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Minimizing Base Station Power Consumption

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TLDR
A new radio resource management algorithm which aims at minimizing the base station supply power consumption for multi-user MIMO-OFDM and optimizes the trade-off between three basic power-saving mechanisms: antenna adaptation, power control and discontinuous transmission.
Abstract
We propose a new radio resource management algorithm which aims at minimizing the base station supply power consumption for multi-user MIMO-OFDM. Given a base station power model that establishes a relation between the RF transmit power and the supply power consumption, the algorithm optimizes the trade-off between three basic power-saving mechanisms: antenna adaptation, power control and discontinuous transmission. The algorithm comprises two steps: a) the first step estimates sleep mode duration, resource shares and antenna configuration based on average channel conditions and b) the second step exploits instantaneous channel knowledge at the transmitter for frequency selective time-variant channels. The proposed algorithm finds the number of transmit antennas, the RF transmission power per resource unit and spatial channel, the number of discontinuous transmission time slots, and the multi-user resource allocation, such that supply power consumption is minimized. Simulation results indicate that the proposed algorithm is capable of reducing the supply power consumption by between 25% and 40%, dependend on the system load.

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Citations
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Energy-Efficiency Oriented Traffic Offloading in Wireless Networks: A Brief Survey and a Learning Approach for Heterogeneous Cellular Networks

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Proceedings ArticleDOI

Energy Evaluation of Spatial Modulation at a Multi-Antenna Base Station

TL;DR: It is demonstrated that a BS employing SM can be up to 67% more energy efficient compared to a BS under a conventional MIMO transmission scheme, for four transmit antennas, and that SM achieves the highest EE among them.
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Review of latest advances in 3GPP standardization: D2D communication in 5G systems and its energy consumption models

TL;DR: A set of application scenarios for D2D communications in 5G networks is defined and the recent models of 3GPP long term evolution (LTE) and WiFi interfaces are used in analyzing the power consumption from both the infrastructure and user device perspectives.
References
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Journal ArticleDOI

Multiuser OFDM with adaptive subcarrier, bit, and power allocation

TL;DR: The results show that the proposed algorithm outperforms multiuser OFDM systems with static time-division multiple access (TDMA) or frequency-divisionmultiple access (FDMA) techniques which employ fixed and predetermined time-slot or subcarrier allocation schemes.
Journal ArticleDOI

Energy-efficiency of MIMO and cooperative MIMO techniques in sensor networks

TL;DR: This work considers radio applications in sensor networks, where the nodes operate on batteries so that energy consumption must be minimized, while satisfying given throughput and delay requirements, and analyses the best modulation and transmission strategy to minimize the total energy consumption.
Journal ArticleDOI

How much energy is needed to run a wireless network

TL;DR: The most important addenda of the proposed E3F are a sophisticated power model for various base station types, as well as large-scale long-term traffic models, which are applied to quantify the energy efficiency of the downlink of a 3GPP LTE radio access network.
Journal ArticleDOI

Transmit power adaptation for multiuser OFDM systems

TL;DR: A transmit power adaptation method that maximizes the total data rate of multiuser orthogonal frequency division multiplexing (OFDM) systems in a downlink transmission and proposes a simple method where users with the best channel gain for each subcarrier are selected and then the transmit power is equally distributed among the subcarriers.
Journal ArticleDOI

Computationally efficient bandwidth allocation and power control for OFDMA

TL;DR: Numerical results demonstrate that the proposed low complexity algorithms offer comparable performance with an existing iterative algorithm.
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