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.

In this study, we explore a terahertz (THz) wireless power transfer (WPT) system, aiming to determine the optimal transmit waveform that maximizes the rectification efficiency. Since traditional Schottky diodes may be inefficient for THz rectification, the receiver is assumed to be equipped with a resonant tunneling diode (RTD)-based rectifier. Such rectifiers are generally characterized by a non-monotonic energy harvesting (EH) behavior, necessitating the design of transmit waveforms that align with the receiver’s non-monotonic characteristics. To this end, we utilize a waveform-to-energy model and formulate an optimization problem to maximize the rectification efficiency under both average and peak power constraints. The derived closed-form optimal waveform is a pulsed tone whose adaptive duty cycle follows the rectifiers efficiency peaks via a simple, real-time implementable rule. Our results demonstrate consistent gains over multisine baselines across power regimes and clarify when peak/average power constraints change the optimal rectification efficiency.

Wireless power transfer (WPT) has emerged as a promising technology for prolonging the battery life of energy-constrained devices. Waveform optimization enhances energy harvesting efficiency, but depends on the applied energy harvesting model. Conventional models capturing rectifier nonlinearities unveiled multisines’ superiority over continuous waves, but exhibit limitations in supported waveforms and rectifier structures. A recently proposed waveform-to-energy harvesting model addresses these limitations and showed that pulsed radio frequency (RF) signals outperform multisines under ideal conditions. However, practical deployments face channel uncertainty. This paper presents a robust optimization framework for WPT systems under a bounded channel estimation error model. We develop an efficient algorithm maximizing worst-case harvested energy and propose single-and multi-frequency pulsed RF designs that maintain performance despite channel uncertainty. Numerical results demonstrate significant gains of the proposed designs over benchmarks, especially under severe channel uncertainty.

A disruptive radio communication paradigm is proposed, where the dynamic channel reconfiguration capability of the emerging fluid antennas (FA) and hybrid reflecting intelligent surfaces (RIS) technologies is exploited to ensure favorable propagation and substantially boost multiple-input multiple-output capacity in a cost-effective and energy-efficient manner, offering fiber-like user experience improvement. The joint optimization of RIS scheduling and beamforming, precoding, and FAs’ positions is studied in a fully- or sub-connected active/passive RIS-aided multiple-input single-output broadcast channel with a FAs-equipped base station (BS), such that the energy efficiency is maximized subject to the BS’s and RIS’s power consumption constraints. An efficient iterative algorithm based on fractional programming techniques, the block coordinate ascent framework, big-M formulation, penalty-based optimization, and the gradient ascent method is developed to tackle this challenging mixed-integer nonlinear program. Numerical simulation results validate the proposed concept and unveil its significant performance gains over benchmarks.

Wireless information and energy transfer (WIET) represents an emerging paradigm that 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 sixth-generation (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 codesign 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 and also practical aspects. The comprehensive overview of the state of the art of WIET presented in this article highlights the potential of WIET systems and their overall benefits in 6G networks.

In this work, we introduce novel hybrid reflecting intelligent surface (RIS) architectures where at least one reflecting sub-surface (RS) resembles a sub-connected RIS. Next, assuming a multiple-input single-output broadcasting system where the base station (BS) is aided by a hybrid RIS adopting either one of the proposed structures or the fully-connected active/passive design, we jointly optimize the transmit precoding and reflect beamforming schemes to maximize the energy efficiency subject to the power consumption constraints of the BS and any active RS. We develop efficient, low-complexity iterative algorithms based on the Fractional Programming, Block Coordinate Descent, Lagrange multipliers, and Majorization-Minimization methods to tackle these challenging non-convex optimization problems and obtain closed-form expressions of the optimal solutions. We also derive the necessary conditions for feasible allocation of RIS elements. Numerical evaluations unveil the performance gains of the proposed designs over benchmarks and provide insights.

