2006 IEEE International Conference on Communications 2006
DOI: 10.1109/icc.2006.255547
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An Efficient Decoding Algorithm for STBC with Multi-dimensional Rotated Constellations

Abstract: In this paper, we present a novel maximum likelihood (ML) decoding algorithm for space-time block codes (STBC) over fading channels. Using a lattice representation for space-time codes by transforming complex channel models into real matrix equations, we propose a new efficient ML decoding algorithm with performance identical to the conventional ML decoder. We show that the complex orthogonal space-time codes, in fact, allow a separate ML decoding for each inphase and quadrature-phase component. For rate one q… Show more

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Cited by 19 publications
(42 citation statements)
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References 16 publications
(24 reference statements)
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“…The complexity of the decoding algorithm proposed in [23] is in the order O(L N/4 ) which is equal to the complexity of the algorithm presented in [26] and will be denoted C [23], [26] in the sequel. In Table I, we give a comparison between ML, algorithms in [23] and [26], and PR in terms of the number of real multiplications and real additions considering N = 4 for different constellation sizes. The number of multiplications and additions shown include the computation of QR andȳ = Q Hỹ .…”
Section: Simulation Resultsmentioning
confidence: 99%
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“…The complexity of the decoding algorithm proposed in [23] is in the order O(L N/4 ) which is equal to the complexity of the algorithm presented in [26] and will be denoted C [23], [26] in the sequel. In Table I, we give a comparison between ML, algorithms in [23] and [26], and PR in terms of the number of real multiplications and real additions considering N = 4 for different constellation sizes. The number of multiplications and additions shown include the computation of QR andȳ = Q Hỹ .…”
Section: Simulation Resultsmentioning
confidence: 99%
“…Note that this simplification is obtained through the orthogonality properties in (13) and the QR decomposition in (14), resulting in (15) and (16). This is similar to but simpler than [23] and [26] in that all have joint detection of two real symbols (N/2 times in [23] and [26] and n s times in this work) but with minimizing a norm of size 2N in [23] and [26] while minimizing a norm of size 2 in this work. This means that the original complex ML problem is decomposed into n s = 4 parallel real-valued upper triangular problems, each of dimension 2.…”
mentioning
confidence: 89%
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“…For example, the ISI-free QO-STBC is achieved through the rotation of one-half of the symbol constellation set [35][36][37], multidimensional rotation [38][39][40], Givens-rotations [16], EVD [17,18] and Hadamard matrices [1,17]. Although the EVD approach is less complex and will be followed, the results can be enhanced if an equivalent modal matrix can be derived without zeros terms.…”
Section: Full-diversity Qo-stbc Using Evd and The Proposedmentioning
confidence: 99%
“…Whereupon, QOSTBCs with constellation rotation were proposed in [19,20], which can provide full diversity and higher code rate. However, the ML decoding for QOSTBCs is generally more complex than that of OSTBCs.…”
Section: Introductionmentioning
confidence: 99%