Abstract:
A gateway situated between the RAN and the core network may provide 2G/3G/4G/Wi-Fi convergence for nodes in a network on a plurality of radio access technologies. In some embodiments, a convergence gateway is described that allows for legacy radio access network functions to be provided by all-IP core network nodes. A multi-RAT gateway provides 2G/3G Iuh to IuPS interworking, IuCS to VoLTE interworking via a VoLTE proxy, IuPS and 4G data local breakout or S1-U interworking, and 2G A/IP and Gb/IP to VoLTE and S1-U/local breakout interworking. The multi-RAT gateway may thereby support all voice calls via VoLTE, and all data over S1 or local breakout, including VoLTE. The multi-RAT gateway may provide self-organizing network (SON) capabilities for all RATs. A multi-RAT base station may provide 2G and 3G front-end interworking to Iuh.
Abstract:
A gateway situated between the RAN and the core network may provide 2G/3G/4G/Wi-Fi convergence for nodes in a network on a plurality of radio access technologies. In some embodiments, a convergence gateway is described that allows for legacy radio access network functions to be provided by all-IP core network nodes. A multi-RAT gateway provides 2G/3G Iuh to IuPS interworking, IuCS to VoLTE interworking via a VoLTE proxy, IuPS and 4G data local breakout or S1-U interworking, and 2G A/IP and Gb/IP to VoLTE and S1-U/local breakout interworking. The multi-RAT gateway may thereby support all voice calls via VoLTE, and all data over S1 or local breakout, including VoLTE. The multi-RAT gateway may provide self-organizing network (SON) capabilities for all RATs. A multi-RAT base station may provide 2G and 3G front-end interworking to Iuh.
Abstract:
A method for scheduling resources in a network where the scheduling activity is split across two nodes in the network is disclosed, comprising: receiving, from a local scheduler in a first radio access network, access network information at a global scheduler; accessing information regarding a second radio access network allocating, at the global scheduler, resources for secondary allocation by the local scheduler; applying a hash function to map the allocated resources for secondary allocation to a set of hash values; and sending, from the global scheduler, the set of hash values to the local scheduler.
Abstract:
A method may be disclosed in accordance with some embodiments, comprising: receiving, at a virtualizing gateway between the eNodeB and a first core network, a service request from a first user equipment (UE) via an eNodeB; applying a filter to an identifier of the UE to authenticate the UE; and forwarding, based on the applied filter, the service request from the first UE to the first core network. The identifier may be an international mobile subscriber identity (IMSI). The filter may be a whitelist containing a plurality of IMSIs to be granted service or a blacklist containing a plurality of IMSIs to be denied service, the service request may be a Long Term Evolution (LTE) attach request, and the method may further comprise forwarding the message from the first UE to a first mobility management entity (MME) in the first core network.
Abstract:
In this invention, we disclose methods directed toward integrating an ad hoc cellular network into a fixed cellular network. The methods disclosed herein automate the creation and integration of these networks. In additional embodiments, we disclose methods for establishing a stand-alone, ad hoc cellular network. In either of these implementations, we integrate or establish an ad hoc cellular network using mobile ad hoc cellular base stations configured to transmit and receive over a variety of frequencies, protocols, and duplexing schemes. The methods flexibly and dynamically choose an access or backhaul configuration and radio characteristics to optimize network performance. Additional embodiments provide for enhancing an existing network's coverage as needed, establishing a local network in the event of a loss of backhaul coverage to the core network, and providing local wireless access service within the ad hoc cellular network.
Abstract:
This application discloses methods for creating self-organizing networks implemented on heterogeneous mesh networks. The self-organizing networks can include a computing cloud component coupled to the heterogeneous mesh network. In the methods and computer-readable mediums disclosed herein, a processor receives an environmental condition for a mesh network. The processor may have measured the environmental condition, or it could have received it from elsewhere, e.g., internally stored information, a neighboring node, a server located in a computing cloud, a network element, user equipment ("UE"), and the like. After receiving the environmental condition, the processor evaluates it and determines whether an operational parameter within the mesh network should change to better optimize network performance.
Abstract:
Methods and computer software are disclosed for providing backhaul bandwidth estimation for a network. In one embodiment a method is disclosed, comprising: performing active measurements of a maximum achievable bandwidth for the network; determining an uplink direction bandwidth estimation for the network; determining a downlink direction bandwidth estimation for the network; and determining, using the uplink direction bandwidth estimation and the downlink direction estimation bandwidth, a bandwidth estimation conclusion for the network.
Abstract:
Performing training using superimposed pilot subcarriers to determine training data. Training that includes starting with a training duration (T) equal to a number of antennas (M) and running a Convolutional Neural Network (CNN) model using training samples to determine if a testing variance meets a predefined threshold. When the testing variance meets a predefined threshold, then reducing T by one half and repeating the running Convolutional Neural Network (CNN) model until the testing variance fails to meet the predefined threshold. When the testing variance fails to meet the predefined threshold, then multiplying T by two and using the new value of T as the new training duration to be used. Generating a run-time model based on the training data, updating the run-time model with new feedback data received from a User Equipment (UE), producing a DL channel estimation and an optimal precoding matrix from the DL channel estimation.
Abstract:
Systems and methods are disclosed for performing training using superimposed pilot subcarriers to determine training data. The training includes starting with a training duration (T) equal to a number of antennas (M) and running a Convolutional Neural Network (CNN) model using training samples to determine if a testing variance meets a predefined threshold. When the testing variance meets a predefined threshold, then reducing T by one half and repeating the running Convolutional Neural Network (CNN) model until the testing variance fails to meet the predefined threshold. When the testing variance fails to meet the predefined threshold, then multiplying T by two and using the new value of T as the new training duration to be used. Generating a run-time model based on the training data, updating the run-time model with new feedback data received from a User Equipment (UE), producing a DL channel estimation from the run-time model; and producing an optimal precoding matrix from the DL channel estimation.
Abstract:
Systems and methods are disclosed for performing computations on data at an intelligent data pipe en route to a data store. In one embodiment, a method is disclosed, comprising: receiving metadata regarding a data stream from a data source; performing an analysis of the metadata at a service orchestrator; creating at least one container instance based on the analysis; streaming the data stream from the data source to a data sink via the at least one container; and processing the data stream as it passes through the at least one container instance, thereby enabling application-aware processing of data streams in real time prior to arrival at the data store.