Optimization-Based Phase-Shift Codebook Design for Large IRSs

In this letter, we focus on large intelligent reflecting surfaces (IRSs) and propose a new codebook construction method to obtain a set of pre-designed phase-shift configurations for the IRS unit cells. Since the complexity of online optimization and the overhead for channel estimation scale with the size of the phase-shift codebook, the design of small codebooks is of high importance. We consider both continuous and discrete phase-shift designs and formulate the codebook construction as optimization problems. To solve the optimization problems, we propose an optimal algorithm for the discrete phase-shift design and a locally optimal solution for the continuous design. Simulation results show that the proposed algorithms facilitate the construction of codebooks of different sizes and with different beamwidths. Moreover, the performance of the discrete phase-shift design with 2-bit quantization is shown to approach that of the continuous phase-shift design. Finally, our simulation results show that the proposed designs enable large transmit power savings compared to the existing linear and quadratic codebook designs.

[1]  G. C. Alexandropoulos,et al.  Arbitrary Beam Pattern Approximation via RISs with Measured Element Responses , 2022, 2022 Joint European Conference on Networks and Communications & 6G Summit (EuCNC/6G Summit).

[2]  H. Poor,et al.  Power Efficiency, Overhead, and Complexity Tradeoff of IRS Codebook Design—Quadratic Phase-Shift Profile , 2021, IEEE Communications Letters.

[3]  Derrick Wing Kwan Ng,et al.  Optimal Resource Allocation Design for Large IRS-Assisted SWIPT Systems: A Scalable Optimization Framework , 2021, IEEE Transactions on Communications.

[4]  Derrick Wing Kwan Ng,et al.  Power-Efficient Resource Allocation for Multiuser MISO Systems via Intelligent Reflecting Surfaces , 2020, GLOBECOM 2020 - 2020 IEEE Global Communications Conference.

[5]  H. Vincent Poor,et al.  Physics-Based Modeling and Scalable Optimization of Large Intelligent Reflecting Surfaces , 2020, IEEE Transactions on Communications.

[6]  Rui Zhang,et al.  Intelligent Reflecting Surface Assisted Multi-User OFDMA: Channel Estimation and Training Design , 2020, IEEE Transactions on Wireless Communications.

[7]  Derrick Wing Kwan Ng,et al.  Robust and Secure Wireless Communications via Intelligent Reflecting Surfaces , 2019, IEEE Journal on Selected Areas in Communications.

[8]  Zhu Han,et al.  Hybrid Beamforming for Reconfigurable Intelligent Surface based Multi-User Communications: Achievable Rates With Limited Discrete Phase Shifts , 2019, IEEE Journal on Selected Areas in Communications.

[9]  Jie Xu,et al.  Joint Transmit and Reflective Beamforming Design for IRS-Assisted Multiuser MISO SWIPT Systems , 2019, ICC 2020 - 2020 IEEE International Conference on Communications (ICC).

[10]  Rui Zhang,et al.  Intelligent Reflecting Surface-Enhanced OFDM: Channel Estimation and Reflection Optimization , 2019, IEEE Wireless Communications Letters.

[11]  Qingqing Wu,et al.  Beamforming Optimization for Wireless Network Aided by Intelligent Reflecting Surface With Discrete Phase Shifts , 2019, IEEE Transactions on Communications.

[12]  Chau Yuen,et al.  Reconfigurable Intelligent Surfaces for Energy Efficiency in Wireless Communication , 2018, IEEE Transactions on Wireless Communications.

[13]  Qingqing Wu,et al.  Intelligent Reflecting Surface Enhanced Wireless Network via Joint Active and Passive Beamforming , 2018, IEEE Transactions on Wireless Communications.

[14]  Le Thi Hoai An,et al.  Exact penalty and error bounds in DC programming , 2012, J. Glob. Optim..

[15]  G. C. Alexandropoulos,et al.  An Idea Whose Time Has Come , 1997, International Society of Hair Restoration Surgery.

[16]  R. Horst,et al.  Global Optimization: Deterministic Approaches , 1992 .