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Symeon Chatzinotas

Publications and source records attributed to Symeon Chatzinotas.

At least 235 records · Page 13Linked to original sources
arXiv2023

Perpetual Reconfigurable Intelligent Surfaces Through In-Band Energy Harvesting: Architectures, Protocols, and Challenges

Reconfigurable intelligent surfaces (RISs) are considered to be a key enabler of highly energy-efficient 6G and beyond networks. This property arises from the absence of power amplifiers in the structure, in contrast to active nodes, such as small cells and relays. However, still an amount of power is required for their operation. To improve their energy efficiency further, we propose the notion of perpetual RISs, which secure the power needed to supply their functionalities through wireless energy harvesting of the impinging transmitted electromagnetic signals. Towards this, we initially explain the rationale behind such RIS capability and proceed with the presentation of the main RIS controller architecture that can realize this vision under an in-band energy harvesting consideration. Furthermore, we present a typical energy-harvesting architecture followed by two harvesting protocols. Subsequently, we study the performance of the two protocols under a typical communications scenario. Finally, we elaborate on the main research challenges governing the realization of large-scale networks with perpetual RISs.

cs.IT↗
arXiv2023

IRS Assisted MIMO Full Duplex: Rate Analysis and Beamforming Under Imperfect CSI

Intelligent reflecting surfaces (IRS) have emerged as a promising technology to enhance the performance of wireless communication systems. By actively manipulating the wireless propagation environment, IRS enables efficient signal transmission and reception. In recent years, the integration of IRS with full-duplex (FD) communication has garnered significant attention due to its potential to further improve spectral and energy efficiencies. IRS-assisted FD systems combine the benefits of both IRS and FD technologies, providing a powerful solution for the next generation of cellular systems. In this manuscript, we present a novel approach to jointly optimize active and passive beamforming in a multiple-input-multiple-output (MIMO) FD system assisted by an IRS for weighted sum rate (WSR) maximization. Given the inherent difficulty in obtaining perfect channel state information (CSI) in practical scenarios, we consider imperfect CSI and propose a statistically robust beamforming strategy to maximize the ergodic WSR. Additionally, we analyze the achievable WSR for an IRS-assisted MIMO FD system under imperfect CSI by deriving both the lower and upper bounds. To tackle the problem of ergodic WSR maximization, we employ the concept of expected weighted minimum mean squared error (EWMMSE), which exploits the information of the expected error covariance matrices and ensures convergence to a local optimum. We evaluate the effectiveness of our proposed design through extensive simulations. The results demonstrate that our robust approach yields significant performance improvements compared to the simplistic beamforming approach that disregards CSI errors, while also outperforming the robust half-duplex (HD) system considerably

eess.SP↗
arXiv2023

Robust Beamforming for IRS Aided MIMO Full Duplex Systems

In this paper, a novel robust beamforming for an intelligent reflecting surface (IRS) assisted FD system is presented. Since perfect channel state information (CSI) is often challenging to acquire in practice, we consider the case of imperfect CSI and adopt a statistically robust beamforming approach to maximize the ergodic weighted sum rate (WSR). We also analyze the achievable WSR of an IRS-assisted FD with imperfect CSI, for which the lower and the upper bounds are derived. The ergodic WSR maximization problem is tackled based on the expected Weighted Minimum Mean Squared Error (WMMSE), which is guaranteed to converge to a local optimum. The effectiveness of the proposed design is investigated with extensive simulation results. It is shown that our robust design achieves significant performance gain compared to the naive beamforming approaches and considerably outperforms the robust Half-Duplex (HD) system.

eess.SP↗
arXiv2023

Phase Shift Design for RIS-Aided Cell-Free Massive MIMO with Improved Differential Evolution

This paper proposes a novel phase shift design for cell-free massive multiple-input and multiple-output (MIMO) systems assisted by reconfigurable intelligent surface (RIS), which only utilizes channel statistics to achieve the uplink sum ergodic throughput maximization under spatial channel correlations. Due to the non-convexity and the scale of the derived optimization problem, we develop an improved version of the differential evolution (DE) algorithm. The proposed scheme is capable of providing high-quality solutions within reasonable computing time. Numerical results demonstrate superior improvements of the proposed phase shift designs over the other benchmarks, particularly in scenarios where direct links are highly probable.

