arXiv subjects

Marco Di Renzo

Publications and source records attributed to Marco Di Renzo.

At least 271 records · Page 15Linked to original sources

arXiv2020

Ambient backscatters-friendly 5G networks: creating hot spots for tags and good spots for readers

In this paper, we present an ambient backscatters-friendly 5G network that creates locations with large power (hot spots) for tags and good reception locations (quiet spots or coherent spots) for readers. The massive multiple input multiple output (M-MIMO) antenna and beamforming capability of the 5G network is used as follows. In a first step, a training device (separate from tags and readers) is used to send pilots and train the 5G network to perform focusing and/or nulling onto marked locations. In a second step, tags and readers are positioned onto the marked locations. The robustness of M-MIMO beamforming to slight changes in the environment is exploited. Our initial simulation results, in a multipath propagation channel environment, show that creating a hot spot on a tag improves the tag-reader range however with a low probability of detection. Creating a hot spot on a tag and a good reception spot on a reader at the same time, improves the tag-reader range with 99% probability of detection. The study also shows that beamforming does not degrade the performance of a legacy 5G communication.

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arXiv2020

Intelligent Reflecting Surface Aided Network: Power Control for Physical-Layer Broadcasting

As a recently proposed idea for future wireless systems, intelligent reflecting surface (IRS) can assist communications between entities which do not have high-quality direct channels in between. Specifically, an IRS comprises many low-cost passive elements, each of which reflects the incident signal by incurring a phase change so that the reflected signals add coherently at the receiver. In this paper, for an IRS-aided wireless network, we study the problem of power control at the base station (BS) for physical-layer broadcasting under quality of service (QoS) constraints at mobile users, by jointly designing the transmit beamforming at the BS and the phase shifts of the IRS units. Furthermore, we derive a lower bound of the minimum transmit power at the BS to present the performance bound for optimization methods. Simulation results show that, the transmit power at the BS approaches the lower bound with the increase of the number of IRS units, and is much lower than that of the communication system without IRS.

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arXiv2020

Analytical Modeling of the Path-Loss for Reconfigurable Intelligent Surfaces -- Anomalous Mirror or Scatterer ?

Reconfigurable intelligent surfaces (RISs) are an emerging field of research in wireless communications. A fundamental component for analyzing and optimizing RIS-empowered wireless networks is the development of simple but sufficiently accurate models for the power scattered by an RIS. By leveraging the general scalar theory of diffraction and the Huygens-Fresnel principle, we introduce simple formulas for the electric field scattered by an RIS that is modeled as a sheet of electromagnetic material of negligible thickness. The proposed approach allows us to identify the conditions under which an RIS of finite size can or cannot be approximated as an anomalous mirror. Numerical results are illustrated to confirm the proposed approach.

physics.class-ph↗

arXiv2020

Artificial Intelligence Enabled Wireless Networking for 5G and Beyond: Recent Advances and Future Challenges

The fifth generation (5G) wireless communication networks are currently being deployed, and beyond 5G (B5G) networks are expected to be developed over the next decade. Artificial intelligence (AI) technologies and, in particular, machine learning (ML) have the potential to efficiently solve the unstructured and seemingly intractable problems by involving large amounts of data that need to be dealt with in B5G. This article studies how AI and ML can be leveraged for the design and operation of B5G networks. We first provide a comprehensive survey of recent advances and future challenges that result from bringing AI/ML technologies into B5G wireless networks. Our survey touches different aspects of wireless network design and optimization, including channel measurements, modeling, and estimation, physical-layer research, and network management and optimization. Then, ML algorithms and applications to B5G networks are reviewed, followed by an overview of standard developments of applying AI/ML algorithms to B5G networks. We conclude this study by the future challenges on applying AI/ML to B5G networks.

