23,071 research outputs found
An Adaptive Conditional Zero-Forcing Decoder with Full-diversity, Least Complexity and Essentially-ML Performance for STBCs
A low complexity, essentially-ML decoding technique for the Golden code and
the 3 antenna Perfect code was introduced by Sirianunpiboon, Howard and
Calderbank. Though no theoretical analysis of the decoder was given, the
simulations showed that this decoding technique has almost maximum-likelihood
(ML) performance. Inspired by this technique, in this paper we introduce two
new low complexity decoders for Space-Time Block Codes (STBCs) - the Adaptive
Conditional Zero-Forcing (ACZF) decoder and the ACZF decoder with successive
interference cancellation (ACZF-SIC), which include as a special case the
decoding technique of Sirianunpiboon et al. We show that both ACZF and ACZF-SIC
decoders are capable of achieving full-diversity, and we give sufficient
conditions for an STBC to give full-diversity with these decoders. We then show
that the Golden code, the 3 and 4 antenna Perfect codes, the 3 antenna Threaded
Algebraic Space-Time code and the 4 antenna rate 2 code of Srinath and Rajan
are all full-diversity ACZF/ACZF-SIC decodable with complexity strictly less
than that of their ML decoders. Simulations show that the proposed decoding
method performs identical to ML decoding for all these five codes. These STBCs
along with the proposed decoding algorithm outperform all known codes in terms
of decoding complexity and error performance for 2,3 and 4 transmit antennas.
We further provide a lower bound on the complexity of full-diversity
ACZF/ACZF-SIC decoding. All the five codes listed above achieve this lower
bound and hence are optimal in terms of minimizing the ACZF/ACZF-SIC decoding
complexity. Both ACZF and ACZF-SIC decoders are amenable to sphere decoding
implementation.Comment: 11 pages, 4 figures. Corrected a minor typographical erro
A Scalable VLSI Architecture for Soft-Input Soft-Output Depth-First Sphere Decoding
Multiple-input multiple-output (MIMO) wireless transmission imposes huge
challenges on the design of efficient hardware architectures for iterative
receivers. A major challenge is soft-input soft-output (SISO) MIMO demapping,
often approached by sphere decoding (SD). In this paper, we introduce the - to
our best knowledge - first VLSI architecture for SISO SD applying a single
tree-search approach. Compared with a soft-output-only base architecture
similar to the one proposed by Studer et al. in IEEE J-SAC 2008, the
architectural modifications for soft input still allow a one-node-per-cycle
execution. For a 4x4 16-QAM system, the area increases by 57% and the operating
frequency degrades by 34% only.Comment: Accepted for IEEE Transactions on Circuits and Systems II Express
Briefs, May 2010. This draft from April 2010 will not be updated any more.
Please refer to IEEE Xplore for the final version. *) The final publication
will appear with the modified title "A Scalable VLSI Architecture for
Soft-Input Soft-Output Single Tree-Search Sphere Decoding
Generalized feedback detection for spatial multiplexing multi-antenna systems
We present a unified detection framework for spatial multiplexing multiple-input multiple-output (MIMO) systems by generalizing Heller’s classical feedback decoding algorithm for convolutional codes. The resulting generalized feedback detector (GFD) is characterized by three parameters: window size, step size and branch factor. Many existing MIMO detectors are turned out to be special cases of the GFD. Moreover, different parameter choices can provide various performance-complexity tradeoffs. The connection between MIMO detectors and tree search algorithms is also established. To reduce redundant computations in the GFD, a shared computation technique is proposed by using a tree data structure. Using a union bound based analysis of the symbol error rates, the diversity order and signal-to-noise ratio (SNR) gain are derived analytically as functions of the three parameters; for example, the diversity order of the GFD varies between 1 and N. The complexity of the GFD varies between those of the maximum-likelihood (ML) detector and the zero-forcing decision feedback detector (ZFDFD). Extensive computer simulation results are also provided
Low-complexity dominance-based Sphere Decoder for MIMO Systems
The sphere decoder (SD) is an attractive low-complexity alternative to
maximum likelihood (ML) detection in a variety of communication systems. It is
also employed in multiple-input multiple-output (MIMO) systems where the
computational complexity of the optimum detector grows exponentially with the
number of transmit antennas. We propose an enhanced version of the SD based on
an additional cost function derived from conditions on worst case interference,
that we call dominance conditions. The proposed detector, the king sphere
decoder (KSD), has a computational complexity that results to be not larger
than the complexity of the sphere decoder and numerical simulations show that
the complexity reduction is usually quite significant
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