Search Results for author: Marco Di Renzo

Found 68 papers, 3 papers with code

A Tractable Closed-Form Approximation of the Ergodic Rate in Poisson Cellular Networks

no code implementations3 Dec 2024 Alexis I. Aravanis, Thanh Tu Lam, Olga Muñoz, Antonio Pascual-Iserte, Marco Di Renzo

In this course, the present paper introduces an accurate approximation for the moment generating function (MGF) of the aggregate other-cell interference created by base stations whose positions follow a Poisson point process of given spatial density.

Rate Region of RIS-Aided URLLC Broadcast Channels: Diagonal versus Beyond Diagonal Globally Passive RIS

no code implementations28 Oct 2024 Mohammad Soleymani, Alessio Zappone, Eduard Jorswieck, Marco Di Renzo, Ignacio Santamaria

We analyze the finite-block-length rate region of wireless systems aided by reconfigurable intelligent surfaces (RISs), employing treating interference as noise.

Degrees of Freedom of Holographic MIMO in Multi-user Near-field Channels

no code implementations7 Oct 2024 Houfeng Chen, Shaohua Yue, Marco Di Renzo, Hongliang Zhang

In this paper, we propose a novel method to calculate the DoF of HMIMO in multi-user near-field channels.

Blocking

Practical Challenges for Reliable RIS Deployment in Heterogeneous Multi-Operator Multi-Band Networks

no code implementations28 Aug 2024 Mehdi Monemi, Mehdi Rasti, Arthur S. de Sena, Mohammad Amir Fallah, Matti Latva-aho, Marco Di Renzo

While not an ideal solution, we discuss how ultra-narrowband metasurfaces can be incorporated into the manufacturing of RISs to mitigate some challenges of RIS deployment in wireless networks.

Secrecy Energy Efficiency Maximization in RIS-Aided Wireless Networks with Statistical CSI

no code implementations17 Aug 2024 Robert Kuku Fotock, Agbotiname Lucky Imoize, Alessio Zappone, Marco Di Renzo, Roberto Garello

The complexity of the proposed method is analyzed and numerical results are provided to show the performance of the proposed optimization method.

Energy Efficiency Comparison of RIS Architectures in MISO Broadcast Channels

no code implementations8 Aug 2024 Mohammad Soleymani, Ignacio Santamaria, Eduard Jorswieck, Marco Di Renzo, Jesús Gutiérrez

In a globally passive RIS, the power of the output signal of the RIS is not greater than its input power, but some RIS elements can amplify the signal.

CmWave and Sub-THz: Key Radio Enablers and Complementary Spectrum for 6G

no code implementations26 Jun 2024 Mayur V. Katwe, Aryan Kaushik, Keshav Singh, Marco Di Renzo, Shu Sun, Doohwan Lee, Ana G. Armada, Yonina C. Eldar, Octavia A. Dobre, Theodore S. Rappaport

Sixth-generation (6G) networks are poised to revolutionize communication by exploring alternative spectrum options, aiming to capitalize on strengths while mitigating limitations in current fifth-generation (5G) spectrum.

Secrecy Energy Efficiency Maximization in RIS-Aided Wireless Networks

no code implementations11 Jun 2024 Robert Kuku Fotock, Alessio Zappone, Marco Di Renzo

This work proposes a provably convergent and low complexity optimization algorithm for the maximization of the secrecy energy efficiency in the uplink of a wireless network aided by a Reconfigurable Intelligent Surface (RIS), in the presence of an eavesdropper.

Sustainable Wireless Networks via Reconfigurable Intelligent Surfaces (RISs): Overview of the ETSI ISG RIS

no code implementations9 Jun 2024 Ruiqi Liu, Shuang Zheng, Qingqing Wu, Yifan Jiang, Nan Zhang, Yuanwei Liu, Marco Di Renzo, and George C. Alexandropoulos

Reconfigurable Intelligent Surfaces (RISs) are a novel form of ultra-low power devices that are capable to increase the communication data rates as well as the cell coverage in a cost- and energy-efficient way.

