Energy Efficiency Maximization for a Relay Assisted Parasitic Symbiotic Radio Network

ABSTRACT Relay‐assisted symbiotic radio (SR) has been recently proposed to overcome the blocking of the direct link from the primary transmitter (PT) or backscatter node (BN) to the destination node (DN). However, the energy efficiency (EE), which is an important performance metric for SR networks, has been largely ignored in existing studies of the relay‐assisted SR. To fill the gap, this work maximizes the EE of a relay‐assisted parasitic SR network, which comprises a PT, a BN, a relay node (RN), and a DN. More specifically, we formulate a mixed‐integer programming optimization problem that maximizes the system EE by jointly optimizing the transmit power of the PT, the power reflection coefficient of the BN, the transmit power and the power allocation ratio at the RN as well as the successive interference cancellation (SIC) decoding order at the DN. We decompose the formulated non‐convex problem into two subproblems corresponding to the two different SIC decoding orders, respectively. For each subproblem, we convert its objective function from a fractional form into a subtractive form by using a Dinkelbach‐based method, and then utilize the block coordinate descent (BCD) method to further decouple it into two subsubproblems that are proved to be convex. Based on the obtained solutions, we devise an iterative algorithm to solve each subproblem by solving its two subsubproblems alternately. The optimal solution to the subproblem with a higher system EE returns a near‐optimal solution to the original problem. Simulation results demonstrate the rapid convergence of the proposed algorithms and validate the significant advantages of our proposed algorithms over the baseline schemes.

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Publication Details

Journal
IET Communications
Published
2025-01-01
DOI
https://doi.org/10.1049/cmu2.70058
Primary Topic
Advanced MIMO Systems Optimization
Type
article
Field-Weighted Citation Impact
0.00

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article

Energy Efficiency Maximization for a Relay Assisted Parasitic Symbiotic Radio Network

Xiaoli Chu, Liqin Shi, Yinghui Ye, Dongsheng Han et al.
IET Communications
Advanced MIMO Systems Optimization
article

Energy Efficiency Maximization for a Relay Assisted Parasitic Symbiotic Radio Network

Xiaoli Chu, Liqin Shi, Yinghui Ye, Dongsheng Han, Xi Song
article en

Abstract

ABSTRACT Relay‐assisted symbiotic radio (SR) has been recently proposed to overcome the blocking of the direct link from the primary transmitter (PT) or backscatter node (BN) to the destination node (DN). However, the energy efficiency (EE), which is an important performance metric for SR networks, has been largely ignored in existing studies of the relay‐assisted SR. To fill the gap, this work maximizes the EE of a relay‐assisted parasitic SR network, which comprises a PT, a BN, a relay node (RN), and a DN. More specifically, we formulate a mixed‐integer programming optimization problem that maximizes the system EE by jointly optimizing the transmit power of the PT, the power reflection coefficient of the BN, the transmit power and the power allocation ratio at the RN as well as the successive interference cancellation (SIC) decoding order at the DN. We decompose the formulated non‐convex problem into two subproblems corresponding to the two different SIC decoding orders, respectively. For each subproblem, we convert its objective function from a fractional form into a subtractive form by using a Dinkelbach‐based method, and then utilize the block coordinate descent (BCD) method to further decouple it into two subsubproblems that are proved to be convex. Based on the obtained solutions, we devise an iterative algorithm to solve each subproblem by solving its two subsubproblems alternately. The optimal solution to the subproblem with a higher system EE returns a near‐optimal solution to the original problem. Simulation results demonstrate the rapid convergence of the proposed algorithms and validate the significant advantages of our proposed algorithms over the baseline schemes.

IET CommunicationsVol. 19(1)
North China Electric Power University (CN), Xi’an University of Posts and Telecommunications (CN), University of Sheffield (GB)
National Natural Science Foundation of China
Openalex Percentile: Top 88%
Advanced MIMO Systems Optimization
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