughput,” and the “action” may comprise scheduler options (e.g., selecting from among available MAC scheduler algorithms), handover offset configurations, or both. For a second use case of “coverage optimization,” the “reward” may be a composite of a number of connected users and a call retainability metric, and the corresponding “action” may comprise antenna array/RRH tilt, handover offset configurations, or both. Chart 300 further illustrates three additional example use cases of “energy savings,” “mobile device/UE throughput and fairness,” and “load balancing,” each with associated reward-action sets. It should be noted that the state information may be the same for all of the use cases (e.g., shared state information).)
As to claims 2 and 14, Chen teaches the system of claim 1, wherein the machine learning computing system is further configured to:
receive operation mode data for the base station; ([0047] In one example, components of RAN 270 may collect raw operational data of the RAN, such as base station equipment operating temperatures (e.g., BBU temperatures, operational states (e.g., active/inactive, on/off, etc.), device voltages, raw call trace records (CTRs), information regarding mobile devices detected by a base station, such as IMEI/IMSI, the numbers of active mobile devices, the number of active sessions per sector, the number of active sessions per mobile device, transmit power, receive power, RSRP measurement data obtained from mobile devices, information regarding a currently active scheduling algorithm, a currently active handover setting, etc.).)
determine a predicted operation mode for the base station based on the received operation mode data for the base station; and ([0050] In other words, each of the plurality of neural networks 247-249 is to encode (and learn through RL) a respective policy for selecting at least one setting of at least one parameter of the radio access network (an “action”) to increase (e.g., raise or maximize) a respective predicted reward in accordance with the state information.)
perform preemptive action for user equipment based on the predicted operation mode for the base station and available resources of the base station. ([0054] FIG. 3 illustrates a chart 300 with example use cases that may be associated with different sub-agents of a reinforcement learning agent and the respective state information, reward information, and actions that may relate to the different use cases. For a second use case of “coverage optimization,” the “reward” may be a composite of a number of connected users and a call retainability metric, and the corresponding “action” may comprise antenna array/RRH tilt, handover offset configurations, or both. Chart 300 further illustrates three additional example use cases of “energy savings,” “mobile device/UE throughput and fairness,” and “load balancing,” each with associated reward-action sets. It should be noted that the state information may be the same for all of the use cases (e.g., shared state information).)
As to claims 3 and 15, Chen teaches the system of claim 2, wherein the machine learning computing system is configured to perform preemptive action for the user equipment by: handing over the user equipment from the first cell to a second cell; releasing and redirecting the user equipment from the first cell to the second cell; and/or changing an operation mode of the user equipment. ([0054] FIG. 3 illustrates a chart 300 with example use cases that may be associated with different sub-agents of a reinforcement learning agent and the respective state information, reward information, and actions that may relate to the different use cases. For a second use case of “coverage optimization,” the “reward” may be a composite of a number of connected users and a call retainability metric, and the corresponding “action” may comprise antenna array/RRH tilt, handover offset configurations, or both. Chart 300 further illustrates three additional example use cases of “energy savings,” “mobile device/UE throughput and fairness,” and “load balancing,” each with associated reward-action sets. It should be noted that the state information may be the same for all of the use cases (e.g., shared state information).)
