A novel framework for covert communications aided by Reconfigurable Intelligent Surfaces (RIS) is proposed. In this general framework, the use of multiport network theory for modelling the RIS consider various aspects that traditional RIS models in communication theory often overlook, including mutual coupling between elements and the impact of structural scattering. Moreover, considering a two-timescale RIS optimization approach, RIS optimization operates on a long timescale by leveraging statistical knowledge about the intended receiver, namely Bob. The proposed approach is validated through numerical results, demonstrating that communication with the legitimate user is successfully achieved while satisfying the covertness constraint. Moreover, while in classical communication scenarios the RIS offers little to no benefit when the direct link between transmitter and receiver is sufficiently strong, in covert communications the RIS can still provide significant advantages, even in the presence of strong direct paths.

Reconfigurable Intelligent Surfaces (RIS) represent transformative technologies for next-generation wireless communications, offering advanced control over electromagnetic wave propagation. While RIS have been extensively studied, Stacked Intelligent Metasurfaces (SIM), which extend the RIS concept to multi-layered systems, present significant modeling and optimization challenges. This work addresses these challenges by introducing an optimization framework for SIM that, unlike previous approaches, is based on a comprehensive model without relying on specific assumptions, allowing for broader applicability of the results. We first present a model based on multi-port network theory for characterizing a general electromagnetic collaborative object (ECO) and derive a framework for ECO optimization. We then introduce the SIM as an ECO with a specific architecture, offering insights into SIM optimization for various configurations and discussing the complexities associated with each case. Finally, we demonstrate that the comprehensive model considered in this work simplifies to the model traditionally used in the literature when the assumption of unilateral propagation between the levels of the SIM is made, and mutual coupling between the SIM elements is neglected. To assess the applicability of these assumptions, a case study focused on the realization of a 2D DFT was undertaken. In this context, we highlight that these assumptions introduce a significant mismatch between the SIM model and its behavior as described by the complete model, making these approximations inadequate for optimizing the SIM. Conversely, we show that employing the complete model proposed in this paper can yield excellent performance.

This study focuses on the optimization of a single-cell multi-user multiple-input multiple-output (MIMO) system with multiple large-size reconfigurable intelligent surfaces (RISs). The overall transmit power is minimized by optimizing the precoding coefficients and the RIS configuration, with constraints on users’ signal-to-interference-plus-noise ratios (SINRs). The minimization problem is divided into two sub-problems and solved by means of an iterative alternating optimization (AO) approach. The first sub-problem focuses on finding the best precoder design. The second sub-problem optimizes the configuration of the RISs by partitioning them into smaller tiles. Each tile is then configured as a combination of pre-defined configurations. This allows the efficient optimization of RISs, especially in scenarios where the computational complexity would be prohibitive using traditional approaches. Simulation results show the good performance and limited complexity of the proposed method in comparison to benchmark schemes.

Control data for unmanned aerial vehicles (UAVs) are characterized by stringent reliability and latency requirements, thus transmission parameters must be suitably set. In particular, packet retransmission can significantly improve the reliability but risks compromising latency requirements. In this paper an efficient proactive retransmission scheme based on channel classification is proposed. The retransmission occurs without waiting the receiver feedback, and is selectively used for a subset of packets. Differently, classical proactive schemes foresee the retransmission of all packets with a consequent waste of resources. The selection is based on the classification of the channel behavior before the next channel state report and on the aging of the channel state information. A deep recursive neural network is used to classify the UAV channels. Numerical results show that the proposed approach allows to reach a suitable tradeoff between reliability and spectral efficiency compared with different solutions.

