Energy-Efficient Power Control in Cell-Free and User-Centric Massive MIMO at Millimeter Wave

Alonzo, Mario; Buzzi, Stefano; Zappone, Alessio; D'Elia, C.

doi:10.1109/tgcn.2019.2908228

Cited by 173 publications

(121 citation statements)

References 31 publications

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“…A multiuser clustered channel model is introduced, and an uplink multiuser channel estimation scheme is described along with hybrid analog/digital beamforming architectures. Moreover, in [19], the non-convex problem of power allocation for downlink global energy efficiency maximization is addressed. In [20], an uplink TDD-based cell-free massive MIMO system is considered.…”

Section: A Related Workmentioning

confidence: 99%

Efficient Angle-Domain Processing for FDD-Based Cell-Free Massive MIMO Systems

Abdallah

Mansour

2020

IEEE Trans. Commun.

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Cell-free massive MIMO communications is an emerging network technology for 5G wireless communications wherein distributed multi-antenna access points (APs) serve many users simultaneously. Most prior work on cell-free massive MIMO systems assume time-division duplexing mode, although frequency-division duplexing (FDD) systems dominate current wireless standards. The key challenges in FDD massive MIMO systems are channel-state information (CSI) acquisition and feedback overhead. To address these challenges, we exploit the socalled angle reciprocity of multipath components in the uplink and downlink, so that the required CSI acquisition overhead scales only with the number of served users, and not the number of AP antennas nor APs. We propose a low complexity multipath component estimation technique and present linear angle-ofarrival (AoA)-based beamforming/combining schemes for FDDbased cell-free massive MIMO systems. We analyze the performance of these schemes by deriving closed-form expressions for the mean-square-error of the estimated multipath components, as well as expressions for the uplink and downlink spectral efficiency. Using semi-definite programming, we solve a maxmin power allocation problem that maximizes the minimum user rate under per-user power constraints. Furthermore, we present a user-centric (UC) AP selection scheme in which each user chooses a subset of APs to improve the overall energy efficiency of the system. Simulation results demonstrate that the proposed multipath component estimation technique outperforms conventional subspace-based and gradient-descent based techniques. We also show that the proposed beamforming and combining techniques along with the proposed power control scheme substantially enhance the spectral and energy efficiencies with an adequate number of antennas at the APs.Index Terms-FDD mode, cell-free massive MIMO, multipath component estimation, array signal processing, angle-based beamforming/combining, power control.

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Section: A Related Workmentioning

confidence: 99%

Efficient Angle-Domain Processing for FDD-Based Cell-Free Massive MIMO Systems

Abdallah

Mansour

2020

IEEE Trans. Commun.

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“…. , NT} has been generated, where η UL, * (n) corresponds to the power allocation obtained by the optimization methods from [17,18], for the training input x(n). It should be stressed that both the generation of the training set and the execution of the training algorithm can be executed offline and sporadically, i.e.…”

Section: Power Allocation Via Deep Learning and Experimental Resultsmentioning

confidence: 99%

Uplink Power Control in Cell-Free Massive MIMO via Deep Learning

D’Andrea

Zappone

Buzzi

et al. 2019

2019 IEEE 8th International Workshop on Computational Advances in Multi-Sensor Adaptive Processing (CAMSAP)

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This paper focuses on the use of a deep learning approach to perform sum-rate-max and max-min power allocation in the uplink of a cell-free massive MIMO network. In particular, we train a deep neural network in order to learn the mapping between a set of input data and the optimal solution of the power allocation strategy. Numerical results show that the presence of the pilot contamination in the cell-free massive MIMO system does not significantly affect the learning capabilities of the neural network, that gives near-optimal performance. Conversely, with the introduction of the shadowing effect in the system the performance obtained with the deep learning approach gets significantly degraded with respect to the optimal one.

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“…Uplink processing MMSE [59,124,145,146] ZF [147] Power allocation and load balancing [148] Max-min power control [149] Pilot power control [150] Sequential linear MMSE [120] mmWave Limited fronthaul capacity [151] Power control [152] Channel modeling [153] Energy efficiency [154] Energy Efficiency [116,132,[155][156][157] Imperfect Fronthaul [158][159][160][161][162][163][164] Low Resolution ADCs [163,[165][166][167] Hardware Impairments [164,[168][169][170] Non-orthogonal [171][172][173][174][175] Multiple Access…”

Section: Topics Aspects and Referencesmentioning

confidence: 99%

Cell-Free Massive MIMO : Scalability, Signal Processing and Power Control

Interdonato

2020

Linköping Studies in Science and Technology. Dissertations

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Linköping 2020 This is a Swedish Doctor of Philosophy thesis. The Doctor of Philosophy degree comprises 240 ECTS credits of postgraduate studies. Cover: Illustration of the Ericsson Radio Stripes concept, co-invented by the author of this thesis. The Radio Stripes constitute a way to implement a cell-free massive MIMO system. They aim at enabling invisible, low-cost deployment of large number of distributed access points in the vicinity of the users. This is achieved by serial integration of several transmitting and receiving components into a cable (or stripe) which also provides fronthaul communication and power supply.

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Energy-Efficient Power Control in Cell-Free and User-Centric Massive MIMO at Millimeter Wave

Cited by 173 publications

References 31 publications

Efficient Angle-Domain Processing for FDD-Based Cell-Free Massive MIMO Systems

Efficient Angle-Domain Processing for FDD-Based Cell-Free Massive MIMO Systems

Uplink Power Control in Cell-Free Massive MIMO via Deep Learning

Cell-Free Massive MIMO : Scalability, Signal Processing and Power Control

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