This paper designs a #gradient #descent #algorithm for iteratively updating the phase shift of each meta-atom in the #staked #intelligent #metasurface (#SIM) to minimize the fitting error between the SIM's response and the #two-#dimensional (#2D) DFT matrix. To further improve the #direction-#of-#arrival (#DOA) estimation accuracy, they configure the phase shifts in the input layer of the SIM to generate a set of 2D DFT matrices having #orthogonal #spatial #frequency #bins.----Jiancheng An, Chau Yuen, Yong Liang GUAN, @Marco Di Renzo, @Mérouane Debbah, @H. Vincent Poor, @Lajos Hanzo More details can be found at this link: https://lnkd.in/gbBYMDpt
Shannon Wireless
Higher Education
This page aims to share the latest research in wireless communications.
About us
This channel aims to share the latest research in wireless communications. We look forward to exploring these topics together and welcoming your feedback. Please feel free to like, retweet, comment or favorite to stay engaged in the discussion.
- Industry
- Higher Education
- Company size
- 51-200 employees
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- Nonprofit
Updates
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This paper addresses the problem of finding #better #mobile #charger #trajectories for #mobile #sensors, where sensor devices are assumed to be mobile. The authors first introduce two problems. One is the #MaxAC #problem that maximizes the amount of charge from a charger to sensors within a given time constraint; the other is the #MinCD problem that minimizes the charging delay to provide all the sensors with at least a #target #power #level. To this end, they design the charging utility prediction model to estimate how much power can be transferred during a given time interval. Then, two trajectory planning algorithms are proposed, namely #TPA-#MaxAC and #TPA-#MinCD, for each problem.----Rihito Tsuchida, @Kazuya Sakai, @Min-Te Sun, Wei-Shinn "Jeff" Ku More details can be found at this link: https://lnkd.in/g9gsHSMb
On Wireless Charging for Mobile Sensors
ieeexplore.ieee.org
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This paper presents a decentralized and #multi-#tier #framework for #joint #slot #allocation and #transmit-#sleep scheduling in wireless network nodes with thin #energy #budget. The key learning objectives of this architecture are: #collision-#free #transmission #scheduling, #reducing #energy #consumption, and #improving #network #performance. This is achieved using a cooperative and decentralized learning behavior of multiple #Reinforcement #Learning (#RL) agents. The resulting architecture provides throughput-sustainable support for data flows while minimizing energy expenditure and #sleep-#induced #packet #losses. To achieve this, a concept of Context is introduced to the RL framework in order to capture network traffic dynamics. The resulting #Contextual #Deep #Q-#Learning (#CDQL) model makes the system adaptive to dynamic and heterogeneous network load. ----Hrishikesh Dutta, @Amit Kumar Bhuyan, Subir Biswas More details can be found at this link: https://lnkd.in/gExyBzep
Contextual Deep Reinforcement Learning for Flow and Energy Management in Wireless Sensor and IoT Networks
ieeexplore.ieee.org
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#Non-#terrestrial #networks (#NTNs) will complement #terrestrial #networks (#TNs) in #5G and beyond, which can be attributed to recent deployment and standardization activities. Maximizing the efficiency of NTN communications is critical to unlock its full potential and reap its numerous benefits. One method to make communications more efficient is by the usage of #multi-#connectivity (#MC), which allows a user to connect to multiple base stations simultaneously. It is standardized and widely used for TNs, but for MC to be used in the NTN environment, several challenges must be overcome. This article discusses #challenges related to #MC in #NTNs and proposes solutions to the #identified #challenges----Mikko Majamaa More details can be found at this link: https://lnkd.in/gcEYNcQE
Toward Multi-Connectivity in Beyond 5G Non-Terrestrial Networks: Challenges and Possible Solutions
ieeexplore.ieee.org
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This article aim to unfold the opportunities that can be reaped from integrating #large #generative #artificial #intelligence (#GenAI) models into the #Telecom #domain. In particular, the authors first highlight the applications of large GenAI models in future #wireless #networks, defining potential use-cases and revealing insights on the associated theoretical and practical challenges. Furthermore, they unveil how #6G can open up new opportunities through connecting multiple #on-#device large GenAI models, and hence, paves the way to the collective intelligence paradigm. Finally, they put a forward-looking vision on how large GenAI models will be the key to realize #self-#evolving networks.----Lina Bariah, @Qiyang Zhao, @Hang Zou, @Yu Tian, Faouzi Bader, Merouane Debbah More details can be found at this link: https://lnkd.in/gnpUuacK
Large Generative AI Models for Telecom: The Next Big Thing?
