Abstract
In this paper, we present a low-complexity list sphere detection algorithm for achieving near-optimal a posteriori probability (APP) detection in an iterative detection and decoding (IDD). Motivated by the fact that the list sphere decoding searching a fixed number of candidates is computationally inefficient in many scenarios, we design a criterion to search lattice points with non-vanishing likelihood and then derive a hypersphere radius satisfying this condition. Further, in order to exploit the original sphere constraint as it is instead of using necessary conditioned version, we combine a probabilistic tree pruning strategy and the proposed list sphere search. Two features, tightened hypersphere radius and probabilistic tree pruning, collaborate and improve the search efficiency in a complementary fashion. Through simulations on 4x4 MIMO system, we show that the proposed method provides substantial reduction in complexity while achieving negligible performance loss over the conventional list sphere detection.
Original language | English |
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Article number | 6213035 |
Pages (from-to) | 2848-2857 |
Number of pages | 10 |
Journal | IEEE Transactions on Wireless Communications |
Volume | 11 |
Issue number | 8 |
DOIs | |
Publication status | Published - 2012 |
Keywords
- Iterative detection and decoding
- Sphere decoding
- a posteriori probability
- complexity reduction
- multiple-input multiple-output system
- probabilistic radius tightening
ASJC Scopus subject areas
- Computer Science Applications
- Electrical and Electronic Engineering
- Applied Mathematics