A Novel Decoder for Unknown Diversity Channels Employing Space-Time Codes
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A Novel Decoder for Unknown Diversity Channels Employing Space-Time Codes Elona Erez Department of Electrical Engineering-Systems, Tel Aviv University, Tel Aviv, 69978, Israel Email: [email protected]
Meir Feder Department of Electrical Engineering-Systems, Tel Aviv University, Tel Aviv, 69978, Israel Email: [email protected] Received 29 May 2001 and in revised form 11 January 2002 We suggest new decoding techniques for diversity channels employing space time codes (STC) when the channel coefficients are unknown to both transmitter and receiver. Most of the existing decoders for unknown diversity channels employ training sequence in order to estimate the channel. These decoders use the estimates of the channel coefficients in order to perform maximum likelihood (ML) decoding. We suggest an efficient implementation of the generalized likelihood ratio test (GLRT) algorithm that improves the performance with only slight increase in complexity. We also suggest an energy weighted decoder (EWD) that shows additional improvement without further increase in the computational complexity. Keywords and phrases: maximum likelihood, generalized likelihood ratio test, Viterbi algorithm, recursive least square.
1.
INTRODUCTION
Space time coding schemes have been shown to significantly improve the performance of communication over fading channels when multiple antennas are available at transmitter and receiver. Efficient space time codes (STC) for mobile cellular diversity channel are introduced by Tarokh et al. [1] where the channel coefficients are assumed to be known to the decoder. Many of the STC schemes assume full or partial receiver knowledge of the channel coefficients. When no knowledge of the channel is available, training sequences can be used in order to facilitate communication over an unknown channel. Since the sequence is known at the receiver, an estimate of the channel can be achieved at the receiver [2]. The use of training sequences has some drawbacks. First, there is a mismatch penalty. Because the training sequence is of limited length, the channel estimate formed at the receiver is imprecise which results in an increased error rate. Secondly, there is penalty in throughput, because the training sequence carries no information. This penalty is worse the longer the training sequence is, as compared to the length of the data sequence. When the channel changes rapidly over time, using training sequences might be inadequate. Since the channel can be assumed to be constant only over a very short period of time, training sequences would have to be
transmitted very often, which would severely increase the throughput penalty. In broadcast multipoint communication networks, if a channel from the master to one of the receiver stations goes down at any time following the initial training period and it is desired to retain only that receiver, the use of training sequences is unsuitable. In such situations, it is desirable for the receiver to decode without having a known training sequence available. An efficient differential dete
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