  Grant-free non-orthogonal multiple access (NOMA) is promising to achieve low
latency massive access in Internet of Things (IoT) applications. In grant-free
NOMA, pilot signals are often used for user activity detection (UAD) and
channel estimation (CE) prior to multiuser detection (MUD) of active users.
However, the pilot overhead makes the communications inefficient for IoT
devices with sporadic transmissions and short data packets, or when the channel
coherence time is short. Hence, it is desirable to improve the efficiency by
avoiding the use of pilot signals, which can also further achieve lower
latency. This work focuses on Bayesian receiver design for grant-free low
density signature orthogonal frequency division multiplexing (LDS-OFDM), where
each user is allocated a unique low density spreading sequence. We propose to
use the low density spreading sequences for active user detection, thereby
avoiding the use of pilot signals. Firstly, the task of joint UAD, CE and MUD
is formulated as a structured signal estimation problem. Then message passing
based Bayesian approach is developed to solve the structured signal estimation
problem. In particular, belief propagation (BP), expectation propagation (EP)
and mean field (MF) message passing are used to develop efficient hybrid
message passing algorithms to achieve trade-off between performance and
complexity. Simulation results demonstrate the effectiveness of the proposed
receiver for grant-free LDS-OFDM without the use of pilot signals.