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arXiv2019

Reconfigurable Intelligent Surface-Based Wireless Communication: Antenna Design, Prototyping and Experimental Results

One of the key enablers of future wireless communications is constituted by massive multiple-input multiple-output (MIMO) systems, which can improve the spectral efficiency by orders of magnitude. However, in existing massive MIMO systems, conventional phased arrays are used for beamforming, which result in excessive power consumption and hardware cost. Recently, reconfigurable intelligent surface (RIS) has been considered as one of the revolutionary technologies to enable energy-efficient and smart wireless communications, which is a two-dimensional structure with a large number of passive elements. In this paper, we propose and develop a new type of high-gain yet low-cost RIS having 256 elements. The proposed RIS combines the functions of phase shift and radiation together on an electromagnetic surface, where positive intrinsic-negative (PIN) diodes are used to realize 2-bit phase shifting for beamforming. Based on this radical design, the world's first wireless communication prototype using RIS having 256 2-bit elements is designed and developed. Specifically, the prototype conceived consists of modular hardware and flexible software, including the hosts for parameter setting and data exchange, the universal software radio peripherals (USRPs) for baseband and radio frequency (RF) signal processing, as well as the RIS for signal transmission and reception. Our performance evaluation confirms the feasibility and efficiency of RISs in future wireless communications. More particularly, it is shown that a 21.7 dBi antenna gain can be obtained by the proposed RIS at 2.3 GHz, while at the millimeter wave (mmWave) frequency, i.e., 28.5 GHz, a 19.1 dBi antenna gain can be achieved. Furthermore, the over-the-air (OTA) test results show that the RIS-based wireless communication prototype developed is capable of significantly reducing the power consumption.

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arXiv2019

Reconfigurable Intelligent Surface Assisted UAV Communication: Joint Trajectory Design and Passive Beamforming

Thanks to the line-of-sight (LoS) transmission and flexibility, unmanned aerial vehicles (UAVs) effectively improve the throughput of wireless networks. Nevertheless, the LoS links are prone to severe deterioration by complex propagation environments, especially in urban areas. Reconfigurable intelligent surfaces (RISs), as a promising technique, can significantly improve the propagation environment and enhance communication quality by intelligently reflecting the received signals. Motivated by this, the joint UAV trajectory and RIS's passive beamforming design for a novel RIS-assisted UAV communication system is investigated to maximize the average achievable rate in this letter. To tackle the formulated non-convex problem, we divide it into two subproblems, namely, passive beamforming and trajectory optimization. We first derive a closed-form phase-shift solution for any given UAV trajectory to achieve the phase alignment of the received signals from different transmission paths. Then, with the optimal phase-shift solution, we obtain a suboptimal trajectory solution by using the successive convex approximation (SCA) method. Numerical results demonstrate that the proposed algorithm can considerably improve the average achievable rate of the system.

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arXiv2019

Beyond Max-SNR: Joint Encoding for Reconfigurable Intelligent Surfaces

A communication link aided by a Reconfigurable Intelligent Surface (RIS) is studied, in which the transmitter can control the state of the RIS via a finite-rate control link. Prior work mostly assumed a fixed RIS configuration irrespective of the transmitted information. In contrast, this work derives information-theoretic limits, and demonstrates that the capacity is achieved by a scheme that jointly encodes information in the transmitted signal as well as in the RIS configuration. In addition, a novel signaling strategy based on layered encoding is proposed that enables practical successive cancellation-type decoding at the receiver. Numerical experiments demonstrate that the standard max-SNR scheme that fixes the configuration of the RIS as to maximize the Signal-to-Noise Ratio (SNR) at the receiver is strictly suboptimal, and is outperformed by the proposed strategies at all practical SNR levels.

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arXiv2019

Transmit-Receive Generalized Spatial Modulation Based on Dual-layered MIMO Transmission

We propose a novel scheme for downlink multiuser multiple-input multiple-output (MIMO) systems, called dual-layered transmit-receive generalized spatial modulation (DL-TR-GSM). The proposed scheme is based on the concept of dual-layered transmission (DLT) which uses two receive antenna power levels instead of receive antenna activation/inactivation to transmit data in the receive spatial domain. Hence, in order to minimize the bit error rate (BER) for DL-TR-GSM, the optimal ratio between the two power levels is determined. To further characterize DL-TR-GSM, we fully derive the computational complexity and show a significant computational complexity reduction as well as a required hardware complexity reduction of DL-TR-GSM, compared to a state-of-the-art benchmark scheme. Simulation results confirm the performance advantages of DL-TR-GSM.

