Remote and resource-constrained Internet-of Things (IoT) deployments often lack terrestrial connectivity for task offloading, motivating non-terrestrial networks (NTNs) with onboard multiaccess edge computing (MEC) capabilities. Nevertheless, in the presence of malicious actors, authentication needs to be performed to avoid non-authorized nodes from draining the computing resources of the NTN nodes. As a solution, we propose a four-layer MEC-enabled NTN with unmanned aerial vehicles (UAVs) acting as access nodes, a high altitude platform station (HAPS) acting as coordinator and authenticator, and a constellation of low-Earth orbit satellites (LEOSats) acting as remote MEC servers. We consider a tag-based physical-layer authentication (PLA) scheme to authenticate legitimate users, and formulate a joint task offloading decision and resource allocation for the admitted tasks, which is solved via block coordinate descent. Numerical results show that the PLA scheme is efficient and performs better than the benchmark schemes. We also demonstrate that the proposed scheme is robust against malicious attacks even under relaxed false-alarm constraints.

peer reviewed

In this paper, we address the resource allocation problem for task offloading from Internet of Things (IoT) devices to a non-terrestrial network. The proposed architecture contains clusters of IoT devices that can either execute their computing tasks locally or offload them to a dedicated unmanned aerial vehicle (UAV) functioning as a multi-access edge computing (MEC) server. The UAV can process the tasks itself or further offload them to an available high-altitude platform station (HAPS) or to a low-earth orbit (LEO) satellite within line-of-sight for remote computing. We formulate an optimization problem that aims to minimize the weighted sum of the total task-execution delay and the energy consumption of the IoT devices. Due to non-convexity of the problem and the inherent complexity-performance trade-off in optimization algorithms, we propose a set of low-complexity solutions. These include optimal methods based on convex subproblem decomposition and a greedy heuristic guided by convex optimization criteria. The framework jointly optimizes the computing resources and transmission power of IoT devices, the digital precoders and combiners at the UAV, the computing resources at the remote nodes (UAV, HAPS, and LEO), as well as task offloading decisions and subchannel allocation through a one-shot block coordinate descent approach. Simulation results highlight the performance gains of the proposed methods, demonstrating the impact of algorithmic complexity on key system metrics and the benefits of incorporating multiple non-terrestrial nodes compared to architectures lacking such capabilities.

This paper investigates the design of distributed precoding for multi-satellite massive MIMO transmissions. We first conduct a detailed analysis of the transceiver model, in which delay and Doppler precompensation is introduced to ensure coherent transmission. In this analysis, we examine the impact of precompensation errors on the transmission model, emphasize the near-independence of inter-satellite interference, and ultimately derive the received signal model. Based on such signal model, we formulate an approximate expected rate maximization problem that considers both statistical channel state information (sCSI) and compensation errors. Unlike conventional approaches that recast such problems as weighted minimum mean square error (WMMSE) minimization, we demonstrate that this transformation fails to maintain equivalence in the considered scenario. To address this, we introduce an equivalent covariance decomposition-based WMMSE (CDWMMSE) formulation derived based on channel covariance matrix decomposition. By exploiting the channel characteristics, we develop a low-complexity decomposition method and propose an optimization algorithm. To further reduce computational complexity, we introduce a model-driven scalable deep learning (DL) approach that leverages the equivariance of the mapping from sCSI to the unknown variables in the optimal closed-form solution, enhancing performance through novel dense Transformer network and scaling-invariant loss function design. Simulation results validate the effectiveness and robustness of the proposed method in some practical scenarios. We also demonstrate that the DL approach can adapt to dynamic settings with varying numbers of users and satellites.

