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Integrated sensing and communications (ISAC) is an emerging critical technique for the next generation of communication systems. However, due to multiple performance metrics used for communication and sensing, the limited degrees-of-freedom (DoF) in optimizing ISAC systems poses a challenge. Reconfigurable intelligent surfaces (RIS) can introduce new DoF for beamforming in ISAC systems, thereby enhancing the performance of communication and sensing simultaneously. In this paper, we propose two optimization techniques for beamforming in RIS-assisted ISAC systems. The first technique is an alternating optimization (AO) algorithm based on the semidefinite relaxation (SDR) method and a one-dimension iterative (ODI) algorithm, which can maximize the radar mutual information (MI) while imposing constraints on the communication rates. The second technique is an AO algorithm based on the Riemannian gradient (RG) method, which can maximize the weighted ISAC performance metrics. Simulation results verify the effectiveness of the proposed schemes. The AO-SDR-ODI method is shown to achieve better communication and sensing performance, than the AO-RG method, at a higher complexity. It is also shown that the mean-squared-error (MSE) of the estimates of the sensing parameters decreases as the radar MI increases.

The reconfigurable intelligent surface (RIS) has been recognized as an essential enabling technology for sixth-generation (6G) mobile communication networks. An RIS comprises a large number of small and low-cost reflecting elements whose parameters can be dynamically adjusted with a programmable controller. Each of these elements can effectively reflect a phase-shifted version of the incident electromagnetic wave. By configuring the wave phases in real time, the propagation environment of the information-bearing signals can be dynamically manipulated to enhance communication reliability, boost transmission rate, expand cellular coverage, and strengthen communication security. In this study, we provide an overview on RIS-assisted wireless communications. Specifically, we elaborate on the state-of-the-art enabling techniques for the RIS technology as well as their corresponding substantial benefits from the perspectives of RIS reflection and RIS modulation. With these benefits, we envision the integration of RISs into emerging applications for 6G. In addition, communication security is of unprecedented importance in future 6G networks with ubiquitous wireless services in multifarious verticals and areas. We highlight potential contributions of RISs to physical-layer security, in terms of secrecy rate and secrecy outage probability, exemplified by a typical case study from both theoretical and numerical aspects. Finally, we discuss challenges and opportunities on the deployment of RISs in practice to motivate future research. © 2023, Science China Press.

Industrial environments are considered to be severe from the point of view of electromagnetic (EM) wave propagation. When dealing with a wide range of industrial environments and deployment setups, ray-tracing channel emulation can capture many distinctive characteristics of a propagation scenario. Ray-tracing tools often require a detailed and accurate description of the propagation scenario. Consequently, industrial environments composed of complex objects can limit the effectiveness of a ray-tracing tool and lead to computationally intensive simulations. This study analyzes the impact of using different propagation models by evaluating the number of allowed ray path interactions and digital scenario representation for an industrial environment. This study is realized using the Volcano ray-tracing tool at frequencies relevant to 5G industrial networks: 2 GHz (mid-band) and 28 GHz (high-band). This analysis can help in enhancing a ray-tracing tool that relies on a digital representation of the propagation environment to produce deterministic channel models for Indoor Factory (InF) scenarios, which can subsequently be used for industrial network design., Comment: copyright 2023 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works

We consider a reconfigurable intelligent surface (RIS) assisted multiple-input multiple-output (MIMO) system in the presence of scattering objects. The MIMO transmitter and receiver, the RIS, and the scattering objects are modeled as mutually coupled thin wires connected to load impedances. We introduce a novel numerical algorithm for optimizing the tunable loads connected to the RIS, which does not utilize the Neumann series approximation. The algorithm is provably convergent, has polynomial complexity with the number of RIS elements, and outperforms the most relevant benchmark algorithms while requiring fewer iterations and converging in a shorter time.

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. We focus on uplink cascaded channel estimation, where known and fixed base station combining and RIS phase control matrices are considered for collecting observations. To boost the estimation performance and reduce the training overhead, the inherent channel sparsity of mmWave channels is leveraged in the deep unfolding method. It is verified that the proposed deep unfolding network architecture can outperform the least squares (LS) method with a relatively smaller training overhead and online computational complexity.

