IEEE Transactions on Green Communications and Networking · 2017 · 853 citations · 25 references
EngineeringComputer EngineeringElectrical EngineeringAntennasOptimizationWireless CommunicationsBeamformingChannel EstimationEnergy EfficiencyMassive Multiple-input Multiple-outputPower OptimizationWireless Channel ModelingCell-free Massive MimoChannel State InformationMassive Mimo Energy EfficiencyMassive Mimo Power ControlMassive Mimo Spectral EfficiencyOptimal Power ControlPower AllocationSpectral Efficiency
The study examines cell‑free massive MIMO downlink, where many distributed multi‑antenna access points serve numerous single‑antenna users in the same time‑frequency resource. It derives a closed‑form spectral‑efficiency expression and uses it to design an optimal power‑allocation algorithm under per‑user spectral‑efficiency and per‑AP power constraints, while also proposing AP‑selection schemes that reduce backhaul power consumption. The authors employ distributed conjugate beamforming based on locally estimated CSI obtained via uplink training in a TDD system, derive a closed‑form spectral‑efficiency expression accounting for estimation errors, and propose AP‑selection schemes to cut backhaul power. The proposed power‑allocation and AP‑selection schemes double and further increase total energy efficiency, respectively, and cell‑free massive MIMO outperforms colocated MIMO in energy efficiency while meeting quality‑of‑service requirements.
We consider the cell-free massive multiple-input multiple-output (MIMO) downlink, where a very large number of distributed multiple-antenna access points (APs) serve many single-antenna users in the same time-frequency resource. A simple (distributed) conjugate beamforming scheme is applied at each AP via the use of local channel state information (CSI). This CSI is acquired through time-division duplex operation and the reception of uplink training signals transmitted by the users. We derive a closed-form expression for the spectral efficiency taking into account the effects of channel estimation errors and power control. This closed-form result enables us to analyze the effects of backhaul power consumption, the number of APs, and the number of antennas per AP on the total energy efficiency, as well as, to design an optimal power allocation algorithm. The optimal power allocation algorithm aims at maximizing the total energy efficiency, subject to a per-user spectral efficiency constraint and a per-AP power constraint. Compared with the equal power control, our proposed power allocation scheme can double the total energy efficiency. Furthermore, we propose AP selections schemes, in which each user chooses a subset of APs, to reduce the power consumption caused by the backhaul links. With our proposed AP selection schemes, the total energy efficiency increases significantly, especially for large numbers of APs. Moreover, under a requirement of good quality-of-service for all users, cell-free massive MIMO outperforms the colocated counterpart in terms of energy efficiency.
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