• Journal of Communications and Networks
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• v.18 no.4
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• pp.649-657
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• 2016

In this paper, we investigate energy efficiency (EE) of the traditional co-located and the distributed massive multiple-input multiple-output (MIMO) systems. First, we derive an approximate EE expression for both the idealistic and the realistic power consumption models. Then an optimal energy-efficient remote access unit (RAU) selection algorithm based on the distance between the mobile stations (MSs) and the RAUs are developed to maximize the EE for the downlink distributed massive MIMO systems under the realistic power consumption model. Numerical results show that the EE of the distributed massive MIMO systems is larger than the co-located massive MIMO systems under both the idealistic and realistic power consumption models, and the optimal EE can be obtained by the developed energy-efficient RAU selection algorithm.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.9 no.11
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• pp.4387-4404
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• 2015

The energy-efficient design of sensing-based spectrum sharing of a multi-input and multi-output (MIMO) cognitive radio (CR) system with imperfect multiple antenna spectrum sensing is investigated in this study. Optimal resource allocation strategies, including sensing time and power allocation schemes, are studied to maximize the energy efficiency (EE) of the secondary base station under the transmit power and interference power constraints. EE problem is formulated as a nonlinear stochastic fractional programming of a nonconvex optimal problem. The EE problem is transformed into its equivalent nonlinear parametric programming and solved by one-dimension search algorithm. To reduce searching complexity, the search range was founded by demonstration. Furthermore, simulation results confirms that an optimal sensing time exists to maximize EE, and shows that EE is affected by the spectrum detection factors and corresponding constraints.

• Asia Pacific Journal of Business Review
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• v.5 no.2
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• pp.19-44
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• 2021

Sub Saharan Africa (SSA) is receiving increased investments in the energy sector under the belt and road initiative (BRI) project since its inception in 2013. SSA has a worse energy efficiency ratio coupled with deficient electricity access, through analysis showed varied impacts on the SSA countries due to the BRI initiative. This study dilves into the influencing factors for Energy Efficiency (EE) in 38 SSA countries, applying the probit and logit approach for 2000-2018. The Multiple-regression model shows significant results of some variables such as foreign direct investment, gross domestic product, and port infrastructure quality being significant on EE under BRI initiative countries. However, the logit and probit models produce similar results and the marginal effect for the entire variable, except energy imports that do not likely impact EE. Furthermore, the interaction of quality of port infrastructure and foreign direct investment variables produces significant results, highlighting the increased investments SSA receives under the BRI initiative in the energy and transport sectors. The model Percent correctly predicted (PCP) value was about 84%, indicating it correctly classified the variables and about 16% not classified. The study recommends EE performance standards should be incorporated on energy projects in SSA to ensure that these projects are energy efficient and decouple SSA's energy demand from economic growth. The research proffers suggestions for policy regarding the BRI initiative in SSA and the implications on sustainable energy and building a community with a shared future.

• Journal of Satellite, Information and Communications
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• v.11 no.2
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• pp.55-59
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• 2016

Conventional RF energy harvesting systems can harvest energy and decode information from same source as an Hybirid-AP (H-AP). However, harvesting efficiency is seriously dependent on distance between users and H-AP. Therefore, in this paper, we proposed a transmission model for RF harvesting consisting of information and power source separately called Decoupled RF Energy harvesting networks. Main purpose of this paper is to maximize energy efficiency under various constraints of transmit power from H-AP and power beacon (PB), minimum quality of service and quality of harvested power of each users. To measure proposed model's performance, we proposed optimal time scheduling algorithms for energy efficiency (EE) maximization using Lagrangian dual decomposition theory that locally maximizes the EE by obtaining suboptimal values of three arguments : transmit power of H-AP, transmit power of PB, frame splitting factor. Experiment results show that the proposed energy-efficient algorithms converge within a few iterations with its optimality and greatly improve the EE compared to that of baseline schemes.

• ETRI Journal
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• v.45 no.6
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• pp.963-973
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• 2023

Non-orthogonal multiple access (NOMA) is considered a key candidate technology for next-generation wireless communication systems due to its high spectral efficiency and massive connectivity. Incorporating the concepts of multiple-input-multiple-output (MIMO) into NOMA can further improve the system efficiency, but the hardware complexity increases. This study develops an energy-efficient (EE) subchannel assignment framework for MIMO-NOMA systems under the quality-of-service and interference constraints. This framework handles an energy-efficient co-training-based semi-supervised learning (EE-CSL) algorithm, which utilizes a small portion of existing labeled data generated by numerical iterative algorithms for training. To improve the learning performance of the proposed EE-CSL, initial assignment is performed by a many-to-one matching (MOM) algorithm. The MOM algorithm helps achieve a low complex solution. Simulation results illustrate that a lower computational complexity of the EE-CSL algorithm helps significantly minimize the energy consumption in a network. Furthermore, the sum rate of NOMA outperforms conventional orthogonal multiple access.