Abstract We consider a multiple‐input multiple‐output (MIMO) channel in the presence of a reconfigurable intelligent surface (RIS). Specifically, our focus is on analysing the spatial multiplexing gains in line‐of‐sight and low‐scattering MIMO channels in the near field. We prove that the channel capacity is achieved by diagonalising the end‐to‐end transmitter‐RIS‐receiver channel, and applying the water‐filling power allocation to the ordered product of the singular values of the transmitter‐RIS and RIS‐receiver channels. The obtained capacity‐achieving solution requires an RIS with a non‐diagonal matrix of reflection coefficients. Under the assumption of nearly‐passive RIS, that is, no power amplification is needed at the RIS, the water‐filling power allocation is necessary only at the transmitter. We refer to this design of RIS as a linear, nearly‐passive, reconfigurable electromagnetic object (EMO). In addition, we introduce a closed‐form and low‐complexity design for RIS, whose matrix of reflection coefficients is diagonal with unit‐modulus entries. The reflection coefficients are given by the product of two focusing functions: one steering the RIS‐aided signal towards the mid‐point of the MIMO transmitter and one steering the RIS‐aided signal towards the mid‐point of the MIMO receiver. We prove that this solution is exact in line‐of‐sight channels under the paraxial setup. With the aid of extensive numerical simulations in line‐of‐sight (free‐space) channels, we show that the proposed approach offers performance (rate and degrees of freedom) close to that obtained by numerically solving non‐convex optimization problems at a high computational complexity. Also, we show that it provides performance close to that achieved by the EMO (non‐diagonal RIS) in most of the considered case studies.

The explosive growth of mobile traffic needs that the fifth generation of wireless systems supports a thousand-fold increase of network capacity, thanks to new paradigms such as network densification. However, in dense networks interference can be a limiting factor, and must be suitably managed. In this paper multiple cells coordinate to mitigate the mutual interference: cross-tier interference is limited by means of the traditional enhanced Inter Cell Interference Coordination approach, while a new co-tier interference management strategy based on Matching Theory is proposed. The objective is to provide a stable matching between the set of small cells and the set of available subframes left almost blank by the macrocell and used by the small cells to communicate with their most vulnerable users. The proposed solution aims at improving the throughput of most critical users with an affordable computational complexity. The effectiveness of the proposed scheme has been verified in comparison with benchmark methods.

One of the main challenges of supporting massive machine-type-communications (MTCs) by future mobile networks, is the radio access network (RAN) congestion. A viable solution is a hierarchical architecture where cluster-grouped machines are indirectly connected to the mobile network through a cluster gateway. This paper considers such an architecture and focuses on radio resource allocation to machine-clusters where resources can be spatially reused for concurrent transmissions. In particular, resources can be accessed simultaneously by machines of different clusters and by different communication phases, thus resulting in an interference-limited system. Three different resource partitioning schemes are proposed and evaluated, mainly differing on how interference is taken into account. The aim is to minimize the packet loss probability, and thus maximizing the spectral efficiency. Numerical results show the effectiveness of the proposed schemes in comparison to benchmark methods. In particular, by joining resource reuse and interference-mitigation, the efficiency improves significantly.

The use of satellite communications to support the Internet of Things paradigm is of paramount importance for application scenarios where machines are spread in remote and wide areas. In such context, this paper proposes an efficient hierarchical radio access scheme. Ground machines randomly access the radio channel to communicate with their cluster heads, which in turn relay the cluster's aggregated traffic toward the satellite. The aim is optimizing the resource allocation to different communication stages, allowing resource reuse among clusters, in order to minimize the packet drop probability, and hence, to maximize the system throughput. First, the optimal solution is derived for the single cluster, then it is extended to the multi-cluster scenario. Numerical results show the effectiveness of the proposed method, also in comparison with benchmark approaches.

Ultra Dense Networks (UDNs) are considered a key element to address the expected growing of communication capacity demand in an affordable and sustainable way, improving both spectral and energy efficiency. This paper focuses on the energy efficiency of a coordinated multicell multicast transmission in UDNs. In particular, a new iterative algorithm is proposed, whose goal is to maximize the energy efficiency of a single-group multicast service. Toward this goal, the cells are activated opportunistically, only when needed, using suitable transmission power. Moreover, small cells use adaptive beamforming weights in order to increase the efficiency of the radiated power. The effectiveness of the proposed method is proven in terms of energy efficiency and in comparison with different benchmark solutions. Benefits and limits of network densification are evaluated.

