In this article, a new system model for sphere decoding （SD） algorithm is introduced. For the 2 × 2 multipleinput multiple-out （MIMO） system, a simplified maximum likelihood （SML） decoding algorithm is prop...In this article, a new system model for sphere decoding （SD） algorithm is introduced. For the 2 × 2 multipleinput multiple-out （MIMO） system, a simplified maximum likelihood （SML） decoding algorithm is proposed based on the new model. The SML algorithm achieves optimal maximum likelihood （ML） performance, and drastically reduces the complexity as compared to the conventional SD algorithm. The improved algorithm is presented by combining the sphere decoding algorithm based on Schnorr-Euchner strategy （SE-SD） with the SML algorithm when the number of transmit antennas exceeds 2. Compared to conventional SD, the proposed algorithm has low complexity especially at low signal to noise ratio （SNR）. It is shown by simulation that the proposed algorithm has performance very close to conventional SD.展开更多
In this article a bridge between the expected complexity and performance of sphere decoding （SD） is built. The expected complexity of SD for infinite lattices is then investigated, which naturally is the upper-bound...In this article a bridge between the expected complexity and performance of sphere decoding （SD） is built. The expected complexity of SD for infinite lattices is then investigated, which naturally is the upper-bound of those for all the finite lattices if given by the same channel matrix and signal noise ratio （SNR）. Such expected complexity is an important characterization of SD in multi-antenna systems, because no matter what modulation scheme is used in practice （generally it has finite constellation size） this upper-bound holds. Above bridge also leads to a new method of determining the radius for SD. The numerical results show both the real value and upper-bound of average searched number of candidates in SD for 16-QAM modulated system using the proposed sphere radius determining method. Most important of all new understandings of expected complexity of SD are given based on above mentioned theoretic analysis and numerical results.展开更多
基金supported by the Beijing University of Posts and Telecommunications and Qualcomm Joint Research Program
文摘In this article, a new system model for sphere decoding （SD） algorithm is introduced. For the 2 × 2 multipleinput multiple-out （MIMO） system, a simplified maximum likelihood （SML） decoding algorithm is proposed based on the new model. The SML algorithm achieves optimal maximum likelihood （ML） performance, and drastically reduces the complexity as compared to the conventional SD algorithm. The improved algorithm is presented by combining the sphere decoding algorithm based on Schnorr-Euchner strategy （SE-SD） with the SML algorithm when the number of transmit antennas exceeds 2. Compared to conventional SD, the proposed algorithm has low complexity especially at low signal to noise ratio （SNR）. It is shown by simulation that the proposed algorithm has performance very close to conventional SD.
基金supported by the National Natural Science Foundation of China (60572120, 60602058)the Hi-Tech Research and Development Program of China (2006AA01Z257)the National Basic Research Program of China (2007CB310602)
文摘In this article a bridge between the expected complexity and performance of sphere decoding （SD） is built. The expected complexity of SD for infinite lattices is then investigated, which naturally is the upper-bound of those for all the finite lattices if given by the same channel matrix and signal noise ratio （SNR）. Such expected complexity is an important characterization of SD in multi-antenna systems, because no matter what modulation scheme is used in practice （generally it has finite constellation size） this upper-bound holds. Above bridge also leads to a new method of determining the radius for SD. The numerical results show both the real value and upper-bound of average searched number of candidates in SD for 16-QAM modulated system using the proposed sphere radius determining method. Most important of all new understandings of expected complexity of SD are given based on above mentioned theoretic analysis and numerical results.
