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Training-based channel estimation involves probing of the channel in time, frequency, and space by the transmitter with known signals, and estimation of channel parameters from the output signals at the receiver. Traditional training-based methods, often comprising of maximum likelihood estimators, are known to be optimal under the assumption of rich multipath channels. Numerous measurement campaigns...
Time varying nature of uplink/downlink channels in wireless communications, calls for prior knowledge about the channel parameters in the receiver side. This accounts for different techniques of estimating the parameters of the so called channel between the transmitter and the receiver. In this paper beside using a channel model for a typical multi-input multi-output (MIMO) wireless communication...
A low-complexity approach to the joint estimation of frequency offset and channel matrix in a MIMO flat-fading channel using a training sequence is presented. The frequency offsets for all transmit antennas are assumed to be equal. The proposed algorithm first computes an initial estimation. This estimation is good enough for most applications. To increase the accuracy of the estimation, approximation...
Effects of improved channel estimation are studied for a proposed IEEE 802.11n OFDM MIMO system. Three channel estimation methods are considered: maximum likelihood (ML), time-domain truncation (TDT) and model-based (MB). TDT and MB are particularly useful when the channel delay spread is short. For an MMSE receiver, MB shows a 1-2.5 dB improvement over ML on the packet error rate performance for...
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