arXiv subjects

Ioannis Krikidis

Publications and source records attributed to Ioannis Krikidis.

At least 55 records · Page 3Linked to original sources

arXiv2025

An Unsupervised Machine Learning to Optimize Hybrid Quantum Noise Clusters for Gaussian Quantum Channel

This work focuses on optimizing the hybrid quantum noise model to improve the capacity of Gaussian quantum channels using Machine Learning (ML) generated clusters. The work specifically leverages Gaussian Mixture Model (GMM) and the Expectation-Maximization (EM) algorithm to model the complex noise characteristics of quantum channels. Hybrid quantum noise, which includes both quantum shot noise and classical Additive-White-Gaussian Noise (AWGN), is modeled as an infinite mixture of Gaussian distributions weighted by Poissonian parameters. The study proposes a method to reduce the number of clusters within this noise model, simplifying visualization and improving the accuracy of channel capacity estimations without compromising essential noise characteristics. Key contributions include the reduction of Gaussian clusters while maintaining error tolerances and using the EM algorithm to update quantum channel parameters, leading to more accurate channel capacity. The approach is validated through simulations, demonstrating that ML-enhanced quantum noise clustering significantly improves the channels performance in satellite-based quantum communication systems, specifically for Quantum Key Distribution (QKD). The work demonstrates that GMM and EM algorithms provide a practical solution for modeling quantum noise in real-time applications, advancing the optimization of quantum communication networks.

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arXiv2024

A Hybrid Noise Approach to Modelling of Free-Space Satellite Quantum Communication Channel for Continuous-Variable QKD

This paper significantly advances the application of Quantum Key Distribution (QKD) in Free- Space Optics (FSO) satellite-based quantum communication. We propose an innovative satellite quantum channel model and derive the secret quantum key distribution rate achievable through this channel. Unlike existing models that approximate the noise in quantum channels as merely Gaussian distributed, our model incorporates a hybrid noise analysis, accounting for both quantum Poissonian noise and classical Additive-White-Gaussian Noise (AWGN). This hybrid approach acknowledges the dual vulnerability of continuous variables (CV) Gaussian quantum channels to both quantum and classical noise, thereby offering a more realistic assessment of the quantum Secret Key Rate (SKR). This paper delves into the variation of SKR with the Signal-to-Noise Ratio (SNR) under various influencing parameters. We identify and analyze critical factors such as reconciliation efficiency, transmission coefficient, transmission efficiency, the quantum Poissonian noise parameter, and the satellite altitude. These parameters are pivotal in determining the SKR in FSO satellite quantum channels, highlighting the challenges of satellitebased quantum communication. Our work provides a comprehensive framework for understanding and optimizing SKR in satellite-based QKD systems, paving the way for more efficient and secure quantum communication networks.

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arXiv2024

Hybrid Semantic-Shannon Communications

Semantic communications are considered a promising beyond-Shannon/bit paradigm to reduce network traffic and increase reliability, thus making wireless networks more energy efficient, robust, and sustainable. However, the performance is limited by the efficiency of the semantic transceivers, i.e., the achievable "similarity" between the transmitted and received signals. Under strict similarity conditions, semantic transmission may not be applicable and bit communication is mandatory. In this paper, for the first time in the literature, we propose a multi-carrier Hybrid Semantic-Shannon communication system where, without loss of generality, the case of text transmission is investigated. To this end, a joint semantic-bit transmission selection and power allocation optimization problem is formulated, aiming to minimize two transmission delay metrics widely used in the literature, subject to strict similarity thresholds. Despite their non-convexity, both problems are decomposed into a convex and a mixed linear integer programming problem by using alternating optimization, both of which can be solved optimally. Furthermore, to improve the performance of the proposed hybrid schemes, a novel association of text sentences to subcarriers is proposed based on the data size of the sentences and the channel gains of the subcarriers. We show that the proposed association is optimal in terms of transmission delay. Numerical simulations verify the effectiveness of the proposed hybrid semantic-bit communication scheme and the derived sentence-to-subcarrier association, and provide useful insights into the design parameters of such systems.

