Paper Info
Title
Stochastic Transceiver Optimization In Multi-Tags Symbiotic Radio Systems
Abstract
Symbiotic radio (SR) is emerging as a spectrum-and energy-efficient communication paradigm for future passive Internet of Things (IoT), where some single-antenna backscatter devices, referred to as Tags, are parasitic in an active primary transmission. The primary transceiver is designed to assist both direct-link (DL) and backscatter-link (BL) communication. In multi-Tags SR systems, the transceiver designs become much more complicated due to the presence of DL and inter-Tag interference, which further poses new challenges to the availability and reliability of DL and BL transmission. To overcome these challenges, we formulate the stochastic optimization of transceiver design as the general network utility maximization problem (GUMP). The resultant problem is a stochastic multiple-ratio fractional nonconvex problem, and consequently challenging to solve. By leveraging some fractional programming techniques, we tailor a surrogate function with the specific structure and subsequently develop a batch stochastic parallel decomposition (BSPD) algorithm, which is shown to converge to stationary solutions of the GNUMP. The simulation results verify the effectiveness of the proposed algorithm by numerical examples in terms of the achieved system throughput.
Year
DOI
Venue
2020
10.1109/JIOT.2020.3003473
IEEE INTERNET OF THINGS JOURNAL
Keywords
DocType
Volume
Optimization, Interference, Transceivers, Backscatter, Receivers, Signal to noise ratio, Convergence, Batch stochastic parallel decomposition (BSPD) algorithm, fractional programming (FP), multi-Tags symbiotic radio (SR), stochastic transceiver optimization
Journal
7
Issue
ISSN
Citations 
9
2327-4662
0
PageRank 
References 
Authors
0.34
0
5
Name
Order
Citations
PageRank
Xi Chen133370.76
Hei Victor Cheng27010.01
Kaiming Shen301.69
An Liu450046.79
Minjian Zhao522434.77