A growing amount of available data and computational power makes training neural networks over a network of devices, and distribution optimization in general, more realizable. As a consequence, efficient communication becomes more important and can often be the bottleneck of such algorithms. Traditional data compression techniques for these scenarios consider the correlation of the data values over time: although the transmitted data itself may not be sparse, the changes within the data are increasingly sparse as the algorithm converges. In this work, we propose an encoding scheme for discrete symbols that adapts a source encoder to consider the positional correlations through time. We provide a bound on the bit rate yielded by our scheme and verify our results through experiments.

IoT-enabled electric vehicles (IoEVs) enable intelligent charging coordination that accounts for grid congestion. However, increased data exchange raises privacy concerns, as charging patterns can reveal sensitive driver behavior to grid operators. We propose a differentially private (DP) EV charging framework that enables coordinated control while protecting driver data with theoretical privacy guarantees. Nevertheless, integrating DP inevitably introduces uncertainty into the control strategy for EVs, which can lead to infeasible solutions. To tackle this challenge, we develop a feasible and scalable control algorithm based on constrained reinforcement learning (CRL) and convex hulls. While our framework is designed to handle the uncertainty introduced by DP, it is general and also applicable to other sources of uncertainty in EV charging, such as the stochastic nature of driver behavior and renewable variability. This ensures feasible and privacy-preserving coordination of EV charging at scale. Our method constructs convex hulls within the action space to guarantee feasibility under stochastic constraints and incorporates constraint reduction techniques to improve scalability. Case studies based on IEEE benchmark systems demonstrate that the proposed approach effectively balances feasibility under uncertainty, scalability, and privacy in large-scale EV charging control.

Given the availability of more comprehensive measurement data in modern power systems, reinforcement learning (RL) has gained significant interest in operation and control. Conventional RL relies on trial-and-error interactions with the environment and reward feedback, which often leads to exploring unsafe operating regions and executing unsafe actions, especially when deployed in real-world power systems. To address these challenges, safe RL has been proposed to optimize operational objectives while ensuring safety constraints are met, keeping actions and states within safe regions throughout both training and deployment. Rather than relying solely on manually designed penalty terms for unsafe actions, as is common in conventional RL, safe RL methods reviewed here primarily leverage advanced and proactive mechanisms. These include techniques such as Lagrangian relaxation, safety layers, and theoretical guarantees like Lyapunov functions to rigorously enforce safety boundaries. This article provides a comprehensive review of safe RL methods and their applications across various power system operations and control domains, including security control, real-time operation, operational planning, and emerging areas. It summarizes existing safe RL techniques, evaluates their performance, analyzes suitable deployment scenarios, and examines algorithm benchmarks and application environments. This article also highlights real-world implementation cases and identifies critical challenges such as scalability in large-scale systems and robustness under uncertainty, providing potential solutions and outlining future directions to advance the reliable integration and deployment of safe RL in modern power systems.

Graph matching over two known graphs is a method for de-anonymizing obscured node labels within an anonymous graph, finding the corresponding nodes in a second graph. In this paper, we consider a new case where a set of graph signals originate from a hidden graph. We want to match their components to a reference graph to reveal labels of asymmetric nodes. We refer to this as the graph-signal-to-graph matching (GS2GM) problem. We introduce a symmetry detection method to pinpoint the asymmetric nodes in the reference graph. Then, we adapt the existing blind graph matching algorithm, originally designed for asymmetric graphs, to align the detected nodes with signals generated from the target hidden graph. Furthermore, we establish sufficient conditions for perfect node de-anonymization through graph signals, showing that graph signals can leak substantial private information on the concealed labels of the underlying graph.

In this paper, we propose a source coding scheme that represents data from unknown distributions through frequency and support information. Existing encoding schemes often compress data by sacrificing computational efficiency or by assuming the data follows a known distribution. We take advantage of the structure that arises within the spatial representation and utilize it to encode run-lengths within this representation using Golomb coding. Through theoretical analysis, we show that our scheme yields an overall bit rate that nears entropy without a computationally complex encoding algorithm and verify these results through numerical experiments.

Abstract The differential privacy (DP) literature often centers on meeting privacy constraints by introducing noise to the query, typically using a pre-specified parametric distribution model with one or two degrees of freedom. However, this emphasis tends to neglect the crucial considerations of response accuracy and utility, especially in the context of categorical or discrete numerical database queries, where the parameters defining the noise distribution are finite and could be chosen optimally. This paper addresses this gap by introducing a novel framework for designing an optimal noise probability mass function (PMF) tailored to discrete and finite query sets. Our approach considers the modulo summation of random noise as the DP mechanism, aiming to present a tractable solution that not only satisfies privacy constraints but also minimizes query distortion. Unlike existing approaches focused solely on meeting privacy constraints, our framework seeks to optimize the noise distribution under an arbitrary $$(\epsilon , \delta )$$ ( ϵ , δ ) constraint, thereby enhancing the accuracy and utility of the response. We demonstrate that the optimal PMF can be obtained through solving a mixed-integer linear program. Additionally, closed-form solutions for the optimal PMF are provided, minimizing the probability of error for two specific cases. Numerical experiments highlight the superior performance of our proposed optimal mechanisms compared to state-of-the-art methods. This paper contributes to the DP literature by presenting a clear and systematic approach to designing noise mechanisms that not only satisfy privacy requirements but also optimize query distortion. The framework introduced here opens avenues for improved privacy-preserving database queries, offering significant enhancements in response accuracy and utility.

Graph matching over two given graphs is a well-established method for re-identifying obscured node labels within an anonymous graph by matching the corresponding nodes in a reference graph. This paper studies a new application, termed the graph-signal-to-graph matching (GS2GM) problem, where the attacker observes a set of filtered graph signals originating from a hidden graph. These signals are generated through an unknown graph filter activated by certain input excitation signals. Our goal is to match their components to a labeled reference graph to reveal the labels of asymmetric nodes in this unknown graph, where the excitations can be either known or unknown to the attacker. To this end, we integrate the existing blind graph matching algorithm with techniques of graph filter inference and covariance-based eigenvector estimation. Furthermore, we establish sufficient conditions for perfect node de-anonymization through graph signals, showing that graph signals can leak substantial private information on the concealed labels of the underlying graph. Experimental results validate our theoretical insights and demonstrate that the proposed attack effectively reveals many of the hidden labels, particularly when the graph signals are adequately uncorrelated and sampled.

In this paper, we present a notion of differential privacy (DP) for data that comes from different classes. Here, the class-membership is private information that needs to be protected. The proposed method is an output perturbation mechanism that adds noise to the release of query response such that the analyst is unable to infer the underlying class-label. The proposed DP method is capable of not only protecting the privacy of class-based data but also meets quality metrics of accuracy and is computationally efficient and practical. We illustrate the efficacy of the proposed method empirically while outperforming the baseline additive Gaussian noise mechanism.We also examine a real-world application and apply the proposed DP method to the autoregression and moving average (ARMA) forecasting method, protecting the privacy of the underlying data source. Case studies on the real-world advanced metering infrastructure (AMI) measurements of household power consumption validate the excellent performance of the proposed DP method while also satisfying the accuracy of forecasted power consumption measurements.

We propose a novel Federated Edge Network Utility Maximization (FEdg-NUM) architecture for solving a large-scale distributed network utility maximization (NUM) problem. In FEdg-NUM, clients with private utilities communicate to a peer-to-peer network of edge servers. This represents a departure from the classical distributed NUM master-slave configuration and enables distributed computing harnessing local communications. Compared to a solution using cloud synchronization via Ring AllReduce, we prove that our federated edge computing model has shorter run-time in the presence of network congestion, thanks to its configuration and its ability to make progress in the presence of intermittent links. The paper studies its convergence and run-time performance both analytically and numerically, and illustrates several possible networking applications.

In federated learning, models are learned from users’ data that are held private in their edge devices, by aggregating them in the service provider’s “cloud” to obtain a global model. Such global model is of great commercial value in, e.g., improving the customers’ experience. In this paper we focus on two possible areas of improvement of the state of the art. First, we take the difference between user habits into account and propose a quadratic penalty-based formulation, for efficient learning of the global model that allows to personalize local models. Second, we address the latency issue associated with the heterogeneous training time on edge devices, by exploiting a hierarchical structure modeling communication not only between the cloud and edge devices, but also within the cloud. Specifically, we devise a tailored block coordinate descent-based computation scheme, accompanied with communication protocols for both the synchronous and asynchronous cloud settings. We characterize the theoretical convergence rate of the algorithm, and provide a variant that performs empirically better. We also prove that the asynchronous protocol, inspired by multi-agent consensus technique, has the potential for large gains in latency compared to a synchronous setting when the edge-device updates are intermittent. Finally, experimental results are provided that corroborate not only the theory, but also show that the system leads to faster convergence for personalized models on the edge devices, compared to the state of the art.

A futuristic infrastructure model is envisioned with distributed modules that can produce solar energy, have a storage system and provide services of lighting, electric-vehicle charging and communications. A stochastic model is formulated for the solar power production and overall consumption of power. Resource allocation in such a system translates to taking decisions in a foresighted manner while maximizing a social surplus with the goal of serving all power demands optimally. A stochastic dynamic programming approach is formulated for the same and a myopic policy is illustrated while discussing the trade-offs between serving competing demands.

