arXiv subjects

Pandelis Kourtessis

Publications and source records attributed to Pandelis Kourtessis.

At least 19 recordsLinked to original sources

arXiv2025

Flexible Intelligent Metasurface for Downlink Communications under Statistical CSI

Flexible intelligent metasurface (FIM) is a recently developed, groundbreaking hardware technology with promising potential for 6G wireless systems. Unlike conventional rigid antenna array (RAA)-based transmitters, FIM-assisted transmitters can dynamically alter their physical surface through morphing, offering new degrees of freedom to enhance system performance. In this letter, we depart from prior works that rely on instantaneous channel state information (CSI) and instead address the problem of average sum spectral efficiency maximization under statistical CSI in a FIM-assisted downlink multiuser multiple-input single-output setting. To this end, we first derive the spatial correlation matrix for the FIM-aided transmitter and then propose an iterative FIM optimization algorithm based on the gradient projection method. Simulation results show that with statistical CSI, the FIM-aided system provides a significant performance gain over its RAA-based counterpart in scenarios with strong spatial channel correlation, whereas the gain diminishes when the channels are weakly correlated.

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arXiv2025

Visual Event Detection over AI-Edge LEO Satellites with AoI Awareness

Non terrestrial networks (NTNs), particularly low Earth orbit (LEO) satellite systems, play a vital role in supporting future mission critical applications such as disaster relief. Recent advances in artificial intelligence (AI)-native communications enable LEO satellites to act as intelligent edge nodes capable of on board learning and task oriented inference. However, the limited link budget, coupled with severe path loss and fading, significantly constrains reliable downlink transmission. This paper proposes a deep joint source-channel coding (DJSCC)-based downlink scheme for AI-native LEO networks, optimized for goal-oriented visual inference. In the DJSCC approach, only semantically meaningful features are extracted and transmitted, whereas conventional separate source-channel coding (SSCC) transmits the original image data. To evaluate information freshness and visual event detection performance, this work introduces the age of misclassified information (AoMI) metric and a threshold based AoI analysis that measures the proportion of users meeting application specific timeliness requirements. Simulation results show that the proposed DJSCC scheme provides higher inference accuracy, lower average AoMI, and greater threshold compliance than the conventional SSCC baseline, enabling semantic communication in AI native LEO satellite networks for 6G and beyond.

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arXiv2025

Ergodic Mutual Information and Outage Probability for SIM-Assisted Holographic MIMO Communications

Stacked intelligent metasurface (SIM) is a promising enabler for next-generation high-capacity networks that exhibit better performance compared to its single-layer counterpart by means of just wave propagation. However, the study of ergodic mutual information (EMI) and outage probability for SIM-assisted multiple-input-multiple-output (MIMO) systems is not available in the literature. To this end, we obtain the distribution of the MI by using large random matrix theory (RMT) tools. Next, we derive a tight closed-form expression for the outage probability based on statistical channel state information (CSI). Moreover, we apply the gradient descent method for the minimization of the outage probability. Simulation results verify the analytical results and provide fundamental insights such as the performance enhancements compared to conventional MIMO systems and the single-layer counterpart. Notably the proposed optimization algorithm is faster than the alternating optimization (AO) benchmark by saving significant overhead.

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arXiv2025

Channel Estimation for Stacked Intelligent Metasurfaces in Rician Fading Channels

The recent combination of the rising architectures, known as stacked intelligent metasurface (SIM) and holographic multiple-input multiple-output (HMIMO), drives toward breakthroughs for next-generation wireless communication systems. Given the fact that the number of elements per surface of the SIM is much larger than the base station (BS) antennas, the acquisition of the channel state information (CSI) in SIM-aided multi-user systems is challenging, especially when a line-of-sight (LoS) component is present. Thus, in this letter, we address the channel procedure under conditions of Rician fading by proposing a protocol in terms of a minimum mean square error (MMSE) estimator for wave-based design in a single phase. Moreover, we derive the normalized mean square error (NMSE) of the suggested estimator, and provide the optimal phase shifts minimising the NMSE. Numerical results illustrate the performance of the new channel estimation protocol.

