Distributed Algorithms for Spectral and Energy-Efficiency Maximization of K-User Interference Channels

In this paper, we propose a cooperative distributed framework to optimize a variety of rate and energy-efficiency (EE) utility functions, such as the minimum-weighted rate or the global EE, for the <inline-formula> <tex-math notation="LaTeX">K </tex-math></inline-formu...

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Vydané v:IEEE access Ročník 9; s. 96948 - 96963
Hlavní autori: Soleymani, Mohammad, Santamaria, Ignacio, Schreier, Peter J.
Médium: Journal Article
Jazyk:English
Vydavateľské údaje: Piscataway IEEE 2021
The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Predmet:
Algorithms
Beamforming
Convergence
Cost function
Deep learning
Distributed algorithms
Energy conversion efficiency
energy-efficiency region
fairness rate
global energy efficiency
Interference
majorization minimization
Measurement
MIMO communication
Network topologies
Noise
Optimization
Signal to noise ratio
SIMO systems
sum-rate maximization
Utility functions
Wireless communication
ISSN:2169-3536, 2169-3536
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Shrnutí:In this paper, we propose a cooperative distributed framework to optimize a variety of rate and energy-efficiency (EE) utility functions, such as the minimum-weighted rate or the global EE, for the <inline-formula> <tex-math notation="LaTeX">K </tex-math></inline-formula>-user interference channel. We focus on the single-input multiple-output (SIMO) case, where each user, based solely on local channel state information (CSI) and limited exchange information from other users, optimizes its transmit power and receive beamformer, although the framework can also be extended to the multiple-output multiple-input (MIMO) case. The distributed framework combines an alternating optimization approach with majorization-minimization (MM) techniques, thus ensuring convergence to a stationary point of the centralized cost function. Closed-form power update rules are obtained for some utility functions, thus obtaining very fast convergence algorithms. The receivers treat interference as noise (TIN) and apply the beamformers that maximize the signal-to-interference-plus-noise (SINR). The proposed cooperative distributed algorithms are robust against channel variations and network topology changes and, as our simulation results suggest, they perform close to the centralized solution that requires global CSI. As a benchmark, we also study a non-cooperative distributed framework based on the so-called "signal-to-leakage-plus-noise ratio" (SNLR) that further reduces the overhead of the cooperative version.
Bibliografia:ObjectType-Article-1
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ISSN:2169-3536
2169-3536
DOI:10.1109/ACCESS.2021.3094976