Energy Efficiency Tradeoff Research Articles

DVB-T2 Energy and Spectral Efficiency Trade-off Optimization based on Genetic Algorithm

DVB-T2 Energy and Spectral Efficiency Trade-off Optimization based on Genetic Algorithm

Multi-speed configuration of AS/RS amidst responsiveness and energy efficiency trade-off: metamodel-based simulation–optimization

Multi-speed configuration of AS/RS amidst responsiveness and energy efficiency trade-off: metamodel-based simulation–optimization

No immediate attentional bias towards or choice bias for male secondary sexual characteristics in Bornean orang-utans (Pongo pygmaeus).

Primate faces provide information about a range of variant and invariant traits, including some that are relevant for mate choice. For example, faces of males may convey information about their health or genetic quality through symmetry or facial masculinity. Because perceiving and processing such information may have bearing on the reproductive success of an individual, cognitive systems are expected to be sensitive to facial cues of mate quality. However, few studies have investigated this topic in non-human primate species. Orang-utans are an interesting species to test mate-relevant cognitive biases, because they are characterised by male bimaturism: some adult males are fully developed and bear conspicuous flanges on the side of their face, while other males look relatively similar to females. Here, we describe two non-invasive computerised experiments with Bornean orang-utans (Pongo pygmaeus), testing (i) immediate attention towards large flanges and symmetrical faces using a dot-probe task (N = 3 individuals; 2F) and (ii) choice bias for pictures of flanged males over unflanged males using a preference test (N = 6 individuals; 4F). In contrast with our expectations, we found no immediate attentional bias towards either large flanges or symmetrical faces. In addition, individuals did not show a choice bias for stimuli of flanged males. We did find exploratory evidence for a colour bias and energy efficiency trade-offs in the preference task. We discuss our null results and exploratory results in the context of the evolutionary history of Bornean orang-utans, and provide suggestions for a more biocentric approach to the study oforang-utan cognition.

Medium spatiotemporal characteristics based global optimization method for energy efficiency trade-off issue in variable flow rate HVAC system

During the optimization and control processes of variable flow rate heating, ventilation, and air conditioning (HVAC) systems, the medium temporal and spatial characteristics often significantly influence the control effectiveness and energy efficiency of the overall system. Unfortunately, much of the existing research predominantly concentrates on enhancing the performance of specific individual components, neglecting the impact of medium temporal and spatial characteristics on the overall HVAC system performance. Consequently, issues such as energy efficiency trade-offs in practical systems remain inadequately addressed. In this paper, the medium spatiotemporal characteristics of HVAC systems are systematically illustrated and modeled. A novel optimization method based on these characteristics is proposed to improve the overall performance of the HVAC system. This method adopts an event-driven optimization strategy to determine the optimal timing with consideration of temporal characteristics, and an adaptive hybrid algorithm is proposed to determine the optimal setpoints with consideration of spatial characteristics. By comparing to conventional methods with constant setpoints, genetic algorithm (GA) based setpoints, and differential evolution (DE) based setpoints in both simulation test and experiment, the proposed method demonstrates the ability to reduce electricity consumption by 21.2%, 13.1%, and 10.0%, respectively. The methodology in this research provides significant guidance for engineers to promote the decoupling control and overall optimization of HVAC systems. Additionally, the results have referable values for implementing the most appropriate algorithm in practice.

Game-Based Adaptive FLOPs and Partition Point Decision Mechanism With Latency and Energy-Efficient Tradeoff for Edge Intelligence

Game-Based Adaptive FLOPs and Partition Point Decision Mechanism With Latency and Energy-Efficient Tradeoff for Edge Intelligence

AC-FPSK modulation for low-energy and low data rate optical wireless communications

