Compressive Sensing-Based Joint Activity and Data Detection for Grant-Free Massive IoT Access

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 design random schemes, which invokes need efficient joint activity and data detection (JADD) algorithms. By exploiting feature sporadic traffic access, beacon-aided slotted grant-free solution is proposed. Specifically, we spread uplink signals multiple subcarriers with pre-equalization processing formulate JADD as measurement vectors (MMV) compressive sensing problem. Moreover, leverage structured sparsity among time slots, develop two computationally algorithms, termed orthogonal approximate message passing (OAMP)-MMV algorithm simplified structure learning (SSL) accurate (ASL). To achieve detection, expectation maximization exploited ratio noise variance. further improve performance, channel coding applied successive interference cancellation (SIC)-based OAMP-MMV-SSL OAMP-MMV-ASL algorithms developed, where likelihood obtained soft-decision can be refining identification. Finally, state evolution proposed derived predict performance theoretically. Simulation results verify that solutions outperform various state-of-the-art baseline enabling high-reliable IoT overloading.