• Preventive Nutrition and Food Science
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• v.1 no.2
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• pp.239-243
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• 1996

Three hundred and twenty-four 1 day old chicks were used to determine the effects of fat source and energy to protein ratio on growth performance , carcass composition and the efficiency on nutrient utilization. Chicks were assigned. in a completely randomized design, to 3*3 factorial arrangement of treatments. Chicks received one of three fat sources (n0 fat, tallow, corn oil) and one of three energy to protein ratios(16, 14 and 12kcal ME/g CP). All diets were formulated to be isocaloric(3.2Mcal ME/kg diets) using published ME values for the diet ingredients. Addition of tat to the diet increased ADG, average daily feed intake, and gain to feed, Chicks fed diets containing fat had increased percentage body DM and ether extract(EE), but percentage CP was not different, Chicks fed diets containing fat had increased efficiency of protein and energy deposition. Addition of fat ad either fallow or corn oil yielded similar results. Reducing the energy to protein ratio of the diet did not affect ADG or gain to feed, but tended to decrease average daily feed intake(p=0.80), as well as resulting in linear(p<0.05) reductions in body percentage DM., EE and also total EE. Increasing the energy to protein ratio did not affect percentage or total body Cp. Adding fat to poultry diets improved growth performance and the efficiency of growth chicks. Decreasing the energy to protein ratio did not affect growth performance, but reduced EE in the body of Chicks.

• ETRI Journal
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• v.36 no.4
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• pp.686-689
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• 2014

This paper studies energy-efficiency (EE) power allocation for cognitive radio MIMO-OFDM systems. Our aim is to minimize energy efficiency, measured by "Joule per bit" metric, while maintaining the minimal rate requirement of a secondary user under a total power constraint and mutual interference power constraints. However, since the formulated EE problem in this paper is non-convex, it is difficult to solve directly in general. To make it solvable, firstly we transform the original problem into an equivalent convex optimization problem via fractional programming. Then, the equivalent convex optimization problem is solved by a sequential quadratic programming algorithm. Finally, a new iterative energy-efficiency power allocation algorithm is presented. Numerical results show that the proposed method can obtain better EE performance than the maximizing capacity algorithm.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.10 no.2
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• pp.462-483
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• 2016

The research on optimization of cellular network's energy efficiency (EE) towards environmental and economic sustainability has attracted increasing attention recently. In this survey, we discuss the opportunities, trends and challenges of this challenging topic. Two major contributions are presented namely 1) survey of proposed energy efficiency metrics; 2) survey of proposed energy efficient solutions. We provide a broad overview of the state of-the-art energy efficient methods covering base station (BS) hardware design, network planning and deployment, and network management and operation stages. In order to further understand how EE is assessed and improved through the heterogeneous network (HetNet), BS's energy-awareness and several typical HetNet deployment scenarios such as macrocell-microcell and macrocell-picocell are presented. The analysis of different HetNet deployment scenarios gives insights towards a successful deployment of energy efficient cellular networks.

• Journal of Communications and Networks
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• v.18 no.6
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• pp.873-883
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• 2016

In this paper, we focus on maximizing weighted sum energy efficiency (EE) for a multi-cell multi-user channel. In order to solve this non-convex problem, we first decompose the original problem into a sequence of parallel subproblems which can optimized separately. For each subproblem, a base station employs dirty paper coding to maximize the EE for users within a cell while regulating interference induced to other cells. Since each subproblem can be transformed to a convex multiple-access channel problem, the proposed method provides a closed-form solution for power allocation. Then, based on the derived optimal covariance matrix for each subproblem, a local optimal solution is obtained to maximize the sum EE. Finally, simulation results show that our algorithm based on non-linear precoding achieves about 20 percent performance gains over the conventional linear precoding method.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.15 no.2
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• pp.673-694
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• 2021

In this paper, we intend to study the energy efficiency (EE) optimization for a simultaneous wireless information and power transfer (SWIPT)-based distributed antenna system (DAS). Firstly, a DAS-SWIPT model is formulated, whose goal is to maximize the EE of the system. Next, we propose an optimal resource allocation method by means of the Karush-Kuhn-Tucker condition as well as an ergodic method. Considering the complexity of the ergodic method, a suboptimal scheme with lower complexity is proposed by using an antenna selection scheme. Numerical results illustrate that our suboptimal method is able to achieve satisfactory performance of EE similar to an optimal one while reducing the calculation complexity.