This article proposes an amalgam of phase-shift keying and asymmetrically clipped frequency-shift keying (i.e., AC-FPSK) for low-energy/low-data rate optical wireless communications. We design a near-optimal, low-complexity harmonic receiver by analyzing the AC-FPSK time-domain (TD) and frequency-domain (FD) waveforms. The Euclidean distance analysis between AC-FPSK waveform pairs yields lower and upper bounds for theoretical bit error probability. Using numerical simulations, we present an exhaustive evaluation of the optimal cardinality of PSK, which illustrates the superiority of AC-FPSK. We show that AC-FPSK provides better energy efficiency versus spectral efficiency trade-off than state-of-the-art AC-FSK, direct current-FPSK, pulse amplitude modulation, and asymmetrically clipped optical orthogonal frequency division multiplexing. We also demonstrate that by making a suitable choice for the cardinality of PSK, the proposed 2-tap harmonic receiver for AC-FPSK can reach a similar complexity as the AC-FSK harmonic receiver.

Robust Beamforming Design for RSMA-Integrated Full-Duplex Communications: Energy and Spectral Efficiency Trade-off

Robust Beamforming Design for RSMA-Integrated Full-Duplex Communications: Energy and Spectral Efficiency Trade-off

Energy efficient power cap configurations through Pareto front analysis and machine learning categorization

The growing demand for more computing resources has increased the overall energy consumption of computer systems. To support this increasing demand, power and energy consumption must be considered as a constraint on software execution. Modern architectures provide tools for managing the power constraints of a system directly. The Intel Power Cap is a relatively new tool developed to give users fine-grained control over power usage at the central processing unit (CPU) level. The complexity of these tools, in addition to the high variety of modern heterogeneous architectures, hinders predictions of the energy consumption and the performance of any target software. The application of power capping technologies usually leads to the bi-objective optimization problem for energy efficiency and execution time but optimal power constraints could also produce exceeding performance losses. Thus, methods and tools are needed to calculate the proper parameters for power capping technologies, and to optimize energy efficiency. We propose a methodology to analyze the performance and the energy efficiency trade-offs using this power cap technology for a given application. A Pareto front is extracted for the multi-objective performance and energy problem, which represents multiple feasible configurations for both objectives. An extensive experimentation is carried out to categorize the different applications to determine the overall optimal power cap configurations. We propose the use of machine learning (ML) clustering techniques to categorize each application in the target architecture. The use of ML allows us to automate the process and simplifies the effort required to solve the optimization problem. A practical case is presented where we categorize the applications using ML techniques, with the possibility of adding a new application into an existing categorization.

Spectral and Energy Efficiency Trade-Off in UAV-Based Olive Irrigation Systems

Precision agriculture, also referred to as smart farming, is one of the main pillars of modern society to achieve the Sustainable Development Goals (SDGs). Precision agriculture aims to improve the quality and quantity of production while conserving scarce natural resources. Smart farming has grown in recent years thanks to the adoption of modern technologies, including artificial intelligence (AI) and the Internet of Things (IoT). In this work, we consider an irrigation system for olive orchards based on unmanned aerial vehicles (UAVs). Specifically, UAVs ensure remote sensing (RS), which offers the advantage of collecting vital information on a large temporal and spatial scale (which cannot be achieved with traditional technologies). However, UAV-based irrigation systems face tremendous challenges due to the various requirements of a powerful computing ability, battery capacity, energy efficiency, and spectral efficiency for different connected devices. This paper addresses the energy efficiency and spectral efficiency trade-off problem of UAV-based irrigation systems. We propose to adopt massive multiple input, multiple output (M-MIMO) technology to ensure wireless communication. In fact, this technology plays a significant role in future sixth-generation (6G) wireless mobile networks and has the potential to enhance the energy efficiency as well as the spectral efficiency. We design a network model with a three-layered architecture and analytically compute the achievable spectral efficiency and the energy efficiency of the studied system. Then, we numerically determine the optimal number of ground base station antennas as well as the optimal number of IoT devices that should be used to ensure the maximum energy efficiency while guaranteeing a high spectral efficiency. The numerical results prove that the proposed UAV-based irrigation system outperforms conventional systems and demonstrate that the best spectral and energy efficiency trade-off is obtained by using the M-MMSE combiner.

