Ekant Sharma

PAPR analysis of superimposed training based SISO/MIMO-OFDM systems with orthogonal affine precoder

Abstract Superimposed training (ST) is used to estimate channel in orthogonal frequency division multiplexing (OFDM) systems which results in higher throughput than the time/frequency multiplexed training schemes. Orthogonal affine precoders (OAP) are used in ST-based SISO/MIMO-OFDM system to cancel the interference caused due to simultaneous data transmission. We analyze the peak to average power ratio (PAPR) of SISO/MIMO-OFDM systems which use OAP-based ST scheme. We show that the OAP-based ST scheme has higher PAPR than the conventional OFDM (without OAP-based ST) systems. We design an OAP using Zadoff–Chu (ZC) sequence, which has lower PAPR than the conventional OAPs. We further reduce the PAPR of the ST systems using selective mapping (SLM), but by increasing the system complexity. We show that the PAPR of the ST-based SISO/MIMO-OFDM systems with ZC-based OAP and SLM is identical to that of the conventional OFDM using SLM.

BER Efficient Interleaved OFDM System

Precoded orthogonal frequency division multiplexed (OFDM) systems have low bit error rate (BER) performance compared to conventional OFDM systems. In this work, we have proposed a new buffer-interleaver based system architecture. The proposed system is found to have a better BER performance than the conventional precoded OFDM system when minimum mean square error (MMSE) equalization is performed. Using block-diagonal matrices, we have mathematically modelled buffer-interleaver architecture to derive a closed-form expression for the BER performance under frequency-selective fading channels, for zero-forcing (ZF) and MMSE equalizers. Our simulation results match the theoretical expression. It is shown that, as the buffer size increases, BER performance of MMSE equalizer improves, whereas in the case of ZF equalizer the BER performance nearly remains the same as the buffer size increases. A theoretical argument to show the BER performance enhancement is also provided.

Training sequence optimization for estimating the channel in the presence of colored interference for MIMO-OFDM systems

In this paper, the problem of channel estimation for multi-input multi-output (MIMO) orthogonal frequency division multiplexing (OFDM) is addressed. Colored interference at the receiver of the MIMO-OFDM is assumed. The linear minimum mean square error (LMMSE) estimator of the channel is derived in the presence of colored interference. The optimal training signal is also designed which minimizes the mean square error (MSE) of the channel estimate under the total transmit power constraint.

PAPR analysis of superimposed training based MIMO-OFDM systems using an orthogonal affine precoder

Precoder matrices are used in various contexts in the MIMO-OFDM systems. In particular, orthogonal affine precoder (OAP) is used in superimposed training (ST) based MIMO-OFDM system to nullify the data training interference. Precoders are also used to reduce the peak to average power ratio (PAPR) of MIMO-OFDM. In general, the OAP does not reduce the PAPR. In this paper a ZC based OAP is proposed that has lower PAPR compared to random OAPs. We also derive the distribution of PAPR considering ST based MIMO-OFDM systems which uses ZC based OAP and compare with the simulation results.

PAPR and BER minimized OFDM systems with low complexity channel independent precoders

In this paper a channel independent precoder is proposed for orthogonal frequency division multiplexed (OFDM) signals which reduces the peak to average power ratio (PAPR) and also achieves optimal BER performance in frequency selective Rayleigh fading channels. The complexity of the proposed system is lower than the Zadoff-Chu transform based precoded systems. We have compared the proposed system with the Zadoff-Chu transform (ZCT), single carrier cyclic prefixed (SC-CP) and Tpro-OFDM systems.