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) are one of the emerging technologies aimed at meeting the expectations of next-generations wireless systems. In this field, the use of multi-port network models for the characterization and optimization of RIS has emerged in recent years. These models take into account aspects traditionally not considered in communication theory, such as mutual coupling of RIS elements and the presence of structural scattering. In this work, we refer to this model and focus on the problem of maximizing the average achievable rate in a multi-user uplink scenario by leveraging statistical Channel State Information (CSI). This approach significantly reduces the computational burden and communication overhead in CSI estimation compared to schemes requiring instantaneous CSI estimation. These benefits are achieved with performance that, in many cases, is reasonably close to that of the perfect CSI scenario. This is one of the outcomes achievable with the proposed optimization scheme. Moreover, it is shown how in multi-user scenarios, namely in the presence of interference, the use of inadequate models to characterize RIS can lead to very poor performances. For example, models that do not consider structural scattering may fail to account for interference caused by RIS.

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.

Recent advancements in smart radio environment technologies aim to enhance wireless network performance through low-cost electromagnetic (EM) devices. Among these, metaprisms (MTPs)—a class of static, frequency-selective metasurfaces—stand out for their ability to create multiple beams at different frequencies without requiring channel state information (CSI) or active reconfiguration. Unlike reconfigurable intelligent surfaces (RIS), which rely on programmable elements and periodic tuning, MTPs operate passively, significantly reducing system complexity and overhead. The working principle of MTPs is specifically tailored for scenarios in which a large number of devices must be served simultaneously, each requiring low data rate and low latency, as envisioned by, e.g., Industrial Internet-of-Things (IoT). In this paper, we address the design of an ideal MTP by considering frequency-dependent reflection coefficients, and by identifying the general properties that are necessary to achieve the desired beam steering function in the angle-frequency domain. We also discuss the limitations of previous studies that employed oversimplified models, which may compromise performance. Key contributions include a detailed exploration of the equivalence of the MTP to an ideal S-parameter multiport (MP) model and an analysis of its implementation using Foster’s circuits. Additionally, we introduce a realistic MP network model that incorporates aspects overlooked by ideal scattering models, along with an ad-hoc optimization strategy for the obtained model. The performance of the proposed optimization approach and circuits implementation are validated through simulations using a commercial full-wave EM simulator, showcasing the effectiveness of the proposed method.

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.

Multiport network theory has been proved to be a suitable abstraction model for analyzing and optimizing reconfigurable intelligent surfaces (RISs) in an electromagnetically consistent manner, especially for studying the impact of the electromagnetic mutual coupling among radiating elements that are spaced less than half of the wavelength apart and for considering the interrelation between the amplitude and phase of the reflection coefficients. Both representations in terms of Z-parameter (impedance) and S-parameter (scattering) matrices are widely utilized. In this paper, we embrace multiport network theory for analyzing and optimizing the reradiation properties of RIS-aided channels, and provide four new contributions. (i) First, we offer a thorough comparison between the Z-parameter and S-parameter representations. This comparison allows us to unveil that typical scattering models utilized for RIS-aided channels ignore the structural scattering from an RIS, which is well documented in antenna theory. We show that the structural scattering results in an unwanted specular reflection. (ii) Then, we develop an iterative algorithm for optimizing, in the presence of electromagnetic mutual coupling, the tunable loads of an RIS based on the S-parameters representation. We prove that small perturbations of the step size of the algorithm result in larger variations of the S-parameter matrix compared with the Z-parameter matrix, resulting in a faster convergence rate. (iii) Subsequently, we generalize the proposed algorithm to suppress the specular reflection due to the structural scattering, while maximizing the received power towards the direction of interest, and analyze the effectiveness and tradeoffs of the proposed approach. (iv) Finally, we validate the theoretical findings and algorithms with numerical simulations and a commercial full-wave electromagnetic simulator based on the method of moments.

Consider a communication system in which a single-antenna user equipment exchanges information with a multi-antenna base station via a reconfigurable intelligent surface (RIS) in the presence of spatially correlated channels and electromagnetic interference (EMI). To exploit the attractive advantages of RIS technology, accurate configuration of its reflecting elements is crucial. In this paper, we use statistical knowledge of channels and EMI to optimize the RIS elements for 1i) accurate channel estimation and 2) reliable data transmission. In both cases, our goal is to determine the RIS coefficients that minimize the mean square error, resulting in the formulation of two non-convex problems that share the same structure. To solve these two problems, we present an alternating optimization approach that reliably converges to a locally optimal solution. The incorporation of the diagonally scaled steepest descent algorithm, derived from Newton’s method, ensures fast convergence with manageable complexity. Numerical results demonstrate the effectiveness of the proposed method under various propagation conditions. Notably, it shows significant advantages over existing alternatives that depend on a suboptimal configuration of the RIS and are derived on the basis of different criteria.