In this paper, the performance limits of faults localization are investigated using synchrophasor data. The focus is on a non-trivial operating regime where the number of Phasor Measurement Unit (PMU) sensors available is insufficient to have full observability of the grid state. Proposed analysis uses the Kullback Leibler (KL) divergence between the distributions corresponding to different fault location hypotheses associated with the observation model. This analysis shows that the most likely locations are concentrated in clusters of buses more tightly connected to the actual fault site akin to graph communities. Consequently, a PMU placement strategy is derived that achieves a near-optimal resolution for localizing faults for a given number of sensors. The problem is also analyzed from the perspective of sampling a graph signal, and how the placement of the PMUs i.e. the spatial sampling pattern and the topological characteristic of the grid affect the ability to successfully localize faults. To highlight the superior performance of presented fault localization and placement algorithms, the proposed strategy is applied to a modified IEEE 34, IEEE-123 bus test cases and to data from a real distribution grid. Additionally, the detection of cyber-physical attacks is also examined where PMU data and relevant Supervisory Control and Data Acquisition (SCADA) network traffic information are compared to determine if a network breach has affected the integrity of the system information and/or operations.

We describe a distributed framework for resource sharing problems that arise in communications, micro-economics, and various networking applications. In particular, we consider a hierarchical multi-layer decomposition for network utility maximization (ML-NUM), where functionalities are assigned to different layers. The proposed methodology creates solutions with central management and distributed computations to the resource allocation problems. In non-stationary environments, the technique aims to respond quickly to the dynamics of the network by decreasing delay by partially shifting the communication and computational burden to the network edges. Our main contribution is a detailed analysis under the assumption that the network changes are on the same time-scale as the convergence time of the algorithms used for local computations. Moreover, assuming strong concavity and smoothness of the users' objective functions, and under some stability conditions for each layer, we present convergence rates and optimality bounds for the ML-NUM framework. In addition, the main benefits of the proposed method are demonstrated with numerical examples.

In this paper, we present a distributed array processing algorithm to analyze the power output of solar photo-voltaic (PV) installations, leveraging the low-rank structure inherent in the data to estimate possible faults. Our multi-agent algorithm requires near-neighbor communications only and is also capable of jointly estimating the common low rank cloud profile and local shading of panels. To illustrate the workings of our algorithm, we perform experiments to detect shading faults in solar PV installations within a single ZIP code. Additionally, we also derive a Bayesian lower bound on the shading parameter's mean squared estimation error. The results are promising and show that we can successfully estimate the fraction of partial shading in solar installations that can usually go unnoticed.

In this paper, we consider the problem of solving a distributed (consensus-based) optimization problem in a network that contains regular and malicious nodes (agents). The regular nodes are performing a distributed iterative algorithm to solve their associated optimization problem, while the malicious nodes inject false data with a goal to steer the iterates to a point that serves their own interest. The problem consists of detecting and isolating the malicious agents, thus allowing the regular nodes to solve their optimization problem. We propose a method to dwarf data injection attacks on distributed optimization algorithms, which is based on the idea that the malicious nodes (individually or in collaboration) tend to give themselves away when broadcasting messages with the intention to drive the consensus value away from the optimal point for the regular nodes in the network. In particular, we provide a new gradient-based metric to detect the neighbors that are likely to be malicious. We also provide some simulation results demonstrating the performance of the proposed approach.

Many recent works on inference of graph structure assume that the graph signals are fully observable. For large graphs with thousands or millions of nodes, this entails high complexity on the data collection and processing steps. Here, we study a community inference problem on partially observed (sub-sampled) graph signals which sidesteps topology inference, while revealing the coarse structure of the graph directly. Two variants of the inference task are studied: (i) a blind method that infers the communities that the observable nodes belong to; and (ii) a semi-blind method that infers the communities of all nodes using, in addition, side information about the sub-graph between observable and hidden nodes. These techniques for community inference are shown to be efficient and suitable for large graphs analytically and empirically.

In this paper, a mathematical relationship between temperature and solar irradiance is established in order to reduce the sample space and provide joint probabilistic forecasts. These forecasts can then be used for the purpose of stochastic optimization in power systems. A Volterra system type of model is derived to characterize the dependence of temperature on solar irradiance. A dataset from NOAA weather station in California is used to validate the fit of the model. Using the model, probabilistic forecasts of both temperature and irradiance are provided and the performance of the forecasting technique highlights the efficacy of the proposed approach. Results are indicative of the fact that the underlying correlation between temperature and irradiance is well captured and will therefore be useful to produce future scenarios of temperature and irradiance while approximating the underlying sample space appropriately.

We provide a compressive-measurement based method to detect susceptible agents who may receive misinformation through their contact with `stubborn agents' whose goal is to influence the opinions of agents in the network. We consider a DeGroot-type opinion dynamics model where regular agents revise their opinions by linearly combining their neighbors' opinions, but stubborn agents, while influencing others, do not change their opinions. Our proposed method hinges on estimating the temporal difference vector of network-wide opinions, computed at time instances when the stubborn agents interact. We show that this temporal difference vector has approximately the same support as the locations of the susceptible agents. Moreover, both the interaction instances and the temporal difference vector can be estimated from a small number of aggregated opinions. The performance of our method is studied both analytically and empirically. We show that the detection error decreases when the social network is better connected, or when the stubborn agents are `less talkative'.

This paper considers the problem of inferring the topology of a graph from noisy outputs of an unknown graph filter excited by low-rank signals. Limited by this low-rank structure, we focus on solving the community detection problem, whose aim is to partition the node set of the unknown graph into subsets with high edge densities. We propose to detect the communities by applying spectral clustering on the low-rank output covariance matrix. To analyze the performance, we show that the low-rank covariance yields a sketch of the eigenvectors of the unknown graph. Importantly, we provide theoretical bounds on the error introduced by this sketching procedure based on spectral features of the graph filter involved. Finally, our theoretical findings are validated via numerical experiments.

This paper studies the security aspect of gossip-based decentralized optimization algorithms for multi agent systems against data injection attacks. Our contributions are two-fold. First, we show that the popular distributed projected gradient method (by Nedić et al.) can be attacked bycoordinated insiderattacks, in which the attackers are able to steer the final state to a point of their choosing. Second, we propose a metric that can be computed locally by the trustworthy agents processing their own iterates and those of their neighboring agents. This metric can be used by the trustworthy agents to detect and localize the attackers. We conclude the paper by supporting our findings with numerical experiments.

Principal component analysis (PCA) is a fundamental primitive of many data analysis, array processing, and machine learning methods. In applications where extremely large arrays of data are involved, particularly in distributed data acquisition systems, distributed PCA algorithms can harness local communications and network connectivity to overcome the need of communicating and accessing the entire array locally. A key feature of distributed PCA algorithm is that they defy the conventional notion that the first step toward computing the principal vectors is to form a sample covariance. This paper is a survey of the methodologies to perform distributed PCA on different data sets, their performance, and of their applications in the context of distributed data acquisition systems.

This paper proposes a fast and privacy preserving distributed algorithm for handling low-rank regression problems with nuclear norm constraint. Traditional projected gradient algorithms have high computation costs due to their projection steps when they are used to solve these problems. Our gossip-based algorithm, called the fast DeFW algorithm, overcomes this issue since it is projection-free. In particular, the algorithm incorporates a carefully designed decentralized power method step to reduce the complexity by distributed computation over network. Meanwhile, privacy is preserved as the agents do not exchange the private data, but only a random projection of them. We show that the fast DeFW algorithm converges for both convex and non-convex losses. As an application example, we consider the low-rank matrix completion problem and provide numerical results to support our findings.

This work proposes a decentralized and adaptive subspace estimation method, called the Power-Oja (P-Oja) method. Existing decentralized subspace tracking algorithms have slow convergence rate or are unable to adapt to time varying statistics. To resolve these issues, the P-Oja method is developed by combining the power method with Oja's learning rule. Our key innovation lies on the design of a modified objective function with enhanced spectral gap property. This allows the P-Oja method to track the principal subspace more quickly with a finite number of samples. Interestingly, the resulting method coincides with the conventional Oja's learning rule in some special cases. To enable decentralized signal processing, we further demonstrate that the proposed method can be implemented by using a gossip algorithm. Our simulation results show that the proposed P-Oja outperforms the conventional Oja's method in terms of estimation accuracy, and the power method in terms of tracking performance. The effect of the communication graph on the tracking performance is also studied.

This work examines a cross-layered caching problem for distributed estimation in wireless sensor networks (WSNs). In WSNs, large amounts of data are produced continuously over time, and storing all the data collected from the sensors can be costly. In distributed estimation applications, sensors first gather information about a common phenomenon, and then forward the information to a fusion center where the final estimate is computed. By assuming that the parameters are correlated over time, the estimation quality at the fusion center can be improved by combining both present and past information, where the latter can be obtained from cached data. Different from conventional caching problems, where the goal is to reconstruct the sensors' observations, our caching strategy is designed to minimize the long term average mean-square error (MSE) of the final estimate. This problem can be modelled as a Markov decision process but, due to the curse of dimensionality, is solved here using a greedy one-step-ahead caching strategy, which only minimizes the expected MSE in the next time slot. This results in a nonlinear fractional programming problem that is solved approximately using semi-definite relaxation and a modified Dinkelbach's algorithm. The effectiveness of the proposed scheme is demonstrated through numerical simulations.

This paper provides new insights on the convergence of a locally connected network of pulse coupled oscillator (PCOs) (i.e., a bioinspired model for communication networks) to synchronous and desynchronous states, and their implication in terms of the decentralized synchronization and scheduling in communication networks. Bioinspired techniques have been advocated by many as fault-tolerant and scalable alternatives to produce self-organization in communication networks. The PCO dynamics, in particular, have been the source of inspiration for many network synchronization and scheduling protocols. However, their convergence properties, especially in locally connected networks, have not been fully understood, prohibiting the migration into mainstream standards. This paper provides further results on the convergence of PCOs in locally connected networks and the achievable convergence accuracy under propagation delays. For synchronization, almost sure convergence is proved for three nodes and accuracy results are obtained for general locally connected networks, whereas for scheduling (or desynchronization), results are derived for locally connected networks with mild conditions on the overlapping set of maximal cliques. These issues have not been fully addressed before in the literature.

