Next-generation real-time compute-intensive applications, such as extended reality, multi-user gaming, and autonomous transportation, are increasingly composed of heterogeneous AI-intensive functions with diverse resource requirements and stringent latency constraints. While recent advances have enabled very efficient algorithms for joint service placement, routing, and resource allocation for increasingly complex applications, current models fail to capture the non-linear relationship between delay and resource usage that becomes especially relevant in AI-intensive workloads. In this paper, we extend thecloud network flowoptimization framework to support queueing-delay-aware orchestration of distributed AI applications over edge-cloud infrastructures. We introduce two execution models, Guaranteed-Resource (GR) and Shared-Resource (SR), that more accurately capture how computation and communication delays emerge from system-level resource constraints. These models incorporate M/M/1 and M/G/1 queue dynamics to represent dedicated and shared resource usage, respectively. The resulting optimization problem is non-convex due to the non-linear delay terms. To overcome this, we develop SPARQ, an iterative approximation algorithm that decomposes the problem into two convex sub-problems, enabling joint optimization of service placement, routing, and resource allocation under nonlinear delay constraints. The modeling approach is validated against real-world data. Simulation results demonstrate that the SPARQ not only offers a more faithful representation of system delays, but also substantially improves resource efficiency and the overall cost-delay tradeoff compared to existing state-of-the-art methods.

Semantic communication, rather than on a bit-by-bit recovery of the transmitted messages, focuses on the meaning and the goal of the communication itself. In this paper, we propose a novel semantic image coding scheme that preserves the semantic content of an image, while ensuring a good trade-off between coding rate and image quality. The proposed Semantic-Preserving Image Coding based on Conditional Diffusion Models (SPIC) transmitter encodes a Semantic Segmentation Map (SSM) and a low-resolution version of the image to be transmitted. The receiver then reconstructs a high-resolution image using a Denoising Diffusion Probabilistic Models (DDPM) doubly conditioned to the SSM and the low-resolution image. As shown by the numerical examples, compared to state-of-the-art (SOTA) approaches, the proposed SPIC exhibits a better balance between the conventional rate-distortion trade-off and the preservation of semantically-relevant features. Code available at https://github.com/frapez1/SPIC

In this work, we study the problem of stability of Graph Convolutional Neural Networks (GCNs) under random small perturbations in the underlying graph topology, i.e. under a limited number of insertions or deletions of edges. We derive a novel bound on the expected difference between the outputs of unperturbed and perturbed GCNs. The proposed bound explicitly depends on the magnitude of the perturbation of the eigenpairs of the Laplacian matrix, and the perturbation explicitly depends on which edges are inserted or deleted. Then, we provide a quantitative characterization of the effect of perturbing specific edges on the stability of the network. We leverage tools from small perturbation analysis to express the bounds in closed, albeit approximate, form, in order to enhance interpretability of the results, without the need to compute any perturbed shift operator. Finally, we numerically evaluate the effectiveness of the proposed bound.

This paper introduces topological Slepians, i.e., a novel class of signals defined over topological spaces (e.g., simplicial complexes) that are maximally concentrated on the topological domain (e.g., over a set of nodes, edges, triangles, etc.) and perfectly localized on the dual domain (e.g., a set of frequencies). These signals are obtained as the principal eigenvectors of a matrix built from proper localization operators acting over topology and frequency domains. Then, we suggest a principled procedure to build dictionaries of topological Slepians, which theoretically provide non-degenerate frames. Finally, we evaluate the effectiveness of the proposed topological Slepian dictionary in two applications, i.e., sparse signal representation and denoising of edge flows.

Weighing the topological domain over which data can be represented and analysed is a key strategy in many signal processing and machine learning applications, enabling the extraction and exploitation of meaningful data features and their (higher order) relationships. Our goal in this paper is to present topological signal processing tools for weighted simplicial complexes. Specifically, relying on the weighted Hodge Laplacian theory, we propose efficient strategies to jointly learn the weights of the complex and the filters for the solenoidal, irrotational and harmonic components of the signals defined over the complex. We numerically assess the effectiveness of the proposed procedures.

Since their introduction, graph attention networks achieved outstanding results in graph representation learning tasks. However, these networks consider only pairwise relations between features associated to the nodes and then are unable to fully exploit higher-order and long-range interactions present in many real world data-sets. In this paper, we introduce a neural architecture operating on data defined over the nodes and the edges of a graph, represented as the 1-skeleton of a regular cell complex, able to capture insightful higher-order and long-range interactions. In particular, we exploit the lower and upper neighborhoods, as encoded in the cell complex, to design two independent masked self-attention mechanisms, thus generalizing the conventional graph attention strategy. The approach used is hierarchical and it incorporates the following steps: i) a lifting algorithm that learns (additional) edge features from node features; ii) a cell attention mechanism to find the optimal combination of edge features over both lower and upper neighbors; iii) a hierarchical edge pooling mechanism to extract a compact meaningful set of features. The experimental results show that this method compares favorably with state of the art results on graph-based learning tasks while maintaining a low complexity.

In band Network Telemetry (INT) is a technique aiming at collecting telemetry information by inserting it inside the data packets, instead of relying on classical centralized monitoring elements that periodically query the network devices. The main drawback of INT is represented by the introduced per-packet overhead, that could negatively affect some traffic flows, especially those having stringent QoS requirements. To deal with the increase in the packet length caused by INT, in this paper we introduce the Sampling and Recovering paradigm to overcome the classical Collect Everything approach where all the INT data must be gathered. The proposed approach hinges on signal processing strategies to sample and recover sparse flow signals. The key idea is to reduce the number of INT data to collect and exploit signal reconstruction algorithms to obtain the unseen samples. The preliminary performance evaluation shows that the 18% of INT data are enough to get an accurate reconstruction of the overall network situation, while allowing for 90% of overhead reduction with respect to the Collect Everything case.

The aim of this work is to propose a novel dynamic resource allocation strategy for adaptive Federated Learning (FL), in the context of beyond 5G networks endowed with Reconfigurable Intelligent Surfaces (RISs). Due to time-varying wireless channel conditions, communication resources (e.g., set of transmitting devices, transmit powers, bits), computation parameters (e.g., CPU cycles at devices and at server) and RISs reflectivity must be optimized in each communication round, in order to strike the best trade-off between power, latency, and performance of the FL task. Hinging on Lyapunov stochastic optimization, we devise an online strategy able to dynamically allocate these resources, while controlling learning performance in a fully data-driven fashion. Numerical simulations implement distributed training of deep convolutional neural networks, illustrating the effectiveness of the proposed FL strategy endowed with multiple reconfigurable intelligent surfaces.

This work aims to propose a novel architecture and training strategy for graph convolutional networks (GCN). The proposed architecture, named Autoencoder-Aided GCN (AA-GCN), compresses the convolutional features in an information-rich embedding at multiple hidden layers, exploiting the presence of autoencoders before the point-wise nonlinearities. Then, we propose a novel end-to-end training procedure that learns different graph representations per layer, jointly with the GCN weights and auto-encoder parameters. As a result, the proposed strategy improves the computational scalability of the GCN, learning the best graph representations at each layer in a data-driven fashion. Several numerical results on synthetic and real data illustrate how our architecture and training procedure compares favorably with other state-of-the-art solutions, both in terms of robustness and learning performance.

Whenever communication takes place to fulfill a goal, an effective way to encode the source data to be transmitted is to use an encoding rule that allows the receiver to meet the requirements of the goal. A formal way to identify the relevant information with respect to a goal can be obtained exploiting the information bottleneck (IB) principle. In this paper, we propose a goal-oriented communication system, based on the combination of IB and stochastic optimization. The IB principle is used to design the encoder in order to find an optimal balance between representation complexity and relevance of the en-coded data with respect to the goal. Stochastic optimization is then used to adapt the parameters of the IB to find an efficient resource allocation of communication and computation resources. Our goal is to minimize the average energy consumption under constraints on average service delay and accuracy of the learning task applied to the received data in a dynamic scenario. Numerical results assess the performance of the proposed strategy in two cases: regression from Gaussian random variables, where we can exploit closed-form solutions, and image classification using deep neural networks, with adaptive network splitting between transmit and receive sides.