\beginabstract In the context of the Factories at Major Accident Risk (FMAR), we consider a scenario consisting of multiple sensor nodes that detect dangerous conditions and raise alarms over a LoRaWAN network. Upon event detection, a large number of nodes try to transmit concurrent alarm messages and the reception of at least one message is sufficient for the central server to react. We propose a scheme in which nodes operate in a slotted-time mode after the detection of the triggering event and they can only transmit one packet in a randomly chosen slot. The choice of the transmission slot follows a specific probability distribution that maximizes the probability of successful reception of at least one packet subject to real-time latency constraints. We provide an optimization framework to find the optimal distribution for choosing a transmission slot. We validate the proposed scheme by simulations and provide numerical results to compare the performance for three different slot choice distributions: i) uniform, ii) the distribution used in the Sift protocol \cite Tay2004, and iii) the proposed optimal distribution. The results show that the proposed optimal distribution leads to much better probability of successful packet delivery than other distributions. \endabstract

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.

Abstract Densification of networks through heterogeneous cells deployment is considered a key technology to satisfy the huge traffic growth in future wireless systems. In addition to achieving the required communication capacity and efficiency, another significant challenge arises from the broadcast nature of wireless channels: vulnerability to wiretapping. Physical-layer security is envisaged as an additional level of security to provide confidentiality of radio communications. Typical characteristics of the wireless channel (noise, interference) can be exploited to keep a message confidential from potential eavesdroppers. In particular, heterogeneous networks (HetNet) have inherent security features: while the legitimate user can benefit of the HetNet architecture, the eavesdropper is strongly affected by the inter-cell interference. This paper presents an overview of HetNets intrinsic security benefits, mainly focusing on users association and resource allocation policies. In particular, allocation of radio resources is a poorly investigated topic when related to information security. However, in systems with a large radio resource reuse like HetNets, co-channel interference can be suitably exploited to resist to the eavesdropper. This paper presents a new framework for radio resources allocation using reinforcement learning (Q-learning) to increase the security level in HetNets. A coordinated scheduling among different cells using the same radio resources is proposed based on the exploitation of the spatial information. The goal is to optimize the security at physical layer. The reinforcement learning approach represents a feasible and efficient solution to the proposed problem.

Reconfigurable intelligent surfaces (RISs) have tremendous potential to boost communication performance, especially when the line-of-sight (LOS) path between the user equipment (UE) and base station (BS) is blocked. To control the RIS, channel state information (CSI) is needed, which entails significant pilot overhead. To reduce this overhead and the need for frequent RIS reconfiguration, we propose a novel framework for integrated localization and communications, where RIS configurations are fixed during location coherence intervals, while BS precoders are optimized every channel coherence interval. This framework leverages accurate location information obtained with the aid of several RISs as well as novel RIS optimization and channel estimation methods. Performance in terms of localization accuracy, channel estimation error, and achievable rate demonstrates the effectiveness of the proposed approach.

Joint localization and communication services have received growing interest for the next 6G cellular scenarios. Therefore, this paper studies a multi-Reconfigurable Intelligent Surface (RIS) single-user Multiple Input Multiple Output (MIMO) scenario from a dual perspective. From one side, we analyze the capabilities of an Extended Kalman Filter (EKF) to track the trajectory of the user by processing the positioning information gathered by the signals backscattered from the RISs, whereas the RISs are in turn optimized accounting for the outcomes of the EKF estimator. On the other side, we analyze the impact of positioning onto the communication performance in terms of achievable rate. Numerical results have shown that it is possible to track moving nodes with high accuracy leveraging on, solely, the radio signal reflected by the RISs in the absence of a line-of-sight (LOS) path between the transmitter and the receiver ensuring, at the same time, good communications performance.

With the advent of 5G and the research into beyond 5G (B5G) networks, a novel and very relevant research issue is how to manage the coexistence of different types of traffic, each with very stringent but completely different requirements. We propose a Deep Reinforcement Learning (DRL) algorithm to slice the available physical layer resources between ultra-reliable low-latency communications (URLLC) and enhanced Mobile BroadBand (eMBB) traffic. Specifically, in our setting the time-frequency resource grid is fully occupied by eMBB traffic and we train the DRL agent to employ Proximal Policy Optimization (PPO), a state-of-the-art DRL algorithm, to dynamically allocate the incoming URLLC traffic by puncturing eMBB codewords. Assuming that each eMBB codeword can tolerate a certain limited amount of puncturing beyond which is in outage, we show that the policy devised by the DRL agent never violates the latency requirement of URLLC traffic and, at the same time, manages to keep the number of eMBB codewords in outage at minimum levels, when compared to other state-of-the-art schemes.