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arXiv2025

STARS-Enabled Full-Duplex Two-Way mMIMO System Under Spatially-Correlated Channels

\underline{S}imultaneous \underline{t}ransmitting \underline{a}nd \underline{r}eflecting \underline{s}urface (STARS)-assisted systems have emerged to fill this gap by providing $ 360^{\circ}$ wireless coverage. In parallel, full-duplex (FD) communication offers a higher achievable rate through efficient spectrum utilization compared to the half-duplex (HD) counterpart. Moreover, two-way/bi-directional communications in an FD system can further enhance the system's spectral efficiency. Hence, in this paper, we propose a STARS-enabled massive MIMO deployment in an FD two-way communication network for highly efficient spectrum utilization, while covering the dead zones around the STARS. This model enables simultaneous information exchange between multiple nodes, while \emph{potentially} doubling the spectral efficiency (SE). By invoking the use-and-then-forget (UaTF) combining scheme, we derive a closed-form expression for an achievable SE at each user of the system considering both uplink and downlink communications based on statistical channel state information (CSI), while also accounting for imperfect CSI and correlated fading conditions. Moreover, we formulate an optimization problem to obtain an optimal passive beamforming matrix design at the STARS that maximizes the sum achievable SE. The considered problem is non-convex and we propose a provably-convergent low-complexity algorithm, termed as \underline{pro}jected \underline{gr}adient \underline{a}scent \underline{m}ethod (ProGrAM), to obtain a stationary solution. Extensive numerical results are provided to establish the performance superiority of the FD STARS-enabled system over the HD STARS-enabled and FD conventional RIS (cRIS)-enabled counterparts, and also to show the effect of different parameters of interest on the system performance.

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arXiv2024

Near-Field Beamforming for Stacked Intelligent Metasurfaces-assisted MIMO Networks

Stacked intelligent metasurfaces (SIMs) have recently gained significant interest since they enable precoding in the wave domain that comes with increased processing capability and reduced energy consumption. The study of SIMs and high frequency propagation make the study of the performance in the near field of crucial importance. Hence, in this work, we focus on SIM-assisted multiuser multiple-input multiple-output (MIMO) systems operating in the near field region. To this end, we formulate the weighted sum rate maximisation problem in terms of the transmit power and the phase shifts of the SIM. By applying a block coordinate descent (BCD)-relied algorithm, numerical results show the enhanced performance of the SIM in the near field with respect to the far field.

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arXiv2024

Achievable Rate Optimization for Large Stacked Intelligent Metasurfaces Based on Statistical CSI

Stacked intelligent metasurface (SIM) is an emerging design that consists of multiple layers of metasurfaces. A SIM enables holographic multiple-input multiple-output (HMIMO) precoding in the wave domain, which results in the reduction of energy consumption and hardware cost. On the ground of multiuser beamforming, this letter focuses on the downlink achievable rate and its maximization. Contrary to previous works on multiuser SIM, we consider statistical channel state information (CSI) as opposed to instantaneous CSI to overcome challenges such as large overhead. Also, we examine the performance of large surfaces. We apply an alternating optimization (AO) algorithm regarding the phases of the SIM and the allocated transmit power. Simulations illustrate the performance of the considered large SIM-assisted design as well as the comparison between different CSI considerations.

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arXiv2024

Achievable Rate Optimization for Stacked Intelligent Metasurface-Assisted Holographic MIMO Communications

Stacked intelligent metasurfaces (SIM) is a revolutionary technology, which can outperform its single-layer counterparts by performing advanced signal processing relying on wave propagation. In this work, we exploit SIM to enable transmit precoding and receiver combining in holographic multiple-input multiple-output (HMIMO) communications, and we study the achievable rate by formulating a joint optimization problem of the SIM phase shifts at both sides of the transceiver and the covariance matrix of the transmitted signal. Notably, we propose its solution by means of an iterative optimization algorithm that relies on the projected gradient method, and accounts for all optimization parameters simultaneously. We also obtain the step size guaranteeing the convergence of the proposed algorithm. Simulation results provide fundamental insights such the performance improvements compared to the single-RIS counterpart and conventional MIMO system. Remarkably, the proposed algorithm results in the same achievable rate as the alternating optimization (AO) benchmark but with a less number of iterations.

