Abstract: Architectures and techniques are provided for intelligently selecting a modulation coding scheme (MCS) table that is to be used for a connection between an access point (e.g., a distributed unit) and a user equipment (UE). Based on an initial signal-to-noise ratio (SNR) value between the access point and UE and data points determined from previous connections involving the access point or a given cell of the access point an adaptive access point-specific SNR threshold can be determined. This SNR threshold can evolve over time based on the collected data and can be used to choose a particular MCS table for subsequent connections.

Abstract: Technology described herein can employ dynamically changing network variables and/or context, in realtime, to determine one or more possible contexts that can lead to network function (NF) misconfiguration, NF mismatch and/or key performance indicator (KPI) degradation. In an embodiment, an example system can comprise a processor, and a memory that stores executable instructions that, when executed by the processor, facilitate performance of operations, comprising determining a working configuration of a network function, and detecting a misconfiguration between a specified configuration and the working configuration of the network function, wherein the detecting is based on a context known to be associated with the misconfiguration. Analysis of context can comprise analyzing, based on an artificial intelligence model, the contexts of the network function as compared to known and/or stored contexts.

Abstract: The technology described herein is directed towards near-instantaneous optimization of radio resource control (RRC) rejection wait times per user equipment, which can be per user equipment of a group of multiple user equipment. In inference mode, a trained wait time prediction model (e.g., an artificial intelligence/deep reinforcement learning (AI/DRL) model in a base station) adaptively determines the per-UE rejection wait times based on current (measured) network state data, including current centralized unit and distributed unit utilization levels. The technology described herein operates to decreases the overall RRC request rate, while maintaining very high utilization of gNodeB resources, which results in increased per-UE energy efficiency and decreased signaling traffic loads.

Abstract: The described technology is generally directed towards scheduling, by a distributed unit, the injection of custom traffic (signals/data) into a radio unit communications path. The custom traffic can be used to improve the performance of the radio unit, such as to perform antenna calibration, to perform test and measurement operations to obtain performance data, which can be used, for example, to modify operating parameters of the radio unit to improve performance of the radio unit. The distributed unit coordinates with the radio unit to schedule and synchronize such custom traffic in unscheduled (physical resource block), such as interleaved with to live-air and non-live-air traffic. Further, the distributed unit, which has scheduling knowledge of upcoming traffic of the radio unit, can communicate such knowledge to the radio unit for the radio unit to change its operating state based on the upcoming traffic knowledge.

Abstract: A method facilitating dynamic redistribution of random access channel (RACH) preamble resources includes facilitating, by a system including at least one processor, submitting a network performance measurement, relating to a performance indicator of a communication network, to a reinforcement learning model; adjusting, by the system based on an output generated by the reinforcement learning model in response to the network performance measurement, a partitioning of a group of RACH preambles, used by the communication network, between a first subgroup of contention-based RACH preambles and a second subgroup of non-contention-based RACH preambles, resulting in an adjusted partitioning of the RACH preambles; and causing, by the system in response to the adjusting, a base station to transmit data relating to the adjusted partitioning of the RACH preambles via a system information broadcast.

Abstract: A method facilitating efficient resource grants for radio resource control (RRC) messaging includes generating, by centralized unit equipment including at least one processor, scheduling instructions for an RRC message, including embedding a resource grant request, for uplink communication resources to be allocated for an uplink message to be transmitted by a user equipment in response to the RRC message, into the scheduling instructions; generating, by the centralized unit equipment, a downlink F1 application protocol (F1AP) message including the RRC message and the scheduling instructions; and transmitting, by the centralized unit equipment, the downlink F1AP message to distributed unit equipment serving the user equipment.

Abstract: SID frame duration during communication session between device and base station can be determined and bypassing of certain uplink grants of the session can be controlled. Uplink communication manager (UCM) can determine when session is in silent period based on a silent period detection threshold value, and determine SID frame duration of session based on determination of a number of consecutive slots without uplink voice-related data packet or SID frame being received from the device during silent period. UCM can determine number of slots to bypass for periodic uplink grants, or number of configured slots to bypass for configured grant, during portion of silent period, based on SID frame duration, silent period detection threshold value, and periodic uplink grant period or configured grant periodicity. During silent period, UCM can control bypassing of periodic uplink grants for the number of slots or the number of configured grant slots.

