Multi-Tag Selection in Cognitive Ambient Backscatter Communications for Next-Generation IoT Networks

Abstract

In this paper, we propose multi-tag selection schemes to improve achievable rate of a secondary ambient backscatter communication (ABC) system with multiple tags and a single power beacon in cognitive radio (CR) environments. Using the secondary power beacon, which plays a role as radio frequency (RF) source when there is no ambient RF source signals, with spectrum sensing capability, the secondary ABC system determines operation modes according to activity of the licensed primary system. When the primary system is active, a tag is randomly selected due to a lack of channel information, while when it is inactive, the best tag maximizing achievable rate is selected using the secondary power beacon. Moreover, we apply a successive interference cancellation (SIC) technique when the secondary power beacon is able to work. The performance of the proposed multi-tag selection schemes is mathematically analyzed in terms of bit error rate (BER) and achievable rate. Finally, the performance of the proposed schemes is evaluated through simulations in terms of BER and achievable rate. It is shown that our approach can significantly outperform a conventional ABC system in CR environments, especially under a low-active primary system regime.