Abstract: Provided herein are methods and systems for radio access network (RAN) control including receiving, at a base station controller deployed at a base station of the RAN, a data input corresponding to a real-time operational status of the base station; determining a locally optimal signal configuration of the base station; controlling the base station to change a configuration of a signaling component to implement the locally optimal signal configuration; receiving, at an edge controller deployed at an edge of the RAN, a plurality of the locally optimal signal configurations corresponding to a plurality of the base stations within the RAN; determining a coordinated optimal signal configuration of each base station; and instructing, by the edge controller, the base station controller to change a configuration of the signaling component of the base station from the locally optimal signal configuration to the coordinated optimal signal configuration.

Abstract: Methods and systems are provided for controlling an unmanned aerial vehicle (UAV) swarm network including receiving, at a control terminal, an operator input defining a plurality of network performance directives, constructing a network control model representing the plurality of network performance directives, decomposing the network control model into a plurality of sub-models, generating, via the control terminal, a distributed UAV control model corresponding to each of the distributed sub-models, transmitting the generated distributed UAV control models to each of a plurality of UAVs of the UAV swarm network, executing, at each of the plurality of UAVs, at least one of the distributed UAV control models, and changing, responsive to the execution of the distributed UAV control models, an operating condition of at least one of the plurality of UAVs to satisfy the network performance directives.

Abstract: Provided herein are systems for controlling a network of distributed non-terrestrial nodes including a control framework operative to train and control a plurality of the non-terrestrial nodes, the control framework including a control interface in communication with a network operator to receive one or more specified control objectives, and a learning engine operative to train a virtual non-terrestrial network, wherein the control framework is further operative to transfer knowledge gained through the training of the virtual non-terrestrial network to the network of distributed non-terrestrial nodes as data-driven logic unit configurations tailored for the specified control objectives.

Abstract: A system and method for beam management in a wireless network are provided. A learning module having a trained classification module processes received I/Q input samples to determine transmitted beam information of incoming RF transmissions. The learning module includes a beam inference engine to determine waveforms waveform characteristics of incoming RF transmissions beams, and an angle of arrival engine operative to determine an angle of arrival of the incoming RF transmissions beams on an antenna array. An incoming RF transmission beam and angle of arrival are selected based on the determined waveforms for beam management operations.

Abstract: A self-optimizing operating system for next-generation cellular networks is provided. The system provides telecommunications operators with an efficient and flexible network control platform that hides low-level network details through a virtual network abstraction and allows them to define centralized and high-level control objectives with no need for in-depth and detailed knowledge of cross-layer optimization theory or the underlying wireless protocol stack implementation.

Abstract: A visible light communication (VLC) system transmits and receives visible light signals across a VLC channel that includes an air-water interface. A transmitter may be configured generates and transmits a visible light signal across the VLC channel to a remote device, and a signal modulator controls the transmitter to generate the visible light signal from a digital transmission signal in accordance with a modulation setting. A receiver processes a remote visible light signal received across the VLC channel from the remote device. A signal demodulator converts the remote visible light signal to a received digital signal.

Abstract: Provided herein are methods and systems for deployment and orchestration of network intelligence in an Open RAN including receiving requests at a request collector, selecting one, by an orchestration engine, or more ML/Al models applicable for satisfying the plurality of collected requests, assigning at least one Open RAN resource to execute each of the ML/Al models, automatically generating, by an orchestration engine, executable software components embedding at least one of the ML/Al models, dispatching each executable software component to the assigned Open RAN resource, and instantiating, at the Open RAN resource, at least one of the ML/AI models embedded in the executable software component to configure the Open RAN to satisfy the requests.

Abstract: Embodiments disclosed herein may be implemented in the form of a method or corresponding apparatus for receiving or transmitting network communications carried at acoustic wavelengths via an acoustic medium. The corresponding method or apparatus may include a gate-level digital hardware module communicatively coupled to a communications module and define therein logic blocks configured to perform respective primitive processing functions, sequences of the logic blocks being capable of processing data units in accordance with any of the multiple communications protocols on a data unit-by-data unit basis without reconfiguring.

Abstract: Methods and systems are provided for allocating resources to users in a wireless network including a plurality of edge nodes that provide wireless network access and multi-access edge computing functions. Slice requests are received from the users for a type of resource, including one or more of networking resources, storage resources, and computation resources. A set of slice requests to be admitted is determined based on resource availability constraints among one or more of the networking resources, the storage resources, and the computation resources at each edge node.

Abstract: A radio access network (RAN) intelligent controller (RIC) and corresponding method may be implemented within RAN and in next-generation cellular networks to improve performance. The RIC comprises an interface to a RAN and further comprises a data-driven logic unit. The data-driven logic unit (i) produces, based on data received from the RAN via the interface, a representation describing a state of the RAN and (ii) based on the representation describing the state, instructs an action associated with at least one network element. The interface transmits a message based on the action instructed. The message is to be routed to the at least one network element. The representation is based on a context of the RAN. The message transmitted enabling re-configuration of the at least one network element. The re-configuration improves performance of the at least one network element within the context.