Beamforming Optimization for Active RIS-Aided Multiuser Communications With Hardware Impairments

no code implementations16 Feb 2024 Zhangjie Peng, Zhibo Zhang, Cunhua Pan, Marco Di Renzo, Octavia A. Dobre, Jiangzhou Wang

Specifically, by exploiting the majorization-minimization approach, each subproblem is reformulated into a tractable surrogate problem, whose closed-form solutions are obtained by Lagrange dual decomposition approach and element-wise alternating sequential optimization method.

Unlocking Metasurface Practicality for B5G Networks: AI-assisted RIS Planning

no code implementations16 Oct 2023 Guillermo Encinas-Lago, Antonio Albanese, Vincenzo Sciancalepore, Marco Di Renzo, Xavier Costa-Pérez

The advent of reconfigurable intelligent surfaces(RISs) brings along significant improvements for wireless technology on the verge of beyond-fifth-generation networks (B5G). The proven flexibility in influencing the propagation environment opens up the possibility of programmatically altering the wireless channel to the advantage of network designers, enabling the exploitation of higher-frequency bands for superior throughput overcoming the challenging electromagnetic (EM) propagation properties at these frequency bands.

Deep Reinforcement Learning Management

Beyond Diagonal Reconfigurable Intelligent Surfaces with Mutual Coupling: Modeling and Optimization

no code implementations4 Oct 2023 Hongyu Li, Shanpu Shen, Matteo Nerini, Marco Di Renzo, Bruno Clerckx

This work studies the modeling and optimization of beyond diagonal reconfigurable intelligent surface (BD-RIS) aided wireless communication systems in the presence of mutual coupling among the RIS elements.

Tuning of Ray-Based Channel Model for 5G Indoor Industrial Scenarios

no code implementations12 Sep 2023 Gurjot Singh Bhatia, Yoann Corre, Marco Di Renzo

This paper presents an innovative method that can be used to produce deterministic channel models for 5G industrial internet-of-things (IIoT) scenarios.

A Transmit-Receive Parameter Separable Electromagnetic Channel Model for LoS Holographic MIMO

no code implementations28 Aug 2023 Tierui Gong, Chongwen Huang, Jiguang He, Marco Di Renzo, Mérouane Debbah, Chau Yuen

To support the extremely high spectral efficiency and energy efficiency requirements, and emerging applications of future wireless communications, holographic multiple-input multiple-output (H-MIMO) technology is envisioned as one of the most promising enablers.

Terahertz Communications and Sensing for 6G and Beyond: A Comprehensive Review

no code implementations19 Jul 2023 Wei Jiang, Qiuheng Zhou, Jiguang He, Mohammad Asif Habibi, Sergiy Melnyk, Mohammed El Absi, Bin Han, Marco Di Renzo, Hans Dieter Schotten, Fa-Long Luo, Tarek S. El-Bawab, Markku Juntti, Merouane Debbah, Victor C. M. Leung

Different from earlier surveys, this paper presents a comprehensive treatment and technology survey on THz communications and sensing in terms of advantages, applications, propagation characterization, channel modeling, measurement campaigns, antennas, transceiver devices, beamforming, networking, the integration of communications and sensing, and experimental testbeds.

Survey

Space-Time Phase Coupling in STMM-based Wireless Communications

no code implementations1 Jun 2023 Marouan Mizmizi, Dario Tagliaferri, Marco Di Renzo, Umberto Spagnolini

Space-time modulated metasurfaces (STMMs) are a recently proposed generalization of reconfigurable intelligent surfaces, which include a proper time-varying phase at the metasurface elements, enabling higher flexibility and control of the reflected signals.

Low Complexity Optimization for Line-of-Sight RIS-Aided Holographic Communications

no code implementations13 Apr 2023 Juan Carlos Ruiz-Sicilia, Marco Di Renzo, Merouane Debbah, H. Vincent Poor

The synergy of metasurface-based holographic surfaces (HoloS) and reconfigurable intelligent surfaces (RIS) is considered a key aspect for future communication networks.

Energy Efficiency Maximization in RIS-Aided Networks with Global Reflection Constraints

no code implementations6 Mar 2023 Robert K. Fotock, Alessio Zappone, Marco Di Renzo

This work addresses the issue of energy efficiency maximization in a multi-user network aided by reconfigurable intelligent surface (RIS) with global reflection capabilities.

