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Joint User Scheduling and Precoding for XL-MIMO Systems with Imperfect CSI

Jose P. Gonzalez-Coma, F. Javier Lopez-Martinez, Luis Castedo

Abstract

We propose an algorithm for joint precoding and user selection in multiple-input multiple-output systems with extremely-large aperture arrays, assuming realistic channel conditions and imperfect channel estimates. The use of long-term channel state information (CSI) for user scheduling, and a proper selection of the set of users for which CSI is updated allow for obtaining an improved achievable sum spectral efficiency. We also confirm that the effect of imperfect CSI in the precoding vector design and the cost of training must be taken into consideration for realistic performance prediction.

Joint User Scheduling and Precoding for XL-MIMO Systems with Imperfect CSI

Abstract

We propose an algorithm for joint precoding and user selection in multiple-input multiple-output systems with extremely-large aperture arrays, assuming realistic channel conditions and imperfect channel estimates. The use of long-term channel state information (CSI) for user scheduling, and a proper selection of the set of users for which CSI is updated allow for obtaining an improved achievable sum spectral efficiency. We also confirm that the effect of imperfect CSI in the precoding vector design and the cost of training must be taken into consideration for realistic performance prediction.
Paper Structure (7 sections, 1 theorem, 23 equations, 3 figures, 1 table, 1 algorithm)

This paper contains 7 sections, 1 theorem, 23 equations, 3 figures, 1 table, 1 algorithm.

Table of Contents

  1. Introduction
  2. System model
  3. User scheduling with imperfect CSI
  4. Equivalent channel gains
  5. Imperfect CSI scheduling and precoding
  6. Numerical Results
  7. Conclusion

Key Result

Lemma 1

Let $[{\bf{R}}]_{m,n}$ be the element at row $m$ and column $n$ of the spatial correlation matrix ${\bf{R}}$ given by where $\beta$ is the average gain, $r_m$ and $r_n$ are the distances corresponding to the $m$ and $n$ antennas, and $f(\theta)$ is the probability density function. Then, $[{\bf{R}}]_{m,n}$ can be computed in closed-form as where $\phi(\cdot)$ is the error function, and

Figures (3)

  • Figure 1: Achievable SE vs. SNR (dB) for a different number of paths $S_k$ and power ratios $\kappa$. The remaining parameter values are given in Table \ref{['tab:Sim1']}.
  • Figure 2: Achievable SE vs. SNR (dB) for $S_k=4$, $\kappa=2$ and different CSI delays $\tau_s$. Parameter values are given in Table \ref{['tab:Sim1']}.
  • Figure 3: Achievable SE vs. SNR (dB) for $S_k=4$, $\kappa=2$, $\tau_s=10^4$, and several training overheads $\dot\tau$. Parameter values are given in Table \ref{['tab:Sim1']}.

Theorems & Definitions (2)

  • Lemma 1
  • proof