Federated learning (FL) has attracted great attention lately due to many advantages it provides. The community has noted that FL performance can be greatly degraded by heterogeneous data or any data that is unsuitable for the learning problem under consideration. Here we focus on the gradient descent and heavy ball algorithms in a worker-server architecture and describe a simple but effective approach to reject data from workers with unsuitable data that would significantly harm the training performance. The approach uses a very short pre-training phase where only a few workers who have known suitable data are involved. Using the theory of order statistics, this pre-training allows accurate estimation of the range of gradient directions that should be present during the initial iterations of training, allowing workers with unsuitable data to be recognized. The approach is tested on real data sets and shows promising results for all cases tested.

Reinforcement learning (RL) is a promising approach for robotic navigation, allowing robots to learn through trial and error. However, real-world robotic tasks often suffer from sparse rewards, leading to inefficient exploration and suboptimal policies due to sample inefficiency of RL. In this work, we introduce Confidence-Controlled Exploration (CCE), a novel method that improves sample efficiency in RL-based robotic navigation without modifying the reward function. Unlike existing approaches, such as entropy regularization and reward shaping, which can introduce instability by altering rewards, CCE dynamically adjusts trajectory length based on policy entropy. Specifically, it shortens trajectories when uncertainty is high to enhance exploration and extends them when confidence is high to prioritize exploitation. CCE is a principled and practical solution inspired by a theoretical connection between policy entropy and gradient estimation. It integrates seamlessly with on-policy and off-policy RL methods and requires minimal modifications. We validate CCE across REINFORCE, PPO, and SAC in both simulated and real-world navigation tasks. CCE outperforms fixed-trajectory and entropy-regularized baselines, achieving an 18% higher success rate, 20-38% shorter paths, and 9.32% lower elevation costs under a fixed training sample budget. Finally, we deploy CCE on a Clearpath Husky robot, demonstrating its effectiveness in complex outdoor environments.

In this work, we highlight vulnerabilities in robotic systems integrating large language models (LLMs) and vision-language models (VLMs) due to input modality sensitivities. While LLM/VLM-controlled robots show impressive performance across various tasks, their reliability under slight input variations remains underexplored yet critical. These models are highly sensitive to instruction or perceptual input changes, which can trigger misalignment issues, leading to execution failures with severe real-world consequences. To study this issue, we analyze the misalignment-induced vulnerabilities within LLM/VLM-controlled robotic systems and present a mathematical formulation for failure modes arising from variations in input modalities. We propose empirical perturbation strategies to expose these vulnerabilities and validate their effectiveness through experiments on multiple robot manipulation tasks. Our results show that simple input perturbations reduce task execution success rates by 22.2% and 14.6% in two representative LLM/VLM-controlled robotic systems. These findings underscore the importance of input modality robustness and motivate further research to ensure the safe and reliable deployment of advanced LLM/VLM-controlled robotic systems.

Deceptive behavior involves an intelligent agent creating plans that conceal its true intentions while appearing to pursue different goals. It is crucial for inducing confusion in various applications, including security, military, and competitive environments, where the ability to conceal true intentions can lead to significant strategic advantages. Developing better artificial intelligence (AI) for adversarial environments or strategic decision-making scenarios (e.g., more realistic testing of human or AI decision-making capabilities in games and simulations) requires effective deceptive planning. In this paper, we propose a novel polynomial path planner that enables an agent to deceive its observers. Our contributions include the following: (i) we develop a framework for obtaining ambiguous functions; (ii) we introduce new deception metrics; (iii) we present a method for standardizing trajectories to enable shape-based comparisons independent of speed; (iv) we conduct a human survey to evaluate deception and its relation to goal recognition; and (v) we outperform the state of the art on a multitude of deception metrics. Furthermore, our findings show that using more complex functions and increasing the level of misdirection greatly enhances agent deception effectiveness.

The emergence of the Internet of Things (IoT) has revolutionized service automation, enabling the development of smart applications. However, the vast interconnectivity of IoT devices not only produces large volumes of data but also creates multiple potential attack surfaces. While Machine Learning (ML) offers insights from IoT data, inherent data privacy and security challenges hinder its effective utilization. Federated Learning (FL) offers privacy-preserving ML for distributed edge devices. Nevertheless, the susceptibility to attacks poses a threat to the integrity of IoT data impacting ML for IoT services and applications. To tackle this challenge and identify IoT devices compromised by attacks like label-flipped data, this article introduces an innovative defense mechanism modeled after the human immune system. Analogous to ‘B’ cells, which detect viruses within the human body, the Reinforcement Learning (RL) agent identifies malicious IoT nodes that participate in federated learning enabled IoT. Subsequently, the FL server, similar to ‘T’ cells in Human immune systems eliminate/destroy infected cells, quarantines/discards the malicious IoT nodes (that are FL clients) and their reported parameters. Like ‘B’ cells and ‘T’ cells work together to defend the human body against infections and diseases, RL agent and FL server work together to defend/secure FL enabled IoT from compromised/malicious IoT devices. Specifically, with the help of Deep Reinforcement Learning (DRL), the RL agent continually monitors model updates from participating IoT nodes during training phase to find malicious nodes and then to isolate or remove those malicious nodes (i.e., parameters) at FL server while aggregating parameters for the global model. The effectiveness of the proposed approach is demonstrated through experiments, where RL agent detects malicious/compromised IoT nodes and FL server discards the parameters from such malicious/compromised IoT nodes. We evaluate our proposed approach using numerical results obtained from experiments where we observe that our approach outperforms the existing state-of-the-art approaches in terms of detection rate, error, and accuracy for enhancing IoT security in FL enabled IoT.

We develop provably safe and convergent reinforcement learning (RL) algorithms for control of nonlinear dynamical systems, bridging the gap between the hard safety guarantees of control theory and the convergence guarantees of RL theory. Recent advances at the intersection of control and RL follow a two-stage, safety filter approach to enforcing hard safety constraints: model-free RL is used to learn a potentially unsafe controller, whose actions are projected onto safe sets prescribed, for example, by a control barrier function. Though safe, such approaches lose any convergence guarantees enjoyed by the underlying RL methods. In this paper, we develop a single-stage, sampling-based approach to hard constraint satisfaction that learns RL controllers enjoying classical convergence guarantees while satisfying hard safety constraints throughout training and deployment. We validate the efficacy of our approach in simulation, including safe control of a quadcopter in a challenging obstacle avoidance problem, and demonstrate that it outperforms existing benchmarks.

Recent developments in spectrum-sharing technologies include integrated sensing and communications (ISAC) systems to save resources, cost, and power. In this paper, we consider a co-existence topology with n-tuple radar and communications transmitters, wherein neither the transmitted signal nor the channels are known. Estimating these unknown quantities is modeled as a n-tuple blind deconvolution problem (NTBD). The receiver is considered to be a uniform linear antenna array. Thus, the channels are modeled as continuous-valued time delay, Doppler modulation, and direction of arrival (DoA). Also, harnessing the sparse nature of the channels and their continuousvalued parametrization, we propose a 3D n-tuple atomic norm minimization (NANM). Casting the NANM problem to its corresponding dual optimization problem, and employing the theory of positive trigonometric polynomial, we formulate a semidefinite program for the estimation of the unknown channel parameters. Performance guarantees of the proposed algorithm are provided in terms of the minimum number of samples required for exact recovery. Finally, numerical simulations validate our theoretical insights.