The goal of this paper is to investigate the impact of perturbations of topological descriptors, such as graphs and simplicial complexes, on the robustness of filters acting on signals observed over such domains. Given a nominal graph that may undergo small perturbations of its edges, we design robust FIR filters using approximate closed form expressions for the perturbed eigendecomposition of the Laplacian matrix associated with the nominal graph. Then, we extend the analysis to simplicial complexes and show how the perturbation of a few triangles affect the homology class of the simplicial complex. Our small perturbation analysis of a second order simplicial complex yields approximate closed form expressions of the high order Laplacian eigenvalue/eigenvector perturbation, which are useful for the design of robust FIR filters acting on solenoidal signals. Numerical results assess the accuracy of the derived analysis and the effectiveness of the proposed method.

We propose a dynamic resource allocation algorithm in the context of future wireless networks endowed with edge computing, to enable accurate energy efficient classification with end-to-end delay guarantees. In our scenario, sensor devices continuously upload data to an Edge Server (ES) for classification purposes. Merging Lyapunov stochastic optimization and ensemble inference, we propose DEsIreE, a low-complexity method that dynamically selects the data quantization level, the device transmit power, and the ES's CPU scheduling, without any prior knowledge of the statistics of wireless channels and data arrivals. Numerical simulations run on two real datasets assess the effectiveness of our algorithm in optimizing sensors' energy consumption and classification accuracy, with the ensemble yielding considerable gain.

The aim of this paper is to propose a novel dynamic resource allocation strategy for energy-efficient federated learning at the wireless network edge, with latency and learning performance guarantees. We consider a set of devices collecting local data and uploading processed information to an edge server, which runs stochastic gradient descent (SGD) to perform distributed learning and adaptation. Hinging on Lyapunov stochastic optimization tools, we dynamically optimize radio parameters (i.e., set of transmitting devices, transmit powers) and computation resources (i.e., CPU cycles at devices and at server) in order to strike the best trade-off between energy, latency, and performance of the federated learning task. The general framework is then customized to the case of federated least mean squares (LMS) estimation. Numerical results illustrate the effectiveness of our strategy to perform energy-efficient, low-latency, federated machine learning at the wireless network edge.

The aim of this paper is to propose a method for online learning of time-varying graphs from noisy observations of smooth graph signals collected over the vertices. Starting from an initial graph, and assuming that the topology can undergo the perturbation of a small percentage of edges over time, the method is able to track the graph evolution by exploiting a small perturbation analysis of the Laplacian matrix eigendecomposition, while assuming that the graph signal is bandlimited. The proposed method alternates between estimating the time-varying graph signal and recovering the dynamic graph topology. Numerical results corroborate the effectiveness of the proposed learning strategy in the joint online recovery of graph signal and topology.

The goal of this paper is to promote the idea that including semantic and goal-oriented aspects in future 6G networks can produce a significant leap forward in terms of system effectiveness and sustainability.Semantic communication goes beyond the common Shannon paradigm of guaranteeing the correct reception of each single transmitted bit, irrespective of the meaning conveyed by the transmitted bits.The idea is that, whenever communication occurs to convey meaning or to accomplish a goal, what really matters is the impact that the received bits have on the interpretation of the meaning intended by the transmitter or on the accomplishment of a common goal.Focusing on semantic and goal-oriented aspects, and possibly combining them, helps to identify the relevant information, i.e. the information strictly necessary to recover the meaning intended by the transmitter or to accomplish a goal.Combining knowledge representation and reasoning tools with machine learning algorithms paves the way to build semantic learning strategies enabling current machine learning algorithms to achieve better interpretation capabilities and contrast adversarial attacks.6G semantic networks can bring semantic learning mechanisms at the edge of the network and, at the same time, semantic learning can help 6G networks to improve their efficiency and sustainability.

We study decentralized estimation of time-varying signals at a fusion center, when energy harvesting sensors transmit sampled data over rate-constrained links. We propose dynamic strategies to select radio parameters, sampling set, and harvested energy at each node, with the aim of estimating a time-varying signal while ensuring: 1) the accuracy of the recovery procedure and 2) the stability of the batteries around a prescribed operating level. The approach is based on stochastic optimization tools, which enable adaptive optimization without the need of apriori knowledge of the statistics of radio channels and energy arrivals processes. Numerical results validate the proposed approach for decentralized signal estimation under communication and energy constraints typical of Internet-of-Things scenarios.

The aim of this work is to develop a dynamic optimization strategy to allocate communication and computation resources in a Multi-access Edge Computing (MEC) scenario, where Unmanned Aerial Vehicles (UAVs) act as flying base station platforms endowed with computation capabilities to provide edge cloud services on demand. Hinging on stochastic optimization tools, we propose a dynamic algorithmic framework that minimizes the overall energy spent by the system, while imposing latency constraints, and optimizing the altitude of the UAV in an online fashion. The method does not require a priori knowledge of channels and/or task arrival statistics. Numerical results illustrate the advantages of the proposed approach.

In this paper, we address the problem of dynamic allocation of communication and computation resources for Edge Machine Learning (EML) exploiting Multi-Access Edge Computing (MEC). In particular, we consider an IoT scenario, where sensor devices collect data from the environment and upload them to an edge server that runs a learning algorithm based on Stochastic Gradient Descent (SGD). The aim is to explore the optimal tradeoff between the overall system energy consumption, including IoT devices and edge server, the overall service latency, and the learning accuracy. Building on stochastic optimization tools, we devise an algorithm that jointly allocates radio and computation resources in a dynamic fashion, without requiring prior knowledge of the statistics of the channels, task arrivals, and input data. Finally, we test our algorithm in the specific case the edge server runs a Least Mean Squares (LMS) algorithm on the data acquired by each sensor device.

This paper proposes a novel algorithmic solution for dynamic computation offloading, aimed at reducing the energy consumption of a mobile network endowed with multi-access edge computing. The dynamic evolution of the system is modeled through three queues: a local queue at the user side, a computation queue at the edge server, and a queue of results at the network access point. The optimization problem is cast as the minimization of the long-term average energy consumption of the whole system, comprising user devices, servers, and access points. Quality of service constraints for end users are imposed in terms of probability that the \textit{sum of the queues} exceeds a given threshold. A suitable weighting parameter can be tuned to drive the system toward a user-centric, a network-centric, or a hybrid solution. Exploiting stochastic optimization tools, the problem is solved thanks to a dynamic optimization algorithm, based on the solution of deterministic convex problems in each time slot. The algorithm does not assume any knowledge on the task input and output random sizes and the radio channel statistics. Several numerical results illustrate the advantages of the proposed method.

We study decentralized estimation of time-varying signals at a fusion center (FC), when energy harvesting sensors transmit sampled data over rate-constrained links. We propose a dynamic strategy based on stochastic optimization for selecting radio parameters, sampling set, and harvested energy at each node, with the aim of estimating a time-varying signal with guaranteed performance while ensuring stability of the batteries around a prescribed operating level. Numerical results validate the proposed approach for dynamic signal estimation under communication and energy constraints.

We study distributed processing of subspace-constrained signals in multi-agent networks with sparse connectivity. We introduce the first optimization framework based on distributed subspace projections, aimed at minimizing a network cost function depending on the specific processing task, while imposing subspace constraints on the final solution. The proposed method hinges on (sub)gradient techniques while leveraging distributed projections as a mechanism to enforce subspace constraints in a cooperative and distributed fashion. Asymptotic convergence to optimal solutions of the problem is established under different assumptions (e.g., nondifferentiability, nonconvexity, etc.) on the objective function. Finally, numerical tests assess the performance of the proposed distributed strategy.

Multi-Access Edge Computing (MEC) is one of the key technology enablers of the 5G ecosystem, in combination with the high speed access provided by mmWave communications. In this paper, among all services enabled by MEC, we focus on computation offloading, devising an algorithm to optimize computation and communication resources jointly with the assignment of mobile users to Access Points and Mobile Edge Hosts, in a dynamic scenario where computation tasks are continuously generated according to (unknown) random arrival processes at each user. To formulate and solve the dynamic allocation/assignment problem, we merge tools from stochastic optimization and matching theory, thus developing a low complexity algorithmic solution that works in an online fashion. Numerical results illustrate the potential advantages of the proposed approach.

