Massive machine-type communications (mMTC) are poised to provide ubiquitous connectivity for billions of Internet-of-Things (IoT) devices. However, the required low-latency massive access necessitates a paradigm shift in the design of random access schemes, which invokes a need of efficient joint activity and data detection (JADD) algorithms. By exploiting the feature of sporadic traffic in massive access, a beacon-aided slotted grant-free massive access solution is proposed. Specifically, we spread the uplink access signals in multiple subcarriers with pre-equalization processing and formulate the JADD as a multiple measurement vector (MMV) compressive sensing problem. Moreover, to leverage the structured sparsity of uplink massive access signals among multiple time slots, we develop two computationally efficient detection algorithms, which are termed as orthogonal approximate message passing (OAMP)-MMV algorithm with simplified structure learning (SSL) and accurate structure learning (ASL). To achieve accurate detection, the expectation maximization algorithm is exploited for learning the sparsity ratio and the noise variance. To further improve the detection performance, channel coding is applied and successive interference cancellation (SIC)-based OAMP-MMV-SSL and OAMP-MMV-ASL algorithms are developed, where the likelihood ratio obtained in the soft-decision can be exploited for refining the activity identification. Finally, the state evolution of the proposed OAMP-MMV-SSL and OAMP-MMV-ASL algorithms is derived to predict the performance theoretically. Simulation results verify that the proposed solutions outperform various state-of-the-art baseline schemes, enabling low-latency random access and high-reliable massive IoT connectivity with overloading.
翻译:大规模机型通信(MMTC)即将为数十亿个互联网测试设备提供无处不在的轨迹变异性连接;然而,所需的低延迟大规模接入要求需要在随机访问计划的设计中进行范式转变,这需要高效的联合活动和数据检测算法。通过利用大规模接入中零星通信的特征,提出了信标辅助定档次无定档的大规模接入解决方案。具体地,我们通过在具有前均匀处理的多子容器中传播了连接信号,并将JADD设计成一个多度测量矢量的 OMV 递增量矢量矢量矢量矢量矢量。此外,为了利用在多个时段中将大型访问信号连通性增强的结构性宽度,我们开发了两种计算高效的检测算法,即随机近似传递信息(OAMMP)-MV算盘算法,以及精确结构学习(SS),为了实现准确的检测,正在利用最大值共算法来学习OSLSL的螺旋流流流流流流流流流流流流率率率,在OSL进行不断的检测活动,从而改进了OSICA的测试。