Ioannis Krikidis

Nizar Khalfet, Ioannis Krikidis

This paper introduces a new framework for quantum simultaneous information and power transfer (QSIPT), enabling the joint use of quantum states for classical information and energy transfer in quantum communication systems. We propose a novel model in which quantum states are simultaneously used to transmit classical information through a quantum channel and transfer energy to an energy harvesting (EH) receiver. The trade-off between communication rate and harvested energy is characterized by the capacity-power function, which is defined and characterized for both discrete-variable (DV) and continuous-variable (CV) quantum channels. For DV channels, we derive the properties of the capacity-power function, providing analytical upper and lower bounds for the amplitude damping channel and an exact closed-form characterization for the quantum erasure channel. For CV channels, we extend the mathematical framework by introducing a generalized beam-splitter (BS) receiver with adjustable transmissivity, jointly optimized with a transmitter mean-photon-number budget, that splits the channel output between the information decoder and the EH receiver. Specifically, we analyze the capacity-power trade-off under various Gaussian encoding schemes including coherent, squeezed, and thermal states for both lossy bosonic and additive Gaussian noise channels. Closed-form expressions are derived for coherent-state encoding under the joint photon-number-budget and adjustable-transmissivity formulation; squeezed-state inputs are evaluated numerically. Our results show that, within the considered displaced Gaussian encoding class, coherent states achieve the best capacity-power trade-off, squeezed states do not outperform coherent-state encoding under the phase-insensitive channel and passive receiver architecture, and thermal states enable energy transfer without supporting reliable communication.

Ioannis Krikidis

In this letter, we deal with the age of information (AoI) for a sensor network with wireless power transfer (WPT) capabilities. Specifically, we study a simple network topology, where a sensor node harvests energy from radio frequency signals (transmitted by a dedicated energy source) to transmit real-time status updates. The sensor node generates an update when its capacitor/battery becomes fully charged and transmits by using all the available energy without further energy management. The average AoI performance of the considered greedy policy is derived in closed form and is a function of the capacitor's size. The optimal value of the capacitor that maximizes the freshness of the information, corresponds to a simple optimization problem requiring a one-dimensional search. The derived theoretical results provide useful performance bounds for practical WPT networks.

Nizar Khalfet, Kapila W. S. Palitharathna, Symeon Chatzinotas et al.

This paper presents a comprehensive analysis of the information-energy capacity region for simultaneous lightwave information and power transfer (SLIPT) systems over lognormal fading channels. Unlike conventional studies that primarily focus on additive white Gaussian noise channels, we study the complex impact of lognormal fading, which is prevalent in optical wireless communication systems such as underwater and atmospheric channels. By applying the Smith's framework for these channels, we demonstrate that the optimal input distribution is discrete, characterized by a finite number of mass points. We further investigate the properties of these mass points, especially at the transition points, to reveal critical insights into the rate-power trade-off inherent in SLIPT systems. Additionally, we introduce a novel cooperative information-energy capacity learning framework, leveraging generative adversarial networks, to effectively estimate and optimize the information-energy capacity region under practical constraints. Numerical results validate our theoretical findings, illustrating the significant influence of channel fading on system performance. The insights and methodologies presented in this work provide a solid foundation for the design and optimization of future SLIPT systems operating in challenging environments.

Elio Faddoul, Konstantinos Ntougias, Ioannis Krikidis

The emerging reconfigurable antenna (RA) array technology promises capacity enhancement through dynamic antenna positioning. Traditional approaches enforce half-wavelength or greater spacing among RA elements to avoid mutual coupling, limiting the solution space. Additionally, achieving sufficient spatial channel sampling requires numerous discrete RA positions (ports), while high-frequency scenarios with hybrid processing demand many physical RAs to maintain array gains. This leads to exponential growth in the solution space. In this work, we propose two techniques to address the former challenge: (1) surrounding a limited number of active RAs with passive ones terminated to tunable analog loads to \textit{exploit} mutual coupling and increase array gain, and (2) employing tunable loads on each RA in an all-active design to \textit{eliminate} mutual coupling in the analog domain. Both methods enable arbitrary RA spacing, unlocking the full solution space. Regarding the latter challenge, we develop greedy and meta-heuristic port selection algorithms, alongside low-complexity heuristic variants, that efficiently handle over $10^{20}$ array configurations. Furthermore, we optimize the loading values to maximize the sum-rate in a multiple-input single-output broadcast channel under transmission power constraints, assuming a heuristic linear precoder. In addition, we analyze performance degradation from quantized loads and propose corresponding robust designs. Numerical simulations reveal 20-56\% sum-rate gains over benchmarks and around 60\% performance recovery under quantization errors.

Ioannis Krikidis

We investigate a quantum integrated communication and computation (QICC) scheme for a single-mode bosonic multiple-access channel (MAC) with coherent-state signalling. By exploiting the natural superposition property of the quantum MAC, a common receiver simultaneously performs over-the-air computation (OAC) on the analogue symbols transmitted by one set of devices and decodes multiple-access data from another. The joint design of the transmit power control and the receive coefficient leads to a non-convex optimization problem that maximizes computation accuracy under a prescribed sum-rate communication constraint. To address this challenge, we develop a low-complexity alternating-optimization framework that incorporates: (i) closed-form linear minimum-mean square error updates for the receive coefficient, (ii) monotonicity properties of the quantum sum-rate constraint, and (iii) projected-gradient refinements for the communication powers. The proposed QICC scheme achieves an effective computation-communication trade-off with fast convergence and low computational complexity.

Nikolaos Nomikos, Spyros Zoupanos, Themistoklis Charalambous et al.

Mobile networks are experiencing tremendous increase in data volume and user density. An efficient technique to alleviate this issue is to bring the data closer to the users by exploiting the caches of edge network nodes, such as fixed or mobile access points and even user devices. Meanwhile, the fusion of machine learning and wireless networks offers a viable way for network optimization as opposed to traditional optimization approaches which incur high complexity, or fail to provide optimal solutions. Among the various machine learning categories, reinforcement learning operates in an online and autonomous manner without relying on large sets of historical data for training. In this survey, reinforcement learning-aided mobile edge caching is presented, aiming at highlighting the achieved network gains over conventional caching approaches. Taking into account the heterogeneity of sixth generation (6G) networks in various wireless settings, such as fixed, vehicular and flying networks, learning-aided edge caching is presented, departing from traditional architectures. Furthermore, a categorization according to the desirable performance metric, such as spectral, energy and caching efficiency, average delay, and backhaul and fronthaul offloading is provided. Finally, several open issues are discussed, targeting to stimulate further interest in this important research field.