Assume that a multi-user multiple-input multipleoutput (MIMO) system is designed from scratch to uniformly cover a given area with maximal energy efficiency (EE). What are the optimal number of antennas, active users, and transmit power? The aim of this paper is to answer this fundamental question. We consider jointly the uplink and downlink with different processing schemes at the base station and propose a new realistic power consumption model that reveals how the above parameters affect the EE. Closed-form expressions for the EE-optimal value of each parameter, when the other two are fixed, are provided for zero-forcing (ZF) processing in single-cell scenarios. These expressions prove how the parameters interact. For example, in sharp contrast to common belief, the transmit power is found to increase (not to decrease) with the number of antennas. This implies that energy-efficient systems can operate in high signal-to-noise ratio regimes in which interference-suppressing signal processing is mandatory. Numerical and analytical results show that the maximal EE is achieved by a massive MIMO setup wherein hundreds of antennas are deployed to serve a relatively large number of users using ZF processing. The numerical results show the same behavior under imperfect channel state information and in symmetric multi-cell scenarios. 1 and all the BS antennas are processed coherently [6]- [10] . We stress that the original massive MIMO definition in [7] also assumed M K 1, while we consider the more general definition from [8] and [9] where M K can also be a small constant. The way that the number of antennas M impacts the EE has been recently investigated in [11]- [16] . In particular, in [11] 1536-1276
In REF , the optimal number of antennas, number of active users, and transmission power were optimized for maximal energy efficiency.
Optimal Design of Energy-Efficient Multi-User MIMO Systems: Is Massive MIMO the Answer?
IEEE Transactions on Wireless Communications
IEEE Transactions on Wireless Communications
Mathematics Computer Science