  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.