Reconfigurable surfaces (RS) have recently emerged as an enabler for smart radio environments where they are used to actively tailor/control the radio propagation (e.g., to support users under adverse channel conditions). If multiple RSs are deployed (e.g., coated on various buildings) to support different groups of users, it is critical to jointly optimize the phase-shifts of all the RSs to mitigate interference amongst them as well as to leverage the secondary reflections amongst them. Motivated by these considerations, this paper considers the uplink transmissions of multiple users that are grouped and supported by multiple RSs to communicate with a multi-antenna base station (BS). We first formulate two optimization problems: the weighted sum-rate maximization and the minimum achievable rate (from all users) maximization. Unlike existing works that considered single user or single RS or multiple RSs without inter-RS reflections, the considered problems require the joint optimization of the phase-shifts of all RS elements and all beamformers at the multi-antenna BS. The two problems turn out to be non-convex and thus are difficult to be solved in general. Moreover, the inter-RS reflections give rise to the coupling of the phase-shifts amongst the RSs, making the optimization problems even more challenging to solve. To tackle them, we design alternating optimization algorithms that provably converge to locally optimal solutions. Simulation results reveal that by effectively mitigating interference and leveraging the secondary reflections amongst the RSs, there is a great benefit of deploying more RSs to support different groups of users so as to achieve a higher rate per user. This gain is even more significant with a larger number of elements per RS. Without properly dealing with the secondary reflections, by contrast, increasing the number of RSs can adversely impact the network throughput, especially for high transmit power.

We investigate the performance of multi-user multiple-antenna downlink systems in which a base station (BS) serves multiple users via a shared wireless medium. In order to fully exploit the spatial diversity while minimizing the passive energy consumed by radio frequency (RF) components, the BS is equipped with M RF chains and N antennas, where M < N. Upon receiving pilot sequences to obtain the channel state information (CSI), the BS determines the best subset of M antennas for serving the users. We propose a joint antenna selection and precoding design (JASPD) algorithm to maximize the system sum rate subject to a transmit power constraint and quality of service (QoS) requirements. The JASPD overcomes the non-convexity of the formulated problem via a doubly iterative algorithm, in which an inner loop successively optimizes the precoding vectors, followed by an outer loop that tries all valid antenna subsets. Although approaching the (near) global optimality, the JASPD suffers from a combinatorial complexity, which may limit its application in real-time network operations. To overcome this limitation, we propose a learning-based antenna selection and precoding design algorithm (L-ASPA), which employs a deep neural network (DNN) to establish underlaying relations between the key system parameters and the selected antennas. The proposed L-ASPD is robust against the number of users and their locations, BS’s transmit power, as well as the small-scale channel fading. With a well-trained learning model, it is shown that the L-ASPD significantly outperforms baseline schemes based on the block diagonalization [5] and a learning-assisted solution for broadcasting systems [29] and achieves higher effective sum rate than that of the JASPA under limited processing time. In addition, we observed that the proposed L-ASPD can reduce the computation complexity by 95% while retaining more than 95% of the optimal performance., QC 20210603

This paper proposes a precoded optical space-domain index modulation scheme for indoor visible light communications, which is based on optimization of the minimum Euclidean distance in optical spatial modulation (OSM) with real-valued modulation constellations. We find that the precoding matrix design can be formulated as a non-convex quadratically constrained quadratic program (QCQP) problem, whose solution is generally intractable. To tackle this problem, we first consider the case of two optical transmit antennas (Nt = 2) in precoded OSM and derive the closed-form solution for arbitrary M-order pulse amplitude modulation (PAM). Based on the derived solutions and the error vector reduction method, we then propose a low-complexity iterative (LCI) algorithm to identify the precoding matrix for the cases of Nt > 2. To strike a flexible complexity-BER (bit error rate) tradeoff, we propose a successive convex approximation (SCA)-assisted matrix-based optimization method to transform the non-convex QCQP problem into a series of linear convex subproblems, which can easily be solved by low-complexity solvers. Simulation results show that these proposed algorithms are capable of substantially improving the system error performance compared with conventional OSM systems. Besides, the symbol-based SCA algorithm is introduced and it outperforms the matrix-based SCA and the suboptimal LCI algorithm in terms of the BER., QC 20210319

We investigate the performance of multi-user multiple-antenna downlink systems in which a base station (BS) serves multiple users via a shared wireless medium. In order to fully exploit the spatial diversity while minimizing the passive energy consumed by radio frequency (RF) components, the BS is equipped with M RF chains and N antennas, where M <; N. Upon receiving pilot sequences to obtain the channel state information (CSI), the BS determines the best subset of M antennas for serving the users. We propose a joint antenna selection and precoding design (JASPD) algorithm to maximize the system sum rate subject to a transmit power constraint and quality of service (QoS) requirements. The JASPD algorithm overcomes the non-convexity of the formulated problem via a doubly iterative algorithm, in which an inner loop successively optimizes the precoding vectors, followed by an outer loop that tests all valid antenna subsets. Although approaching (near) global optimality, the JASPD suffers from a combinatorial complexity, which may limit its application in real-time network operations. To overcome this limitation, we propose a learning-based antenna selection and precoding design algorithm (L-ASPA), which employs a deep neural network (DNN) to establish underlaying relations between key system parameters and the selected antennas. The proposed L-ASPD algorithm is robust against the number of users and their locations, the transmit power of the BS, as well as the small-scale channel fading. With a well-trained learning model, it is shown that the L-ASPD algorithm significantly outperforms baseline schemes based on the block diagonalization and a learning-assisted solution for broadcasting systems and achieves a better effective sum rate than that of the JASPA under limited processing time. In addition, we observed that the proposed L-ASPD algorithm can reduce the computation complexity by 95% while retaining more than 95% of the optimal performance.

