Shannon Wireless

This study introduces a #Projection-#Calibrated #Web #Energy-#Carbon #Calculator (#PC-#WECC) that maps web workloads to energy and emissions by integrating data-center #power #usage #effectiveness (#PUE), network energy intensity, and country-level grid carbon intensity projections through 2030. To capture application-level effects, they develop an #Empirically-#Fitted #Cache #Efficiency #Regression #Model (#ECERM) based on controlled server experiments that measure joules-per-gigabyte for cached and non-cached delivery. ---- Sebastian Soler Domínguez, Rajesh K Ahir Jagruti Thakur More details can be found at this link: https://lnkd.in/gnQSbtxv

In this paper, the authors present a novel iterative #MMSE based #channel #estimation technique for full duplex #Ambient #Backscatter #Communication (#AmBC) systems with an efficient pilot utilization scheme. They then use these MMSE estimates to derive expressions for the rates achievable over all the links in the considered AmBC system. Finally, using extensive numerical simulations, validate their derived results and show that the proposed algorithm outperforms the state of the art machine learning based channel estimation algorithms considered in the literature. ---- B. Srikar Ranganath, Ribhu Chopra, Kumar Appaiah More details can be found at this link: https://lnkd.in/gd7ywnYx

This paper proposes a hardware-agnostic, automated, and non-invasive framework for exposure assessment in real-world deployments. By leveraging a blind #Downlink #Control #Information (#DCI) decoding mechanism, their method extracts scheduling information to isolate user-specific #Physical #Downlink #Shared #Channel (#PDSCH) resources. Experimental validation in #frequency #range (#FR)1 and FR2 demonstrates that this approach enables a procedural mapping of radiated power while obviating the need for full carrier bandwidth saturation. By abstracting the acquisition hardware as an IQ sample provider, the pipeline is designed to operate across heterogeneous receivers, including low-cost #Software-#Defined #Radios (#SDRs). While absolute metrological accuracy remains dependent on specific device calibration, this methodology supports a streamlined, user-specific #Maximum-#Power #Extrapolation (#MPE) analysis workflow at the signal-processing level that is independent of network traffic conditions. ---- Daniele Franci, Ivan Palama, Stefano Mangione, Sara Adda, Mattia Vaccarono, PhD, Giuseppe Bianchi More details can be found at this link: https://lnkd.in/g6MEGi7D

In this work, the authors investigate the deployment of active #reconfigurable #intelligent #surfaces (#RIS) mounted on #unmanned #aerial #vehicles (#UAV) for dynamically controlling the propagation environment. In contrast to classical centralized optimization, they model a joint optimization problem, including UAV trajectory, active RIS beamforming, amplification, phase shifts, and non-orthogonal multiple access power allocation, as a decentralized one. They propose a #multi-#agent #reinforcement #learning (#MARL) framework based on the algorithm multi-agent proximal policy optimization with a paradigm called centralized training and decentralized execution. The Base Station, UAV, and RIS play the role of independent agents. They learn cooperative policies in order to maximize a multi-objective reward function, which combines network sum-rate, energy efficiency, and user fairness. ---- Monzur Morshed, Mostafa Zaman Chowdhury, PhD, Yeong Min Jang More details can be found at this link: https://lnkd.in/gQPwN_-Y

This paper studys the sum rate maximization problem in an #stacked #intelligent #metasurfaces (#SIM)-based #multiuser (#MU) #multiple-#input #single-#output (#MISO) downlink system. A vast majority of pioneer studies, if not all, address this fundamental problem using the prevailing #alternating #optimization (#AO) framework, where the #digital #beamforming (#DB) and SIM phase shifts are optimized alternately. However, many of these approaches suffer from suboptimal performance, quickly leading to performance saturation, when the number of SIM layers increases assuming a fixed SIM thickness. In this letter, they demonstrate that significant performance gains can still be achieved, and such saturation does not occur with the proposed method in the considered setting. To this end, they provide practical design guidelines to improve AO-based optimization of digital precoders and SIM phase shifts. Specifically, they show that (i) optimizing the SIM phase shifts first yields significant performance improvements, compared to optimizing the DB first; and (ii) when applying #projected #gradient (#PG) methods, which are gradually becoming more popular to optimize the phase shifts thanks to their scalability, they find that using an iterative PG method achieves better performance than a single PG step, which is commonly used in existing solutions. ---- Eduard E. Bahingayi, Shuying Lin, Murat Uysal, Marco Di Renzo, Le-Nam Tran More details can be found at this link: https://lnkd.in/gTW6cKup

