Title | ||
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Channel-aware adaptive receivers for linearly precoded MIMO-OFDM systems with imperfect CSIT. |
Abstract | ||
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Within the context of linearly precoded MIMO-OFDM (combination of multiple antenna techniques with multicarrier transmission schemes such as orthogonal frequency division multiplexing) systems with multiple-stream transmission, maximum likelihood detection (MLD) has been shown to offer large performance gains when compared to an all-linear setup (i.e., linear transmitter/receiver) when either perfect or imperfect channel state information at the transmitter (CSIT) is available. Unfortunately, these gains come at the cost of a higher complexity. In particular, the increase in computational cost is more significant when the receiver is designed to operate with soft information and even more dramatic when, in order to optimise error rate performance, iterative decoding is allowed. In order to exploit the best features of each detection technique, this paper proposes a method to selectively choose the detection strategy (ML or linear) for each individual subcarrier as a function of the instantaneous channel conditions and CSIT accuracy. Numerical results show that a cautious and selective use of ML detection substantially reduces complexity while still reaping most of the performance advantage. |
Year | DOI | Venue |
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2013 | 10.1186/1687-1499-2013-240 | EURASIP J. Wireless Comm. and Networking |
Keywords | Field | DocType |
Orthogonal Frequency Division Multiplex, Power Allocation, Minimum Mean Square Error, Orthogonal Frequency Division Multiplex Symbol, Soft Information | Subcarrier,MIMO-OFDM,Transmitter,Telecommunications,Computer science,Word error rate,Communication channel,Minimum mean square error,Electronic engineering,Real-time computing,Decoding methods,Orthogonal frequency-division multiplexing | Journal |
Volume | Issue | ISSN |
2013 | 1 | 1687-1499 |
Citations | PageRank | References |
8 | 0.35 | 18 |
Authors | ||
2 |
Name | Order | Citations | PageRank |
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Felip Riera-Palou | 1 | 211 | 31.02 |
Guillem Femenias | 2 | 354 | 45.43 |