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

Core Concepts

This paper proposes an analytical model to characterize the distribution of the total unknown interference power in the uplink of a cell-free massive MIMO network. The model is used to enable robust rate adaptation with guaranteed target outage performance.

Abstract

The paper addresses the challenge of unknown interference from neighboring cell-free clusters in a cell-free massive MIMO network. The key points are:

In a cell-free massive MIMO network, each user equipment (UE) is served by a cluster of access points (APs). The interference from UEs in the same serving cluster can be estimated and suppressed, but the interference from UEs in neighboring clusters is unknown.

The authors derive an analytical model to characterize the distribution of the total unknown interference power at the central processing unit (CPU) of the serving cluster. They show that the unknown interference power at each AP can be modeled as an independent Inverse-Gamma random variable.

Using the derived distribution of the total unknown interference power, the authors propose a method for robust uplink rate adaptation that can guarantee a target outage probability. This is in contrast to a baseline scheme that uses a fixed fade margin to account for the unknown interference.

Numerical results validate the accuracy of the analytical model and demonstrate the effectiveness of the proposed rate adaptation approach in maintaining the target outage probability, even with changes in the number of unknown interferers or the location of the desired UE.

Stats

The paper provides the following key figures and metrics:

The network simulation parameters, including the number of serving APs, total number of APs, number of antennas per AP, number of known interferers, pathloss exponent, uplink transmit power, uplink noise power, coherence block length, and pilot sequence length.
The large-scale fading model used to generate the channel coefficients, including the path loss and correlated shadowing.

Quotes

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Key Insights Distilled From

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

by Mahm... at arxiv.org 04-19-2024

https://arxiv.org/pdf/2404.12148.pdf

Deeper Inquiries

How would the proposed model and rate adaptation approach need to be modified if the cross-covariance between the unknown interference power at different APs is non-negligible

In the scenario where the cross-covariance between the unknown interference power at different APs is non-negligible, the proposed model and rate adaptation approach would need to be modified to account for the interdependence between the unknown interference powers. This would involve considering the correlation between the unknown interference powers at different APs when computing the total unknown interference power at the CPU. The characteristic function of the total unknown interference power would need to incorporate the cross-covariance terms between the APs to accurately capture the joint distribution of the unknown interference powers. Additionally, the parameters of the Inverse-Gamma distribution for each AP's unknown interference power would need to be adjusted to reflect the inter-AP correlations, ensuring a more accurate representation of the total unknown interference power.

What are the potential drawbacks or limitations of relying solely on the statistical characterization of the unknown interference power, rather than attempting to measure or estimate it directly

Relying solely on the statistical characterization of the unknown interference power, without directly measuring or estimating it, may have certain drawbacks and limitations. One limitation is the assumption that the statistical properties of the unknown interference power remain constant over time, which may not hold true in dynamic wireless environments with varying user mobility and network conditions. This could lead to inaccuracies in the estimated distribution of the unknown interference power, impacting the effectiveness of the rate adaptation approach. Additionally, the statistical characterization approach may not capture sudden changes or anomalies in the unknown interference power, potentially leading to suboptimal rate adaptation decisions. Furthermore, the reliance on statistical properties alone may overlook specific interference patterns or behaviors that could be crucial for optimizing system performance.

Can the insights from this work on unknown interference modeling be extended to other wireless network architectures beyond cell-free massive MIMO, such as traditional cellular networks or ad-hoc networks

The insights gained from this work on unknown interference modeling in cell-free massive MIMO networks can be extended to other wireless network architectures beyond cell-free massive MIMO. The concept of characterizing and modeling unknown interference power can be applied to traditional cellular networks, ad-hoc networks, and other wireless communication systems where co-channel interference poses a challenge. By understanding and quantifying the unknown interference power distribution, network operators can develop more robust rate adaptation strategies, interference mitigation techniques, and resource allocation schemes to improve overall network performance and reliability. The principles of modeling unknown interference can be adapted and tailored to suit the specific characteristics and requirements of different wireless network architectures, providing valuable insights for optimizing system design and operation.