A Joint Optimization Framework for IRS-Assisted Energy Self-Sustainable IoT Networks

Xie, Xie; Luan, Huixu; Dong, Yangrui; Yang, Kun; Gao, Feifei; Wang, Z. Jane

doi:10.1109/jiot.2022.3142850

Cited by 22 publications

(10 citation statements)

References 44 publications

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“…Next, we focus on the second term in (10). Due to the concavity of the logarithm function ln(u) ≤ ln (u 0 ) + u u0 − 1, we have [45], [46] − ln (1 + z l ) ≥ − ln 1 + z t l −…”

Section: A Lower Bound On the Objectivementioning

confidence: 99%

“…Furthermore, the authors of [8] introduced energy efficiency (EE) optimization in RIS-assisted systems. Nowadays, RIS-assisted transmission has been investigated in various scenarios such as IoT networks [9], [10], simultaneous wireless information and power transfer (SWIPT) [11], nonorthogonal multiple access (NOMA) [12], hybrid terrestrialaerial networks [13], [14], anti-jamming communications [15], [16], physical layer security communications [17], [18], and cognitive radio networks [19].…”

Section: Introductionmentioning

confidence: 99%

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Joint Beamforming Design for Secure RIS-Assisted IoT Networks

Niu

Lin

Chu

et al. 2023

IEEE Internet Things J.

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“…Next, we focus on the second term in (10). Due to the concavity of the logarithm function ln(u) ≤ ln (u 0 ) + u u0 − 1, we have [45], [46] − ln (1 + z l ) ≥ − ln 1 + z t l −…”

Section: A Lower Bound On the Objectivementioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Joint Beamforming Design for Secure RIS-Assisted IoT Networks

Niu

Lin

Chu

et al. 2023

IEEE Internet Things J.

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show abstract

“…Theorem 1. The general expression of 𝛾-order IRS network channel H 𝐼,𝛾 can be written as (16) where X 𝛾 ∈ N + 𝐾 𝑃 𝛾 ×𝛾 is the index matrix of 𝛾 orders.…”

Section: The Mumor Network Channelmentioning

confidence: 99%

“…Single IRS assisted communication systems have been considered in many works from different aspects, including EE maximization and weighted sum rate maximization [15][16][17][18]. An IRS network, which is defined as deploying multipiece IRS in the transmission environment, has been studied to further enhance the EE and SE.…”

Section: Introductionmentioning

confidence: 99%

Intelligent Reflecting Surface Networks With Multiorder-Reflection Effect: System Modeling and Critical Bounds

Liu

Zhang

Gao

et al. 2022

IEEE Trans. Commun.

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In this paper, we model, analyze and optimize the multi-user and multi-order-reflection (MUMOR) intelligent reflecting surface (IRS) networks. We first derive a complete MUMOR IRS network model applicable for the arbitrary times of reflections, size and number of IRSs/reflectors. The optimal condition for achieving sum rate upper bound with one IRS in a closed-form function and the analytical condition to achieve interference-free transmission are derived, respectively. Leveraging this optimal condition, we obtain the MUMOR sum rate upper bound of the IRS network with different network topologies, where the linear graph (LG), complete graph (CG) and null graph (NG) topologies are considered. Simulation results verify our theories and derivations and demonstrate that the sum rate upper bounds of different network topologies are under a 𝐾-fold improvement given 𝐾-piece IRS.

show abstract

“…A single IRS assisted communication systems have been considered in many works from different aspects, including EE maximization and weighted sum-rate maximization [15][16][17]. An IRS network, which is defined as deploying multipiece IRS in the transmission environment, has been studied to further enhance the EE and SE.…”

Section: Introductionmentioning

confidence: 99%

Intelligent Reflecting Surface Networks with Multi-Order-Reflection Effect: System Modelling and Critical Bounds

Liu¹,

Zhang²,

Gao³

et al. 2022

Preprint

Self Cite

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In this paper, we model, analyze and optimize the multi-user and multi-order-reflection (MUMOR) intelligent reflecting surface (IRS) networks. We first derive a complete MUMOR IRS network model that is applicable for arbitrary times of reflections, arbitrary size and number of IRSs/reflectors. The optimal condition for achieving sum-rate upper bound with one IRS in a closed-form function and the analytical condition to achieve interference-free transmission, are derived respectively. Leveraging this optimal condition, we obtain the MUMOR sum-rate upper bound of IRS network with different network topology, where the linear graph (LG), complete graph (CG) and null graph (NG) topologies are considered. Simulation results verify our theories and derivations and demonstrate that the sum-rate upper bounds of different network topologies is under a 𝐾-fold improvement given 𝐾-piece IRS.

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A Joint Optimization Framework for IRS-Assisted Energy Self-Sustainable IoT Networks

Cited by 22 publications

References 44 publications

Joint Beamforming Design for Secure RIS-Assisted IoT Networks

Joint Beamforming Design for Secure RIS-Assisted IoT Networks

Intelligent Reflecting Surface Networks With Multiorder-Reflection Effect: System Modeling and Critical Bounds

Intelligent Reflecting Surface Networks with Multi-Order-Reflection Effect: System Modelling and Critical Bounds

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