Internet of Things (IoT) envisions an intelligent infrastructure of networked smart devices offering task-specific monitoring and control services. The unique features of IoT include extreme heterogeneity, massive number of devices, and unpredictable dynamics partially due to human interaction. These call for foundational innovations in network design and management. Ideally, it should allow efficient adaptation to changing environments, and low-cost implementation scalable to a massive number of devices, subject to stringent latency constraints. To this end, the overarching goal of this paper is to outline a unified framework for online learning and management policies in IoT through joint advances in communication, networking, learning, and optimization. From the network architecture vantage point, the unified framework leverages a promising fog architecture that enables smart devices to have proximity access to cloud functionalities at the network edge, along the cloud-to-things continuum. From the algorithmic perspective, key innovations target online approaches adaptive to different degrees of nonstationarity in IoT dynamics, and their scalable model-free implementation under limited feedback that motivates blind or bandit approaches. The proposed framework aspires to offer a stepping stone that leads to systematic designs and analysis of task-specific learning and management schemes for IoT, along with a host of new research directions to build on.

The goal of this paper is to derive a small perturbation analysis for networks subject to random changes of a small number of edges. Small perturbation theory allows us to derive, albeit approximate, closed form expressions that make possible the theoretical statistical characterization of the network topology changes. The analysis is instrumental to formulate a graph-based optimization algorithm, which is robust against edge failures. In particular, we focus on the optimal allocation of the overall transmit powers in wireless communication networks subject to fading, aimed at minimizing the variation of the network connectivity, subject to a constraint on the overall power necessary to maintain network connectivity.

We study centralized interpolation of bandlimited graph signals at a fusion center (FC), when sampled data are transmitted over rate-constrained links. In such a scenario, the performance of the reconstruction task is inevitably affected by several sources of errors such as observation noise and quantization due to source encoding. In this paper, we propose two strategies for optimally selecting transmission powers, quantization bits, and the sampling set, with the aim of interpolating a graph signal with guaranteed performance. Numerical results validate the proposed approach for interpolation of bandlimited graph signals under communication constraints.

A key tool to analyze signals defined over a graph is the so called Graph Fourier Transform (GFT). Alternative definitions of GFT have been proposed, based on the eigen-decomposition of either the graph Laplacian or adjacency matrix. In this paper, we introduce an alternative approach, valid for the general case of directed graphs, that builds the graph Fourier basis as the set of orthonormal vectors that minimize a well-defined continuous extension of the graph cut size, known as Lovász extension. To cope with the non-convexity of the problem, we exploit a recently developed method devised for handling orthogonality constraints, with provable convergence properties.

Controlling the behavior of a signal defined over a graph by acting on a limited set of nodes is a problem that finds application in many fields. In this paper, we merge recently developed tools in graph signal processing with control theory of complex networks and consider the reconstruction of bandlimited graph signals from their samples through a diffusion process properly driven by a subset of control nodes. Then, we propose an optimization algorithm aimed at minimizing the control energy incorporating a regularization term whose goal is to promote sparsity across time and nodes jointly.

Developing tools to analyze signals defined over a graph is a research area that is attracting a significant amount of contributions because of its many applications. However, a graph representation does not capture the overall information about the data, as it implicitly takes into account only pairwise relations. The goal of this paper is to extend signal processing tools to signals defined over hypergraphs, which represent a formal framework to describe multi-way relations among the data. First, we suggest alternative ways to introduce a Fourier Transform (FT) for signals defined over hypergraphs and, in particular, for simplicial complexes. Then, building on the notion of Fourier Transform, we derive a sampling theorem aimed at identifying the minimum number of samples necessary to encode all information about band-limited hypergraph signals.

Edge computing through local mobile cloud computing platforms is a key enabler for coping with the ever increasing data traffic requirements. A key enabler for this technology is the awaited ultra-dense deployment of radio access points for future 5G networks. Local cloud platforms allow maintaining a scalable network design by jointly managing local radio and computational resources. The Fog, a platform with rich services, introduces distributed intelligence at the edge of the network where entities such as radio access points form a local computing resources pool. In this paper, we address the problem of radio access points clustering for fog computing applications. We focus on the multi-user case where the local cloud resources are to be shared by several devices. We propose a novel clustering algorithm in which management functionalities are split into two layers: centralized and decentralized. The proposed strategy compromises centralized optimality with decentralized distribution intelligence for faster and less complex decision making. We compare, through simulations, the performance of the proposed algorithm to centralized and decentralized strategies, and show how it can achieve good quality of experience.

The goal of this paper is to propose a network formation game where strategic agents decide whether to form or sever a link with other agents depending on the net balance between the benefit resulting from the additional information coming from the new link and the cost associated to establish the link. Differently from previous works, where the benefits are functions of the distances among the involved agents, in our work the benefit is a function of the mutual information that can be exchanged among the agents, conditioned to the information already available before setting up the link. An interesting result of our network formation game is that, under certain conditions, the final network topology tends to match the topology of the Markov graph describing the conditional independencies among the random variables observed in each node, at least when the cost of forming a link is small.

In 5G future wireless networks, the (ultra)-dense deployment of radio access points is a key drive for satisfying the increase of traffic demand and improving perceived users' quality. (Ultra)-dense deployment combined with capillary edge cloud, the fog, leads the way for optimization of users' Quality of Experience (QoE) and network performance. In this paper, we focus on improving users' QoE by addressing the issue of load balancing in fog computing. In this paper, we consider the challenging case of multiple users requiring computation offloading, where all requests should be processed by local computation clusters resources. We propose a low complexity small cell clusters establishment and resources management customizable algorithm for fog clustering. Our simulation results show that the proposed algorithm yields high users' satisfaction percentage of a minimum of 90% for up to 4 users per small cell, moderate power consumption, and/or high latency gain.

Ultra-dense deployment of radio access points is a key enabler for future 5G networks. It allows the network to cope with the ever increasing mobile data traffic. In addition, these radio access points can serve as an infrastructure for a local mobile cloud computing platform referred to as fog computing. The fog is a capillary edge cloud that enables joint optimization of communication and computational resources for maintaining an efficient and scalable network design. In this paper, we address the problem of radio access points clustering for fog computing applications. We focus on the case where multiple users require fog computing services. We formulate the distributed clustering problem as a joint optimization of the computation and communication resources. We transform the non-convex original problem into an equivalent convex one. Our simulation results show that the clustering solution derived from this problem yields high users' satisfaction ratio while keeping low the communication power consumption of the computation cluster.

In this paper we propose distributed strategies for the estimation of sparse vectors over adaptive networks. The measurements collected at different nodes are assumed to be spatially correlated and distributed according to a Gaussian Markov random field (GMRF) model. We derive optimal sparsity-aware algorithms that incorporate prior information about the statistical dependency among observations. Simulation results show the potential advantages of the proposed strategies for online recovery of sparse vectors.

It is widely recognized that one of the factors that are going to yield the most significant capacity increase in wireless networks is spatial reuse of radio resources through dense deployment of radio access points. This leads to the development of small cell networks where different size cells, e.g. macro cells, picocells, femtocells, relays, coexist under the same standard. Of course, dense deployment is able to unravel its potential benefits only provided that interference is properly managed. In this paper, we propose an algorithm able to perform cell association and radio resource allocation jointly, in order to maximize the sum rate in a MIMO (interference) network. Cell selection is inherently a combinatorial problem. To deal with the nonconvexity, we introduce a suitably chosen convex relaxation of the objective function and develop a fast algorithm converging to a locally optimal solution of the nonconvex problem.

Femto-cloud is a novel networking architecture that joins femtocells networks and computation offloading to the cloud in a single framework. This allows to form server farms of femtocell access points providing cloud services. However, femtocells cannot offer the same computation and storage capacities as traditional cloud servers. In the femto-cloud platform, femtocells cooperate together through cluster formation. Effective cooperation between femtocells through clustering has to take into account many challenging limitations such as radio resources availability, base stations deployment scenarios, delay constraints and power consumption limitations. These parameters affect the choice of the femtocells cluster size and the computation load distribution. In this paper, we evaluate different strategies of clustering in the femto-cloud framework and show their effect on cluster characteristics in terms of size, latency, and power consumption.

