Power Consumption Model Research Articles

Integer Programming Based Optimization of Power Consumption for Data Center Networks

With the quickly developing data centers in smart cities, reducing energy consumption and improving network performance, as well as economic benefits, are essential research topics. In particular, Data Center Networks do not always run at full capacity, which leads to significant energy consumption. This paper experiments with a range of optimization tools to find the optimal solutions for the Integer Linear Programming (ILP) model of network power consumption. The study reports on experiments under three communication patterns (near, long, and random), measuring runtime and memory consumption in order to evaluate the performance of different ILP solvers.While the results show that, for near traffic pattern, most of the tools rapidly converge to the optimal solution, CP-SAT provides the most stable performance and outperforms the other solvers for the long traffic pattern. On the other hand, for random traffic pattern, Gurobi can be considered to be the best choice, since it is able to solve all the benchmark instances under the time limit and finds solutions faster by 1 or 2 orders of magnitude than the other solvers do.

Provably Energy Efficiency and Lower Power Consumption Based on HOA in 5G MIMO-NOMA Systems

The rapid expansion of 5G communication networks necessitates improved energy efficiency and reduced power consumption. This article explores the integration of Hybrid Optimization Algorithms (HOA) in 5G MIMO-NOMA systems, aiming to enhance energy efficiency and minimize power usage. The proposed methodology leverages MIMO technology and Non-Orthogonal Multiple Access (NOMA). We introduce a new power consumption model based on HOA, recognizing MIMO-NOMA as pivotal in future wireless communication systems. HOA allows simultaneous service for more users, leading to heightened energy efficiency and reduced power consumption compared to conventional MIMO or NOMA systems. A streamlined user admission scheme is presented, admitting users based on ascending power requirements to meet Quality of Service criteria. Numerical results demonstrate the efficacy of HOA and the power allocation strategy in enhancing energy efficiency and user admission. Comparative analysis shows lower power consumption and approximately a 10% increase in energy efficiency compared to traditional methods and other algorithms like GA, PSO, SPPA, and the water-filling algorithm.

A comparative assessment of the power generation via S-shape and M-shape PV system and its impact on a residential consumer

This paper compares the differences in power generation and resident experience between S-shape and M-shape photovoltaic (PV) systems at the same location with the same number of modules. First, the sky anisotropy model (Hay-Davies-Klucher-Reindl, HDKR) is used to simulate solar irradiance (direct, diffuse, and reflection radiation) received by solar modules based on the laws of movement between the Earth and the Sun in the solar system. Next, the annual output power of two different PV system arrangements is calculated based on the solar irradiation received. Furthermore, because the solar module receives three types of solar radiation, interference is added to each of the three parts one by one to simulate shadows cast by moving objects such as birds, leaves, or clouds. Subsequently, off-grid and grid-connected power consumption models are established, and two different photovoltaic power generation systems are used at the power generation end. Lastly, the photovoltaic systems were tested based on residents’ electricity consumption habits, considering both off-grid and grid-connected households. Among them, the grid-connected model adopts the Particle Swarm Optimization (PSO) algorithm to generate an intelligent and flexible power consumption strategy. The results, based on data from Naha, Japan, show that with the same number of modules, the M-shape arrangement generates less annual power than the S-shape. In summer, though, the M-shape can generate more power, and the two structures have similar immunity to disturbances. The S-shape and M-shape have little difference in the power consumption experience. However, the general applicability of these results to other regions remains uncertain, and further testing is needed to account for environmental variations.

Container Power Consumption Prediction Based on GBRT-PL for Edge Servers in Smart City

Edge computing and IoT devices have been widely deployed in smart city applications due to the rapid promotion and implementation of 5G communication technology. Limited by power supply and hardware computing capability, applications on edge servers are mostly deployed and run in the form of container micro-services to improve resource utilization. Therefore, identifying the power consumption at the container granularity is of great importance for the load scheduling and service quality assurance of edge servers. In this paper, a GBRT-PL-based container power prediction method is proposed. Performance metrics with a strong correlation between container and server power consumption are selected for power consumption modeling. To effectively suit the nonlinear relationship between container performance metrics and server power consumption, the integrated predictive capabilities of multiple regression trees and the segmented linear model of single regression tree leaf nodes are applied. The data from the experiments prove that the GBRT-PL model predicts power consumption more accurately than other models for single and multiple container groups. The highest average relative error rate in the four multi-container group tests is 6.72%, whereas the highest relative error rate in the 90% quantile is 11.66%. In addition, it can accurately predict the majority of power consumption peaks, which contributes to the precise detection of power consumption anomalies.