A review on energy efficiency in autonomous mobile robots

PurposeThis paper aims to provide a comprehensive analysis of the state of the art in energy efficiency for autonomous mobile robots (AMRs), focusing on energy sources, consumption models, energy-efficient locomotion, hardware energy consumption, optimization in path planning and scheduling methods, and to suggest future research directions.Design/methodology/approachThe systematic literature review (SLR) identified 244 papers for analysis. Research articles published from 2010 onwards were searched in databases including Google Scholar, ScienceDirect and Scopus using keywords and search criteria related to energy and power management in various robotic systems.FindingsThe review highlights the following key findings: batteries are the primary energy source for AMRs, with advances in battery management systems enhancing efficiency; hybrid models offer superior accuracy and robustness; locomotion contributes over 50% of a mobile robot’s total energy consumption, emphasizing the need for optimized control methods; factors such as the center of mass impact AMR energy consumption; path planning algorithms and scheduling methods are essential for energy optimization, with algorithm choice depending on specific requirements and constraints.Research limitations/implicationsThe review concentrates on wheeled robots, excluding walking ones. Future work should improve consumption models, explore optimization methods, examine artificial intelligence/machine learning roles and assess energy efficiency trade-offs.Originality/valueThis paper provides a comprehensive analysis of energy efficiency in AMRs, highlighting the key findings from the SLR and suggests future research directions for further advancements in this field.

Rate, Power, and Energy Efficiency Trade-Offs in Massive MIMO Systems With Carrier Aggregation

This work considers a multi-cell, multi-carrier massive MIMO network with carrier aggregation capabilities, and tackles both the rate versus power consumption and the rate versus energy efficiency (EE) trade-offs, by jointly optimizing the number of employed carriers, transmit antennas, base station density, and transmit power. Provably convergent algorithms for both trade-off problems are developed, together with closed-form results for the individual optimization of the considered resources, taking three main carrier aggregation techniques into account, namely inter-carrier, intra-carrier contiguous, and intra-carrier non-contiguous. Numerical results show how the use of carrier aggregation represents an effective way of increasing the network rate and EE, while keeping the power consumption at bay. By using carrier aggregation, it is possible to reduce the number of deployed antennas, without sacrificing the rate performance and increasing the system EE.

Active RIS Assisted Rate-Splitting Multiple Access Network: Spectral and Energy Efficiency Tradeoff

With the increasing demand of high data rate and massive access in both ultra-dense and industrial Internet-of-things networks, spectral efficiency (SE) and energy efficiency (EE) are regarded as two important and inter-related performance metrics for future networks. In this paper, we investigate a novel integration of rate-splitting multiple access (RSMA) and reconfigurable intelligent surface (RIS) into cellular systems to achieve a desirable tradeoff between SE and EE. Different from the commonly used passive RIS, we adopt reflection elements with active load to improve a newly defined metric, called resource efficiency (RE), which is capable of striking a balance between SE and EE. This paper focuses on the RE optimization by jointly designing the base station (BS) transmit precoding and RIS beamforming (BF) while guaranteeing the transmit and forward power budgets of the BS and RIS, respectively. To efficiently tackle the challenges for solving the RE maximization problem due to its fractional objective function, coupled optimization variables, and discrete coefficient constraint, the formulated nonconvex problem is solved by proposing a two-stage optimization framework. For the outer stage problem, a quadratic transformation is used to recast the fractional objective into a linear form, and a closed-form solution is obtained by using auxiliary variables. For the inner stage problem, the system sum rate is approximated into a linear function. Then, an alternating optimization (AO) algorithm is proposed to optimize the BS precoding and RIS BF iteratively, by utilizing the penalty dual decomposition (PDD) method. Simulation results demonstrate the superiority of the proposed design compared to other benchmarks.

