Bae Systems Information and Electronic Systems Integration Inc.

Other

Georgia Tech Research Corporation

Intellectual Ventures Ii LLC

Taffco Three Fund, L.l.c.

Apurva N Mody

Jack Chuang

Gordon Stuber

Ranga Reddy

Matthew J Sherman

Thomas Kiernan

Chad M Spooner

Michael P Anthony

Diane Grieselhuber Mills

Alan M Rosenwinkel

Thomas P McElwain

Stephen Robert Blatt

Ned B Thammakhoune

Alex Lackpour

Ronald Lawrence

Kenneth Carrigan

Gordon L Stuber

Joseph Farkas

Michael J Shea

Phong C Khuu

Paul David Fiore

Prabahan Basu

David J Couto

Sharon K Sputz

Kwok N Yau

Alan Trojan

Timothy X Brown

Dan E Dudgeon

Kevin McNeil

A multi-user system to simultaneously perform operations such as communication, RADAR, Light Detection and Ranging (LIDAR) and Relative Navigation (RELNAV). The techniques according to an embodiment includes generating a Fourier based orthogonal chirp sequence of length P, a prime number greater than the number of users targeted for communication. The orthogonal chirp sequence is based on an identifier, in the range of one to P&#x2212;1, associated with one of the targeted users. The method further includes using the orthogonal chirp sequence to generate a spread user signal based on a message directed to the one targeted users. The method further includes generating a sequence of training pulses for insertion into the spread user signal to facilitate reception of the signal. The method further includes transmitting and receiving a reflection of the spread user signal from one of the targeted users, the reflection used to detect and range the user.

Orthogonal chirps for Radar, relative navigation and ranging, Light Detection and Ranging, and communications fungibility

A spectrum sharing system includes an advanced beacon (e.g. a low latency RF link) as part of an information sharing subsystem. The advanced beacon signal carries radar spectrum transmission schedule in an obfuscated way such as not to reveal the geolocation of the radar. The information sharing subsystem directs nodes, such as cell phones, to share spectrum based on spectrum sharing instructions contained in the advanced beacon. The spectrum sharing system permits out-of-band sharing of spectrum white space, as well as sharing of in-band spectrum gray space.

Spectrum sharing system and method thereof

Techniques are provided for implementing a portable spectrum analyzer. An example system, according to an embodiment, includes a signal analyzer including an RF receiver to receive RF signals from an antenna, an analog-to-digital converter to generate a sampled signal based on the received RF signals, and a signal analysis co-processor to perform cognitive scanning analysis of the sampled signal. The cognitive scanning analysis includes detection, identification, and characterization of digital/analog signal(s) embedded in the sampled signal. The system may further include a communications interface circuit to provide communication between the signal analyzer and an associated mobile host platform (e.g., smartphone). The communication includes transmitting results of the cognitive scanning analysis to the mobile host and receiving parameters from the mobile host to control the operation of the cognitive scanning analysis. The signal analyzer and mobile host are coupled through a shared enclosure such as a hinged case, sleeve, or wallet.

Techniques for implementing a portable spectrum analyzer

A system and method that uses tunnelizing for analyzing frequency spectrum. The method may include the steps of under sampling an input signal to take samples in one or more tunnels each with a tunnel bandwidth that is equal to or less than a total analysis bandwidth of the input signal; detecting one or more cyclostationary features of the input signal based on the samples; and determining one or more signal types of the one or more cyclostationary features based on the one or more cyclostationary features.

Process for tunnelized cyclostationary to achieve low-energy spectrum sensing

A system and method use tunnelizing for analyzing frequency spectrum. The method may include the steps of under sampling an input signal to take samples in one or more tunnels each with a tunnel bandwidth that is equal to or less than a total analysis bandwidth of the input signal; detecting one or more cyclostationary features of the input signal based on the samples; and determining one or more signal types of the one or more cyclostationary features based on the one or more cyclostationary features.

