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The current era of connectivity and information transfer demands high data rates from broadband wireless systems. The main problem faced by such high data rate systems is multipath fading as it causes inter symbol interference (ISI). Thus, there is a need for a technique that avoids ISI while still providing high speed data. Orthogonal Frequency Division Multiplexing (OFDM) emerged as a technique that combines the advantages of high achievable data rates, relatively easy implementation, high spectral efficiency and robustness to multipath fading. This is reflected by many standards that considered and adopted OFDM including digital audio and video broadcasting (DAB and DVB), WIMAX (Worldwide Inter-operability for Microwave Access), high speed modems over digital subscriber lines, and local area wireless broadband standards such as the HIPERLAN/2 and IEEE 802.11a (Stuber et al., 2004). In a wireless communication system, data is sent over a channel. This channel is time variant and undergoes fading. The exact state of the channel is unknown at the receiver. The transmitted signal is received at the receiver convolved with the channel and corrupted with noise. The main interest at the receiver is to recover the transmitted data. If we consider that the channel state information is known at the receiver, we can faithfully extract the transmitted data from the received signal with this knowledge (through equalization). In practice however, we do not have prior knowledge of the channel state information and hence we have to employ some estimation technique to obtain an estimate of the channel. Channel estimation is thus an important step in receiver design. We will start this chapter by reviewing the various approaches to channel estimation available in literature. We will then present an iterative channel estimation technique based on the Forward-Backward Kalman filter for simple OFDM systems and later extend the concept for multi-access OFDM and Multi Input Multi Output (MIMO) OFDM systems.

Iterative Forward-Backward Kalman Filtering for Data Recovery in (Multiuser) OFDM Communications