cs.IT↗
arXiv2023

Joint Device Identification, Channel Estimation, and Signal Detection for LEO Satellite-Enabled Random Access

This paper investigates joint device identification, channel estimation, and signal detection for LEO satellite-enabled grant-free random access, where a multiple-input multipleoutput (MIMO) system with orthogonal time-frequency space modulation (OTFS) is utilized to combat the dynamics of the terrestrial-satellite link (TSL). We divide the receiver structure into three modules: first, a linear module for identifying active devices, which leverages the generalized approximate message passing (GAMP) algorithm to eliminate inter-user interference in the delay-Doppler domain; second, a non-linear module adopting the message passing algorithm to jointly estimate channel and detect transmit signals; the third aided by Markov random field (MRF) aims to explore the three dimensional block sparsity of channel in the delay-Doppler-angle domain. The soft information is exchanged iteratively between these three modules by careful scheduling. Furthermore, the expectation-maximization algorithm is embedded to learn the hyperparameters in prior distributions. Simulation results demonstrate that the proposed scheme outperforms the conventional methods significantly in terms of activity error rate, channel estimation accuracy, and symbol error rate.

cs.IT↗
arXiv2023

Performance of Joint Symbol Level Precoding and RIS Phase Shift Design in the Finite Block Length Regime with Constellation Rotation

In this paper, we tackle the problem of joint symbol level precoding (SLP) and reconfigurable intelligent surface (RIS) phase shift design with constellation rotation in the finite block length regime. We aim to increase energy efficiency by minimizing the total transmit power while satisfying the quality of service constraints. The total power consumption can be significantly minimized through the exploitation of multiuser interference by symbol level precoding and by the intelligent manipulation of the propagation environment using reconfigurable intelligent surfaces. In addition, the constellation rotation per user contributes to energy efficiency by aligning the symbol phases of the users, thus improving the utilization of constructive interference. The formulated power minimization problem is non-convex and correspondingly difficult to solve directly. Hence, we employ an alternating optimization algorithm to tackle the joint optimization of SLP and RIS phase shift design. The optimal phase of each user's constellation rotation is obtained via an exhaustive search algorithm. Through Monte-Carlo simulation results, we demonstrate that the proposed solution yields substantial power minimization as compared to conventional SLP, zero forcing precoding with RIS as well as the benchmark schemes without RIS.

cs.IT↗
arXiv2023

Reconfigurable Intelligent Surfaces Enhanced NOMA D2D Communications Underlaying UAV Networks

Device-to-device (D2D) communications offers high spectral efficiency, low energy consumption and transmission latency. However, one of the main limitations of D2D communications is co-channel interference from underlaying wireless system. Reconfigurable intelligent surfaces (RIS) is a promising technology because it can manipulate the electromagnetic waves in their environment to overcome interference and enhance wireless communications. This paper considers RIS enhanced D2D communications underlaying unmanned aerial vehicle (UAV) networks with non-orthogonal multiple access (NOMA). The objective is to maximize the sum rate of NOMA D2D communications by simultaneously optimizing the power budget of D2D transmitter, NOMA power allocation coefficients of D2D receivers and passive beamforming of RIS while guaranteeing the quality of services of UAV user. Due to non-convexity, the optimization problem is intractable and challenging to handle. Therefore, it is solved in two parts using alternating optimization. Simulation results unviel the performance of the proposed RIS enhanced D2D communications scheme. Results demonstrate that the proposed scheme achieves 15\% and 27\% higher sum rates compared to the fixed power D2D and orthogonal D2D schemes.

eess.SP↗
arXiv2023

A Hybrid Optimization and Deep RL Approach for Resource Allocation in Semi-GF NOMA Networks

Semi-grant-free non-orthogonal multiple access (semi-GF NOMA) has emerged as a promising technology for the fifth-generation new radio (5G-NR) networks supporting the coexistence of a large number of random connections with various quality of service requirements. However, implementing a semi-GF NOMA mechanism in 5G-NR networks with heterogeneous services has raised several resource management problems relating to unpredictable interference caused by the GF access strategy. To cope with this challenge, the paper develops a novel hybrid optimization and multi-agent deep (HOMAD) reinforcement learning-based resource allocation design to maximize the energy efficiency (EE) of semi-GF NOMA 5G-NR systems. In this design, a multi-agent deep Q network (MADQN) approach is employed to conduct the subchannel assignment (SA) among users. While optimization-based methods are utilized to optimize the transmission power for every SA setting. In addition, a full MADQN scheme conducting both SA and power allocation is also considered for comparison purposes. Simulation results show that the HOMAD approach outperforms other benchmarks significantly in terms of the convergence time and average EE.