As to claim 4, Chen teaches the system of claim 2, wherein the machine learning computing system is further configured to determine a predicted cause for the predicted operation mode for the base station. ([0040] It should be noted that different sub-agents may have different combinations of (state, reward, action) depending upon the purpose(s) and configuration(s) of the different sub-agents. It should also be noted that in accordance with the present disclosure, a reinforcement learning agent (RLA) may comprise a plurality of sub-agents, which may have different rewards, and which may select different actions with respect to a given state. A selected action of one sub-agent may affect the reward that accrues to a different sub-agent, and vice versa. In addition, in some examples, different sub-agents may have associated actions which may relate to a same parameter. see [0050] and chart 300 of fig. 3)
As to claims 9 and 18, Chen teaches the system of claim 1, wherein the machine learning computing system is configured to reserve radio resources for standalone user equipment and non-standalone user equipment based on the one or more predicted traffic parameters for standalone user equipment, the one or more predicted traffic parameters for non-standalone user equipment, and service requirements for the base station. ([0050] In other words, each of the plurality of neural networks 247-249 is to encode (and learn through RL) a respective policy for selecting at least one setting of at least one parameter of the radio access network (an “action”) to increase (e.g., raise or maximize) a respective predicted reward in accordance with the state information. [0054] FIG. 3 illustrates a chart 300 with example use cases that may be associated with different sub-agents of a reinforcement learning agent and the respective state information, reward information, and actions that may relate to the different use cases. For instance, as illustrated in FIG. 3, chart 300 includes various performance indicators (e.g., KPIs) that may comprise state information for RL agents (sub-agents), e.g., one for each respective use case. The state information may include cell-level and mobile device/UE-level KPIs. Each of the use cases may be associated with a respective agent (or sub-agent) with a neural network approximating the Q-function and having different reward-action sets. For instance, for a first use case of “video throughput optimization,” the “reward” may comprise “video user downlink (DL) throughput,” and the “action” may comprise scheduler options (e.g., selecting from among available MAC scheduler algorithms), handover offset configurations, or both. For a second use case of “coverage optimization,” the “reward” may be a composite of a number of connected users and a call retainability metric, and the corresponding “action” may comprise antenna array/RRH tilt, handover offset configurations, or both. Chart 300 further illustrates three additional example use cases of “energy savings,” “mobile device/UE throughput and fairness,” and “load balancing,” each with associated reward-action sets. It should be noted that the state information may be the same for all of the use cases (e.g., shared state information).)
As to claims 10 and 19, Chen teaches the system of claim 1, wherein one or more components of the system are configured to use scaling to increase radio resources for standalone user equipment and non-standalone user equipment based on the one or more predicted traffic parameters for standalone user equipment, the one or more predicted traffic parameters for non-standalone user equipment, and the service requirements for the base station. ([0050] In other words, each of the plurality of neural networks 247-249 is to encode (and learn through RL) a respective policy for selecting at least one setting of at least one parameter of the radio access network (an “action”) to increase (e.g., raise or maximize) a respective predicted reward in accordance with the state information.)
As to claim 11, Chen teaches the system of claim 1, wherein the at least one BBU entity includes a central unit communicatively coupled to a distributed unit, wherein the distributed unit is communicatively coupled to the first radio unit, wherein the central unit is configured to send the one or more performance indicators for standalone user equipment and the one or more performance indicators for non-standalone user equipment to the machine learning computing system, wherein the machine learning computing system is implemented in a radio access network intelligent controller. ([0026] Although cloud RAN infrastructure may include distributed RRHs and centralized baseband units, a heterogeneous network may include cell sites where RRH and BBU components remain co-located at the cell site. [0035] For instance, SON/SDN controller 102 may access cell sites 111-113 and/or any one or more components thereof, such as eNodeB's, remote radio heads (RRHs), BBU pool 114, etc. via one or more respective control interfaces (e.g., vendor APIs) to remotely configure the settings for the plurality of parameters. [0045] For example, SON/SDN controller 102 may include a reinforcement learning agent (RLA) and/or data pre-processing unit, e.g., addition to other functions of SON/SDN controller 102.)
Claim Rejections - 35 USC § 103
The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made.
Claim(s) 5-8, 12, 16, 17, and 20 is/are rejected under 35 U.S.C. 103 as being unpatentable over Chen.
As to claims 5 and 16, Chen teaches the system of claim 1,
But does not specifically teach:
wherein the one or more performance indicators for standalone user equipment and one or more performance indicators for non-standalone user equipment from the at least one BBU entity include:
a total number of RRC Connection Establishment Requests; a total number of RRC Connection Rejections with a cause of resources not being available; a total number of Guaranteed Bit Rate (GBR) bearers mapped to a Protocol Data Unit (PDU) session; a total number of non-GBR bearers mapped to a PDU session; a total number of PDU sessions; and/or a total number of 5G Quality of Service Identifier (SQI) bearers.