― Simultaneous wireless information and power transfer (SWIPT) represents an enabling paradigm for future energysustainable networks. Multiple-input multiple-output (MIMO) technology enhances the performance of SWIPT systems via precoding. We typically rely on heuristic precoding designs in practice, due to their favorable performance-complexity balance, as opposed to their optimization-based counterparts. On the other hand, these designs provide limited flexibility. Furthermore, the standard precoding heuristics are SWIPT-agnostic. In this paper, we propose a novel MIMO precoding framework for SWIPT that is based on the notion of controllable residual interference to resolve the aforementioned issues. Specifically, we consider a multiple-input single-output (MISO) broadcast system for SWIPT with separate energy and information receivers. In this context, we formulate interference-constrained problems and apply a relaxation to obtain low-complexity solutions that admit closed-form expressions. We also extend our approach to hybrid precoding designs for the case where the base station adopts an analog/digital architecture due to particularly stringent cost and energy consumption constraints. Numerical simulations unveil the performance gains of the proposed precoding schemes over the solutions based on semi-definite relaxation (SDR) and heuristic baseline methods and provide valuable insights.

We compare exact and approximate performance evaluation methods of the Least Recently Used (LRU) caching strategy, which is widely applied in local caches and in distributed web cache architectures in core and edge networks. Based on the independent reference model (IRM), the LRU startup behaviour and convergence time (CT) is derived. The result is related to hit ratio approximations by Fagin and Che et al. We evaluate the precision of the approximations by identifying maximum errors, which are shown to decrease with the cache size. For different object sizes, we extend the analytical LRU hit ratio formula, which is tractable for small caches. The Che and CT approximations are subject to larger deviations for high variability of the object sizes and small caches due to partially unused cache space. We propose an estimation scheme for the fraction of unused LRU cache space, which is shown to improve the accuracy.

This study considers the Joint Transmit/Reflect Beamforming and Power Splitting (JTRBPS) optimization problem in a spectrum underlay setting, such that the transmit sum-energy of the intelligent reflecting surface (IRS)-aided secondary transmitter (ST) is minimized subject to the quality-of-service requirements of the PS-simultaneous wireless information and power transfer (SWIPT) secondary receivers and the interference constraints of the primary receivers (PR). The interference at the PRs caused by the reception of IRS-reflected signals sent by the primary transmitter is taken into account. A coordinated channel state information (CSI) acquisition protocol is proposed. Next, assuming availability at the ST of perfect CSI for all direct and IRS-cascaded transmitter–receiver channels, two penalty-based iterative algorithms are developed: an alternating minimization algorithm that involves semi-definite relaxation in JTBPS design and successive convex approximation in RB optimization, and a block coordinate descent algorithm that employs the Riemannian conjugate gradient algorithm in RB updates. Finally, an outage-constrained robust design under imperfect CSI is devised. Numerical simulations highlight the performance gains of the proposed strategies over benchmarks, corroborate the benefits of using an IRS, and provide valuable insights.

In this paper, we study the employment of conventional orthogonal frequency division multiplexing (OFDM) signals in simultaneous wireless information and power transfer (SWIPT) systems. Specifically, we investigate the impact of high peak-to-average power ratio (PAPR) on OFDM-based information/energy transfer under the nonlinearities and memory effects introduced by high power amplifier (HPA) at the transmitter. A closed-form expression of the symbol error rate (SER) as a function of the PAPR is derived for a Gaussian channel in this context. We notice that even under these realistic non-idealities, PAPR deteriorates the SER performance while facilitating energy transfer efficiency, as expected. To further enlarge the rate-energy (R-E) region, a predistortion scheme that allows the transmission of unclipped high PAPR OFDM signals is also proposed. Numerical simulations based on MATLAB and ADS software validate the impact of PAPR in SWIPT systems, regarding both the information detection and energy harvesting processes.

We consider an intelligent reflecting surface (IRS)-aided secondary multiple-input single-output (MISO) broadcast system for simultaneous wireless information and power transfer (SWIPT) in a spectrum underlay setup. The secondary transmitter (ST) regards the primary receivers (PR) as possible eavesdroppers. We propose an inter-system coordination protocol that enables acquisition at the ST of control information to facilitate interference management. We assume availability of imperfect channel state information (CSI) regarding the relevant direct and IRS-cascaded links at the ST. We aim at jointly optimizing the transmit precoding, artificial noise (AN) covariance, and reflect beamforming matrices, so that the transmit power of the ST is minimized subject to the quality-of-service (QoS) requirements of the information decoding and energy harvesting secondary receivers (IDR/EHR), the security and interference constraints of the PRs, and the unit-modulus constraints of the IRS phase shifts. We obtain convex approximations of the probabilistic constraints by employing Bernstein-type and first-order Taylor inequalities. We derive a robust outage-constrained design by developing an alternating minimization algorithm that makes use of the semi-definite relaxation (SDR) method and the penalty convex-concave procedure (CCP). Our design takes into account the additional interference incurred at the PRs by the IRS-reflected transmissions of the primary transmitter (PT) itself, which serves its users in an IRS-blind manner. Numerical simulation results reveal the performance gains of the proposed scheme over benchmark strategies and highlight the impact of the system parameters on the performance.