eess.SP↗
arXiv2023

Harnessing the Power of Swarm Satellite Networks with Wideband Distributed Beamforming

The space communications industry is challenged to develop a technology that can deliver broadband services to user terminals equipped with miniature antennas, such as handheld devices. One potential solution to establish links with ground users is the deployment of massive antennas in one single spacecraft. However, this is not cost-effective. Aligning with recent \emph{NewSpace} activities directed toward miniaturization, mass production, and a significant reduction in spacecraft launch costs, an alternative could be distributed beamforming from multiple satellites. In this context, we propose a distributed beamforming modeling technique for wideband signals. We also consider the statistical behavior of the relative geometry of the swarm nodes. The paper assesses the proposed technique via computer simulations, providing interesting results on the beamforming gains in terms of power and the security of the communication against potential eavesdroppers at non-intended pointing angles. This approach paves the way for further exploration of wideband distributed beamforming from satellite swarms in several future communication applications.

eess.SP↗
arXiv2023

Integrated Access and Backhaul via Satellites

To allow flexible and cost-efficient network densification and deployment, the integrated access and backhaul (IAB) was recently standardized by the third generation partnership project (3GPP) as part of the fifth-generation new radio (5G-NR) networks. However, the current standardization only defines the IAB for the terrestrial domain, while non-terrestrial networks (NTNs) are yet to be considered for such standardization efforts. In this work, we motivate the use of IAB in NTNs, and we discuss the compatibility issues between the 3GPP specifications on IAB in 5G-NR and the satellite radio regulations. In addition, we identify the required adaptation from the 3GPP and/or satellite operators for realizing an NTN-enabled IAB operation. A case study is provided for a low earth orbit (LEO) satellite-enabled in-band IAB operation with orthogonal and non-orthogonal bandwidth allocation between access and backhauling, and under both time- and frequency-division duplex (TDD/FDD) transmission modes. Numerical results demonstrate the feasibility of IAB through satellites, and illustrate the superiority of FDD over TDD transmission. It is also shown that in the absence of precoding, non-orthogonal bandwidth allocation between the access and the backhaul can largely degrades the network throughput.

cs.NI↗
arXiv2023

Full-Duplex-Enabled Joint Communications and Sensing with Reconfigurable Intelligent Surfaces

The full-duplex (FD) technology has the potential to radically evolve wireless systems, facilitating the integration of both communications and radar functionalities into a single device, thus, enabling joint communication and sensing (JCAS). In this paper, we present a novel approach for JCAS that incorporates a reconfigurable intelligent surface (RIS) in the near-field of an FD multiple-input multiple-output (MIMO) node, which is jointly optimized with the digital beamformers to enable JSAC and efficiently handle self-interference (SI). We propose a novel problem formulation for FD MIMO JCAS systems to jointly minimize the total received power at the FD node's radar receiver while maximizing the sum rate of downlink communications subject to a Cramér-Rao bound (CRB) constraint. In contrast to the typically used CRB in the relevant literature, we derive a novel, more accurate, target estimation bound that fully takes into account the RIS deployment. The considered problem is solved using alternating optimization, which is guaranteed to converge to a local optimum. The simulation results demonstrate that the proposed scheme achieves significant performance improvement both for communications and sensing. It is showcased that, jointly designing the FD MIMO beamformers and the RIS phase configuration to be SI aware can significantly loosen the requirement for additional SI cancellation.