Joint Beamforming for RIS-Assisted Integrated Sensing and Communication Systems

no code implementations3 Mar 2023 Yongqing Xu, Yong Li, J. Andrew Zhang, Marco Di Renzo, Tony Q. S. Quek

However, due to multiple performance metrics used for communication and sensing, the limited degrees-of-freedom (DoF) in optimizing ISAC systems poses a challenge.

On the Road to 6G: Visions, Requirements, Key Technologies and Testbeds

no code implementations28 Feb 2023 Cheng-Xiang Wang, Xiaohu You, Xiqi Gao, Xiuming Zhu, Zixin Li, Chuan Zhang, Haiming Wang, Yongming Huang, Yunfei Chen, Harald Haas, John S. Thompson, Erik G. Larsson, Marco Di Renzo, Wen Tong, Peiying Zhu, Xuemin, Shen, H. Vincent Poor, Lajos Hanzo

A series of white papers and survey papers have been published, which aim to define 6G in terms of requirements, application scenarios, key technologies, etc.

RIS-Position and Orientation Estimation in MIMO-OFDM Systems with Practical Scatterers

no code implementations9 Feb 2023 Sheng Hong, Minghui Li, Cunhua Pan, Marco Di Renzo, Wei zhang, Lajos Hanzo

A two-step positioning scheme is exploited, where the channel parameters are first acquired, and the position-related parameters are then estimated.

Position

Cooperative Beamforming and RISs Association for Multi-RISs Aided Multi-Users MmWave MIMO Systems through Graph Neural Networks

no code implementations8 Feb 2023 Mengbing Liu, Chongwen Huang, Marco Di Renzo, Merouane Debbah, Chau Yuen

Reconfigurable intelligent surface (RIS) is considered as a promising solution for next-generation wireless communication networks due to a variety of merits, e. g., customizing the communication environment.

Blocking Graph Neural Network

Localization as a key enabler of 6G wireless systems: A comprehensive survey and an outlook

no code implementations4 Feb 2023 Stylianos E. Trevlakis, Alexandros-Apostolos A. Boulogeorgos, Dimitrios Pliatsios, Konstantinos Ntontin, Panagiotis Sarigiannidis, Symeon Chatzinotas, Marco Di Renzo

Finally, insights that arise from the presented analysis are summarized and used to highlight the most important future directions for localization in 6G wireless systems.

Throughput analysis of RIS-assisted UAV wireless systems under disorientation and misalignment

no code implementations10 Dec 2022 Alexandros-Apostolos A. Boulogeorgos, Angeliki Alexiou, Marco Di Renzo

Reconfigurable intelligent surface (RIS)-assisted unmanned areal vehicles (UAV) communications have been identified as a key enabler of a number of next-generation applications.

Two-Timescale Transmission Design and RIS Optimization for Integrated Localization and Communications

no code implementations6 Sep 2022 Fan Jiang, Andrea Abrardo, Kamran Keykhoshravi, Henk Wymeersch, Davide Dardari, Marco Di Renzo

Reconfigurable intelligent surfaces (RISs) have tremendous potential to boost communication performance, especially when the line-of-sight (LOS) path between the user equipment (UE) and base station (BS) is blocked.

A Novel SCA-Based Method for Beamforming Optimization in IRS/RIS-Assisted MU-MISO Downlink

1 code implementation25 Aug 2022 Vaibhav Kumar, Rui Zhang, Marco Di Renzo, Le-Nam Tran

In this letter, we consider the fundamental problem of jointly designing the transmit beamformers and the phase-shifts of the intelligent reflecting surface (IRS) / reconfigurable intelligent surface (RIS) to minimize the transmit power, subject to quality-of-service constraints at individual users in an IRS-assisted multiuser multiple-input single-output downlink communication system.