Integrated sensing and communications (ISAC) has emerged as a key enabler for 6G and beyond. However, ISAC systems face significant challenges, including the sensing function that introduces interference and degrades communication performance, as well as high sensing power consumption that reduces overall communication efficiency, particularly in complex urban environments. To address these issues, we propose a reconfigurable intelligent surface (RIS)-assisted orthogonal frequency division multiplexing (OFDM) multiple-input multiple-output (MIMO) ISAC system, where a RIS enhances connectivity for users in localized coverage gaps. We formulate and study two optimization problems: i) maximizing system sum spectral efficiency and ii) maximizing global energy efficiency, by jointly optimizing transmit precoding, subcarrier allocation, and RIS phase shifts under power, quality of service, and sensing accuracy constraints. These problems are classified as mixed-integer nonlinear programs, which are generally difficult to solve optimally. To tackle this, we develop efficient iterative algorithms leveraging successive convex approximation, alternating optimization, Riemannian manifolds, and Dinkelbach’s method to obtain at least locally optimal solutions. Simulation results validate the effectiveness of the proposed designs, demonstrating their superiority over benchmark schemes, achieving up to 40% higher spectral efficiency and up to 60% improvement in energy efficiency compared to conventional overlap and random-phase approaches.

This paper studies the distributed precoding design for multi-satellite massive MIMO transmission. We first conduct a detailed analysis of the transceiver process, examining the effects of delay and Doppler compensation errors and emphasizing the nearly independent nature of inter-satellite interference. Based on the derived signal model, an approximate expected sum rate maximization problem is formulated, incorporating statistical channel state information and compensation errors. Unlike conventional approaches that recast such problems as weighted minimum mean square error (WMMSE) minimization, we demonstrate that this transformation cannot hold equivalence in the considered scenario. To address this, we propose a modified WMMSE formulation leveraging channel covariance matrix decomposition. By exploiting channel characteristics, a low-complexity decomposition method is then developed, accompanied by an efficient algorithm. Simulation results validate the effectiveness and robustness of the proposed method in some practical simulated scenarios.

Satellite communications (SatComs) have recently been through a renaissance, both technologically and entrepreneurially. Ambitious plans have already come into fruition with the operation of low-Earth orbit (LEO) constellations including thousands of satellites and supported by state of the art but proprietary technologies, such as active antenna arrays and intersatellite links (ISLs). In this context, this article aims to provide a forward-looking vision of use cases and a deep dive into technological enablers that will be prominent in space communications beyond 2030. In parallel, it motivates how open standards can play a role in delivering affordable communication services in space. Starting from the 5G plans for nonterrestrial networks, we provide a survey and roadmap toward artificial intelligence (AI)-supported satellite systems, space-enabled quantum networks, and joint communications and positioning (JCAP) for space missions and interplanetary exploration.

In this work, we consider a typical scenario in a harsh urban propagation environment which is typical for a smart city scenario where multiple reconfigurable intelligent surfaces (RISs) are deployed in different hotspot areas to overcome signal blockage between the base station and users. Our goal is to ensure uninterrupted service availability to users in different hotspot areas regardless of their location. Consistent service availability can be achieved by guaranteeing that each RIS deployed in a hotspot area can support a certain number of users. This plays a critical role in smart city applications in the context of emergency communications and ubiquitous connectivity since the design ensures service availability to as many users as possible in all relevant locations. Taking into consideration the challenges in obtaining channel state information (CSI) given the passive nature of RIS and dynamic environments, we formulate a robust fairness problem to maximize the minimum expected number of served users in proximity to each RIS while considering the available transmit power and the worst-case quality of service (QoS) constraints within the bounded CSI error model framework. The resulting problem is a mixed integer non-convex program which is highly coupled and challenging to solve in polynomial time. Thus, we resort to binary variable relaxation, convex approximation techniques, and alternating optimization to tackle the problem. Additionally, we handle the semi-infinite uncertainty constraints by employing the S-procedure and general sign-definiteness. Simulation results demonstrate the effectiveness of the proposed design in obtaining consistent and reliable service in different hotspot areas compared to the relevant benchmark schemes. In addition, the proposed design shows flexibility in serving users with their target QoS given different channel uncertainty levels.