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arXiv2024

Transmit and Receive Antenna Port Selection for Channel Capacity Maximization in Fluid-MIMO Systems

In this letter, we study a discrete optimization problem, namely, the maximization of channel capacity in fluid multiple-input multiple-output (fluid-MIMO) systems through the selection of antenna ports/positions at both the transmitter and the receiver. First, we present a new joint convex relaxation (JCR) problem by using an upper bound on the channel capacity and exploiting the binary nature of optimization variables. Then, we develop and analyze two optimization algorithms with different performance-complexity tradeoffs. The first algorithm is based on JCR and reduced exhaustive search (JCR&RES), while the second on JCR and alternating optimization (JCR&AO).

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arXiv2024

Hybrid Quantum Noise Approximation and Pattern Analysis on Parameterized Component Distributions

Noise is a vital factor in determining the accuracy of processing the information of the quantum channel. One must consider classical noise effects associated with quantum noise sources for more realistic modelling of quantum channels. A hybrid quantum noise model incorporating both quantum Poisson noise and classical additive white Gaussian noise (AWGN) can be interpreted as an infinite mixture of Gaussians with weightage from the Poisson distribution. The entropy measure of this function is difficult to calculate. This research developed how the infinite mixture can be well approximated by a finite mixture distribution depending on the Poisson parametric setting compared to the number of mixture components. The mathematical analysis of the characterization of hybrid quantum noise has been demonstrated based on Gaussian and Poisson parametric analysis. This helps in the pattern analysis of the parametric values of the component distribution, and it also helps in the calculation of hybrid noise entropy to understand hybrid quantum noise better.

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arXiv2024

An RIS-enabled Time Reversal Scheme for Multipath Near-Field Channels

Time reversal (TR) is a promising technique that exploits multipaths for achieving energy focusing in high-frequency wideband communications. In this letter, we focus on a TR scheme facilitated by a reconfigurable intelligent surface (RIS) which, due to the higher frequency and large array aperture, operates in the near-field region. The proposed scheme enriches the propagation environment for the TR in such weak scattering conditions and does not need channel knowledge for the RIS configuration. Specifically, the RIS is employed to create multiple virtual propagation paths that are required to efficiently apply the TR. We derive a performance bound for the proposed scheme under near-field modeling through the received signal-to-noise ratio (SNR) and we examine how various system design parameters affect the performance. We observe that a linear RIS topology maximizes the number of resolvable paths. It is also demonstrated that the proposed scheme improves the SNR, while for a large number of elements it can outperform the conventional passive beamforming at the RIS.

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arXiv2024

Advanced Channel Coding Designs for Index-Modulated Fluid Antenna Systems

The concept of fluid antennas (FAs) has emerged as a promising solution to enhance the spectral efficiency of wireless networks, achieved by introducing additional degrees of freedom, including reconfigurability and flexibility. In this paper, we investigate the use of index-modulated (IM) transmissions within the framework of FA systems, where an FA position is activated during each transmission interval. This approach is motivated by the common characteristics exhibited by FAs and IM transmissions, which entails the use of a single radio-frequency chain. From this perspective, we derive a closed-form expression for the bit error rate of IM-FAs considering spatial correlation, and demonstrating superior performance compared to conventional IM systems. To enhance the performance of IM-FAs in correlated conditions, channel coding techniques are applied. We first analyze a set partition coding (SPC) scheme for IM-FAs to spatially separate the FA ports, and provide a tight performance bound over correlated channels. Furthermore, the spatial SPC scheme is extended to turbo-coded modulation where the performance is analyzed for low and high signal-to-noise ratios. Our results reveal that through the implementation of channel coding techniques designed for FAs and IM transmission, the performance of coded IM-FAs exhibits notable enhancements, particularly in high correlation scenarios.