Artificial Intelligence Empowered Multiple Access for Ultra Reliable and Low Latency THz Wireless Networks

no code implementations17 Aug 2022 Alexandros-Apostolos A. Boulogeorgos, Edwin Yaqub, Rachana Desai, Tachporn Sanguanpuak, Nikos Katzouris, Fotis Lazarakis, Angeliki Alexiou, Marco Di Renzo

In more detail, a fast and centralized joint user association, radio resource allocation, and blockage avoidance by means of a novel metaheuristic-machine learning framework is documented, that maximizes the THz networks performance, while minimizing the association latency by approximately three orders of magnitude.

Deep Reinforcement Learning Management

Multi-Point Integrated Sensing and Communication: Fusion Model and Functionality Selection

no code implementations16 Aug 2022 Guoliang Li, Shuai Wang, Kejiang Ye, Miaowen Wen, Derrick Wing Kwan Ng, Marco Di Renzo

Integrated sensing and communication (ISAC) represents a paradigm shift, where previously competing wireless transmissions are jointly designed to operate in harmony via the shared use of the hardware platform for improving the spectral and energy efficiencies.

Intelligent Omni-Surfaces (IOSs) for the MIMO Broadcast Channel

no code implementations27 Jun 2022 Abdelhamed Mohamed, Nemanja Stefan Perović, Marco Di Renzo

In this paper, we consider intelligent omni-surfaces (IOSs), which are capable of simultaneously reflecting and refracting electromagnetic waves.

A Novel RIS-Aided EMF Exposure Aware Approach using an Angularly Equalized Virtual Propagation Channel

no code implementations14 Jun 2022 Nour Awarkeh, Dinh-Thuy Phan-Huy, Marco Di Renzo

However, in some cases, where the BS serves the same user for a long period, and in some propagation conditions, such systems reduce their transmit power to avoid creating unwanted regions of electromagnetic field exposure exceeding the regulatory threshold, beyond the circle around the BS that limits the distance between people and the BS antenna.

A Novel RIS-Aided EMF-Aware Beamforming Using Directional Spreading, Truncation and Boosting

no code implementations14 Jun 2022 Nour Awarkeh, Dinh-Thuy Phan-Huy, Raphael Visoz, Marco Di Renzo

In some propagation conditions, when the base station serves the same target user equipment for a long period, it reduces the transmit power (and degrades the received power) to avoid creating high exposure regions located in the vicinity of the antenna and concentrated in few directions (corresponding to the best propagation paths between the antenna and the receiver).

Performance Analysis of Multi-Reconfigurable Intelligent Surface-Empowered THz Wireless Systems

no code implementations13 Feb 2022 Alexandros-Apostolos A. Boulogeorgos, Nestor Chatzidiamantis, Harilaos G. Sandalidis, Angeliki Alexiou, Marco Di Renzo

In this paper, we introduce a theoretical framework for analyzing the performance of multi-reconfigurable intelligence surface (RIS) empowered terahertz (THz) wireless systems subject to turbulence and stochastic beam misalignment.

Outage performance analysis of RIS-assisted UAV wireless systems under disorientation and misalignment

no code implementations28 Jan 2022 Alexandros-Apostolos A. Boulogeorgos, Angeliki Alexiou, Marco Di Renzo

In this paper, we analyze the performance of a reconfigurable intelligent surface (RIS)-assisted unmanned aerial vehicle (UAV) wireless system that is affected by mixture-gamma small-scale fading, stochastic disorientation, and misalignment, as well as transceivers hardware imperfections.

Diversity

An Overview of Signal Processing Techniques for RIS/IRS-aided Wireless Systems

no code implementations11 Dec 2021 Cunhua Pan, Gui Zhou, Kangda Zhi, Sheng Hong, Tuo Wu, Yijin Pan, Hong Ren, Marco Di Renzo, A. Lee Swindlehurst, Rui Zhang, Angela Yingjun Zhang

In the past as well as present wireless communication systems, the wireless propagation environment is regarded as an uncontrollable black box that impairs the received signal quality, and its negative impacts are compensated for by relying on the design of various sophisticated transmission/reception schemes.

Polarization-Based Reconfigurable Tags for Robust Ambient Backscatter Communications

no code implementations2 Nov 2021 Romain Fara, Dinh-Thuy Phan-Huy, Abdelwaheb Ourir, Yvan Kokar, Jean-Christophe Prévotet, Maryline Hélard, Marco Di Renzo, Julien de Rosny

In this paper, we propose a polarization-based reconfigurable antenna in order to improve the robustness of the tag-to-reader link against the source-to-reader direct interference.

