Feedback-aided complexity reductions in ML and Lattice decoding

Singh, Arun; Elia, Petros
ISIT 2012, IEEE International Symposium on Information Theory, July 1-6, 2012, Cambridge, MA, USA

The work analyzes the computational-complexity savings that a single bit of feedback can provide in the computationally intense setting of non-ergodic MIMO communications. Specifically we derive upper bounds on the feedback-aided complexity exponent required for the broad families of ML-based and lattice based decoders to achieve the optimal diversitymultiplexing behavior. The bounds reveal a complexity that is reduced from being exponential in the number of codeword bits, to being at most exponential in the rate. Finally the derived savings are met by practically constructed ARQ schemes, as well as simple lattice designs, decoders, and computation-halting policies.


DOI
HAL
Type:
Conférence
City:
Cambridge
Date:
2012-07-01
Department:
Systèmes de Communication
Eurecom Ref:
3625
Copyright:
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PERMALINK : https://www.eurecom.fr/publication/3625