Systems, devices and processes are described herein to improve the reliable delivery of content in a satellite system by facilitating the prediction of likely future signal interference. Specifically, the various embodiments provide a technique whereby a likelihood of future plant growth interfering with received signal strength at a satellite dish is determined. In one embodiment, the technique analyses one or more images of a plant in the vicinity of the satellite dish and from that image generates prediction of future growth of the plant over a future time period. From this prediction of future plant growth, a likelihood of future signal interference being caused by the plant can be determined.

A communication system, includes a satellite receiver in operable communication with a central server, a cellular node configured to operate within a proximity of the satellite receiver, and at least one mobile communication device configured to communicate (i) with the cellular node, (ii) within the proximity of the satellite receiver, and (iii) using a transmission signal capable of causing interference to the satellite receiver. The satellite receiver is configured to detect a repeating portion of the transmission signal and determine a potential for interference from the at least one mobile communication device based on the detected repeating portion.

Aspects of the subject disclosure may include, for example, determining a coherence block for each user equipment (UE) of a plurality of UEs being served by the first cell, resulting in a plurality of coherence blocks, responsive to the determining, identifying a smallest coherence block from the plurality of coherence blocks, identifying a pilot sequence length based on the smallest coherence block, determining a plurality of orthogonal pilot sequences based on the identifying the pilot sequence length, designating, from the plurality of orthogonal pilot sequences, a first group of orthogonal pilot sequences for use in the first cell, and distributing, to each neighboring cell of a plurality of neighboring cells adjacent to the first cell, a respective group of orthogonal pilot sequences from a remainder of the plurality of orthogonal pilot sequences, to prevent pilot contamination between the first cell and the plurality of neighboring cells. Other embodiments are disclosed.

A non-real time control unit (Non-RT RIC) and a near-real time control unit (Near-RT RIC) are hierarchized, a learning and inference unit (11, 12, 13, 16,17, 18), that controls the radio access network based on a result of inference performed by applying newest data to a learning model generated based on data collected from an O-RAN base station device 10, is arranged in the near-real time control unit, and a retraining control unit (14, 15, 19, 20), that detects concept drift based on a history of the data collected and causes the learning and inference unit to retrain the learning model when the concept drift is detected, is arranged in the non-real time control unit.

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.

A device may include a memory configured to store channel quality data comprising information indicating a quality of a communication channel between a base station (BS) and a user equipment (UE). The device may further include a processor configured to provide an input comprising the channel quality data to a machine learning model configured to predict a channel quality indicator (CQI) based on the input and encode a channel quality information based on the predicted CQI for a transmission to the BS.

A system uses pulsed lasers to enhance a signal propagation path in a telecommunications network. The system can estimate a signal propagation path, which varies based on a current location of a mobile device relative to a base station. The system can detect a propagation loss due to a condition of a propagation medium including the signal propagation path and determine whether the mobile device is in Line-of-Sight (LOS) of a laser emitter. In response to detection of the propagation loss, the laser emitter can emit a pulsed laser that can enhance signal propagation by mitigating the propagation loss on the signal propagation path. The pulsed laser has a propagation path overlapping the signal propagation path when the mobile device is in LOS of the laser emitter, which is mounted on the base station.

The present disclosure relates to a device for use in a wireless network, the device including: a processor configured to: provide input data to a trained machine learning model, the input data representative of a network environment of the wireless network, wherein the trained machine learning model is configured to provide, based on the input data, output data representative of an expected performance of a plurality of configurations of network components with respect to power consumption and performance of the wireless network; select a configuration of a network component from the plurality of configurations based on the output data of the trained machine learning model; and instruct an operation of the network component according to the selected configuration; and a memory coupled with the processor, the memory storing the input data provided to the trained machine learning model and/or the output data from the trained machine learning model.

Systems and methods are disclosed for adjusting Radio Link Monitoring (RLM), Radio Link Failure (RLF) detection, RLF recovery, and/or connection establishment failure detection for wireless devices (16) in a cellular communications network (10) depending on mode of operation. In one embodiment, a node (14, 16) in the cellular communications network (10) determines whether a wireless device (16) (e.g., a Machine Type Communication (MTC) device) is to operate in a long range extension mode of operation or a normal mode of operation. The node (14, 16) then applies different values for at least one parameter depending on whether the wireless device (16) is to operate in the long range extension mode or the normal mode. The at least one parameter includes one or more RLM parameters, one or more RLF detection parameters, and/or one or more RLF recovery parameters. In doing so, signaling overhead and energy consumption within the wireless device (16) when operating in the long range extension mode is substantially reduced.

Artificial Intelligence (AI) means are disclosed for enabling network operators to optimize 5G and 6G messaging performance, in real-time. AI models, or fieldable algorithms derived therefrom, can select an appropriate modulation scheme according to network conditions. Modulation variables can then be adjusted to optimize performance, such as throughput or failure rates, for low or high traffic densities. Three development phases are described: network data acquisition including faults experienced under various network conditions, AI structure tuning for accurate prediction of performance, and implementation of a fieldable algorithm based on the AI structure. Network operators can use the fieldable algorithm to compare predicted performance metrics in real-time, according to various operating conditions (such as available modulation schemes), and thereby adjust particular modulation parameters (such as amplitude or phase levels).