In this paper, we consider a multi-user multiple-input multiple-output (MIMO) system aided by multiple intelligent reflecting surfaces (IRSs) that are deployed to increase the coverage and, possibly, the rank of the channel. We propose an optimization algorithm to configure the IRSs, which is aimed at maximizing the network sum-rate by exploiting only the statistical characterization of the locations of the mobile users. As a consequence, the proposed approach does not require the estimation of either instantaneous channel state information (CSI) or second-order channel statistics for IRS optimization, thus significantly relaxing (or even avoiding) the need of frequently reconfiguring the IRSs, which constitutes one of the most critical issues in IRS-assisted systems. Numerical results confirm the validity of the proposed approach. It is shown, in particular, that IRS-assisted wireless systems that are optimized based on statistical position information still provide large performance gains as compared to the baseline scenarios in which no IRSs are deployed.

In this article, we study the problem of power and channel allocation for multicarrier non-orthogonal multiple access (NOMA) full duplex (FD) systems. In such a system there are multiple interfering users transmitting over the same channel and the allocation task is a non-convex and extremely challenging problem. Following a block coordinated descent approach, we propose two algorithms based on the decomposition of the original allocation problem in lower-complexity sub-problems, which can be solved in the Lagrangian dual domain with a great reduction of the computational load. Numerical results show the effectiveness of the proposed approach, which outperforms other schemes designed to address NOMA-FD allocation and attains performance similar to the optimal solution with much lower complexity.

The Fifth Generation of mobile networks is emerging as a key enabler for Ultra Reliable and Low Latency Communications. However, to effectively design and provide safety-critical applications through mobile systems, many research issues still need to be deeply investigated. The most important ones include: (1) the dynamic and flexible management of radio resources of a new Radio Access Network jointly used by many virtual mobile operators, (2) the optimized and realtime configuration of network slices, and (3) the harmonious integration of Multi-access Edge Computing services. Starting from the efficient methodologies and solutions available in the current state of the art, this position paper sheds some important basis for the design of a comprehensive architecture enabling Radio Access Network slicing for Ultra Reliable and Low Latency Communications, including design criteria, system components and their baseline interactions, and critical open issues to be investigated in future research initiatives.

Device-to-device (D2D) communications are capable of enhancing the total cell throughput, reducing power consumption and increasing the instantaneous data rate. In this paper we propose a distributed power allocation scheme for D2D communications overlaying an OFDMA network, so that D2D communications take place on dedicated resources but are supervised by the BS. The proposed scheme addresses the problem of maximizing the users' sum rate subject to power constraints, which is known to be nonconvex. By modelling the power allocation problem as a potential game, we can take advantage of the potential games property of converging under better response dynamics. Accordingly, we propose a fully distributed iterative algorithm, where each user updates sequentially and autonomously its power allocation. The proposed method exhibit performance close to the maximum achievable optimum and outperform other schemes presented in the literature.

The implementation of device-to-device (D2D) underlaying or overlaying preexisting cellular networks has received much attention due to the potential of enhancing the total cell throughput, reducing the power consumption, and increasing the instantaneous data rate. In this paper, we propose a distributed power allocation scheme for D2D OFDMA communications and, in particular, we consider the two operating modes amenable to a distributed implementation: dedicated and reuse modes. The proposed schemes address the problem of maximizing the users' sum rate subject to power constraints, which is known to be nonconvex and, as such, extremely difficult to be solved exactly. We propose here a fresh approach to this well-known problem, capitalizing on the fact that the power allocation problem can be modeled as a potential game. Exploiting the potential games property of converging under better response dynamics, we propose two fully distributed iterative algorithms, one for each operation mode considered, where each user updates sequentially and autonomously its power allocation. Numerical results, computed for several different user scenarios, show that the proposed methods, which converge to one of the local maxima of the objective function, exhibit performance close to the maximum achievable optimum and outperform other schemes presented in the literature.

