Machine Learning

Using a Vector Space Approach, QuantumSine Modulation can take advantage of advances in machine learning and enables the concept of a Universal Receiver. The Universal Receiver is a machine Learning based demodulator using Neural Network Architectures which is adaptable and flexible to different physical attributes of the QuantumSine Modulation while learning and adapting to the physical attributes of the modulation to the channel conditions

Noise Recovery & Channel Effects Mitigation

The QuantumSine modulation signal can be described using vector space representation where each symbol is an element of a vector space. Noise and other channel effects within that symbol may be modeled as an additive vector of the vector space and recovered. QuantumSine modulation allows the design of noise recovery and channel effects recovery algorithms.

Higher Order Modulation

QuantumSine Modulation can support higher order modulation with improved spectral efficiencies by increasing number of bits per dimension in the generating matrix. Comparable spectral efficiency to traditional higher order modulations like 256QAM, and 1024QAM with the capability to pack considerably higher number of bits per symbol with comparable BER performance.

Physical Layer Encryption

The operation of Modulation and Demodulation of QuantumSine can be described in the form of vector space and the utility of such modulation can be extended by additional functionalities such as the addition of cryptographic functions (or encryption and decryption) directly or indirectly as a property of physical layer processing thus allowing for physical layer encryption of the QuantumSine signal. Information Security is implemented as part of physical layer modulation. Cryptographic primitives are derived from vector space representation with Lattice-Based Cryptographic Primitives providing potential immunity to quantum attacks. Both Symmetric and Asymmetric cryptosystems can be defined.