cs.IT↗

arXiv2019

Resource Allocation Using Gradient Boosting Aided Deep Q-Network for IoT in C-RANs

In this paper, we investigate dynamic resource allocation (DRA) problems for Internet of Things (IoT) in real-time cloud radio access networks (C-RANs), by combining gradient boosting approximation and deep reinforcement learning to solve the following two major problems. Firstly, in C-RANs, the decision making process of resource allocation is time-consuming and computational-expensive, motivating us to use an approximation method, i.e. the gradient boosting decision tree (GBDT) to approximate the solutions of second order cone programming (SOCP) problem. Moreover, considering the innumerable states in real-time C-RAN systems, we employ a deep reinforcement learning framework, i.e., deep Q-network (DQN) to generate a robust policy that controls the status of remote radio heads (RRHs). We propose a GBDT-based DQN framework for the DRA problem, where the heavy computation to solve SOCP problems is cut down and great power consumption is saved in the whole C-RAN system. We demonstrate that the generated policy is error-tolerant even the gradient boosting regression may not be strictly subject to the constraints of the original problem. Comparisons between the proposed method and existing baseline methods confirm the advantages of our method.

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arXiv2019

Wireless Communications Through Reconfigurable Intelligent Surfaces

The future of mobile communications looks exciting with the potential new use cases and challenging requirements of future 6th generation (6G) and beyond wireless networks. Since the beginning of the modern era of wireless communications, the propagation medium has been perceived as a randomly behaving entity between the transmitter and the receiver, which degrades the quality of the received signal due to the uncontrollable interactions of the transmitted radio waves with the surrounding objects. The recent advent of reconfigurable intelligent surfaces in wireless communications enables, on the other hand, network operators to control the scattering, reflection, and refraction characteristics of the radio waves, by overcoming the negative effects of natural wireless propagation. Recent results have revealed that reconfigurable intelligent surfaces can effectively control the wavefront, e.g., the phase, amplitude, frequency, and even polarization, of the impinging signals without the need of complex decoding, encoding, and radio frequency processing operations. Motivated by the potential of this emerging technology, the present article is aimed to provide the readers with a detailed overview and historical perspective on state-of-the-art solutions, and to elaborate on the fundamental differences with other technologies, the most important open research issues to tackle, and the reasons why the use of reconfigurable intelligent surfaces necessitates to rethink the communication-theoretic models currently employed in wireless networks. This article also explores theoretical performance limits of reconfigurable intelligent surface-assisted communication systems using mathematical techniques and elaborates on the potential use cases of intelligent surfaces in 6G and beyond wireless networks.

eess.SP↗

arXiv2019

A Survey on Spatial Modulation in Emerging Wireless Systems: Research Progresses and Applications

Spatial modulation (SM) is an innovative and promising digital modulation technology that strikes an appealing trade-off between spectral efficiency and energy efficiency with a simple design philosophy. SM enjoys plenty of benefits and shows great potential to fulfill the requirements of future wireless communications. The key idea behind SM is to convey additional information typically through the ON/OFF states of transmit antennas and simultaneously save the implementation cost by reducing the number of radio frequency chains. As a result, the SM concept can have widespread effects on diverse applications and can be applied in other signal domains such as frequency/time/code/angle domain or even across multiple domains. This survey provides a comprehensive overview of the latest results and progresses in SM research. Specifically, the fundamental principles, variants of system design, and enhancements of SM are described in detail. Furthermore, the integration of the SM family with other promising techniques, applications to emerging communication systems, and extensions to new signal domains are also extensively studied.