Novel Heuristic Subcarrier Allocation for Spectral: Energy Efficiency Tradeoff Improvement in Underlay Cognitive Radio Network

Novel Heuristic Subcarrier Allocation for Spectral: Energy Efficiency Tradeoff Improvement in Underlay Cognitive Radio Network

Energy efficiency and delay determinacy tradeoff in energy harvesting-powered zero-touch deterministic industrial M2M communications

Data transmission for Industrial Internet of Things (IIoTs) is of the utmost importance, especially in the Industry 5.0 era, where human–machine collaboration is increasingly intensive. On the one hand, as a key characteristic for Industry 5.0, deterministic transmission aims to ensure the data arrive at destination devices accurately, has attracted remarkable attentions. On the other hand, energy efficiency problem is another major concern in Industry 5.0 since the massive Machine-Type Devices (MTDs). Consequently, in this paper, we investigate the tradeoff between energy efficiency and delay determinacy in energy harvesting-powered Zero-Touch Deterministic Industrial Machine-to-Machine (ZT-DI-M2M) communications. In particular, we first derive the probability of the transmission delay falling within a certain time window to characterize the delay determinacy and then figure out the relationship between delay determinacy and the energy efficiency by taking the system control performance into account. After that, in order to model the uncertainty of the stochastic environment, we formulate the problem as a stochastic game by jointly considering transmission power control, frequency spectrum allocation and the selection of base stations. Afterwards, a random graph-based sparse Long Short-Term Memory (LSTM) network is proposed to solve the optimization problem while reducing the computational complexity. Finally, numerical result demonstrates that the proposed sparse LSTM algorithm enables to achieve a specific delay determinacy with a higher energy efficiency as compared to Deep Q-Learning Network (DQN) and Deep Deterministic Policy Gradient (DDPG) algorithms. In addition, we also analysis the influence of sparse coefficient and the size of time window on the relationship between energy efficiency and delay determinacy.

Tethered UAV Deployment Strategies: The Coverage and Energy Efficiency Trade-Off

Tethered UAV Deployment Strategies: The Coverage and Energy Efficiency Trade-Off

Total and Minimum Energy Efficiency Tradeoff in Robust Multigroup Multicast Satellite Communications

Satellite communication is an indispensable part of future wireless communications given its global coverage and long-distance propagation. In satellite communication systems, channel acquisition and energy consumption are two critical issues. To this end, we investigate the tradeoff between the total energy efficiency (TEE) and minimum EE (MEE) for robust multigroup multicast satellite communication systems in this paper. Specifically, under the total power constraint, we investigate the robust beamforming aimed at balancing the TEE-MEE, so as to achieve the balance between the fairness and total performance on the system EE. For this optimization problem, we first model the balancing problem as a nonconvex problem while deriving its approximate closed-form average user rate. Then, the nonconvex problem is handled by solving convex programs sequentially with the help of the semidefinite relaxation and the concave-convex procedure. In addition, depending on the solution rank value, Gaussian randomization and eigenvalue decomposition method are applied to generate the feasible solutions. Finally, simulation results illustrate that the proposed approach can effectively achieve the balance between the TEE and MEE, thus realizing a tradeoff between fairness and system EE performance. It is also indicated that the proposed robust approach outperforms the conventional baselines in terms of EE performance.

Coordinated rate splitting and power allocation in energy-spectral efficiency tradeoff-based multicell networks

In order to support the energy efficiency (EE) and spectral efficiency (SE) in beyond 5G (B5G) wireless communication networks, we propose a flexible tradeoff design of these key performance metrics to obtain simultaneous EE and SE performance enhancement compared to conventional investigations where EE and SE are optimized independently. In this paper, a multi-objective optimization problem (MOOP) in the coordinated rate splitting (Co-RS) based multicell network is formulated to jointly optimize the private power, the coordinated common power and the common rate, by which both network EE and SE can be simultaneously achieved under the consideration of estimated channel error. To make the formulated MOOP tractable, we first transform the independent EE and SE maximization MOOP into a EE and SE tradeoff based single-objective optimization problem (SOOP) equivalently by introducing a flexible weight parameter. Then, fractional optimization is used to handle the multi-ratio objective function, by which an alternative optimization (AO) based sub-optimal solution is proposed to optimize the private power allocation, the joint common power and the spitted rate allocation iteratively with guaranteed convergence. Numerical results have demonstrated that the proposed Co-RS framework is able to achieve flexible EE-SE tradeoff in the multicell network, where better tradeoff has been achieved compared to the related benchmarks.