In the context of average-reward reinforcement learning, the requirement for oracle knowledge of the mixing time, a measure of the duration a Markov chain under a fixed policy needs to achieve its stationary distribution, poses a significant challenge for the global convergence of policy gradient methods. This requirement is particularly problematic due to the difficulty and expense of estimating mixing time in environments with large state spaces, leading to the necessity of impractically long trajectories for effective gradient estimation in practical applications. To address this limitation, we consider the Multi-level Actor-Critic (MAC) framework, which incorporates a Multi-level Monte-Carlo (MLMC) gradient estimator. With our approach, we effectively alleviate the dependency on mixing time knowledge, a first for average-reward MDPs global convergence. Furthermore, our approach exhibits the tightest available dependence of $\mathcal{O}(\sqrt{\tau_{mix}})$ known from prior work. With a 2D grid world goal-reaching navigation experiment, we demonstrate that MAC outperforms the existing state-of-the-art policy gradient-based method for average reward settings.

In this work, we introduce PIPER: Primitive-Informed Preference-based Hierarchical reinforcement learning via Hindsight Relabeling, a novel approach that leverages preference-based learning to learn a reward model, and subsequently uses this reward model to relabel higher-level replay buffers. Since this reward is unaffected by lower primitive behavior, our relabeling-based approach is able to mitigate non-stationarity, which is common in existing hierarchical approaches, and demonstrates impressive performance across a range of challenging sparse-reward tasks. Since obtaining human feedback is typically impractical, we propose to replace the human-in-the-loop approach with our primitive-in-the-loop approach, which generates feedback using sparse rewards provided by the environment. Moreover, in order to prevent infeasible subgoal prediction and avoid degenerate solutions, we propose primitive-informed regularization that conditions higher-level policies to generate feasible subgoals for lower-level policies. We perform extensive experiments to show that PIPER mitigates non-stationarity in hierarchical reinforcement learning and achieves greater than 50$\%$ success rates in challenging, sparse-reward robotic environments, where most other baselines fail to achieve any significant progress.

Signal processing over hypercomplex numbers arises in many optical imaging applications. In particular, spectral image or color stereo data are often processed using octonion algebra. Recently, the eight-band multispectral image phase recovery has gained salience, wherein it is desired to recover the eight bands from the phaseless measurements. In this letter, we tackle this hitherto unaddressed hypercomplex variant of the popular phase retrieval (PR) problem. We propose octonion Wirtinger flow (OWF) to recover an octonion signal from its intensity-only observation. However, contrary to the complex-valued Wirtinger flow, the non-associative nature of octonion algebra and the consequent lack of octonion derivatives make the extension to OWF non-trivial. We resolve this using the pseudo-real-matrix representation of octonion to perform the derivatives in each OWF update. We demonstrate that our approach recovers the octonion signal up to a right-octonion phase factor. Numerical experiments validate OWF-based PR with high accuracy under both noiseless and noisy measurements.

Model-based approaches to reinforcement learning (MBRL) exhibit favorable performance in practice, but their theoretical guarantees in large spaces are mostly restricted to the setting when transition model is Gaussian or Lipschitz, and demands a posterior estimate whose representational complexity grows unbounded with time. In this work, we develop a novel MBRL method (i) which relaxes the assumptions on the target transition model to belong to a generic family of mixture models; (ii) is applicable to large-scale training by incorporating a compression step such that the posterior estimate consists of a Bayesian coreset of only statistically significant past state-action pairs; and (iii) exhibits a sublinear Bayesian regret. To achieve these results, we adopt an approach based upon Stein's method, which, under a smoothness condition on the constructed posterior and target, allows distributional distance to be evaluated in closed form as the kernelized Stein discrepancy (KSD). The aforementioned compression step is then computed in terms of greedily retaining only those samples which are more than a certain KSD away from the previous model estimate. Experimentally, we observe that this approach is competitive with several state-of-the-art RL methodologies, and can achieve up-to 50 percent reduction in wall clock time in some continuous control environments.

Consider a target being tracked by a cognitive radar network. If the target can intercept noisy radar emissions, how can it detect coordination in the network? By ‘coordination’ we mean that the radar emissions satisfy Pareto optimality with respect to multi-objective optimization over the objective functions of each radar and a constraint on total network power output. This paper provides a novel inverse multi-objective optimization approach for statistically detecting Pareto optimal (’coordinating’) behavior, from a finite dataset of noisy radar emissions. Specifically, we develop necessary and sufficient conditions for radar network emissions to be consistent with multi-objective optimization (coordination), and we provide a statistical detector with theoretical guarantees for determining this consistency when radar emissions are observed in noise. We also provide numerical simulations which validate our approach. Note that while we make use of the specific framework of a radar network coordination problem, our results apply more generally to the field of inverse multi-objective optimization.

Retrieving a signal from its triple correlation spectrum, also called bispectrum, arises in a wide range of signal processing problems. Conventional methods do not provide an accurate inversion of bispectrum to the underlying signal. In this paper, we present an approach that uniquely recovers signals with finite spectral support (band-limited signals) from at least 3B measurements of its bispectrum function (BF), where B is the signal’s bandwidth. Our approach also extends to time-limited signals. We propose a two-step trust region algorithm that minimizes a non-convex objective function. First, we approximate the signal by a spectral algorithm and then refine the attained initialization based on a sequence of gradient iterations. Numerical experiments suggest that our proposed algorithm is able to estimate band-/time-limited signals from its BF for both complete and undersampled observations.

Consider a target being tracked by a cognitive radar network. If the target can intercept some radar network emissions, how can it detect coordination among the radars? By 'coordination' we mean that the radar emissions satisfy Pareto optimality with respect to multiobjective optimization over each radar's utility. This paper provides a novel multi-objective inverse reinforcement learning approach which allows for both detection of such Pareto optimal ('coordinating') behavior and subsequent reconstruction of each radar's utility function, given a finite dataset of radar network emissions. The method for accomplishing this is derived from the micro-economic setting of revealed preferences, and also applies to more general problems of inverse detection and learning of multi-objective optimizing systems.