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arXiv2024

STAR-RIS-Assisted Communication Radar Coexistence: Analysis and Optimization

Integrated sensing and communication (ISAC) is expected to play a prominent role among emerging technologies in future wireless communications. In particular, a communication radar coexistence system is degraded significantly by mutual interference. In this work, given the advantages of promising reconfigurable intelligent surface (RIS), we propose a simultaneously transmitting and reflecting RIS (STAR-RIS)-assisted radar coexistence system where a STAR-RIS is introduced to improve the communication performance while suppressing the mutual interference and providing full space coverage. Based on the realistic conditions of correlated fading, and the presence of multiple user equipments (UEs) at both sides of the RIS, we derive the achievable rates at the radar and the communication receiver side in closed forms in terms of statistical channel state information (CSI). Next, we perform alternating optimization (AO) for optimizing the STAR-RIS and the radar beamforming. Regarding the former, we optimize the amplitudes and phase shifts of the STAR-RIS through a projected gradient ascent algorithm (PGAM) simultaneously with respect to the amplitudes and phase shifts of the surface for both energy splitting (ES) and mode switching (MS) operation protocols. The proposed optimization saves enough overhead since it can be performed every several coherence intervals. This property is particularly beneficial compared to reflecting-only RIS because a STAR-RIS includes the double number of variables, which require increased overhead. Finally, simulation results illustrate how the proposed architecture outperforms the conventional RIS counterpart, and show how the various parameters affect the performance. Moreover, a benchmark full instantaneous CSI (I-CSI) based design is provided and shown to result in higher sum-rate but also in large overhead associated with complexity.

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arXiv2024

Performance of Double-Stacked Intelligent Metasurface-Assisted Multiuser Massive MIMO Communications in the Wave Domain

Although reconfigurable intelligent surface (RIS) is a promising technology for shaping the propagation environment, it consists of a single-layer structure within inherent limitations regarding the number of beam steering patterns. Based on the recently revolutionary technology, denoted as stacked intelligent metasurface (SIM), we propose its implementation not only on the base station (BS) side in a massive multiple-input multiple-output (mMIMO) setup but also in the intermediate space between the base station and the users to adjust the environment further as needed. For the sake of convenience, we call the former BS SIM (BSIM), and the latter channel SIM (CSIM). Hence, we achieve wave-based combining at the BS and wave-based configuration at the intermediate space. Specifically, we propose a channel estimation method with reduced overhead, being crucial for SIMassisted communications. Next, we derive the uplink sum spectral efficiency (SE) in closed form in terms of statistical channel state information (CSI). Notably, we optimize the phase shifts of both BSIM and CSIM simultaneously by using the projected gradient ascent method (PGAM). Compared to previous works on SIMs, we study the uplink transmission, a mMIMO setup, channel estimation in a single phase, a second SIM at the intermediate space, and simultaneous optimization of the two SIMs. Simulation results show the impact of various parameters on the sum SE, and demonstrate the superiority of our optimization approach compared to the alternating optimization (AO) method.

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arXiv2024

Two-Timescale Design for Active STAR-RIS Aided Massive MIMO Systems

Simultaneously transmitting and reflecting \textcolor{black}{reconfigurable intelligent surface} (STAR-RIS) is a promising implementation of RIS-assisted systems that enables full-space coverage. However, STAR-RIS as well as conventional RIS suffer from the double-fading effect. Thus, in this paper, we propose the marriage of active RIS and STAR-RIS, denoted as ASTARS for massive multiple-input multiple-output (mMIMO) systems, and we focus on the energy splitting (ES) and mode switching (MS) protocols. Compared to prior literature, we consider the impact of correlated fading, and we rely our analysis on the two timescale protocol, being dependent on statistical channel state information (CSI). On this ground, we propose a channel estimation method for ASTARS with reduced overhead that accounts for its architecture. Next, we derive a \textcolor{black}{closed-form expression} for the achievable sum-rate for both types of users in the transmission and reflection regions in a unified approach with significant practical advantages such as reduced complexity and overhead, which result in a lower number of required iterations for convergence compared to an alternating optimization (AO) approach. Notably, we maximize simultaneously the amplitudes, the phase shifts, and the active amplifying coefficients of the ASTARS by applying the projected gradient ascent method (PGAM). Remarkably, the proposed optimization can be executed at every several coherence intervals that reduces the processing burden considerably. Simulations corroborate the analytical results, provide insight into the effects of fundamental variables on the sum achievable SE, and present the superiority of 16 ASTARS compared to passive STAR-RIS for a practical number of surface elements.

