Logo
ScienceStack
Table of Contents
Fetching ...
Sign In

Metasurface-Based Receivers with $1$-bit ADCs for Multi-User Uplink Communications

Panagiotis Gavriilidis, Italo Atzeni, George C. Alexandropoulos

TL;DR

This work tackles the power and cost challenges of massive MIMO by introducing metasurface-based receivers with $1$-bit ADCs for multi-user uplink. It develops a Bussgang-based analytical framework to jointly optimize the analog and digital combiners, providing closed-form SINDR and rate expressions and a practical design that outperforms fully digital systems at moderate SNRs. The proposed approach includes a precise digital combiner via a generalized eigenvalue solution and a rate-maximization based analog combiner with SDP relaxation and a low-complexity eigenvector solution, augmented by large-$K$ approximations to decouple the quantization effects. Numerical results reveal substantial energy efficiency gains and show that increasing metamaterial density per microstrip can compensate for 1-bit quantization, supporting scalable, low-power uplink communications with metasurface-based transceivers.

Abstract

The massive Multiple-Input Multiple-Output (mMIMO) concept has been recently moving forward to extreme scales to address the envisioned requirements of next generation networks. However, the extension of conventional architectures will result in significant cost and power consumption. To this end, metasurface-based transceivers, consisting of microstrips of metamaterials, have recently emerged as an efficient enabler of extreme mMIMO systems. In this paper, we consider metasurface-based receivers with a $1$-bit Analog-to-Digital Converter (ADC) per microstrip and develop an analytical framework for the optimization of the analog and digital combining matrices. Our numerical results, including comparisons with fully digital, infinite-resolution MIMO, provide useful insights into the role of various system parameters.

Metasurface-Based Receivers with $1$-bit ADCs for Multi-User Uplink Communications

TL;DR

This work tackles the power and cost challenges of massive MIMO by introducing metasurface-based receivers with -bit ADCs for multi-user uplink. It develops a Bussgang-based analytical framework to jointly optimize the analog and digital combiners, providing closed-form SINDR and rate expressions and a practical design that outperforms fully digital systems at moderate SNRs. The proposed approach includes a precise digital combiner via a generalized eigenvalue solution and a rate-maximization based analog combiner with SDP relaxation and a low-complexity eigenvector solution, augmented by large- approximations to decouple the quantization effects. Numerical results reveal substantial energy efficiency gains and show that increasing metamaterial density per microstrip can compensate for 1-bit quantization, supporting scalable, low-power uplink communications with metasurface-based transceivers.

Abstract

The massive Multiple-Input Multiple-Output (mMIMO) concept has been recently moving forward to extreme scales to address the envisioned requirements of next generation networks. However, the extension of conventional architectures will result in significant cost and power consumption. To this end, metasurface-based transceivers, consisting of microstrips of metamaterials, have recently emerged as an efficient enabler of extreme mMIMO systems. In this paper, we consider metasurface-based receivers with a -bit Analog-to-Digital Converter (ADC) per microstrip and develop an analytical framework for the optimization of the analog and digital combining matrices. Our numerical results, including comparisons with fully digital, infinite-resolution MIMO, provide useful insights into the role of various system parameters.
Paper Structure (8 sections, 1 theorem, 19 equations, 1 figure)

This paper contains 8 sections, 1 theorem, 19 equations, 1 figure.

Table of Contents

  1. Introduction
  2. System model
  3. Proposed RX DMA Design
  4. Design of the Digital Combiner $\mathbf{W}$
  5. Design of the Analog Combiner $\mathbf{Q}$
  6. Approximation of $\mathbf{G}$ and $\mathbf{C_d}$ for Large $K$
  7. Numerical Results and Discussion
  8. Conclusion

Key Result

Proposition 1

For $K \to \infty$, we have that $\frac{1}{K} \mathbf{[HH}^{\mathrm{H}}]_{n,n}\to 1$ and $\left|\frac{[\mathbf{HH}^{\mathrm{H}}]_{n,n}}{[\mathbf{HH}^{\mathrm{H}}]_{i,j}}\right|\to \infty$$\forall\, i\neq j$ and $\forall n,i,j=1,\dots,N$.

Figures (1)

  • Figure 1: (a) Rate vs $\rho$ for $K=2$ users and $N_{\rm RF}=K$, with $N_d=5$ and $N_e=10$ for the 1-bit RX DMA. (b) Rate vs $N_d$ for $K=5$ users, $N_{\rm RF}=K$, $N_e=20$, and $\rho=10$ dB. (c) Rate vs $N_e$ for $K=\{4,5\}$ users, $N_{\rm RF}=N_d=K$, and $\rho=10$ dB.

Theorems & Definitions (2)

  • Proposition 1
  • proof