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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., Comment: Proceedings of the IEEE, 36 pages, 33 figures

The emerging reflecting intelligent surface (RIS) technology promises to enhance the capacity of wireless communication systems via passive reflect beamforming. However, the product path loss limits its performance gains. Fully-connected (FC) active RIS, which integrates reflect-type power amplifiers into the RIS elements, has been recently introduced in response to this issue. Also, sub-connected (SC) active RIS and hybrid FC-active/passive RIS variants, which employ a limited number of reflect-type power amplifiers, have been proposed to provide energy savings. Nevertheless, their flexibility in balancing diverse capacity requirements and power consumption constraints is limited. In this direction, this study introduces novel hybrid RIS structures, wherein at least one reflecting sub-surface (RS) adopts the SC-active RIS design. The asymptotic signal-to-noise-ratio of the FC-active/passive and the proposed hybrid RIS variants is analyzed in a single-user single-input single-output setup. Furthermore, the transmit and RIS beamforming weights are jointly optimized in each scenario to maximize the energy efficiency of a hybrid RIS-aided multi-user multiple-input single-output downlink system subject to the power consumption constraints of the base station and the active RSs. Numerical simulation and analytic results highlight the performance gains of the proposed RIS designs over benchmarks, unveil non-trivial trade-offs, and provide valuable insights.

We survey analytical methods and evaluation results for the performance assessment of caching strategies. Knapsack solutions are derived, which provide static caching bounds for independent requests and general bounds for dynamic caching under arbitrary request pattern. We summarize Markov- and time-to-live-based solutions, which assume specific stochastic processes for capturing web request streams and timing. We compare the performance of caching strategies with different knowledge about the properties of data objects regarding a broad set of caching demands. The efficiency of web caching must regard benefits for network wide traffic load, energy consumption and quality-of-service aspects in a tradeoff with costs for updating and storage overheads., Comment: 25 pages

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 the Internet of Things, learning is one of most prominent tasks. In this paper, we consider an Internet of Things scenario where federated learning is used with simultaneous transmission of model data and wireless power. We investigate the trade-off between the number of communication rounds and communication round time while harvesting energy to compensate the energy expenditure. We formulate and solve an optimization problem by considering the number of local iterations on devices, the time to transmit-receive the model updates, and to harvest sufficient energy. Numerical results indicate that maximum ratio transmission and zero-forcing beamforming for the optimization of the local iterations on devices substantially boost the test accuracy of the learning task. Moreover, maximum ratio transmission instead of zero-forcing provides the best test accuracy and communication round time trade-off for various energy harvesting percentages. Thus, it is possible to learn a model quickly with few communication rounds without depleting the battery., Comment: Accepted to appear in the IEEE International Workshop on Signal Processing Advances in Wireless Communications (SPAWC) in Lucca, Italy, Sep. 2021

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., Comment: This version of the work has been accepted for publication in the IEEE 31st PIMRC 2020 - Workshop on UAV Communications for 5G and Beyond; 7 pages; 5 figures

Single-user multiple-input / multiple-output (SU-MIMO) communication systems have been successfully used over the years and have provided a significant increase on a wireless link's capacity by enabling the transmission of multiple data streams. Assuming channel knowledge at the transmitter, the maximization of the mutual information of a MIMO link is achieved by finding the optimal power allocation under a given sum-power constraint, which is in turn obtained by the water-filling (WF) algorithm. However, in spectrum sharing setups, such as Licensed Shared Access (LSA), where a primary link (PL) and a secondary link (SL) coexist, the power transmitted by the SL transmitter may induce harmful interference to the PL receiver. While such co-existing links have been considered extensively in various spectrum sharing setups, the mutual information of the SL under a constraint on the interference it may cause to the PL receiver has, quite astonishingly, not been evaluated so far. In this paper, we solve this problem, find its unique optimal solution and provide the power allocation policy and corresponding precoding solution that achieves the optimal capacity under the imposed constraint. The performance of the optimal solution and the penalty due to the interference constraint are evaluated over some indicative Rayleigh fading channel conditions and interference thresholds. We believe that the obtained results are of general nature and that they may apply, beyond spectrum sharing, to a variety of applications that admit a similar setup., Comment: Work very similar to this was reported in another related area. This is something that was not acknowledged at the time of the submission

Recently, several advanced multi-antenna radio communications technologies have emerged to meet the increased capacity demands in wireless multi-user networks. Despite their great potential, the extent of these techniques' practical applicability still remains questionable, since they have to face either backhaul limitations or cost and hardware constraints. In this paper, we propose a new system solution which includes network architecture, antenna technology and radio transmission protocol to reduce drastically the hardware complexity and cost as well as the channel state information / user data feedback requirements of multi-user multi-antenna wireless networks. We focus on the forward link of an interference channel in a cloud radio access network setup wherein an arbitrary number of remote radio heads are each equipped with a single radio frequency module parasitic antenna array and wish to send data to their respective single-antenna user terminals, while co-existing in time and frequency. Base stations select cooperatively the optimal combination of pre-determined beams prior transmission. Our proposed approach is able to achieve the aforementioned goals, while offering significant downlink sum-rate gains due to the available spatial degrees of freedom.