Today, mobile handsets are more and more capable to run complex applications. Mobile computation offloading offers the potential of extending mobile devices capabilities and battery lifetime. Traditional mobile computation offloading decision algorithms are mainly based on the offloading energy trade-off between locally consumed energy and offloading energy. Femtocloud is a novel paradigm introduced by the European project TROPIC [1] that merges cloud computing services and the benefits of femtocell networks. In this paper we present a novel offloading algorithm that takes decision about offloading mobile computation to a femtocloud. The proposed offloading algorithm incorporates a multitude of parameters in the offloading decision process while reducing the mobile handset energy consumption and keeping a good user quality of experience. Simulations results show that our proposed algorithm is able to extend the mobile battery life and to assure the computation of all the applications while respecting latency and memory constraints.

Polynomial-Phase Signals (PPS) appear in a variety of applications and several algorithms are available to estimate their parameters in the presence of noise. Among the available tools, the Product High order Ambiguity Function (PHAF) has the merit of performing well in the presence of a superposition of PPS's. In this work, we generalize the PHAF to handle two-dimensional PPS's. Then we show an example of application motivating such an extension: the high resolution imaging of moving targets from synthetic aperture radars (SAR). Using the 2D-PHAF, we will propose an algorithm that compensates jointly for the range cell migration and the phase modulation induced by the relative radar-target motion in order to produce a focused image of the moving target.

In this paper, we propose a decentralized algorithm for the estimation and control of connectivity of random ad hoc networks. First, we introduce a novel stochastic power iteration method that allows each node to estimate and track the expected algebraic connectivity of a random graph. The proposed method is then used to adapt the power transmitted by each node in order to drive the network connectivity toward a desired value. Numerical results illustrate the main features of the algorithm and its robustness to fluctuations of the network graph due to the presence of random link failures.

Online adaptive algorithms have been largely applied for recursive estimation and tracking of sparse signals. In this paper we propose a distributed recursive least squares (RLS) algorithm incorporating an l1-norm regularization with time-varying regularization coefficient that enables a recursive distributed solution with no losses with respect to the centralized solution. The method is especially useful in cooperative sensing when the parameters to be estimated are structurally sparse and time-varying. As well known, the l1-norm is useful to recover sparsity, but it also introduces a non negligible bias. To tackle this issue, we further apply a garotte correction to our distributed mechanism that strongly reduces the bias. Numerical results are included to validate the estimation and tracking capabilities of the proposed algorithm.

The goal of this letter is to propose an adaptive and distributed approach to cooperative sensing for wireless small cell networks. The method uses a basis expansion model of the power spectral density (PSD) to be estimated, and exploits spectral sparsity to improve estimation accuracy and adaptation capabilities. An estimator of the model coefficients is developed based on sparse diffusion strategies, which are able to exploit and track sparsity while at the same time processing data in real-time and in a fully decentralized manner. Simulation results illustrate the advantages of the proposed sparsity-aware strategies for cooperative spectrum sensing applications.

The goal of this paper is to propose diffusion LMS techniques for distributed estimation over adaptive networks, which are able to exploit sparsity in the underlying system model. The approach relies on convex regularization, common in compressive sensing, to improve the performance of the diffusion strategies. We provide convergence and performance analysis of the proposed method, showing under what conditions it outperforms the unregularized diffusion version. Simulation results illustrate the advantage of the proposed filter under the sparsity assumption on the true coefficient vector.

One of the most critical issues in femtocell network deployment is interference management, especially for femtocells sharing the spectrum occupied by conventional cellular networks. In this work we propose and analyze an optimal power allocation strategy based on modeling the interferer's activity as a two-state Markov chain. In the single femto-user access, we show how to maximize the expected value of femto-user rate, averaged over the interference statistical model. Then, we extend the approach to the multiuser case, adopting a game-theoretic formulation to devise decentralized access strategies, particularly suitable in view of potential massive deployment of femto access points.

This paper proposes a distributed resource allocation strategy for cognitive radio networks based on a swarming model that incorporates random link failures and quantized communications. The swarming mechanism is used to minimize the interference produced by the cognitive users, take advantage of cooperative sensing, avoid collisions among the users and limit the spread of resources in the time-frequency domain. The mechanism assumes only local exchange of data among nearby nodes. The communications among the nodes are assumed to be affected by noise and random fading. Packet drops are taken into account as inducing a random topology, where a link is on or off depending on the decision errors. Using classical results from stochastic approximation theory, we prove that the swarm always converges in probability to a final allocation even in the presence of non ideal communications among the nodes. Numerical results show how the convergence rate of the algorithm is affected by the probability of link failures. The proposed procedure is applied to a bi-dimensional allocation in the time-frequency plane where the primary users' activity is modeled as a set of continuous-time Markov processes.

The observations gathered by the individual nodes of a sensor network may be unreliable due to malfunctioning, observation noise or low battery level. Global reliability is typically recovered by collecting all the measurements in a fusion center which takes proper decisions. However, centralized networks are more vulnerable and prone to congestion around the sink nodes. To relax the congestion problem, decrease the network vulnerability and improve the network efficiency, it is appropriate to bring the decisions at the lowest possible level. In this paper, we propose a distributed algorithm allowing each node to improve the reliability of its own reading thanks to the interaction with the other nodes, assuming that the field monitored by the network is a smooth function. In mathematical terms, this only requires that the useful field belongs to a subspace of dimension smaller than the number of nodes. Although fully decentralized, the proposed algorithm is globally optimal, in the sense that it performs the projection of the overall set of observations onto the signal subspace through an iterative decentralized algorithms, that requires minimum convergence time, for any given node coverage.

In most sensor network applications, the vector containing the observations gathered by the sensors lies in a space of dimension equal to the number of nodes, typically because of observation noise, even though the useful signal belongs to a subspace of much smaller dimension. This motivates smoothing or rank reduction. We formulate a convex optimization problem, where we incorporate a fidelity constraint that prevents the final smoothed estimate from diverging too far from the observations. This leads to a distributed algorithm in which nodes exchange updates only with neighboring nodes. We show that the widely studied consensus algorithm is indeed only a very specific case of our more general formulation. Finally, we study the convergence rate and propose some approaches to maximize it.

In this work we propose a physical layer design, based on pulse position modulated (PPM) signals, for a decentralized wireless sensor network implementing an iterative consensus algorithm. The proposed scheme does not require any MAC protocol to avoid or resolve collisions, and is also suitable for a half-duplex implementation. The considered network model assumes only local coupling among the nodes, thus allowing for low transmit power even in large scale networks. Furthermore, we show how to remove the effect of propagation delays, multipath, and non perfect synchronization among the nodes, without requiring any channel parameter estimate. As an example of application, we consider a simple parameter estimation problem, which is instrumental to discuss the fundamental trade-offs arising in the system parameters settings, when both observation noise and coupling noise are considered in the performance analysis.

In this paper we focus on the maximization of the information rates subject to transmit power constraints for noncooperative multiple-input multiple-output (MIMO) systems, using the same physical resources, i.e., time, bandwidth and space. To derive decentralized solutions that do not require any cooperation among the systems, the optimization problem is formulated as a static noncooperative game. The analysis of the game for arbitrary MIMO interference channels is quite involved, since it requires the study of a set of nonlinear nondifferentiable matrix-valued equations, based on the MIMO waterfilling solution. To overcome this difficulty, we provide a new interpretation of the waterfilling operator, for the general MIMO multiuser case, as a matrix projection. This key result allows us to simplify the study of the game and to obtain sufficient conditions for both uniqueness of the Nash equilibrium (NE) and convergence of the proposed totally asynchronous distributed algorithms. The proposed approach provides a general framework that encompasses all previous works, mostly concerned with the particular case of SISO Gaussian frequency-selective interference channel.