cs.IT↗
arXiv2023

RIS-Assisted Energy-Efficient LEO Satellite Communications with NOMA

This paper proposes an energy-efficient RIS-assisted downlink NOMA communication for LEO satellite networks. The proposed framework simultaneously optimizes the transmit power of ground terminals of the LEO satellite and the passive beamforming of RIS while ensuring the quality of services. Due to the nature of the considered system and optimization variables, the energy efficiency maximization problem is non-convex. In practice, obtaining the optimal solution for such problems is very challenging. Therefore, we adopt alternating optimization methods to handle the joint optimization in two steps. In step 1, for any given phase shift vector, we calculate satellite transmit power towards each ground terminal using the Lagrangian dual method. Then, in step 2, given the transmit power, we design passive beamforming for RIS by solving the semi-definite programming. We also compare our solution with a benchmark framework having a fixed phase shift design and a conventional NOMA framework without involving RIS. Numerical results show that the proposed optimization framework achieves 21.47\% and 54.9\% higher energy efficiency compared to the benchmark and conventional frameworks.

eess.SP↗
arXiv2023

Intelligent Reflecting Surfaces Assisted Millimeter Wave MIMO Full Duplex Systems

Full duplex (FD) systems suffer from very high hardware cost and high power consumption to mitigate the self-interference (SI) in the analog domain. Moreover, in millimeter wave (mmWave) they rely on hybrid beamforming (HYBF) as a signal processing tool to partially deal with the SI, which presents many drawbacks such as high insertion loss and high power consumption. This article proposes the use of near-field (NF-) IRSs for FD systems with the objective to solve the aforementioned issues cost-efficiently. Namely, we propose to truncate the analog/hybrid beamforming stage of the mmWave FD systems and compensate it with an NF-IRS, to simultaneously and smartly control the uplink (UL) and downlink (DL) channels, while assisting in shaping the SI channel: this to obtain very strong passive SI cancellation. A novel joint active and passive beamforming design for the weighted sum-rate (WSR) maximization of a NF-IRS-assisted mmWave point-to-point FD system is presented. Results show that the proposed solution fully reaps the benefits of the IRSs only when they operate in the NF, which leads to considerably higher gains compared to the conventional massive MIMO (mMIMO) mmWave FD and half duplex (HD) systems.

cs.IT↗
arXiv2023

Energy Efficient RAN Slicing and Beams Selection for Multiplexing of Heterogeneous Services in 5G mmWave Networks

In this paper, we study a RAN resource-slicing problem for energy-efficient communication in an orthogonal frequency division multiple access (OFDMA) based millimeter-wave (mmWave) downlink (DL) network consisting of enhanced mobile broadband (eMBB) and ultra-reliable low-latency communication (URLLC) services. Specifically, assuming a fixed set of predefined beams, we address an energy efficiency (EE) maximization problem to obtain the optimal beam selection, Resource Block (RB), and transmit power allocation policy to serve URLLC and eMBB users on the same physical radio resources. The problem is formulated as a mixed-integer non-linear fractional programming (MINLFP) problem considering minimum data rate and latency in packet delivery constraints. By leveraging the properties of fractional programming theory, we first transform the formulated non-convex optimization problem in fractional form into a tractable subtractive form. Subsequently, we solve the transformed problem using a two-loop iterative algorithm. The main resource-slicing problem is solved in the inner loop utilizing the difference of convex (DC) programming and successive convex approximation (SCA) techniques. Subsequently, the outer loop is solved using the Dinkelbach method to acquire an improved solution in every iteration until it converges. Our simulation results illustrate the performance gains of the proposed methodology with respect to baseline algorithms with the fixed and mixed resource grid models.

cs.IT↗
arXiv2023

Parallel and Distributed Hybrid Beamforming for Multicell Millimeter Wave MIMO Full Duplex

Full duplex (FD) is an auspicious wireless technology that holds the potential to double data rates through simultaneous transmission and reception. This paper proposes two innovative designs of hybrid beamforming (HYBF) for a multicell massive multiple-input-multiple-output (mMIMO) millimeter wave (mmWave) FD system. Initially, we introduce a novel centralized HYBF (C-HYBF) scheme, which employs the minorization-maximization (MM) method. However, while centralized beamforming designs offer superior performance, they suffer from high computational complexity, substantial communication overhead, and demand expensive computational resources. To surmount these challenges, we present a framework that facilitates per-link parallel and distributed HYBF (P&D- HYBF) in the mmWave frequency band. This cooperative approach enables each base station (BS) to independently solve its local, low-complexity sub-problems in parallel, resulting in a substantial reduction in communication overhead and computational complexity. Simulation results demonstrate that P&D-HYBF achieves comparable performance to C-HYBF, and with only a few radio-frequency (RF) chains, both designs surpass the capabilities of fully digital half duplex (HD) systems.

