Sparse Bayesian learning (SBL)-based approximately sparse channel estimation schemes are conceived for space-time trellis coded (STTC) multiple-input multiple-output (MIMO) orthogonal frequency division multiplexing (OFDM) systems relying on trellis-based encoding and decoding over the data subcarriers. First, a pilot-aided channel estimation scheme is developed employing the multiple response extension of SBL (MSBL) framework. Subsequently, a novel data-aided joint channel estimation and data decoding framework relying on optimal maximum likelihood sequence detection (MLSD) is intrinsically amalgamated with our powerful EM-based MSBL algorithm. Explicitly, an MSBL-based MIMO channel estimate is gleaned in the E-step followed by a novel modified path-metric-based Viterbi decoder in the M-step. Our theoretical analysis characterizes the performance of the proposed schemes in terms of the associated frame error rate (FER) upper bounds by explicitly considering the effect of estimation errors along with evaluating the product measure of the STTC under consideration. Finally, our simulation results are complemented by the Bayesian Cramér-Rao bound (BCRB), the associated complexity analysis and the performance of the proposed schemes for validating the theoretical bounds.

中文翻译：

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空时网格编码 MIMO-OFDM 系统中的稀疏贝叶斯学习辅助联合稀疏信道估计和 ML 序列检测

基于稀疏贝叶斯学习 (SBL) 的近似稀疏信道估计方案被设想用于时空网格编码 (STTC) 多输入多输出 (MIMO) 正交频分复用 (OFDM) 系统，该系统依赖于基于网格的编码和解码数据副载波。首先，采用 SBL (MSBL) 框架的多响应扩展开发了一种导频辅助信道估计方案。随后，依赖于最优最大似然序列检测 (MLSD) 的新型数据辅助联合信道估计和数据解码框架本质上与我们强大的基于 EM 的 MSBL 算法相结合。明确地，在 E 步骤中收集基于 MSBL 的 MIMO 信道估计，然后在 M 步骤中收集新的基于路径度量的新型维特比解码器。我们的理论分析通过明确考虑估计误差的影响以及评估所考虑的 STTC 的产品度量，根据相关的帧错误率 (FER) 上限来表征所提出方案的性能。最后，我们的模拟结果得到了贝叶斯克拉默-拉奥界限 (BCRB)、相关复杂性分析和所提出的用于验证理论界限的方案的性能的补充。