Abstract: A steganographic communication system and method are provided. A covert packet generator can embed a stream of covert data as covert data symbols within primary I/Q symbols of a primary data stream in a covert packet. The covert packet has a data structure having a header, a payload, and a payload error detecting code. The header includes information on how to demodulate the covert packet by a receiver. The covert packet generator can also determine if a number of primary I/Q symbols is large enough to generate the header and can generate displacements in the primary I/Q symbols in a constellation diagram randomly in a plurality of transmissions to mimic channel noise. A transmitter and receiver can provide mutual authentication for covert transmissions.

Abstract: Provided herein are methods and systems for identifying one or more unused or underused portions of a wireless radio frequency (RF) spectrum including providing a multi-label multi-class machine learning classifier trained using a set of RF transmission data, receiving, by a receiver, wireless RF signals in an environment suspected of containing unused or underused portions of said RF spectrum, classifying the received wireless RF signals using the classifier; and identifying unused or underused portions of said RF spectrum.

Abstract: Embodiments disclosed herein may be implemented in the form of a method or corresponding apparatus for receiving or transmitting network communications carried at acoustic wavelengths via an acoustic medium. The corresponding method or apparatus may include a gate-level digital hardware module communicatively coupled to a communications module and define therein logic blocks configured to perform respective primitive processing functions, sequences of the logic blocks being capable of processing data units in accordance with any of the multiple communications protocols on a data unit-by-data unit basis without reconfiguring.

Abstract: Provided herein are open radio access network (Open RAN) adapters for using non-open RAN nodes in a wireless network having Open RAN infrastructure, the Open RAN adapters including one or more control application programming interfaces (Control APIs) for controlling the non-open RAN nodes, one or more Data Capture APIs for capturing operational data from the non-open RAN nodes, one or more Open RAN interfaces for communication between the Open RAN adapter and the Open RAN infrastructure and one or more software modules each configured to translate data received via the Data Capture APIs to Open RAN compatible data for use by the Open RAN infrastructure and/or translate Open RAN commands received via the Open RAN interfaces to non-open RAN compatible commands for instantiation at one or more of the non-open RAN nodes via the Control APIs.

Abstract: Provided herein are methods and systems for sharing spectrum, computing resources. and Radio Access Network (RAN) elements in a wireless communication network, the system including a centralized service management and orchestration (SMO) entity, the SMO including an optimization engine for determining one or more resource allocation policies responsive to one or more received tenant requests and based on network state information received from a radio access network (RAN) and deploying the determined resource allocation policies on the network, a plurality of edge datacenters configured to instantiate virtualized networking services in response to the deployed resource allocation policies from the optimization engine, and a plurality of cell sites, the cell sites operating the network in response to instructions from the edge datacenters consistent with the deployed resource allocation policies.

Abstract: Provided herein are systems for controlling a network of distributed non-terrestrial nodes including a control framework operative to train and control a plurality of the non-terrestrial nodes, the control framework including a control interface in communication with a network operator to receive one or more specified control objectives, and a learning engine operative to train a virtual non-terrestrial network, wherein the control framework is further operative to transfer knowledge gained through the training of the virtual non-terrestrial network to the network of distributed non-terrestrial nodes as data-driven logic unit configurations tailored for the specified control objectives.

Abstract: Systems and methods for wireless deep brain stimulation using ultrasonic waves. Implantable device(s) for intracranial use within a subject may comprise at least one stimulation means, one or more circuits for collecting system data, a receiver, and a transmitter for communications using ultrasonic waves. A wearable controller external to the subject, the wearable controller configured to: communicate with the implantable device(s) using the ultrasonic waves and obtain the system data, analyze the system data to determine whether the subject is experiencing or is expected to experience an adverse medical condition, and communicate with the implantable device(s) using the ultrasonic waves to apply a stimulation to treat the adverse medical condition. The system may be used to treat Parkinson's Disease, for example.

Abstract: A deep learning medical device implantable in a body is provided. The device includes a processing and communication unit and a sensing and actuation unit. The processing and communication unit includes a deep learning module including a neural network trained to process the input samples, received from the sensing and actuation unit, through a plurality of layers to classify physiological parameters and provide classification results. A communication interface in communication with the deep learning module receives the classification results for ultrasonic transmission through biological tissue. Methods of sensing and classifying physiological parameters of a body and methods of embedding deep learning into an implantable medical device are also provided.

Abstract: Methods and systems for allocating radio access network (RAN) spectrum resources among a plurality of mobile virtual network operators (MVNOs) of a network of base stations. The methods and systems include determining a slicing enforcement policy that assigns resource blocks (RBs) of frequency units and time slots of spectrum resources to each MVNO according to a slicing policy in which each MVNO is allocated an amount of the spectrum resources on at least one base station in a determined time span. The slicing enforcement policy minimizes overlap between each MVNO's set of RBs with another MVNO's set of RBs on a same base station, and interference between each MVNO's set of RBs with another MVNO's set of RBs on an interfering base station.

Abstract: A polymorphic platform for wireless communication systems is provided that employs trained classification techniques to determine physical layer parameters from a transmitter at a receiver. The system includes a learning module to determine transmitted physical layer parameters of the signal using a trained classification module, such as a deep learning neural network. The trained classification module receives I/Q input samples from receiver circuitry and processes the I/Q input samples to determine transmitted physical layer parameters from the transmitter. The system includes a polymorphic processing unit that demodulates data from the signal based on the determined transmitted parameters.