Reconfigurable Intelligent Surfaces (RISs) have attracted major attention in the last few years, thanks to their useful characteristics. An RIS is a nearly passive thin surface that can dynamically change the reradiated field, and can therefore realize anomalous reflection, refraction, focalization, or other wave transformations for engineering the radio propagation environment or realizing novel surface-type antennas. Evaluating the performance and optimizing the deployment of RISs in wireless networks need physically consistent frameworks that account for the electromagnetic characteristics of dynamic metasurfaces. In this paper, we introduce a general macroscopic model for evaluating the scattering from an RIS. The proposed method decomposes the wave reradiated from an RIS into multiple scattering contributions and is aimed at being embedded into ray-based models. Since state-of-the-art ray-based models can already efficiently simulate specular wave reflection, diffraction, and diffuse scattering, but not anomalous reradiation, we enhance them with an approach based on Huygens' principle and propose two possible implementations for it. Multiple reradiation modes can be modeled through the proposed approach, using the power conservation principle. We validate the accuracy of the proposed model by benchmarking it against several case studies available in the literature, which are based on analytical models, full-wave simulations, and measurements.

Reconfigurable intelligent surfaces (RISs) have the potential of realizing the emerging concept of smart radio environments by leveraging the unique properties of meta-surfaces. In this article, we discuss the potential applications of RISs in wireless networks that operate at high-frequency bands, e.g., millimeter wave (30-100 GHz) and sub-millimeter wave (greater than 100 GHz) frequencies. When used in wireless networks, RISs may operate in a manner similar to relays. This paper elaborates on the key differences and similarities between RISs that are configured to operate as anomalous reflectors and relays. In particular, we illustrate numerical results that highlight the spectral efficiency gains of RISs when their size is sufficiently large as compared with the wavelength of the radio waves. In addition, we discuss key open issues that need to be addressed for unlocking the potential benefits of RISs., Comment: Submitted for journal publication (revised version)

In this report, we summarize the end-to-end signal-to-noise ratio and the rate of half-duplex, full-duplex, amplify-and-forward, and decode-and-forward relay-aided communications, and well as the signal-to-noise ratio and the rate of the emerging technology known as reconfigurable intelligent surfaces., Comment: unpublished report

An emerging and promising vision of wireless networks consists of coating the environmental objects with reconfigurable metasurfaces that are capable of modifying the radio waves impinging upon them according to the generalized law of reflection. By relying on tools from point processes, stochastic geometry, and random spatial processes, we model the environmental objects with a modified random line process of fixed length, and with random orientations and locations. Based on the proposed modeling approach, we develop the first analytical framework that provides one with the probability that a randomly distributed object that is coated with a reconfigurable metasurface acts as a reflector for a given pair of transmitter and receiver. In contrast to the conventional network setup where the environmental objects are not coated with reconfigurable metasurfaces, we prove that the probability that the typical random object acts as a reflector is independent of the length of the object itself. The proposed analytical approach is validated against Monte Carlo simulations, and numerical illustrations are given and discussed., Comment: Submitted for journal publication

Future wireless networks are expected to adopt many different network technologies and architectures that promise to greatly enhance data rate and provide ubiquitous coverage for end users, all while enabling higher spectral efficiency. These benefits come with an increased risk of co-channel interference and possible correlation in the aggregated interference, which impacts the communication performance. This paper introduces a mathematical framework to quantify the spatial correlation of the interference in finite and clustered wireless networks with signals subject to Rayleigh fading. Expressions are derived for the correlation coefficient of the outage probability when the interferers are located according to some of the most common point processes used in the literature to model the spatial distribution of the devices: binomial point process (i.e., relay networks with fixed users), Poisson point process (i.e., heterogeneous cellular networks), and Thomas point process (i.e., device-to-device networks)., Comment: 7 pages, and 3 figures - in IEEE International Symposium on Personal, Indoor and Mobile Radio Communications (PIMRC) 2018