In this paper, we propose an energy-efficient optimal altitude for an aerial access point (AAP), which acts as a flying base station to serve a set of ground user equipment (UE). Since the ratio of total energy consumed by the aerial vehicle to the communication energy is very large, we include the aerial vehicle's energy consumption in the problem formulation. After considering the energy consumption model of the aerial vehicle, our objective is translated into a non-convex optimization problem of maximizing the global energy efficiency (GEE) of the aerial communication system, subject to altitude and minimum individual data rate constraints. At first, the non-convex fractional objective function is solved by using sequential convex programming (SCP) optimization technique. To compare the result of SCP with the global optimum of the problem, we reformulate the initial problem as a monotonic fractional optimization problem (MFP) and solve it using the polyblock outer approximation (PA) algorithm. Numerical results show that the candidate solution obtained from SCP is the same as the global optimum found using the monotonic fractional programming technique. Furthermore, the impact of the aerial vehicle's energy consumption on the optimal altitude determination is also studied.

This paper investigates the hybrid precoding (HP) design for simultaneous wireless information and power transfer in a multiple-input single-output broadcast channel setup where the terminals adopt the power splitting architecture. The problem of interest is the maximization of the signal-to-interference-plus-noise-ratio and the harvested power for all terminals under a total transmit power constraint. Our focus is on the derivation of frequency- and setup-agnostic low-complexity HP methods. Two baseline approaches for the determination of the analog precoder are considered. In the first one, the phases are computed via the singular value decomposition (SVD) of the channel matrix, while in the second they are selected randomly. Then, the baseband precoder is computed by applying semidefinite relaxation (SDR) to the problem under study. Alternatively, we combine the aforementioned analog precoders with a fixed zero-forcing baseband pre-coder, in order to further reduce the computational load. Another proposed strategy focuses on the minimization of the Euclidean distance between the optimal fully-digital precoder, which is obtained via SDR, and the hybrid one. To this end, an alternating minimization algorithm that employs Gaussian smoothing to convexify the problem and utilizes stochastic gradient descent to update the phases is introduced. The performance of the proposed HP methods is comparatively evaluated versus the one achieved by the optimal fully-digital precoder via numerical simulations. The simulation results indicate that the stochastic optimization approach presents a favorable performance-complexity trade-off as well as substantial power gains.

The combination of coordinated multi-point (CoMP) and underlay spectrum sharing promises substantial spectral efficiency (SE) gains for future cellular networks. However, this concept has been largely overlooked in the literature. Moreover, none of the few relevant studies consider the use of "standard" transmission strategies to facilitate the adoption of the aforementioned communication paradigm by 5G networks. The use of load-controlled antenna arrays (LC-AA) and symbol-level (SL) precoding can further enhance the performance of CoMP cellular networks, as it has been shown in the literature. Nevertheless, the corresponding research works do not consider a spectrum sharing setup. In this paper, we fill this gap in the literature by deriving the optimal power allocation strategy (in the sum-SE sense) and the corresponding algorithm that implements this solution for scenarios where various linear or SL precoding schemes are applied. Numerical simulations indicate the feasibility of the proposed approach for LC-AA-equipped CoMP networks and shed light on the effect of various parameters on system performance.

We compare web caching strategies based on the least recently used (LRU) and the least frequently used (LFU) replacement principles with optimum caching according to Belady's algorithm. The achievable hit rates of the strategies are shown to improve with the exploited knowledge about the request pattern while the computation effort is also increasing. The results give an overview of performance tradeoffs in the whole relevant range for web caching with Zipf request pattern. In a second part, we study a combined approach of the optimum strategy for a limited look-ahead with LRU, LFU or other non-predictive methods. We evaluate the hit rate gain depending on the extent of the look-ahead for request traces and for the independent reference model (IRM) via simulation and derive an analytic confirmation of the observed behaviour. It is shown that caching for video streaming can benefit from the proposed look-ahead technique, when replacement decisions can be partly revised due to new requests being encountered during long lasting content updates.