Amplifier-Aware Multiple-Input Multiple-Output Power Allocation

Persson, Daniel; Eriksson, Thomas; Larsson, Erik G.

doi:10.1109/lcomm.2013.043013.130050

Cited by 57 publications

(58 citation statements)

References 15 publications

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“…Note that P PA,b (w) can adopt either linear model [8] or nonlinear model [9] which are in general the convex function of transmit beamforming w. On the other hand, P dyn accounts for the dynamic power radiation of all circuit blocks in each active radio frequency chain, and P sta is the static power for power supply, site cooling and the baseband signal processing circuits (e.g., coding/decoding, channel estimation, synchronization, backhaul transmission, etc.). For simplicity we assume that P dyn and P sta are fixed.…”

Section: B Power Consumption Modelmentioning

confidence: 99%

Energy-efficient transmission strategies for multiantenna downlink

Nguyen

Tervo

et al. 2017

2017 European Conference on Networks and Communications (EuCNC)

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Abstract-Energy efficiency (EE) is becoming one of the important criteria in wireless transmission design. This paper discusses the recently proposed energy-efficient transmit beamforming designs for multicell multiuser multiple-input single-output (MISO) systems, including maximizing overall network EE, sum weighted EE and fairness EE. Generally, the EE optimization problems are NP-hard nonconvex programs for which finding the globally optimal solutions is challenging. For low-complexity suboptimal approaches, there is a class of solutions conventionally developed based on parametric transformations. However, those have been revealed problematic in terms of computational complexity and convergence. To overcome these issues, novel algorithms have been recently developed based on the state-of-the-art successive convex approximation (SCA) framework. Here we sum up the basic concepts of the algorithms and provide numerical results which illustrate the solution quality compared to the existing methods.I. INTRODUCTION Fifth generation (5G) wireless networks visions foresee the challenges of traffic demand set by the upcoming explosive growth of wireless devices and applications [1]. To increase the achievable rates, the total energy consumption inevitably increases due to the high power feeding for the multipleantenna transmissions and involved circuit components in wireless transceivers. Thus energy efficiency (EE) has become an important criteria in cellular communications, also due to the concerns on greenhouse gas emission [2], [3].Energy efficiency is generally defined as the ratio of the total throughput over the total power consumption of the network. It is notable that increasing the network throughput by increasing the transmit power does not always improve the achievable EE, because the power consumption also increases. Thus, finding the optimal energy-efficient operating point is essential and has become the focus in a large portion of recent works [2], [3], which investigate energy-efficient transmission strategies regarding to three main criteria, i.e., network EE (NEE), sum weighted EE (SWEE) and max-min fairness EE (maxminEE) [4]. While the first metric optimizes the EE gain of the entire network, the two other ones aim at satisfying the specific EE requirements on individual parties involved. In general, the EE maximization (EEmax) problem for each metric belongs to the class of NP-hard problems, namely fractional program for which finding a globally optimal solution is challenging. Thus, suboptimal approaches that achieve a stationary solution (i.e., a solution that satisfies the Karush-Kuhn-Tucker (KKT)

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Section: B Power Consumption Modelmentioning

confidence: 99%

Energy-efficient transmission strategies for multiantenna downlink

Nguyen

Tervo

et al. 2017

2017 European Conference on Networks and Communications (EuCNC)

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“…Actually, in practical wireless transmission system, energy consumption does not only include transmit power but also include circuit power for nonideal transmitter [16]. In [17], it has been demonstrated that the circuit power is consumed by signal processing and the device working in the active mode. The authors in [18] designed an optimal power allocation scheme to maximize EE for two-way amplify and forward (AF) relay networks with consideration of circuit power.…”

Section: Introductionmentioning

confidence: 99%

Adaptive wireless transmission strategy for maximizing energy efficiency

Cai

Qiu

Jiang

et al. 2017

J Wireless Com Network

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In a traditional wireless transmission system, studies about energy efficiency (EE) are usually for a fixed transmission mode. In this paper, we propose an adaptive wireless transmission (AWT) strategy with consideration of circuit power. In this AWT strategy, the transmission mode is switched between the direct transmission (DT) mode and the two-way relay transmission (TWRT) mode. The switch strategy is based on a transmission rate threshold R th which makes the EEs of the DT mode and the TWRT mode equal. Furthermore, we propose a transmission rate threshold determining (TRTD) algorithm with a bisection method to find the threshold R th . The simulation results also show that our AWT strategy has the maximum EE at a reasonable range of transmission rate.

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“…Adopting the circuit power changes the behavior of the EE from a strictly decreasing function of the SE to a bellshaped function where the maximum is given for a non-zero power [19]. In other studies, there was a focus on the amplifier power which was considered separately [27], [28] and was shown to be a function of the transmission power. In [27], it was shown that the total power consumption is proportional to the to the square root of the transmission power.…”

Section: Introductionmentioning

confidence: 99%

“…In other studies, there was a focus on the amplifier power which was considered separately [27], [28] and was shown to be a function of the transmission power. In [27], it was shown that the total power consumption is proportional to the to the square root of the transmission power. In [28], a more general form is presented where the square root is generalized by an exponent between 0.5 and 0.64.…”

Section: Introductionmentioning

confidence: 99%

Energy-Efficient Power Allocation for Underlay Cognitive Radio Systems

Sboui

Rezki

Alouini

2015

IEEE Trans. Cogn. Commun. Netw.

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Abstract-We present a power allocation framework for spectrum sharing Cognitive Radio (CR) systems based on maximizing the energy efficiency (EE). First, we show that the relation between the EE and the spectral efficiency (SE) is strictly increasing in contrast with the SE-EE trade-off discussed in the literature. We also solve a non-convex problem and explicitly derive the optimal power for the proposed average EE under either a peak or an average power constraint. We apply our results to the underlay CR systems where the power is limited by an additional interference constraint. When the instantaneous channel is not available, we provide a necessary and sufficient condition for the optimal power and present a simple sub-optimal power. In the numerical results, we show that the proposed EE corresponds to a higher SE at mid-range and high power regime compared to the classical EE. We also show that the sup-optimal solution is very close to the optimal solution. In addition, we deduce that the absence of instantaneous CSI affects the EE and the SE at high power regime compared to full CSI. In the CR context, we show that the interference threshold has a minimal effect on the EE compared to the SE.Index Terms-Energy efficiency, spectral efficiency, optimal power allocation, underlay cognitive radio.

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Amplifier-Aware Multiple-Input Multiple-Output Power Allocation

Cited by 57 publications

References 15 publications

Energy-efficient transmission strategies for multiantenna downlink

Energy-efficient transmission strategies for multiantenna downlink

Adaptive wireless transmission strategy for maximizing energy efficiency

Energy-Efficient Power Allocation for Underlay Cognitive Radio Systems

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