This paper investigates the subcarrier allocation problem for uplink transmissions in a multi-cell network, where device-to-device communications are enabled. We focus on maximizing the aggregate transmission rate in the system accounting for both inter- and intra-cell interference. This problem is computationally hard due to its nonconvex and combinatorial nature. However, we show that it can be described by a potential game, and thus a Nash equilibrium can be found using iterative algorithms based on best/better response dynamics. In particular, we propose a simple iterative algorithm with limited signaling that is guaranteed to converge to an equilibrium point, corresponding to a local maximum of the potential function. Using extensive simulations, we show that the algorithm converges quickly also for dense networks, and that the distance to the true optimum is often small, at least for the small-sized networks for which we were able to compute the true optimum.

In this paper, we analyze a cognitive wireless scenario, where a primary wireless network (PWN) coexists with a cognitive (or secondary) wireless network (CWN). The PWN uses licensed spectrum and the nodes of the CWN cooperate to detect idle subchannels (not used by the PWN's nodes), possibly taking into account the knowledge of their positions'. On the basis of this scenario, we present a simple, yet effective, framework to analyze the tradeoff between the CWN detection capabilities, i.e., the probability of detecting an unused lincensed subchannel, and the energy consumption needed to detect this subchannel. To this end, we introduce a novel performance indicator, denoted as detection energy efficiency. Our results show that there is an optimal working point, i.e., an optimal number of collaborating CWN nodes that allows to achieve the highest detection energy efficiency.

Optimum decision fusion in the presence of malicious nodes - often referred to as Byzantines - is hindered by the necessity of exactly knowing the statistical behavior of Byzantines. In this paper, we focus on a simple, yet widely adopted, setup in which a fusion center (FC) is asked to make a binary decision about a sequence of system states by relying on the possibly corrupted decisions provided by local nodes. We propose a game-theoretic framework, which permits to exploit the superior performance provided by optimum decision fusion, while limiting the amount of a priori knowledge required. We use numerical simulations to derive the optimum behavior of the FC and the Byzantines in a game-theoretic sense, and to evaluate the achievable performance at the equilibrium point of the game. We analyze several different setups, showing that in all cases, the proposed solution permits to improve the accuracy of data fusion. We also show that, in some cases, it is preferable for the Byzantines to minimize the mutual information between the status of the observed system and the reports submitted to the FC, rather than always flipping the decision made by the local nodes.

We consider a cooperative cognitive wireless network scenario where a primary wireless network is co-located with a cognitive (or secondary) network. In the considered scenario, the nodes of the secondary network make local binary decisions about the presence of a signal emitted by a primary node. Then, they transmit their decisions to a fusion center (FC). The final decision about the channel state is up to the FC by means of a proper fusion rule. In this scenario, we derive the optimum decision strategy for the FC and the optimum local decision thresholds of the secondary nodes in a Neyman-Pearson setup. In particular, the overall system performance are derived by making the realistic assumption that the position of the primary user is completely unknown to the FC.

Many assignment problems, and channel allocation in OFDMA networks is a typical example, can be formulated as bipartite weighted b-matching (BWBM) problems. In this letter we provide a proof of the convergence and the optimality of the reweighted message passing (ReMP) algorithm when applied to solve BWBM problems in a distributed fashion. To this aim, we first show that the ReMP rule is a contraction mapping under a maximum mapping norm. Then, we show that the fixed convergence point is an optimal solution for the original assignment problem.

In this paper, we consider orthogonal multiple access coding schemes, where correlated sources are encoded in a distributed fashion and transmitted through additive white Gaussian noise (AWGN) channels to an access point (AP). At the AP, component decoders, which are associated with the source encoders, iteratively exchange soft information by taking into account the source correlation. The first goal of this paper is to investigate the ultimate achievable performance limits in terms of a multi-dimensional feasible region in the space of channel parameters, deriving insights on the impact of the number of sources. The second goal is the design of pragmatic schemes, where the sources use “off-the-shelf” channel codes. In order to analyze the performance of given coding schemes, we propose an extrinsic information transfer-based approach, which allows to determine the corresponding multi-dimensional feasible regions. On the basis of the proposed analytical framework, the performance of pragmatic coded schemes, based on serially concatenated convolutional codes, is discussed.

We investigate the performance of a watermarking system in which the encoder is forced to use a binning strategy based on antipodal binary-valued sequences. The use of antipodal binary random binning has several advantages, including the possibility of relying on simple and effective binary code constructions and the ease with which this kind of schemes can cope with amplitude scaling. By relying on a novel binning strategy, we derive a lower bound of the Gelfand-Pinsker capacity of the watermark channel when the encoder is forced to use an antipodal binary auxiliary random variable, showing that for low to moderate bit-rates, the bound coincides with Costa's capacity. We exploit the properties of the new binning strategy, to develop a practical watermarking system and show that the new scheme outperforms previous constructions, exhibiting very good performance also in the presence of gain attack. Preliminary results on audio signals show that the new scheme retains its good performance also when used for the watermarking of real multimedia data.