This paper considers an online optimization algorithm for actively learning trusts on social networks. We first introduce a DeGroot model for opinion dynamics under the influence of stubborn agents and demonstrate how an observer with estimates of the individuals opinions can actively learn the relative trusts among different agents, by fitting the opinions to the steady state equations of the social system equations. The main contribution of this article is an online algorithm for extracting the trust parameters from streaming data of randomly sampled, noisy opinion estimates. The algorithm is based on the stochastic proximal gradient method and it is proven to converge almost surely. Finally, numerical results are presented to corroborate our findings.

The pulse-coupled synchronization and scheduling (PulseSS) protocol is proposed in this paper for simultaneous synchronization and scheduling of communication activities in clustered wireless sensor networks (WSNs), by emulating the emergent behavior of pulse-coupled oscillator (PCO) networks in mathematical biology. Different from existing works that address synchronization and scheduling (i.e., desynchronization) separately, PulseSS provides a coordination signaling mechanism that achieves decentralized network synchronization and time division multiple access scheduling simultaneously at different time scales for clustered WSNs. Here, we assume that the nodes are connected only locally via their respective cluster heads. Moreover, PulseSS addresses the issue of propagation delays, that may plague the accuracy of PCO synchronization in practice, by providing ways to estimate and precompensate for these values locally at the sensors (i.e., PCOs). At the same time the protocol retains the adaptivity and light-weight nature of PCO protocols both in terms of signaling and computations. Simulations of both physical and medium access control layers show a synchronization accuracy of factions of microseconds above 15 dB of signal to interference and noise ratio for a five cluster network. A hardware implementation of PulseSS using TinyOS is also provided to corroborate the real world applicability of our protocol.

In handling massive-scale signal processing problems arising from `big-data' applications, key technologies could come from the development of decentralized algorithms. In this context, consensus-based methods have been advocated because of their simplicity, fault tolerance and versatility. This paper presents a new consensus-based decentralized algorithm for a class of non-convex optimization problems that arises often in inference and learning problems, including `sparse dictionary learning' as a special case. For the proposed algorithm, we provide sufficient conditions for convergence to a stationary point. Numerical results demonstrate the efficacy of the proposed algorithm and provide evidence that validates our convergence claim.

In this paper, we propose a cognitive radio scheme that allows a secondary user (SU) to transmit over the same time-frequency slot of a primary user (PU), even when the PU is active. In our scheme, the SU amplifies and forwards the signal of the PU, by using as scaling factor the value of its information symbol to be transmitted towards the secondary receiver. The information-theoretic limits of the proposed protocol are investigated in terms of ergodic channel capacities of both the PU and SU links. It is shown that: 1) under certain operating conditions, the SU can superimpose its information symbols on the PU signal, without violating the cognitive radio principle of protecting the PU transmission; and 2) when the primary link is busy, the SU offers the PU its own transmitting power in exchange for a low-capacity communication channel, which improves the packet delay performance of the SU. In this barter, the tempting incentive for the PU consists of a noticeable improvement of its achievable rate at the price of a slight increase in the computational complexity of the primary receiver.

In this paper, we describe our novel use of network intrusion detection systems (NIDS) for protecting automated distribution systems (ADS) against certain types of cyber attacks in a new way. The novelty consists of using the hybrid control environment rules and model as the baseline for what is normal and what is an anomaly, tailoring the security policies to the physical operation of the system. NIDS sensors in our architecture continuously analyze traffic in the communication medium that comes from embedded controllers, checking if the data and commands exchanged conform to the expected structure of the controllers interactions, and evolution of the system's physical state. Considering its importance in future ADSs, we chose the fault location, isolation and service restoration (FLISR) process as our distribution automation case study for the NIDS deployment. To test our scheme, we emulated the FLISR process using real programmable logic controllers (PLCs) that interact with a simulated physical infrastructure. We used this test bed to examine the capability of our NIDS approach in several attack scenarios. The experimental analysis reveals that our approach is capable of detecting various attacks scenarios including the attacks initiated within the trusted perimeter of the automation network by attackers that have complete knowledge about the communication information exchanged.

In this paper we formulate a Multi-Armed Bandit Compressive Spectrum Sensing (MAB-CSS) problem, in which a Cognitive Receiver (CR) decides dynamically how to best sense N sub-channels states, that switch from being occupied to being available as independent and statistically identical Markov chains. We assume that the CR is endowed with K CSS samplers each sensing an arbitrary mixture of the N signals in the sub-channels, and upon deciding what channels are available, it collects an equal reward from each channel unoccupied that is sensed. The MAB-CSS problem accounts for the ability of the CR of sweeping a large spectrum and being able to reconstruct the exact support of the N channels occupancy pattern, as long as the latter is sufficiently sparse. This is a generalization of the typical model in which the CR can sense K out of the N sub-channels. In choosing the compressive sensing strategy, the CR needs to consider how to gather the most informative statistics on the spectrum while not exceeding the limits beyond which the occupancy is no longer identifiable. In this work, we study a simplified and noiseless discrete sensing model and establish the structure of the optimum MAB-CSS myopic policy.

In this paper, we propose an opportunistic amplify-and-forward relaying scheme for a cognitive radio network, which is aimed at allowing a secondary user (SU) to transmit over the same time-frequency slot of a primary user (PU). In our scheme, the SU amplifies and transmits the PU signal it receives, by using as relaying gain the information symbols that the SU wishes to transmit towards its own secondary receiver. The information theoretic limits of the proposed protocol are investigated by showing that, in some operative conditions of practical interest, the SU can embed its information symbols in the PU signal, without violating the cognitive radio principle of protecting the PU transmission and, at the same time, by attaining low transmission rates.

In the Cognitive Compressive Sensing (CCS) problem, a Cognitive Receiver (CR) seeks to optimize the reward obtained by sensing an underlying N dimensional random vector, by collecting at most K arbitrary projections of it. The N components of the latent vector represent sub-channels states, that change dynamically from “busy” to “idle” and vice versa, as a Markov chain that is biased towards producing sparse vectors. To identify the optimal strategy we formulate the Multi-Armed Bandit Compressive Sensing (MAB-CS) problem, generalizing the popular Cognitive Spectrum Sensing model, in which the CR can sense K out of the N sub-channels, as well as the typical static setting of Compressive Sensing, in which the CR observes K linear combinations of the N dimensional sparse vector. The CR opportunistic choice of the sensing matrix should balance the desire of revealing the state of as many dimensions of the latent vector as possible, while not exceeding the limits beyond which the vector support is no longer uniquely identifiable.

In this paper, we formulate a cognitive radio (CR) systems spectrum sensing (SS) problem in which Secondary Users (SU), with multiple receive antennae, sense a channel shared among multiple asynchronous Primary Users (PU) transmitting Multiple Input Multiple Output (MIMO) Orthogonal Frequency Division Multiplexing (OFDM) signals. The method we propose to estimate the opportunities available to the SUs combines advances in array processing and compressed channel sensing, and leverages on both the so called “shrinkage method” as well as on an over-complete basis expansion of the PUs interference covariance matrix to detect the occupied and idle angles of arrivals and subcarriers. The covariance “shrinkage” step and the sparse modeling step that follows, allow to resolve ambiguities that arise when the observations are scarce, reducing the sensing cost for the SU, thereby increasing its spectrum exploitation capabilities compared to competing sensing methods. Simulations corroborate these claims.

This paper proposes a decentralized state estimation scheme via network gossiping with applications in smart grid wide-area monitoring. The proposed scheme allows distributed control areas to solve for an accurate global state estimate collaboratively using the proposed Gossip-based Gauss-Newton (GGN) algorithm. Furthermore, the proposed scheme mitigates the influence of bad data by adaptively updating the noise variances and re-weighting the contributions of the most recent measurements for state estimation. Compared with other distributed techniques, our scheme via gossiping is more flexible and resilient in case of network reconfigurations and failures. We further prove that the power flow equations satisfy the sufficient condition for the GGN algorithm to converge to the desired solution. Simulations of the IEEE-118 system show that the proposed scheme estimates and tracks the global state robustly, and degrades gracefully when there are random failures and bad data.

A critical task in smart grid is to gain situational awareness by performing state estimation. In this paper, we consider the problem of placing a type of special sensors, called Phasor Measurement Units (PMU), to optimize the performance and convergence of state estimation. We derive a metric to evaluate how the placement impacts the convergence and accuracy of state estimation solved by Gauss-Newton (GN) algorithm. Using the proposed metric, we formulate and solve the placement problem as a semi-definite program (SDP). Simulations of the IEEE 30 and 118 systems corroborate our analysis, showing that the proposed placement stabilizes and accelerates state estimation, while maintaining optimal estimation performance.

The papers in this special issue explore advances in communication technologies that have the potential for improving energy efficiency and realizing the smart grid vision.

