Low complexity list detection for high-rate multiple-antenna channels

Reid, A.B.; Grant, Alex; Kind, Adriel

doi:10.1109/isit.2003.1228288

Cited by 17 publications

(10 citation statements)

References 3 publications

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“…On the other hand, since any increases in the number of effective vectors may bring significant improvements, the number of N L can be much less than that in [7] or [9], i.e., 512, as we will also show in simulations. Consequently, the computational complexity, memory requirement, and search time, can be significantly reduced.…”

Section: B Competing Vectors Generation and Soft Detection For Each mentioning

confidence: 62%

“…Before the iterative soft detection starts, we apply the hard decision SDA for (2), with an initial search radius as (9) which, does not decrease after a candidate vector for (2) is found. The metric Ilx -Hsl1 2 of each newly found candidate is compared with those of the vectors stored in the list, and the N L vectors with minimal metrics are stored.…”

Section: A List Selection Via Modified Hard Decision Sphere Decodingmentioning

confidence: 99%

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Near-capacity performance soft output sphere decoding based on short list detection and metrics combining

Vojcic

2009

2009 43rd Annual Conference on Information Sciences and Systems

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where H is the N r x Nt flat fading channel matrix, s is the Nt x 1 transmitted symbol vector, x the N r x 1 received II. SYSTEM MODEL We consider fast Rayleigh fading channels. The MIMO system is described by Nt log2(M). Among many reduced complexity algorithms, the sphere decoding based algorithms provide best candidates for near-capacity performance [7] [9] [14]. The principle of sphere decoding algorithms (SDA) [2] is, instead of an exhaustive search in the lattice formed by all the possible received symbol vectors, it restricts the search to be within a small sphere so that the ML or near-ML solution can be found efficiently. It is well known that for uncoded data, SDA can achieve optimal or near-optimal performance. In iterative detection with channel decoding, however, standard hard decision SDA does not fit in. To produce reliable soft output, the list sphere decoding (LSD) algorithm in [7] applies the max-log approximation to MAP detection by selecting the best vector candidate searched by the sphere decoder. To ensure the reliability of the selected vector, the sphere decoder has to provide enough candidate vectors which requires a relatively large search radius. The subsequent soft detection also has to compare the likelihood metrics from the long list of candidates. Consequently, heavy computational complexity and/or large memories are required. The approach in [14] applies modified hard decision SDA after each iteration to find the exact best candidate. But the overall complexity is shown as similar to LSD. Besides, in general there are performance losses by the max-log approximation. In this paper, we show that with careful selection, combining the likelihood metrics of multiple candidate vectors instead of selecting only one for the max-log approximation can be more effective. An important feature is that a very short list for combining can be sufficient. As a result, for hard decision SDA, a smaller search radius can be used. The subsequent soft output detection also benefit from the much shorter list. Overall, both performance improvements and complexity reduction can be achieved.

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Section: B Competing Vectors Generation and Soft Detection For Each mentioning

confidence: 62%

Section: A List Selection Via Modified Hard Decision Sphere Decodingmentioning

confidence: 99%

Near-capacity performance soft output sphere decoding based on short list detection and metrics combining

Vojcic

2009

2009 43rd Annual Conference on Information Sciences and Systems

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“…This can be done efficiently by using two separate lists (one from the channel information and one from the a priori information) [8] . This point significantly improves the performance of the detector when the number of candidates is limited.…”

Section: A List M App Detectormentioning

confidence: 99%

“…Using numerical simulations, we will compare the performance of these APP MIMO detectors with the list version of the sphere decoder. A modified version of the M algorithm has also been independently proposed in [8].…”

Section: Introductionmentioning

confidence: 99%

Breadth first algorithms for APP detectors over MIMO channels

Ruyet

Bertozzi

Özbek

2004

2004 IEEE International Conference on Communications (IEEE Cat. No.04CH37577)

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Abstract-For iterative decoding of multiple antenna systems concatenated with an outer error correcting code, it is important to use an a posteriori probability detector for the MIMO detection to achieve near capacity performance. To avoid full APP detection, we propose a reduced complexity detector based on breadth first algorithms. Although these algorithms are suboptimal, we show that they can provide a good list of candidates for the APP calculation. Furthermore, by exploiting the a priori information delivered from the outer decoder, it is possible to decrease the MIMO detector complexity at each iteration. Using simulation results, we will compare the performance of the proposed detectors with the list sphere detector.

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“…using various different list detectors [12][13][14][15][16][17][18][19]. Most of these list-detection based methods rely on Cholesky decomposition of the correlation matrix as a first step.…”

Section: Introductionmentioning

confidence: 99%

A tree search method for iterative decoding of underdetermined multiuser systems

Kind¹,

Grant

2005

Proceedings. International Symposium on Information Theory, 2005. ISIT 2005.

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Abstract-Application of the turbo principle to multiuser decoding results in an exchange of probability distributions between two sets of constraints. Firstly, constraints imposed by the multiple-access channel, and secondly, individual constraints imposed by each users' error control code. A-posteriori probability computation for the first set of constraints is prohibitively complex for all but a small number of users. Several lower complexity approaches have been proposed in the literature. One class of methods is based on linear filtering (e.g. LMMSE).A more recent approach is to compute approximations to the posterior probabilities by marginalising over a subset of sequences (list detection). Most of the list detection methods are restricted to non-singular systems. In this paper, we introduce a transformation that permits application of standard tree-search methods to underdetermined systems. We find that the resulting tree-search based receiver outperforms existing methods.

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Low complexity list detection for high-rate multiple-antenna channels

Cited by 17 publications

References 3 publications

Near-capacity performance soft output sphere decoding based on short list detection and metrics combining

Near-capacity performance soft output sphere decoding based on short list detection and metrics combining

Breadth first algorithms for APP detectors over MIMO channels

A tree search method for iterative decoding of underdetermined multiuser systems

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