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arXiv2019

Model-Aided Wireless Artificial Intelligence: Embedding Expert Knowledge in Deep Neural Networks Towards Wireless Systems Optimization

Deep learning based on artificial neural networks is a powerful machine learning method that, in the last few years, has been successfully used to realize tasks, e.g., image classification, speech recognition, translation of languages, etc., that are usually simple to execute by human beings but extremely difficult to perform by machines. This is one of the reasons why deep learning is considered to be one of the main enablers to realize the notion of artificial intelligence. In order to identify the best architecture of an artificial neural network that allows one to fit input-output data pairs, the current methodology in deep learning methods consists of employing a data-driven approach. Once the artificial neural network is trained, it is capable of responding to never-observed inputs by providing the optimum output based on past acquired knowledge. In this context, a recent trend in the deep learning community is to complement pure data-driven approaches with prior information based on expert knowledge. In this work, we describe two methods that implement this strategy, which aim at optimizing wireless communication networks. In addition, we illustrate numerical results in order to assess the performance of the proposed approaches compared with pure data-driven implementations.

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arXiv2019

Wireless Networks Design in the Era of Deep Learning: Model-Based, AI-Based, or Both?

This work deals with the use of emerging deep learning techniques in future wireless communication networks. It will be shown that data-driven approaches should not replace, but rather complement traditional design techniques based on mathematical models. Extensive motivation is given for why deep learning based on artificial neural networks will be an indispensable tool for the design and operation of future wireless communications networks, and our vision of how artificial neural networks should be integrated into the architecture of future wireless communication networks is presented. A thorough description of deep learning methodologies is provided, starting with the general machine learning paradigm, followed by a more in-depth discussion about deep learning and artificial neural networks, covering the most widely-used artificial neural network architectures and their training methods. Deep learning will also be connected to other major learning frameworks such as reinforcement learning and transfer learning. A thorough survey of the literature on deep learning for wireless communication networks is provided, followed by a detailed description of several novel case-studies wherein the use of deep learning proves extremely useful for network design. For each case-study, it will be shown how the use of (even approximate) mathematical models can significantly reduce the amount of live data that needs to be acquired/measured to implement data-driven approaches. For each application, the merits of the proposed approaches will be demonstrated by a numerical analysis in which the implementation and training of the artificial neural network used to solve the problem is discussed. Finally, concluding remarks describe those that in our opinion are the major directions for future research in this field.

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arXiv2019

Smart Radio Environments Empowered by AI Reconfigurable Meta-Surfaces: An Idea Whose Time Has Come

Future wireless networks are expected to constitute a distributed intelligent wireless communications, sensing, and computing platform, which will have the challenging requirement of interconnecting the physical and digital worlds in a seamless and sustainable manner. Currently, two main factors prevent wireless network operators from building such networks: 1) the lack of control of the wireless environment, whose impact on the radio waves cannot be customized, and 2) the current operation of wireless radios, which consume a lot of power because new signals are generated whenever data has to be transmitted. In this paper, we challenge the usual "more data needs more power and emission of radio waves" status quo, and motivate that future wireless networks necessitate a smart radio environment: A transformative wireless concept, where the environmental objects are coated with artificial thin films of electromagnetic and reconfigurable material (that are referred to as intelligent reconfigurable meta-surfaces), which are capable of sensing the environment and of applying customized transformations to the radio waves. Smart radio environments have the potential to provide future wireless networks with uninterrupted wireless connectivity, and with the capability of transmitting data without generating new signals but recycling existing radio waves. This paper overviews the current research efforts on smart radio environments, the enabling technologies to realize them in practice, the need of new communication-theoretic models for their analysis and design, and the long-term and open research issues to be solved towards their massive deployment. In a nutshell, this paper is focused on discussing how the availability of intelligent reconfigurable meta-surfaces will allow wireless network operators to redesign common and well-known network communication paradigms.