However Chen describes in [0047] In one example, components of RAN 270 may collect raw operational data of the RAN, such as base station equipment operating temperatures (e.g., BBU temperatures, operational states (e.g., active/inactive, on/off, etc.), device voltages, raw call trace records (CTRs), information regarding mobile devices detected by a base station, such as IMEI/IMSI, the numbers of active mobile devices, the number of active sessions per sector, the number of active sessions per mobile device, transmit power, receive power, RSRP measurement data obtained from mobile devices, information regarding a currently active scheduling algorithm, a currently active handover setting, etc.).
It would have been obvious to one of ordinary skill in the art at the time of the effective filing date of the claimed invention that the information regarding mobile devices detected by a base station, as stated in Chen, would include the list of performance indicators stated in the claimed limitations of the claimed invention in order to determine operational data for determining an action that would increase or maximize rewards for the system.
As to claim 6, Chen teaches the system of claim 1,
But does not specifically teach:
wherein the one or more performance indicators for standalone user equipment from the at least one BBU entity include a number of inactive RRC contexts and a number of inactive RRC contexts that became active.
However Chen describes in [0036] The various parameters may include… historical information [0047] In one example, components of RAN 270 may collect raw operational data of the RAN, such as base station equipment operating temperatures (e.g., BBU temperatures, operational states (e.g., active/inactive, on/off, etc.), device voltages, raw call trace records (CTRs), information regarding mobile devices detected by a base station, such as IMEI/IMSI, the numbers of active mobile devices, the number of active sessions per sector, the number of active sessions per mobile device, transmit power, receive power, RSRP measurement data obtained from mobile devices, information regarding a currently active scheduling algorithm, a currently active handover setting, etc.).
It would have been obvious to one of ordinary skill in the art at the time of the effective filing date of the claimed invention that historical information and the information regarding mobile devices detected by a base station…number of active sessions per mobile device, as stated in Chen, would include the list of performance indicators stated in the claimed limitations of the claimed invention in order to determine operational data for determining an action that would increase or maximize rewards for the system.
As to claims 7 and 17, Chen teaches the system of claim 1,
But does not specifically teach:
wherein the one or more predicted traffic parameters for standalone user equipment and the one or more predicted traffic parameters for non- standalone user equipment include: a predicted total number of RRC Connection Establishment Requests for standalone user equipment and non-standalone user equipment; a predicted total number of Guaranteed Bit Rate (GBR) bearers mapped to a Protocol Data Unit (PDU) session for standalone user equipment and non-standalone user equipment; a predicted total number of non-GBR bearers mapped to a PDU session for standalone user equipment and non-standalone user equipment; a predicted total number of PDU sessions for standalone user equipment and non- standalone user equipment; and/or a predicted total number of 5G Quality of Service Identifier (SQI) bearers for standalone user equipment and non-standalone user equipment.
However Chen describes in [0036] The various parameters may include… historical information [0047] In one example, components of RAN 270 may collect raw operational data of the RAN, such as base station equipment operating temperatures (e.g., BBU temperatures, operational states (e.g., active/inactive, on/off, etc.), device voltages, raw call trace records (CTRs), information regarding mobile devices detected by a base station, such as IMEI/IMSI, the numbers of active mobile devices, the number of active sessions per sector, the number of active sessions per mobile device, transmit power, receive power, RSRP measurement data obtained from mobile devices, information regarding a currently active scheduling algorithm, a currently active handover setting, etc [0050] In other words, each of the plurality of neural networks 247-249 is to encode (and learn through RL) a respective policy for selecting at least one setting of at least one parameter of the radio access network (an “action”) to increase (e.g., raise or maximize) a respective predicted reward in accordance with the state information. [0054] FIG. 3 illustrates a chart 300 with example use cases that may be associated with different sub-agents of a reinforcement learning agent and the respective state information, reward information, and actions that may relate to the different use cases. For a second use case of “coverage optimization,” the “reward” may be a composite of a number of connected users and a call retainability metric, and the corresponding “action” may comprise antenna array/RRH tilt, handover offset configurations, or both. Chart 300 further illustrates three additional example use cases of “energy savings,” “mobile device/UE throughput and fairness,” and “load balancing,” each with associated reward-action sets. It should be noted that the state information may be the same for all of the use cases (e.g., shared state information).)