Device-to-device (D2D) communications integrated into cellular networks is a means to take advantage of the proximity of devices and thereby to increase the user bitrates and system capacity. D2D communications has recently been proposed for the 3GPP Long Term Evolution (LTE) system as a method to increase the spectrum- and energy-efficiency. Such systems support a wide range of power control schemes based on a combination of open-loop and closed-loop components and there is a need to set the associated control parameters such that spectrum- and energy-efficiency targets are met. In this paper we study the performance of various power control strategies applicable to D2D communications in LTE networks and compare them with a utility function maximization approach that balances spectrum efficiency and the total transmission power. Our reference scheme is based on a fully distributed algorithm that iteratively sets the signal-to-interference-plus-noise (SINR) targets and corresponding transmit power levels. We find that the LTE-based power control approach performs close to the optimal scheme provided that the associated parameters are properly set1.

One of the major sources of energy waste in wireless sensor networks (WSNs) is idle listening, that is, the cost of actively listening for potential packets. This article focuses on reducing idle-listening time via a dynamic duty-cycling technique which aims at optimizing the sleep interval between consecutive wake-ups. We considered a receiver-initiated MAC method for WSNs in which the sender waits for a beacon signal from the receiver before starting to transmit. Since each sender receives beacon signals from several nodes, the data are routed on multiple paths in a data collection network. In this context, we propose an optimization framework for minimizing the energy waste of the most power-hungry node of the network. To this aim, we first derive an analytic model that predicts nodes' energy consumption. Then, we use the model to derive a distributed optimization technique. Simulation results via NS-2 simulator are included to illustrate the accuracy of the model, and numerical results assess the validity of the proposed scheme.

This paper proposes a distributed and low-complexity resource allocation scheme for cellular OFDMA networks. In particular, we consider ReMP, a reweighted message passing algorithm that perturbs the standard max-sum algorithm by suitably reweighting messages. In a single-cell scenario, such a scheme allows to achieve convergence to a fixed and provably optimum point without employing any central controller. The ReMP algorithm is then adapted to a multi-cell environment. To this aim, we devise X-ReMP, a ReMP-based algorithm that combines cross-cell signaling and the regular ReMP routine that still runs within each cell. The cross-signaling among cells aids ReMP to deal with the inter-cell multiple-access interference, so that X-ReMP allows convergence to a good working point in terms of system throughput even in presence of strong inter-cell interference.

One of the major sources of energy waste in a Wireless Sensor Network (WSN) is idle listening, i.e., the cost of actively listening for potential packets. This paper focuses on reducing the idle-listening time via a dynamic duty cycling technique which aims at optimizing the sleep interval between consecutive wakeups. We considered receiver-initiated MAC method for WSNs, in which the sender waits for a beacon signal from the receiver before starting to transmit. In this context, we propose an optimization framework for minimizing the energy waste of the most power hungry node of the network. To this aims, we first derive an analytic model that predicts nodes' energy consumption. Then, we use the model to derive an heuristic distributed optimization technique. Simulation results via NS-2 simulator are included to illustrate the accuracy of the model, and to assess the validity of the proposed scheme.

We propose two novel distributed resource allocation (RA) schemes for the uplink of a cellular multi-carrier multi-format system based on the message passing (MP) technique. In the proposed approaches each transmitter iteratively sends and receives information messages to/from the base station with the goal of achieving an optimal RA strategy. The exchanged messages are the solution of small distributed allocation problems. Hence, despite the NP-hardness of the original RA problem, they distribute the computational effort in the cell among all the transmitters and the base station. Specifically, the first algorithm combines MP with a dynamic programming formula solved at each step, while the second method initially solves to optimality a simplified single-format RA via MP, and eventually performs format allocation to satisfy the rate constraints. Compared to alternatives, numerical results assess the validity of MP-based schemes both in terms of efficiency and complexity.

Lossy compression of hyperspectral and ultraspectral images is traditionally performed using 3D transform coding. This approach yields good performance, but its complexity and memory requirements are unsuitable for onboard compression. In this paper we propose a low-complexity lossy compression scheme based on prediction, uniform threshold quantization, and rate-distortion optimization. Its performance is competitive with that of state-of-the-art 3D transform coding schemes, but the complexity is immensely lower. The algorithm is able to limit the scope of errors, and is amenable to parallel implementation, making it suitable for onboard compression at high throughputs.

