In one embodiment, a method includes receiving SRS received from a plurality of UEs associated with the base station from an RU associated with the base station, estimating strengths or signal-to-noise ratios (SNRs) for pre-determined beams for each of the plurality of UEs based on the received SRS, selecting a subset of the plurality of UEs to which downlink data is to be transmitted for a RBG in a TTI based on the estimated strengths or SNRs of the pre-determined beams for the plurality of UEs, computing a precoding matrix for the RBG based on the selected subset, preparing multi-layered UE data for the RBG based on the selected subset and the computed precoding matrix, sending the multi-layered UE data and the precoding matrix for the RBG to the RU, where the RU transmits pre-coded multi-layered UE data to the UEs in the subset using MIMO technologies.

The present invention relates to a method for predicting indoor three-dimensional space signal field strength by an outdoor-to-indoor propagation model, which comprises the steps of: establishing a three-dimensional space scene model from a transmitting base station to a target building; predicting space field strength of an outer envelope of the target building according to an extended COST-231-Walfisch-Ikegami propagation model; generating, on the outer envelope of the target building, a series of out-door-to-indoor virtual rays in accordance with a certain resolution; simulating a propagation procedure of the virtual rays using a ray tracing propagation model algorithm, to predict three-dimensional space signal field strength in the target building. In the present invention, an extended COST231-Walfisch-Ikegami propagation model is adopted for the transmitting base station and the outdoor region of the target building, while a ray tracing propagation model algorithm is adopted for the indoor region of the target building, which effectively combines an outdoor empirical propagation model and an indoor deterministic propagation model, so that a good equilibrium is achieved between calculation efficiency and calculation accuracy, and the algorithm has a strong engineering applicability.

A central command system may determine a mission plan for resilient execution by a swarm of drones comprising one or more sensors to capture data in accordance with the mission plan. The mission plan may specify requirements for fault tolerance or parallelism and a redundancy structure for the swarm. The mission plan may be transmitted to a remote drone swarm controller device that determines a swarm configuration based on the mission plan and available drones. The controller may transmit instructions regarding the swarm configuration to dispatch a resilient swarm of drones. During execution of the mission plan, drones in the resilient swarm may be monitored by other drones in the swarm, by the remote drone swarm controller, and/or by the central command system. The redundancy structure provides for failover options for one or more drones in the resilient swarm.

A communication band calculation device includes a memory; and a processor configured to execute acquiring user-based traffic information; defining non-overlapping separate section columns having any section width as a state space and sets a user distribution of a traffic amount according to the state space based on the traffic information; generating a transition probability matrix in which a transition probability that a transition from a separate section to another in the state space occurs is set as an element based on the traffic information; obtaining prediction of the user distribution at a desired future time using a product of the user distribution which is a starting point of prediction and the transition probability matrix; and calculating an amount of band facilities required to achieve quality of a communication service at a future point in time based on the user distribution in future.

An information handling system executing an intelligent throughput performance analysis and issue detection system may comprise a network interface device to establish a wireless link with a wireless network and a processor to execute a neural network trained to predict wireless link throughput values based on controlled connectivity testing metrics gathered in a controlled laboratory from tested information handling systems. The processor may gather measured throughput of the wireless link and operational connectivity metrics for the information handling system that describe antenna positional information, antenna adaptation controller parameters, signal strength measurements, and wireless link performance metrics. The neural network may output, based on the gathered operational connectivity metrics a predicted throughput value that differs from the measured throughput by a maximum tolerance. The network interface device may transmit a notification of erroneously predicted throughput, the neural network output, and the operational connectivity metrics to a remote neural network training platform for retraining.

Aspects of the subject disclosure may include, for example, receiving sounding reference signal (SRS) symbols from antenna elements of each of multiple modular antenna arrays, wherein the multiple modular antenna arrays are operatively combined to form a coherent antenna system, performing an uplink (UL) channel estimation and a downlink (DL) channel estimation, across a plurality of physical resource blocks (PRBs), based on the SRS symbols, calculating, for the antenna elements, a plurality of uplink (UL) combining weights based on the UL channel estimation and a plurality of downlink (DL) precoder weights based on the DL channel estimation, and causing the plurality of UL combining weights and the plurality of DL precoder weights to be applied to the antenna elements, thereby adjusting beamforming of the coherent antenna system. Other embodiments are disclosed.

Facilitating an interference leakage dependent resource reservation protocol in advanced networks (e.g., 5G, 6G, and beyond) is provided herein. Operations of a method can comprise defining, by a system comprising a memory and a processor, a resource reservation procedure that associates respective amounts of reserved resources available for a mobile device based on a transmission beam width and a transmission power level of the mobile device. The method also can comprise selecting, by the system, an amount of reserved resources from the respective amounts of reserved resources available based on the transmission beam width and the transmission power level of the mobile device.

A system and method for transmitting a reference signal in a cell that uses an unlicensed frequency band. The method includes determining a candidate resource set that is used when a first reference signal is transmitted in the cell that uses the unlicensed frequency band, where the candidate resource set includes a preset resource and at least one flexible candidate resource, determining a first candidate resource that is used when the first reference signal is transmitted in the cell that uses the unlicensed frequency band, where a channel on the unlicensed frequency band corresponding to the first candidate resource is in an idle state, and the first candidate resource is the preset resource or a flexible candidate resource in the candidate resource set, and sending the first reference signal on the first candidate resource.

A server, method, and computer-readable storage medium for coverage prediction in wireless networks. The server includes a memory storing instructions and a processor operably connected to the memory, which is configured to execute the instructions to cause the server to identify a region of interest (RoI) for the coverage prediction; determine, using a neural network, a set of values for a system performance metric for areas in the RoI, respectively; and generate the coverage prediction for the RoI which associates the areas in the RoI with a determined value in the set of values. The set of values for the system performance metric is determined based on a plurality of data samples for a set of RoIs which include at least one of building height, terrain height, foliage height, clutter data that classifies land cover, line-of-sight indication data, antenna height, and ground truth data for the system performance metric.

A wireless network can generate candidate signal configurations for physical transmissions to or from a user equipment (UE) in a radio environment. The generation of candidate signal configurations can be performed using a first neural network that is associated with the UE. These signal configurations can then be evaluated using a second neural network that is associated with the radio environment. The second neural network can be trained using measurements from previous physical transmissions in the radio environment. The trained second neural network generates a reward value that is associated with the candidate signal configurations. The first neural network is then trained using the reward values from the second neural network to produce improved candidate signal configurations. When a signal configuration that produces a suitable reward value is generated, this signal configuration can be used for the physical transmission in the radio environment.