【作者】 林宏志；

【导师】 朱光喜；

【作者基本信息】 华中科技大学 ， 信息与通信工程， 2008， 博士

【摘要】 多天线和OFDM的结合极大地提高了系统容量,OFDM能为系统提供平坦衰落信道,而多天线则提供空间增益。传统的多天线系统要么采用BLAST算法获得复用增益,要么采用STC算法获得分集增益,而这两种增益都是来源于多天线的空间维度显然可以统一为空间增益。本文于是提出一种平衡空间增益的HARQ方案,该方案利用空时码的发射矩阵特性,将发射矩阵不同行的发送等效为HARQ的重传机制,即通过时间维度的引入将复用增益和分集增益统一起来。本文首先分析了多径衰落信道的信道脉冲响应模型作为系统工作的无线环境,以及获得复用增益的BLAST算法和获得分集增益的STBC算法,还有能获得全速率和满分集增益的最优模型-阿拉莫提模型作为本文的研究基础。其次本文提出了BLAST和STBC的结合算法即B-S算法。由于空时码发射矩阵分为正交和准正交矩阵,其中正交矩阵可以获得满分集增益但达不到全速率,而准正交矩阵可以获得全速率却只能获得部分分集增益,唯一的特例是能获得满分集增益和全速率的阿拉莫提模型,因此本文在研究了基于阿拉莫提模型的B-S算法的基础上,从满分集增益的方向发展了基于正交发射矩阵的B-S算法,以及从全速率方向发展了基于准正交发射矩阵的B-S算法。通过比较两者的性能差异,发现在中低信噪比下正交B-S算法可以获得更优性能,而在高信噪比下准正交B-S算法可以获得更优性能。由于解码性能是基于星座图的欧氏距离,而高阶调制的各个星座点之间的欧氏距离各不相同,在重传数据时使用不同的星座图并采用软信息解码就能得到更大的欧氏距离从而改善性能。B-S算法是将发射矩阵的数据依次发送的,显然可以和星座图重排自然的结合起来,结合的类型分为:矩阵的每行数据都进行重排,以及逐矩阵的重排。基于每行重排的B-S算法有最优的性能,但是由于破坏了矩阵的固有结构使得解码变得复杂,可以使用最大似然及其简化算法球形译码。基于每矩阵重传保留了矩阵的特定结构所以译码变得十分简单可以直接采用软信息解码,但是由于重排的颗粒度降低导致性能会有轻微下降。系统需要在复杂度和性能两方面作一个权衡。基于以上的分析,本文将B-S算法推广为HARQ机制并结合星座图重排,提出基于星座图重排的B-S-HARQ方案。依据重排的‘颗粒度’可以分为两种:一种是低复杂度的每矩阵星座图重排HARQ方案,该方案采用正交发射矩阵,在重传矩阵内部行的时候保持星座图不变,而当再次重传该矩阵时才改变星座图,这样的方案保留了发射矩阵的结构因而具有低的复杂度;另一种是基于球形译码的每行数据重排和星座图重排的HARQ方案,该方案采用准正交空时码发射矩阵,在HARQ重传的时候矩阵的每一行都改变星座图,这样就保证了能使系统获得最优的性能,因为系统在获得空间分集增益的同时还获得最大的重排分集增益,但是为了获得这样的最优性能必须使用最大似然译码,本文使用能获得相似性能但是复杂度更低的球形译码来降低部分复杂度。最后本文对以上方法都进行了仿真,表明提出的算法有良好的性能,并在此基础上提出了未来工作的展望。更多还原

【Abstract】 The combination of MIMO and OFDM greatly increases the system capacity in the sense that OFDM and MIMO can respectively provide flat fading channel and spatial gain. The traditional MIMO system either uses BLAST to get spatial multiplexing gain or STC to get spatial diversity gain, but they both can be uniformly treated as spatial gain as they are obtained from the spatial dimension of multiple antenna. This paper proposes an HARQ method that mainly depends on the structure of STBC matrix to trade-off between the spatial gains. In this method the HARQ retransmission scheme is equivalent as transmitting different matrix rows, so by introducing the time dimension, the notions of multiplexing and diversity gains can be successfully unified.At first, this paper analyses the impulse response model of a multi-path fading channel. It gives an overview of the BLAST and STBC algorithms. Based on Alamouti’s model, we also study a system that can get full spatial multiplexing and full spatial diversity gains.Secondly, this paper proposes a method combining BLAST and STBC called the B-S algorithm. As we already know, there are two types of STBC matrixes: Orthogonal and quasi-orthogonal matrixes. The orthogonal matrix can get full diversity gain but it cannot reach full rate and conversely, the quasi-orthogonal matrix can get full rate but can only get part of the diversity gain. The Alamouti model is the only one that can get full diversity gain and full rate at the same time. The basic B-S algorithm is based on Alamouti’s model, but while trying to achieve full diversity, we derive an orthogonal B-S method using an orthogonal matrix; similarly for the full rate scheme, a quasi-orthogonal B-S algorithm is also derived. The simulation result shows that orthogonal B-S outperforms at middle and lower SNR but quasi-orthogonal B-S is better at higher SNR.Knowing that the decoding performance depends on the Euclidean distance between constellation points and that high level modulations have different Euclidean distances within their respective constellations, the overall performance can be improved by using different symbol mappings and soft-value decoding to get larger Euclidean distances at retransmissions. As the B-S algorithm relies on the transmitter matrix, constellation remapping can be divided in two types as well: The row-by-row remapping and the matrix remapping. The line remapping has better performance but higher complexity, it can make use of ML and Sphere Decoding. On the other hand, the second method based on the whole matrix retransmission keeps the structure of the matrix intact and so the decoding procedure becomes very simple. The LLR values can even be used directly to decode but the performance decreases slightly due to loss of diversity during remapping. Therefore, the system must trade-off between complexity and performance.Bases on the above analysis, this paper proposes a B-S-HARQ scheme, a low complexity approach based on matrix of LLR values constellation remapping and therefore a orthogonal matrix is used. At an HARQ retransmission, the matrix rows are kept inside the matrix and the constellation mapping remains unchanged until the next iteration where the following matrix is sent. This approach guarantees a low complexity decoding as it can use the matrix inverse and the LLR values between different matrixes to improve the system performance. This paper also proposes an optimization algorithm based on SD (Sphere Dedocing) where row and constellation remappings are applied. This method uses quasi-orthogonal matrixes, at an HARQ retransmission, each row of the matrix changes the constellation mapping and therefore guaranteeing the best system performance. The reason is because while the system gets spatial diversity, the highest remapping diversity gain is also attained. However, ML must be used to obtain such high performance therefore, this paper proposes the use of SD as it is less complex and its performance is relatively close to that of ML.Finally, simulations were conducted on every scheme indicated above, good results demonstrated their performance and on this basis, further research topics are proposed.更多还原