This paper considers distributed protocol design for resource allocation (RA) problems. We propose a fully decentralized RA scheme based on the min-sum message passing (MP) approach in which each message is the solution of small distributed allocation problems. Due to the presence of cycles in the network graph, the MP routine may not converge to a fixed point. To this end, we introduce a reweighted MP (ReMP) algorithm that perturbs the ordinary min-sum algorithm by suitably re-weighting messages. ReMP distributes the computational effort of achieving an optimal RA among nodes. Such feature makes ReMP particularly attractive in wireless networks allowing the convergence to a fixed and provably optimum point without employing any central controller. Numerical results show that ReMP outperforms conventional MP-based algorithms for RA problems in terms of computation time.

In this paper, we study the performance of non-cooperative multiple access systems with noisy separated channels, where correlated sources communicate to an access point (AP) through block-faded links. In the considered scenario, perfect channel state information (CSI) is assumed at the receiver while no CSI is available at the transmitters. We first consider uncoded transmissions from the sources to the AP, which exploits the source correlation to carry out joint channel detection (JCD). In this scenario, we propose an analytical approach to evaluate the achievable performance in terms of average bit error rate (BER). We then investigate the impact of coding, considering the same fixed coding scheme at each source. Then, we consider an extrinsic information transfer (EXIT) chart-based framework to optimize the design of concatenated and low-density parity-check (LDPC) codes for JCD schemes.

In this paper, we focus on the design of optimized binary convolutional codes (CCs) and serially concatenated convolutional codes (SCCCs) in the presence of a-priori information (API) at the receiver. For large signal-to-noise ratios (SNRs), we first propose a CC design criterion based on the minimization of a union bound on the bit error probability (BEP). In this case, relevant performance gains, with respect to previously proposed CCs, are obtained. These gains persist even in the presence of estimation errors on the API. Then, we apply the same union bound-based design criterion to SCCCs. Since the BEP of SCCCs is characterized by a typical waterfall shape, the proposed union bound-based design criterion is accurate only at large SNR, to estimate the BEP floor. In order to complement this analysis, we propose a density evolution-based approach to optimize the SCCC design in terms of minimization of the SNR of the "knee" of the BEP curve. The obtained simulation results show substantial gains with respect to previously proposed parallel concatenated convolutional coding (PCCCing) schemes optimized under the assumption of no API at the decoder. Moreover, in the presence of strong API the proposed SCCCs allow to approach the Shannon limit (SL) more than any previously proposed turbo coding scheme.

In this paper, we focus on the design of optimized binary convolutional codes (CCs) and serially concatenated convolutional codes (SCCCs) in the presence of a-priori information (API) at the receiver. First, we propose a design criterion for CCs based on the minimization of the bit error proabability (BEP). In this case, relevant performance gains, with respect to previously proposed CCs, are obtained. These gains persist even in the presence of estimation errors on the API. Then, we apply the same BEP-based design criterion to SCCCs and derive good encoders' structures. In the SCCC case with API at the receiver, simulation results show substantial gains with respect to previously proposed parallel concatenated convolutional coding (PCCCing) schemes optimized under the assumption of no API at the decoder. Moreover, in the presence of strong API the proposed SCCCs allow to approach the Shannon limit (SL) more than any previously proposed turbo coding scheme.

Lossy compression of hyperspectral and ultraspectral images is traditionally performed using 3D transform coding. This approach yields good performance, but the complexity and memory requirements make it unsuitable for onboard compression. In this paper we propose a low-complexity lossy compression scheme based on prediction, quantization and rate-distortion optimization. The scheme employs coset codes coupled with the newconcept of “informed quantization”, and requires no entropy coding. The performance of the resulting algorithm is competitive with that of state-of-the-art 3D transform coding schemes, but the complexity is immensely lower, making it suitable for onboard compression at high throughputs.

In this paper, we propose a lossless compression algorithm for hyperspectral images inspired by the distributed-source-coding (DSC) principle. DSC refers to separate compression and joint decoding of correlated sources, which are taken as adjacent bands of a hyperspectral image. This concept is used to design a compression scheme that provides error resilience, very low complexity, and good compression performance. These features are obtained employing scalar coset codes to encode the current band at a rate that depends on its correlation with the previous band, without encoding the prediction error. Iterative decoding employs the decoded version of the previous band as side information and uses a cyclic redundancy code to verify correct reconstruction. We develop three algorithms based on this paradigm, which provide different tradeoffs between compression performance, error resilience, and complexity. Their performance is evaluated on raw and calibrated AVIRIS images and compared with several existing algorithms. Preliminary results of a field-programmable gate array implementation are also provided, which show that the proposed algorithms can sustain an extremely high throughput.

