论文标题
基于压缩传感的大规模访问物联网依赖媒体调制辅助机器类型通信
Compressive Sensing Based Massive Access for IoT Relying on Media Modulation Aided Machine Type Communications
论文作者
论文摘要
大规模的大规模挑战的基本挑战在于如何支持大规模的机器型通信（MMTC）。这封信提出了一种基于媒体调制的MMTC解决方案，用于增加吞吐量，其中大规模多输入基于多输出的基站（BS）用于增强检测性能。对于这种MMTC方案，可靠的主动设备检测和数据解码构成了严重的挑战。通过利用BS收到的MMTC上行链路访问信号的稀疏性，提出了一种基于压缩感应的大规模访问解决方案来应对这一挑战。具体而言，我们提出了一个用于检测活动设备的块稀疏自适应匹配追踪算法，从而利用了连续的时间插槽中展示的上行链路访问信号的块Sparsity，并利用了媒体调制符号的结构化稀疏性来增强检测性能。此外，基于连续的干扰取消基于结构化的子空间追求算法是为了对活动设备的数据解调，从而利用了每个时间插槽中基于媒体调制的符号的结构化稀疏性来提高检测性能。最后，我们的仿真结果验证了所提出的方案比最先进的解决方案的优越性。
A fundamental challenge of the large-scale Internet-of-Things lies in how to support massive machine-type communications (mMTC). This letter proposes a media modulation based mMTC solution for increasing the throughput, where a massive multi-input multi-output based base station (BS) is used for enhancing the detection performance. For such a mMTC scenario, the reliable active device detection and data decoding pose a serious challenge. By leveraging the sparsity of the uplink access signals of mMTC received at the BS, a compressive sensing based massive access solution is proposed for tackling this challenge. Specifically, we propose a block sparsity adaptive matching pursuit algorithm for detecting the active devices, whereby the block-sparsity of the uplink access signals exhibited across the successive time slots and the structured sparsity of media modulated symbols are exploited for enhancing the detection performance. Moreover, a successive interference cancellation based structured subspace pursuit algorithm is conceived for data demodulation of the active devices, whereby the structured sparsity of media modulation based symbols found in each time slot is exploited for improving the detection performance. Finally, our simulation results verify the superiority of the proposed scheme over state-of-the-art solutions.
