Electromagnetic Model of Reflective Intelligent Surfaces

Costa, Filippo; Borgese, Michele

doi:10.1109/ojcoms.2021.3092217

Cited by 50 publications

(41 citation statements)

References 50 publications

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“…Except C n [t], all of these parameters are dependent on the incident angle θ [t], which makes the reflection behavior of meta-atom angle-dependent. This phenomenon is also observed in [12], [14], [15].…”

Section: Fig 1(b) Denote θ [T]supporting

confidence: 62%

“…III-A. This dependency is observed and explained in [12]- [15], but not yet incorporated in the canonical signal model for IRS-assisted communications.…”

Section: Fig 1(b) Denote θ [T]mentioning

confidence: 96%

“…Motivated by this observation, the authors of [13] propose an angle-dependent reflection coefficient model for each IRS meta-atom using an equivalent circuit model. In parallel, the authors in [14], [15] also demonstrate the reflection response varies with the incident angle of the EM wave. To the best of our knowledge, the angle-dependent property of the IRS reflection coefficient has not been incorporated into uplink/downlink signal transmission models for wireless communication systems.…”

Section: Introductionmentioning

confidence: 94%

“…Through tuning the capacitance of the meta-atoms, their impedance can be adjusted. However, the impedance is also dependent on the incident angle of the incoming EM wave [15]. Both of these factors should be considered in IRS reflection behavior design.…”

Section: A Reflection Behavior Of Irs Meta-atomsmentioning

confidence: 99%

“…For the DNN Q-function, the input and output sizes are 20 and 1, respectively. We normalize the values for the state and action to match with the scale of the values, such that h eff (•) ← P/(σ 2 N BS N G ) × h eff (•) and q m ← 10 12 × q m in (14), and a m ← 10 13 × a m in(15).…”

mentioning

confidence: 99%

See 4 more Smart Citations

Learning-Based Adaptive IRS Control with Limited Feedback Codebooks

Kim¹,

Hosseinalipour²,

Marcum³

et al. 2021

Preprint

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Intelligent reflecting surfaces (IRS) consist of configurable meta-atoms, which can change the wireless propagation environment through design of their reflection coefficients. We consider a practical setting where (i) the IRS reflection coefficients are achieved by adjusting tunable elements embedded in the meta-atoms, (ii) the IRS reflection coefficients are affected by the incident angles of the incoming signals, (iii) the IRS is deployed in multi-path, time-varying channels, and (iv) the feedback link from the base station to the IRS has a low data rate. Conventional optimization-based IRS control protocols, which rely on channel estimation and conveying the optimized variables to the IRS, are not applicable in this setting due to the difficulty of channel estimation and the low feedback rate. Therefore, we develop a novel adaptive codebook-based limited feedback protocol where only a codeword index is transferred to the IRS. We propose two solutions for adaptive codebook design: random adjacency (RA) and deep neural network policy-based IRS control (DPIC), both of which only require the end-toend compound channels. We further develop several augmented schemes based on the RA and DPIC.Numerical evaluations show that the data rate and average data rate over one coherence time are improved substantially by our schemes.

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Section: Fig 1(b) Denote θ [T]supporting

confidence: 62%

“…III-A. This dependency is observed and explained in [12]- [15], but not yet incorporated in the canonical signal model for IRS-assisted communications.…”

Section: Fig 1(b) Denote θ [T]mentioning

confidence: 96%