OFDM frame synchronization using decision-directed maximum-likelihood estimation

Abstract

Orthogonal frequency division multiplexing (OFDM), which is a kind of frequency division multiplexing, has many applications for data transmission because of its several merits as following. OFDM shows high spectral efficiency because of the orthogonality between the overlapped adjacent frequency bins called subchannels or subcarriers whose bandwidth are very narrow, and the symbol period of the OFDM modulated signal, which is generally propotional to the number of the subcarriers, is considerably long compared to single carrier schemes. So, OFDM is robust against the impulse noise and need comparatively simple equalization scheme. OFDM needs symbol timming synchronization called frame synchronizaton in order to extract the data exactly from the symbol. OFDM, however, has some problem in synchronization because it is composed of a lot of subcarriers and so we can not use the conventional synchronization scheme like the phase locked loop, square law device, etc., which is used for single carrier modulation. In OFDM systems, the symbol timming error i.e., the frame synchronization offset results in phase errors of every subcarriers and so increases the symbol error rate during QAM decoding like single carrier modulation. So, if we can compute the offset by examining the every subcarrier``s phase error, it can be used as a measure for frame synchronism and we can, furthermore, decrease the symbol error rate due to the synchronization alignment error by compensating the offset. In most OFDM systems, after initial acquisition, fine frame synchronism is usually maintained using several subcarriers as pilot tones. This results in lower channel efficiency, and degraded system performance when the pilot channels are impaired by several interferences. In this thesis, we proposed a method to estimate the frame synchronization offset for orthogonal frequency division multiplexing(OFDM) frame alignment without resort to pilot tones. We first derive the probability den...