Mean Energy Efficiency Research Articles

Performance Analysis of NOMA-Based Hybrid Satellite-Terrestrial Relay Networks With CCI

Hybrid satellite-terrestrial relay networks (HSTRNs) which exploit relay cooperative technology in the integrated system of satellites and terrestrial base stations (BSs) are expected to offer enhanced transmission performance benefited from the spatial gain of independent fading paths. To enable multi-user accessing and resource sharing among users, non-orthogonal multiple access (NOMA) technique can be applied in HSTRNs. In this paper, we consider a NOMA-based HSTRN where a satellite transmits signals to a relay base station (RBS) and a destination ground station (DGS), and RBS also serves a cellular user (CU) and DGS simultaneously by leveraging NOMA scheme. We assume that the satellite shares spectrum with a number of cellular small BSs (SBSs), hence SBSs will cause co-channel interference (CCI) to DGS, RBS and CU. We examine the transmission characteristics of the satellite and terrestrial links, and derive the closed-form expressions of the outage probability (OP) and average symbol error rate (ASER) of DGS and CU, respectively. Specially, we consider the effect of perfect and imperfect successive interference cancellation (SIC) on the ASER performance of CU. Then, considering the stochastic characteristics of satellite links and CCI, we analyze the mean energy efficiency (MEE) of the satellite and propose an optimal power allocation scheme which maximizes the MEE of the satellite and achieves system performance optimization. Simulation results demonstrate the effectiveness of the theoretical analysis and the proposed power allocation scheme.

Role of renewable, non-renewable energy consumption and carbon emission in energy efficiency and productivity change: Evidence from G20 economies

The challenge of achieving sustainable economic development with a secure environmental system is a global challenge faced by both developed and developing countries. Energy Efficiency (EE) is crucial in achieving sustainable economic growth while reducing ecological impacts. This research utilizes the Slack-Based Measure Data Envelopment Analysis (SBM-DEA) and the Malmquist-Luenberger Index (MLI) method to evaluate EE and productivity changes from 1995 to 2020 across G20 countries. The study uses four different input–output bundles to gauge the impact of renewable and non-renewable energy consumption and carbon emissions on EE and productivity changes. The study results show that including renewable energy consumption improves the average EE from 0.783 to 0.8578, but energy productivity declines from 1.0064 to 0.9988. Incorporating bad output (carbon emissions) in the estimation process enhances renewable EE and productivity change, resulting in an average EE of 0.6678 and MLI of 1.0044. Technological change is identified as the primary determinant of energy productivity growth in scenarios 1 and 2, while technical efficiency determines energy productivity change in scenarios 3 and 4. The Kruskal-Wallis test reveals a significant statistical difference between the mean EE and MLI scores of G20 countries.

Optimization of Resource Allocation Model With Energy-Efficient Cooperative Sensing in Green Cognitive Radio Networks

Green cognitive radios show promise for high energy efficiency (EE) in the future of wireless communications. Spectrum sensing refers to an energy-consuming procedure that allows cognitive users to independently identify unused radio spectrum segments and prevent interference to primary users, and it should be minimized due to resource limitations. In this paper, we present a wireless multiple-access channel function to establish the primary users’ presence and the mean EE optimization problem of the cognitive radio systems with mathematical structure computation purposes. EE, as the average throughput to the average energy consumption ratio, is used to measure the network’s performance subject to detection constraint. More specifically, since secondary users are generally battery-powered devices, saving on energy is crucial. We aim to decrease the energy consumption of secondary users while maximizing the total EE and preserving the accurate sensing by maximizing the sensing time detection subject to secondary user power constraints and minimum data rate. To address the non-convexity optimization problem, one can consider energy-efficient power allocation based on an iterative method. Simulation results show the optimum of our framework when combined by the multiple-access channel computation-based scheme. Green cognitive radio should consider the tradeoff against its complexity and maximum available EE metric. However, one can observe from the simulation results that the improved EE presented in this work yields much higher when compared with others with the same detection performance.

Energy-Efficient Cooperative Spectrum Sensing With Reporting Errors in Hybrid Spectrum Sharing CRNs

Recently, cooperative spectrum sensing (CSS) in cognitive radio networks has been extensively researched. However, most of the studies mainly focus on maximizing the spectral efficiency while the energy consumption is generally ignored. However, since the secondary users (SUs) are usually battery-powered devices, energy saving is very important. This paper studies the mean energy efficiency (EE) maximization problem of the CSS system using the hybrid spectrum sharing (HSS) scheme. Specifically, the effects of imperfect spectrum sensing and reporting channel errors on the EE are considered. Our goal is to maximize the mean EE of SUs while maintaining the detection accuracy by jointly optimizing the sensing time and the number of cooperative SUs, subject to the SUs' average/peak transmission power constraints (ATPC/PTPC) and minimum data rate constraint, and the average interference power constraint of primary user. To address the non-convexity of the optimization problem, an energy-efficient iterative power allocation algorithm is developed. Simulations compare the achievable mean EE under ATPC/PTPC with three hard combining fusing rules using the HSS scheme and the opportunistic spectrum access (OSA) scheme, respectively; the results show that our proposed HSS scheme can obtain higher mean EE than the conventional OSA scheme and that the EE achieved under ATPC is better than that under PTPC. Moreover, among the three hard combining fusing rules, the Majority rule has the best EE.

Mean Energy Efficiency Maximization in Cognitive Radio Channels With PU Outage Constraint

Energy efficiency (EE) is very crucial for future green wireless communication systems, particularly for cognitive radio networks (CRNs). However, the EE design for CRNs in fast-fading scenarios has not been fully studied. This letter investigates the mean EE maximization problem for the secondary user (SU) in the fading cognitive radio channels consisting of both SUs and primary users (PUs). We adopt PU outage probability constraint to ensure the quality of service (QoS) of PUs and consider both the peak and mean transmit power constraints for SUs. However, this problem is nontrivial since the mean EE is nonconvex and PU outage probability constraint belongs to chance constraints. With the aid of the fractional programming and Lagrangian duality theory, we propose an efficient algorithm to derive the optimal power allocation strategy for SU to maximize its mean EE while guaranteeing the QoS of PUs. Simulation results assess the performance of our proposed scheme and show the tradeoff between SUs' mean EE and PU outage probability threshold only in some certain range.