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Intelligent Reflecting Surface Aided MIMO Networks: Distributed or Centralized Architecture?

Guangji Chen, Qingqing Wu, Wen Chen, Yanzhao Hou, Mengnan Jian, Shunqing Zhang, Jun Li

TL;DR

This paper investigates the capacity of a broadcast channel with a multi-antenna base station (BS) sending independent messages to multiple users, aided by IRSs with N elements, and derives the maximum capacity achieved by the distributed and centralized IRS under the assumption of line-of-sight (LoS) propagation and homogeneous channel setups.

Abstract

We investigate the capacity of a broadcast channel with a multi-antenna base station (BS) sending independent messages to multiple users, aided by IRSs with N elements. In particular, both the distributed and centralized IRS deployment architectures are considered. Regarding the distributed IRS, the N IRS elements form multiple IRSs and each of them is installed near a user cluster; while for the centralized IRS, all IRS elements are located in the vicinity of the BS. To draw essential insights, we first derive the maximum capacity achieved by the distributed IRS and centralized IRS, respectively, under the assumption of line-of-sight propagation and homogeneous channel setups. By capturing the fundamental tradeoff between the spatial multiplexing gain and passive beamforming gain, we rigourously prove that the capacity of the distributed IRS is higher than that of the centralized IRS provided that the total number of IRS elements is above a threshold. Motivated by the superiority of the distributed IRS, we then focus on the transmission and element allocation design under the distributed IRS. By exploiting the user channel correlation of intra-clusters and inter-clusters, an efficient hybrid multiple access scheme relying on both spatial and time domains is proposed to fully exploit both the passive beamforming gain and spatial DoF. Moreover, the IRS element allocation problem is investigated for the objectives of sum-rate maximization and minimum user rate maximization, respectively. Finally, extensive numerical results are provided to validate our theoretical finding and also to unveil the effectiveness of the distributed IRS for improving the system capacity under various system setups.

Intelligent Reflecting Surface Aided MIMO Networks: Distributed or Centralized Architecture?

TL;DR

This paper investigates the capacity of a broadcast channel with a multi-antenna base station (BS) sending independent messages to multiple users, aided by IRSs with N elements, and derives the maximum capacity achieved by the distributed and centralized IRS under the assumption of line-of-sight (LoS) propagation and homogeneous channel setups.

Abstract

We investigate the capacity of a broadcast channel with a multi-antenna base station (BS) sending independent messages to multiple users, aided by IRSs with N elements. In particular, both the distributed and centralized IRS deployment architectures are considered. Regarding the distributed IRS, the N IRS elements form multiple IRSs and each of them is installed near a user cluster; while for the centralized IRS, all IRS elements are located in the vicinity of the BS. To draw essential insights, we first derive the maximum capacity achieved by the distributed IRS and centralized IRS, respectively, under the assumption of line-of-sight propagation and homogeneous channel setups. By capturing the fundamental tradeoff between the spatial multiplexing gain and passive beamforming gain, we rigourously prove that the capacity of the distributed IRS is higher than that of the centralized IRS provided that the total number of IRS elements is above a threshold. Motivated by the superiority of the distributed IRS, we then focus on the transmission and element allocation design under the distributed IRS. By exploiting the user channel correlation of intra-clusters and inter-clusters, an efficient hybrid multiple access scheme relying on both spatial and time domains is proposed to fully exploit both the passive beamforming gain and spatial DoF. Moreover, the IRS element allocation problem is investigated for the objectives of sum-rate maximization and minimum user rate maximization, respectively. Finally, extensive numerical results are provided to validate our theoretical finding and also to unveil the effectiveness of the distributed IRS for improving the system capacity under various system setups.
Paper Structure (21 sections, 8 theorems, 90 equations, 9 figures)

This paper contains 21 sections, 8 theorems, 90 equations, 9 figures.

Table of Contents

  1. Introduction
  2. System Model
  3. Distributed IRS
  4. Centralized IRS
  5. Distributed IRS Versus Centralized IRS
  6. Theoretical Performance Comparison
  7. Capacity Characterization for Distributed IRS
  8. Capacity Characterization for Centralized IRS
  9. Distributed IRS versus Centralized IRS
  10. Numerical Results
  11. Transmission and Element Allocation design for distributed IRS
  12. Hybrid SDMA-TDMA Scheme
  13. IRS Element Allocation Design
  14. Minimum User Rate Maximization
  15. Sum-rate Maximization
  16. ...and 6 more sections

Key Result

Proposition 1

Under the condition that ${{\cal C}^{\rm{D}}}$ is given by under the constraint of $\sum\nolimits_{k = 1}^K {{p_k} = {P_{\max }}}$, where Accordingly, the maximum sum-rate of the $K$ users is obtained as which is achieved by

Figures (9)

  • Figure 1: An IRS-aided multi-user communication system with different IRS deployment strategies..
  • Figure 2: Sum-rate versus ${P_{\max }}$ with $N=200$.
  • Figure 3: Sum-rate versus $N$.
  • Figure 4: $N$ required for ensuring distributed IRS to outperform centralized IRS.
  • Figure 5: Minimum user rate versus $N$ under the heterogeneous channel setup.
  • ...and 4 more figures

Theorems & Definitions (19)

  • Proposition 1
  • proof 1
  • Proposition 2
  • proof 2
  • Theorem 1
  • proof 3
  • Theorem 2
  • proof 4
  • Remark 1
  • Remark 2
  • ...and 9 more