Patent classifications
H04K1/00
Secure communications through distributed phase alignment
Various embodiments comprise systems, methods, architectures, mechanisms or apparatus for wireless secret communication with a device.
HEADSET WITH VOICE SCRAMBLING FOR PRIVATE CONVERSATIONS
An electronic device may be configurable to operate in a scrambling mode and a non-scrambling mode while processing chat audio and microphone audio for a first player participating in an online multiplayer game. While operating in the non-scrambling mode, the electronic device may be configured to transmit the microphone audio without scrambling the microphone audio. While operating in the scrambling mode, the electronic device may be configured to scramble the microphone audio and transmit the scrambled microphone audio. The electronic device may be operable to select a scrambling key used to scramble the microphone audio based on a signal received by the electronic device that indicates a role of the player in the online multiplayer game. The role of the player may correspond to which of two or more opposing teams the first player is a member of in the online multiplayer game.
HEADSET WITH VOICE SCRAMBLING FOR PRIVATE CONVERSATIONS
An electronic device may be configurable to operate in a scrambling mode and a non-scrambling mode while processing chat audio and microphone audio for a first player participating in an online multiplayer game. While operating in the non-scrambling mode, the electronic device may be configured to transmit the microphone audio without scrambling the microphone audio. While operating in the scrambling mode, the electronic device may be configured to scramble the microphone audio and transmit the scrambled microphone audio. The electronic device may be operable to select a scrambling key used to scramble the microphone audio based on a signal received by the electronic device that indicates a role of the player in the online multiplayer game. The role of the player may correspond to which of two or more opposing teams the first player is a member of in the online multiplayer game.
APPARATUS AND METHOD FOR GENERATING JAMMING SIGNAL, COMMUNICATION SYSTEM
This application relates to an apparatus and method for generating a jamming signal, and a communication system. In one aspect, the method may include converting binary bits of a first communication signal to be transmitted and obtaining at least one codeword matched previously to the binary bits. The method may also include generating a second communication signal by superimposing a first conversion signal being obtained by converting the first communication signal to an up-chirp signal and a second conversion signal being obtained by converting the first communication signal to a down-chirp signal. The method further include generating a pseudo jamming signal by computing the second communication signal and the codeword.
Physical unclonable function-based encryption schemes with combination of hashing methods
A system is configured to derive a set of encryption keys from measured device characteristics of at least one PUF device and communicate with a remote device by performing a cryptographic operation secured by the set of encryption keys. The cryptographic operation includes segmenting a first data stream into a first plurality of data stream fragments, segmenting a first data stream fragment of the first plurality of data stream fragments into a first numeric value and a second numeric value, identifying, using the first numeric value, a first encryption key of the set of encryption keys, and applying a one-way cryptographic function to the first encryption key a first number of times determined by the second numeric value to generate a transformed fragment having a value that depends on the values of the first numeric value and the second numeric value from the first data stream fragment and a value of the first encryption key.
Technique for efficient soft-decision demodulation of HE-CPM
A receiver system for demodulating a high-entropy continuous phase modulation (HE-CPM) signal is disclosed. A plurality of complex multipliers is configured to receive the synchronized HE-CPM signal. Each of the complex multipliers removes a phase associated with a respective one of a plurality of inter-symbol interference (ISI) hypotheses and generates a respective one of a plurality of complex multiplier outputs. Each ISI hypothesis includes a previous chip hypothesis corresponding to a binary value for a previous chip, and a next chip hypothesis corresponding to a binary value for a next chip. A summer is configured to combine real parts of the plurality of complex multiplier outputs to generate a soft decision for a current chip of the HE-CPM signal.
JOINT RANDOM SUBCARRIER SELECTION AND CHANNEL-BASED ARTIFICIAL SIGNAL DESIGN AIDED PLS
In the area of Joint Random Subcarrier Selection and Channel-Based Artificial Signal Design Aided PLS, a method for providing physical layer security (PLS) depending on the randomness of wireless channel is proposed. Specifically, a channel-based joint random subcarrier selection and artificial signal design are introduced to protect the communication in the presence of a passive eavesdropper which can be even stronger than the legitimate receiver. Our analysis assumes a window-based subcarrier selection method in which the strongest subcarriers in each window are selected. Chosen subcarriers are considered for secret sequence extraction. The generated channel dependent secret sequence is used for both random subcarrier selection and artificial signal design. We evaluate the efficiency of the proposed method through some representative metrics, such as secret sequence disagreement rate (SSDR), throughput and bit error rate (BER), in both perfect and imperfect channel estimation cases. Simulation results are presented and insightful discussions are drawn.
Physical layer secure communication against an eavesdropper with arbitrary number of eavesdropping antennas
A method for physical layer secure transmission against an arbitrary number of eavesdropping antennas includes: S1: communication between legitimate transmitter Alice and legitimate receiver Bob is confirmed; S2: Alice randomly generates a key bit b.sub.k with M.sub.S bits, maps the key bit b.sub.k into a key symbol K, and performs an XOR on the key bit b.sub.k and to-be-transmitted confidential information b to obtain an encrypted bits b.sub.s; S3: Bob transmits a pilot sequence to Alice, and Alice calculates a candidate precoding space W and transmits modulated symbol streams s=(s.sub.1, . . , s.sub.N) by using precoding W(e); S4: Bob measures received signal strength of each antenna, estimates the corresponding antenna vector e, inversely maps the vector e to obtain key symbols and key bits, and demodulates the received symbol streams in sequence at each activated antenna to obtain demodulated ciphertext bits; S5: Bob performs an XOR on observed key bits and the demodulated ciphertext bits to obtain the confidential information.
Determining an environmental parameter from sensor data of a plurality of automobiles using a cellular network
An automobile device receives first data from one or more transmitters located in an automobile. A random access preamble is transmitted on an uplink carrier to a base station in response to a pre-defined condition being met based on at least one of the following: the first data; a value of an internal timer; and a user input. A first message is transmitted to a network server via the base station over a bearer. The first message is configured to trigger establishment of a connection to the network server. A second message is received from the network server via the base station over the bearer. The second message is configured to cause transmission of the first data to the network server. The first data is transmitted to the base station via an established bearer.
ARTIFICIAL INTELLIGENCE (AI) BASED GARBLED SPEECH ELIMINATION
An AI-based approach to Garbled speech (GS) detection. Machine learning (ML) models are created that can distinguish between GS speech and non-GS speech with high accuracy. The machine learning models take as input an encoded speech frame that has passed a CRC check. The input data/predictors to the models are a selected set of information elements (IEs) (i.e., a set of one or more bits) of the encoded speech frame. The selected IEs are a part of the input parameters to the speech decoder. It is possible to operate on single encoded speech frames, in contrast to using decoded frames, which requires taking a previous encoded frame into account for being able to perform the decoding.