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arXiv2024

Robust IRS-Element Activation for Energy Efficiency Optimization in IRS-Assisted Communication Systems With Imperfect CSI

In this paper, we study an intelligent reflecting surface (IRS)-aided communication system with single-antenna transmitter and receiver, under imperfect channel state information (CSI). More specifically, we deal with the robust selection of binary (on/off) states of the IRS elements in order to maximize the worst-case energy efficiency (EE), given a bounded CSI uncertainty, while satisfying a minimum signal-to-noise ratio (SNR). In addition, we consider not only continuous but also discrete IRS phase shifts. First, we derive closed-form expressions of the worst-case SNRs, and then formulate the robust (discrete) optimization problems for each case. In the case of continuous phase shifts, we design a dynamic programming (DP) algorithm that is theoretically guaranteed to achieve the global maximum with polynomial complexity $O(L\,{\log L})$, where $L$ is the number of IRS elements. In the case of discrete phase shifts, we develop a convex-relaxation-based method (CRBM) to obtain a feasible (sub-optimal) solution in polynomial time $O(L^{3.5})$, with a posteriori performance guarantee. Furthermore, numerical simulations provide useful insights and confirm the theoretical results. In particular, the proposed algorithms are several orders of magnitude faster than the exhaustive search when $L$ is large, thus being highly scalable and suitable for practical applications. Moreover, both algorithms outperform a baseline scheme, namely, the activation of all IRS elements.

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arXiv2024

MIMO with 1-bit Pre/Post-Coding Resolution: A Quantum Annealing Approach

In this paper, we study the problem of digital pre/post-coding design in multiple-input multiple-output (MIMO) systems with 1-bit resolution per complex dimension. The optimal solution that maximizes the received signal-to-noise ratio relies on an NP-hard combinatorial problem that requires exhaustive searching with exponential complexity. By using the principles of alternating optimization and quantum annealing (QA), an iterative QA-based algorithm is proposed that achieves near-optimal performance with polynomial complexity. The algorithm is associated with a rigorous mathematical framework that casts the pre/post-coding vector design to appropriate real-valued quadratic unconstrained binary optimization (QUBO) problems. Experimental results in a state-of-the-art D-WAVE QA device validate the efficiency of the proposed algorithm. To further improve the efficiency of the D-WAVE quantum device, a new pre-processing technique which preserves the quadratic QUBO matrix from the detrimental effects of the Hamiltonian noise through non-linear companding, is proposed. The proposed pre-processing technique significantly improves the quality of the D-WAVE solutions as well as the occurrence probability of the optimal solution.

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arXiv2024

Wireless Information and Energy Transfer in the Era of 6G Communications

Wireless information and energy transfer (WIET) represents an emerging paradigm which employs controllable transmission of radio-frequency signals for the dual purpose of data communication and wireless charging. As such, WIET is widely regarded as an enabler of envisioned 6G use cases that rely on energy-sustainable Internet-of-Things (IoT) networks, such as smart cities and smart grids. Meeting the quality-of-service demands of WIET, in terms of both data transfer and power delivery, requires effective co-design of the information and energy signals. In this article, we present the main principles and design aspects of WIET, focusing on its integration in 6G networks. First, we discuss how conventional communication notions such as resource allocation and waveform design need to be revisited in the context of WIET. Next, we consider various candidate 6G technologies that can boost WIET efficiency, namely, holographic multiple-input multiple-output, near-field beamforming, terahertz communication, intelligent reflecting surfaces (IRSs), and reconfigurable (fluid) antenna arrays. We introduce respective WIET design methods, analyze the promising performance gains of these WIET systems, and discuss challenges, open issues, and future research directions. Finally, a near-field energy beamforming scheme and a power-based IRS beamforming algorithm are experimentally validated using a wireless energy transfer testbed. The vision of WIET in communication systems has been gaining momentum in recent years, with constant progress with respect to theoretical but also practical aspects. The comprehensive overview of the state of the art of WIET presented in this paper highlights the potentials of WIET systems as well as their overall benefits in 6G networks.

