Type
ArticleKAUST Department
Computer, Electrical and Mathematical Sciences and Engineering (CEMSE) DivisionElectrical Engineering Program
Physical Science and Engineering (PSE) Division
Sensors Lab
Date
2011-11-21Online Publication Date
2011-11-21Print Publication Date
2010-08Permanent link to this record
http://hdl.handle.net/10754/561492
Metadata
Show full item recordAbstract
While Maximum-Likelihood (ML) is the optimum decoding scheme for most communication scenarios, practical implementation difficulties limit its use, especially for Multiple Input Multiple Output (MIMO) systems with a large number of transmit or receive antennas. Tree-searching type decoder structures such as Sphere decoder and K-best decoder present an interesting trade-off between complexity and performance. Many algorithmic developments and VLSI implementations have been reported in literature with widely varying performance to area and power metrics. In this semi-tutorial paper we present a holistic view of different Sphere decoding techniques and K-best decoding techniques, identifying the key algorithmic and implementation trade-offs. We establish a consistent benchmark framework to investigate and compare the delay cost, power cost, and power-delay-product cost incurred by each method. Finally, using the framework, we propose and analyze a novel architecture and compare that to other published approaches. Our goal is to explicitly elucidate the overall advantages and disadvantages of each proposed algorithms in one coherent framework. © 2010 World Scientific Publishing Company.Citation
SHEN, C.-A., ELTAWIL, A. M., & SALAMA, K. N. (2010). EVALUATION FRAMEWORK FOR K-BEST SPHERE DECODERS. Journal of Circuits, Systems and Computers, 19(05), 975–995. doi:10.1142/s0218126610006554Publisher
World Scientific Pub Co Pte Ltae974a485f413a2113503eed53cd6c53
10.1142/S0218126610006554