We propose a novel soft-output joint channel estimation and data detection (JED) algorithm for multiuser (MU) multiple-input multiple-output (MIMO) wireless communication systems. Our algorithm approximately solves a maximum a-posteriori JED optimization problem using deep unfolding and generates soft-output information for the transmitted bits in every iteration. The parameters of the unfolded algorithm are computed by a hyper-network that is trained with a binary cross entropy (BCE) loss. We evaluate the performance of our algorithm in a coded MU-MIMO system with 8 basestation antennas and 4 user equipments and compare it to state-of-the-art algorithms separate channel estimation from soft-output data detection. Our results demonstrate that our JED algorithm outperforms such data detectors with as few as 10 iterations.
翻译:我们建议为多用户(MU)多输入多输出多输出无线通信系统采用新型的软输出联合频道估计和数据探测算法。我们的算法大约能通过深度的开发解决最大一个离子 JED优化问题,并为每个迭代中传输的位数生成软输出信息。展开算法的参数由超网络计算,该超网络经过二元交叉星(BCE)损失的培训。我们用8个基站天线和4个用户设备来评估我们在一个编码的MU-MIMO系统中的算法性能,并将其与最先进的算法从软输出数据探测中分离的频道估计值进行比较。我们的结果表明,我们的JED算法比数据探测器高出10倍。