This study focuses on using large language models (LLMs) as a planner for embodied agents that can follow natural language instructions to complete complex tasks in a visually-perceived environment. The high data cost and poor sample efficiency of existing methods hinders the development of versatile agents that are capable of many tasks and can learn new tasks quickly. In this work, we propose a novel method, LLM-Planner, that harnesses the power of large language models to do few-shot planning for embodied agents. We further propose a simple but effective way to enhance LLMs with physical grounding to generate and update plans that are grounded in the current environment. Experiments on the ALFRED dataset show that our method can achieve very competitive few-shot performance: Despite using less than 0.5% of paired training data, LLM-Planner achieves competitive performance with recent baselines that are trained using the full training data. Existing methods can barely complete any task successfully under the same few-shot setting. Our work opens the door for developing versatile and sample-efficient embodied agents that can quickly learn many tasks.1

Many existing reinforcement learning (RL) methods employ stochastic gradient iteration on the back end, whose stability hinges upon a hypothesis that the data-generating process mixes exponentially fast with a rate parameter that appears in the step-size selection. Unfortunately, this assumption is violated for large state spaces or settings with sparse rewards, and the mixing time is unknown, making the step size inoperable. In this work, we propose an RL methodology attuned to the mixing time by employing a multi-level Monte Carlo estimator for the critic, the actor, and the average reward embedded within an actor-critic (AC) algorithm. This method, which we call Multi-level Actor-Critic (MAC), is developed specifically for infinite-horizon average-reward settings and neither relies on oracle knowledge of the mixing time in its parameter selection nor assumes its exponential decay; it is therefore readily applicable to applications with slower mixing times. Nonetheless, it achieves a convergence rate comparable to SOTA actor-critic algorithms. We experimentally show that these alleviated restrictions on the technical conditions required for stability translate to superior performance in practice for RL problems with sparse rewards.

Scene understanding in a contested battlefield is one of the very difficult tasks for detecting and identifying threats. In a complex battlefield, multiple autonomous robots for multi-domain operations are likely to track the activities of the same threat/objects leading to inefficient and redundant tasks. To address this problem, we propose a novel and effective object clustering framework that takes into account the position and depth of objects scattered in the scene. This framework enables the robot to focus solely on the objects of interest. Our system model incorporates YOLOv5 object detection model (ODM) and a pre-trained depth estimation model (DEM), whose outputs are fed into the KMeans Clustering (KM C) model. To train YOLOv5, we create our own battlefield dataset entitled “Common Objects in Battlefield (COBA)” which is split into a training dataset and a validation dataset using our customized object-aware stratified 7-fold cross-validation technique. We present the creation of minimum optimal clusters assuming the minimum number of available battlefield robots. These clusters exhibit accurate relative distances among objects, further validating the effectiveness of our approach for depth and object-aware clustering for scene understanding. Overall, our proposed approach facilitates efficient work division among robots, enhances decision-making capabilities, and helps to improve situational awareness in battlefield scenarios.

A high peak-to-average power ratio (PAPR) is a major disadvantage of orthogonal frequency division multiplexing (OFDM) communications systems. In this letter, we present a graph-theoretic heuristic to mitigate high PAPR. In particular, we focus on searching for an optimal Gray-coded mapping to encode user messages such that minimum PAPR is obtained for a given message sequence in an$M$-ary quadrature amplitude modulation (QAM). We exploit the bijection between vertex-weighted lattice constellations andhypercube graphsto formulate the OFDM PAPR optimization as a computationally efficient integer linear program (ILP) through the application ofBirkhoff's theoremtodoubly stochastic matrices. Our numerical experiments show an average PAPR reduction of 9–10 dB using the hypercube-graph-based constellation map over the worst map while still within 0.5 dB of the brute-force method.

Unmanned aerial vehicles (UAVs) have increasingly shown to be useful in civilian applications (such as agriculture, public safety, surveillance) and mission critical military applications. Despite the growth in popularity and applications, UAVs have also been used for malicious purposes. In such instances, their timely detection and identification has garnished rising interest from government, industry and academia. While much work has been done for detecting UAVs, there still exist limitations related to the impact of extreme environmental conditions and big dataset requirements. This paper proposes a novel ensemble deep learning framework that has hybrid synthetic and deep features to detect unauthorized or malicious UAVs by using acoustic, image/video and wireless radio frequency (RF) signals for robust UAV detection and classification. We present the performance evaluation of the proposed approach using numerical results obtained from experiments using acoustic, image/video and wireless RF signals. The proposed approach outperforms the existing related approaches for detecting malicious UAVs.

We study the problem of developing autonomous agents that can follow human instructions to infer and perform a sequence of actions to complete the underlying task. Significant progress has been made in recent years, especially for tasks with short horizons. However, when it comes to long-horizon tasks with extended sequences of actions, an agent can easily ignore some instructions or get stuck in the middle of the long instructions and eventually fail the task. To address this challenge, we propose a modelagnostic milestone-based task tracker (M-TRACK) to guide the agent and monitor its progress. Specifically, we propose a milestone builder that tags the instructions with navigation and interaction milestones which the agent needs to complete step by step, and a milestone checker that systemically checks the agent's progress in its current milestone and determines when to proceed to the next. On the challenging ALFRED dataset, our M-Track leads to a notable 33% and 52% relative improvement in unseen success rate over two competitive base models.

We consider a general spectral coexistence scenario, wherein the channels and transmit signals of both radar and communications systems are unknown at the receiver. In this dual-blind deconvolution (DBD) problem, a common receiver admits the multi-carrier wireless communications signal that is overlaid with the radar signal reflected-off multiple targets. When the radar receiver is not collocated with the transmitter, such as in passive or multistatic radars, the transmitted signal is also unknown apart from the target parameters. Similarly, apart from the transmitted messages, the communications channel may also be unknown in dynamic environments such as vehicular networks. As a result, the estimation of unknown target and communications parameters in a DBD scenario is highly challenging. In this work, we exploit the sparsity of the channel to solve DBD by casting it as an atomic norm minimization problem. Our theoretical analyses and numerical experiments demonstrate perfect recovery of continuous-valued range-time and Doppler velocities of multiple targets as well as delay-Doppler communications channel parameters using uniformly-spaced time samples in the dual-blind receiver.

We focus on parameterized policy search for reinforcement learning over continuous action spaces. Typically, one assumes the score function associated with a policy is bounded, which {fails to hold even for Gaussian policies. } To properly address this issue, one must introduce an exploration tolerance parameter to quantify the region in which it is bounded. Doing so incurs a persistent bias that appears in the attenuation rate of the expected policy gradient norm, which is inversely proportional to the radius of the action space. To mitigate this hidden bias, heavy-tailed policy parameterizations may be used, which exhibit a bounded score function, but doing so can cause instability in algorithmic updates. To address these issues, in this work, we study the convergence of policy gradient algorithms under heavy-tailed parameterizations, which we propose to stabilize with a combination of mirror ascent-type updates and gradient tracking. Our main theoretical contribution is the establishment that this scheme converges with constant batch sizes, whereas prior works require these parameters to respectively shrink to null or grow to infinity. Experimentally, this scheme under a heavy-tailed policy parameterization yields improved reward accumulation across a variety of settings as compared with standard benchmarks.

The 17 papers in this special section focus on resilience in networked robotic systems. This collection of articles aims to provide a deeper understanding of resilience as it pertains to multirobot systems, and to disseminate the current advances in designing and operating networked robotic systems. We understand resilience to be a characteristic that enables amultirobot system to withstand or overcome unexpected adverse conditions or shocks, and unknown, unmodeled disturbances. It refers to the contingent nature of the robots’ behaviors that is aimed at preserving their functionality or minimizing the time periods during which their functionality is compromised. The papers explore new algorithmic and mathematical foundations toward resilience.