In this paper, we study the resource allocation problem in multi-RIS-aided outdoor-to-indoor communications. Specifically, we aim to provide geographical fairness to ensure that users in different hotspot areas in a smart city can be served regardless of their location. We consider a scenario where RISs are deployed to extend coverage to indoor users in different buildings where there is limited network accessibility. This design is crucial in smart cities in the context of emergency communication and ubiquitous connectivity since it ensures service availability to as many users as possible independently of the locations. Thus, to achieve geographical fairness, we formulate a max-min fairness problem to maximize the minimum number of users served by each RIS by jointly optimizing the active precoding and RIS-based beamforming subject to power and quality of service constraints. The geographical location of users is directly linked to the RIS which means that users are served by the RIS closest to them. In this case, we ensure that a certain number of users can be supported by each RIS. The formulated problem is a mixed integer nonlinear program, which is challenging to solve directly using methods of convex optimization. Accordingly, we propose an efficient successive convex approximation-based alternating optimization algorithm to tackle the complexity of the formulated problem. The presented results show the performance gain of the proposed design in providing geographical fairness compared to the relevant benchmark schemes.

Simultaneously transmitting and reflecting reconfigurable intelligent surface (STAR-RIS) is an emerging technology that enables full-space ($\mathbf{3 6 0}$ degrees) coverage on both sides of the surface. To harness the benefits of the dynamic configuration of STAR-RIS while avoiding co-channel interference, we investigate the performance of a multi-user network assisted by STARRIS. In this setup, users are divided into multiple groups, each comprising two users located on opposite sides of the STARRIS. Orthogonal time resources are allocated to each group such that the groups are served sequentially. Based on the Gamma moment matching method, we introduce a Gamma distribution to model the product of Rician, Rayleigh and mixed fading STAR-RIS channels. We then derive exact closed-form expressions for the outage probability and diversity order per user in the proposed system model. Moreover, simulation results are provided to substantiate the analytical derived expressions. Our findings highlight a reliability trade-off associated with the number of grouped users per time slot, STAR-RIS elements, and the user targeted data rates. This balance is crucial for optimizing network performance.

In this paper, we analytically study the distribution of the main-lobe gain in a low-Earth orbit (LEO) satellite swarm scenario under imperfect phase synchronization, based on an open-loop process. The provided analytical framework determines the maximum tolerable error in phase estimation under which the desired beamforming gain can still be achieved. Consequently, we are able to determine the highest value of accuracy (worst-case scenario) in position estimation between satellites (inter-node ranging) required for phase synchronization. Notably, the analytical framework reveals that in millimeter-wave bands, the required maximum precision in inter-node ranging to guarantee just a small performance deterioration is in the order of millimetres. Finally, numerical results based on Monte Carlo simulations validate the analytical framework.

Due to the fact that large swathes on Earth still lack broadband communication coverage, especially in remote/rural areas and developing countries, there have been several attempts, starting from 3GPP Release 17, to lay out the architectural amendments needed for the integration of terrestrial networks with their non-terrestrial counterparts. Such attempts have led to recent projects regarding such integration that consider either 5G/5G-Advanced networks or more revolutionary approaches for the forthcoming 6G networks. In this manuscript, we give an overview of the architectural framework, technical innovations, and considered use cases of the Horizon Europe ETHER project.

To harness the benefits of non-orthogonal multiple access (NOMA) and reconfigurable intelligent surfaces (RISs), we propose a novel RIS-empowered decode-and-forward (RIS-DF) relaying scheme tailored to cooperative NOMA transmissions. This integration is a promising direction for improving communication reliability, extending coverage, and enhancing the overall performance in sixth-generation (6G) wireless networks. This paper focuses on enhancing signal reception of the cell-edge users with weak channel conditions by deploying multiple RISs within cooperative NOMA systems. In this setting, we investigate system outage performance and derive closed-form analytical expressions for both cooperative NOMA and its orthogonal mul-tiple access (OMA) counterpart, which is used as a benchmark for comparison. To validate the proposed solutions, numerical results are provided to demonstrate the performance gains of the proposed scheme compared to the existing conventional DF relays developed for cooperative NOMA. Further, the impact of RIS placement within the system on performance is also examined, offering useful practical design insights.

