Signal Scrambling Based Joint Blind Channel Estimation, Activity Detection, and Decoding for Massive Random Access

A signal scrambling based joint blind channel estimation, activity detection, and data decoding (SS-JCAD) scheme is proposed for coded massive random access. This signal scrambling technique imposes symbol-wise phase rotation to each user's modulated data, and the scrambling pattern serves as a user-specific signature which is free from any bandwidth expansion or pilot signaling overhead. Building on this scrambling signature, we further propose a simple yet efficient receiver design, which integrates the blind channel state information (CSI) estimation module with the forward error correction (FEC) decoder. Specifically, according to the scrambling signature, a user-specific posterior probability density function of the CSI is derived, based on which both the CSI and activity of each user can be blindly detected using a low-complexity single-user maximum a posteriori estimation. Given the estimated CSI as a priori information, a joint CSI (including user activity) estimation and data decoding algorithm is proposed, where the soft information is iteratively updated between the FEC decoder and the CSI estimation module to refine the detection reliability. Simulation shows that for massive random access systems with moderate code length and system load factor less than 1.5, the SS-JCAD scheme achieves almost the same bit error rate as the ideal case aided with perfect CSI, implying the SS-JCAD scheme as a near-optimal solution to the massive random access scenario.