The influence of the static component of the water supply network on the change in the power consumption of the pumping unit with frequency control

RELEVANCE. The effective functioning of urban water supply systems plays an important role in maintaining the normal life of cities and towns. Of particular importance is the management of pressure and the optimization of hydraulic regimes in water-wired networks, since they directly affect the reliability of water supply and the efficient use of resources. However, the determination of the optimal parameters and methods of regulation in each specific case requires careful research and analysis. In the context of a constant increase in requirements for automation of water supply and growing investments in its infrastructure, the problem of a correct feasibility study for such investments is of particular relevance.PURPOSE. For smooth regulation of the pressure characteristic of the pump in the conditions of the need to maintain a given pressure of the water supply network, frequency converters are widely used. In the context of the significance of an accurate assessment of investment costs for the automation of pumping units, it becomes necessary to obtain equations that take into account the features of the operation of the water supply network at the pre-design stage. In this regard, the authors of the article set themselves the goal of studying the degree of influence of the static component of the water supply network on the change in the power consumption of the pumping unit with frequency regulation and testing the obtained dependencies on real statistics of the water and energy consumption modes of the Sozh water intake of the Gomel water utility.METHODS. To solve the tasks, classical formulas of similarity of the pumping unit were used, reflecting the relationship between flow, pressure and power consumption. The mean squared error, the mean absolute error and the average absolute error in percentages were used as the evaluation metric for verifying the electric power supply model of the pumping unit.RESULTS. The study demonstrates a significant improvement in the accuracy of power consumption modeling when using a modified coefficient reflecting the degree of power change when the frequency of the supply network changes. When using this approach, the standard error is reduced more than twice, from 0.35 to 0.167, the average absolute error is reduced from 0.347 to 0.165, and the average absolute percentage error is reduced from 0.20% to 0.08%.CONCLUSION The conducted research confirms the effectiveness of the use of frequency control of pumping units, which provides a nonlinear change in electrical power, and demonstrates the possibility of more accurate forecasting of electricity consumption, taking into account the specifics of the water supply network. The results of this work can be useful for projects to optimize urban water supply systems, providing more accurate planning and use of resources.

Performance modeling and analysis for randomly walking mobile users with Markov chains

We treat user equipments (UEs) and mobile edge clouds (MECs) as M/G/1 queueing systems, which are the most suitable, powerful, and manageable models. We propose a computation offloading strategy which can satisfy all UEs served by an MEC and develop an efficient method to find such a strategy. We use discrete-time Markov chains, continuous-time Markov chains, and semi-Markov processes to characterize the mobility of UEs, and calculate the joint probability distribution of the locations of UEs at any time. We extend our Markov chains to incorporate mobility cost into consideration, and are able to obtain the average response time of a UE with location change penalty. We can algorithmically predict the overall average response time of tasks generated on a UE and also demonstrate numerical data and examples. We consider the power constrained MEC speed setting problem and develop an algorithm to solve the problem for two power consumption models.

TinyML based Deep Learning Model for Activity Detection

Our physical and emotional well-being are directly impacted by our body positions. In addition to promoting a confident, upright image, maintaining good body posture during various activities also ensures that our musculoskeletal system is properly aligned. On the other side, bad posture can result in a number of musculoskeletal conditions, discomfort, and reduced productivity. Accurate systems that can detect posture in real time, activity detection, are required due to the rising use of wearable technology and the growing interest in health and fitness tracking. The goal of this project is to create a TinyML model for wearable activity detection that will allow users to assess their posture and make necessary corrections in order to improve their health and general well-being. The project intends to contribute to the creation of useful posture detection technologies that can be quickly implemented on wearable devices for widespread usage by leveraging machine learning algorithms and wearable sensor data. For reliable posture categorization, the model architecture combines deep neural networks (DNN) and LSTM layers. With the development and implementation of the TinyML model, a significant decrease in the model's power consumption, memory, and latency was achieved without any compromise in the accuracy. This work can be used in the fields of health, wellness, rehabilitation, corporate life, sports and fitness to keep track of calories burned, activity duration, distance traveled, posture analysis, and real-time tracking.

