DEEP LEARNING-BASED BLIND CHANNEL ESTIMATION IN MASSIVE MIMO SYSTEMS UNDER SPATIAL CORRELATION

Abstract

The scalability of modern Massive MIMO systems and prospective 6G networks is fundamentally constrained by the “pilot contamination” effect and the prohibitive overhead of time-frequency resources required for orthogonal pilot transmission. In ultra-dense deployment scenarios, traditional pilot-aided channel estimation methods exhibit a critical degradation in spectral efficiency. The study aims to develop a resource-efficient method for blind channel estimation that is invariant to antenna array topology, with the goal of minimizing signaling overhead and maximizing throughput capacity under conditions of complex spatial correlation. The proposed approach is based on the statistical processing of the sample covariance matrix of received signals utilizing a deep convolutional neural network. In contrast to direct reconstruction techniques, the algorithm employs a residual learning strategy to isolate and mitigate estimation noise arising from finite sample sizes. To resolve the phase ambiguity of the signal subspace, a “virtual pilot” concept (a single reference symbol) is introduced, ensuring that resource overhead approaches zero asymptotically. The study is validated across a wide spectrum of configurations, including linear, rectangular, and circular arrays, as well as distributed antenna systems. Simulation results confirm that the proposed method yields a significant gain in the system’s aggregate spectral efficiency by liberating resources previously allocated to pilot sequences. Despite a marginal degradation in estimation accuracy compared to conventional methods, the algorithm demonstrates high robustness to various spatial correlation profiles and antenna geometries. The proposed method facilitates the realization of massive connectivity scenarios on existing base station hardware architectures, effectively overcoming throughput limitations imposed by the coherence interval length.