LAUSR

The conventional grant-based network relies on the handshaking between the base station and active devices to achieve dynamic multi-user scheduling, which may result in large signaling overheads as well as system latency. To address those problems, a grant-free receiver design is considered in this paper based on sparse code multiple access (SCMA), one of the promising air interface technologies for 5G wireless networks. With the presence of unknown multipath fading, the proposed receiver performs joint channel estimation and data decoding without knowing the user activity in the network. Formulating a factor graph representation for the problem, we devise a message-passing receiver for the uplink SCMA that performs joint estimation iteratively. Motivated by the idea of approximate inference, we use expectation propagation to project the intractable distributions into Gaussian families such that a linear complexity decoder is obtained. The simulation results show that the proposed receiver can detect active devices in the network with a high accuracy and can achieve an improved bit-error-rate performance compared with existing methods.