On the Identifiability of Semi-Blind Estimation in Cell-Free Massive MIMO Networks

2026-05-20

AI Summary

This work addresses the identifiability challenge in semi-blind joint channel estimation and data detection caused by pilot contamination in cell-free massive MIMO networks. It establishes, for the first time, a theoretical framework grounded in network topology and spatial randomness: access points and users are modeled via a Poisson point process, local connectivity is captured using a bipartite random geometric graph, and the degree distribution of user–access point matching is approximated through an independent-edge random association graph. Recursive probabilistic analysis yields an identifiability region within which successful recovery occurs with high probability. The analysis quantitatively characterizes how access point density, user density, and connection radius jointly influence identifiability. Simulations confirm the accuracy of the theoretical predictions, offering principled guidelines for system parameter design and pilot length selection.

Abstract

Semi-blind joint channel estimation and data detection (JCD) is a promising approach to mitigate pilot contamination in cell-free massive multiple-input multiple-output (CF-MaMIMO) networks. The effectiveness of such methods fundamentally depends on identifiability, i.e., the ability to unambiguously recover the unknown channel coefficients and transmitted data signals from the received uplink observations. In this work, we investigate the identifiability of semi-blind JCD from a large-scale system design perspective. We consider a CF-MaMIMO network in which access points (APs) and user equipments (UEs) are spatially distributed according to Poisson point processes (PPPs). The resulting network topology is modeled as bipartite random geometric graph (BRGG) that captures local connectivity induced by wireless propagation. To enable a tractable analysis, the spatially dependent graph model is approximated by a surrogate independent-edge random graph with matched degree distributions. Building on this model, we develop a recursive probabilistic analysis that characterizes the conditions under which semi-blind recovery succeeds with high probability. The proposed analysis reveals an identifiability region as a function of key system parameters, including AP and UE densities and the connectivity radius beyond which channel coefficients are assumed negligible. Monte Carlo simulations validate the predicted identifiability region and assess the accuracy of the proposed graph approximation. The proposed framework provides system level insights into how network density and connectivity affect identifiability in large-scale CF-MaMIMO systems and offers guidelines for selecting deployment parameters and pilot sequence lengths that enable reliable semi-blind recovery.

Problem

• Identifiability
• Cell-Free Massive MIMO
• Semi-Blind Estimation
• Joint Channel Estimation and Data Detection
• Pilot Contamination

Innovation

• semi-blind JCD
• cell-free massive MIMO
• identifiability
• random geometric graph
• Poisson point process