Abstract: The technology described herein is directed towards dynamically determining, based on current environment state data, a number of coverage zones within a base station's coverage area, and physical uplink shared channel (PUSCH) closed-loop power control-related data for user equipment in each zone. In one implementation, a deep reinforcement learning (DRL)-based system includes a first DRL agent that, based on the current environment state, outputs the optimal number of zones. Based on the current environment state data and the number of zones, a second DRL agent outputs optimal per-zone target signal-to-interference-plus-noise ratio (SINR) values. The SINR values are used in outputting transmit power control data to the UEs in each coverage zone. Also described is deep reinforcement learning by the system based on a reward function that can balance enhanced power efficiency with enhanced throughput to determine the optimal number of zones and SINR values for various current environment state data.

Abstract: The described technology is generally directed towards scheduling, by a distributed unit, the injection of custom traffic (signals/data) into a radio unit communications path. The distributed unit coordinates with the radio unit to schedule and synchronize such custom traffic in unscheduled (physical resource block), such as interleaved with to live-air and non-live-air traffic. The radio unit can request the unscheduled physical resource blocks for custom traffic to be inserted by the radio unit. Alternatively, the distributed unit can inject the custom traffic in otherwise unscheduled physical resource blocks for sending to the radio unit. The custom traffic is configured to perform some action by the radio unit, such as to perform antenna calibration, to perform test and measurement operations to obtain performance data, and the like. Performance data can be used, for example, to modify operating parameters of the radio unit to improve performance of the radio unit.

Abstract: The technology described herein is directed towards near-instantaneous optimization of radio resource control (RRC) rejection wait times per user equipment, which can be per user equipment of a group of multiple user equipment. In inference mode, a trained wait time prediction model (e.g., an artificial intelligence/deep reinforcement learning (AI/DRL) model in a base station) adaptively determines the per-UE rejection wait times based on current (measured) network state data, including current centralized unit and distributed unit utilization levels. The technology described herein operates to decreases the overall RRC request rate, while maintaining very high utilization of gNodeB resources, which results in increased per-UE energy efficiency and decreased signaling traffic loads.

Abstract: Improved techniques for selecting a handover target cell are described herein, for example in connection with a 5G disaggregated radio network. For example, in addition to evaluating signal quality metrics that are reported by the user equipment subject to the handover, the disclosed techniques can also evaluate additional metrics. For example, a load associated with potential target cells can be considered as neighbor load can cause handover issues. As another example, a history of success rates associated with a handover to the target cell and/or from the serving cell can be considered as well since handover failures can sometimes result due to issues that can be surfaced by the success rate history.

Abstract: Herein is a self-tuning database management system (DBMS) storing JavaScript object notation (JSON) documents and operating a JSON datatype as native to the DBMS. In an embodiment, a computer hosts a DBMS that executes a data definition language (DDL) statement that defines, in a database dictionary of the DBMS, a JSON document column of a database table that stores JSON documents as instances of the JSON datatype that is native in the DBMS. The DBMS may autonomously set or adjust configuration settings that control behaviors such as a default width of a JSON document column, in lining or not of the JSON document column, kind and scope and duration of indexing of the JSON document column, and/or caching of the JSON document column such as in an in memory columnar unit (IMCU). The DBMS may use the various configuration settings to control how JSON documents and the native JSON datatype are stored and/or processed.

Abstract: Technology described herein can employ dynamically changing network variables and/or context, in realtime, to determine one or more possible contexts that can lead to network function (NF) misconfiguration, NF mismatch and/or key performance indicator (KPI) degradation. In an embodiment, an example system can comprise a processor, and a memory that stores executable instructions that, when executed by the processor, facilitate performance of operations, comprising determining a working configuration of a network function, and detecting a misconfiguration between a specified configuration and the working configuration of the network function, wherein the detecting is based on a context known to be associated with the misconfiguration. Analysis of context can comprise analyzing, based on an artificial intelligence model, the contexts of the network function as compared to known and/or stored contexts.

