论文标题
数据驱动的压缩传感用于大规模无线访问
Data-Driven Compressed Sensing for Massive Wireless Access
论文作者
论文摘要
大型机器类型通信（MMTC）的核心挑战是通过有限的频谱连接大量不协调的设备。典型的MMTC通信模式是零星的，具有短数据包。这可以在无授予的随机访问中利用，在这种随机访问中，活动检测，通道估计和数据恢复作为稀疏恢复问题，并通过压缩传感算法解决。这种方法在高计算复杂性和延迟方面带来了新的挑战。我们介绍了如何将数据驱动的方法应用于无授予的随机访问中并证明性能提高。讨论了有关该问题的神经网络的变化，以及未来的挑战和潜在方向。
The central challenge in massive machine-type communications (mMTC) is to connect a large number of uncoordinated devices through a limited spectrum. The typical mMTC communication pattern is sporadic, with short packets. This could be exploited in grant-free random access in which the activity detection, channel estimation, and data recovery are formulated as a sparse recovery problem and solved via compressed sensing algorithms. This approach results in new challenges in terms of high computational complexity and latency. We present how data-driven methods can be applied in grant-free random access and demonstrate the performance gains. Variations of neural networks for the problem are discussed, as well as future challenges and potential directions.
