Abstract: A cognitive radio device may include an RF receiver configured to receive interfering RF signals, an RF transmitter configured to be selectively operated, a quantum computing circuit configured to perform quantum subset summing, and a processor. The processor may be configured to generate a game theory reward matrix for a plurality of different deep learning models, cooperate with the quantum computing circuit to perform quantum subset summing of the game theory reward matrix, select a deep learning model from the plurality thereof based upon the quantum subset summing of the game theory reward matrix, and process the received interfering RF signals using the selected deep learning model for selectively operating the RF transmitter.

Abstract: A cognitive radio device may include a radio frequency (RF) detector, an RF transmitter having a selectable hopping frequency window, and a controller. The controller may be configured to cooperate with the RF detector and RF transmitter to detect a jammer signal, determine a jammer type associated with the jammer signal from among a plurality of different jammer types based upon a game theoretic model, and operate the RF transmitter to change the selectable hopping frequency window responsive to the determined jammer type of the jammer signal.

Abstract: A cognitive radio system may include cognitive radio frequency (RF) radios and a controller configured to selectively change at least one operating parameter of the cognitive RF radios based upon a cognitive group hierarchy. The cognitive group hierarchy may include a first group based upon a signal modulation classification, a second group based upon a waveform requirement, a third group based upon an optimal cognitive RF radio path, a fourth group based upon a cognitive RF radio dynamic spectrum allocation, and a fifth group based upon frequency hopping.

Abstract: Systems and methods for operating a communication device. The methods comprise: receiving a signal at the communication device; performing, by the communication device, one or more machine learning algorithms using at least one feature of the signal as an input to generate a plurality of scores (each score representing a likelihood that the signal was modulated using a given modulation type of a plurality of different modulation types); assigning a modulation class to the signal based on the plurality of scores; determining whether a given wireless channel is available based at least on the modulation class assigned to the signal; and selectively using the given wireless channel for communicating signals based on results of the determining.

Abstract: Systems and methods for operating a communication device. The methods comprise: receiving a signal at the communication device; performing, by the communication device, one or more machine learning algorithms using at least one feature of the signal as an input to generate a plurality of scores (each score representing a likelihood that the signal was modulated using a given modulation type of a plurality of different modulation types); assigning a modulation class to the signal based on the plurality of scores; determining whether a given wireless channel is available based at least on the modulation class assigned to the signal; and selectively using the given wireless channel for communicating signals based on results of the determining.

Abstract: Systems and methods for operating a receiver. The methods comprise: receiving, at the receiver, a combined waveform comprising a combination of a desired signal and an interference signal; and performing, by the receiver, demodulation operations to extract the desired signal from the combined waveform. The demodulation operations comprise: obtaining, by the receiver, machine learned models for recovering the desired signal from combined waveforms; comparing, by the receiver, at least one characteristic of the received combined waveform to at least one of the machine learned models; and determining a value for at least one symbol of the desired signal based on results of the comparing.

Abstract: Systems and methods for operating a receiver. The methods comprise: receiving, at the receiver, a combined waveform comprising a combination of a desired signal and an interference signal; and performing, by the receiver, demodulation operations to extract the desired signal from the combined waveform. The demodulation operations comprise: obtaining, by the receiver, machine learned models for recovering the desired signal from combined waveforms; comparing, by the receiver, at least one characteristic of the received combined waveform to at least one of the machine learned models; and determining a value for at least one symbol of the desired signal based on results of the comparing.

Abstract: Systems and methods for operating a communication device. The methods comprise: receiving a signal at the communication device; performing, by the communication device, one or more machine learning algorithms using at least one feature of the signal as an input to generate a plurality of scores (each score representing a likelihood that the signal was modulated using a given modulation type of a plurality of different modulation types); assigning a modulation class to the signal based on the plurality of scores; determining whether a given wireless channel is available based at least on the modulation class assigned to the signal; and selectively using the given wireless channel for communicating signals based on results of the determining.

Abstract: Systems (100) and methods (700) for dynamically managing Secondary User Node (“SUN”) access to a segment of a wireless spectrum licensed for use by Primary User Nodes (“PUNs”). The methods comprise: detecting physical data transfers by PUNs (110-122) at first licensed frequencies (f1, f2, f3, f4) during slot sample times of a first epoch (t1-t15); generating a report comprising sensed spectral data indicating (a) during which of the slot sample times each physical data transfer was detected by a respective SUN (102-108) and (b) at which of the first licensed frequencies each physical data transfer occurred; receiving a report broadcasted from a remote SUN at a non-licensed frequency during a respective slot report times (t16-t19) of the first epoch; and analyzing the sensed spectral data of the reports to determine a time for using a first licensed frequency without interfering with or only minimally interfering with use thereof by PUNs.

