In machine-type communication (MTC), since multiple active devices are located randomly in a cell, the arrival time of multiple signals can be different. A priori information on the arrival time of multiple signals is helpful to estimate more specific parameters such as the number of signals, which is essential to employ the successive interference cancellation (SIC) to improve throughput of a non-orthogonal random access (NORA) system. In this letter, we propose an approach to estimate the round-trip delay (RTD) of multiple signals based on Markov chain Monte Carlo (MCMC) and Genetic Algorithm (GA). Simulation results show our proposed GA-based MCMC method can achieve high performance with low complexity.

中文翻译：

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在非正交随机访问中使用基于遗传算法的 MCMC 进行多重延迟估计

在机器类型通信 (MTC) 中，由于多个有源设备随机位于一个小区中，因此多个信号的到达时间可能不同。多个信号到达时间的先验信息有助于估计更具体的参数，例如信号数量，这对于采用连续干扰消除 (SIC) 来提高非正交随机接入 (NORA) 系统的吞吐量至关重要. 在这封信中，我们提出了一种基于马尔可夫链蒙特卡罗 (MCMC) 和遗传算法 (GA) 来估计多个信号的往返延迟 (RTD) 的方法。仿真结果表明，我们提出的基于 GA 的 MCMC 方法可以以低复杂度实现高性能。