This paper considers the noncooperative maximization of mutual information in the Gaussian interference channel in a fully distributed fashion via game theory. This problem has been studied in a number of papers during the past decade for the case of frequency-selective channels. A variety of conditions guaranteeing the uniqueness of the Nash Equilibrium (NE) and convergence of many different distributed algorithms have been derived. In this paper we provide a unified view of the state-of- the-art results, showing that most of the techniques proposed in the literature to study the game, even though apparently different, can be unified using our recent interpretation of the waterfilling operator as a projection onto a proper polyhedral set. Based on this interpretation, we then provide a mathematical framework, useful to derive a unified set of sufficient conditions guaranteeing the uniqueness of the NE and the global convergence of waterfilling based asynchronous distributed algorithms. The proposed mathematical framework is also instrumental to study the extension of the game to the more general MIMO case, for which only few results are available in the current literature. The resulting algorithm is, similarly to the frequency-selective case, an iterative asynchronous MIMO waterfilling algorithm. The proof of convergence hinges again on the interpretation of the MIMO waterfilling as a matrix projection, which is the natural generalization of our results obtained for the waterfilling mapping in the frequency-selective case.

This paper considers the maximization of information rates for the Gaussian frequency-selective interference channel, subject to power and spectral mask constraints on each link. To derive decentralized solutions that do not require any cooperation among the users, the optimization problem is formulated as a static noncooperative game of complete information. To achieve the so-called Nash equilibria of the game, we propose a new distributed algorithm called asynchronous iterative water-filling algorithm. In this algorithm, the users update their power spectral density (PSD) in a completely distributed and asynchronous way: some users may update their power allocation more frequently than others and they may even use outdated measurements of the received interference. The proposed algorithm represents a unified framework that encompasses and generalizes all known iterative water-filling algorithms, e.g., sequential and simultaneous versions. The main result of the paper consists of a unified set of conditions that guarantee the global converge of the proposed algorithm to the (unique) Nash equilibrium of the game.

Achieving consensus on common global parameters through totally decentralized algorithms is a topic that has attracted considerable attention in the last few years. Several algorithms have been developed, among which the most popular is the average consensus method. The main advantage of these approaches is that they do not require a fusion center. But, on the other hand, they are typically based on iterative algorithms, whose energy consumption is proportional to the time necessary to achieve consensus. This time depends on the network topology, as well as on the transmit power of each node. In this paper, we show that there exists an optimal transmit power that minimizes the overall energy consumption necessary to achieve the global estimate within a given accuracy and that this power depends on the network topology.

In this paper we consider multiple access systems where users and access points are equipped with multiple antennas. In order to exploit some of the MIMO potentials we have to resort to space-time coding. Unfortunately, such a processing may induce severe loss in terms of achievable information rates. In this paper we prove the necessary and sufficient condition ensuring that the space-time coding is information lossless, in the sense that it does not induce any modification in certain regions of achievable rates. In particular information lossless property is guaranteed if each user makes use of a trace-orthogonal design (TOD), that is a linear space-time code whose encoding matrices are orthogonal with respect to the trace inner product. Noteworthy, users can also use the same set of encoding matrices.

This paper considers the competitive maximization of information rates in the Gaussian frequency-selective interference channel, subject to global power and spectral mask constraints. We focus on the practical case in which the transmission by the different users contains time and frequency synchronization offsets. We propose a unified framework based on a distributed algorithm called asynchronous iterative waterfilling algorithm. In this algorithm, the users update their power spectral density in a completely distributed and asynchronous way: some users may update their power allocation more frequently than others and they may even use outdated measurements of the received interference. Moreover the users are not required to know time and frequency offsets. Our main contribution is to provide a unified set of convergence conditions for the whole class of algorithms obtained from the asynchronous iterative waterfilling algorithm.

The mathematical models of populations of mutually coupled oscillators having self-synchronization capabilities are a powerful tool for designing sensor networks with high energy efficiency, fault tolerance and scalability. In this work, we derive the conditions for the existence the asymptotic stability of the equilibrium of a system capable to provide maximum likelihood estimates through only local coupling and without the need for a fusion center, provided that the whole network observes the same phenomenon. Interestingly, we show that the network global consensus capability is strictly related to the network topology. Finally we test the performance taking into account propagation delays and possible parameter fluctuations among the network nodes

Trace-orthogonality is an important property of linear space-time encoders that has emerged relatively recently. In this work we carry out the theoretical performance analysis of a low complexity decoder for trace-orthogonal space-time codes based on the linear MMSE estimator. We derive the diversity order of such a decoder in the case of MIMO systems affected by uncorrelated flat Rayleigh fading, when information symbols are carved from a QPSK constellation, and are encoded using what we term a unitary trace-orthogonal design. Then we propose an iterative decoder that dramatically improves the performance over the linear MMSE decoder. Simulation results give evidence of the effectiveness of the proposed scheme

The detection and localization of an event through a sensor network is a topic that has attracted considerable attention recently because of many potential applications. Typically, these decisions are taken by conveying the sensor measurements to a sink node that processes the data and provides an estimate. However, the presence of a sink node creates a bottleneck that is the cause of potential congestions and it poses problems of scalability. In this work, we propose a decentralized decision scheme that is capable to achieve optimal decisions without requiring a fusion center. The network is composed of a set of mutually coupled oscillators, where each node is coupled only to the nearest nodes. We show how to achieve optimal detection for both deterministic and random signals by properly selecting the parameters of the coupling mechanism. Furthermore, if the nodes know their own positions and the network is connected, we show how to make each node able to perform a totally distributed energy-based source localization

In this paper we propose a unified framework, based on the emergent potential games to deal with a variety of network resource allocation problems. We generalize the existing results on potential games to the cases where there exists coupling among the (possibly vector) strategies of all players. We derive sufficient conditions for the existence and uniqueness of the Nash equilibrium, and provide different distributed algorithms along their convergence properties. Using this new framework, we then show that many power control problems (standard and non-standard) with coupled constraints among the users, can be naturally formulated as potential games and, hence, efficiently solved. Finally, we point out an interesting interplay existing between potential games, classical optimization theory, and Lyapunov stability theory

The sequential iterative water-filling algorithm (IWFA) proposed by Yu et al. is by now a popular low-complexity algorithm to compute the Nash equilibrium point of the power allocation game in a Gaussian frequency-selective multiuser interference channel. The algorithm is based on a distributed sequential updating where, at each iteration, the users choose their power allocation, one after the other. However, this sequential updating strategy may slow down its convergence time excessively when the number of users is high. In this paper, we propose an alternative distributed algorithm, called simultaneous iterative water-filling algorithm (SIWFA), where at each iteration, all the users update their power allocations simultaneously, rather than sequentially. This reduces the convergence time considerably, specially when the number of users is large. Our main contribution is to provide a unified set of sufficient conditions for the convergence of both IWFA and SIWFA, that are less stringent than those known in the literature for IWFA. These conditions guarantee the convergence of both algorithms also in the presence of spectral mask constraints imposed on the power allocations of the users

In this paper we prove that trace-orthogonal space-time coding provides a necessary and sufficient condition for information lossless coding in a multiple access system, where each user encodes its own symbols independently of the other users. Then, we show that the sub-optimal MMSE decoder can be implemented very simply as a set of scalar decoders and we prove that, under the assumption of using the (sub-optimal) MMSE decoder, the trace-orthogonal design with scaled unitary coding matrices yields minimum BER for each user.

Cooperation among mobile users (MUs) in a wireless network can be very useful to reduce the total radiated power necessary to insure the delivery of the information with the desired quality of service. A systematic framework for achieving such a gain consists in making the cooperating nodes act as the antennas of a virtual transmit array, operating according to a distributed space-time coding (DSTC) strategy. However, cooperation implies the allocation of dedicated resources, typically power and time slots, for the exchange of data between source and intermediate nodes (relays). It is then necessary to design the system properly to make possible a final net gain, taking into account all resources involved in the communication. In this paper, we consider regenerative relays and we analyze the effect of intermediate decision errors at the relay nodes. We derive the optimal maximum-likelihood (ML) detector, at the final destination, in case of binary phase-shift keying (BPSK) transmission, and a suboptimal scalar detector, whose bit-error rate (BER) is expressed in (approximate) closed form. Since with DSTC the transmit antennas are not colocated, we show how to allocate the power among source and relay terminals in order to minimize the average BER at the final destination. Finally, we compare alternative cooperation and decoding strategies.

