Sentence examples for multiple signal representations from inspiring English sources

Several other schemes exploit multiple signal representations at the expense of complexity and signaling overhead.

The multiple signal representations are generated by applying different instances of the precoder, which has to be applied within the multi-user downlink scenario.

Multiple signal representations such as partial transmit sequences (PTS) techniques [4, 5] and clipping techniques [6 8, 10] are much more flexible and suitable for OFDM signals with a large number of subcarriers.

These techniques include amplitude clipping and filtering, coding [1], tone reservation (TR) [2, 3] and tone injection (TI) [2], active constellation extension (ACE) [4, 5], and multiple signal representation methods, such as partial transmit sequence (PTS), selected mapping (SLM), and interleaving [6].

Both schemes are based on the idea of generating multiple redundant signal representations and selecting the one exhibiting the lowest PAR and are thus based on the philosophy of the SLM family.

Initial guesses are chosen from a direct imaging algorithm, multiple signal classification (MUSIC), along with a level set representation at a certain wavenumber, where the Born approximation may not be valid.

The methods of Prony, Pisarenko, and MUltiple SIgnal Classification (MUSIC) are next shown to be targeted at analyzing signals with sparse frequency domain representations.

Multiple Signal Classification.

In the article "Joint DOD/DOA estimation in MIMO radar exploiting time-frequency signal representations"[1] Yimin Zhang et al. deal with the joint estimation of direction-of-departure (DOD) and direction-of-arrival (DOA) information of maneuvering targets in a bistatic multiple-input multiple-output (MIMO) radar system when exploiting spatial time-frequency distribution (STFD).

The main contribution is the representation of a sparse channel model and the exploitation of a modified approach based on Multiple Measurement Vector (MMV) greedy sparse framework and subspace method of Multiple Signal Classification (MUSIC) which work together to recover the indices of non-zero elements of an unknown channel matrix when the rank of the channel matrix is defected.

This is done by using conventional signal representations [11].