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Intelligent Reflecting Surfaces and Next Generation Wireless Systems

Yashuai Cao, Hetong Wang, Tiejun Lv, Wei Ni

TL;DR

This chapter investigates the slot-by-slot IRS reflection pattern design and two-timescale reflection pattern design schemes, respectively, and presents a two-timescale reflection optimization scheme.

Abstract

Intelligent reflecting surface (IRS) is a potential candidate for massive multiple-input multiple-output (MIMO) 2.0 technology due to its low cost, ease of deployment, energy efficiency and extended coverage. This chapter investigates the slot-by-slot IRS reflection pattern design and two-timescale reflection pattern design schemes, respectively. For the slot-by-slot reflection optimization, we propose exploiting an IRS to improve the propagation channel rank in mmWave massive MIMO systems without need to increase the transmit power budget. Then, we analyze the impact of the distributed IRS on the channel rank. To further reduce the heavy overhead of channel training, channel state information (CSI) estimation, and feedback in time-varying MIMO channels, we present a two-timescale reflection optimization scheme, where the IRS is configured relatively infrequently based on statistical CSI (S-CSI) and the active beamformers and power allocation are updated based on quickly outdated instantaneous CSI (I-CSI) per slot. The achievable average sum-rate (AASR) of the system is maximized without excessive overhead of cascaded channel estimation. A recursive sampling particle swarm optimization (PSO) algorithm is developed to optimize the large-timescale IRS reflection pattern efficiently with reduced samplings of channel samples.

Intelligent Reflecting Surfaces and Next Generation Wireless Systems

TL;DR

This chapter investigates the slot-by-slot IRS reflection pattern design and two-timescale reflection pattern design schemes, respectively, and presents a two-timescale reflection optimization scheme.

Abstract

Intelligent reflecting surface (IRS) is a potential candidate for massive multiple-input multiple-output (MIMO) 2.0 technology due to its low cost, ease of deployment, energy efficiency and extended coverage. This chapter investigates the slot-by-slot IRS reflection pattern design and two-timescale reflection pattern design schemes, respectively. For the slot-by-slot reflection optimization, we propose exploiting an IRS to improve the propagation channel rank in mmWave massive MIMO systems without need to increase the transmit power budget. Then, we analyze the impact of the distributed IRS on the channel rank. To further reduce the heavy overhead of channel training, channel state information (CSI) estimation, and feedback in time-varying MIMO channels, we present a two-timescale reflection optimization scheme, where the IRS is configured relatively infrequently based on statistical CSI (S-CSI) and the active beamformers and power allocation are updated based on quickly outdated instantaneous CSI (I-CSI) per slot. The achievable average sum-rate (AASR) of the system is maximized without excessive overhead of cascaded channel estimation. A recursive sampling particle swarm optimization (PSO) algorithm is developed to optimize the large-timescale IRS reflection pattern efficiently with reduced samplings of channel samples.
Paper Structure (14 sections, 4 theorems, 65 equations, 13 figures, 2 tables, 3 algorithms)

This paper contains 14 sections, 4 theorems, 65 equations, 13 figures, 2 tables, 3 algorithms.

Table of Contents

  1. Intelligent Reflecting Surfaces and Next Generation Wireless Systems
  2. Introduction
  3. Related Work
  4. Intelligent Reflecting Surfaces Hardware and Architectures
  5. Intelligent Reflecting Surfaces and the Path Loss Model
  6. IRS-empowered Slot Scheduling and Cost-efficient Reflection Optimization
  7. Slot-by-slot Reflection Pattern Design
  8. Low-complexity Reflection Pattern Algorithm
  9. Two-timescale Reflection Pattern Design
  10. Temporally Correlated Channel and Two-timescale Transmission Strategy
  11. Reflection Pattern Design with Statistical CSI
  12. Results and Discussions
  13. Conclusion
  14. Future Work

Key Result

Theorem 10.3.1

Given the angle $\varphi_{n}^{\mathrm{i}/\mathrm{d}}$ which is the $n$-th entry of vector $\mathbf{a}_{\mathrm{i}/\mathrm{d}}$, the array gain of the IRS from the incident wave to the departure wave is equivalent to: The maximum array gain can be achieved by setting

Figures (13)

  • Figure 1: Comparison of power consumption between 4G and 5G BSs shurdi20215g.
  • Figure 2: IRS-enabled new functions: (a) modulate the pure carrier and transmit information symbols by IRS reflection; (b) overcome the signal blindspot to ensure robust transmissions; (c) IRS performs beamforming and provides desirable RF propagation properties for sensing; (d) realize the specified computation tasks by controlling the wave propagation.
  • Figure 3: Array geometry structure of IRS.
  • Figure 4: $N$-port single-connected reconfigurable impedance network, which explains the physical significance of the IRS reflection pattern.
  • Figure 5: Product-distance path loss model in IRS-assisted mmWave system.
  • ...and 8 more figures

Theorems & Definitions (8)

  • Theorem 10.3.1
  • proof
  • Theorem 10.4.1
  • proof
  • Lemma 10.5.1
  • proof
  • Proposition 10.5.1
  • proof