Higher spectral and energy efficiencies are the envisioned defining characteristics of next-generation high data-rate sixth-generation (6G) wireless networks. One of the enabling technologies to meet these requirements is index modulation (IM), which transmits information through permutations of indices of spatial, frequency, or temporal media. In this paper, we propose novel electromagnetics-compliant designs of reconfigurable intelligent surface (RIS) apertures for realizing IM in 6G transceivers. We consider RIS modeling and implementation of spatial and subcarrier IMs, including beam steering, spatial multiplexing, and phase modulation capabilities. Numerical experiments for our proposed implementations show that the bit-error-rates obtained via RIS-aided IM outperform traditional implementations. We further establish the programmable ability of these transceivers to vary the reflection phase and generate frequency harmonics for IM through full-wave electromagnetic analyses of a specific reflect-array metasurface implementation.

Decentralized coordination of a robot swarm requires addressing the tension between local perceptions and actions, and the accomplishment of a global objective. In this work, we propose to learn decentralized controllers based solely on raw visual inputs. For the first time, this integrates the learning of two key components: communication and visual perception, in one end-to-end framework. More specifically, we consider that each robot has access to a visual perception of the immediate surroundings, and communication capabilities to transmit and receive messages from other neighboring robots. Our proposed learning framework combines a convolutional neural network (CNN) for each robot to extract messages from the visual inputs, and a graph neural network (GNN) over the entire swarm to transmit, receive and process these messages in order to decide on actions. The use of a GNN and locally-run CNNs results naturally in a decentralized controller. We jointly train the CNNs and the GNN so that each robot learns to extract messages from the images that are adequate for the team as a whole. Our experiments demonstrate the proposed architecture in the problem of drone flocking and show its promising performance and scalability, e.g., achieving successful decentralized flocking for large-sized swarms.

This paper presents a uniqueness result which states that a band- limited signal can be recovered from at least 3B measurements where B is the bandwidth from the radar ambiguity function (AF). This function is a two-dimensional mapping of the propagation delay and Doppler frequency. This formal model represents the distortion of a returned pulse due to the receiver matched filter. To estimate a time/band-limited signal from its radar AF, a trust region algorithm that minimizes a smoothed non-convex least-squares objective function is proposed. The method consists of two steps. First, we approximate the signal by an iterative spectral algorithm. Then, the attained initialization is refined based upon a sequence of gradient iterations. To the best of our knowledge this work is seminal in the sense of solving the radar phase retrieval problem for band-limited signals. Simulations results suggest that the proposed algorithm is able to estimate band-limited signals from its radar AF for both complete and incomplete radar cases. The AF is incomplete when only few shifts are considered. Numerical results show that the proposed algorithm estimates the signal with mean-square-error of 5 × 10-2for both complete and incomplete noisy cases.

We study the problem of securely communicating a sequence of information bits with a client in the presence of multiple adversaries at unknown locations in the environment. We assume that the client and the adversaries are located in the far-field region, and all possible directions for each adversary can be expressed as a continuous interval of directions. In such a setting, we develop a periodic transmission strategy, i.e., a sequence of joint beamforming gain and artificial noise pairs, that prevents the adversaries from decreasing their uncertainty on the information sequence by eavesdropping on the transmission. We formulate a series of nonconvex semi-infinite optimization problems to synthesize the transmission strategy. We show that the semi-definite program (SDP) relaxations of these nonconvex problems are exact under an efficiently verifiable sufficient condition. We approximate the SDP relaxations, which are subject to infinitely many constraints, by randomly sampling a finite subset of the constraints and establish the probability with which optimal solutions to the obtained finite SDPs and the semi-infinite SDPs coincide. We demonstrate with numerical simulations that the proposed periodic strategy can ensure the security of communication in scenarios in which all stationary strategies fail to guarantee security.

We consider the recovery of a complex band-limited radar waveform from the magnitude of the fractional Fourier transform (FrFT) formulation of its ambiguity function (AF). This is essentially a phase retrieval (PR) problem applied to radar waveform design. The FrFT-based AF is mathematically obtained by correlating the signal with its frequency-rotated, Doppler-shifted, and delayed replicas. It completely characterizes the radar's capability to discriminate closely-spaced targets in the delay-Doppler plane. Unlike prior works which largely involved analytical approaches, our method WaveMax formulates the recovery of the waveform via the FrFT-based AF PR as a convex optimization problem. Specifically, we retrieve the signal by solving a basis pursuit that requires a designed approximation of the radar signal obtained by extracting the leading eigenvector of a matrix depending on the AF. Our theoretical analysis shows that unique waveform reconstruction is possible using signal samples no more than thrice the number of signal frequencies or time samples. Numerical experiments demonstrate that our method recovers band-limited signals from both even-sparse and random samples of the AFs with a mean squared error of$1\times 10^{-6}$and$5\times 10^{-2}$for full noiseless samples and sparse noisy samples, respectively.

Existing studies on question answering on knowledge bases (KBQA) mainly operate with the standard i.i.d. assumption, i.e., training distribution over questions is the same as the test distribution. However, i.i.d. may be neither achievable nor desirable on large-scale KBs because 1) true user distribution is hard to capture and 2) randomly sampling training examples from the enormous space would be data-inefficient. Instead, we suggest that KBQA models should have three levels of built-in generalization: i.i.d., compositional, and zero-shot. To facilitate the development of KBQA models with stronger generalization, we construct and release a new large-scale, high-quality dataset with 64,331 questions, GrailQA, and provide evaluation settings for all three levels of generalization. In addition, we propose a novel BERT-based KBQA model. The combination of our dataset and model enables us to thoroughly examine and demonstrate, for the first time, the key role of pre-trained contextual embeddings like BERT in the generalization of KBQA.1

Fingerprint embedding at the physical layer is a highly tunable authentication framework for wireless communication that achieves information-theoretic security by hiding a traditional HMAC tag in noise. In a multiantenna scenario, artificial noise (AN) can be transmitted to obscure the tag even further. The AN strategy, however, relies on perfect knowledge of the channel state information (CSI) between the legitimate users. When the CSI is not perfectly known, the added noise leaks into the receiver's observations. In this article, we explore whether AN still improves security in the fingerprint embedding authentication framework with only imperfect CSI available at the transmitter and receiver. Specifically, we discuss and design detectors that account for AN leakage and analyze the adversary's ability to recover the key from observed transmissions. We compare the detection and security performance of the optimal perfect CSI detector with the imperfect CSI robust matched filter test and a generalized likelihood ratio test (GLRT). We find that utilizing AN can greatly improve security, but suffers from diminishing returns when the quality of CSI knowledge is poor. In fact, we find that in some cases allocating additional power to AN can begin to decrease key security.