We propose a novel distributed resource allocation scheme for the up-link of a cellular multi-carrier system based on the message passing (MP) algorithm. In the proposed approach each transmitter iteratively sends and receives information messages to/from the base station with the goal of achieving an optimal resource allocation strategy. The exchanged messages are the solution of small distributed allocation problems. To reduce the computational load, the MP problems at the terminals follow a dynamic programming formulation. The advantage of the proposed scheme is that it distributes the computational effort among all the transmitters in the cell and it does not require the presence of a central controller that takes all the decisions. Numerical results show that the proposed approach is an excellent solution to the resource allocation problem for cellular multi-carrier systems.

This paper presents a novel cross-layer design for joint power and end-to-end rate control optimization in DS-CDMA wireless networks, along with a detailed implementation and evaluation in the network simulator ns-2. Starting with a network utility maximization formulation of the problem, we derive distributed power control, transport rate and queue management schemes that jointly achieve the optimal network operation. Our solution has several attractive features compared to alternatives: it adheres to the natural time-scale separation between rapid power control updates and slower end-to-end rate adjustments, and uses simplified power control mechanisms with reduced signalling requirements. We argue that these features are critical for a successful real-world implementation. To validate these claims, we present a detailed implementation of a cross-layer adapted networking stack for DS-CSMA ad-hoc networks in ns-2. We describe several critical issues that arise in the implementation, but are typically neglected in the theoretical protocol design, and evaluate the alternatives in extensive simulations.

In this article we focus on the channel decoding problem in presence of a-priori information. In particular, assuming that the a-priori information reliability is not perfectly estimated at the receiver, we derive a novel analytical framework for evaluating the decoder's performance. It is derived the important result that a ldquogood coderdquo, i.e., a code which allows to fully exploit the potential benefit of a-priori information, must associate information sequences with high Hamming distances to codewords with low Hamming distances. Basing on the proposed analysis, we analyze the performance of random codes and turbo codes.

Efficient resource allocation in cellular OFDMA systems envisages the assignment of the number of sub-carriers and the relative transmission format on the basis of the experimented link quality. In this way, a higher number of sub-carriers with low per-carrier cshould be assigned to users at cell border. This strategy has already proved its efficiency in the single-cell scenario, while no study has been provided in the multi-cell scenario with reuse factor equal to one, i.e., in presence of severe interference conditions. In this paper we propose an optimum centralized radio resource allocator for the multi-cell scenario of an OFDMA cellular system which allows to highly outperform iterative decentralized allocation strategies based on local optimization criteria. The proposed scheme is characterized by huge implementation complexity, and, hence, it can be hardly implemented in the real world. However, it can help the system designer in catching the essence of interference limitations in OFDMA cellular systems, thus allowing the elaboration of efficient heuristic decentralized approaches. As an example, we prove that the sub-carrier transmission format adaptation is not useful in a multi-cell scenario. This is because users at cell border tends to consume the most of the resources (i.e., they are assigned the most of sub-carriers), thus producing interference for the neighbor cells over a large set of sub-carriers. Hence, since in this case neighbor cells are forced to use those (few) sub-carriers which experiment low interference, the diversity gain tends to be missed.

A first attempt to exploit distributed source coding (DSC) principles for the lossless compression of hyperspectral images is presented. The DSC paradigm is exploited to design a very light coder which minimizes the exploitation of the correlation between the image bands. In this way we managed to move the computational complexity from the encoder to the decoder, thus matching the needs of classical acquisition system where compression is achieved on board of the aerial platform and decoding at the ground station. Though the encoder does not explicitly exploit inter-band correlation, the achieved bit rate is about 1 bit/pixel lower than classical 2D schemes such as JPEG-LS or CALID 2D, and only about 1 b/p higher than the best performing, and much more complex, 3D schemes.

A novel quantization-based data-hiding method, named rational dither modulation (RDM), is presented. This method amounts to simple modifications of the well-known dither modulation (DM) scheme, which is largely vulnerable to scaling attacks. With such modifications, RDM becomes invariant to those attacks. Since RDM does not work by trying to estimate the step-size of the quantizers, it does not need any pilot-sequence. Moreover, RDM is suitable for a scalar operation, thus avoiding the cumbersome constructions of spherical codes. It is also shown that RDM approaches the performance of DM asymptotically with the size of the memory needed for the method to operate. Simulation results show the accuracy of our theoretical analysis and the superiority of RDM compared to the improved spread spectrum method.

