Abstract

Orthogonal Frequency Division Multiplexing (OFDM) has several desirable attributes, such as high immunity to inter-symbol interference, robustness with respect to multi-path fading, and ability for high data rates, all of which are making OFDM to be incorporated in wireless standards like IEEE 802.11a/g/n WLAN and ETSI terrestrial broadcasting. However one of the major problems posed by OFDM is its high Peak-to-Average-Power Ratio (PAPR), which seriously limits the power efficiency of the transmitter’s High Power Amplifier (HPA). This is because PAPR forces the HPA to operate beyond its linear range with a consequent nonlinear distortion in the transmitted signal. One of good solutions to mitigate this nonlinear distortion is put a Pre-Distorter before the High Power Amplifier and increase linear dynamic range up to a saturation region (1) (2) (3). However, the main disadvantage of Pre-Distorter technique is that these PD techniques only work in a limited range, that is, up to the saturation region of the amplifier. In this situation, Peak-to-Average Power Ratio (PAPR) reduction techniques which pull down high PAPR of OFDM signal to an acceptable range can be a good complementary solution. Due to practical importance of this, there are various PAPR reduction techniques for OFDM signals (4) (5) (6) (7) (8) (9). Among them, the PTS (Partial Transmit Sequence) technique is very promising because it does not give rise to any signal distortion (9). However, its high complexity makes it difficult to use in a practical system. To solve the complexity problem of the PTS technique, Cimini and Sollenberger proposed an iterative flipping algorithm (10). Even though the iterative flipping algorithm greatly reduces the complexity of the PTS technique, there is still some performance gap between the ordinary PTS and the iterative flipping algorithm. In this chapter, we propose an enhanced version of the iterative flipping algorithm to reduce the performance gap between the iterative flipping algorithm and the ordinary PTS technique. In the proposed algorithm, there is an adjustable parameter to allow a performance/complexity trade-off.

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