论文标题
改进了基于MMTC中短包URLLC的优化扩散矩阵的稀疏矢量代码
Improved Sparse Vector Code Based on Optimized Spreading Matrix for Short-Packet URLLC in mMTC
论文作者
论文摘要
最近，稀疏矢量代码（SVC）正在成为大型机器类型通信（MMTC）以及超可靠和低延迟通信（URLLC）中短包传输的有前途的解决方案。在SVC过程中，编码和解码阶段共同建模为标准压缩感应（CS）问题。因此，本文旨在通过优化扩散矩阵（即CS中的测量矩阵）来提高SVC的解码性能。为此，提出了两种贪婪的算法，以最大程度地减少SVC中扩散矩阵的相互相干值。特别是，对于实际应用，进一步的扩展矩阵必须为双极，其条目被限制为+1或-1。结果，优化的扩展矩阵对于存储，计算和硬件实现非常有效。仿真结果表明，与现有工作相比，通过优化的扩散矩阵可以显着提高SVC的块错误率（BLER）性能。
Recently, the sparse vector code (SVC) is emerging as a promising solution for short-packet transmission in massive machine type communication (mMTC) as well as ultra-reliable and low-latency communication (URLLC). In the SVC process, the encoding and decoding stages are jointly modeled as a standard compressed sensing (CS) problem. Hence, this paper aims at improving the decoding performance of SVC by optimizing the spreading matrix (i.e. measurement matrix in CS). To this end, two greedy algorithms to minimize the mutual coherence value of the spreading matrix in SVC are proposed. Specially, for practical applications, the spreading matrices are further required to be bipolar whose entries are constrained as +1 or -1. As a result, the optimized spreading matrices are highly efficient for storage, computation, and hardware realization. Simulation results reveal that, compared with the existing work, the block error rate (BLER) performance of SVC can be improved significantly with the optimized spreading matrices.
