Abstract:
Briefly, in accordance with one or more embodiments, a mechanism disclosed herein groups transmissions to machine-to-machine (M2M) devices in the downlink which can significantly reduce the overhead of transmission. One or more bursts to be transmitted in the downlink to one or more respective devices are aggregated and concatenated into a concatenated burst comprising one or more sub-bursts corresponding to the one or more bursts. The concatenated burst is encoded as a single payload to be transmitted, and the payload is transmitted to the one or more devices such that the devices are capable of decoding their respective sub-bursts in the concatenated burst.
Abstract:
Embodiments of a base station and method for resource allocation using localized and distributed resource blocks are generally described herein. The base station comprises processing circuitry to allocate localized resources to user stations based on receipt of channel quality information received from the user stations and to allocate distributed resource to user stations based on non-receipt of channel quality information. The base station also comprises physical layer circuitry to transmit control information on a physical channel to indicate the resources that are allocated to each scheduled user station.
Abstract:
Techniques are described that can be used to determine a transmitter power level of a mobile station at cell edge. To determine transmitter power level, the technique considers at least a balance of power transmitted by mobile stations near cell edge and power transmitted by mobile stations closer to cell center, target mean received power by the base station from mobile stations near center cell, target mean power transmitted from cell edge mobile stations, signal-to-interference-power ratio between signals transmitted from base stations of different cells to the mobile station at cell edge, and channel gain.
Abstract:
Beamforming with nulling techniques for wireless communications networks are disclosed. For example, an apparatus may include a beamforming module and a weight determination module. The beamforming module applies beamforming weights to a downlink user channel with a first mobile station. The weight determination module determines the beamforming weights based on user channel information and interfering channel information. This user channel information is received from the first mobile station and includes characteristics of the downlink user channel. However, the interfering channel information includes characteristics of one or more downlink interfering channels received by one or more further mobile stations. These downlink interfering channels are associated with transmissions across the downlink user channel with the first mobile station.
Abstract:
A computing node to implement an RL management entity in an NG wireless network includes a NIC and processing circuitry coupled to the NIC. The processing circuitry is configured to generate a plurality of network measurements for a corresponding plurality of network functions. The functions are configured as a plurality of ML models forming a multi-level hierarchy. Control signaling from an ML model of the plurality is decoded, the ML model being at a predetermined level (e.g., a lowest level) in the hierarchy. The control signaling is responsive to a corresponding network measurement and at least second control signaling from a second ML model at a level that is higher than the predetermined level. A plurality of reward functions is generated for training the ML models, based on the control signaling from the MLO model at the predetermined level in the multi-level hierarchy.
Abstract:
Systems, apparatuses, methods, and computer-readable media, are provided for distributed machine learning (ML) training using heterogeneous compute nodes in a heterogeneous computing environment, where the heterogeneous compute nodes are connected to a master node via respective wireless links. ML computations are performed by individual heterogeneous compute nodes on respective training datasets, and a master combines the outputs of the ML computations obtained from individual heterogeneous compute nodes. The ML computations are balanced across the heterogeneous compute nodes based on knowledge of network conditions and operational constraints experienced by the heterogeneous compute nodes. Other embodiments may be described and/or claimed.
Abstract:
Technology for a user equipment (UE) to communicate in a multiple radio access technology (multi-RAT) heterogeneous network (HetNet) is described. A radio-link-selection hysteresis threshold can be determined at the UE for a radio link between the UE and a node in the multi-RAT HetNet. A reliability value of a throughput estimate can be measured for the radio link in the multi-RAT HetNet. The radio-link-selection hysteresis threshold can be adjusted at the UE based on the reliability value to increase network stability in the multi-RAT HetNet.
Abstract:
A technique for setting the transmission powers of individual D2D (device-to-device) transmitters using a distributed power control technique is described. Each individual D2D transmitter learns the interference levels that it imposes on an eNB (evolved Node B) and on D2D receivers other than its partner D2D receiver. The D2D transmitter is then able to adjust its transmission power accordingly. Such managing of interference temperature via distributed power control enables the network to maximize its reuse of time-frequency resources.
Abstract:
Certain embodiments herein are directed to managing wireless spectrum, which may include recommending or transmitting spectrum usage changes to one or more wireless devices. A spectrum management system comprising one or more computers may receive spectrum usage information associated with one or more wireless devices. The spectrum management system may generate a spectrum usage map based on the received information. Based on the spectrum usage map, a spectrum usage change is determined and transmitted to one or more wireless devices. The wireless devices may change their operation in accordance with the spectrum usage change.
Abstract:
This disclosure is directed to a composable thin computing device. An example device may comprise at least a device interface module, a communication module, a processing module, a memory module, a composable computing module and a power module. The device interface module may couple the device to an operational environment via at least one of a physical connector or a wireless connection. The communication module may at least one of transmit or receive data via the device interface module. The processing module may process the data. The memory module may store at least a portion of the data. The composable computing module may cause at least one of the above modules to perform certain functionality related to the operational environment. The power module may power at least one of the above modules.