This paper considers the secrecy performance of several schemes for multi-antenna transmission to single-antenna users with full-duplex (FD) capability against randomly distributed single-antenna eavesdroppers (EDs). These schemes and related scenarios include transmit antenna selection (TAS), transmit antenna beamforming (TAB), artificial noise (AN) from the transmitter, user selection based their distances to the transmitter, and colluding and non-colluding EDs. The locations of randomly distributed EDs and users are assumed to be distributed as Poisson Point Process (PPP). We derive closed form expressions for the secrecy outage probabilities (SOP) of all these schemes and scenarios. The derived expressions are useful to reveal the impacts of various environmental parameters and user's choices on the SOP, and hence useful for network design purposes. Examples of such numerical results are discussed.

Arrays of closely-spaced antennas with mutual coupling have been considered recently with analogies to the hearing mechanism in small insects that exhibit excellent direction finding capabilities. We develop a model for a two-element array system that includes three distinct noise sources and a 4-port electrical network that couples the antennas to the measurement loads. The optimum coupling network that minimizes the Cramer-Rao bound (CRB) for angle of arrival (AOA) estimation is derived and a design method is presented to synthesize the network. A compact expression for the CRB provides insights about the fundamental value of mutual coupling and the importance of an optimum coupling network, and we show simulation results for an AOA estimator that achieves the CRB.

We study strategies to deploy defenders/sensors to detect intruders that approach a targeted region. This scenario is formulated as a barrier coverage, which aims to minimize the number of unseen paths. The problem becomes challenging when the number of defenders is insufficient for a full coverage, requiring us to find the most effective location to deploy them. To this end, we use ideas from game theory to account for various paths that the intruders may take. Specifically, we propose an iterative algorithm to refine the set of candidate defender formations, which uses the payoff matrix to directly evaluate the utility of different formations. Given the set of candidate formations, a mixed Nash equilibrium gives a stochastic policy to deploy the defenders. The efficacy of the proposed strategy is demonstrated by a numerical analysis that compares our method with an existing graph-theoretic method.

In this paper, we investigate the problem of distributed coherent beamforming in wireless networks where multiple distributed transmitters adjust the phases of their signals to form a directional and targeted communication link to a client receiver. The quality-of-service (QoS) and security are key components of robust and covert wireless networks. Although the security can be improved by exploiting information about potential adversaries, such information may not be available in practical networks since the adversaries are often passive. Therefore, we introduce transmission strategies which not only send a confidential message by forming a beam towards the client but also broadcast interference with the aim of obfuscating adversaries without having any information about them. Two different client feedback scenarios are considered, two-bit and rich feedback. The proposed algorithms can be performed in a fully distributed manner without any knowledge about potential adversaries. Numerical simulations validate the effectiveness of the proposed schemes.

In this paper, we investigate a wireless network where multiple distributed transmitters adjust the phases of their signals so that they can be constructively added at an intended receiver (client). Unlike conventional beamforming with co-located and phase-synchronized antennas, geographically separated transmitters may have phase offsets induced by individual local carrier oscillators, that pose a challenge for coherent distributed beamforming. This is especially true for transmitters that are far apart, when distributed clock synchronization protocols may be more difficult to implement. There may also be a desired spatial repulsion among the positions of the transmitters in order to mitigate mutual coupling effects and extend the coverage region. In this regard, we analyze the performance of distributed beamforming with phase offsets by modeling the spatial distribution of the transmitters as a $\beta$-Ginibre point process that models the repulsive behavior. We consider two transmission strategies: (i) Transmitter selection in which the client chooses the transmitter providing the highest received power at the client, and (ii) Coherent beamforming in which multiple transmitters simultaneously send their signals to the client. From numerical simulations, we examine the impact of the phase offsets on the performance and confirm the accuracy of our analysis. It is shown that even with significant phase offset errors, employing coherent beamforming can be an effective strategy.

Mining entity synonym sets (i.e., sets of terms referring to the same entity) is an important task for many entity-leveraging applications. Previous work either rank terms based on their similarity to a given query term, or treats the problem as a two-phase task (i.e., detecting synonymy pairs, followed by organizing these pairs into synonym sets). However, these approaches fail to model the holistic semantics of a set and suffer from the error propagation issue. Here we propose a new framework, named SynSetMine, that efficiently generates entity synonym sets from a given vocabulary, using example sets from external knowledge bases as distant supervision. SynSetMine consists of two novel modules: (1) a set-instance classifier that jointly learns how to represent a permutation invariant synonym set and whether to include a new instance (i.e., a term) into the set, and (2) a set generation algorithm that enumerates the vocabulary only once and applies the learned set-instance classifier to detect all entity synonym sets in it. Experiments on three real datasets from different domains demonstrate both effectiveness and efficiency of SynSetMine for mining entity synonym sets.

Given a text description, most existing semantic parsers synthesize a program in one shot. However, it is quite challenging to produce a correct program solely based on the description, which in reality is often ambiguous or incomplete. In this paper, we investigate interactive semantic parsing, where the agent can ask the user clarification questions to resolve ambiguities via a multi-turn dialogue, on an important type of programs called “If-Then recipes.” We develop a hierarchical reinforcement learning (HRL) based agent that significantly improves the parsing performance with minimal questions to the user. Results under both simulation and human evaluation show that our agent substantially outperforms non-interactive semantic parsers and rule-based agents.1

We present a statistical analysis of the secrecy capacity for two downlink transmission schemes: transmitantenna selection (TAS) and transmit- antenna beamforming (TAB), where the transmitter (Alice) has multiple antennas, the receiver (Bob) is a single-antenna full-duplex radio, and the eavesdroppers are randomly distributed each with a single antenna. We focus on the secrecy outage probability (SOP) or its related measure, show closed-form expressions of SOP for the two schemes, and present insights into how various system parameters such as the jamming powers from both Alice and Bob affect SOP. The SOP performance of TAB is shown to be significantly better than that of TAS although TAB requires more on channel estimation than TAS.

In this paper, we focus on the problem of distributed coherent beamforming in wireless networks where multiple distributed transmitters adjust the phases of their signals to form a directional and targeted communication link to a client receiver. The quality-of-service (QoS) and security are key aspects of robust and covert wireless networks. Although the security can be enhanced by exploiting information about the locations of adversaries, such information may not be available in practical networks since the adversaries are often passive. Therefore, we propose transmission strategies which divide transmitters into two groups where one group forms a beam towards the client and the other group broadcasts interference in order to obfuscate adversaries. As the interference may degrade the QoS of the client, the latter group steers a null to the client to alleviate the interference at the client. The proposed scheme can be performed in a fully distributed manner with only two bits of feedback information from the client and without any knowledge about the locations of potential adversaries.