Abstract: Generally provided is a system for determining unknown, new and/or changed radio system architecture/topology, including available system connections between nodes, NFs, CU-UPs, DUs and/or CU-CPs. An example system can comprise a processor; and a memory that stores executable instructions that, when executed by the processor, facilitate performance of operations, comprising determining a request from a user equipment (UE) for network access, and selecting a central unit user plane (CU-UP) to which to assign the UE based on a known aspect of a network topology of a communication network comprising the CU-UP. The system can provide a learned and dynamic approach to network topology analysis, and can allow for determining a CU-UP based on network topology, connection availability, loading, downtime, traffic connections, bandwidth and/or one or more other KPIs.

Abstract: The described technology is generally directed towards scheduling, by a distributed unit, the injection of custom traffic (signals/data) into a radio unit communications path. The custom traffic can be used to improve the performance of the radio unit, such as to perform antenna calibration, to perform test and measurement operations to obtain performance data, which can be used, for example, to modify operating parameters of the radio unit to improve performance of the radio unit. The distributed unit coordinates with the radio unit to schedule and synchronize such custom traffic in unscheduled (physical resource block), such as interleaved with to live-air and non-live-air traffic. Further, the distributed unit, which has scheduling knowledge of upcoming traffic of the radio unit, can communicate such knowledge to the radio unit for the radio unit to change its operating state based on the upcoming traffic knowledge.

Abstract: The described technology is generally directed towards scheduling, by a distributed unit, the injection of custom traffic (signals/data) into a radio unit communications path. The distributed unit coordinates with the radio unit to schedule and synchronize such custom traffic in unscheduled (physical resource block), such as interleaved with to live-air and non-live-air traffic. The radio unit can request the unscheduled physical resource blocks for custom traffic to be inserted by the radio unit. Alternatively, the distributed unit can inject the custom traffic in otherwise unscheduled physical resource blocks for sending to the radio unit. The custom traffic is configured to perform some action by the radio unit, such as to perform antenna calibration, to perform test and measurement operations to obtain performance data, and the like. Performance data can be used, for example, to modify operating parameters of the radio unit to improve performance of the radio unit.

Abstract: Herein is a self-tuning database management system (DBMS) storing JavaScript object notation (JSON) documents and operating a JSON datatype as native to the DBMS. In an embodiment, a computer hosts a DBMS that executes a data definition language (DDL) statement that defines, in a database dictionary of the DBMS, a JSON document column of a database table that stores JSON documents as instances of the JSON datatype that is native in the DBMS. The DBMS may autonomously set or adjust configuration settings that control behaviors such as a default width of a JSON document column, in lining or not of the JSON document column, kind and scope and duration of indexing of the JSON document column, and/or caching of the JSON document column such as in an in memory columnar unit (IMCU). The DBMS may use the various configuration settings to control how JSON documents and the native JSON datatype are stored and/or processed.

Abstract: Described is a system, method, and computer program product to perform bi-directional mapping of hierarchical data (e.g. JSON, XML) to database object types (e.g., user defined database object types).

Abstract: Techniques are introduced herein for maintaining geometry-type data on persistent storage and in memory. Specifically, a DBMS that maintains a database table, which includes at least one column storing spatial data objects (SDOs), also maintains metadata for the database table that includes definition data for one or more virtual columns of the table. According to an embodiment, the definition data includes one or more expressions that calculate minimum bounding box values for SDOs stored in the geometry-type column in the table. The one or more expressions in the metadata maintained for the table are used to create one or more in-memory columns that materialize the bounding box data for the represented SDOs. When a query that uses spatial-type operators to perform spatial filtering over data in the geometry-type column is received, the DBMS replaces the spatial-type operators with operators that operate over the scalar bounding box information materialized in memory.

Abstract: Herein is a self-tuning database management system (DBMS) storing JavaScript object notation (JSON) documents and operating a JSON datatype as native to the DBMS. In an embodiment, a computer hosts a DBMS that executes a data definition language (DDL) statement that defines, in a database dictionary of the DBMS, a JSON document column of a database table that stores JSON documents as instances of the JSON datatype that is native in the DBMS. The DBMS may autonomously set or adjust configuration settings that control behaviors such as a default width of a JSON document column, in lining or not of the JSON document column, kind and scope and duration of indexing of the JSON document column, and/or caching of the JSON document column such as in an in memory columnar unit (IMCU). The DBMS may use the various configuration settings to control how JSON documents and the native JSON datatype are stored and/or processed.