cs.IT↗
arXiv2023

Network-Aided Intelligent Traffic Steering in 6G O-RAN: A Multi-Layer Optimization Framework

To enable an intelligent, programmable and multi-vendor radio access network (RAN) for 6G networks, considerable efforts have been made in standardization and development of open RAN (O-RAN). So far, however, the applicability of O-RAN in controlling and optimizing RAN functions has not been widely investigated. In this paper, we jointly optimize the flow-split distribution, congestion control and scheduling (JFCS) to enable an intelligent traffic steering application in O-RAN. Combining tools from network utility maximization and stochastic optimization, we introduce a multi-layer optimization framework that provides fast convergence, long-term utility-optimality and significant delay reduction compared to the state-of-the-art and baseline RAN approaches. Our main contributions are three-fold: i) we propose the novel JFCS framework to efficiently and adaptively direct traffic to appropriate radio units; ii) we develop low-complexity algorithms based on the reinforcement learning, inner approximation and bisection search methods to effectively solve the JFCS problem in different time scales; and iii) the rigorous theoretical performance results are analyzed to show that there exists a scaling factor to improve the tradeoff between delay and utility-optimization. Collectively, the insights in this work will open the door towards fully automated networks with enhanced control and flexibility. Numerical results are provided to demonstrate the effectiveness of the proposed algorithms in terms of the convergence rate, long-term utility-optimality and delay reduction.

cs.LG↗
arXiv2023

Task-oriented Communication Design in Cyber-Physical Systems: A Survey on Theory and Applications

Communications system design has been traditionally guided by task-agnostic principles, which aim at efficiently transmitting as many correct bits as possible through a given channel. However, in the era of cyber-physical systems, the effectiveness of communications is not dictated simply by the bit rate, but most importantly by the efficient completion of the task in hand, e.g., controlling remotely a robot, automating a production line or collaboratively sensing through a drone swarm. In parallel, it is projected that by 2023, half of the worldwide network connections will be among machines rather than humans. In this context, it is crucial to establish a new paradigm for designing communications strategies for multi-agent cyber-physical systems. This is a daunting task, since it requires a combination of principles from information, communication, control theories and computer science in order to formalize a general framework for task-oriented communication design. In this direction, this paper reviews and structures the relevant theoretical work across a wide range of scientific communities. Subsequently, it proposes a general conceptual framework for task-oriented communication design, along with its specializations according to the targeted use case. Furthermore, it provides a survey of relevant contributions in dominant applications, such as industrial internet of things, multi-UAV systems, tactile internet, autonomous vehicles, distributed learning systems, smart manufacturing plants and 5G and beyond self-organizing networks. Finally, it highlights the most important open research topics from both the theoretical framework and application points of view.

cs.IT↗
arXiv2023

Task-Oriented Communication Design at Scale

With countless promising applications in various domains such as IoT and industry 4.0, task-oriented communication design (TOCD) is getting accelerated attention from the research community. This paper presents a novel approach for designing scalable task-oriented quantization and communications in cooperative multi-agent systems (MAS). The proposed approach utilizes the TOCD framework and the value of information (VoI) concept to enable efficient communication of quantized observations among agents while maximizing the average return performance of the MAS, a parameter that quantifies the MAS's task effectiveness. The computational complexity of learning the VoI, however, grows exponentially with the number of agents. Thus, we propose a three-step framework: i) learning the VoI (using reinforcement learning (RL)) for a two-agent system, ii) designing the quantization policy for an $N$-agent MAS using the learned VoI for a range of bit-budgets and, (iii) learning the agents' control policies using RL while following the designed quantization policies in the earlier step. We observe that one can reduce the computational cost of obtaining the value of information by exploiting insights gained from studying a similar two-agent system - instead of the original $N$-agent system. We then quantize agents' observations such that their more valuable observations are communicated more precisely. Our analytical results show the applicability of the proposed framework under a wide range of problems. Numerical results show striking improvements in reducing the computational complexity of obtaining VoI needed for the TOCD in a MAS problem without compromising the average return performance of the MAS.

cs.IT↗