Abstract: Distributed unit monitoring of radio unit transmitter performance (e.g., using a computerized tool), is enabled. For example, a system can comprise a processor and a memory that stores executable instructions that, when executed by the processor, facilitate performance of operations. The operations can comprise determining an average of differences between respective uplink path losses between a cellular node and a group of user equipment, and corresponding downlink path losses between the cellular node and the group of user equipment. The operations can further comprise, in response to the average of the differences between the respective uplink path losses and the corresponding downlink path losses being determined to satisfy a defined difference threshold, determining that the cellular node is not transmitting according to a specified power level.

Abstract: The technology described herein is directed towards radio unit and distributed unit splits, including for massive MIMO systems and/or O-RAN splits. Demodulation reference signal (DMRS)-based receive beamforming is split between a radio unit and a distributed unit, in which the computational resources needed for channel estimation, DMRS weight calculation and equalization are located in the distributed unit rather than the radio unit. In one implementation, the radio unit extracts the DMRS symbols and resource elements (REs) and sends them with little or no beamforming to the distributed unit; channel estimation and beamforming coefficients are computed at the distributed unit based on the DMRS data, and returned to the radio unit for use in receive beamforming for the current slot or a future slot. The radio unit can send candidate beams to the distributed unit for combining to reduce any non-optimality of the beams. A future optimal beam can be predicted.

Abstract: A method for low-complexity artificial intelligence-based channel link adaptation includes converting, by a device including a processor, a first vector of signal quality values corresponding to respective frequency subcarriers utilized by a cell of a communication network into a second vector of model input parameters. The first vector has a first size that is smaller than a second size of the second vector. The method further includes selecting, by the device, a modulation and coding scheme based on applying the second vector to a machine learning model. The method additionally includes facilitating, by the device, conducting a transmission from the cell using the modulation and coding scheme, resulting in the transmission having a first throughput that is higher than a second throughput associated with the cell before the facilitating.

Abstract: Consecutive slot scheduling for physical uplink shared channel resources (e.g., using a computerized tool), is enabled. For example, a 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 first demodulation reference signal of a special slot of a physical uplink shared channel, and based on the first demodulation reference signal, scheduling a second demodulation reference signal of a subsequent slot of the physical uplink shared channel, subsequent to the special slot, for uplink transmission via a user equipment common to the special slot and the subsequent slot.

Abstract: Satellites provide connectivity in remote and rural areas as well as providing applications and services elsewhere on earth and in space. Existing satellite networks will be increasingly augmented by ultra-dense deployments of interconnected satellites providing low Earth orbit (LEO) constellations. However, such satellites only offer short-term line-of-sight access requiring ongoing handovers during the duration of a terminal's access. Accordingly, to exploit these LEO constellations the inventors have established methodologies exploiting distributed massive multiple-input multiple-output technology for a user terminal to be connected to a cluster of LEO satellites.

Abstract: A system and methods for conducting Internet based financial transactions between a client and a server. The client has a processor, a printer, a client authentication module, a module for issuing a transaction request, and a unique digital signature. The server has a network including a transaction server, a transaction database, a server authentication module, and a receipt generation module. An internet connection is used between the client and the server network. The transaction execution system includes authentication, wherein the client authentication module and the server authentication modules communicate via the internet connection and are authenticated to each other.