serted between or around the data subcarriers) in the upper and/or lower end of the fre- quency region. The goal of equalization is to compensate the effects of channel distortion due to frequency selectivity and to restore the original signal. One approach to signal equalization is in the time domain using a linear equalizer, which consists of a linear filter with an impulse response w(t) operating on the received signal. By selecting different filter impulse responses, different receiver/equalizer strategies can be implemented. For example, the receiver filter can be selected to compensate the radio channel frequency selectivity. This can be achieved by configuring the receiver filter impulse response to satisfy w(t)*h(t) = 1 where the operator denotes linear convolution. This method of filtering is known as zero-forcing equalization, which compensates the channel frequency selectivity. However, the ZF equalization may lead to significant increase in the noise level after equalization, degrading the overall link performance. This will be the case especially when the channel has large variations in its frequency response. Another alternative is to select a filter which provides a tradeoff between signal distortion due to channel frequency selectivity and the corruption due to noise/interference, resulting in a filter impulse response that minimizes the mean squared error between the equalizer output and the transmitted signal. The linear equalizers are typically implemented as a discrete-time FIR-filter with certain number of taps. In general, the complexity of such a discrete-time equalizer increases with increasing bandwidth of the signal [30,46]. An alternative to time-domain equalization is frequency-domain equalization which can sig- nificantly reduce the complexity of linear equalization. In this method, the equalization is performed on a block of data. The received signal is transformed to frequency domain using a DFT operation. The equalization is done as a frequency-domain filtering operation, where the