A State-of-the-Art Review of AI-Assisted Cell-Free Massive MIMO for 6G Networks

As wireless communication systems advance toward the sixth generation (6G), intensive research and development efforts are underway. To identify enabling technologies that meet the unprecedented requirements of next-generation networks, including ubiquitous connectivity, sustainability, ultra-high throughput, and near-zero latency. To fulfill these ambitious goals, emerging paradigms including Digital Twin (DT), High Altitude Platform Systems (HAPS), terahertz and petahertz communications, Internet of Things (IoTs), Integrated Sensing and Communication (ISAC), Orthogonal Time Frequency Space (OTFS) modulation, Network Slicing (NS), and Quantum Communication (QC) are being integrated with Cell-Free (CF) network architecture. Although CF networks offer significant advantages in coverage and capacity, their distributed nature introduces several challenges, including antenna coordination, resource allocation, channel estimation, security, task offloading, and computational complexity. Recent advances in Artificial Intelligence (AI), particularly Deep Learning (DL), present promising solutions to mitigate these issues to cater global coverage with seamless connectivity and enhanced security. This paper provides a comprehensive review of state-of-the-art technologies synergized with CF communications and explores DL-driven methodologies to realise the performance targets of future 6G wireless networks.