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arXiv2023

STAR-RIS Assisted Cell-Free Massive MIMO System Under Spatially-Correlated Channels

This paper investigates the performance of downlink simultaneous transmitting and reflecting reconfigurable intelligent surface (STAR-RIS)-assisted cell-free (CF) massive multiple-input multiple-output (mMIMO) systems, where user equipments (UEs) are located on both sides of the RIS. We account for correlated Rayleigh fading and multiple antennas per access point (AP), while the maximum ratio (MR) beamforming is applied for the design of the active beamforming in terms of instantaneous channel state information (CSI). Firstly, we rely on an aggregated channel estimation approach that reduces the overhead required for channel estimation while providing sufficient information for data processing. We obtain the normalized mean square error (NMSE) of the channel estimate per AP, and design the passive beamforming (PB) of the surface based on the long-time statistical CSI. Next, we derive the received signal in the asymptotic regime of numbers of APs and surface elements. Then, we obtain a closed-form expression of the downlink achievable rate for arbitrary numbers of APs and STAR-RIS elements under statistical CSI. Finally, based on the derived expressions, the numerical results show the feasibility and the advantages of deploying a STAR-RIS into conventional CF mMIMO systems. In particular, we theoretically analyze the properties of STAR-RIS-assisted CF mMIMO systems and reveal explicit insights in terms of the impact of channel correlation, the number of surface elements, and the pilot contamination on the achievable rate.

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arXiv2023

Max-Min SINR Analysis of STAR-RIS Assisted Massive MIMO Systems with Hardware Impairments

Reconfigurable intelligent surface (RIS) has emerged as a cost-effective solution to improve wireless communication performance through just passive reflection. Recently, the concept of simultaneously transmitting and reflecting RIS (STAR-RIS) has appeared but the study of minimum signal-to-interference-plus-noise ratio (SINR) and the impact of hardware impairments (HWIs) remain open. In addition to previous works on STAR-RIS, we consider a massive multiple-input multiple-output (mMIMO) base station (BS) serving multiple user equipments (UEs) at both sides of the RIS. Specifically, in this work, focusing on the downlink of a single cell, we derive the minimum SINR obtained by the optimal linear precoder (OLP) with HWIs in closed form. The OLP maximises the minimum SINR subject to a given power constraint for any given passive beamforming matrix (PBM). Next, we obtain deterministic equivalents (DEs) for the OLP and the minimum SINR, which are then used to optimise the PBM. Notably, based on the DEs and statistical channel state information (CSI), we optimise simultaneously the amplitude and phase shift by using a projected gradient ascent algorithm (PGAM) for both energy splitting (ES) and mode switching (MS) STAR-RIS operation protocols with reduced feedback, \textcolor{black}{which is quite crucial for STAR-RIS systems that include the double number or variables compared to reflecting only RIS.} Simulations verify the analytical results, shed light on the impact of HWIs, and demonstrate the better performance of STAR-RIS compared to conventional RIS.

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arXiv2023

Cooperative RIS and STAR-RIS assisted mMIMO Communication: Analysis and Optimization

Reconfigurable intelligent surface (RIS) has emerged as a cost-effective and promising solution to extend the wireless signal coverage and improve the performance via passive signal reflection. Different from existing works which do not account for the cooperation between RISs or do not provide full space coverage, we propose the marriage of cooperative double-RIS with simultaneously transmitting and reflecting RIS (STAR-RIS) technologies denoted as RIS/STAR-RIS under correlated Rayleigh fading conditions to assist the communication in a massive multiple-input multiple-output (mMIMO) setup. The proposed architecture is superior since it enjoys the benefits of the individual designs. We introduce a channel estimation approach of the cascaded channels with reduced overhead. Also, we obtain the deterministic equivalent (DE) of the downlink achievable sum spectral efficiency (SE) in closed form based on large-scale statistics. Notably, relied on statistical channel state information (CSI), we optimise both surfaces by means of the projected gradient ascent method (PGAM), and obtain the gradients in closed form. The proposed optimization achieves to maximise the sum SE of such a complex system, and has low complexity and low overhead since it can be performed at every several coherence intervals. Numerical results show the benefit of the proposed architecture and verify the analytical framework. In particular, we show that the RIS/STAR-RIS architecture outperforms the conventional double-RIS or its single-RIS counterparts.

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arXiv2023

Achievable Rate of a STAR-RIS Assisted Massive MIMO System Under Spatially-Correlated Channels