This paper proposes a fully decentralized adaptive re-weighted state estimation (DARSE) scheme for power systems via network gossiping. The enabling technique is the proposed Gossip-based Gauss-Newton (GGN) algorithm, which allows to harness the computation capability of each area (i.e. a database server that accrues data from local sensors) to collaboratively solve for an accurate global state. The DARSE scheme mitigates the influence of bad data by updating their error variances online and re-weighting their contributions adaptively for state estimation. Thus, the global state can be estimated and tracked robustly using near-neighbor communications in each area. Compared to other distributed state estimation techniques, our communication model is flexible with respect to reconfigurations and resilient to random failures as long as the communication network is connected. Furthermore, we prove that the Jacobian of the power flow equations satisfies the Lipschitz condition that is essential for the GGN algorithm to converge to the desired solution. Simulations of the IEEE-118 system show that the DARSE scheme can estimate and track online the global power system state accurately, and degrades gracefully when there are random failures and bad data.

We consider a variation of the Deffuant-Weisbuch model introduced by Deffuant et al. in 2000, to provide new analytical insights on the opinion dynamics in a social group. We model the trust that may exist between like-minded agents through a trust function, which is a discontinuous (hard-interaction) non-increasing function of the opinion distance. In this model, agents exchange their opinions with their neighbors and move their opinions closer to each other if they are like-minded (that is, the distance between opinions is smaller than a threshold). We first study the dynamics of opinion formation under random interactions with a fixed rate of communication between pairs of agents. Our goal is to analyze the convergence properties of the opinion dynamics and explore the underlying characteristics that mark the phase transition from opinion polarization to consensus. Furthermore, we extend the hard-interaction model to a strategic interaction model by considering a time-varying rate of interaction. In this model, social agents themselves decide the time and energy that should be expended on interacting each of their neighbors, based on their utility functions. The aim is to understand how and under what conditions clustering patterns emerge in opinion space. Extensive simulations are provided to validate the analytical results of both the hard-interaction model and the strategic interaction model. We also offer evidence that suggests the validity of the proposed model, using the location and monthly survey data collected in the Social Evolution experiment over a period of nine months.

Starting from September 2013, prospective authors are allowed to submit the letters with one additional fifth page devoted exclusively to references. The Editor-in Chief (EiC) also takes this opportunity of this short editorial to let you know that she will leave her post of EiC one year earlier, compared to the usual three-year term.

A sensitive receiver operation in multi-access reception is to detect the presence of training signals and identify sources from which the signals are sent, with a certain physical delay and center frequency. This is typically a sequential search, where the receiver tests the presence of specific signals and then acquires synchronization parameters (delays, Dopplers), for each component. In this paper, we develop an optimal compressive multichannel sampling (CMS) architecture, the output samples of which are fed to the proposed Sparsity Regularized (SR) Generalized Likelihood Ratio Test (GLRT). It is shown that SR-GLRT using the optimal sampling scheme exhibits better performance than conventional compressed sensing structure, and furthermore effectively scales down the storage requirement and complexity with greater flexibility than conventional architectures.

Gossiping models have been increasingly applied to study social network phenomena, in particular, to model the dynamics of social behavior or belief through local interactions. In this context, this paper investigates how the opinions of social agents diffuse in a network under a so-called hard-interaction model, in which the agents interact more strongly with neighbors that share their beliefs and have no influence on the neighbors whose opinions differ by more than a threshold. We analyze the convergence properties of the opinion dynamics and provide analytical insights to characterize the phase transition from a society of radicalized opinions to one of convergent behavior.

At present, the power grid has tight control over its dispatchable generation capacity but a very coarse control on the demand. Energy consumers are shielded from making price-aware decisions, which degrades the efficiency of the market. This state of affairs tends to favor fossil fuel generation over renewable sources. Because of the technological difficulties of storing electric energy, the quest for mechanisms that would make the demand for electricity controllable on a day-to-day basis is gaining prominence. The goal of this paper is to provide one such mechanisms, which we call Digital Direct Load Scheduling (DDLS). DDLS is a direct load control mechanism in which we unbundle individual requests for energy and digitize them so that they can be automatically scheduled in a cellular architecture. Specifically, rather than storing energy or interrupting the job of appliances, we choose to hold requests for energy in queues and optimize the service time of individual appliances belonging to a broad class which we refer to as "deferrable loads". The function of each neighborhood scheduler is to optimize the time at which these appliances start to function. This process is intended to shape the aggregate load profile of the neighborhood so as to optimize an objective function which incorporates the spot price of energy, and also allows distributed energy resources to supply part of the generation dynamically.

"Smart Grid" refers to the modernization of electric grid to allow for more efficient generation, transmission, distribution, and usage of energy. This is becoming necessary in order to transition to a more sustainable energy generation and consumption and to reduce any adverse effects on the environment. While more advances in areas like renewable energies and network control are necessary, advances in communication technologies, data fusion and mining, as well as scheduling and optimization are also critical in order to achieve this vision. It is anticipated that the same communication technologies that have revolutionized our way of life in the past decades, ranging from sensor networks, mobile Internet, cloud computing, smart phones and many others, will have direct applicability to Smart Grid. Our objectives for this series of IEEE JSAC are focused on identifying the numerous communication challenges posed by the various aspects and functionalities of the Smart Grid and exploring research avenues for addressing them. We have received 67 papers, and after thorough review and careful deliberations, we have accepted 10 papers in this first issue. The articles in this issue can be grouped into 4 main sub-topic areas, namely, scheduling and load balancing, energy pricing, routing, and security.

We investigate the impact of cooperative relaying on uplink multi-user (MU) wireless video transmissions. We analyze and simplify a MU Markov decision process (MDP), whose objective is to maximize the long-term sum of utilities across the video terminals in a decentralized fashion, by jointly optimizing the packet scheduling and physical layer, under the assumption that some nodes are willing to act as cooperative relays. The resulting MU-MDP is a pricing-based distributed resource allocation algorithm, where the price reflects the expected future congestion in the network. Compared to a non-cooperative setting, we observe that the resource price increases in networks supporting low transmission rates and decreases for high transmission rates. Additionally, cooperation allows users with feeble direct signals to significantly improve their video quality, with a moderate increase in total network energy consumption that is far less than the energy these nodes would require to achieve the same video quality without cooperation.

From now on if one's paper is accepted for publication in the IEEE Signal Processing Letters the authors may present the same work at one of our two flagship conferences, ICASSP and ICIP. The work must be accepted by the deadline set by the conference for SPL papers, which will be one or two weeks prior to the "notification of acceptance" deadline, but not be published prior to the end of the last conference, about twelve months before the event. The new model avoids double publication by providing the electronic version in the proceedings distributed to registered conference attendees but including only the Signal Processing Letters paper in IEEEXplore. Thus, you will have only one official publication. This initiative is aimed at encouraging the most innovative contributions in our broad field to be submitted to Signal Processing Letters for consideration.

When cooperative terminals transmit concurrently, the receiver experiences additional distortion by the presence of radios carrier offsets and time delays. With the inevitable inaccuracy in estimating the channel at the receiving end, is it realistic to assume that relays transmissions do not collide? To address this question, we provide an analytical framework to model the effect of channel estimation methods that treat the channel as deterministic and unknown, and derive the average symbol error rate (SER) of a maximum likelihood sequence decoder (MLSD) and of a linear minimum mean squared error (LMMSE) equalizer using such estimates. We select the appropriate architecture by mapping two different channel estimation techniques onto the proposed analytical framework, and derive their diversity gain. First we analyze the well known linear least square channel estimation (LLSE), then, we show how to cast a compressed channel sensing (CCS) method onto our analytical model, by a novel approximation of the estimator noise and modeling mismatch. We observe that the latter is the key bottleneck in LLSE whereas the CCS appears sufficiently flexible to deliver the promised gains. This conclusion shows that collisions models are overly pessimistic, but it also underscores the need for an advanced receiver synchronization design.

Relay-assisted cooperative wireless communication has been shown to have significant performance gains over the legacy direct transmission scheme. Compared with single relay based cooperation schemes, utilizing multiple relays further improves the reliability and rate of transmissions. Distributed space-time coding (DSTC), as one of the schemes to utilize multiple relays, requires tight coordination between relays and does not perform well in a distributed environment with mobility. In this paper, a cooperative medium access control (MAC) layer protocol, called STiCMAC, is designed to allow multiple relays to transmit at the same time in an IEEE 802.11 network. The transmission is based on a novel DSTC scheme called randomized distributed space-time coding (R-DSTC), which requires minimum coordination. Unlike conventional cooperation schemes that pick nodes with good links, STiCMAC picks a transmission mode that could most improve the end-to-end data rate. Any station that correctly receives from the source can act as a relay and participate in forwarding. The MAC protocol is implemented in a fully decentralized manner and is able to opportunistically recruit relays on the fly, thus making it robust to channel variations and user mobility. Simulation results show that the network capacity and delay performance are greatly improved, especially in a mobile environment.

We investigate the impact of cooperative relaying on uplink multi-user (MU) wireless video transmission. We formulate the problem as an MU Markov decision process (MDP) that explicitly considers the cooperation at the physical layer and the medium access control sublayer, the video users' heterogeneous traffic characteristics, and the dynamically varying network conditions. Although MDPs notoriously suffer from the curse of dimensionality, our study shows that the complexity of the MU-MDP can be mitigated. Our simulation results show that cooperation allows users with feeble direct signals to achieve improvements in video quality on the order of 5-10 dB peak signal-to-noise ratio, with less than 0.8 dB quality loss by users with strong direct signals.

Electrical Vehicles are gaining increasing attention, due to the opportunities and challenges they present for the energy market. On the one hand, they will allow to drastically reduce the need for oil; on the other hand they may require a significant shift in the day to day management of the electricity generation. This paper is concerned with finding appropriate models for residential load in light of a widespread penetration of electric vehicles. The analysis is aimed at finding a SmartGrid solution that would enable us to optimize the generation dispatch in real time and allow to plug cars in any SmartGrid enabled plug. The key idea is to discriminate between regular load and the load due to the EVs, gathering in real time aggregate information about the sensed EV arrivals and their associated charging times in a demand matrix, that can be readily used to optimize the dispatch, while updating without real time constraints the billing record for the EV.