cs.IT↗

arXiv2019

STORNS: Stochastic Radio Access Network Slicing

Recently released 5G networks empower the novel Network Slicing concept. %which enables novel business models; Network slicing introduces new business models such as allowing telecom providers to lease a virtualized slice of their infrastructure to tenants such as industry verticals, e.g. automotive, e-health, factories, etc. However, this new paradigm poses a major challenge when applied to Radio Access Networks (RAN): how to achieve revenue maximization while meeting the diverse service level agreements (SLAs) requested by the infrastructure tenants? In this paper, we propose a new analytical framework, based on stochastic geometry theory, to model realistic RANs that leverage the business opportunities offered by network slicing. We mathematically prove the benefits of slicing radio access networks as compared to non-sliced infrastructures. Based on this, we design a new admission control functional block, STORNS, which takes decisions considering per slice SLA guaranteed average experienced throughput. A radio resource allocation strategy is introduced to optimally allocate transmit power and bandwidth (i.e., a slice of radio access resources) to the users of each infrastructure tenant. Numerical results are illustrated to validate our proposed solution in terms of potential spectral efficiency, and compare it against a non-slicing benchmark.

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arXiv2019

SDN-enabled MIMO Heterogeneous Cooperative Networks with Flexible Cell Association

Small-cell densification is a strategy enabling the offloading of users from macro base stations (MBSs), in order to alleviate their load and increase the coverage, especially, for cell-edge users. In parallel, as the network increases in density, the BS cooperation emerges as an efficient design method towards the demands for drastic improvement of the system performance against the detrimental overall interference. In addition, the tiers are enhanced with cell association policies by introducing the concept of the association probability. Above this and motivated by the advantages of cooperation among BSs, the small base stations (SBSs) are enriched with this property in their design. SBS cooperation allows shedding light into its impact on the cell selection rules in multi-antenna HetNets. Under these settings, software-defined networking (SDN) is introduced smoothly to play the leading role in the orchestration of the network. {In particular, heavy operations such as the coordination and the cell association are undertaken by virtue of an SDN controller performing and managing efficiently the corresponding computations due to its centralized adaptability and dynamicity towards the enhancement and potential scalability of the network}. In this context, we derive the coverage probability and the mean achievable rate. Not only we show the outperformance of BS cooperation over uncoordinated BSs, but we also demonstrate that the SBS cooperation enables the admittance of more users from the macro-cell BSs (MBSs). Moreover, we investigate the performance of different transmission techniques, and we identify the optimal bias in each case when SBSs cooperate. Finally, we depict that the SBS densification is beneficial until a specific density value since a further increase does not increase the coverage probability.

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arXiv2019

Spatial Modulation Based on Reconfigurable Antennas: Performance Evaluation by Using the Prototype of a Reconfigurable Antenna

In this paper, we study the performance of spatial modulation based on reconfigurable antennas. Two main contributions are provided. We introduce an analytical framework to compute the error probability, which is shown to be accurate and useful for system optimization. We design and implement the prototype of a reconfigurable antenna that is specifically designed for application to spatial modulation, and that provides multiple radiation patterns that are used to encode the information bits. By using the measured antenna radiation patterns, we show that spatial modulation based on reconfigurable antennas work in practice, and that its performance can be optimized by appropriately optimizing the radiation patterns to use for a given data rate.

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arXiv2018

Treating Interference as Noise in Cellular Networks: A Stochastic Geometry Approach

The interference management technique that treats interference as noise (TIN) is optimal when the interference is sufficiently low. Scheduling algorithms based on the TIN optimality condition have recently been proposed, e.g., for application to device-to-device communications. TIN, however, has never been applied to cellular networks. In this work, we propose a scheduling algorithm for application to cellular networks that is based on the TIN optimality condition. In the proposed scheduling algorithm, each base station (BS) first randomly selects a user equipment (UE) in its coverage region, and then checks the TIN optimality conditions. If the latter conditions are not fulfilled, the BS is turned off. In order to assess the performance of TIN applied to cellular networks, we introduce an analytical framework with the aid of stochastic geometry theory. We develop, in particular, tractable expressions of the signal-to-interference-and-noise ratio (SINR) coverage probability and average rate of cellular networks. In addition, we carry out asymptotic analysis to find the optimal system parameters that maximize the SINR coverage probability. By using the optimized system parameters, it is shown that TIN applied to cellular networks yields significant gains in terms of SINR coverage probability and average rate. Specifically, the numerical results show that average rate gains of the order of $21\%$ over conventional scheduling algorithms are obtained.

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