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Unknown Interference Modeling for Rate Adaptation in Cell-Free Massive MIMO Networks

Mahmoud Zaher, Emil Björnson, Marina Petrova

TL;DR

This work addresses outages caused by unknown uplink interference in a cell-free massive MIMO network. It extends a Bayesian approach by modeling per-AP unknown interference as independent Inverse-Gamma random variables and deriving a semi-analytical CDF for the total interference via characteristic functions, enabling epsilon-outage rate calculations. The authors propose a practical four-step rate-adaptation procedure using locally estimated AP statistics to obtain per-AP parameters and a global outage threshold, validated against Monte Carlo simulations. The framework provides guaranteed outage performance and is well-suited for robust uplink operation in densely deployed, cluster-based cell-free networks.

Abstract

Co-channel interference poses a challenge in any wireless communication network where the time-frequency resources are reused over different geographical areas. The interference is particularly diverse in cell-free massive multiple-input multiple-output (MIMO) networks, where a large number of user equipments (UEs) are multiplexed by a multitude of access points (APs) on the same time-frequency resources. For realistic and scalable network operation, only the interference from UEs belonging to the same serving cluster of APs can be estimated in real-time and suppressed by precoding/combining. As a result, the unknown interference arising from scheduling variations in neighboring clusters makes the rate adaptation hard and can lead to outages. This paper aims to model the unknown interference power in the uplink of a cell-free massive MIMO network. The results show that the proposed method effectively describes the distribution of the unknown interference power and provides a tool for rate adaptation with guaranteed target outage.

Unknown Interference Modeling for Rate Adaptation in Cell-Free Massive MIMO Networks

TL;DR

This work addresses outages caused by unknown uplink interference in a cell-free massive MIMO network. It extends a Bayesian approach by modeling per-AP unknown interference as independent Inverse-Gamma random variables and deriving a semi-analytical CDF for the total interference via characteristic functions, enabling epsilon-outage rate calculations. The authors propose a practical four-step rate-adaptation procedure using locally estimated AP statistics to obtain per-AP parameters and a global outage threshold, validated against Monte Carlo simulations. The framework provides guaranteed outage performance and is well-suited for robust uplink operation in densely deployed, cluster-based cell-free networks.

Abstract

Co-channel interference poses a challenge in any wireless communication network where the time-frequency resources are reused over different geographical areas. The interference is particularly diverse in cell-free massive multiple-input multiple-output (MIMO) networks, where a large number of user equipments (UEs) are multiplexed by a multitude of access points (APs) on the same time-frequency resources. For realistic and scalable network operation, only the interference from UEs belonging to the same serving cluster of APs can be estimated in real-time and suppressed by precoding/combining. As a result, the unknown interference arising from scheduling variations in neighboring clusters makes the rate adaptation hard and can lead to outages. This paper aims to model the unknown interference power in the uplink of a cell-free massive MIMO network. The results show that the proposed method effectively describes the distribution of the unknown interference power and provides a tool for rate adaptation with guaranteed target outage.
Paper Structure (8 sections, 1 theorem, 21 equations, 3 figures, 1 table)

This paper contains 8 sections, 1 theorem, 21 equations, 3 figures, 1 table.

Table of Contents

  1. Introduction
  2. System Model
  3. Channel Estimation
  4. Uplink Data Transmission
  5. Unknown Interference Power Distribution
  6. $\epsilon$-Outage Rate
  7. Numerical Evaluation
  8. Conclusions

Key Result

Theorem 1

The CDF of the total unknown interference power is given by where $\phi\left(t\right)$ represents the characteristic function of the total unknown interference power at the CPU, $\phi_l\left(t\right)$ denotes the characteristic function of the Inverse-Gamma random variable representing the unknown interference power at the $l^{th}$ AP, $K_{\alpha}\left(\cdot

Figures (3)

  • Figure 1: Site map and UE distribution with $K_u = 100$ unknown interferers. Serving cluster is located inside the marked circle.
  • Figure 2: Outage probability for different numbers of unknown interferers and different desired UE locations.
  • Figure 3: $\epsilon$-outage SE with RZF.

Theorems & Definitions (1)

  • Theorem 1