In this paper, we are interested in modeling diffusion of innovations on social networks. We focus on a scenario where innovation emerges at a small number of nodes in the society, and each individual needs certain portion of his neighbors (thresholds) to adopt the innovation before he does so. We analyze the dynamics of the diffusion process under deterministic topologies and threshold values. We show that the diffusion process depends on both the topology and threshold values through so called coherent sets. We investigate several interesting topologies, and utilize coherent set argument to determine the behavior of the process on these topologies.

Are Power Line Communications (PLC) a good candidate for Smart Grid applications? The objective of this paper is to address this important question. To do so, we provide an overview of what PLC can deliver today by surveying its history and describing the most recent technological advances in the area. We then address Smart Grid applications as instances of sensor networking and network control problems and discuss the main conclusions one can draw from the literature on these subjects. The application scenario of PLC within the Smart Grid is then analyzed in detail. Because a necessary ingredient of network planning is modeling, we also discuss two aspects of engineering modeling that relate to our question. The first aspect is modeling the PLC channel through fading models. The second aspect we review is the Smart Grid control and traffic modeling problem which allows us to achieve a better understanding of the communications requirements. Finally, this paper reports recent studies on the electrical and topological properties of a sample power distribution network. Power grid topological studies are very important for PLC networking as the power grid is not only the information source but also the information delivery system-a unique feature when PLC is used for the Smart Grid.

The Pulse-Coupled Oscillator (PCO) is a novel protocol inspired by models used in mathematical biology to justify the emergence of synchrony in the natural world. Our paper introduces and demonstrates the efficacy of a new PCO protocol implementation that, by disabling all collision resolution mechanisms for a suitable portion of the node operations, lets the rapid establishment of a common clock and its maintenance. The key idea is to allow signals to be superimposed in time, a feature that is absent in previous implementations, because it is prevented by traditional medium access schemes. We map the PCO protocol into an event-driven asynchronous coloring algorithm, based on the local exchange of information to explain its convergence properties. The event-based description of the PCO protocol sets the stage for our experimental comparison with a competing decentralized network synchronization approach, namely, the Reference Broadcast Protocol (RBS). For comparison, we combined RBS with an asynchronous average consensus protocol, running exactly on the same MicaZ platforms. The experimental results showcase the better scalability of the PCO scheme compared to the competing method based on RBS, proving that the PCO primitive is a reasonable option to consider for wireless sensor network applications.

Randomized space-time block coding (RSTBC) is a decentralized cooperative technique that ensures diversity gains through the recruitment of multiple uncoordinated relays, with virtually no signaling overhead. In this paper, RSTBC is applied to two-way relaying wireless networks which, when two terminals want to send a message to each other, can potentially improve the network throughput by allowing them to exchange data over two or three time slots via bidirectional relay communications. Specifically, two decode-and-forward relaying strategies are considered which take up only two time slots. In the first slot the two sources transmit simultaneously. In the former scheme which we refer to as decode and forward both (DFB) RSTBC, only relays which can reliably decode both source blocks via joint maximum likelihood decoding cooperate, and do so by modulating the bit-level XOR of the decoded data through a single RSTBC. In the latter scheme called decode and forward any (DFA) RSTBC, the relays cooperate in the second slot also when they can decode only one of the two source data. In this case each source data that is decoded is mapped into an independent RSTBC. If the relay decoded reliably both sources, after cancellation of the strong interference, then it sends the two RSTBCs encoding the symbol vectors from each of the sources. A randomized forwarding scheme is also proposed for three-time-slot relaying, which is also a DFA strategy, although without joint decoding or interference cancellation after the first slot. The diversity orders achievable through the three proposed schemes are calculated and the obtained theoretical results are validated by means of Monte Carlo numerical simulations.

We study a distributed protocol for time-division multiple access (TDMA) with applications in wireless body area networks. Our scheme is derived from a bio-inspired algorithm known as Pulse-Coupled Oscillator, in which each node in the network update its state based on its knowledge of the neighborhood. The protocol we propose is suitable for negotiation of a shared resource in a distributed manner, and is adaptive to changes in the network size or in the nodes' demands. We discuss the implementation of such scheme from an application perspective, and discuss the impact of the physical layer on its performance.

Recently, much progress has been made on the cross-layer optimization of video streams in multiple access networks. The key idea is to use the granular data structure of compressed video to trade quality with bits, and optimally prioritize transmissions given the available bandwidth, in order to obtain proportionally optimal video quality across video streams. Herein, we discuss the effect and potential benefit of using a cooperative-relay strategy in a wireless network, where proportionally optimal video schedules are computed via a multi-user Markov decision process. The idea is that feeble signals of nodes that are located far away from the destination can be enhanced via the cooperation of intermediate nodes, acting as cooperative relays. Our contribution is to indicate a possible solution that would require relatively modest changes to the multi-user optimization framework, while warranting a uniformly better experience to the video users thanks to cooperative coding.

Cooperation diversity schemes employing space-time block coding (STBC) techniques have been proposed for wireless networks to increase network capacity and coverage even when each node is equipped with a single antenna. Such schemes allow several relay stations distributed in space to assist the transmission between a given source-destination pair. A key design problem in cooperative networks is to take advantage from spatial diversity by reducing the amount of signaling and processing overhead as far as possible. In this paper, capitalizing on randomized STBC (RSTBC), a coding method which has been recently developed for decode-and-forward (D&F) relay nodes, a totally decentralized cooperative communication scheme is proposed for amplify-and-forward (A&F) relays, where each relay is unaware of both the effective STBC being employed by the other nodes and the number of cooperating stations. Numerical results are provided to highlight the effectiveness of the proposed scheme in comparison to its D&F counterpart.

We consider the design of sufficiently informative inputs for estimating the parameters of a time-varying linear system. Specifically, we assume the output of the system may be modeled as linear combinations of known time-varying elements whose parameters are points in an appropriately chosen grid for the parameter space, such that these parameters are sparse in comparison with the grid size. The sufficiently informative inputs we study are signals that guarantee a unique sparsest solution thereby satisfying minimum requirements for numerically solving the estimation problem. Since in practice the parameters of interest may not lie on the grid constructed for the parameter space, we discuss how our input designs can be modified to provide robustness in the presence of modeling error and noise.

In recent years, several models introduced in mathematical biology and natural science have been used as the foundation of networking algorithms. These bio-inspired algorithms often solve complex problems by means of simple and local interactions of individuals. In this work, we consider the development of decentralized scheduling in a small network of self-organizing devices that are modeled as pulse-coupled oscillators (PCOs). By appropriately designing the dynamics of the PCO, the network of devices can converge to a desynchronous state where the nodes naturally separate their transmissions in time. Specifically, by following Peskin's PCO model with inhibitory coupling, we first show that round-robin scheduling can be achieved with weak convergence, where the nodes' transmissions are separated by a constant duration, but the differences of their local clocks continue to shift over time. Then, by having each node accept coupling only from the pulses emitted by a subset of neighboring nodes, we show that it is possible to achieve strict desynchronization, where the difference between local clocks remain fixed over time. More interestingly, by having each node maintain two local clocks, we show that it is possible to further achieve proportional fair scheduling, where the time alloted to each node is proportional to their demands. The convergence of these algorithms is studied both analytically and numerically.

We consider the problem of coordinating a group of mobile nodes communicating through a wireless medium. The objective of the network is the alignment of all the nodes towards a common direction through local interactions, without the need for global knowledge such as the network topology or the maximum degree of the network, or even local parameters, such as the number of neighbors. The key feature of our algorithm is that each node state update is done through voting, where the probability of each vote is biased by the state of the node neighbors. We propose two possible physical implementations for our algorithm. The first is based on the explicit exchange of packetized messages, while the second is a cross-layer approach. Our analysis unveils key convergence properties of this simple class of alignment algorithms, via analytical and simulated results.

Gossip algorithms are attractive for in-network processing in sensor networks because they do not require any specialized routing, there is no bottleneck or single point of failure, and they are robust to unreliable wireless network conditions. Recently, there has been a surge of activity in the computer science, control, signal processing, and information theory communities, developing faster and more robust gossip algorithms and deriving theoretical performance guarantees. This paper presents an overview of recent work in the area. We describe convergence rate results, which are related to the number of transmitted messages and thus the amount of energy consumed in the network for gossiping. We discuss issues related to gossiping over wireless links, including the effects of quantization and noise, and we illustrate the use of gossip algorithms for canonical signal processing tasks including distributed estimation, source localization, and compression.

Physical (PHY) layer cooperation in a wireless network allows neighboring nodes to share their communication resources in order to create a virtual antenna array by means of distributed transmission and signal processing. A novel medium access control (MAC) protocol, called CoopMAC, has been recently proposed to integrate cooperation at the PHY layer with the MAC sublayer, thereby achieving substantial throughput and delay performance improvements. CoopMAC capitalizes on the broadcast nature of the wireless channel and rate adaptation, recruiting a single relay on the fly to support the communication of a particular source-destination pair. In this paper, we propose a cross-layer rate-adaptive design that opportunistically combines the recruitment of multiple cooperative nodes and carrier sensing multiple access with collision avoidance. We focus on a single-source single-destination setup, and develop a randomized cooperative framework, which is referred to as randomized CoopMAC (RCoopMAC). Thanks to the randomization of the coding rule, the RCoopMAC approach enables the blind participation of multiple relays at unison relying only on the mean channel state information (CSI) of the potential cooperating nodes, without introducing additional signaling overhead to coordinate the relaying process. The proposed RCoopMAC scheme is not only beneficial in substantially improving the link quality and therefore the sustainable data rates but, thanks to the decentralized and agnostic coding rule, it also allows to effectively recruit multiple relays in a robust fashion, i.e., even when the required mean CSI is partially outdated.