Energy Efficiency and Delay Tradeoff in an MEC-Enabled Mobile IoT Network

Mobile-edge computing (MEC) has recently emerged as a promising technology in the 5G era. It is deemed an effective paradigm to support computation intensive and delay-critical applications even at energy-constrained and computation-limited Internet of Things (IoT) devices. To effectively exploit the performance benefits enabled by MEC, it is imperative to jointly allocate radio and computational resources by considering nonstationary computation demands, user mobility, and wireless fading channels. This article aims to study the tradeoff between energy efficiency (EE) and service delay for multiuser multiserver MEC-enabled IoT systems when provisioning offloading services in a user mobility scenario. Particularly, we formulate a stochastic optimization problem with the objective of minimizing the long-term average network EE with the constraints of the task queue stability, peak transmit power, maximum CPU-cycle frequency, and maximum user number. To tackle the problem, we propose an online offloading and resource allocation algorithm by transforming the original problem into several individual subproblems in each time slot based on the Lyapunov optimization theory, which are then solved by convex decomposition and submodular methods. Theoretical analysis proves that the proposed algorithm can achieve a <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$[O(1/V), O(V)]$ </tex-math></inline-formula> tradeoff between EE and service delay. Simulation results verify the theoretical analysis and demonstrate our proposed algorithm can offer much better EE-delay performance in task offloading challenges, compared to several baselines.

Intelligent Network Solution for Improved Efficiency in 6G-Enabled Expanded IoT Network

The fast-moving world relies on intelligent connected networks to support the numerous applications of the expanded Internet-of-Things (IoT). The evolving communication requirements of this connected world require a new sixth generation (6G) radio to enable intelligent interaction with the massive number of connected objects. The energy management of billions of connected devices supporting massive Internet-of-Things (IoT) applications is the main challenge. These IoT devices and connected nodes are energy limited, and hence, energy-aware solutions are needed to enable seamless information flow between these communicating nodes. This paper presents an intelligent network solution for improved energy efficiency in a 6G-enabled expanded IoT network. A cell-free massive multiple input multiple output (mMIMO) technology is utilized for maximum energy efficiency with optimum network resource allocation. A practical power consumption model is proposed for the designed network topology which contains all the power components related to data transmission and circuit power. The proposed scheme aims to achieve maximum energy efficiency by the optimal allocation of pilot reuse factor and access point (AP) density for a given number of antennas at each AP and number of users. It is observed that the maximum energy efficiency of 5.2362 Mbit/Joule is achieved at the AP density of 29 and pilot reuse factor of 4 with PMMSE receive combining. In the end, the role of energy efficiency and area throughput tradeoff on the system performance is also evaluated, which suggests that both the energy efficiency and area throughput can be jointly increased until maximum energy efficiency is reached at a point.

Multi-Objective Optimization for Spectrum and Energy Efficiency Tradeoff in IRS-Assisted CRNs With NOMA

Non-orthogonal multiple access (NOMA) is a promising candidate for the sixth generation wireless communication networks due to its high spectrum efficiency (SE), energy efficiency (EE), and better connectivity. It can be applied in cognitive radio networks (CRNs) to further improve SE and user connectivity. However, the interference caused by spectrum sharing and the utilization of non-orthogonal resources can downgrade the achievable performance. In order to tackle this issue, intelligent reflecting surface (IRS) is exploited in a downlink multiple-input-single-output (MISO) CRN with NOMA. To realize a desirable tradeoff between SE and EE, a multi-objective optimization (MOO) framework is formulated under both the perfect and imperfect channel state information (CSI). An iterative block coordinate descent (BCD)-based algorithm is exploited to optimize the beamforming design and IRS reflection coefficients iteratively under the perfect CSI case. A safe approximation and the <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$ \mathcal {S}$ </tex-math></inline-formula> -procedure are used to address the non-convex infinite inequality constraints of the problem under the imperfect CSI case. Simulation results demonstrate that the proposed scheme can achieve a better balance between SE and EE than baseline schemes. Moreover, it is shown that both SE and EE of the proposed algorithm under the imperfect CSI can be significantly improved by exploiting IRS.