Joint Transceiver Design Based on Dictionary Learning Algorithm for SCMA

TL;DR

This paper introduces a novel grant-free access protocol for massive connectivity in cellular networks, combining sparse code multiple access with dictionary learning for efficient receiver processing, reducing scheduling overhead.

Contribution

It proposes a new joint transceiver design using dictionary learning for SCMA, enabling massive connectivity with minimal scheduling overhead.

Findings

Promising performance in massive connectivity scenarios

Reduced scheduling overhead compared to existing schemes

Effective receiver processing with dictionary learning

Abstract

With the explosively increasing demands on the network capacity, throughput and number of connected wireless devices, massive connectivity is an urgent problem for the next generation wireless communications. In this paper, we propose a grant-free access protocol for massive connectivity that utilizes a large number of antennas in a base station (BS) and is expected to be widely deployed in cellular networks. The scheme consists of a sparse structure in sparse code multiple access (SCMA) and receiver processing based on dictionary learning (DL). A large number of devices can transmit data without any scheduling process. Unlike existing schemes, whose signal schedulings require a lot of overhead, the scheduling overhead required by the proposed scheme is negligible, which is attractive for resource utilization and transmission power efficiency. The numerical results show that the proposed scheme has promising performance in massive connectivity scenario of cellular networks.