Pulse coupled oscillators (PCOs) are pulsing devices that pulse individually in a periodic manner but alter their pulsing patterns in response to the pulsing of other nodes. A network of PCOs can produce a number of different dynamics from their pulsing activities, among which the synchrony of pulsing is perhaps the most well known. In this paper, we study the primitive that falls into the class of ldquodesynchronizationrdquo. Specifically, we propose a simple pulse-coupling mechanism that allows each node in the network to converge to a desynchronized state where the nodes will pulse periodically with a constant spacing among each others firing times. We discuss the convergence of the PCO mechanism and propose to apply this primitive to resolve contention in the reservation phase of a reservation-based MAC protocol.

Given a network of N nodes with the i-th sensor's observation xiisin RM, the matrix containing all Euclidean distances among measurements ||xi- xj|| foralli, j isin {1,..., N} is a useful description of the data. While reconstructing a distance matrix has wide range of applications, we are particularly interested in the manifold reconstruction and its dimensionality reduction for data fusion and query. To make this map available to the all of the nodes in the network, we propose a fully decentralized consensus gossiping algorithm which is based on local neighbor communications, and does not require the existence of a central entity. The main advantage of our solution is that it is insensitive to changes in the network topology and it is fully decentralized. We describe the proposed algorithm in detail, study its complexity in terms of the number of inter-node radio transmissions and showcase its performance numerically.

Recently, we proposed a decentralized cooperative communication scheme [1]. In this paper we show that randomized decentralized schemes perform well even when the cooperating nodes are asynchronous. In particular we study the attainable diversity for frequency selective channels, and the coding gain in comparison to a centralized scheme.

In a distributed wireless network, it is possible to employ several relays and mimic a multiple antenna transmission system. In this paper we propose a MAC layer solution that allows multiple relays to send information to the receiver at unison, using a randomized distributed space time code. The randomized space-time coding can recruit relays on the fly, thus significantly reducing signaling overhead. The cross-layer design between physical layer and MAC layer involves relay discovery and rate adaptation, and results in improvements in throughput and delay performance. The design is dynamic and can be adapted to changing network conditions. The proposed MAC scheme can be integrated into various wireless technologies such as distributed contention based networks (e.g., IEEE 802.11 BSS and ad hoc mode) as well as centralized multiple access networks (e.g., IEEE 802.16).

Kalman filtering is a classical technique with a number of potential distributed applications in sensor networks. In this paper we consider a specific algorithm for distributed Kalman filtering proposed recently by Olfati-Saber [Olfati-Saber, 2005 ]. We design a communication access protocol for wireless sensor networks that is tailored to converge rapidly to the desired estimate and provides scalable error performance as number of sensors increases. By exploiting the structure of the distributed filtering computations, we derive an optimal communication resource allocation policy for minimizing the component-wise state estimation error. We provide simulation results demonstrating the performance of our architecture.

We examine the application of current research in sparse signal recovery to the problem of channel estimation. Specifically, using an orthogonal frequency division multiplexed (OFDM) transmission scheme with pilot symbol assisted modulation (PSAM), we consider the problem of identifying a frequency selective channel from a limited number Q out of a possible M tones of an OFDM symbol. The main observation is that if M is chosen as prime, one can identify the channel uniquely if Q ges 2T, where T is the number of nonzero taps in the frequency-selective channel. The identifiability result requires the minimization of the lo norm, leading to an intractable combinatorial search problem. Several methods have been proposed to deal with these issues, and the one we examine involves l1norm regularization known as basis pursuit, We apply these methods specifically to the problem of estimating a frequency selective channel with PSAM. As a result, the bandwidth efficiency of the system is increased due to the sparsity of the channel.

Average consensus algorithms are gossiping protocols for averaging original sensor measurements via near neighbor communications. In this paper, we consider the average consensus algorithm under communication rate constraints. Without any communication rate restrictions, the algorithm ideally allows every node state to converge to the initial average in the limit. Noting that brute force quantization does not guarantee convergence due to error propagation effects, in our recent work we proposed two source coding methods which use side information (predictive coding and Wyner-Ziv coding) to achieve convergence with vanishing quantization rates in the case of block coding. In this work, we focus on a simplified predictive coding scheme with variable quantization rates over the iterations and on a communication network with regular topology. We characterize the asymptotic rate which allows to achieve a bounded convergence in terms of the initial conditions (i. e, the rate at the first iteration, and the initial state correlation), and the connectivity of the network. Moreover, we study the optimal rate allocation among the average consensus iterations subject to the constraints that the total number of quantization bits is fixed.

Motivated by applications to wireless sensor, peer-to-peer, and ad hoc networks, we study distributed broadcasting algorithms for exchanging information and for computing in an arbitrarily connected network of nodes. Specifically, we propose a broadcasting-based gossiping algorithm to compute the (possibly weighted) average of the initial measurements of the nodes at every node in the network. We show that the broadcast gossip algorithms almost surely converge to a consensus. In addition, the random consensus value is, in expectation, equal to the desired value, i.e., the average of initial node measurements. However, the broadcast gossip algorithms do not converge to the initial average in absolute sense because of the fact that the sum is not preserved at every iteration. We provide theoretical results on the mean square error performance of the broadcast gossip algorithms. The results indicate that the mean square error strictly decreases through iterations until the consensus is achieved. Finally, we assess and compare the communication cost of the broadcast gossip algorithms required to achieve a given distance to consensus through numerical simulations.

Group testing has been used in many applications to efficiently identify rare events in a large population. In this paper, the concept of group testing is generalized to applications with correlated source models to derive scheduling policies for sensors' adopting cooperative transmissions. The tenet of our work is that in a wireless sensor network it is advantageous to allocate the same channel dimensions to all sensor sources that have the same response to a sequence of queries or tests. That is, nodes that have the same data attributes should transmit as a cooperative super-source. Specifically, we consider the case where sensors' data are modeled spatially as a one-dimensional Markov chain. Two strategies are considered: the recursive algorithm and the tree-based algorithm. The recursive scheme allows us to illustrate the performance of group testing for finite populations while the tree-based algorithm is used to derive the achievable scaling performances of the class of group testing strategies as the number of sensors increases. We show that the total number of queries required to gather all sensors' data scales in the order of the joint entropy. A further generalization of this concept provides the basis of deriving efficient data-gathering algorithms for correlated sources.

In this paper we consider the problem of transmitting quantized data while performing an average consensus algorithm. Average consensus algorithms are protocols to compute the average value of all sensor measurements via near neighbors communications. The main motivation for our work is the observation that consensus algorithms offer the perfect example of network communications where there is an increasing correlation between the data exchanged, as the system updates its computations. Henceforth, it is possible to utilize previously exchanged data and current side information to reduce significantly the demands of quantization bit rate for a certain precision. We analyze the case of a network with a topology built as that of a random geometric graph and with links that are assumed to be reliable at a constant bit rate. Numerically we show that in consensus algorithms, increasing number of iterations does not have the effect of increasing the error variance. Thus, we conclude that noisy recursions lead to a consensus if the data correlation is exploited in the messages source encoders and decoders. We briefly state the theoretical results which are parallel to our numerical experiments.

Sensor networks have been used in a wide range of applications. Considerable research effort is currently devoted to design protocols that allow networks of inexpensive sensors to perform reliable remote control and monitoring functions, in spite of the limitations of each device.Our objective is to demonstrate a fully software implementation of the so called Pulse Coupled Oscillator Protocol, proposed in [2, 6] for the decentralized synchronization of radio devices. The PCO protocol is inspired by a model found in mathematical biology [5]. It is decentralized, scalable and simple, as we will show in our demonstration.

A fundamental problem in large scale wireless networks is the energy efficient broadcast of source messages to the whole network. The energy consumption increases as the network size grows, and the optimization of broadcast efficiency becomes more important. In this paper, we study the optimal power allocation problem for cooperative broadcast in dense large-scale networks. In the considered cooperation protocol, a single source initiates the transmission and the rest of the nodes retransmit the source message if they have decoded it reliably. Each node is allocated an-orthogonal channel and the nodes improve their receive signal-to-noise ratio (SNR), hence the energy efficiency, by maximal-ratio combining the receptions of the same packet from different transmitters. We assume that the decoding of the source message is correct as long as the receive SNR exceeds a predetermined threshold. Under the optimal cooperative broadcasting, the transmission order (i.e., the schedule) and the transmission powers of the source and the relays are designed so that every node receives the source message reliably and the total power consumption is minimized. In general, finding the best scheduling in cooperative broadcast is known to be an NP-complete problem. In this paper, we show that the optimal scheduling problem can be solved for dense networks, which we approximate as a continuum of nodes. Under the continuum model, we derive the optimal scheduling and the optimal power density. Furthermore, we propose low-complexity, distributed and power efficient broadcasting schemes and compare their power consumptions with those-of-a traditional noncooperative multihop transmission.