Abstract: System and methods for providing a cognitive network (300). The methods involve generating Initialization Parameters (“IPs”) for a first Multi-Objective Optimization (“MOO”) algorithm based on project requirements; determining a first Pareto Front (“PF”) for a first Protocol Stack Layer (“PSL”) of a protocol stack by solving the first MOO algorithm using IPS (450, 550); initialize or constrain a second MOO algorithm using the first PF (100); determining a second PF for a second PSL succeeding the first PSL using the second MOO algorithm; analyzing the first and second PFs to develop Best Overall Network Solutions (“BONSs”); ranking the BONSs according to a pre-defined criteria; identifying a top ranked solution for BONSs that complies with current regulatory and project policies; computing configuration parameters for protocols of PSLs that enable implementation of the top ranked solution within the cognitive network; and dynamically re-configuring network resources of PSLs using the configuration parameters.

Abstract: System (300) and methods (400, 600) for providing a Cognitive Network (“CN”). The methods involve: partially solving Multi-Objective Optimization Algorithms (“MOOAs”) for Protocol Stack Layers (“PSLs”) using initialization parameters generated based on project requirements (572); and monitoring the convergence behaviors of MOOAs (584) to identify when solutions (106) thereof start to converge toward Pareto-Optimal solutions (104). In response to said identification, a convergence of a solution trajectory for at least one MOOA is “biased” so that compatible non-dominated solutions are generated at PSLs. A Pareto Front (100) for each PSL is determined by generating remaining solutions for MOOAs. The Pareto Fronts are analyzed in aggregate to develop Best Overall Network Solutions (“BONSs”). BONSs are ranked according to a pre-defined criteria. A Top Ranked Solution (“TRS”) is identified for BONSs that complies with current regulatory/project policies.

Abstract: Systems (100) and methods (700) for dynamically managing Secondary User Node (“SUN”) access to a segment of a wireless spectrum licensed for use by Primary User Nodes (“PUNs”). The methods comprise: detecting physical data transfers by PUNs (110-122) at first licensed frequencies (f1, f2, f3, f4) during slot sample times of a first epoch (t1-t15); generating a report comprising sensed spectral data indicating (a) during which of the slot sample times each physical data transfer was detected by a respective SUN (102-108) and (b) at which of the first licensed frequencies each physical data transfer occurred; receiving a report broadcasted from a remote SUN at a non-licensed frequency during a respective slot report times (t16-t19) of the first epoch; and analyzing the sensed spectral data of the reports to determine a time for using a first licensed frequency without interfering with or only minimally interfering with use thereof by PUNs.

Abstract: System and methods for providing a cognitive network (300). Initialization parameters for distributed Multi-Objective Optimization (“MOO”) algorithms are generated based on project requirements. A Pareto Front (100) is determined for each protocol stack layer by solving the distributed MOO algorithms using the initialization parameters. The Pareto Fronts are analyzed in aggregate to develop best overall network solutions. The best overall network solutions are then ranked according to a pre-defined criteria. A top ranked solution is identified for the best overall network solutions that comply with current regulatory and project/mission policies. Subsequently, operations are performed to compute configuration parameters for protocols of the protocol stack layers that enable implementation of the top ranked solution within the cognitive network. Network resources of the protocol stack layers are then configured in accordance with the configuration parameters.

Abstract: Systems (100) and methods for selectively controlling access to data streams communicated from a first communication device (FCD) using a timeslotted shared frequency spectrum and shared spreading codes. Protected data signals (1301, . . . , 130S) are modulated to form first modulated signals (1321, . . . , 132S). The first modulated signals are combined with first chaotic spreading codes to form digital chaotic signals. The digital chaotic signals are additively combined to form a protected data communication signal (PDCS). The PDCS (136) and a global data communication signal (GDCS) are time division multiplexed to form an output communication signal (OCS). The OCS (140) is transmitted from FCD (102) to a second communication device (SCD) over a communications channel. The SCD (106, 108, 110) is configured to recover (a) only global data from the OCS, or (b) global data and at least some protected data from the OCS.

