MUSA Grant-Free Access Framework and Blind Detection Receiver

Abstract

Recently, a non-orthogonal multiple access scheme called multi-user shared access (MUSA) was proposed to provide massive connection capability of low-complexity devices in the 5G networks. MUSA achieves higher spectral efficiency allowing independent devices to transmit data on the same physical layer time-frequency resources. Furthermore, MUSA introduces a grant-free transmission and a blind multi-user detection at the receiver, reducing the complexity on the transmit side. This approach is interesting for Internet of Things applications over mobile communication networks, where the devices have limited power and processing capacity. The references available in the literature about this multiple access scheme do not bring sufficient details about the MUSA multi-user detector. This limitation makes it difficult to evaluate the MUSA performance and to propose improvements for this new technique. The main goal of this paper is to provide a framework describing the entire communication chain using MUSA as multiple access. This paper also brings a proposal for a blind multi-user detection, where the information about the MUSA parameters and the channel state information are unknown at the receiver side. The performance of the MUSA multi-user detector is improved by a deep learning based processing that improves the quality of the channel estimation provided by a initial minimum mean square error estimator. The proposed deep neural network architecture employed to improve the channel estimation allows more users to share the same time-frequency resources for a given target block error rate, increasing the overall spectrum efficiency of the system.