In a portable device, such as a digital camera, limitations on storage are an important consideration. In addition, due to constraints on the complexity of available hardware, image coding algorithms must be fairly simple in implementation. This work presents one such efficient method for coding multiple images of a scene, in a manner that complements a post-processing-based enhancement system. Super-resolution, image restoration and de-noising algorithms have demonstrated the ability to improve the quality of an image using multiple blurry, noisy copies of the same scene. This additional quality does not come without cost, however, since an image capture system must store each image. The proposed encoding scheme is derived from a general linear system model, and encodes multiple images of the same scene, with different amounts of blurring. It is also compared with a variety of methods based on current camera compression technology. For the tested images, this approach requires one-half the rate required by other methods at lower rates. In addition, for a small performance loss, it is essentially implementable without using any compression hardware

We study the problem of designing distributed space-time codes for cooperative communication. A major challenge in distributed cooperative transmissions is to find a way to coordinate the relay transmissions without requiring extra control information overhead. Most of the previous works on the subject assume each node emulates a predetermined antenna of a multiple-antenna system. However, this requires a centralized "antenna allocation" procedure. Here, we introduce randomized strategies that decentralize the transmission of a space time code from a set of distributed relays. The simple idea we propose is to let each node transmit a random linear combination of the codewords that would be transmitted by all the antennas in a centralized space time coding scheme. We provide different code designs that achieve the diversity order (min(N, L)) when number of nodes N is different than the number of virtual antennas L. In this paper, we focus on the case when N = L and we show that under certain designs the achieved diversity order is fractional

Considering two correlated vector sources x, y isin RN, we address the problem of lossy coding of x with uncoded side information y available at the decoder. The general non-linear mapping between y and x capturing their correlation can be approximated through a linear model y = Hx + n in which x is independent of x. Viewing this model as a virtual communication channel with input x and output y we utilize linear precoding and decoding technique to convert the original vector source coding problem into a set of manageable scalar source coding problems. The scalar source coding problems can be solved using the existing distributed source coding algorithms that are primarily designed for the simple correlation model y = x + n where x and y are scalar jointly Gaussian sources

In this paper, we study the problem of designing distributed space-time codes for cooperative communication. We introduce randomized strategies that decentralize the transmission of a space time code from a set of distributed relays and analyze their performance. In our methods, as in other cooperative transmission strategies, the relay signals are encoded so as to provide diversity and coding gains. However, our methods enable the relays to act independently and to achieve such gains in a decentralized fashion. The simple idea we propose is to let each node transmit a random linear combination of the codewords that would be transmitted by all the antennas in a centralized space time coding scheme. Different decentralized strategies correspond to different choices for the statistics of the random coefficients. We provide sufficient conditions on the distributions of the random coefficients and on the number of nodes that guarantee to achieve the maximum diversity gain. We show that the proposed scheme achieves full diversity (N) if NL.

We study error propagation in wireless cooperative broadcast protocols. In our model, nodes in the network are randomly deployed in a fixed region. The message from a certain source node is relayed by multiple groups of cooperating relays that are located in predetermined consecutive swaths of the network area. We refer to these groups of relays as "levels". Our analysis is based on the derivation of recursive equations that express the error rate at each relay in a given level as a function of the error probabilities of the nodes in the previous levels. To provide analytical results we take the limit as the number of nodes goes to infinity while the relay power goes to zero, so that the relay power density per unit area is constant. In the limit, we show that the relay power density and area of the regions that correspond to the levels lead to fixed points in the error rate that prevent catastrophic error propagation, regardless of the distance the message is transmitted over.

We study the optimal power allocation problem for cooperative broadcast in dense large-scale networks. In our set-up, a single source initiates the transmission and the nodes that receive the message with sufficient signal-to-noise ratio (SNR) retransmit. The goal is to design the transmission order (schedule) and the transmission powers of the relays so that the message reaches to the entire network with the minimum possible power expenditure. In general, finding the best scheduling in cooperative broadcast is known to be NP-complete. In this paper, we show that the optimal scheduling is easily resolved for certain network topologies and channel models. Furthermore, we approximate dense large-scale networks with a continuum of nodes and provide optimal power density for the continuum network under pathloss attenuation

We consider the transmission of space-time block codes (STBC) over doubly-selective channels, which are encountered in highly mobile environments or high-frequency (HF) communications. In particular, we propose two extensions of time-reversal STBC with superior performance under these fast-fading conditions, and compare these schemes for various decision-feedback equalizers. The tradeoffs between performance and complexity are illustrated, and simulation results for the severely doubly-selective HF-channel corroborate our analysis

Cooperative broadcast aims to deliver a source message to a locally connected network by means of collaborating nodes. In traditional architectures, node cooperation has been at the network layer. Recently, physical layer cooperative schemes have been shown to offer several advantages over the network layer approaches. This form of cooperation employs distributed transmission resources at the physical layer as a single radio with spatial diversity. In decentralized cooperation schemes, collaborating nodes make transmission decisions based on the quality of the received signal, which is the only parameter available locally. In this case, critical parameters that influence the broadcast performance include the source/relay transmission powers and the decoding threshold (the minimum signal-to-noise ratio (SNR) required to decode a transmission). We study the effect of these parameters on the number of nodes reached by cooperative broadcast. In particular, we show that there exists a phase transition in the network behavior: if the decoding threshold is below a critical value, the message is delivered to the whole network. Otherwise, only a fraction of the nodes is reached, which is proportional to the source transmit power. Our approach is based on the idea of continuum approximation, which yields closed-form expressions that are accurate when the network density is high.

Broadcasting is a method that allows the distributed nodes in a wireless sensor network to share its data efficiently among each other. Due to the limited energy supplies of a sensor node, energy efficiency has become a crucial issue in the design of broadcasting protocols. In this paper, we analyze the energy savings provided by a cooperative form of broadcast, called the opportunistic large arrays (OLA), and compare it to the performance of conventional multi-hop networks where no cooperation is utilized for transmission. The cooperation in OLA allows the receivers to utilize for detection the accumulation of signal energy provided by the transmitters that are relaying the same symbol. In this work, we derive the optimal energy allocation policy that minimizes the total energy cost of the OLA network subject to the SNR (or BER) requirements at all receivers. Even though the cooperative broadcast protocol provides significant energy savings, we prove that the optimum energy assignment for cooperative networks is an NP-complete problem and, thus, requires high computational complexity in general. We then introduce several suboptimal yet scalable solutions and show the significant energy-savings that one can obtain even with the approximate solutions

The goal of group testing is to efficiently classify the state of a set of distributed agents through a sequence of tests by imposing each test simultaneously upon groups of agents. In this work, we describe the concept of group testing in a generalized framework and propose to apply this concept to solve the scheduling and multiple access problem in a large scale wireless sensor network. Since the standard approach is to dedicate a single channel to each sensor, we discuss the efficiency of group testing by comparing it to the case where each sensor is tested individually. Through the sequence of tests, the group testing strategy successively refines the observation space of the set of sensors and eventually identifies the status of each sensor when the space is refined to only one element. We show that the successive refinement property of group testing (similar to that of arithmetic coding) plays an important role in its performance. Based on this concept, we provide insight into choosing optimal group testing strategies for general applications.

We consider a wireless network in which a single source transmits its message with the help of multiple cooperative relays. In our model, the source broadcasts the message and the relays retransmit it as soon as they are able to produce an inference of acceptable quality. If the network is dense, multiple nodes quasi-synchronously retransmit the source message, acting as groups of cooperative relays, thereby increasing the range of transmission. We analyze the behavior of such cooperative networks with respect to parameters such as the source and relay transmission powers by deriving a dynamic system model using a continuum asymptote as the number of relay nodes goes to infinity. The methodology developed can be used to analyze the performance of other cooperative protocols.

We consider a set of wireless stations that transmit and receive real-time video over a wireless channel. In our analysis, each video packet experiences a single flat fading state (block fading model) and additive Gaussian noise. We assume that the flat fading parameters are known at both transmitter and receiver sides and that each packet is sufficiently long such that, through error correction, it it possible to recover it correctly at the receiver end. Under an analogous model optimal rate allocation policies have been developed in an information theoretic framework. These policies maximize aggregate throughput or minimize queueing delays, but do not explicitly consider the application layer parameters in the rate allocation. We are interested in developing the framework to derive the optimum rate adaptation for video sources and evaluating the aggregate PSNR achievable with an optimal cross-layer design combining application-MAC-PNY layers.

We consider a multi-hop wireless network in which a single source-destination pair communicates with the help of multiple cooperative relays. The relays deliver the source message by transmitting it in groups at every hop. The group transmissions increase the signal-to-noise ratio (SNR) of the received signal, and improve the range of communication. In this paper, we analyze the behavior of cooperative network with respect to the network parameters such as source/relay transmission powers and the decoding threshold (the minimum SNR required to decode a transmission). It is shown that if the decoding threshold is below a critical value, the message is delivered to the destination regardless of the distance between the source and the destination. Otherwise, the number of transmitting nodes diminishes at every hop, and the message does not reach to a destination far away. Our approach is based on the idea of continuum approximation, which is valid when the network density is high.

Synchronization is considered a particularly difficult task in wireless sensor networks due to its decentralized structure. Interestingly, synchrony has often been observed in networks of biological agents (e.g., synchronously flashing fireflies, or spiking of neurons). In this paper, we propose a bio-inspired network synchronization protocol for large scale sensor networks that emulates the simple strategies adopted by the biological agents. The strategy synchronizes pulsing devices that are led to emit their pulses periodically and simultaneously. The convergence to synchrony of our strategy follows from the theory of Mirollo and Strogatz, 1990, while the scalability is evident from the many examples existing in the natural world. When the nodes are within a single broadcast range, our key observation is that the dependence of the synchronization time on the number of nodes N is subject to a phase transition: for values of N beyond a specific threshold, the synchronization is nearly immediate; while for smaller N, the synchronization time decreases smoothly with respect to N. Interestingly, a tradeoff is observed between the total energy consumption and the time necessary to reach synchrony. We obtain an optimum operating point at the local minimum of the energy consumption curve that is associated to the phase transition phenomenon mentioned before. The proposed synchronization protocol is directly applied to the cooperative reach-back communications problem. The main advantages of the proposed method are its scalability and low complexity.

