Double-Active-IRS Aided Wireless Communication: Deployment Optimization and Capacity Scaling
In this letter, we consider a double-active-intelligent reflecting surface (IRS) aided wireless communication system, where two active IRSs are properly deployed to assist the communication from a base station (BS) to multiple users located in a given zone via the double-reflection links. Under the...
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Published in | IEEE wireless communications letters Vol. 12; no. 11; p. 1 |
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Main Authors | , , |
Format | Journal Article |
Language | English |
Published |
Piscataway
IEEE
01.11.2023
The Institute of Electrical and Electronics Engineers, Inc. (IEEE) |
Subjects | |
Online Access | Get full text |
ISSN | 2162-2337 2162-2345 |
DOI | 10.1109/LWC.2023.3294258 |
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Abstract | In this letter, we consider a double-active-intelligent reflecting surface (IRS) aided wireless communication system, where two active IRSs are properly deployed to assist the communication from a base station (BS) to multiple users located in a given zone via the double-reflection links. Under the assumption of fixed per-element amplification power for each active-IRS element, we formulate a rate maximization problem subject to practical constraints on the reflection design, elements allocation, and placement of active IRSs. To solve this non-convex problem, we first obtain the optimal active-IRS reflections and BS beamforming, based on which we then jointly optimize the active-IRS elements allocation and placement by using the alternating optimization (AO) method. Moreover, we show that given the fixed per-element amplification power, the received signal-to-noise ratio (SNR) at the user increases asymptotically with the square of the number of reflecting elements; while given the fixed number of reflecting elements, the SNR does not increase with the per-element amplification power when it is asymptotically large. Last, numerical results are presented to validate the effectiveness of the proposed AO-based algorithm and compare the rate performance of the considered double-active-IRS aided wireless system with various benchmark systems. |
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AbstractList | In this letter, we consider a double-active-intelligent reflecting surface (IRS) aided wireless communication system, where two active IRSs are properly deployed to assist the communication from a base station (BS) to multiple users located in a given zone via the double-reflection links. Under the assumption of fixed per-element amplification power for each active-IRS element, we formulate a rate maximization problem subject to practical constraints on the reflection design, elements allocation, and placement of active IRSs. To solve this non-convex problem, we first obtain the optimal active-IRS reflections and BS beamforming, based on which we then jointly optimize the active-IRS elements allocation and placement by using the alternating optimization (AO) method. Moreover, we show that given the fixed per-element amplification power, the received signal-to-noise ratio (SNR) at the user increases asymptotically with the square of the number of reflecting elements; while given the fixed number of reflecting elements, the SNR does not increase with the per-element amplification power when it is asymptotically large. Last, numerical results are presented to validate the effectiveness of the proposed AO-based algorithm and compare the rate performance of the considered double-active-IRS aided wireless system with various benchmark systems. In this letter, we consider a double-active-intelligent reflecting surface (IRS) aided wireless communication system, where two active IRSs assist the communication from a base station (BS) to multiple users via double-reflection links. Under the assumption of fixed per-element amplification power for each active-IRS element, we formulate a rate maximization problem subject to practical constraints on the reflection design, elements allocation, and placement of active IRSs. To solve this non-convex problem, we first obtain the optimal active-IRS reflections and BS beamforming, based on which we then jointly optimize the active-IRS elements allocation and placement by using the alternating optimization (AO) method. Moreover, we show that given the fixed per-element amplification power, the received signal-to-noise ratio (SNR) at the user increases asymptotically with the square of the number of reflecting elements; while given the fixed number of reflecting elements, the SNR does not increase with the per-element amplification power when it is asymptotically large. Finally, numerical results validate the proposed algorithm and compare the rate performance to benchmark systems. |
Author | Kang, Zhenyu You, Changsheng Zhang, Rui |
Author_xml | – sequence: 1 givenname: Zhenyu orcidid: 0000-0002-1476-5210 surname: Kang fullname: Kang, Zhenyu organization: Department of Electrical and Computer Engineering, National University of Singapore, Singapore, Singapore – sequence: 2 givenname: Changsheng orcidid: 0000-0003-3245-9361 surname: You fullname: You, Changsheng organization: Department of Electronic and Electrical Engineering, Southern University of Science and Technology (SUSTech), Shenzhen, China – sequence: 3 givenname: Rui orcidid: 0000-0002-8729-8393 surname: Zhang fullname: Zhang, Rui organization: Department of Electrical and Computer Engineering, National University of Singapore, Singapore, Singapore |
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Snippet | In this letter, we consider a double-active-intelligent reflecting surface (IRS) aided wireless communication system, where two active IRSs are properly... In this letter, we consider a double-active-intelligent reflecting surface (IRS) aided wireless communication system, where two active IRSs assist the... |
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SubjectTerms | active IRS Algorithms Amplification Array signal processing Asymptotic properties Azimuth Beamforming capacity scaling order double IRS Intelligent reflecting surface (IRS) Optimization Placement Reflection Resource management Signal to noise ratio Wireless communication Wireless communication systems Wireless communications |
Title | Double-Active-IRS Aided Wireless Communication: Deployment Optimization and Capacity Scaling |
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