Rate-Adaptive Multiple Access for Uplink Grant-Free Transmission - Hou, Xiaolin; Liu, Wenjia; Li, Xiangming; Wang, Aihua; Ye, Neng; Yu, Hanxiao (Wireless communications and mobile computing, 2018)
Grant-free transmission, which simplifies the signaling procedure via uplink instant transmission, has been recognized as a promising multiple access protocol to address the massive connectivity and low latency requirements for future machine type communications. The major drawback of grant-free transmission is that the contaminations among uncoordinated transmissions can reduce the data throughput and deteriorate the outage performance. In this paper, we propose a rate-adaptive multiple access (RAMA) scheme to tackle the collision problems caused by the grant-free transmission. Different from the conventional grant-free (conv-GF) scheme which transmits a single signal layer, RAMA transmits the signals with a multilayered structure, where different layers exhibit unequal protection property. At the receiver, the intra- and interuser successive interference cancellation (SIC) receiving algorithm is employed to detect multiple data streams. In RAMA, the users can achieve rate adaptation without the prior knowledge of the channel conditions, since the layers with high protection property can be successfully recovered when the interference is severe, while other layers can take advantage of the channel when the interference is less significant. Besides, RAMA also facilitates the SIC receiving since the multiple layers in the transmission signals can provide more opportunities for interference cancellation. To evaluate the system performance, we analyze the exact expressions of the throughout and the outage probability of both conv-GF and RAMA. Finally, theoretical analysis and simulation results validate that the proposed RAMA scheme can simultaneously achieve higher average throughput and lower outage performance than conv-GF. Meanwhile, RAMA shows its robustness with large user activation probability, where the collisions among users are severe.