Energy efficiency (EE) is one of the main requirements for future fifth generation wireless networks to allow a sustainable network development. Toward this goal, this study proposes a novel iterative algorithm to maximise the EE of multicast services in ultra dense networks. Macrocells and small cells cooperate using a multicast beamforming strategy to efficiently radiate the power in the considered area. Moreover, small cells are activated opportunistically, and their power levels are adjusted suitably. Due to the high complexity of the optimisation problem, the proposed algorithm works calculating iteratively beamforming weights and power adjustments, thus reducing the set of possible solutions. Then, an exhaustive search is performed among the elements of this restricted set. This solution has reduced computational complexity, and the achieved EE is higher than benchmark alternatives. Performance in terms of EE and complexity is provided together with a solution feasibility evaluation.

Packet collision is a well-known severe impairment in ALOHA-based networks and, more generally, in any random access network. As a consequence, several methodologies have been considered with the aim of lowering the resulting performance degradation. A novel multiuser detection scheme exploiting channel diversity to separate collided signals is proposed here to enable multiple packet reception capability in random access schemes. The performance of this approach is analytically derived and optimized in terms of network throughput, and compared with that of different alternatives. The overall performance in terms of throughput shows the effectiveness of the proposed scheme in different operating conditions.

The most recent trend in the Information and Communication Technology world is toward an ever growing demand of mobile heterogeneous services that imply the management of different quality of service requirements and priorities among different type of users. The long-term evolution (LTE)/LTE-advanced standards have been introduced aiming to cope with this challenge. In particular, the resource allocation problem in downlink needs to be carefully considered. Herein, a solution is proposed by resorting to a modified multidimensional multiple-choice knapsack problem modeling, leading to an efficient solution. The proposed algorithm is able to manage different traffic flows taking into account users priority, queues delay, and channel conditions achieving quasi-optimal performance results with a lower complexity. The numerical results show the effectiveness of the proposed solution with respect to other alternatives.

Heterogeneous network deployment represents one of the main enhancements of future LTE-A networks to increase spectrum efficiency. However, the achievable improvements are limited by the interference that arises from a co-channel allocation of the cells. This paper proposes two cognitive resource allocation methods that work using the angle of arrival of the signals instead of the channel state information. Both methods reduce the interference towards the macro-cell User Equipments (MUEs) using Zero Forcing Beamforming to avoid transmission in the MUEs direction. At the same time they maximize the capacity of the femto-cell with a suitable allocation of the time-frequency resources to the femto-cell UEs (FUEs). The performance of the proposed methods is compared with that of a conventional maximum gain beamforming and conventional zero forcing beamforming. The results show that the proposed methods achieve a good trade off between maximization of the femto-cell capacity and reduction of the interference level at the MUEs side.

We consider a heterogeneous Long Term Evolution Advanced (LTE-A) network where a cognitive femtocell operates in the coverage area of a traditional macrocell sharing the same frequency band. A new method is proposed here to avoid the interference generated by the femtocell on the macrocell downlink communications. In particular, the estimation of the direction of arrival of the primary user signals is used to place nulls in the femtocell transmission paths by means of a suitable beamforming algorithm. The novelty of the proposed approach is that it works on a physical resource basis, taking into account the specific LTE-A access scheme. In this way it is able to manage the interference towards several macrocell users characterized by a high angle dispersion of the multipath components. The obtained numerical results show that the system is able to significantly reduce the interference in different operational conditions, especially when the physical resource blocks belonging to the same macrocell user can be clustered, thus increasing the dimension of the snapshot used for the direction of arrival estimation.

Heterogeneous network deployment is one of the key issues of future Long Term Evolution Advanced systems. However, the coexistence of high power macro-cells and low power femto-cells leads to co-channel interference problems that must be suitably counteracted. Enhanced Inter-Cell Interference Coordination allows a joint management of the resources among the cells so as to reduce the interference. The behaviour of this approach depends on the choice of some parameters, and in particular on the Almost Blank Subframes muting ratio. This paper proposes a simple approach to determine adaptively the most suitable value of the muting ratio as a function of the network traffic load, based on suitable threshold values. The performance is given in terms of network throughput percentage gain.