Distributed space-time coding (DSTC) is a rather novel paradigm that merges ideas from space-time coding (STC) and multihop networks (MHN) to design a wireless network capable of improving the performance considerably with respect to single hop networks (SHN). The basic advantage of DSTC comes from allowing multiple nodes to share their antennas to create a virtual transmit array and then implement a distributed space-time coding technique over the virtual array. The major differences between DSTC and conventional STC are: (i) detection errors at the relay nodes; and (ii) possible lack of synchronization between source and relay nodes. In this work, we study these problems and compare different DSTC techniques based on decode and forward and amplify and forward strategies. Finally, we show the trade-off curves between rate and diversity gain for DSTC systems.

In this work we provide the optimal coding strategy for meshed wireless networks, where more links are active simultaneously, assuming as optimality criterion the rates of all the links. We formulate the rate maximization problem as a multi-objective optimization problem (MOP). Assuming a multi-carrier modulation for each user, we show how to allocate the power of each user optimally according to a centralized power distribution algorithm. We also propose a decentralized (suboptimal) but simpler algorithm, based on the idea of Nash equilibrium (NE). Finally, we compare the two strategies showing that the loss, in terms of information rate, of the decentralized strategy based on the iterative water-filling algorithm can be very small with respect to the optimal centralized solution, as the distance between the interfering links is just a few times the distance of each link, thus making the decentralized approach a viable solution.

Cooperation among mobile users in a wireless network can be exploited to induce diversity and/or rate gain, using distributed space-time coding. In this work we consider a distributed block Alamouti scheme, valid for frequency selective channels, where we take explicitly into account the errors in the source-relay link and we derive a closed form expression for the bit error rate in the simple case of BPSK transmission. Building on such derivations, we show how to allocate the power among source and relay terminals in order to minimize the average bit error rate. Since cooperation inevitably requires a proper allocation of resources between source and relay nodes, we show the final balance in terms of rate and diversity gain, incorporating the rate loss due to the exchange of information between source and relays.

We propose a multihop cellular network architecture which takes advantage of cooperation among users to induce a diversity gain. First, mobile terminals (MT), willing to cooperate, share their data during a time slot reserved to MT-to-MT links, and then, in a successive time slot, they send their data to the base station (BS) through a virtual array of antennas, constituted by the antennas of the cooperating users. We derive the coding strategy, for such a virtual array, that maximizes the sum of the rates from the MTs to the BS, under the constraint of a given total available power. We assume at the beginning that the channels from the MT to the BS are perfectly known. This allows us to derive, for each MT, a closed form expression for the optimal power allocation, as a function of frequency. Then, we remove this assumption and we use a first order perturbation analysis to compute the loss resulting from imperfect channel knowledge.

Multi-carrier (MC) systems are known for being very effective in cancelling intersymbol and multi-user interference provided that a cyclic prefix (CP) of length at least equal to the channel order is inserted at the beginning of each transmitted block. However, this insertion causes a reduction of the transmission speed which is not negligible when the channel delay spread is not small with respect to the block-length. We propose an algorithm for designing time-domain pre-equalizers which shorten the channel impulse response. The method requires neither the a-priori knowledge of the channel impulse response nor the transmission of training sequences. The only basic assumption underlying our method is that the transmitted MC stream contains null (virtual) sub-carriers, a condition which is verified in many current MC transmission systems.

We propose a flexible method for designing space-time block codes capable of achieving the desired trade-off between diversity and coding gain. The proposed system is valid for frequency selective, block fading channels and refers to a block coding scheme capable of achieving full rate transmission, for any number of transmit antennas. We derive a closed form expression for the pairwise error probability and the maximum diversity and coding gain. These expressions are instrumental in designing a coding strategy able to yield the required trade-off between coding and diversity gain, in order to reach the desired average BER with the smallest SNR. Finally, we check our theoretical derivations with simulations and compare our approach with alternative ones.

In this work, we propose a concatenated space-time block coding scheme for transmissions of over block fading frequency selective channels, which guarantees maximum diversity gain and high coding gain, with affordable receiver complexity. We derive a closed form expression for the bound of the pairwise error probability, which is instrumental to devise the optimal coding strategy, and then we check out theoretical derivations with simulations and compare our approach with alternative ones.

It is well known that the optimal coding strategy, maximizing the mutual information under an average transmit power constraint and additive Gaussian noise, for single-input/single-output (SISO) transmission over a time-invariant, dispersive channel is water-filling. The extension to a multiple-input/single-output (MISO) channel can also be derived in a straightforward manner using a numerical approach. The aim of this work is to provide a closed form expression for the optimal coding and power/bit allocation for MISO channel, which has a direct interesting physical interpretation and it establishes a direct link between water-filling, beamforming and maximal ratio combining. We test then our theoretical findings with numerical results.

In this paper we consider a CDMA system equipped with multiple antenna transceivers to implement space-time block coding (STBC). The codes incorporate a cyclic prefix (CP), to facilitate channel equalization and simplify the rejection of multiuser interference (MUI) in broadband transmissions over frequency-selective channels. The only price paid for the introduction of CP is a rate reduction, depending on the relative length of the CP with respect to the code length. To limit, and possibly avoid, this loss, we propose a CDMA/STBC scheme using CP of length smaller than the channel. To prevent interblock interference which would require a sophisticated decoding procedure, we equip the receiver with a MIMO channel shortening filterbank. We derive the conditions under which we can achieve perfect shortening using an FIR filterbank. Finally, we show that the choice of a CP length represents a trade-off between the rate reduction factor and the SNR loss resulting from the insertion of the channel shortening filter.

In this paper we derive and compare designs for the optimal linear precoderes to be used in transmissions over frequency selective multiple-input multiple-output (MIMO) channels. We assume as alternative design constraints the average transmit power and the peak power. The design criteria are scalable with respect to the number of antennas, size of the coding block and transmit average/peak power. The solutions are shown to convert in both cases the MIMO channel with memory into a set of parallel independent fiat fading subchannels, regardless of the design criterion, while appropriate power/bits loading on the sub-channels is the specific signature of the different designs.

We develop synchronization algorithms for both the downlink and the uplink of quasi-synchronous and asynchronous orthogonal frequency division multiple access systems. Unlike existing alternatives, the proposed time- and carrier-offset estimators do not require the transmission of known sequences and exhibit performance independent of the underlying channel zero locations. The only necessary assumption is that there are virtual subcarriers which are not occupied by any user. We derive a closed-form variance expression for the carrier-offset estimator at high signal-to-noise ratio (SNR), as a function of the number of active users and the SNR. We compare our method with alternative ones and validate our theoretical derivations with simulation results.

In this work we show that the optimal digital communication strategy for transmissions over time-varying channels with spread function maximally concentrated along a line of the delay-Doppler domain consists in multiplexing the input symbol block with an IFFT, as in OFDM, and modulating the IFFT output with a chirp signal whose sweep rate is matched to the channel. We show how to allocate the transmit power optimally across the chirped subcarriers, derive the limits of applicability of the proposed chirped-OFDM scheme and compute the resulting BER curves.

We develop and analyze timing and carrier frequency offset synchronization algorithms for generalized asynchronous and quasi-synchronous orthogonal frequency division multiple access systems using null subcarriers and subcarrier hopping. We derive an approximate analytic expression for the variance of the frequency offset estimators as a function of the number of active users and the SNR and show that the performance of our algorithms is asymptotically independent of the channel zero locations for quasi-synchronous systems. Finally, we validate our theoretical expressions with simulations.

We propose a non-data aided synchronization algorithm for orthogonal frequency division multiple access systems. The method is applied to both quasisynchronous and asynchronous systems. The proposed algorithm does not require the transmission of training sequences or of repeated symbols and, differently from all available methods, our frequency estimator has performance independent of the channel zero locations. We provide an analytic expression for the variance of the frequency offset estimator and then we validate our theoretical findings with simulation results.

The many advantages responsible for the widespread application of OFDM are primarily limited by its sensitivity to carrier frequency-offsets. Most of the literature dealing with this problem focuses on data-aided frequency-offset estimation only. In this contribution, several non-data-aided frequency-offset and channel estimation schemes are developed for OFDM transmissions over frequency-selective channels. Timing offset is also accounted for because it is incorporated as a pure delay in the unknown channel. The resulting blind estimation methods include subspace based deterministic or statistical algorithms. Simulations illustrate the performance tradeoffs of these schemes.

