Orthogonal frequency-division multiplexingIn telecommunications, orthogonal frequency-division multiplexing (OFDM) is a type of digital transmission used in digital modulation for encoding digital (binary) data on multiple carrier frequencies. OFDM has developed into a popular scheme for wideband digital communication, used in applications such as digital television and audio broadcasting, DSL internet access, wireless networks, power line networks, and 4G/5G mobile communications. OFDM is a frequency-division multiplexing (FDM) scheme that was introduced by Robert W.
Frequency-hopping spread spectrumFrequency-hopping spread spectrum (FHSS) is a method of transmitting radio signals by rapidly changing the carrier frequency among many frequencies occupying a large spectral band. The changes are controlled by a code known to both transmitter and receiver. FHSS is used to avoid interference, to prevent eavesdropping, and to enable code-division multiple access (CDMA) communications. The frequency band is divided into smaller sub-bands. Signals rapidly change ("hop") their carrier frequencies among the center frequencies of these sub-bands in a determined order.
FM broadcastingFM broadcasting is a method of radio broadcasting that uses frequency modulation (FM) of the radio broadcast carrier wave. Invented in 1933 by American engineer Edwin Armstrong, wide-band FM is used worldwide to transmit high-fidelity sound over broadcast radio. FM broadcasting offers higher fidelity—more accurate reproduction of the original program sound—than other broadcasting techniques, such as AM broadcasting. It is also less susceptible to common forms of interference, having less static and popping sounds than are often heard on AM.
Feature learningIn machine learning, feature learning or representation learning is a set of techniques that allows a system to automatically discover the representations needed for feature detection or classification from raw data. This replaces manual feature engineering and allows a machine to both learn the features and use them to perform a specific task. Feature learning is motivated by the fact that machine learning tasks such as classification often require input that is mathematically and computationally convenient to process.