Energy-Efficient Joint Scheduling of Pumps and Valves in Water Distribution Network Using Hybrid Optimization Algorithm

Traditionally, pumping efficiency and pressure control have been studied as separate disciplines in water companies, with a focus on optimizing pump operation and managing leakage. Present-day siphons are frequently outfitted with variable speed drives; consequently, the siphon outlet tension could be constrained by controlling siphon speed. If there are siphons upstream from a tension-decreasing valve (PRV) with no moderate tank, the PRV gulf strain could be diminished by changing siphoning in the upstream piece of the organization. In addition, when optimizing pump operation, consideration should be given to the impact of pressure-dependent leakage on the resulting schedules. Thus, this article considers the streamlining of siphon and valve plans for complex huge-scope water circulation organizations (WDN). The review takes care of the siphoning booking issue, which tries to acquire the timetable of on/off switches for each siphon and valve that limits energy power cost, taking into account the energy consumed by the functioning siphons. This schedule generates the flow and pressure through the network and it has to satisfy the demand for all nodes, conserve energy, to minimize head loss. To execute and safeguard clean water assets, functional improvement of WDSs should consider both energy and upkeep costs while deciding the ideal timetable for siphons and valves. The point of this examination is to create and confirm an energy model that can work on the effectiveness of equal siphon frameworks. Accordingly, the research proposed the mathematical model of shaft power consumption using the quadratic polynomial fitting. Additionally, investigated the potential application of Variable Speed Pumps (VSPs) to improve pressure reliability, leakage, and electrical power consumption in Water Distribution Networks (WDNs). To execute and safeguard clean water assets, functional improvement of Water Circulation Frameworks (WDSs) should consider both energy and upkeep costs while deciding the ideal timetable for siphons and valves. The point of this examination is to create and confirm an energy model that can work on the effectiveness of equal siphon frameworks. The hybrid binary dragonfly-enhanced Particle Swarm Optimization (PSO) algorithm is proposed for joint pumping and valve scheduling problems in WDS. The optimal scheduling is conducted based on three different energy tariffs. The Matlab programming climate is utilized to demonstrate this strategy with the assistance of the EPANet Matlab tool compartment. Analyzing a range of scenarios with varying time frames, operational limitations, and network changes, the proposed WDN solution showed its capability to efficiently create and address optimization challenges tailored to diverse needs.

Power Consumption Modeling of Wheeled Mobile Robots With Multiple Driving Modes

Wheeled mobile robots (WMRs) are being used in many real-world applications. The main challenge in deployment of WMRs is their limited battery power. In order to complete a given task autonomously, it becomes necessary to estimate a priori the total energy consumption for the task assigned to a WMR. An accurate power consumption model allows to estimate the energy consumption of a WMR. This paper presents the power consumption modeling of WMRs using a power-based graphical modeling tool called bond graph. First, the dynamic bond graph model is developed for the generalized configuration of a WMR according to number and types of wheels attached in chassis. Then, the developed bond graph model is utilized for the power consumption modeling of a WMR. The proposed power consumption model includes the various driving modes of a WMR, by considering its degree of steerability. Finally, the proposed methodology is applied to a reconfigurable outdoor WMR with a degree of steerability varying from 0 to 2. The estimated power from the model is compared with the measured power from the experiments. Further, the proposed power model is used for the energy planning in a trajectory tracking task based on changing driving modes of a reconfigurable WMR.

Energy Maximization for Ground Nodes in UAV-Enabled Wireless Power Transfer Systems

In UAV-enabled wireless power transfer (WPT) systems, the UAV is usually planned to fly and charge the ground nodes (GNs), so as to prolong the lifetime of wireless sensor networks. But in fact, the long charging distance between the UAV and the GNs makes the GNs receive less energy. In order to maximize the received energy of GNs, this paper focuses on the charging process and proposes a V-shaped WPT scheme, where the UAV descends to the optimal hover position and charges the GNs, so as to transfer more energy to the GNs. Moreover, considering that the GNs far from the hover position receive little energy in the V-shaped WPT scheme, we further develop an Inverted Trapezoidal WPT scheme to improve the fairness of the energy received by the GNs, in which the UAV continuously charges the GNs whether it is hovering or flying horizontally after lowering its altitude. Because the UAV’s flight and maneuvering are strictly constrained by its loading energy, we establish the UAV’s propulsion power consumption model in different flight attitudes and formulate the optimization problems of the GNs’ received energy in two schemes, and then develop two algorithms to solve them. The simulation results show that the performance metrics of UAV’s energy consumption, the efficiency and fairness of GNs’ received energy are better than the compared schemes.

