Content-Aware AP Selection with LSTM-enabled Proactive Caching in Cell-Free Massive MIMO Networks

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Abstract
Cell-Free massive MIMO (CF-mMIMO) networks face significant challenges in achieving Ultra-Reliable Low-Latency Communication (URLLC) requirements due to inherent delays in content retrieval from central processing units (CPUs). This paper presents an integrated framework that jointly optimizes access point (AP) selection and content caching to minimize latency while maintaining reliability. We develop a novel content-aware user-centric clustering scheme that considers both cached content availability and channel conditions. The scheme features a Content Query Beacon (CQB) mechanism, which verifies content availability prior to connection establishment. To address the dynamic nature of content popularity, we design a novel proactive content caching strategy using Long Short-Term Memory (LSTM) to minimize CPU-dependent data retrieval. Extensive simulations demonstrate that our proposed framework achieves a 75% reduction in content delivery latency, 31.87% improvement in Quality of Experience (QoE), and a 26.8% increase in cache hit rates compared to conventional approaches. This comprehensive solution significantly enhances the capability of CF-mMIMO networks to deliver URLLC services, particularly in densely populated areas with diverse content demands.

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