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arXiv2024

On User Association in Large-Scale Heterogeneous LEO Satellite Network

In this paper, we investigate the performance of large-scale heterogeneous low Earth orbit (LEO) satellite networks in the context of three association schemes. In contrast to existing studies, where single-tier LEO satellite-based network deployments are considered, the developed framework captures the heterogeneous nature of real-world satellite network deployments. More specifically, we propose an analytical framework to evaluate the performance of multi-tier LEO satellite-based networks, where the locations of LEO satellites are approximated as points of independent Poisson point processes, with different density, transmit power, and altitude. We propose three association schemes for the considered network topology based on: 1) the Euclidean distance, 2) the average received power, and 3) a random selection. By using stochastic geometry tools, analytical expressions for the association probability, the downlink coverage probability, as well as the spectral efficiency are derived for each association scheme, where the interference is considered. Moreover, we assess the achieved network performance under several different fading environments, including low, typical, and severe fading conditions, namely non-fading, shadowed-Rician and Rayleigh fading channels, respectively. Our results reveal the impact of fading channels on the coverage probability, and illustrate that the average power-based association scheme outperforms in terms of achieved coverage and spectral efficiency performance against the other two association policies. Furthermore, we highlight the impact of the proposed association schemes and the network topology on the optimal number of LEO satellites, providing guidance for the planning of multi-tier LEO satellite-based networks in order to enhance network performance.

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arXiv2024

Chaotic Waveform-based Signal Design for Noncoherent SWIPT Receivers

This paper proposes a chaotic waveform-based multi-antenna receiver design for simultaneous wireless information and power transfer (SWIPT). Particularly, we present a differential chaos shift keying (DCSK)-based SWIPT multiantenna receiver architecture, where each antenna switches between information transfer (IT) and energy harvesting (EH) modes depending on the receiver's requirements. We take into account a generalized frequency-selective Nakagami-m fading model as well as the nonlinearities of the EH process to derive closed-form analytical expressions for the associated bit error rate (BER) and the harvested direct current (DC), respectively. We show that, both depend on the parameters of the transmitted waveform and the number of receiver antennas being utilized in the IT and EH mode. We investigate a trade-off in terms of the BER and energy transfer by introducing a novel achievable `success rate - harvested energy' region. Moreover, we demonstrate that energy and information transfer are two conflicting tasks and hence, a single waveform cannot be simultaneously optimal for both IT and EH. Accordingly, we propose appropriate transmit waveform designs based on the application specific requirements of acceptable BER or harvested DC or both. Numerical results demonstrate the importance of chaotic waveform-based signal design and its impact on the proposed receiver architecture.

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arXiv2024

A Novel Temperature-based Model for SWIPT

In this letter, a novel communication paradigm for simultaneous wireless information and power transfer (SWIPT) is proposed, which leverages the thermal characteristics of electromagnetic signals. In particular, the proposed scheme exploits the inherent thermal dynamics of electromagnetic signals, enabling the seamless integration of information decoding and energy harvesting (EH). As a consequence, in contrast to conventional SWIPT techniques, the proposed model eliminates the need to divide the received signal into orthogonal components. By exploiting the thermal correlation between consecutive time slots, the communication channel is converted to a virtual multiple-input multiple-output (MIMO) channel with memory. We evaluate the achievable rate of the proposed temperature-modulated channel for uniform and exponential input distributions and assess its performance in terms of harvested energy through a non-linear harvesting model. Our numerical results reveal that the exponential distribution outperforms the uniform distribution in rate and harvested energy at low input power levels, while the uniform distribution achieves a better EH performance at high input power levels.

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arXiv2024

MIMO with Analogue 1-bit Phase Shifters: A Quantum Annealing Perspective

In this letter, we study the analogue pre/post-coding vector design for a point-to-point multiple-input multiple-output (MIMO) system with 1-bit phase shifters. Specifically, we focus on the signal-to-noise ratio (SNR) maximization problem which corresponds to a combinatorial NP-hard due to the binary phase resolution. Two classical computation heuristics are proposed i.e., i) an 1-bit real-valued approximation of the optimal digital designs, and ii) an alternating optimization where a Rayleigh quotient problem is solved at each iteration. An iterative quantum annealing (QA)-based heuristic is also investigated, which outperforms classical counterparts and achieves near-optimal performance while ensuring polynomial time complexity. Experimental results in a real-world D-WAVE QA device validate the efficiency of the proposed QA approach.

