Deep Learning-Based Active User Detection for Grant-free SCMA Systems
This addresses latency and overhead reduction in massive machine-type communication, but it is incremental as it builds on existing grant-free and NOMA approaches.
The paper tackled the problem of active user detection in grant-free SCMA systems for mMTC by proposing two group-based deep neural network schemes, which achieved more than twice the probability of detection compared to conventional methods over a range of signal-to-noise ratios.
Grant-free random access and uplink non-orthogonal multiple access (NOMA) have been introduced to reduce transmission latency and signaling overhead in massive machine-type communication (mMTC). In this paper, we propose two novel group-based deep neural network active user detection (AUD) schemes for the grant-free sparse code multiple access (SCMA) system in mMTC uplink framework. The proposed AUD schemes learn the nonlinear mapping, i.e., multi-dimensional codebook structure and the channel characteristic. This is accomplished through the received signal which incorporates the sparse structure of device activity with the training dataset. Moreover, the offline pre-trained model is able to detect the active devices without any channel state information and prior knowledge of the device sparsity level. Simulation results show that with several active devices, the proposed schemes obtain more than twice the probability of detection compared to the conventional AUD schemes over the signal to noise ratio range of interest.