Transmit antenna diversity has been exploited to develop high-performance space-time coders and simple maximum-likelihood decoders for transmissions over flat fading channels. Relying on block precoding, this paper develops generalized space-time coded multicarrier transceivers appropriate for wireless propagation over frequency-selective multipath channels. Multicarrier precoding maps the frequency-selective channel into a set of flat fading subchannels, whereas space-time encoding/decoding facilitates equalization and achieves performance gains by exploiting the diversity available with multiple transmit antennas. When channel state information is unknown at the receiver, it is acquired blindly based on a deterministic variant of the constant-modulus algorithm that exploits the structure of space-time block codes. To benchmark performance, the Cramer-Rao bound of the channel estimates is also derived. System performance is evaluated both analytically and with simulations.

The ability of orthogonal frequency-division multiplexing systems to mitigate frequency-selective channels is impaired by the presence of carrier frequency offsets (CFOs). In this paper, we investigate identifiability issues involving high-resolution techniques that have been proposed for blind CFO estimation based on null subcarriers. We propose new approaches that do not suffer from the lack of identifiability and adopt adaptive algorithms that are computationally feasible. The performance of these techniques in relation to the location of the null subcarriers is also investigated via computer simulations and compared with the modified Cramer-Rao bound.

Starting from a parametric model of time-varying multipath channels with additive white Gaussian noise, we derive the theoretical bound on the channel parameters estimation as a function of number of samples, SNR and model mis-matching errors. We use this bound to derive an approximate closed form expression of the channel mean square prediction error, subsequently used to evaluate the maximum training period. Finally, we illustrate a simple and effective method for estimating the channel parameters (path delays, amplitudes, phases and frequencies), using chirp signals as training sequences.

OFDM renders multipath channels with finite memory equivalent to parallel flat fading subchannels, over which equalization amounts to simple phase compensation. If the channel status information is known at the transmitter side, optimal power loading across subchannels is possible. Users mobility, time and carrier asynchronism introduce variations in the equivalent channel impulse response setting the channel coherence time as a boundary for the OFDM symbol duration. Optimal design should incorporate such variations, possibly avoiding the overhead of frequent training. Most adaptive modulation schemes assume knowledge of the channel response and flat fading. Modeling the channel taps as correlated Gaussian random processes, we develop the optimal power/bit loading strategies for frequency selective fading, based on the knowledge of the channel past estimates and correlation. In practice the channel sample correlation are obtained as a by-product of channel estimation which, however, does not need to be performed on a block by block basis.

OFDM systems enable simple and effective schemes to mitigate frequency-selective fading channels. However, they are extremely sensitive to multiplicative fluctuations induced by time-varying multipath delays, carrier offsets and oscillators phase noise. Although the duration of OFDM symbols is chosen smaller than the channel coherence time to avoid, or reduce, channel time variations, this choice limits system efficiency. In this paper, we propose a generalized OFDM scheme capable of estimating and then compensating channel frequency selectivity and multiplicative noise effects using a deterministic method that exploits the redundancy added to the transmitted sequence in the form of pilot tones and null guard intervals in the frequency domain. The proposed method applies to channels modeled as the cascade of dispersive filters whose output is corrupted by both additive and multiplicative noise.

Suppression of multiuser interference (MUI) and mitigation of multipath effects constitute major challenges in the design of third-generation wireless mobile systems. Most wide-band and multicarrier uplink code-division multiple-access (CDMA) schemes suppress MUI statistically in the presence of unknown multipath. For fading resistance, they all rely on transmit- or receive-diversity and multichannel equalization based on bandwidth-consuming training sequences or self-recovering techniques at the receiver end. Either way, they impose restrictive and difficult to check conditions on the finite-impulse response channel nulls. Relying on block-symbol spreading, we design a mutually-orthogonal usercode-receiver (AMOUR) system for quasi-synchronous blind CDMA that eliminates MUI deterministically and mitigates fading regardless of the unknown multipath and the adopted signal constellation. AMOUR converts a multiuser CDMA system into parallel single-user systems regardless of multipath and guarantees identifiability of users' symbols without restrictive conditions on channel nulls in both blind and nonblind setups. An alternative AMOUR design called Vandermonde-Lagrange AMOUR is derived to add flexibility in the code assignment procedure. Analytic evaluation and preliminary simulations reveal the generality, flexibility, and superior performance of AMOUR over competing alternatives.

Linear time-varying (LTV) channels are often encountered in mobile communications but, as opposed to the linear time-invariant (LTI) channels case, there is no a well established theory for computing the channel capacity, or providing simple bounds to the maximum information rate based only on the channel impulse response, or predicting the structure of the channel eigenfunctions. We provide: (i) a method for computing the mutual information between blocks of transmitted and received sequences, for any finite block length; (ii) the optimal precoding (decoding) strategy to achieve the maximum information rate; (iii) an upper bound for the channel capacity based only on the channel time-varying transfer function; and (iv) a time frequency representation of the channel eigenfunctions, revealing a rather intriguing, but nonetheless intuitively justifiable, bubble structure.

Suppression of multiuser interference (MUI) and mitigation of multipath effects constitute major challenges in the design of third-generation wireless mobile systems. Most wideband and multicarrier uplink CDMA schemes suppress MUI statistically in the presence of unknown multipath. For fading resistance, they all rely on transmit- or receive-diversity and multichannel equalization based on bandwidth-consuming training or blind techniques. Either way, they impose restrictive and difficult to check conditions on the FIR channel nulls. Relying on symbol blocking, we design a mutually-orthogonal usercode-receiver (AMOUR) system for quasi-synchronous blind CDMA that eliminates MUI deterministically and mitigates fading irrespective of the unknown multipath and the adopted signal constellation. Analytic evaluation and preliminary simulations reveal the generality, flexibility, and superior performance of AMOUR over competing alternatives.

A new class of codes was proposed recently for perfect multi-user interference (MUI) suppression in code division multiple access (CDMA) systems. These so called Lagrange-Vandermonde (LV) codes offer deterministic MUI elimination without channel estimation, and convert frequency selective channels into flat fading channels. In this work, we develop dual CDMA transceivers, naturally termed Vandermonde Lagrange (VL), with the precoder-decoder roles interchanged, in order to improve the system flexibility in the code assignment. We also derive and test nonredundant and redundant techniques for combating the residual flat fading.

In this paper we propose a blind deterministic method for channel estimation based on the introduction of minimal redundancy on the transmitted data sequence through linear precoders that map consecutive blocks of information symbols onto higher size blocks. This transmission scheme incorporates, for example, OFDM and CDMA systems. We prove that even allowing the transmitted data block to be longer than the information block by only one sample is sufficient to guarantee the channel identification uniquely with a deterministic algorithm, without any restriction on the channel zero location and under sufficient conditions on the precoder only, which can be easily checked a priori. Our proposed blind estimation method is able to work with any amount of extra redundancy and this renders the method particularly useful for all applications where the channels have long impulse responses, such as in wired as well as in wireless macrocellular communication systems.

The high-order ambiguity function (HAF) was introduced for the estimation of polynomial-phase signals (PPS) embedded in noise. Since the HAF is a nonlinear operator, it suffers from noise-masking effects and from the appearance of undesired cross terms and, possibly, spurious harmonics in the presence of multicomponent (mc) signals. The product HAF (PHAF) was then proposed as a way to improve the performance of the HAF in the presence of noise and to solve the ambiguity problem. In this correspondence we derive a statistical analysis of the PHAF in the presence of additive white Gaussian noise (AWGN) valid for high signal-to-noise ratio (SNR) and a finite number of data samples. The analysis is carried out in detail for single-component PPS but the multicomponent case is also discussed. Error propagation phenomena implicit in the recursive structure of the PHAF-based estimator are explicitly taken into account. The analysis is validated by simulation results for both single- and multicomponent PPSs.

Optimal finite impulse response (FIR) transmit and receive filterbanks are derived for block-based data transmissions over frequency-selective additive Gaussian noise (AGN) channels by maximizing mutual information subject to a fixed transmit-power constraint. Both FIR and pole-zero channels are considered. The inherent flexibility of the proposed transceivers is exploited to derive, as special cases, zero-forcing (ZF) and minimum mean-square error receive filterbanks. The transmit filterbank converts transmission over a frequency-selective fading channel, affected by additive colored noise, into a set of independent flat fading subchannels with uncorrelated noise samples. Two loading algorithms are also developed to distribute transmit power and number of bits across the usable subchannels, while adhering to an upper bound on the bit error rate (BER). Reduction of the signal-to-noise ratio (SNR) margin required to satisfy the prescribed BER is achieved by coding each subchannel's bit stream. The potential of the proposed transceivers is illustrated and compared to discrete multitone (DMT) with simulated examples.

