We propose a coordinated random access scheme for industrial internet-of-things (IIoT) scenarios, with machine-type devices (MTDs) generating sporadic correlated traffic. This occurs, e.g., when external events trigger data generation at multiple MTDs simultaneously. Time is divided into frames, each split into slots and each MTD randomly selects one slot for (re)transmission, with probability density functions (PDFs) specific of both the MTD and the number of the current retransmission. PDFs are locally optimized to minimize the probability of packet collision. The optimization problem is modeled as a repeated Markov game with incomplete information, and the linear reward-inaction algorithm is used at each MTD, which provably converges to a deterministic (suboptimal) slot assignment. We compare our solution with both the slotted ALOHA and the min-max pairwise correlation random access schemes, showing that our approach achieves a higher network throughput with moderate traffic intensity.
翻译:我们为工业互联网(IIoT)情景提出了一个协调随机访问计划,机型设备(MTDs)产生零星相关交通。 发生这种情况时, 例如当外部事件同时触发多个MTDs的数据生成时。 时间被分为框架, 将每个空格分成一个空格, 并且每个MTD随机选择一个空格用于( 重新) 传输, 其概率密度函数( PDFs) 与当前再传输数都有特殊性。 PDF 在当地优化, 以最大限度地减少包件碰撞的概率。 优化问题以重复的Markov 游戏为模型, 且信息不完整, 在每个 MTD 中都使用线性奖励- 不行动算法, 这可以与确定性( 亚优性) 空档任务相匹配 。 我们比较我们的解决方案, 与时间档的 ALOHA 和 微角对角相对等随机访问计划, 显示我们的方法能够以中等流量的流量实现更高的网络。