In the large‐scale distributed antenna system (LS‐DAS), a large number of antenna elements are densely deployed in a distributed way over the coverage area, and all the signals are gathered at the cloud processor (CP) via dedicated fiber links for globally joint processing. Intuitively, the LS‐DAS can inherit the advantage of both large‐scale multiple‐input‐multiple‐output (MIMO) and network densification; thus, it offers enormous gains in terms of both energy efficiency (EE) and spectral efficiency (SE). However, as the number of distributed antenna elements (DAEs) increases, the overhead for acquiring the channel state information (CSI) will increase accordingly. Without perfect CSI at the CP, which is the majority situation in practical applications due to limited overhead, the claimed gain of LS‐DAS cannot be achieved. To solve this problem, this chapter considers a more practical case with only the long‐term CSI including the path loss and shadowing known at the CP. As the long‐term channel fading usually varies much more slowly than the short‐term part, the system overhead can be easily controlled under this framework. Then, the EE‐oriented and SE‐oriented power allocation problems are formulated and solved by fractional programming (FP) and geometric programming (GP) theories, respectively. It is observed that the performance gain with only long‐term CSI is still noticeable and, more importantly, it can be achieved with a practical system cost.
Part of the book: Towards 5G Wireless Networks