Reconfigurable intelligent surfaces (RISs) have emerged as a game-changing technology to improve wireless network performance by intelligently manipulating and customizing the physical propagation environment. Such capability is especially important for the application of smart cities as it increases wireless service offers and quality to end-users. In this paper, we aim to maximize the number of served users in a challenging RIS-aided smart city street by jointly optimizing the multislot scheduling, precoding, and passive RIS-based beamforming design under quality of service and power constraints. Multislot scheduling is introduced in order to benefit from additional time diversity and thus better exploit the available degrees of freedom. The formulated problem is a mixed integer nonlinear programming, which is NP-hard. To solve the problem with affordable complexity, we develop an efficient iterative algorithm based on binary variable relaxation, alternating optimization, and successive convex approximation techniques. Simulation results demonstrate the superiority of the proposed design over the design without RIS and the design without scheduling, especially in the presence of a large number of users. In addition, results illustrate that by introducing a quality of service margin, the proposed design can improve its robustness to outdated channel state information in mobility scenarios.

In this work, we propose a time- and a unit cell-splitting protocol for supplying the energy needs of reconfigurable intelligent surfaces (RISs) through wireless energy harvesting (EH) from information signals. We first compute the RIS energy consumption per frame that is common for both protocols and incorporates the energy burden for channel estimation. Based on it, we subsequently formulate an optimization problem that maximizes the average rate under the constraint of meeting the RIS long-term energy consumption demands. In addition, closed-form solutions regarding the optimal allocation of resources are provided for both protocols in the case of deterministic channel gains for the transmitter-RIS links and a methodology to obtain such a solution in the general case of random channels. Finally, for the optimal resource allocation for both protocols numerical results based on Monte-Carlo simulations reveal that the unit cell-splitting protocol exhibits a superior performance compared to its time-splitting counterpart.

The key advantage of successive relaying (SR) networks is their ability to mimic the full-duplex (FD) operation with half-duplex (HD) relays. However, the main challenge that comes with such schemes is the associated inter-relay interference (IRI). In this work, we propose a reconfigurable intelligent surface (RIS)-enhanced SR network, where one RIS is deployed near each of the two relay nodes to provide spatial suppression of IRI, and to maximize the gain of desired signals. The resultant max-min optimization problem with joint phase-shift design for both RISs is first tackled via the semidefinite programming (SDP) approach. Then, a lower-complexity solution suitable for real-time implementation is proposed based on particle swarm optimization (PSO). Numerical results demonstrate that even relatively small RISs can provide significant gains in achievable rates of SR networks, and the proposed PSO scheme can achieve a near optimal performance.

In this work, we investigate the decode-and-forward (DF) relay-aided double reconfigurable intelligent surface (RIS)-assisted networks, where the signal is subject to reflections from two RISs before reaching the destination. Different relay-aided network architectures are considered for maximum achievable rate under a total power constraint. Phase optimization for the double-RIS channels is tackled via the alternating optimization and majorization-minimization (MM) schemes. Moreover, closed-form solutions are obtained for each case. Numerical results indicate that the deployment of two relays, one near each RIS, achieves higher rates at low and medium signal-to-noise ratios (SNRs) compared to placing a single relay between the two RISs; while at high SNRs, the latter approach achieves higher rates only if the inter-relay interference for the former case is considerably high.

In this paper, we examine the potential for a reconfigurable intelligent surface (RIS) to be powered by energy harvested from information signals. This feature might be key to reap the benefits of RIS technology’s lower power consumption compared to active relays. We first identify the main RIS power-consuming components and then propose an energy harvesting and power consumption model. Furthermore, we formulate and solve the problem of the optimal RIS placement together with the amplitude and phase response adjustment of its elements in order to maximize the signal-to-noise ratio (SNR) while harvesting sufficient energy for its operation. Finally, numerical results validate the autonomous operation potential and reveal the range of power consumption values that enables it.

Among a plethora of new wireless communication technologies, reconfigurable intelligent surface (RIS) emerges as one of the revolutionary solutions to provide energy- and cost-efficient signal transmissions. RIS is capable of reflecting electromagnetic signals in a controlled manner. In this paper, we jointly design the active beamforming at the base station and passive beamforming at the RIS to maximize the number of served users in a practical Smart City street scenario, subject to quality of service (QoS) and power constraints. The formulated problem belongs to the difficult class of mixed-integer non-convex programming, which is NP-hard. To arrive at a low-complexity solution, we first decompose the original problem into two subproblems and then propose an alternating optimization algorithm based on successive convex approximation (SCA) to solve them in an iterative manner. Simulation results are provided to verify the performance improvement of the proposed algorithm as compared to baseline schemes.