MACROMODELING OF LOCAL POWER SUPPLY SYSTEM BALANCE FORECASTING USING FRACTAL PROPERTIES OF LOAD AND GENERATION SCHEDULES

A method of forecasting the balance of electricity consumption of urban development objects, civil purposes using discrete macromodels is proposed. We consider the power supply system (PSS) of the district, which is characterised by power supply from general-purpose power grids, as well as having its own generation of electricity from renewable energy sources (RES). Such a local electric power system (LES) under certain conditions can be operated as an independent balanced electrical facility. For optimal operation of the LES under these conditions, it is necessary to predict its power consumption schedules. The proposed macromodelling method allows to develop deterministic models of power consumption with the required accuracy on the basis of retrospective information without the use of data preprocessing procedures. The solution to the problem of forecasting electricity consumption schedules is simplified by using only basic or deterministic characteristics in the construction of the model. These include fractal properties of PSS load schedules.

Impact of Circuit Power on the Energy Efficiency of Massive Multiple-Input Multiple-Output (MIMO) Cellular Systems

Massive multiple-input multiple-output (MIMO) systems are designed to increase data capacity systems. However, there are growing social and economic concerns in developing these communication technologies. Energy efficient systems are desirable for these systems considering the costs and minimizing resources. Thus, the optimization of these approaches is significant in the advancement of green communication. This paper converges on the circuit power in modelling massive MIMO systems. Specifically, to apply various power consumption of the transceiver chains, to determine the desired power of the base station value for maximum energy efficiency, and to define the effects of a realistic circuit power consumption model in Massive MIMO systems. Mathematical models were developed, and the corresponding codes were simulated using MATLAB. Different processing schemes were also studied, namely, maximum ratio transmission/maximum ratio combining, and zero forcing for the perfect and imperfect single-cells scenario. In contrast, multiple cells consider different re-use factors. As observed, a low power wattage consumption of transceiver chains and base stations is desired to produce a more remarkable relative change in energy efficiency. Local oscillators' power usage is also recommended to be low wattage. Low-power systems utilize low energy consumption but not disregarding higher energy efficiency. Other factors in data throughput – bandwidth, transmit power, noise power spectral density, and path loss, can be studied to optimize energy-efficient massive MIMO systems.

Effects of Spanwise Spacing on the Interaction of Tandem Pitching Hydrofoils

The interaction between a pair of tandem in-line oscillating hydrofoils is presented. The hydrofoils undergo sinusoidal pitching about their leading edges with a fixed Strouhal number of and a Reynolds number of 10,000. The streamwise spacing, spanwise spacing, and phase offset between the hydrofoils are varied. Force measurements are employed to investigate changes in thrust, lift, spanwise force, power consumption, and propulsive efficiency. A method to mitigate confounding factors from connecting rod drag is employed using streamlined fairings. Near and far streamwise spacing regions are identified with a transition occurring near 0.875 chord lengths downstream. Decreasing streamwise spacing in the far region causes a rise in the maximum power consumption of the follower hydrofoil. Decreasing streamwise spacing in the near region results in an opposite trend, with a sharp drop in maximum average power consumption by the follower. An empirical model for power consumption of the follower is developed. Increased spanwise spacing is found to weaken the interaction between the hydrofoils, driving them toward their isolated performance. This phenomenon is related to the spanwise contraction of the wake shed by the leader and is a function of the overlap of the wake region impacting the follower.