TAG

Robust Ambient Backscatter Communications with Polarization Reconfigurable Tags

no code implementations2 Nov 2021 Romain Fara, Dinh-Thuy Phan-Huy, Abdelwaheb Ourir, Marco Di Renzo, Julien de Rosny

In this paper, for the first time, we propose to exploit a "polarization reconfigurable" antenna to improve robustness of the tag-to-reader link against the source-to-reader direct interference.

TAG

Spectrum Learning-Aided Reconfigurable Intelligent Surfaces for 'Green' 6G Networks

no code implementations3 Sep 2021 Bo Yang, Xuelin Cao, Chongwen Huang, Yong Liang Guan, Chau Yuen, Marco Di Renzo, Dusit Niyato, Merouane Debbah, Lajos Hanzo

In the sixth-generation (6G) era, emerging large-scale computing based applications (for example processing enormous amounts of images in real-time in autonomous driving) tend to lead to excessive energy consumption for the end users, whose devices are usually energy-constrained.

Autonomous Driving

Learning to Estimate RIS-Aided mmWave Channels

1 code implementation27 Jul 2021 Jiguang He, Henk Wymeersch, Marco Di Renzo, Markku Juntti

Inspired by the remarkable learning and prediction performance of deep neural networks (DNNs), we apply one special type of DNN framework, known as model-driven deep unfolding neural network, to reconfigurable intelligent surface (RIS)-aided millimeter wave (mmWave) single-input multiple-output (SIMO) systems.

Cascaded Composite Turbulence and Misalignment: Statistical Characterization and Applications to Reconfigurable Intelligent Surface-Empowered Wireless Systems

no code implementations29 Jun 2021 Alexandros-Apostolos A. Boulogeorgos, Nestor Chatzidiamantis, Harilaos G. Sandalidis, Angeliki Alexiou, Marco Di Renzo

Building upon the derived analytical expressions, we present novel closed-form formulas that quantify the joint impact of turbulence and misalignment on the outage performance for two scenarios of high interest namely cascaded multi-RIS-empowered free space optics (FSO) and terahertz (THz) wireless systems.

A Prototype of Reconfigurable Intelligent Surface with Continuous Control of the Reflection Phase

no code implementations25 May 2021 Romain Fara, Philippe Ratajczak, Dinh-Thuy Phan Huy, Abdelwaheb Ourir, Marco Di Renzo, Julien de Rosny

On the other hand, ambient backscatter communications (AmBC) is another promising technology that is tailored for addressing the energy efficiency requirements for the Internet of Things (IoT).

continuous-control Continuous Control

Model-Driven Deep Learning Based Channel Estimation and Feedback for Millimeter-Wave Massive Hybrid MIMO Systems

no code implementations22 Apr 2021 Xisuo Ma, Zhen Gao, Feifei Gao, Marco Di Renzo

To reduce the uplink pilot overhead for estimating the high-dimensional channels from a limited number of radio frequency (RF) chains at the base station (BS), we propose to jointly train the phase shift network and the channel estimator as an auto-encoder.

Reconfigurable Intelligent Surface -Assisted Ambient Backscatter Communications -- Experimental Assessment

no code implementations15 Mar 2021 Romain Fara, Dinh-Thuy Phan-Huy, Philippe Ratajczak, Abdelwaheb Ourir, Marco Di Renzo, Julien de Rosny

On the other hand, in an ambient backscatter system, a device, named tag, communicates towards a reader by backscattering the waves of an ambient source (such as a TV tower).

TAG

Intelligent Spectrum Learning for Wireless Networks with Reconfigurable Intelligent Surfaces

no code implementations2 Mar 2021 Bo Yang, Xuelin Cao, Chongwen Huang, Chau Yuen, Lijun Qian, Marco Di Renzo

Reconfigurable intelligent surface (RIS) has become a promising technology for enhancing the reliability of wireless communications, which is capable of reflecting the desired signals through appropriate phase shifts.

