Channel Estimation For Mimo Wireless Communication

In order to meet the higher demand for future mobile communication, communication system that can adopt multiple transmit and receive antennas is calling more people's attention. By far, the first Multiple Input and Multiple Output (MIMO) system named V-BLAST in the world has been implemented by Bell laboratory. A recognized MIMO standard, however, hasn't yet been formed. The study on characteristic of wireless channels is a key for mobile communication, and MIMO system is also not an exception. In fact, any design for a reliable wireless communication system must take channel characteristic into account. For instance, a receiver must depend on accurate channel estimation to detect information-bearing signals smoothly. Due to complexity of MIMO channel compared with SISO one, one of the difficulties of MIMO communication is MIMO channel estimation. So a thorough study is necessary. In general, people believe that between any transmit antenna and any receive antenna, there exists a sub-channel and moreover, all sub-channels are not mutually independent but correlate to each other to some degree. In this paper, for simplification of MIMO channel estimation, the author assumes the optimal condition of fully independent sub-channels.On the basis of knowledge about MIMO system and characteristic of wireless channel, as the main work of this paper, the author gives several MIMO channel estimation algorithms for time-invariant and time-variant FIR channel model respectively. In chapter three, based on likelihood (ML) criteria and least square (LS) theory, channel estimation is achieved by sending training sequence. Though, these training sequences are not random ones, but are specifically designed to obtain as accurate channel estimation as possible. Recently, a new idea for estimating channel is introduced. By utilizing periodic sequences superimposed on information-bearing symbols, channel estimation can be achieved only based on first-order statistics of receiving symbols. Attractive as this method seems to be, however, it's seldom applied to MIMO case. In chapter four and five, the author generalizes this method to MIMO case for time-invariant and time-variant MIMO channel estimation respectively. As its advantages, low computing complexity is needed and no extra time or frequency resources are occupied. Nevertheless, a higher peak-to-average symbol power ration (PAR) as its disadvantage arises. As a way of addressing this problem, sequences that can reduce PAR are designed while estimating performance is not impaired at all.At last, in the author's opinion, research on channel estimation for MIMO wireless needs to be further penetrated by considering all kinds of practical communicating factors that may impair the given model in this paper. In this sense, this paper will destine to be harbinger that can lead to a thorough solution.