Reconfigurable intelligent surfaces (RIS)-assisted massive multiple-input multiple-output (mMIMO) is a promising technology for applications in next-generation networks. However, reflecting-only RIS provides limited coverage compared to a simultaneously transmitting and reflecting RIS (STAR-RIS). Hence, in this paper, we focus on the downlink achievable rate and its optimization of a STAR-RIS-assisted mMIMO system. Contrary to previous works on STAR-RIS, we consider mMIMO, correlated fading, and multiple user equipments (UEs) at both sides of the RIS. In particular, we introduce an estimation approach of the aggregated channel with the main benefit of reduced overhead links instead of estimating the individual channels. {Next, leveraging channel hardening in mMIMO and the use-and-forget bounding technique, we obtain an achievable rate in closed-form that only depends on statistical channel state information (CSI). To optimize the amplitudes and phase shifts of the STAR-RIS, we employ a projected gradient ascent method (PGAM) that simultaneously adjusts the amplitudes and phase shifts for both energy splitting (ES) and mode switching (MS) STAR-RIS operation protocols.} By considering large-scale fading, the proposed optimization can be performed every several coherence intervals, which can significantly reduce overhead. Considering that STAR-RIS has twice the number of controllable parameters compared to conventional reflecting-only RIS, this accomplishment offers substantial practical benefits. Simulations are carried out to verify the analytical results, reveal the interplay of the achievable rate with fundamental parameters, and show the superiority of STAR-RIS regarding its achievable rate compared to its reflecting-only counterpart.

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arXiv2023

Terahertz-Band Channel and Beam Split Estimation via Array Perturbation Model

For the demonstration of ultra-wideband bandwidth and pencil-beamforming, the terahertz (THz)-band has been envisioned as one of the key enabling technologies for the sixth generation networks. However, the acquisition of the THz channel entails several unique challenges such as severe path loss and beam-split. Prior works usually employ ultra-massive arrays and additional hardware components comprised of time-delayers to compensate for these loses. In order to provide a cost-effective solution, this paper introduces a sparse-Bayesian-learning (SBL) technique for joint channel and beam-split estimation. Specifically, we first model the beam-split as an array perturbation inspired from array signal processing. Next, a low-complexity approach is developed by exploiting the line-of-sight-dominant feature of THz channel to reduce the computational complexity involved in the proposed SBL technique for channel estimation (SBCE). Additionally, based on federated-learning, we implement a model-free technique to the proposed model-based SBCE solution. Further to that, we examine the near-field considerations of THz channel, and introduce the range-dependent near-field beam-split. The theoretical performance bounds, i.e., Cramér-Rao lower bounds, are derived both for near- and far-field parameters, e.g., user directions, beam-split and ranges. Numerical simulations demonstrate that SBCE outperforms the existing approaches and exhibits lower hardware cost.

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arXiv2023

Near-Field Terahertz Communications: Model-Based and Model-Free Channel Estimation

Terahertz (THz) band is expected to be one of the key enabling technologies of the sixth generation (6G) wireless networks because of its abundant available bandwidth and very narrow beam width. Due to high frequency operations, electrically small array apertures are employed, and the signal wavefront becomes spherical in the near-field. Therefore, near-field signal model should be considered for channel acquisition in THz systems. Unlike prior works which mostly ignore the impact of near-field beam-split (NB) and consider either narrowband scenario or far-field models, this paper introduces both a model-based and a model-free techniques for wideband THz channel estimation in the presence of NB. The model-based approach is based on orthogonal matching pursuit (OMP) algorithm, for which we design an NB-aware dictionary. The key idea is to exploit the angular and range deviations due to the NB. We then employ the OMP algorithm, which accounts for the deviations thereby ipso facto mitigating the effect of NB. We further introduce a federated learning (FL)-based approach as a model-free solution for channel estimation in a multi-user scenario to achieve reduced complexity and training overhead. Through numerical simulations, we demonstrate the effectiveness of the proposed channel estimation techniques for wideband THz systems in comparison with the existing state-of-the-art techniques.

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arXiv2022

Vehicular Networks for Combating a Worldwide Pandemic: Preventing the Spread of COVID-19

As a worldwide pandemic, the coronavirus disease-19 (COVID-19) has caused serious restrictions in people's social life, along with the loss of lives, the collapse of economies and the disruption of humanitarian aids. Despite the advance of technological developments, we, as researchers, have witnessed that several issues need further investigation for a better response to a pandemic outbreak. Therefore, researchers recently started developing ideas to stop or at least reduce the spread of the pandemic. While there have been some prior works on wireless networks for combating a pandemic scenario, vehicular networks and their potential bottlenecks have not yet been fully examined. Furthermore, the vehicular scenarios can be identified as the locations, where the social distancing is mostly violated. With this motivation, this article provides an extensive discussion on vehicular networking for combating a pandemic. We provide the major applications of vehicular networking for combating COVID-19 in public transportation, in-vehicle diagnosis, border patrol and social distance monitoring. Next, we identify the unique characteristics of the collected data in terms of privacy, flexibility and coverage, then highlight corresponding future directions in privacy preservation, resource allocation, data caching and data routing. We believe that this work paves the way for the development of new products and algorithms that can facilitate the social life and help controlling the spread of the pandemic.

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