This paper studies Dictionary Learning problems wherein the learning task is distributed over a multi-agent network, modeled as a time-varying directed graph. This formulation is relevant, for instance, in Big Data scenarios where massive amounts of data are collected/stored in different locations (e.g., sensors, clouds) and aggregating and/or processing all data in a fusion center might be inefficient or unfeasible, due to resource limitations, communication overheads or privacy issues. We develop a unified decentralized algorithmic framework for this class of nonconvex problems, which is proved to converge to stationary solutions at a sublinear rate. The new method hinges on Successive Convex Approximation techniques, coupled with a decentralized tracking mechanism aiming at locally estimating the gradient of the smooth part of the sum-utility. To the best of our knowledge, this is the first provably convergent decentralized algorithm for Dictionary Learning and, more generally, bi-convex problems over (time-varying) (di)graphs.

We propose a non-adaptive unequal error protection (UEP) querying policy based on superposition coding for the noisy 20 questions problem. In this problem, a player wishes to successively refine an estimate of the value of a continuous random variable by posing binary queries and receiving noisy responses. When the queries are designed non-adaptively as a single block and the noisy responses are modeled as the outputs of a binary symmetric channel the 20 questions problem can be mapped to an equivalent problem of channel coding with UEP. A new non-adaptive querying strategy based on UEP superposition coding is introduced whose estimation error decreases with an exponential rate of convergence that is significantly better than that of the UEP repetition coding introduced by Variani et al. (2015). In fact, we show that the proposed non-adaptive UEP querying policy achieves the same order convergence rate as the adaptive policy.

Taxonomies are of great value to many knowledge-rich applications. As the manual taxonomy curation costs enormous human effects, automatic taxonomy construction is in great demand. However, most existing automatic taxonomy construction methods can only build hypernymy taxonomies wherein each edge is limited to expressing the is-a relation. Such a restriction limits their applicability to more diverse real-world tasks where the parent-child may carry different relations. In this paper, we aim to construct a task-guided taxonomy from a domain-specific corpus, and allow users to input a seed taxonomy, serving as the task guidance. We propose an expansion-based taxonomy construction framework, namely HiExpan, which automatically generates key term list from the corpus and iteratively grows the seed taxonomy. Specifically, HiExpan views all children under each taxonomy node forming a coherent set and builds the taxonomy by recursively expanding all these sets. Furthermore, HiExpan incorporates a weakly-supervised relation extraction module to extract the initial children of a newly-expanded node and adjusts the taxonomy tree by optimizing its global structure. Our experiments on three real datasets from different domains demonstrate the effectiveness of HiExpan for building task-guided taxonomies.

Taxonomy construction is not only a fundamental task for semantic analysis of text corpora, but also an important step for applications such as information filtering, recommendation, and Web search. Existing pattern-based methods extract hypernym-hyponym term pairs and then organize these pairs into a taxonomy. However, by considering each term as an independent concept node, they overlook the topical proximity and the semantic correlations among terms. In this paper, we propose a method for constructing topic taxonomies, wherein every node represents a conceptual topic and is defined as a cluster of semantically coherent concept terms. Our method, TaxoGen, uses term embeddings and hierarchical clustering to construct a topic taxonomy in a recursive fashion. To ensure the quality of the recursive process, it consists of: (1) an adaptive spherical clustering module for allocating terms to proper levels when splitting a coarse topic into fine-grained ones; (2) a local embedding module for learning term embeddings that maintain strong discriminative power at different levels of the taxonomy. Our experiments on two real datasets demonstrate the effectiveness of TaxoGen compared with baseline methods.

Supporting networks with a large number of nodes in infrastructure-poor and complex propagation environments is an important challenge for military and civilian applications. A major problem when using classical approaches, such as code division multiple access (CDMA), is maintaining inter-link coordination while mitigating multi-user interference (MUI). By contrast, loosely synchronous (LS) codes have perfect code orthogonality within a window of inter-link delays at a cost of the number of available spreading codes. Since sporadic communications naturally involves a low probability of transmission, we investigate the potential for LS code reuse to effectively support more users. We study this problem by simulating inter-user channels using a high-fidelity physics-based model. We focus our study of the channel on the low-VHF band, which has improved penetration and channel coherence in complex environments. We perform initial characterization of different levels of code reuse, synchronization and coordination. Of particular interest is a purely random (uncoordinated) spreading code assignment. The results illustrate good performance of a scalable medium access scheme.

Authentication via fingerprint embedding at the physical layer utilizes noise in the wireless channel to attain a certain degree of information theoretic security that traditional HMAC methods cannot provide. Fingerprint embedding refers to a key-aided process of superimposing a low-power tag to the primary message waveform for the purpose of authenticating the transmission. The tag is uniquely created from the message and key and successful authentication is achieved when the correct tag is detected by the receiver. This paper generalizes a framework for embedding physical layer fingerprints to create an authenticated side-channel for minimal cost. Side-channel information is conveyed to the receiver through the transmitter's choice of tag from a secret codebook generated by the primary message and a shared secret key. In addition, a new linear coding scheme is introduced which enhances the ability to trade off the performance goals of authentication, side-channel rate, secrecy, and privacy.

In this paper, we propose an open-loop unequal-error-protection querying policy based on superposition coding for the noisy 20 questions problem. In this problem, a player wishes to successively refine an estimate of the value of a continuous random variable by posing binary queries and receiving noisy responses. When the queries are designed non-adaptively as a single block and the noisy responses are modeled as the output of a binary symmetric channel, the 20 questions problem can be mapped to an equivalent problem of channel coding with unequal error protection (UEP). A new non-adaptive querying strategy based on UEP superposition coding is introduced, whose estimation error decreases with an exponential rate of convergence that is significantly better than that of the UEP repetition coding introduced by Variani et al. (2015). With the proposed querying strategy, the rate of exponential decrease in the number of queries matches the rate of a closed-loop adaptive scheme, where queries are sequentially designed with the benefit of feedback. Furthermore, the achievable error exponent is significantly better than that of random block codes employing equal error protection.

We show a direct relationship between the variance and the differential entropy for subclasses of symmetric and asymmetric unimodal distributions by providing an upper bound on variance in terms of entropy power. Combining this bound with the well-known entropy power lower bound on variance, we prove that the variance of the appropriate subclasses of unimodal distributions can be bounded below and above by the scaled entropy power. As the differential entropy decreases, the variance is sandwiched between two exponentially decreasing functions in the differential entropy. This establishes that for the subclasses of unimodal distributions, the differential entropy can be used as a surrogate for concentration of the distribution.

We present a semi-automated framework for constructing factoid question answering (QA) datasets, where an array of question characteristics are formalized, including structure complexity, function, commonness, answer cardinality, and paraphrasing.Instead of collecting questions and manually characterizing them, we employ a reverse procedure, first generating a kind of graph-structured logical forms from a knowledge base, and then converting them into questions.Our work is the first to generate questions with explicitly specified characteristics for QA evaluation.We construct a new QA dataset with over 5,000 logical form-question pairs, associated with answers from the knowledge base, and show that datasets constructed in this way enable finegrained analyses of QA systems.The dataset can be found in https://github.com/ysu1989/GraphQuestions.