A Rank Analysis and Ensemble Machine Learning Model for Load Forecasting in the Nodes of the Central Mongolian Power System

Forecasting electricity consumption is currently one of the most important scientific and practical tasks in the field of electric power industry. The early retrieval of data on expected load profiles makes it possible to choose the optimal operating mode of the system. The resultant forecast accuracy significantly affects the performance of the entire electrical complex and the operating conditions of the electricity market. This can be achieved through using a model of total electricity consumption designed with an acceptable margin of error. This paper proposes a new method for predicting power consumption in all nodes of the power system through the determination of rank coefficients calculated directly for the corresponding voltage level, including node substations, power supply zones, and other parts of the power system. The forecast of the daily load schedule and the construction of a power consumption model was based on the example of nodes in the central power system in Mongolia. An ensemble of decision trees was applied to construct a daily load schedule and rank coefficients were used to simulate consumption in the nodes. Initial data were obtained from daily load schedules, meteorological factors, and calendar features of the central power system, which accounts for the majority of energy consumption and generation in Mongolia. The study period was 2019–2021. The daily load schedules of the power system were constructed using machine learning with a probability of 1.25%. The proposed rank analysis for power system zones increases the forecasting accuracy for each zone and can improve the quality of management and create more favorable conditions for the development of distributed generation.

Transition Factors of Power Consumption Models for CPA Attacks on Cryptographic RISC-V SoC

Physical cryptographic devices are vulnerable to side-channel information leakages during operation. They are widely used in software as well as hardware implementations, ranging from microcontrollers and microprocessors to hardware accelerators in System on Chips (SoCs). Nowadays, cryptographic RISC-V SoCs are becoming the most prominent solution compared to the rest. Cryptographic accelerators provide users with a very high level of flexibility and customization of chips suited to specific applications in these systems. First, this research aims to confirm the effectiveness of the Correlation Power Analysis attack on cryptographic SoCs based on three different power consumption models. In each model, the effectiveness of an attack depends on the transition factor, which is a ratio related to different characteristics of the device's power consumption. Then, we focus on modifying the configuration on the SoC and attacking the AES hardware implementation on these designs. The experimental results show that applying the Switching Distance model brings the highest performance. With our suggested range of transition factors, the number of traces needed to find the secret key can be reduced by 13.35% in the best case.

Network-Assisted Full-Duplex Cell-Free Massive MIMO: Spectral and Energy Efficiencies

We consider network-assisted full-duplex (NAFD) cell-free massive multiple-input multiple-output (CF-mMIMO) systems, where full-duplex (FD) transmission is virtually realized via half-duplex (HD) hardware devices. The HD access points (APs) operating in uplink (UL) mode and those operating in downlink (DL) mode simultaneously serve DL and UL user equipments (UEs) in the same frequency bands. We comprehensively analyze the performance of NAFD CF-mMIMO from both a spectral efficiency (SE) and energy efficiency (EE) perspectives. Specifically, we propose a joint optimization approach that designs the AP mode assignment, power control, and large-scale fading (LSFD) weights to improve the sum SE and EE of NAFD CF-mMIMO systems. We formulate two mixed-integer nonconvex optimization problems of maximizing the sum SE and EE, under realistic power consumption models, and the constraints on minimum individual SE requirements, maximum transmit power at each DL AP and UL UE. The challenging formulated problems are transformed into tractable forms and two novel algorithms are proposed to solve them using successive convex approximation techniques. More importantly, our approach can be applied to jointly optimize power control and LSFD weights for maximizing the sum SE and EE of HD and FD CF-mMIMO systems, which, to date, has not been studied. Numerical results show that: (a) our joint optimization approach significantly outperforms the heuristic approaches in terms of both sum SE and EE; (b) in CF-mMIMO systems, the NAFD scheme can provide approximately 30% SE gains, while achieving a remarkable EE gain of up to 200% compared with the HD and FD schemes.