ieeexplore.ieee.org
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This work presents a new techniques provided by #IEEE #802.#11az for enhanced #secured #positioning in the #mmWave #band. Additionally, this paper assesses 802.11az mmWave accuracy using a novel trigonometry solution, compares it with advanced positioning solutions, and identifies open research challenges.----PABLO PICAZO MARTINEZ, Carlos B., @Jorge Martin-Perez, Milan Groshev, Antonio De La Oliva More details can be found at this link: https://lnkd.in/gFParZ96
IEEE 802.11az Indoor Positioning with mmWave
ieeexplore.ieee.org
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This paper analyzes the history and current state of #Artificial #Intelligence (#AI)-powered #traffic #classification (#TC) on the Internet, highlighting unresolved research questions. Indeed, despite extensive research, key desiderata goals to #product-#line implementations remain. AI presents untapped potential for addressing the complex and evolving challenges of TC, drawing from successful applications in other domains. The authors identify novel #Machine #Learning (#ML) topics and solutions that address unmet TC requirements, shaping a comprehensive research landscape for the TC future. They also discuss the #interdependence #of #TC desiderata and identify obstacles hindering AI-powered next-generation solutions. ----Giuseppe Aceto, Domenico Ciuonzo, Antonio Montieri, Valerio Persico, Antonio Pescape' More details can be found at this link: https://lnkd.in/gedYkJFr
AI-powered Internet Traffic Classification: Past, Present, and Future
ieeexplore.ieee.org
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This article provides an overview of state of the art of #hardware #acceleration for #open #radio #access #networks (#RAN) in the #fifth #generation (#5G) cellular networks, by reviewing RAN architectural evolution, presenting the role of hardware acceleration in RAN, and introducing the latest #O-#RAN Alliance's development of acceleration abstraction layer. They also present a practical implementation of inline hardware acceleration for open RAN layer 1 processing and identify several areas for future exploration.----Lopamudra K., Xingqin Lin, Elena A., Elena A., Tim Martin More details can be found at this link: https://lnkd.in/gp4c9wPM
Hardware Acceleration for Open Radio Access Networks: A Contemporary Overview
ieeexplore.ieee.org
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This paper investigates the #optimal #pilot #power #allocation during the channel estimation stage to improve the ergodic channel gain of #reconfigurable #intelligent #surface (#RIS)-assisted systems under practical imperfect #channel #state #information (#CSI). Specifically, the authors commence by deriving an explicit closed-form expression of the #ergodic #channel gain of a #multi-#RIS-#aided #communication system that takes into account channel estimation errors. Then, they formulate the pilot power allocation problem to maximize the ergodic channel gain under #imperfect #CSI, subject to the average pilot power constraint. Then, the method of Lagrange multipliers is invoked to obtain the optimal pilot power allocation solution, which indicates that allocating more power to the pilots for estimating the weak reflection channels is capable of effectively improving the ergodic channel gain under imperfect CSI.----Jiancheng An, Chau Yuen More details can be found at this link: https://lnkd.in/gy6aeZKb
Pilot Power Allocation for Channel Estimation in a Multi-RIS Aided Communication System
ieeexplore.ieee.org
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This paper proposes #user-#wise singular #value #decomposition (#UW-#SVD) to accelerate the convergence of iterative #multiple-#input #multiple-#output (#MIMO) detectors. Its basic principle is to perform SVD on each user’s sub-channel matrix to eliminate intra-user interference. Then, the MIMO signal model is effectively transformed into an #equivalent #signal (#e-#signal) model, comprising an #e-#channel #matrix and an #e-#signal #vector. ----@Jiuyu Liu, Yi Ma, @Jinfei Wang, Rahim Tafazolli More details can be found at this link: https://lnkd.in/eafmyqmt
Accelerating Iteratively Linear Detectors in Multi-User (ELAA-)MIMO Systems with UW-SVD
ieeexplore.ieee.org