We propose a novel approach for demodulating continuous phase modulation (CPM) signals based on the modeling of the instantaneous phase as a piecewise polynomial-phase function. The polynomial modeling can be a good approximation for currently used modulations or it can be exact if the shaping pulse is chosen to be a piecewise polynomial function. The crucial step in the demodulation process is then the estimation of the polynomial coefficients, which is carried out using the so called product high order ambiguity function (PHAF). The proposed approach is suboptimal with respect to the optimal maximum likelihood sequence estimation (MLSE) method, but is much simpler to implement and offers important advantages such as independence of initial phase, tolerance to Doppler shift, and time-offset, blind channel identification. We show theoretical results concerning the minimum distance among sequences, which leads to a lower bound on the error probability, together with some simulation results.

Transmitter redundancy introduced using FIR filterbank precoders offers a unifying framework for single- and multi-user transmissions. With minimal rate reduction, FIR filterbank transmitters with trailing zeros allow for perfect (in the absence of noise) equalization of FIR channels with FIR zero-forcing equalizer filterbanks, irrespective of the input color and the channel zero locations. Methods of exploiting input diversity, blind channel estimators, block synchronizers, and direct self-recovering equalizing filterbanks are derived. The resulting algorithms are computationally simple, require small data sizes, can be implemented online, and remain consistent (after appropriate modifications) even at low SNR colored noise. Simulations illustrate applications to multi-carrier modulation through channels with deep fades, and superior performance relative to the constant modulus algorithm (CMA) and existing output diversity techniques relying on multiple antennas and fractional sampling.

Transmitter redundancy introduced using filterbank precoders generalizes existing modulations including OFDM, DMT, TDMA, and CDMA schemes encountered with single- and multi-user communications. Sufficient conditions are derived to guarantee that, with FIR filterbank preceders, FIR channels are equalized perfectly in the absence of noise by FIR zero-forcing equalizer filterbanks, irrespective of the channel zero locations. Multicarrier transmissions through frequency-selective channels can thus be recovered even when deep fades are present. Jointly optimal transmitter-receiver filterbank designs are also developed based on maximum output SNR and minimum mean-square error criteria under zero-forcing and fixed transmitted power constraints. Analytical performance results are presented for the zero-forcing filterbanks and are compared with mean-square error and ideal designs using simulations.

Polynomial-phase signals (PPSs), i.e., signals parameterized as s(t)=A exp(j2/spl pi//spl Sigma//sub m=0//sup M/ a/sub m/t/sup m/), have been extensively studied and several algorithms have been proposed to estimate their parameters. From both the application and the theoretical points of view, it is particularly important to know the spectrum of this class of signals. Unfortunately, the spectrum of PPSs of generic order is not known in closed form, except for first- and second-order PPSs. The aim of this letter is to provide an approximate behavior of the spectrum of PPSs of any order. More specifically, we prove that: (i) the spectrum follows a power law behavior f/sup -/spl gamma//, with /spl gamma/=(M-2)/(M-1); (ii) the spectrum is symmetric for M even and is strongly asymmetric for M odd; and (iii) the maximum of the spectrum has an upper bound proportional to T/sup (m-1)/M/ and, lower bound proportional to T/sup 1/2/. These results are useful to predict the performance of the so-called high order ambiguity function (HAF) and the Product-HAH (PHAF), specifically introduced to estimate the parameters of PPSs, when applied to multicomponent PPSs.

The aim of this paper is to propose an adaptive method for suppressing wideband interferences in spread spectrum (SS) communications. The proposed method is based on the time-frequency representation of the received signal, from which the parameters of an adaptive time-varying interference excision filter are estimated. The approach is based on the generalized Wigner-Hough transform as an effective way to estimate the instantaneous frequency of parametric signals embedded in noise. The performance of the proposed approach are evaluated in the presence of chirp-like interferences plus noise.

The paper examines the performance analysis of a method for estimating the parameters of multicomponent polynomial-phase signals (PPS) embedded in white Gaussian noise. It initially describes the ambiguity problem arising with similar transforms when dealing with multicomponent PPS having the same highest order phase coefficients. This situation arises in a number of practical applications and is then worth of a careful analysis. A solution of the ambiguity problem is proposed based on a transformation called multiplicative multi-lag high order ambiguity function (MML-HAF). The theoretical performance of the method are evaluated and verified by simulation results.

The aim is to propose a method for detection and parameter estimation of nonlinear FM signals, mono- or multicomponent, embedded in white Gaussian noise. The proposed approach consists in mapping the signal into the time-frequency plane by a time-frequency distribution with reassignment, and then in applying a pattern recognition technique, like the Hough transform, to the time-frequency representation to recognize specific shapes. The advantages of this method over the conventional maximum likelihood estimator are (1) a simpler implementation, because it reduces the dimension of the search space and (2) a consistent attenuation of the interference terms between different components of a signal or between signal and noise.

The authors propose a method for optimizing the decomposition law of a tree-structured wavelet transform in order to maximize the capability of discriminating different textures. The optimization criterion is the maximization of the Fisher's distance. The analysis is carried out theoretically and by simulation on Gaussian Markov random fields and is then applied to the classification of real synthetic aperture radar images.

The aim of this work is the analysis of a method for the detection and parameter estimation of polynomial-phase, mono or multicomponent, signals embedded in white Gaussian noise, based on multilinear time-frequency signal representations. The proposed approach, based on a proper coherent integration of the multilinear time-frequency representation along paths depending on the model assumed for the instantaneous phase of the useful signal, presents some advantages with respect to conventional techniques, based on multilinear time-frequency transforms, in terms of: (i) a closer approach to the Cramer-Rao lower bounds, (ii) a higher output signal-to-noise ratio, and (iii) a better capability of discriminating multicomponent signals.>

Proposes of a method for optimizing configuration and weighting of linear or planar arrays. The criterion chosen for the optimization is the maximization of the integrated sidelobe ratio (ISLR), defined as the ratio between the energy of the mainlobe and the energy of the sidelobes of the system impulse response. The choice of the ISLR allows us to take into account, in the optimization procedure, two important quality factors: resolution and contrast. The nonuniform spacing of the resulting optimized array breaks the regularity responsible for the formation of the so called grating lobes. The method can then be used also as a tool for reducing the number of elements in an array strictly necessary for achieving some desired performance.>

An attempt is made to show that the combined use of the Wigner-Ville distribution (WVD) and the Hough transform (HT) provides an important tool for mapping the signals onto a parameter space where the detection and parameter estimation problems can be made easier, since the important features of the signal are emphasized. This mapping can be used in the detection and parameter estimation of unknown signals embedded in noise. It is shown that this mapping comes directly from the application of the matched filter theory. In particular, the method can be applied to multicomponent signals, since it reduces considerably the effect of the cross terms, produced by the WVD, on the signal parameters estimate.>

A method based on computing the Wigner-Ville distribution is proposed for estimating the instantaneous frequency of undersampled signals whose instantaneous bandwidth is considerably smaller than the overall bandwidth. The approach is not parametric, i.e. the instantaneous frequency can be recovered, whatever its time variation law. The limits of applicability of the method depend on the relationship between the instantaneous bandwidth, the overall bandwidth, and the undersampling factor. The method is applicable to the high-resolution radar imaging of moving objects.>

An antenna pattern design technique for spaceborne synthetic aperture radars (SARs) that optimizes the signal-to-disturbance ratio is presented. It takes into account the ground reflectivity (relative to the transmitted frequency) and the viewing geometry (altitude and off-nadir angle). The technique makes use of the theory of adaptive arrays and takes advantage of a priori knowledge of the ambiguous echo power as a function of the geometry and average ground reflectivity. The optimized antenna weighting turns out to be complex, and asymmetrical patterns are generally obtained for a spaceborne SAR. The technique allows the achievement of better performance compared to the current design values, especially at large off-nadir angles.>