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Register a new userEnergy-Efficient, Large-scale Distributed-Antenna System (L-DAS) for Multiple Users
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Added by
Jingon Joung Dr
Publication date
2013
fields
Informatics Engineering
and research's language is
English
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Large-scale distributed-antenna system (L-DAS) with very large number of distributed antennas, possibly up to a few hundred antennas, is considered. A few major issues of the L-DAS, such as high latency, energy consumption, computational complexity, and large feedback (signaling) overhead, are identified. The potential capability of the L-DAS is illuminated in terms of an energy efficiency (EE) throughout the paper. We firstly and generally model the power consumption of an L-DAS, and formulate an EE maximization problem. To tackle two crucial issues, namely the huge computational complexity and large amount of feedback (signaling) information, we propose a channel-gain-based antenna selection (AS) method and an interference-based user clustering (UC) method. The original problem is then split into multiple subproblems by a cluster, and each clusters precoding and power control are managed in parallel for high EE. Simulation results reveal that i) using all antennas for zero-forcing multiuser multiple-input multiple-output (MU-MIMO) is energy inefficient if there is nonnegligible overhead power consumption on MU-MIMO processing, and ii) increasing the number of antennas does not necessarily result in a high EE. Furthermore, the results validate and underpin the EE merit of the proposed L-DAS complied with the AS, UC, precoding, and power control by comparing with non-clustering L-DAS and colocated antenna systems.
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The combination of energy harvesting and large-scale multiple antenna technologies provides a promising solution for improving the energy efficiency (EE) by exploiting renewable energy sources and reducing the transmission power per user and per antenna. However, the introduction of energy harvesting capabilities into large-scale multiple antenna systems poses many new challenges for energy-efficient system design due to the intermittent characteristics of renewable energy sources and limited battery capacity. Furthermore, the total manufacture cost and the sum power of a large number of radio frequency (RF) chains can not be ignored, and it would be impractical to use all the antennas for transmission. In this paper, we propose an energy-efficient antenna selection and power allocation algorithm to maximize the EE subject to the constraint of users quality of service (QoS). An iterative offline optimization algorithm is proposed to solve the non-convex EE optimization problem by exploiting the properties of nonlinear fractional programming. The relationships among maximum EE, selected antenna number, battery capacity, and EE-SE tradeoff are analyzed and verified through computer simulations.
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Large-scale antenna (LSA) or massive multiple-input multiple-output (MIMO) has gained a lot of attention due to its potential to significantly improve system throughput. As a natural evolution from traditional MIMO-orthogonal frequency division multiplexing (OFDM), LSA has been combined with OFDM to deal with frequency selectivity of wireless channels in most existing works. As an alternative approach, single-carrier (SC) has also been proposed for LSA systems due to its low implementation complexity. In this article, a comprehensive comparison between LSA-OFDM and LSA-SC is presented, which is of interest to the waveform design for the next generation wireless systems.
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