Pan Zihao, Wang Heng, Zhang Bangning, Guo Daoxing
College of Communication Engineering, Army Engineering University of PLA, Nanjing 210001, China.
Sensors (Basel). 2022 Oct 29;22(21):8316. doi: 10.3390/s22218316.
With the standardization and commercialization of 5G, research on 6G technology has begun. In this paper, a new low-complexity soft-input-soft-output (SISO) adaptive detection algorithm for short CPM bursts is proposed for low-power, massive Internet of Things (IoT) connectivity in 6G. First, a time-invariant trellis is constructed on the basis of truncation in order to reduce the number of states. Then, adaptive channel estimators, recursive least squares (RLS), or least mean squares (LMS), are assigned to each hypothetical sequence by using the recursive structure of the trellis, and per-survivor processing (PSP) is used to improve the quality of channel estimation and reduce the number of searching paths. Then, the RLS adaptive symbol detector (RLS-ASD) and LMS adaptive symbol detector (LMS-ASD) could be acquired. Compared to using a least-squares estimator, the RLS-ASD avoids matrix inversion for the computation of branch metrics, while the LMS-ASD further reduces the steps in the RLS-ASD at the cost of performance. Lastly, a soft information iteration process is used to further improve performance via turbo equalization. Simulation results and analysis show that the RLS-ASD improves performance by about 1 dB compared to the state-of-the-art approach in time-variant environments while keeping a similar complexity. In addition, the LMS-ASD could further significantly reduce complexity with a power loss of approximately 1 dB. Thus, a flexible choice of detectors can achieve a trade-off of performance and complexity.
随着5G的标准化和商业化，6G技术的研究已经开始。本文针对6G中低功耗、大规模物联网（IoT）连接，提出了一种用于短CPM突发的新型低复杂度软输入软输出（SISO）自适应检测算法。首先，基于截断构建一个时不变网格以减少状态数量。然后，利用网格的递归结构为每个假设序列分配自适应信道估计器，递归最小二乘法（RLS）或最小均方算法（LMS），并采用逐幸存处理（PSP）来提高信道估计质量并减少搜索路径数量。接着，可以得到RLS自适应符号检测器（RLS - ASD）和LMS自适应符号检测器（LMS - ASD）。与使用最小二乘估计器相比，RLS - ASD在计算分支度量时避免了矩阵求逆，而LMS - ASD以性能为代价进一步减少了RLS - ASD中的步骤。最后，通过Turbo均衡使用软信息迭代过程来进一步提高性能。仿真结果和分析表明，在时变环境中，RLS - ASD与现有方法相比性能提高了约1 dB，同时保持了相似的复杂度。此外，LMS - ASD可以进一步显著降低复杂度，但功率损失约为1 dB。因此，灵活选择检测器可以实现性能和复杂度的权衡。