Abstract: System and methods for providing a cognitive network (300). The methods involve generating Initialization Parameters (“IPs”) for a first Multi-Objective Optimization (“MOO”) algorithm based on project requirements; determining a first Pareto Front (“PF”) for a first Protocol Stack Layer (“PSL”) of a protocol stack by solving the first MOO algorithm using IPS (450, 550); initialize or constrain a second MOO algorithm using the first PF (100); determining a second PF for a second PSL succeeding the first PSL using the second MOO algorithm; analyzing the first and second PFs to develop Best Overall Network Solutions (“BONSs”); ranking the BONSs according to a pre-defined criteria; identifying a top ranked solution for BONSs that complies with current regulatory and project policies; computing configuration parameters for protocols of PSLs that enable implementation of the top ranked solution within the cognitive network; and dynamically re-configuring network resources of PSLs using the configuration parameters.

Abstract: System and methods for providing a cognitive network (200). Initialization parameters for distributed Multi-Objective Optimization (“MOO”) algorithms are generated based on project requirements. A Pareto Front (100) is determined for each protocol stack layer by solving the distributed MOO algorithms using the initialization parameters. The Pareto Fronts are analyzed in aggregate to develop best overall network solutions. The best overall network solutions are then ranked according to a pre-defined criteria. A top ranked solution is identified for the best overall network solutions that comply with current regulatory and project/mission policies. Subsequently, operations are performed to compute configuration parameters for protocols of the protocol stack layers that enable implementation of the top ranked solution within the cognitive network. Network resources of the protocol stack layers are then configured in accordance with the configuration parameters.

Abstract: System (300) and methods (400, 600) for providing a Cognitive Network (“CN”). The methods involve: partially solving Multi-Objective Optimization Algorithms (“MOOAs”) for Protocol Stack Layers (“PSLs”) using initialization parameters generated based on project requirements (572); and monitoring the convergence behaviors of MOOAs (584) to identify when solutions (106) thereof start to converge toward Pareto-Optimal solutions (104). In response to said identification, a convergence of a solution trajectory for at least one MOOA is “biased” so that compatible non-dominated solutions are generated at PSLs. A Pareto Front (100) for each PSL is determined by generating remaining solutions for MOOAs. The Pareto Fronts are analyzed in aggregate to develop Best Overall Network Solutions (“BONSs”). BONSs are ranked according to a pre-defined criteria. A Top Ranked Solution (“TRS”) is identified for BONSs that complies with current regulatory/project policies.

Abstract: System (200) and methods (800) for reducing a PAPR of an OFDM signal (204). The methods comprise: routing the OFDM signal to first and second signal processing paths (FSPP and SSPP); time delaying the OFDM signal traveling along FSPP (206); and determining whether an instantaneous signal magnitude value of the OFDM signal traveling along SSPP (208) is greater than a threshold value that would force a power amplifier (250) into a non-linear operational region. If it is determined that the instantaneous signal magnitude value is greater than the threshold value, then a magnitude of at least one complex symbol sample of the OFDM signal which has been time delayed is scaled to a level that precludes the power amplifier from entering its non-linear operational region. If it is determined that the instantaneous signal magnitude value is not greater than the threshold value, then the complex symbol sample is output without modification to the magnitude thereof.

Abstract: A method for encrypting data is provided. The method includes formatting data represented in a weighted number system into data blocks. The method also includes converting the data blocks into a residue number system representation. The method further includes generating a first error generating sequence and inducing errors in the data blocks after converting the data blocks into a residue number system representation. It should be understood that the errors are induced in the data blocks by using the first error generating sequence. After inducing errors into the data blocks, the data of the data blocks is formatted into a form to be stored or transmitted. The method also includes generating a second error generating sequence synchronized with and identical to the first error generating sequence and correcting the errors in the data blocks using an operation which is an arithmetic inverse of a process used in inducing errors.

Abstract: Embodiments of the present invention provide a system and method for further reducing cyclostationarity and correspondingly energy density in a chaotic spread spectrum data communication channel, by digitally generating a first chaotic sequence of values to form a spreading code. The spreading code is then used to form a digital IF spread spectrum signal having a uniform sampling interval. The digital IF spread spectrum signal is converted to a sampled analog IF spread spectrum signal at a conversion rate substantially equal to the uniform sampling interval. The duration of the sampling interval is then selectively varied in accordance with a first pseudo-random sequence, thereby introducing a known dither in the analog IF spread spectrum signal. After introducing the known dither, the analog IF spread spectrum signal is upconverted to an analog RF spread spectrum signal. The first pseudo-random sequences may be designed to be a chaotic sequence.