In this paper, the authors deploy #stacked #intelligent #metasurface (#SIM) to improve the performance of multi-user #multiple-#input #single-#output (#MISO) wireless systems through a low complexity manner with reduced numbers of transmit #radio #frequency #chains. In particular, an optimization formulation for the joint design of the SIM #phase #shifts and the transmit #power #allocation is presented, which is efficiently tackled via a customized #deep #reinforcement #learning (#DRL) approach that systematically explores pre-designed states of the SIM-parametrized smart wireless environment. The presented performance evaluation results demonstrate the proposed method’s capability to effectively learn from the wireless environment, outperforming both conventional precoding schemes and optimization algorithms. ---- Hao Liu, Jiancheng An, George Alexandropoulos, Derrick Wing Kwan Ng, Chau Yuen, Lu Gan More details can be found at this link: https://lnkd.in/esg9ZrjM

This paper presents an #edge #artificial #intelligence (#AI) system for optimized #energy #management in #smart #buildings using advanced #time-#series #forecasting techniques. To overcome the high latency, network congestion, and reliability limitations of conventional #Internet #of #Things (#IoT)-cloud-based solutions, the authors deploy #deep #learning models directly on edge devices for real-time energy prediction and localized decision-making. The proposed methodology integrates multi-sensor data processing, #machine #learning-based consumption pattern recognition, and local data storage to reduce cloud dependence and minimize latency. The system provides a scalable and efficient framework for intelligent energy optimization in smart building infrastructures. ---- Sid Ahmed Boudaoud, Rayane Aboud, Leïla HAMDAD, Imed ALLAL, PhD More details can be found at this link: https://lnkd.in/gWemRyuJ

In this paper, the authors propose a lightweight #online/#offline #certificateless #signature (#L-#OOCLS) scheme and design a #heterogeneous #remote #anonymous #authentication #protocol (#HRAAP) for secure #Internet #of #Things (#IoT)-based healthcare applications. The proposed framework enables remote #wireless #body #area #networks (#WBANs) users to anonymously access healthcare services while addressing critical #security and #privacy challenges associated with sensitive medical information. The #L-#OOCLS scheme is proven secure under the #random #oracle #model, while the proposed HRAAP is designed to resist various attack types with reduced #computation #overhead and lower #power #consumption for WBAN clients. In addition, the authors provide an application scenario demonstrating the practicality of the proposed scheme in IoT healthcare environments. ---- Mutaz Elradi S. Saeed (PhD); Ying Qin Liu; Guiyun Tian; Bin Gao; Fagen Li More details can be found at this link: https://lnkd.in/gdiqKB-v

This letter investigates the downlink (#DL) performance of a wireless communication system employing #rate-#splitting #multiple #access (#RSMA) in the presence of interference from uplink (#UL) users and a radar target. To account for practical deployment conditions, the authors incorporate imperfections in #channel #state #information (#CSI) estimation and #successive #interference #cancellation (#SIC) at the DL users. For the considered system, they derive a closed-form expression for the #outage #probability (#OP) of the DL user and utilize it to analyze system #throughput. Furthermore, the study proposes an optimal #power #allocation strategy for RSMA aimed at minimizing outage probabilities and maximizing throughput under interference and imperfect CSI/SIC conditions. ---- Shaktimaan Debendra Pratap; Manoj B R; Kuntal Deka More details can be found at this link: https://lnkd.in/gagvpknH

This study investigates the diagnostic value and consistency of #chest #imaging for detecting #COVID-19 using #deep #learning techniques. To address the limited accessibility and feasibility of imaging-based diagnosis, the authors develop a web-based prediction platform named #COVIDz capable of analyzing #chest #X-#ray images to estimate the presence or absence of COVID-19 infection. The proposed framework employs a #Custom #VGG model for automated image classification and disease prediction, enabling rapid and systematic screening of potentially infected patients through #medical #image #analysis. ---- Mohammed Seghir Guellil; samir Ghouali; Emad Kamil Hussein; Mohammed Anis Oukebdane; Amina DINAR; walid cherifi More details can be found at this link: https://lnkd.in/ge_8zW4a