We consider stochastic optimization problems in multi-agent settings, where a network of agents aims to learn decision variables which are optimal in terms of a global objective, while giving preference to locally and sequentially observed information. To do so, we formulate a problem where each agent minimizes a global objective while enforcing network proximity constraints, which includes consensus optimization as a special case. We propose a stochastic variant of the saddle point algorithm proposed by Arrow and Hurwicz to solve it, which yields a decentralized algorithm that is shown to asymptotically converge to a primal-dual optimal pair of the problem in expectation when a diminishing algorithm step-size is chosen. Moreover, the algorithm converges linearly to a neighborhood when a constant step-size is chosen. We apply this method to the problem of sequentially estimating a correlated random field in a sensor network, which corroborates these performance guarantees.

In this paper, we propose an unequal error protection coding strategy based on superposition coding for the noisy 20 questions problem. In this problem, a player wishes to successively refine an estimate of the value of a continuous random variable by posing binary queries and receiving noisy responses. When the queries are designed non-adaptively as a single block and the noisy responses are modeled as the output of a binary symmetric channel the 20 questions problem can be mapped to an equivalent problem of channel coding with unequal error protection (UEP). A superposition coding strategy with UEP is introduced that has error exponent that is significantly better than that of the UEP repetition code introduced by Variani et al. [1].

As the technology for acquiring and storing images becomes more prevalent, we are faced with a growing need to sort and label these images. At this time, computer vision algorithms cannot parse abstract concepts from images like a human. As a result, there may be performance gains possible from the integration of human analysts with computer vision agents. We present an image triage system which facilitates the collaboration of heterogeneous agents through a novel unsupervised meta-learning technique. The system iteratively allocates images for binary classification among heterogeneous agents according to the Generalized Assignment Problem (GAP) and combines the classification results using the Spectral Meta-Learner (SML). In simulation, we demonstrate that the proposed system achieves significant speed-up over a naive parallel assignment strategy without sacrificing accuracy.

We address the issue of wireless ad hoc network connectivity by using a tail model that is derived from the signal to interference and noise ratio (SINR). The SINR model more accurately describes link connectivity than the traditionally used disk model in the real-world. We first assess the network connectivity by measuring the conductance of the network and find the bottleneck location of the network, and then deploy a relay node to improve the connectivity at the bottleneck. A partition algorithm is proposed to address the first problem, and an optimization problem is proposed to address the relay node deployment problem. The relay node deployment problem is solved by using approximate convex optimization models, and the approximation performance is analyzed. Simulation results show that the partition algorithm based on the SINR model identifies the network bottleneck more accurately than the previous methods based on the binary model. It also verifies that the relay node can significantly relieve the bottleneck and make the network more tightly knit.

The lower VHF band shows potential for reliable communications in low power, short range scenarios among near-ground nodes in both indoor and urban environments. Such scenarios are of great interest, for example, in military and search-and-rescue settings. Most prior work at low VHF focuses on modeling path loss at long range. In this paper, we study indoor/outdoor near-ground scenarios through experiments focusing on both line-of-sight (LoS) and non-LoS (NLoS), at ranges up to 200 meters. By transmitting tones and pulses from various locations in a realistic environment, we acquire channel data via a mobile data collection platform which gathers data at hundreds of different locations. We show that the measured channels have a nearly ideal scalar attenuation and delay transfer function, with minimal phase distortion, and little evidence of multipath propagation. We further confirm the absence of small scale fading by measuring bit error rate (BER) versus received signal-to-noise ratio (SNR) for QPSK transmission in an indoor setting. Using only timing and carrier estimation at the receiver, the resulting BER curves coincide with theoretical additive white Gaussian noise channel BER predictions.

Hyperspectral remote sensing often captures imagery where the spectral profiles of the spatial pixels are the result of the reflectance contribution of numerous materials. Spectral unmixing is then used to extract the collection of materials, or endmembers, contained in the measured spectra and a set of corresponding fractions that indicate the abundance of each material present at each pixel. This paper aims at developing a spectral unmixing algorithm directly from compressive measurements acquired using the coded-aperture snapshot spectral imaging (CASSI) system. The proposed method first uses the compressive measurements to find a sparse vector representation of each pixel in a 3-D dictionary formed by a 2-D wavelet basis and a known spectral library of endmembers. The sparse vector representation is estimated by solving a sparsity-constrained optimization problem using an algorithm based on the variable splitting augmented Lagrangian multipliers method. The performance of the proposed spectral unmixing method is improved by taking optimal CASSI compressive measurements obtained when optimal coded apertures are used in the optical system. The optimal coded apertures are designed such that the CASSI sensing matrix satisfies a restricted isometry property with high probability. Simulations with synthetic and real hyperspectral cubes illustrate the accuracy of the proposed unmixing method.

We consider the network connectivity problem in a wireless ad hoc network. Network connectivity is measured by the conductance of the network, also called the Cheeger constant of the graph. A partition algorithm based on this measure is developed that divides the network at the bottleneck area. After the network is bisected, a relay node may be deployed between the two parts to increase the conductance of the network. The relay node deployment problem is formulated as an integer linear program to maximize the number of connections between the nodes on the two sides of the cut, and then a convex optimization algorithm is used to find the precise location of the relay node, which is within the convex hull defined by the radio transmission ranges of all the nodes that can connect to the relay node. The relay node significantly relieves the bottleneck, and the graph connectivity measured by other metrics such as the widely used Fiedler value are also increased.

We study the problem of deploying a high number of low-cost, low-complexity robots inside a known environment with the objective that at least one robotic platform reaches each of N preassigned goal locations. Our study is inspired by SensorFly, a micro-aerial vehicle successfully used for mobile sensor network applications. SensorFly nodes feature limited on-board sensors, so one has to rely on simple navigation strategies and increase performance through redundance in the team. We introduce a simple, fully scalable deployment algorithm exploiting the limited capabilities offered by the SensorFly platform, and we explore its performance by feeding the simulation system with parameters extracted from the real SensorFly platform.

In this paper we consider the problem of multirobot deployment under temporal deadlines. The objective is to compute strategies trading off safety for speed to maximize the probability of reaching a given set of target locations within a pre-assigned temporal deadline. We formulate this problem using the theory of Constrained Markov Decision Processes and we show that thanks to this framework it is possible to determine deploying strategies maximizing the probability of success while satisfying the deadline. Moreover, the formulation allows to exactly compute the failure probability of complex deployment tasks. Simulation results illustrate how the proposed method works in different scenarios and show how informed decisions can be made regarding the size of the robot team.

The predicted ubiquity of light-emitting diodes (LEDs) suggests great potential for dual-use systems with visible light communications (VLC) capabilities. One class of LEDs employs multiple emitters at different wavelengths, making them appropriate for applications requiring colored output and providing multiple channels for communication. We present a design framework for optimizing the signaling constellation of VLC systems employing an arbitrary number of such LEDs, each with an arbitrary number of emitters. In particular, by considering the design of constellation-symbol locations jointly across LED emitters, the framework provides for the precompensation of linear channel distortions arising from, for example, cross-channel leakage, noise correlation, or wavelength-dependent received-signal and/or noise power, as well as the incorporation of design constraints for color-shift keying, dimming, and/or perceived color. Simulation results demonstrate the design approach and the potential performance enhancement that can be achieved for particular system scenarios. Experimental measurements using a prototype VLC system confirm such performance enhancements, providing real-world evidence of the benefit of applying the proposed framework to VLC channel precompensation.