In this paper, we perform a system-level feasibility analysis of full-duplex (FD) relay-aided cellular networks that are equipped with multiple antennas at the base stations (BSs) and the relay nodes (RNs). The aim is to understand whether FD relaying is capable of enhancing the rate of cellular networks. With the aid of tools from stochastic geometry, we develop a tractable approach for computing the percentile rate, which allows us to gain insights on the impact of FD relaying for both the cell-edge and the cell-median mobile terminals subject to network interference. Contrary to previous works that do not consider the network interference, the framework reveals that even in the absence of self-interference at the FD RNs, a network with half-duplex (HD) RNs can outperform its FD counterpart for a moderate number of antennas at the BSs and RNs. On the other hand, the FD-based network can substantially outperform both the HD-based one and the one without RNs for a sufficiently large number of antennas at the BSs and RNs and substantially small self-interference power effect at the RNs. Finally, the aforementioned analytical insights are validated by means of Monte Carlo simulations. © 1972-2012 IEEE.

© 2017 IEEE. We consider a diffusion-based nano-network with N spatially distributed transmitters and one receiver. While the transmitters are linked in a unified entity to perform one transmission, the receiver is large enough to be viewed as a plane. Messages are encoded into the number of nano-scale molecules. Based on these assumptions, we analyze the signal-to-interference ratio, which is based on the average numbers of absorbed molecules. Moreover, we present an approach to interference alignment for molecular communications.

We investigate the performance of a scheduling algorithm where the Mobile Terminals (MTs) may be turned off if they cause a level of interference greater than a given threshold. This approach, which is referred to as Interference Aware Muting (IAM), may be regarded as an interference-aware scheme that is aimed to reduce the level of interference. We analyze its performance with the aid of stochastic geometry and compare it against other interference-unaware and interference-aware schemes, where the level of interference is kept under control in the power control scheme itself rather than in the scheduling process. IAM is studied in terms of average transmit power, mean and variance of the interference, coverage probability, Spectral Efficiency (SE), and Binary Rate (BR), which accounts for the amount of resources allocated to the typical MT. Simplified expressions of SE and BR for adaptive modulation and coding schemes are proposed, which better characterize practical communication systems. Our system-level analysis unveils that IAM increases the BR and reduces the mean and variance of the interference. It is proved that an operating regime exists, where the performance of IAM is independent of the cell association criterion, which simplifies the joint design of uplink and downlink transmissions., Comment: 16 pages, 12 figures. This work has been submitted to the IEEE for possible publication. Copyright may be transferred without notice, after which this version may no longer be accessible

The nowadays power grid deployed and used in every Country worldwide has served relatively well in providing a seamless unidirectional power supply of electricity. Nevertheless, today a new set of challenges is arising, such as the depletion of primary energy resources, the diversification of energy generation and the climate change. This paper proposes recent advancements in this field by introducing SMART-NRG, a Marie Curie project which involves academic and industrial partners from three EU Countries. The project aims to propose new technologies to meet the specific requirements of smart grids applications. In particular, in this paper, a modular and flexible system architecture is presented to face with the challenges imposed by the different application scenarios. © Springer International Publishing Switzerland 2015.

In this work we seek to characterise the performance of spatial modulation (SM) and spatial multiplexing (SMX) with an experimental test bed. Two National Instruments (NI)-PXIe devices are used for the system testing, one for the transmitter and one for the receiver. The digital signal processing that formats the information data in preparation of transmission is described along with the digital signal processing that recovers the information data. In addition, the hardware limitations of the system are also analysed. The average bit error ratio (ABER) of the system is validated through both theoretical analysis and simulation results for SM and SMX under line of sight (LoS) channel conditions., Comment: IEEE Transactions on Vehicular Technology, accepted

Immunoblot analysis with anti-phosphotyrosine antibodies of total extracts from exponentially growing yeast cells reveals a unique cross-reactive polypeptide of about 75 Kd (p75). The specificity of the immunodecorations has been checked by experiments of competition with phosphoaminoacids. A common behaviour has been observed for the 75 kd band and the 170 kd band corresponding to the platelet-derived growth factor receptor from Swiss 3T3 cells, which it has been known to be autophosphorylated on tyrosine upon ligand binding and used as a control throughout this work. We have found that p75 is associated to detergent insoluble cytoplasmic matrices. The stability of p75 detection by antibodies following treatments that specifically hydrolyze phosphohistidine and its susceptibility to potato acid phosphatase treatment provide further evidences that the epitope recognized by these antibodies in the yeast p75 polypeptide is indeed phosphotyrosine