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The evolving landscape of the Internet of Things (IoT) has given rise to a pressing need for an efficient communication scheme. As the IoT user ecosystem continues to expand, traditional communication protocols grapple with substantial challenges in meeting its burgeoning demands, including energy consumption, scalability, data management, and interference. In response to this, the integration of wireless power transfer and data transmission has emerged as a promising solution. This paper considers an energy harvesting (EH)-oriented data transmission scheme, where a set of users are charged by their own multi-antenna power beacon (PB) and subsequently transmits data to a base station (BS) using an irregular slotted aloha (IRSA) channel access protocol. We propose a closed-form expression to model energy consumption for the present scheme, employing average channel state information (A-CSI) beamforming in the wireless power channel. Subsequently, we employ the reinforcement learning (RL) methodology, wherein every user functions as an agent tasked with the goal of uncovering their most effective strategy for replicating transmissions. This strategy is devised while factoring in their energy constraints and the maximum number of packets they need to transmit. Our results underscore the viability of this solution, particularly when the PB can be strategically positioned to ensure a strong line-of-sight connection with the user, highlighting the potential benefits of optimal deployment., Comment: Accepted in EuCNC 2024 conference

Grant-free protocols exploiting compressed sensing (CS) multi-user detection (MUD) are appealing for solving the random access problem in massive machine-type communications (mMTC) with sporadic device activity. Such protocols would greatly benefit from a prior deterministic knowledge of the sparsity level, i.e., instantaneous number of simultaneously active devices $K$. Aiming at this, herein we introduce a framework relying on coordinated pilot transmissions (CPT) over a short phase at the beginning of the transmission block for detecting $K$ in mMTC scenarios under Rayleigh fading. CPT can be implemented either as: i) U-CPT, which exploits only uplink transmissions, or A-CPT, which includes also downlink transmissions for channel state information (CSI) acquisition that resolve fading uncertainty. We discuss two specific implementations of A-CPT: ii) A-CPT-F, which implements CSI-based phase corrections while leveraging the same statistical inverse power control used by U-CPT, and iii) A-CPT-D, which implements a dynamic CSI-based inverse power control, although it requires some active devices to remain in silence if their corresponding channels are too faded. We derive a signal sparsity level detector for each CPT mechanism by relaxing the original integer detection/classification problem to an estimation problem in the continuous real domain followed by a rounding operation. We show that the variance of the relaxed estimator increases with $K^2$ and $K$ when operating with U-CPT and A-CPT mechanisms, respectively. The distribution of the estimators under U-CPT, A-CPT-F and A-CPT-D is found to follow an exponential, Gaussian, and Student's $t-$like distribution, respectively. Analyses evince the superiority of A-CPT-D, which is also corroborated via numerical results. We reveal several interesting trade-offs and highlight potential research directions., Comment: 31 pages, 8 figures, 2 tables. Submitted to IEEE TWC

The recent extra-large scale massive multiple-input multiple-output (XL-MIMO) systems are seen as a promising technology for providing very high data rates in increased user-density scenarios. Spatial non-stationarities and visibility regions (VRs) appear across the XL-MIMO array since its large dimension is of the same order as the distances to the user-equipments (UEs). Due to the increased density of UEs in typical applications of XL-MIMO systems and the scarcity of pilots, the design of random access (RA) protocols and scheduling algorithms become challenging. In this paper, we propose a joint RA and scheduling protocol, namely non-overlapping VR XL- MIMO (NOVR-XL) RA protocol, which takes advantage of the different VRs of the UEs for improving RA performance, besides seeking UEs with non-overlapping VRs to be scheduled in the same payload data pilot resource. Our results reveal that the proposed scheme achieves significant gains in terms of sum rate compared with traditional RA schemes, as well as reducing access latency and improving connectivity performance as a whole., Comment: 10pages, 6 figures, 2 tables. Index terms: Random-access protocol, grant-based protocols, massive MIMO, XL-MIMO, non-stationarity, visibility region (VR), scheduling

