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The paper presents the two transmitter analog paths of 10 W and 400 W peak output power developed for micro- and macro-cell LTE450 base stations, respectively. Each path contains a chain of amplifiers with a final stage designed in the Doherty architecture to improve total transmitter power efficiency. The paths were optimized for linearity and efficiency considering, i.e., an output power vs. input power course shape and tuning of the amplifier's operating points while using low computational complexity MP algorithms.

The paper investigates the impact of Rayleigh-distributed statistical behavior of peak-to-average power ratio (PAPR) associated with a pre-clipped signal on the performance metrics of a direct current-biased optical orthogonal frequency division multiplexing (DCO-OFDM) system. The analytical model for the system takes into consideration a pre-clipped and dc-shifted baseband OFDM signal, driving an optical source over its linear operating range. The model employs a bias-scaling factor, which is heuristically varied over the entire range (0 to 1) to examine improvement in overall power efficiency. Further, it utilizes the cumulative distribution function (CDF) of the pre-clipped signal to get a weighted estimate of the available signal power within the clipped PAPR. The model also takes into consideration the clipping noise effects due to limited linearity of the optical source during electrical-to-optical conversion of baseband OFDM signal. Using this model, the paper aims to arrive at a realistic estimate of the system behavior in terms of bit error rate, electrical power-efficiency and spectral efficiency. Using theoretical simulation results, for a given set of operating parameters viz., signal power, PAPR, bias-scaling factor, modulation order and sub-carrier count, the paper examines the trade-offs involved in optimizing the performance metrics over appropriate dynamic range of the DCO-OFDM transmitter.

Having as a main objective the exploration of power efficiency of microcontrollers running machine learning models, this manuscript contrasts the performance of two types of state-of-the-art microcontrollers, namely ESP32 with an LX6 core and ESP32-S3 with an LX7 core, focusing on the impact of process acceleration technologies like cache memory and vectoring. The research employs experimental methods, where identical machine learning models are run on both microcontrollers under varying conditions, with particular attention to cache optimization and vector instruction utilization. Results indicate a notable difference in power efficiency between the two microcontrollers, directly linked to their respective process acceleration capabilities. The study concludes that while both microcontrollers show efficacy in running machine learning models, ESP32-S3 with an LX7 core demonstrates superior power efficiency, attributable to its advanced vector instruction set and optimized cache memory usage. These findings provide valuable insights for the design of power-efficient embedded systems supporting machine learning for a variety of applications, including IoT and wearable devices, ambient intelligence, and edge computing and pave the way for future research in optimizing machine learning models for low-power, embedded environments.

This paper proposes a new energy-efficient technique for high-frequency switched-capacitor stimulation systems. The proposed sequential high-frequency switched-capacitor (S-HFSC) structure employs a two-step charge transfer technique to reduce the voltage drop across the switches and therefore, achieves a high power efficiency of 77%, without using any external off-chip component. Based on the proposed structure, a high-frequency switched-capacitor stimulation circuit with active charge balancing is designed and simulated in a standard 0.18 µm CMOS process. Post-layout simulation results verify that the proposed structure achieves a peak power efficiency of 77%. Moreover, it occupies an area of 0.14 mm 2 .

Future beyond fifth-generation (B5G) and sixth-generation (6G) communication systems require a higher quality of service (QoS) along with meeting multiple objectives and traffic demands. Consequently, new multi-antenna technologies and massive multiple-input-multiple-output (mMIMO) architectures have been proposed in recent years. This paper delves into the foundational concepts that form the basis for the design of two potential future mMIMO network topologies: cell-free (CF) network and its successor, the radio stripe (RS) system. Key aspects of the mMIMO and CF network concepts are addressed, along with a practical sequential implementation based on RSs. This exploration encompasses intricate details of the channel estimation (CE) phase, as well as the uplink (UL) and downlink (DL) transmission and reception phases. We then focus on analyzing optimization concepts that underpin resource allocation (RA) algorithms, specifically those applied in UL power allocation and access point selection (APS) schemes in both CF and RS networks. This comprehensive understanding serves as a robust foundation for addressing the challenges inherent in achieving the conflicting B5G and 6G major key performance indicators (KPIs), such as enhancements on spectral efficiency (SE), power efficiency (PE), and computational complexity or load balance (LB).