Path Loss Modeling and Measurements for Reconfigurable Intelligent Surfaces in the Millimeter-Wave Frequency Band

no code implementations21 Jan 2021 Wankai Tang, Xiangyu Chen, Ming Zheng Chen, Jun Yan Dai, Yu Han, Marco Di Renzo, Shi Jin, Qiang Cheng, Tie Jun Cui

The refined model gives more accurate estimates of the path loss of RISs comprised of unit cells with a deep sub-wavelength size.

Machine Learning-Enabled Joint Antenna Selection and Precoding Design: From Offline Complexity to Online Performance

no code implementations18 Jan 2021 Thang X. Vu, Symeon Chatzinotas, Van-Dinh Nguyen, Dinh Thai Hoang, Diep N. Nguyen, Marco Di Renzo, Bjorn Ottersten

We investigate the performance of multi-user multiple-antenna downlink systems in which a BS serves multiple users via a shared wireless medium.

Information Theory Information Theory

Optimization of RIS-aided MIMO Systems via the Cutoff Rate

no code implementations9 Dec 2020 Nemanja Stefan Perović, Le-Nam Tran, Marco Di Renzo, Mark F. Flanagan

The main difficulty concerning optimizing the mutual information (MI) in reconfigurable intelligent surface (RIS)-aided communication systems with discrete signaling is the inability to formulate this optimization problem in an analytically tractable manner.

Information Theory Information Theory

Uplink Achievable Rate Maximization for Reconfigurable Intelligent Surface Aided Millimeter Wave Systems with Resolution-Adaptive ADCs

no code implementations27 Nov 2020 Yue Xiu, Jun Zhao, Ertugrul Basar, Marco Di Renzo, Wei Sun, Guan Gui, Ning Wei

In this letter, we investigate the uplink of a reconfigurable intelligent surface (RIS)-aided millimeter-wave (mmWave) multi-user system.

Quantization

MetaSensing: Intelligent Metasurface Assisted RF 3D Sensing by Deep Reinforcement Learning

no code implementations25 Nov 2020 Jingzhi Hu, Hongliang Zhang, Kaigui Bian, Marco Di Renzo, Zhu Han, Lingyang Song

To tackle this challenge, we formulate an optimization problem for minimizing the cross-entropy loss of the sensing outcome, and propose a deep reinforcement learning algorithm to jointly compute the optimal beamformer patterns and the mapping of the received signals.

Deep Reinforcement Learning reinforcement-learning +1

Reconfigurable Intelligent Surfaces for 6G Systems: Principles, Applications, and Research Directions

no code implementations9 Nov 2020 Cunhua Pan, Hong Ren, Kezhi Wang, Jonas Florentin Kolb, Maged Elkashlan, Ming Chen, Marco Di Renzo, Yang Hao, Jiangzhou Wang, A. Lee Swindlehurst, Xiaohu You, Lajos Hanzo

Reconfigurable intelligent surfaces (RISs) or intelligent reflecting surfaces (IRSs), are regarded as one of the most promising and revolutionizing techniques for enhancing the spectrum and/or energy efficiency of wireless systems.

Intelligent Omni-Surface: Ubiquitous Wireless Transmission by Reflective-Transmissive Metasurface

no code implementations2 Nov 2020 Shuhang Zhang, Hongliang Zhang, Boya Di, Yunhua Tan, Marco Di Renzo, Zhu Han, H. Vincent Poor, Lingyang Song

Intelligent reflecting surface (IRS), which is capable to adjust propagation conditions by controlling phase shifts of the reflected waves that impinge on the surface, has been widely analyzed for enhancing the performance of wireless systems.

Stochastic Learning-Based Robust Beamforming Design for RIS-Aided Millimeter-Wave Systems in the Presence of Random Blockages

no code implementations21 Sep 2020 Gui Zhou, Cunhua Pan, Hong Ren, Kezhi Wang, Maged Elkashlan, Marco Di Renzo

To enhance the robustness of hybrid analog-digital beamforming in the presence of random blockages, we formulate a stochastic optimization problem based on the minimization of the sum outage probability.