It would have been obvious to one of ordinary skill in the art at the time of the effective filing date of the claimed invention that changes to handover, load balancing, turning cells off/on, and scheduling would change the number of sessions/connections and is a part of a predicted reward that is expected based on what is described in Chen in order to provide the use case results of chart 300 as shown in fig. 3.
As to claim 8, Chen teaches the system of claim 1,
But does not specifically teach:
wherein the machine learning computing system is further configured to determine a predicted total number of standalone user equipment that will be redirected to non-standalone mode and a predicted total number of non-standalone user equipment that will be redirected to standalone mode; wherein the one or more components of the system are further configured to allocate resources for standalone user equipment and/or non-standalone user equipment based on the predicted total number of standalone user equipment that will be redirected to non-standalone mode and the predicted total number of non-standalone user equipment that will be redirected to standalone mode.
However Chen describes describes in [0036] The various parameters may include… historical information [0047] In one example, components of RAN 270 may collect raw operational data of the RAN, such as base station equipment operating temperatures (e.g., BBU temperatures, operational states (e.g., active/inactive, on/off, etc.), device voltages, raw call trace records (CTRs), information regarding mobile devices detected by a base station, such as IMEI/IMSI, the numbers of active mobile devices, the number of active sessions per sector, the number of active sessions per mobile device, transmit power, receive power, RSRP measurement data obtained from mobile devices, information regarding a currently active scheduling algorithm, a currently active handover setting, etc [0050] In other words, each of the plurality of neural networks 247-249 is to encode (and learn through RL) a respective policy for selecting at least one setting of at least one parameter of the radio access network (an “action”) to increase (e.g., raise or maximize) a respective predicted reward in accordance with the state information. [0054] FIG. 3 illustrates a chart 300 with example use cases that may be associated with different sub-agents of a reinforcement learning agent and the respective state information, reward information, and actions that may relate to the different use cases. For a second use case of “coverage optimization,” the “reward” may be a composite of a number of connected users and a call retainability metric, and the corresponding “action” may comprise antenna array/RRH tilt, handover offset configurations, or both. Chart 300 further illustrates three additional example use cases of “energy savings,” “mobile device/UE throughput and fairness,” and “load balancing,” each with associated reward-action sets. It should be noted that the state information may be the same for all of the use cases (e.g., shared state information).)
It would have been obvious to one of ordinary skill in the art at the time of the effective filing date of the claimed invention that changes to handover, load balancing, turning cells off/on, and scheduling would change the number of sessions/connections and is a part of a predicted reward that is expected based on what is described in Chen in order to provide the use case results of chart 300 as shown in fig. 3.
As to claims 12 and 20, Chen teaches the system of claim 1,
But does not specifically teach:
wherein the machine learning computing system is further configured to: receive updated service requirements for the base station via an interface; and reallocate resources for standalone user equipment and/or non-standalone user equipment based on the one or more predicted traffic parameters for standalone user equipment, the one or more predicted traffic parameters for non-standalone user equipment, and the updated service requirements for the base station.
However Chen describes in [0015] examples of the present disclosure apply reinforcement learning (RL) to explore different configurations in real-time and update policies based upon the outcomes of the changes to determine the optimal configurations (e.g., parameter settings) of the network. [0054] FIG. 3 illustrates a chart 300 with example use cases that may be associated with different sub-agents of a reinforcement learning agent and the respective state information, reward information, and actions that may relate to the different use cases. For a second use case of “coverage optimization,” the “reward” may be a composite of a number of connected users and a call retainability metric, and the corresponding “action” may comprise antenna array/RRH tilt, handover offset configurations, or both. Chart 300 further illustrates three additional example use cases of “energy savings,” “mobile device/UE throughput and fairness,” and “load balancing,” each with associated reward-action sets. It should be noted that the state information may be the same for all of the use cases (e.g., shared state information).)
It would have been obvious to one of ordinary skill in the art at the time of the effective filing date of the claimed invention that changes to handover, load balancing, turning cells off/on, and scheduling would change the number of sessions/connections and is a part of a predicted reward that is expected based on what is described in Chen in order to provide the use case results of chart 300 as shown in fig. 3.
Conclusion
THIS ACTION IS MADE FINAL.
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/Elton Williams/Examiner, Art Unit 2465