In this work, we analytically study the performance of an in-band and self-fronthauling millimeter-wave Cloud-Radio Access Network (C-RAN). By considering a stochastic-geometry approach for the modeling of the position and number of Baseband Units (BBUs), Remote Radio Heads (RRHs), and mobile terminals (MTs), we provide the following three-fold contribution: i) We derive an analytical framework for the MT rate distribution for two types of wireless RRHs, namely half-duplex (HD) and full-duplex (FD); ii) Based on the derived framework, we prove that the maximum performance gain of the FD network over its HD counterpart is achieved for a substantially higher density of the wireless RRHs compared to the fiber-connected ones and an adequately small self-interference power level; iii) Finally, we compute an analytical expression of the total cost required to increase the density of the fiber-connected RRHs in a city that showcases the tradeoff between their density increase and the incurred cost. The aforementioned system-level trends are validated by means of Monte Carlo simulations.

In this paper, we propose a novel policy for device caching that facilitates popular content exchange through high-rate device-to-device (D2D) millimeter-wave (mmWave) communication. The D2D-aware caching policy splits the cacheable content into two content groups and distributes it randomly to the user equipment devices, with the goal to enable D2D connections. By exploiting the high bandwidth availability and directionality of mmWaves, we ensure high rates for the D2D transmissions, while mitigating the co-channel interference that limits the throughput gains of the D2D communication in the sub-6-GHz bands. Furthermore, based on a stochastic-geometry modeling of the network topology, we analytically derive the offloading gain that is achieved by the proposed policy and the distribution of the content retrieval delay considering both half- and full-duplex modes for the D2D communication. The accuracy of the proposed analytical framework is validated through Monte Carlo simulations. In addition, for a wide range of a content popularity indicator, the results show that the proposed policy achieves higher offloading and lower content-retrieval delays than existing state-of-the-art approaches.

In this paper, we perform a system-level feasibility analysis of full-duplex (FD) relay-aided cellular networks that are equipped with multiple antennas at the base stations (BSs) and the relay nodes (RNs). The aim is to understand whether FD relaying is capable of enhancing the rate of cellular networks. With the aid of tools from stochastic geometry, we develop a tractable approach for computing the percentile rate, which allows us to gain insights on the impact of FD relaying for both the cell-edge and the cell-median mobile terminals subject to network interference. Contrary to previous works that do not consider the network interference, the framework reveals that even in the absence of self-interference at the FD RNs, a network with half-duplex (HD) RNs can outperform its FD counterpart for a moderate number of antennas at the BSs and RNs. On the other hand, the FD-based network can substantially outperform both the HD-based one and the one without RNs for a sufficiently large number of antennas at the BSs and RNs and substantially small self-interference power effect at the RNs. Finally, the aforementioned analytical insights are validated by means of Monte Carlo simulations.

We investigate the possibility of wirelessly charging autonomous wearable sensors for patient health-monitoring through dedicated power transfer from Power Beacons (PB). We propose a novel strategy for transmitting energy beams towards patients, where sensors emit a charging request based on a battery lower threshold and receive energy till the battery reaches an upper threshold. These energy beams are steered towards the patient's smartphone rather than towards the sensor, relieving the need for tracking the PB-sensor channel at only negligible performance loss. Furthermore, as PBs cannot serve all requiring sensors simultaneously, we compare different scheduling algorithms, jointly with the battery thresholds and the energy beam width. For the examined configurations, results show that the energy outage probability is slightly affected by the scheduling policy, and most of the energy is harvested from non-intended beams. We show that the strategy for energy-beam transmission should be adjusted to the sensor location on the human body and the patient's velocity for better performance. Finally, proposing new strategies minimizing the waiting duration can be used as a guideline to reduce the energy outage.

International audience

In this paper, we consider Analog Network Coding (ANC) in the Multiple Access Relay Channel (MARC) with multiple relays, and provide the following three-fold contribution: 1) we introduce a tractable mathematical framework for computing the Symbol Error Rate (SER) of Maximum-Likelihood (ML), Zero-Forcing (ZF), and Minimum Mean Square Error (MMSE) receivers; 2) by capitalizing on this tractable mathematical framework, we formulate a power allocation problem that is proved to be convex for ML, ZF and MMSE receivers; and 3) we provide closed-form expressions of the optimal power to be allocated to the sources and the relays for ZF and MMSE receivers. With the aid of Monte Carlo simulations, we validate the accuracy of the proposed mathematical framework for various network topologies and channel conditions, as well as study the effectiveness of optimal power allocation. It is shown, in particular, that power optimization is beneficial as the number of sources increases and if the quality of the source-relay links is better than the quality of the relay-destination links.

