This paper considers an uplink massive machine-type communication (mMTC)
scenario, where a large number of user devices are connected to a base station
(BS). A novel grant-free massive random access (MRA) strategy is proposed,
considering both the sporadic user traffic and short packet features.
Specifically, the notions of active detection time (ADT) and active detection
period (ADP) are introduced so that active user detection can be performed
multiple times within one coherence time. By taking sporadic user traffic and
short packet features into consideration, we model the joint active user
detection and channel estimation issue into a dynamic compressive sensing (CS)
problem with the underlying sparse signals exhibiting substantial temporal
correlation. This paper builds a probabilistic model to capture the temporal
structure and establishes a corresponding factor graph. A novel sequential
approximate message passing (S-AMP) algorithm is designed to sequentially
perform inference and recover sparse signal from one ADT to the next. The Bayes
active user detector and the corresponding channel estimator are then derived.
Numerical results show that the proposed S-AMP algorithm enhances active user
detection and channel estimation performances over competing algorithms under
our scenario.

本文提出了一种新的基于压缩感知和序列近似传递算法的大规模机器型通信系统的无授权随机接入策略，以提高活动用户检测和信道估计的性能。

通过顺序信息传递联合进行活动用户检测和信道估计实现大规模随机接入与零散短数据包

Massive Random Access with Sporadic Short Packets: Joint Active User  Detection and Channel Estimation via Sequential Message Passing

Massive machine-type communication (MTC) with sporadically transmitted small
packets and low data rate requires new designs on the PHY and MAC layer with
light transmission overhead. Compressive sensing based multiuser detection
(CS-MUD) is designed to detect active users through random access with low
overhead by exploiting sparsity, i.e., the nature of sporadic transmissions in
MTC. However, the high computational complexity of conventional sparse
reconstruction algorithms prohibits the implementation of CS-MUD in real
communication systems. To overcome this drawback, in this paper, we propose a
fast Deep learning based approach for CS-MUD in massive MTC systems. In
particular, a novel block restrictive activation nonlinear unit, is proposed to
capture the block sparse structure in wide-band wireless communication systems
(or multi-antenna systems). Our simulation results show that the proposed
approach outperforms various existing algorithms for CS-MUD and allows for
ten-fold decrease of the computing time.

本文提出了一种基于快速深度学习的块限制性激活非线性单元的方法来解决压缩感知多用户检测算法在实际通信系统中计算复杂度高的问题，并在大规模机器类型通信系统中进行了模拟实验，表明该方法在计算时间上减少了十倍，同时优于各种现有的压缩感知多用户检测算法。