We investigate the performance of a switched diversity scheme, where the transmitter selects one of the n/sub t/ available transmit antennas. The selected antenna is chosen in the basis of information feedback from the receiver. In the proposed scheme, vehicle movements cause a mismatch between the state of the channel perceived by the receiver and the actual propagation conditions. Hence, an analytical approach for the evaluation of the impact of mobile speed on the system performance is derived. Then, comparisons between switched diversity and space-time coding performance are given. Such results assess the validity of the considered switched diversity approach for low mobility-low complexity wireless systems.

Non-coherent detection of DS-CDMA signals which make use of M-DPSK modulation in presence of antenna arrays is considered. Firstly, the optimum non-coherent MLSE detection rule is derived for a K dimension DS-CDMA vectorial channel assuming perfect single-user beamforming. Then, in order to exploit the advantages of non-coherent detection fully, a detector which dispenses with array vector estimation is derived. Simulation results show that both the proposed non-coherent detectors succeed in noticeably improving performance by means of antenna arrays. In particular, for the second proposed scheme, this performance improvement can be achieved without any channel state estimation, thus resulting in a fully incoherent detection.

The performance of radiolocation systems which make use of observed time difference of arrival techniques is investigated. We refer to a classical third generation WCDMA wireless system where measurements are performed in the direct connection (from base station to mobile). The system performance is evaluated by using a ray-tracing technique to characterize the behaviour of typical urban environments.

We address the radio resource management problem in high altitude platform stations (HAPS) systems. We propose a centralized resource allocation strategy, named ORA-LF, that allows maximizing the system throughput while preserving the fairness among mobile terminals carrying the same traffic type. We also describe a simplified procedure, named SRA-LF, that allows making the ORA-LF strategy compatible with real time implementations.

This paper envisages a cellular system based on code-division multiple access and investigates the performance of a strength-based closed-loop power control (CLPC) scheme on the basis of different parameters, such as the number of bits of the power command, the quantization step size, and the user speed. On the basis of a log-linear CLPC model, an analytical approach has been developed that has allowed to determine the optimum quantization step size to be used for each value of the number of power command bits. Simulation results have permitted to support the analytical framework developed in this paper.

Future wireless systems will allow the mobile access to multimedia services. This paper proposes a medium access control (MAC) protocol that permits an efficient integration of real time-variable bit rate (rt-VBR) traffic and bursty available bit rate (ABR) traffic. This scheme, named minislotted-dynamic resource assignment (M-DRA), provides rt-VBR sources with a quality of service independent of the ABR traffic load. The M-DRA scheme effectiveness has been validated by comparisons with other protocols.

This paper proposes an analytical study which aims at evaluating the received power statistics in DS-CDMA cellular systems which use a closed-loop power control scheme to compensate for fast multipath fading. The effects of a limited transmission power at the mobile terminals have been also investigated. This study is based on a first order Taylor expansion of the fast fading fluctuations. The accuracy of the obtained results has been verified through computer simulations. Classical frequency-selective channel models have been taken into account which involve the use of a diversity RAKE receiver at the base station. Finally, the system capacity is evaluated under different environmental conditions.

This paper envisages data transmissions in a low-mobility wireless local area network based on a DS-CDMA air interface. A Rayleigh fading channel and suitable Internet-like traffic model have been envisaged to study the impact of the closed-loop power control on an adaptive coding scheme. The following quality of service (QoS) parameters have been considered in this study: the mean datagram transmission delay and the mean transmission energy per useful bit. We have obtained that our proposed adaptive coding scheme permits one to avoid the use of closed-loop fast power control. Such choice permits one to improve both previous QoS parameters, so allowing an efficient management of radio resources.

This paper describes a computational method for estimating the body reflectance function of color surfaces. The experimental apparatus consists of a vision system having seven spectral channels whose wavelength sensitivities cover the visible spectrum. The estimation of the spectral-reflectance function is based on finite-dimensional linear models. In order to use digitized imaged data to evaluate the surface reflectance, a procedure for bypassing the problem of unknown illuminant spectral-power distribution was devised. For the simple objects utilized (color checker charts), it is found that an accuracy greater than 97% can be achieved.

Calibration of the output device used for the reproduction of digital colour images (a color CRT in most cases) can either be achieved through conventional techniques involving mathematical modeling of the CRT, or through a novel neural-network-based scheme, introduced in this work. A Java-based system for CRT remote calibration is also presented, which allows the user to get rid of the computational burden necessary to train the neural network, or to estimate the parameters of the CRT model by relying on a set of measurements of the colours displayed by the CRT. Measurements can be avoided as well, by storing calibration data relative to a wide variety of CRTs and by using them to calibrate monitors with similar characteristics.