Amplitude clipping and iterative reconstruction of MIMO-OFDM signals with optimum equalization

Abstract

Multi-input multi-output orthogonal frequencydivision multiplexing (MIMO-OFDM) has become a promising candidate for next generation broadband wireless communications. However, like a single-input single-output (SISO)-OFDM, one main disadvantage of the MIMO-OFDM is the high peakto-average power ratio (PAPR), which can be reduced by using an amplitude clipping. In this paper, we propose clipped signal reconstruction methods for the MIMO-OFDMs with spatial diversity, such as space-time and space-frequency block codes (STBC/SFBC). The proposed methods are based on the technique called iterative amplitude reconstruction (IAR) for SISO-OFDM. It is shown that the IAR can be easily employed for the STBC-OFDM, but it cannot be directly applied to the SFBC-OFDM, because the transmitted sequences over different antennas are dependent due to the use of space-frequency code. We propose a new SFBC transmitter for clipped OFDM, which has approximately half the computational complexity of conventional SFBC-OFDM. The proposed clipping preserves the orthogonality of transmitted signals, and the clipped signals are iteratively recovered at the receiver. Further, we theoretically analyze the performance of IAR with optimum equalization, and also provide highly accurate channel estimation of the OFDM with amplitude clipping. Simulation results show that the proposed receivers effectively recover contaminated OFDM signals with a moderate computational complexity.Index Terms-Orthogonal frequency-division multiplexing (OFDM), amplitude clipping, space-time and space-frequency block code (STBC/SFBC), iterative amplitude reconstruction (IAR).

Amplitude reconstruction of clipped OFDM by using DFT-based least squares

“…This property is illustrated in Figure 2. Based on this property, only the amplitudes of clipped samples should be reconstructed at the receiver and it is not necessary to reconstruct the phases [10][11]. While Saeedi et al [17] uses the complex value of linear system solution to replace clipped samples, this paper uses its amplitude to replace amplitude of clipped samples.…”

“…At the receiver, clipping noise is mitigated iteratively by initially making decisions in the frequency domain and then converting them back to the time domain. Based on DAR, the Iterative Amplitude Reconstruction (IAR) method is proposed by Kwon et al [10,11], which improves the DAR method and yields better BER performance. The DAR method reconstructs both amplitudes and phases of clipped samples while the IAR only reconstructs amplitudes.…”

“…Thus, methods such as [9][10][11][12] cannot be applied to OFDMA receivers because they must know in advance the modulation types and channel codes employed by the other users to reconstruct the clipped samples. On the other hand, the least square methods [17][18][19] are independent of modulation and channel code; thus, these methods can be used on OFDMAs when users have di erent modulations and channel codes with no knowledge about modulation and channel code of the other users.…”

Reduction of Nonlinear Distortion in Multi-Antenna WiMAX Systems

“…In order to improve BER performance, the receiver needs to estimate clipping that has occurred and conversely, compensate received OFDM signal accordingly. Authors in Kwon et al (2009) propose the new low complexity SFBC transmitter for clipped OFDM signals, which preserves orthogonality of transmitted signals. Furthermore, clipping reconstruction method for SFBC/STBC-OFDM system based on iterative amplitude reconstruction (IAR) in Kwon & Im (2006) is presented.…”

Peak-to-Average Power Ratio Reduction in Orthogonal Frequency Division Multiplexing Systems

“…To address this problem, many techniques for reducing PAPR have been proposed. Some of the most important techniques are clipping (Kwon, et al 2009), windowing (Van Nee and De Wild, 1998), envelope scaling (Foomooljareon and Fernando, 2002), random phase updating (Nikookar and Lidsheim, 2002), peak reduction carrier (Tan and Wassell, 2003), companding (Hao and Liaw, 2008), coding (Wilkison and Jones, 1995), selected mapping (SLM) , partial transmit sequence (PTS) (Muller and Huber, 1997), DSI-PTS , interleaving (Jayalath and Tellambura, 2000), active constellation extension (ACE) (Krongold, et al 2003), tone injection and tone reservation (Tellado, 2000), dummy signal insertion (DSI) (Ryu, et al 2004), addition of Guassian signals (Al-Azoo et al 2008) and etc (Qian, 2005).…”