Resource reuse among heterogeneous cells in LTE-A networks permits to increase the spectral efficiency but requires suitable interference mitigation techniques. Among these Coordinated scheduling and beamforming (CS/CB) is a promising solution. This paper proposes a new CS/CB scheme aiming to maximizing the mean system throughput taking into account the fairness among the cells. First the most suitable CB technique selection is analysed and a sub-optimum criterion based on the reciprocal angular distance between two concurrent users is proposed. Then a smart CS algorithm that allows to pair users with sufficient angular distance is introduced. The results show that the proposed scheme achieves performance close to the optimum CB scheme selection and the smart allocation permits to increase significantly the system throughput.

This paper deals with a cognitive approach used to reduce the co-channel interference arising from the coexistence of an LTE macrocell and an LTE femtocell sharing the same frequency band. Multiple-antennas technology is considered to exploit the angle dimension as a new spectrum opportunity: femtocell acts as a secondary system and inserts nulls in the directions of the UE connected to the macrocell thus protecting the downlink transmission of the primary base station. MuSiC algorithm is used to detect the DOA of the UE signal replicas in an indoor environment assuming a variable number of multipath components characterized by a very large angle spread. The proposed scheme permits to increase the signal to interference ratio at the UE side thus reducing the achieved error rate on the downlink connection even when the number of resolvable paths is higher than the number of antennas. Finally the UE mobility is considered to take into account the effect of variable DOAs.

This paper deals with the performance evaluation of Decode and Forward Physical Layer Network Coding schemes under multipath fading channel propagation conditions. Physical Layer Network Coding in Two Way Relay Networks ideally allows to drastically increase the network throughput. However, under the assumption of actual channel propagation conditions, the performance quickly degrades due to high decoding error probability. With the aim of counteracting this drawback, the paper proposes a suitable approach for Orthogonal Frequency Division Multiplexing systems based on a signal pre-equalization technique. Furthermore, in order to lower the decoding error probability, three subcarriers suppression methods are introduced. These methods differ from each other for the performance achieved and the required amount of signaling that nodes have to exchange. An important result obtained here is that the combined use of channel inversion and subcarriers suppression allows to enhance the system reliability with a slight decrease of the throughput.

This paper analyzes the performance of the Physical Layer Network Coding (PLNC) approach based on Decode-and-Forward (DF) and operating in multipath fading channel. In actual channel affected by multipath fading the DF method has poor performance since the relay node cannot properly perform data decoding. In addition the decoding at the relay requires the knowledge of the channels involved in the communication with a consequent signaling overhead. In order to overcome these problems this paper proposes an Orthogonal Frequency Division Multiplex (OFDM) system where the pre-emphasis approach is considered jointly with an efficient method of sub-carrier suppression named Blind Subcarrier Suppression (BSS). Simulations results show that BSS achieves high throughput with a Bit Error Probability (BER) near to that of a classical OFDM system in multipath channel.

Long Term Evolution (LTE) is considered one of the main candidate to provide wireless broadband access to mobile users. Among main LTE characteristics, flexibility and efficiency can be guaranteed by resorting to suitable resource allocation schemes, in particular by adopting adaptive OFDM schemes. This paper proposes a novel solution to the sub-carrier allocation problem for the LTE downlink that takes into account the queues length, the QoS constraints and the channel conditions. Each user has different queues, one for each QoS class, and can transmit with a different data rate depending on the propagation conditions. The proposed algorithm defines a value of each possible sub-carrier assignment as a linear combination of all the inputs following a cross-layer approach. The problem is formulated as a Multidimensional Multiple-choice Knapsack Problem (MMKP) whose optimal solution is not feasible for our purposes due to the too long computing time required to find it. Hence, a novel efficient heuristic has been proposed to solve the problem. Results shows good performance of the proposed resource allocation scheme both in terms of throughput and delay while guarantees fairness among the users. Performance has been compared also with fixed allocation scheme and round robin.