Reconfigurable intelligent surfaces (RISs) have been proposed as a key enabler to improve the coverage of the signals and mitigate the frequent blockages in millimeter wave (mmWave) multiple-input multiple-output (MIMO) communications. However, the channel state information (CSI) acquisition is one of the major challenges for the practical deployment of the RIS. The passive RIS without any baseband processing capabilities brings difficulty on the channel estimation (CE), since the individual channels or the cascaded one can be estimated only at base station (BS) via uplink training or mobile station (MS) via downlink training. In order to facilitate the CSI acquisition, we focus on the hybrid RIS architecture, where a small number of elements are active and able to receive and process the pilot signals at the RIS. The CE is performed in two stages by following the atomic norm minimization to recover the channel parameters, i.e., angles of departure (AoDs), angles of arrival (AoAs), and propagation path gains. Simulation results show that the proposed scheme can outperform the passive RIS CE under the same training overhead. Furthermore, we also study the theoretical performance limits in terms of mean square error (MSE) via Cram\'er-Rao lower bound (CRLB) analyses., Comment: 30 pages, 11 figures

In this work, we consider a scenario where two multiple-aperture legitimate nodes (Alice and Bob) communicate by means of Free-Space Optical (FSO) communication in the presence of a multiple-aperture eavesdropper (Eve), which is subject to pointing errors. Two different schemes are considered depending on the availability of channel state information (CSI) at Alice: i) the adaptive scheme, where Alice possesses the instantaneous CSI with respect to Bob; ii) the fixed-rate scheme, where such information is not available at Alice. The performance of the aforementioned schemes is evaluated in terms of a recently proposed metric named effective secrecy throughput (EST), which encompasses both the reliability and secrecy constraints. By constraining the system to operate below a given maximum allowed secrecy outage probability, we evaluate the EST analytically and through numerical results, showing that the use of multiple apertures at Alice is very important towards achieving the optimal EST., Comment: to appear in IEEE Transactions on Wireless Communications

In this paper we investigate the secure energy efficiency (SEE) in a cooperative scenario where all nodes are equipped with multiple antennas. Moreover, we employ secrecy rate and power allocation at Alice and at the relay in order to maximize the SEE, subjected to a constraint in terms of a minimal required secrecy outage probability. Only the channel state information (CSI) with respect to the legitimate nodes is available. Then, we compare the Artificial-Noise (AN) scheme with CSI-Aided Decode-and-Forward (CSI-DF), which exploits the CSI to choose the best communication path (direct or cooperative). Our results show that CSI-DF outperforms AN in terms of SEE in most scenarios, except when Eve is closer to the relay or with the increase of antennas at Eve. Additionally, we also show that the maximization of SEE implies in an optimal number of antennas to be used at each node, which is due to the trade-off between secure throughput and power consumption., Comment: 10 pages, 5 figures

Massive MIMO systems promise high data rates by employing large number of antennas, which also increases the power usage of the system as a consequence. This creates an optimization problem which specifies how many antennas the system should employ in order to operate with maximal energy efficiency. Our main goal is to consider a base station with a fixed number of antennas, such that the system can operate with a smaller subset of antennas according to the number of active user terminals, which may vary over time. Thus, in this paper we propose an antenna selection algorithm which selects the best antennas according to the better channel conditions with respect to the users, aiming at improving the overall energy efficiency. Then, due to the complexity of the mathematical formulation, a tight approximation for the consumed power is presented, using the Wishart theorem, and it is used to find a deterministic formulation for the energy efficiency. Simulation results show that the approximation is quite tight and that there is significant improvement in terms of energy efficiency when antenna selection is employed., Comment: To appear in Transactions on Emerging Telecommunications Technologies, 12 pages, 8 figures, 2 tables

We assume a full-duplex (FD) cooperative network subject to hostile attacks and undergoing composite fading channels. We focus on two scenarios: \textit{a)} the transmitter has full CSI, for which we derive closed-form expressions for the \textit{average secrecy rate}; and \textit{b)} the transmitter only knows the CSI of the legitimate nodes, for which we obtain closed-form expressions for the \textit{secrecy outage probability}. We show that secure FD relaying is feasible, even under strong self-interference and in the presence of sophisticated multiple antenna eavesdropper.