In some embodiments, authentication, confidentiality, and privacy are enhanced for a wireless network of cognitive radios by encryption of network management and control messages as well as data traffic, thereby protecting information pertaining to node identification, node location, node-sensed incumbent transmissions, CRN frequency channel selections, and such like. During initial network registration, a temporary ID can be issued to a node, and then replaced once encrypted communication has been established. This prevents association of initial, clear-text messages with later encrypted transmissions. Elliptic curve cryptography can be used for mutual authentication between subscribers and the base station. ECC-based implicit digital certificates can be embedded in co-existence beacons used by CRN nodes to coordinate use of frequency channels, thereby preventing denial of service attacks due to transmitting of falsified beacons. Similar certificates can be embedded within identity beacons used to protect certain incumbents from interference by the CRN.

Method for ensuring security and privacy in a wireless cognitive network

A system and method for converting a non-cognitive radio into a cognitive radio is presented. A cognitive radio system includes, a non-cognitive radio; an electronic device, a spectrum sensing logic and configuration and management logic. The electronic device is connected to the non-cognitive radio so that it receives and/or transmits messages to/from a wireless network. The configuration and management logic is connected between the non-cognitive radio and the spectrum sensing logic. The spectrum sensing logic and the configuration and management logic are removable from the non-cognitive radio allowing the cognitive radio to operate in a non-cognitive mode. The spectrum sensing logic senses a wireless environment to determine available frequencies and available channels. The configuration and management logic transmits available frequencies, available channels or other spectrum data to a remote spectrum manager that is managing access to the wireless network.

Method and system to make current wireless radios cognitive using an external sensor and application level messaging

A method and system of cognitive communication for generating non-interfering transmission, includes conducting radio scene analysis to find grey spaces using external signal parameters for incoming signal analysis without having to decode incoming signals. The disclosed cognitive communications system combines the areas of communications, signal processing, pattern classification and machine learning to detect the signals in the given spectrum of interests, extracts their features, classifies the signals in types, learns the salient characteristics and patterns of the signal and predicts their future behaviors. In the process of signal analysis, a classifier is employed for classifying the signals. The designing of such a classifier is initially performed based on selection of features of a signal detected and by selecting a model of the classifier.

Cognitive radio methodology, physical layer policies and machine learning

A system and method for the operation of a Spectrum Manager (SM) within a cognitive radio system includes two primary states of operation: SM at network initialization and SM at network operation. While in the network Initialization state, the SM's primary responsibility is to find an operating channel. After an operating channel is found, the SM moves from the network initialization state to the network operation state while executing a procedure to establish a network. While in the network operation state, the SM can execute other procedures, a procedure to update list of available channel from the incumbent database of channel and frequency data, a procedure to determine a signal type, a procedure to enter co-existence mode, deciding when to move to a backup channel and issuing calls for the same as well as background procedures such as looking for better backup channels when the SM is otherwise idle.

Method and system for the spectrum manager operation in cognitive radio systems

Apparatus and methods for providing efficient space-time structures for preambles, pilots and data for multi-input, multi-output (MIMO) communications systems are provided. One such embodiment includes providing a computer program that includes logic configured to provide an initial structure. The computer program further includes logic configured to verify that the rows of the initial structure are linearly independent and logic configured to apply an orthonormalization procedure to the initial structure to obtain a space-time structure. Methods are also provided for providing efficient space-time structures for preambles, pilots and data for MIMO communications systems.

Apparatus and methods for providing efficient space-time structures for preambles, pilots and data for multi-input, multi-output communications systems

An algorithm system to detect a broad class of signals in Gaussian noise using higher-order statistics. The algorithm system detects a number of different signal types. The signals may be in the base-band or the pass-band, single-carrier or multi-carrier, frequency hopping or non-hopping, broad-pulse or narrow-pulse etc. In a typical setting this algorithm system provides an error rate of 3/100 at a signal to noise ratio of 0 dB. This algorithm system gives the time frequency detection ratio that may be used to determine if the detected signal falls in Class Single-Carrier of Class Multi-Carrier. Additionally this algorithm system may be used for a number of different applications such as multiple signal identification, finding the basis functions of the received signal and the like.

Efficient detection algorithm system for a broad class of signals using higher-order statistics in time as well as frequency domains

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Lowell, MA, United States of America

Apurva N Mody