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arXiv2024

Optimizing Reconfigurable Antenna MIMO Systems with Coherent Ising Machines

Reconfigurable antenna multiple-input multiple-output (MIMO) is a promising technology for upcoming 6G communication systems. In this paper, we deal with the problem of configuration selection for reconfigurable antenna MIMO by leveraging Coherent Ising Machines (CIMs). By adopting the CIM as a heuristic solver for the Ising problem, the optimal antenna configuration that maximizes the received signal-to-noise ratio is investigated. A mathematical framework that converts the selection problem into a CIM-compatible unconstrained quadratic formulation is presented. Numerical studies show that the proposed CIM-based design outperforms classical counterparts and achieves near-optimal performance (similar to exponentially complex exhaustive searching) while ensuring polynomial complexity.

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arXiv2024

Integrated SWIPT Receiver with Memory Effects: Circuit Analysis and Information Detection

Wireless power transfer has been proposed as a key technology for the foreseen machine type networks. A main challenge in the research community lies in acquiring a simple yet accurate model to capture the energy harvesting performance. In this work, we focus on a half-wave rectifier and based on circuit analysis we provide the actual output of the circuit which accounts for the memory introduced by the capacitor. The provided expressions are also validated through circuit simulations on ADS. Then, the half-wave rectifier is used as an integrated simultaneous wireless information and power transfer receiver where the circuit's output is used for decoding information based on amplitude modulation. We investigate the bit error rate performance based on two detection schemes: (i) symbol-by-symbol maximum likelihood (ML); and (ii) ML sequence detection (MLSD). We show that the symbol period is critical due to the intersymbol interference induced by circuit. Our results reveal that MLSD is necessary towards improving the error probability and achieving higher data rates.

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arXiv2024

Handover Management through Reconfigurable Intelligent Surfaces for VLC under Blockage Conditions

In this paper, we consider an indoor visible light communication (VLC) system with multiple "white" light emitting diodes serving to form overlapping wireless communication cells. In order to maintain seamless connectivity to mobile users, a handover procedure should be implemented. In particular, practical conditions such as blockages due to obstacles inside the room environment and the mobility of users can affect direct VLC connectivity. The use of reconfigurable intelligent surfaces (RISs) in optical wireless systems allows to exploit non-direct connectivity links, thus providing efficient communication links. In this paper, we present a proactive handover mechanism that exploits the presence of a RIS, in order to redirect the communication links in case of blockages. The proposed approach has been implemented both in hard and soft modes and assessed in terms of achievable data rate and handover latency for a user walking in a given reference room at different user speeds and blockage conditions. Our presented results and comparisons with conventional handover methods (i.e., without RIS) are helpful in showing the superiority of the presented algorithm.

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arXiv2024

Deep Learning Driven Buffer-Aided Cooperative Networks for B5G/6G: Challenges, Solutions, and Future Opportunities

Buffer-aided cooperative networks (BACNs) have garnered significant attention due to their potential applications in beyond fifth generation (B5G) or sixth generation (6G) critical scenarios. This article explores various typical application scenarios of buffer-aided relaying in B5G/6G networks to emphasize the importance of incorporating BACN. Additionally, we delve into the crucial technical challenges in BACN, including stringent delay constraints, high reliability, imperfect channel state information (CSI), transmission security, and integrated network architecture. To address the challenges, we propose leveraging deep learning-based methods for the design and operation of B5G/6G networks with BACN, deviating from conventional buffer-aided relay selection approaches. In particular, we present two case studies to demonstrate the efficacy of centralized deep reinforcement learning (DRL) and decentralized DRL in buffer-aided non-terrestrial networks. Finally, we outline future research directions in B5G/6G that pertain to the utilization of BACN.

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