This paper presents a system for jointly managing cooling units and workload assignment in modular data centers. The system aims to minimize power consumption while respecting temperature constraints, all in a thermally heterogeneous environment. Unlike traditional cooling controllers, which may over/under cool certain areas in the data center due to the use of a single setpoint, our framework does not have a single setpoint to satisfy. Instead, using a data-driven thermal model, the proposed system generates an optimal temperature map, the required temperature distribution matrix (RTDM), to be used by the controller, eliminating under/over cooling and improving power efficiency. The RTDM is the resulting temperature distribution when jointly considering workload assignment and cooling control. In addition, we propose the use of model predictive control (MPC) to regulate the operational variables of cooling units in a power-efficient fashion to comply with the RTDM. Within each iteration of the MPC loop, an optimization problem involving the thermal model is solved, and the underlying thermal model is updated. To prove the feasibility of the proposed power efficient system, it has been implemented on an actual modular data center in our facilities. Results from the implementation show the potential for considerable power savings compared to other control methods.

SciNet, one of seven regional HPC consortia operating under the Compute Canada umbrella, runs Canada's first and third fastest computers (as of June 2010) in a state-of-the-art, highly energy-efficient datacentre with a Power Usage Effectiveness (PUE) design-point of 1.16. Power efficiency, computational \"bang for the buck\" and system capability for a handful of flagship science projects were important criteria in choosing the nature of the computers and the data centre itself. Here we outline some of the lessons learned in putting together the systems and the data centre that hosts Canada's fastest computer to date.

We devise a tool-supported framework for achieving power-efficiency of data-flowhardware circuits. Our approach relies on formal control techniques, where the goal is to compute a strategy that can be used to drive a given model so that it satisfies a set of control objectives. More specifically, we give an algorithm that derives abstract behavioral models directly in a symbolic form from original designs described at Register-transfer Level using a Hardware Description Language, and for formulating suitable scheduling constraints and power-efficiency objectives. We show how a resulting strategy can be translated into a piece of synchronous circuit that, when paired with the original design, ensures the aforementioned objectives. We illustrate and validate our approach experimentally using various hardware designs and objectives.

In this work, a fully tin‐based, mixed‐organic‐cation perovskite absorber (FA)x(MA)1−xSnI3 (FA = NH2CH = NH2+, MA = CH3NH3+) for lead‐free perovskite solar cells (PSCs) with inverted structure is presented. By optimizing the ratio of FA and MA cations, a maximum power conversion efficiency of 8.12% is achieved for the (FA)0.75(MA)0.25SnI3‐based device along with a high open‐circuit voltage of 0.61 V, which originates from improved perovskite film morphology and inhibits recombination process in the device. The cation‐mixing approach proves to be a facile method for the efficiency enhancement of tin‐based PSCs. For the first time, an efficiency of over 8% is achieved for tin‐based perovskite solar cells along with a high open‐circuit voltage of 0.61 V by utilizing (FA)0.75(MA)0.25SnI3 as the absorber. The cation‐mixing method is proven to effectively improve the morphology of tin‐based perovskite films and reduce recombination process in the devices.

This paper summarizes the impact of soil/dirt in solar-panel performance in the Colombian Caribbean Region. The corresponding experiment compares the performance of two identical solar panels, which are subjected to different scenarios. The objective of this research is, to categorize or establish on site solar-radiation ranges in order to estimate the actual solar-panel efficiency compared to 1000 W/m2. The maximum power point is calculated using an I vs V transfer function approximation. The soiling related maximum-power-point impact analysis is carried out with a complete Multivariate Analysis of Variance (ANOVA) considering three fundamental factors: dirt/particles, solar radiation and day. According to experimental results, the soiling effect in the solar-panel efficiency reduction has been estimated as up to 6% during times of the day with the maximum solar radiation; while for lower radiations, the effect decreases exponentially becoming negligible on the available electric power. Thus, empirically it is shown that the effect of dirt/particles is significant from a clean solar-panel to one with light accumulation, but such a negative effect rapidly diminishes as accumulation changes from light to heavy. Therefore, it is suggested that once some dirt accumulates on a panel, a cleaning procedure can wait until the particle accumulation becomes heavy. Thus, this study can become a tool that solar-power-plant operators can employ to estimate the trade-off between photovoltaic-system power efficiency and its financial viability.