This paper proposes the use of a distributed synchronization mechanism, which locks in phase the pulse-coupled oscillators, to rapidly alert the nodes in a sensor network of a change detected by a group of the sensors. By encoding into an abrupt variation of the phase their positive detection of a change, the nodes force all other nodes to reach a new synchronization equilibrium. Therefore, the information about the change is implicitly encoded in the phase transitions. While the local detection problem at each sensor can be addressed using the standard change detection algorithms, the interesting aspect of this work is the unconventional way through which the nodes broadcast their information to each other and fuse their decisions. The main advantages of the proposed method is the scalability and low complexity of the fusion algorithm.

Cooperative transmission schemes in multi-hop networks have the advantage of energy efficiency and increased network coverage. Realizing these advantages, however, requires a compatible physical layer. We consider the detection of a signal transmitted by multiple cooperative nodes. Exploiting the structure of the network, we formulate the problem, and propose a generalized likelihood ratio detector. We compare the performance of the proposed detector with two others: estimator-correlator and a genie-aided detector (provides a performance benchmark). The genie-aided detector assumes the knowledge of certain network parameters which may be unknown during reception. Simulations show that the proposed detector performs reasonably close to the genie-aided one, while being considerably better than the estimator-correlator.

We consider a block fading frequency selective multi-input multi-output (MIMO) channel in additive white Gaussian noise (AWGN). The channel input is a training vector superimposed on a linearly precoded vector of Gaussian symbols. To achieve a better performance over the conventional least-squares (LS), we utilize the linear mean square error (LMMSE) symbol estimate to improve the initial LS estimate and update the symbol estimation accordingly. We provide the guidelines to design training which minimizes the MSE of the initial LS estimate.

We consider a block fading frequency selective multi-input multi-output (MIMO) channel in additive white Gaussian noise (AWGN). The channel input is a training vector superimposed on a linearly precoded vector of Gaussian symbols. This form of precoding is referred to as affine precoding. We derive the Cramer-Rao bound (CRB) under two circumstances: the random parameter vector to be estimated contains (i) only fading channel coefficients, (ii) unknown data symbols as well as the channel coefficients. While case (i) corresponds to the decoding schemes in which the channel is estimated first and the channel measurement is utilized to recover the data symbols, case (ii) corresponds to methods in which channel and symbol estimation is performed jointly. The interesting outcome of our investigation is that minimizing trace of the channel CRB for cases (i) and (ii) under a total transmit power constraint leads to different affine precoder design guidelines.

A bio-inspired distributed detection protocol is proposed where the synchronization of pulsing devices is used to reach a consensus in the decision among sensor nodes. Each node encodes the local decision into their pulsing time and reaches an agreement when the pulsing instants converge to a common value. The protocol has low complexity and scales favorably with the size of the network. In fact, we prove that the probability of detection asymptotically goes to 1 as the number of sensors goes to infinity.

Adopting affine precoding as the transmission strategy, we investigate the effect of channel estimation error on the mutual information between the input and the output of a frequency selective fading MIMO channel. For Bayesian receivers which perform joint channel and symbol estimation there is asymptotically no loss in information rate. In contrast, for the receivers which obtain the channel estimation and use it to estimate the symbols, the lower bound on information rate is maximized by enforcing a form of orthogonality between symbols and training

In this paper we consider the multiple access problem with distributed dependent sources. We derive the optimal designs for the case of N correlated binary sources whose data is modelled as a two-state Markov chain. The solution can be classified as a group testing technique where data values at the sensors are determined through the successive refinements of the tests over smaller groups. The tests form, progressively, an accurate map of the sensor data at the central receiver. We derive the conditions on the parameters of the data model for which the group testing approach is superior to time sharing. In contrast to standard multiple access techniques, this is the first method proposed for data retrieval from distributed dependent sources which is content-based rather than user-based.

In this work, we explore the concept of group testing to efficiently acquire data from a distributed sensor field and to reconstruct the sensor field at a central station. We show that group testing techniques are not only an efficient tool to schedule multiple access transmissions, they are also transmission techniques that allow the central node to rapidly discriminate the information from the sensor field when a large number of sources generates data with low aggregate entropy. Our method enables the sensors to reconstruct a map of the entire sensor field with bandwidth requirements that depend on the precision of the reconstructed field and, thus, do not grow linearly with the increased number of nodes when the network density increases.

Reducing power consumption and increasing battery life of nodes in an ad-hoc network requires an integrated power control and routing strategy. Power optimal routing selects the multi-hop links that require the minimum total power cost for data transmission under a constraint on the link quality. This paper studies optimal power routing under the constraint of a fixed end-to-end probability of error and compares the power optimal routes obtained with this criterion with those from the more commonly used fixed per hop error rate constraint. The comparison is carried out by looking at the properties of the power optimal graph, formed by the union of all the power optimal routes. The paper also provides algorithms to determine the power optimal routes.

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 illustrate a novel wireless local area network multiple access control (WLAN MAC) protocol whose allocation policy adopts a strategy similar to the token ring: the mobile stations access the channel upon receiving the token and release the channel by passing the token to the next user. The traffic is divided in two classes: guaranteed bandwidth (GB) and best effort (BE). The base station indirectly controls the traffic access by allotting the tokens and the bandwidth is shared by the same users with a decentralized contention-less protocol in the case of the GB users and on a contention basis for the BE users. The MAC protocol proposed the physical layer adopts a multicarrier code division multiple access multiplexing technique, which mitigates the linear distortions of broadband transmission and allows one to divide the frequency and time resources in elementary units that can be associated to the 'token'. The specification on the average and peak delay and rate are guaranteed by limiting the number of users passing the token and fixing a deadline to release the token. At the same time, similar to a reservation based protocol, the token strategy avoids collisions and is able to guarantee efficiently the integrity of the transmitted data.

We consider a problem of broadcast communication in a multi-hop sensor network, in which samples of a random field are collected at each node of the network, and the goal is for all nodes to obtain an estimate of the entire field within a prescribed distortion value. The main idea we explore in this paper is that of jointly compressing the data generated by different nodes as this information travels over multiple hops, to eliminate correlations in the representation of the sampled field. Our main contributions are: (a) we obtain, using simple network flow concepts, conditions on the rate/distortion function of the random field, so as to guarantee that any node can obtain the measurements collected at every other node in the network, quantized to within any prescribed distortion value; and (b), we construct a large class of physically-motivated stochastic models for sensor data, for which we are able to prove that the joint rate/distortion function of all the data generated by the whole network grows slower than the bounds found in (a). A truly novel aspect of our work is the tight coupling between routing and source coding, explicitly formulated in a simple and analytically tractable model---to the best of our knowledge, this connection had not been studied before.

We consider the problem of designing a wireless communication system where one of the two ends in the link is totally passive and can use very simplified analog technology, such as filters and nonlinearity. This study builds the background and proposes possible solutions for the design of future ultra-miniaturize wireless transceivers, operating on ranges of picocells, that may serve a wide range of civil and military applications.

Several works addressed the problem of deriving the, asymptotic capacity of a wireless system with space diversity in random fading. However, the theory of random matrices was never used in evaluating the asymptotic optimal performance in closed form. By increasing the number of transmit and receive antennas the resulting capacity tend to be a stable value independent of the fading realization. This surprising result is a consequence of Girko's (1984) law, stating that the asymptotic distribution of the eigenvalues of a random matrix, with independent identically distributed zero mean complex entries, is a circle. The conditions on the probability density function of the matrix entries are satisfied by the majority of random non-line of sight fading models. Using this theory in this paper we derive the close form expression for the asymptotic capacity of a system with transmit and receive diversity, assuming independent flat fading for each transmit-receive antenna link, with equal distribution. Our formula fits the numerical results even if the number of transmit an receive antennas as small as ten.

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.

In block transmission systems, transmitter-induced redundancy using finite-impulse response (FIR) filterbanks can be used to suppress intersymbol interference and equalize FIR channels irrespective of channel zeros. At the receiver end, linear or decision-feedback (DF) FIR filterbanks can be applied to recover the transmitted data. Closed-form expressions are derived for the FIR linear or DF filterbank receivers corresponding to varying amounts of transmission redundancy. Our framework encompasses existing block transmission schemes and offers low implementation-cost equalization techniques both when interblock interference is eliminated, and when IBI is present as, e.g., in orthogonal frequency-division multiplexing with insufficient cyclic prefix. By applying blind channel estimation methods, our filterbank transmitters-receivers (transceivers) dispense with bandwidth consuming training sequences. Extensive simulations illustrate the merits of our designs.

A generalized frequency-hopping (GFH) orthogonal frequency-division multiple-access (OFDMA) system is developed in this paper as a structured long code direct-sequence code-division multiple-access (DS-CDMA) system in order to bridge frequency-hopped multicarrier transmissions with long code DS-CDMA. Through judicious code design, multiuser interference is eliminated deterministically in the presence of unknown frequency-selective multipath channels. Thanks to frequency-hopping, no single user suffers from consistent fading effects and constellation-irrespective channel identifiability is guaranteed regardless of channel nulls. A host of blind channel estimation algorithms are developed trading off complexity with performance. Two important variants, corresponding to slow- and fast-hopping, are also addressed with the latter offering symbol recovery guarantees. Performance analysis and simulation results illustrate the merits of GFH-OFDMA relative to conventional OFDMA and long code DS-CDMA with pseudorandom noise codes and RAKE reception.

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.