In this paper, we examine multi-source multi-relay systems that employ Analog Network Coding for which we provide a two-fold contribution: i) We derive a closed-form upper bound of the average symbol error rate (SER) per source of the system, which is shown to be tight in the high-Signal-to-Noise Ratio (SNR) region, especially for an adequate number of relays, and ii) based on this bound, for a given total power budget to be distributed among the source and relay nodes we formulate the power allocation optimization problem with the aim of minimizing the SER per source. Results show that for a common target SER among the sources, an increase in their number results in an increase in the expected energy gains over the equal power allocation for all the nodes policy.

In this paper, we provide a theoretical analysis of the optimal Maximum-Likelihood (ML) detector for the recently proposed multistream Spatial Modulation (SM) concept for multiple-input-multiple-output (MIMO) systems. A Union Bound that is based on closed-form formulas is derived for the average bit error probability (ABEP) of the ML detection, which shows an excellent fit with respect to Monte Carlo simulations for the high signal-to-noise ratio (SNR) region. Furthermore, numerical results for different rates are provided regarding the comparison of the ABEP of multistream SM with ML detection with its recently proposed counterpart with suboptimal detection and with the ABEPs of other well-known MIMO schemes, such as conventional Spatial Multiplexing, Alamouti, and SM, which show its advantage over them. In addition, for the examined MIMO configuration and rates, the relative transmit energy efficiency gain of multistream SM with ML detection over the aforementioned methods is calculated for a low target ABEP.

In this paper, we propose a closed-loop and single RF multiple-input-multiple-output (MIMO) method, particularly suitable for the Uplink of cellular systems, which is based on the low-complexity and recently devised method of Spatial Modulation. By selecting a subset of the available transmit antennas to implement Spatial Modulation based on the instantaneous Bit Error Rate (BER), the proposed method achieves both multiplexing and transmit-diversity gains by utilizing only one RF chain. By taking into account the total power consumption at the transmit side, we numerically show that our proposed method is more energy efficient than the single RF transmit antenna selection method for the same target performance and several MIMO configurations and correlation values among the transmit antennas.

Generalized Space Shift Keying (GSSK) modulation is a recently proposed low-complexity concept for Multiple-Input- Multiple-Output (MIMO) wireless systems. GSSK modulation is a generalized version of Space Shift Keying (SSK) modulation, which provides a better spectral efficiency through multiple active antennas at the transmitter. An apparent weakness of GSSK modulation is that it does not exploit the transmit-antennas to achieve transmit-diversity. In this paper, we propose a precoding method for GSSK modulation, which simultaneously achieves high diversity and coding gains. The solution is based on: i) \emph{co-phasing} the active antennas of each spatial-constellation point; and ii) properly \emph{rotating} the phases among spatial-constellation points. The new scheme requires Channel State Information at the Transmitter (CSIT), i.e., the channel phases of each wireless link, which can be obtained through a feedback channel. For the case of a perfect feedback channel, we analytically show that for three and four antennas at the transmitter a full transmit diversity can be achieved without reducing the achievable rate. Furthermore, for various MISO configurations and achievable rates we show through Monte Carlo simulations that our proposed scheme outperforms state-of-the-art open- loop GSSK schemes, in terms of both diversity and coding gain, when the number of bits allocated for the quantization of each channel phase is between 2 and 4.

Feedback of channel state information (CSI) is necessary to achieve high throughput and low outage probability in multiuser multi antenna systems. There are two types of CSI: directional and quality information. Many papers have analyzed the importance of these in asymptotic regimes. However, we show that such results should be handled with care, as very different conclusions can be drawn depending on the spatial correlation and number of users. There fore, we propose a quantization framework and evaluate the tradeoff between directional and quality feedback under practical conditions.