Deploying a terminal-centric cell-free massive MIMO architecture at scale poses several challenges and trade-offs that need to be carefully considered. One major challenge is the complexity of managing a large number of distributed access points (APs) and coordinating their operations to ensure efficient resource allocation and interference management. As the number of APs increases, the overhead associated with channel estimation, data processing, and coordination also escalates, leading to higher computational complexity and signaling overhead.
Another challenge is the fronthaul capacity required to connect a large number of distributed APs to a central processing unit (CPU). Ensuring high-capacity fronthaul links to support the exchange of data and control information between APs and the CPU is crucial for the proper functioning of the system. Additionally, the scalability of the architecture in terms of power consumption, hardware cost, and maintenance needs to be carefully evaluated to ensure cost-effective deployment at scale.
In terms of trade-offs, one key trade-off is between coverage and capacity. While a terminal-centric cell-free massive MIMO architecture can enhance coverage and provide better service to users at the cell edges, it may come at the expense of overall network capacity due to increased interference levels and resource sharing among users. Balancing coverage and capacity requirements to meet the diverse needs of different applications is essential in designing a scalable and efficient system.
Moreover, trade-offs between spectral efficiency and energy efficiency need to be considered. Increasing the number of APs and antennas can improve spectral efficiency by exploiting spatial diversity and multiplexing gains, but it may also lead to higher energy consumption. Finding the right balance between spectral efficiency and energy efficiency to optimize the overall network performance is crucial in deploying a terminal-centric cell-free massive MIMO architecture at scale.