The optimization of joint source and channel coding for a sequence of numerous progressive packets has been a challenging problem. Further, the problem becomes more complicated if the space-time coding is also involved with the optimization in a multiple-input multiple-output (MIMO) system. This is because the number of ways of jointly assigning channel codes and space-time codes to progressive packets is much larger than that of solely assigning channel codes to the packets. To our knowledge, there is no complete and feasible solution for the optimization of joint source, channel, and space-time coding of progressive packets. This paper applies a parametric approach to address such a highly nonlinear optimization problem in MIMO systems. We use the parametric methodology and derive some relevant properties, which are exploited to propose an efficient optimization method where the joint assignment of channel codes and space-time codes to the packets can be optimized in a packet-by-packet manner. As a result, the computational complexity of the proposed method linearly increases in the number of packets, whereas that of the exhaustive search exponentially increases in the number of packets. The numerical results show that the proposed method significantly improves the peak-signal-to-noise ratio performance of the conventional rate-based optimal solution in a MIMO system.

中文翻译：

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MIMO 信道中渐进比特流的联合源、信道和空时编码

对大量渐进数据包序列的联合源和信道编码的优化一直是一个具有挑战性的问题。此外，如果多输入多输出（MIMO）系统中的优化也涉及空时编码，则问题变得更加复杂。这是因为将信道码和空时码联合分配给逐行数据包的方式比单独为数据包分配信道码的方式要多得多。据我们所知，对于渐进包的联合源、信道和空时编码的优化，目前还没有完整可行的解决方案。本文应用参数方法来解决 MIMO 系统中这种高度非线性的优化问题。我们使用参数化方法并推导出一些相关属性，它们被用来提出一种有效的优化方法，其中信道码和空时码到分组的联合分配可以以逐个分组的方式进行优化。结果，所提出方法的计算复杂度随数据包数量线性增加，而穷举搜索的计算复杂度随数据包数量呈指数增加。数值结果表明，所提出的方法显着提高了MIMO系统中传统的基于速率的最优解的峰值信噪比性能。而穷举搜索的数据包数量呈指数增长。数值结果表明，所提出的方法显着提高了MIMO系统中传统的基于速率的最优解的峰值信噪比性能。而穷举搜索的数据包数量呈指数增长。数值结果表明，所提出的方法显着提高了MIMO系统中传统的基于速率的最优解的峰值信噪比性能。