Stochastic Optimization

Analysis and Optimization for RIS-Aided Multi-Pair Communications Relying on Statistical CSI

no code implementations22 Jul 2020 Zhangjie Peng, Tianshu Li, Cunhua Pan, Hong Ren, Wei Xu, Marco Di Renzo

Simulation results verify the correctness of the obtained results and show that the proposed GA method has almost the same performance as the globally optimal solution.

Quantization

Reconfigurable Intelligent Surfaces: Principles and Opportunities

no code implementations7 Jul 2020 Yuanwei Liu, Xiao Liu, Xidong Mu, Tianwei Hou, Jiaqi Xu, Marco Di Renzo, Naofal Al-Dhahir

In this context, we provide a comprehensive overview of the state-of-the-art on RISs, with focus on their operating principles, performance evaluation, beamforming design and resource management, applications of machine learning to RIS-enhanced wireless networks, as well as the integration of RISs with other emerging technologies.

BIG-bench Machine Learning Management +1

Coverage Analysis and Scaling Laws of Ultra-Dense Networks

no code implementations30 Jun 2020 Imene Trigui, Sofiene Affes, Marco Di Renzo, Dushantha Nalin K. Jayakody

In this paper, we develop an innovative approach to quantitatively characterize the performance of ultra-dense wireless networks in a plethora of propagation environments.

Deep Denoising Neural Network Assisted Compressive Channel Estimation for mmWave Intelligent Reflecting Surfaces

2 code implementations3 Jun 2020 Shicong Liu, Zhen Gao, Jun Zhang, Marco Di Renzo, Mohamed-Slim Alouini

Integrating large intelligent reflecting surfaces (IRS) into millimeter-wave (mmWave) massive multi-input-multi-ouput (MIMO) has been a promising approach for improved coverage and throughput.

Compressive Sensing Denoising

Wireless 2.0: Towards an Intelligent Radio Environment Empowered by Reconfigurable Meta-Surfaces and Artificial Intelligence

no code implementations23 Feb 2020 Haris Gacanin, Marco Di Renzo

We introduce "Wireless 2. 0": The future generation of wireless communication networks, where the radio environment becomes controllable, programmable, and intelligent by leveraging the emerging technologies of reconfigurable metasurfaces and artificial intelligence (AI).

Management reinforcement-learning +2

Reconfigurable Intelligent Surface Aided Power Control for Physical-Layer Broadcasting

no code implementations7 Dec 2019 Huimei Han, Jun Zhao, Zehui Xiong, Dusit Niyato, Wenchao Zhai, Marco Di Renzo, Quoc-Viet Pham, Weidang Lu

Our goalis to minimize the transmit power at the BS by jointly designing the transmit beamforming at the BSand the phase shifts of the passive elements at the RIS.

Holographic MIMO Surfaces for 6G Wireless Networks: Opportunities, Challenges, and Trends

no code implementations27 Nov 2019 Chongwen Huang, Sha Hu, George C. Alexandropoulos, Alessio Zappone, Chau Yuen, Rui Zhang, Marco Di Renzo, Mérouane Debbah

Future wireless networks are expected to evolve towards an intelligent and software reconfigurable paradigm enabling ubiquitous communications between humans and mobile devices.

Robust Beamforming Design for Intelligent Reflecting Surface Aided MISO Communication Systems

no code implementations14 Nov 2019 Gui Zhou, Cunhua Pan, Hong Ren, Kezhi Wang, Marco Di Renzo, Arumugam Nallanathan

In this paper, we study the worst-case robust beamforming design for an IRS-aided multiuser multiple-input single-output (MU-MISO) system under the assumption of imperfect CSI.

Wireless Communications with Reconfigurable Intelligent Surface: Path Loss Modeling and Experimental Measurement

no code implementations13 Nov 2019 Wankai Tang, Ming Zheng Chen, Xiangyu Chen, Jun Yan Dai, Yu Han, Marco Di Renzo, Yong Zeng, Shi Jin, Qiang Cheng, Tie Jun Cui

The measurement results match well with the modeling results, thus validating the proposed free-space path loss models for RIS, which may pave the way for further theoretical studies and practical applications in this field.

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