Optimized Low-Powered Wide Area Network within Internet of Things

The Internet of Things (IoT) is rapidly becoming an integral part of everyday life. LPWANs have been introduced to support the billions of internet-connected devices and the data they produce. LPWANs are capable of providing reliable connectivity even in low-density areas and devices consuming a low amount of energy. The exponential increase in the use of IoT applications across the globe will continue to generate more and more data traffic within the IoT network. Hence, it will increase device battery usage that may reduce the battery life expectancy limits. Thus, End Devices (EDs) within the IoT network in the near future will rise up to billions of devices operating in public, industry, and personal networks, generating a necessity for more correct and reliable energy conservation technology. This prompted the research work on an optimized low-powered wide area network within IoT. This paper focuses on three different strategies: LoRa power consumption model design, simulation of IoT wireless sensor networks, and implementation of SF allocation across the wireless sensor network and results analysis. The experiment has been carried out in various stages: firstly running a simulation over a wireless sensor network without optimization using MATLAB Simulink and obtaining the following result of 6.3997e-17 joules power consumption. Secondly, the authors test the network with power optimization using particle swarm optimization algorithms and obtained a better result of 2.5230e-17 joules. The LoRa energy consumption is reduced by 60%. Lastly, different simulation tests of LoRaWAN protocols with respect to throughput, packet loss, delay, data transmission, buffer size, and network density. The results presented on the graph showed that the proposed model outperforms the existing models. Hence, appropriate spreading factor allocation has increased the power efficiency of LoRa end device battery.

Multi-Objective Optimization of Micro-Milling Parameters—The Trade-Offs between Machining Quality, Efficiency, and Sustainability in the Fabrication of Thin-Walled Microstructures

Micro-milling has found extensive applications in machining components with thin-walled microstructures, such as terahertz slow-wave structures, microfluidic chips, and micro-molds. Due to the influence of size effects, micro-milling exhibits higher specific energy consumption compared with traditional milling, implying that more energy is consumed to remove a unit volume of material, particularly in challenging-to-machine materials like Ti-6Al-4V. Historically, research on parameter optimization for micro-milling has predominantly focused on enhancing machining quality and efficiency, with limited attention given to energy efficiency. However, in the context of the “double carbon” strategy, energy conservation and emissions reduction have garnered significant attention in the manufacturing industry. Therefore, this paper proposes a micro-milling parameter-based power consumption model. Based on this, a specific energy consumption model can be obtained. Moreover, evolutionary algorithms are utilized for the optimization of micro-milling parameters, which aims to achieve comprehensive enhancements in both machinability and sustainability. The optimization objectives encompass improving surface quality, dimensional accuracy, material removal rate, and specific energy consumption during the micro-milling process for thin-walled micro-structures. Among them, NSGA-III achieves the best optimization results. Under conditions in which cutting energy consumption and processing efficiency are very close, the optimization outcomes based on NSGA-III lead to the best machining quality, including the minimum surface roughness and dimensional errors, and the largest surface fractal dimension. The optimal combination of micro-milling parameters is n = 28,800 rpm, fz = 2.6 μm/t, and ap = 62 μm.

RETRACTED ARTICLE: Energy efficient IRS assisted 6G network for Industry 5.0

The real world applications are more prone to difficulties of challenges due to fast growth of technologies and inclusion of artificial intelligence (AI) based logical solutions. The massive internet-of-things (IoT) devices are involved in number of Industry 5.0 applications like smart healthcare, smart manufacturing, smart agriculture, smart transportation. Advanced wireless techniques, customization of services and different technologies are experiencing a major transformation. The desire to increase the communication reliability without adding energy overhead is the major challenge for massive IoT enabled networks. To cope up with the above challenges, Industry 5.0 requirements needs to be monitored at the remote level which again adds on the communication challenge. Use of relays in 6G based wireless networks is denied due to high requirement of energy. Therefore in this paper, Intelligent reflecting surfaces (IRSs) assisted energy constrained 6G wireless networks are studied. To provide seamless connection between the communicating mobile nodes, IRS with an array of reflecting elements are configured in the system set up. A use-case scenario of IRS enabled network in Internet-of-Underwater things (IoUT) for smart ocean transportation is also provided. The IRS assisted wireless network is evaluated for target rates achieved. A power consumption model of the IRS supported system is also proposed to optimise the energy efficiency of the system. Further, the paper evaluates the impact of number of reflecting elements N on the IRS and the phase resolution b of each element on the system performance. The energy efficiency improves by 20% for IRS with N=100\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$N=100$$\\end{document} with b=2\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$b=2$$\\end{document} over IRS with b=1\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$b=1$$\\end{document}.

Analysis of EPON–WiMAX and EPON–LTE integrated networks with power consumption modeling

Analysis of EPON–WiMAX and EPON–LTE integrated networks with power consumption modeling