A MIMO-OFDM wireless communication technique possesses several advantages accrued from combining MIMO and OFDM techniques such as increased channel capacity and improved BER performance. This has made the technique very amiable to current and future generations of communication systems for high data-rate transmission. However, the technique also inherits the high PAPR problem associated with OFDM signals—a problem still requiring a practical solution. This work proposes a PAPR reduction algorithm for solving the problem of high PAPR in MIMO-OFDM systems. The proposed method uses a low-complexity signal mixing concept to combine the original transmit signal and a generated peak-cancelling signal. The computational complexity of the proposed method is O(M) , which is very much less than O(N log2 N) of the FFT algorithms. This is because M, which denotes the number of nonzero peakcancelling samples, is much less than N, the FFT window size. The proposed method was found to achieve high PAPR reductions while utilizing only a few nonzero peak-cancelling samples and it does not significantly change the power of the transmitted signal. For example, with M=5% of 256-point IFFT samples, corresponding to a data rate loss of 4.8%, a large PAPR reduction of 5.9 dB could be achieved at a small power loss of 0.09 dB. Compared with other methods proposed in literature, the proposed method was found to outperform them in terms of PAPR reductions and BER performance.
A Low-Complexity Integer Frequency Offset Estimation Scheme Using Combined Training Symbols for OFDM Systems