Traditional hyperspectral imaging sensors acquire high-dimensional data that are used for the discrimination of objects and features in a scene. Recently, a novel architecture known as the coded-aperture snapshot spectral imaging (CASSI) system has been developed for the acquisition of compressive spectral image data with just a few coded focal plane array measurements. This paper focuses on developing a classification approach with hyperspectral images directly from CASSI compressive measurements, without first reconstructing the full data cube. The proposed classification method uses the compressive measurements to find the sparse vector representation of the test pixel in a given training dictionary. The estimated sparse vector is obtained by solving a sparsity-constrained optimization problem and is then used to directly determine the class of the unknown pixel. The performance of the proposed classifier is improved by taking optimal CASSI compressive measurements obtained when optimal coded apertures are used in the optical system. The set of optimal coded apertures is designed such that the CASSI sensing matrix satisfies a restricted isometry property with high probability. Several simulations illustrate the performance of the proposed classifier using optimal coded apertures and the gain in the classification accuracy obtained over using traditional aperture codes in CASSI.

We consider the problem of 20 questions with noise for multiple players under the minimum entropy criterion in the setting of stochastic search, with application to target localization. Each player yields a noisy response to a binary query governed by a certain error probability. First, we propose a sequential policy for constructing questions that queries each player in sequence and refines the posterior of the target location. Second, we consider a joint policy that asks all players questions in parallel at each time instant and characterize the structure of the optimal policy for constructing the sequence of questions. This generalizes the single player probabilistic bisection method for stochastic search problems. Third, we prove an equivalence between the two schemes showing that, despite the fact that the sequential scheme has access to a more refined filtration, the joint scheme performs just as well on average. Fourth, we establish convergence rates of the mean-square error and derive error exponents. Finally, we obtain an extension to the case of unknown error probabilities. This framework provides a mathematical model for incorporating a human in the loop for active machine learning systems.

We consider the problem of 20 questions with noise for collaborative players under the minimum entropy criterion [1] in the setting of stochastic search, with application to target localization. First, assuming conditionally independent collaborators, we characterize the structure of the optimal policy for constructing the sequence of questions. This generalizes the single player probabilistic bisection method [1, 2] for stochastic search problems. Second, we prove a separation theorem showing that optimal joint queries achieve the same performance as a greedy sequential scheme. Third, we establish convergence rates of the mean-square error (MSE). Fourth, we derive upper bounds on the MSE of the sequential scheme. This framework provides a mathematical model for incorporating a human in the loop for active machine learning systems.

The traditional Nyquist sampling architecture does not provide a feasible solution in a large multi-channel ultrasound imaging system. The main issues are the huge data volume after the analog-to-digital interface, high power consumption, and circuit complexity at both the front-end and mid-end. This paper presents a Compressed Digital Beamformer (CDB) framework for the design of an ultrasound imaging system with a large transducer array (≥ 1024) operating at a moderate carrier frequency (≥ 5 MHz). Simulations demonstrate that the proposed CDB framework achieves a Compression Ratio (CR) of 0.1 and Mean Square Error (MSE) of -27.7 dB with 4 quantization bits.

In this paper we study the multi-robot deployment problem under hard temporal constraints. After proposing a model for this task, we consider the simplest deployment algorithm and we analyze the relationship between three fundamental parameters, the temporal deadline, the probability of success, and the number of robots. Because an exact analysis of even the simplest algorithm is computationally intractable, we derive an approximate bound leading to performance curves useful to answer design questions (how many robots are needed to get a certain performance guarantee?) or analysis questions (what is the probability of success given a certain deadline and number of robots?) Simulations show that the bounds are sharp and provide a useful tool to predict team deployment performance and tradeoffs.

We consider the combined problem of frontier exploration in a complex indoor environment while seeking a radio source. To do this in an efficient manner, we incorporate radio signal strength (RSS) information into the exploration algorithm by locally sampling the RSS and estimating the 2-D RSS gradient. The algorithm exploits the local motion to collect RSS samples for gradient estimation and seeks to explore in a way that brings the robot to the signal source. This strategy avoids random or exhaustive exploration. An indoor experiment demonstrates the exploration algorithm that uses this information to dynamically prioritize candidate frontiers and traverse to a radio source. Simulations, including radio propagation modeling with a ray-tracing algorithm, enable study of control algorithm tradeoffs and statistical performance.

The solar-blind ultraviolet (UV) spectrum has useful properties for wireless communication and sensing. Strong atmospheric scattering in the UV spectrum enables non-line-of-sight (NLOS) communication. The authors present recent experimental and analytical results in NLOS UV channel modelling, including impulse response and path loss. The authors further study the NLOS UV link performance for short-range communication scenarios based on our theoretical modelling results. Relations between power limitation and channel bandwidth limitation are examined. Some link budget results are analysed for long-range communication links up to 5 km.

In the above titled paper (ibid., vol. 27, no. 9, pp. 1535-1544, Dec. 09), two figures were inaccurate due to a software programming error when implementing the path loss prediction algorithm. The corrected Figs. 4 and 6 are presented here. The correction does not otherwise affect results in the above paper.

We consider authentication of a wireless multiple-input-multiple-output (MIMO) system by deliberately introducing a stealthy fingerprint at the physical layer. The fingerprint is superimposed onto the data and uniquely conveys an authentication message as a function of the transmitted data and a shared secret key. A symbol synchronous approach to fingerprint embedding provides low complexity operation. In comparison with a conventional tag-based authentication approach, fingerprinting conveys much less information on the secret key to an eavesdropper. We study the trade-offs between stealth, security, and robustness, and show that very good operating points exist. We consider the cases when deterministic or statistical channel state information is available to the transmitter, and show how precoding and channel mode power allocation can be applied to both the data and the fingerprint in combination to enhance the authentication process.

For ultra-wideband (UWB) impulse radios, noncoherent energy detectors are motivated for their simple circuitry and effective capture of multipath energy. A major performance-degrading factor in energy detection is the noise floor, which is aggravated for low-duty-cycle UWB signals with a large time-bandwidth product. In this paper, weighted energy detection (WED) techniques are developed for effective noise suppression. The received signal is processed by a set of parallel integrators, each corresponding to a different integration time-window within a symbol period. The outputs of these integrators are weighted and linearly combined to generate decision statistics, while the weights are determined by the signal power collected from the corresponding integrators to improve the effective signal to noise ratio. The WED principle is applied to all phases of receiver processing, including signal detection, timing synchronization and data demodulation. For each phase, the optimal linear detector parameters, including decision thresholds and weighting coefficients, are derived analytically. Simulations show that the proposed noncoherent WED receiver enhances the bit-error-rate performance compared to conventional energy detectors.

We develop Ziv-Zakai bounds (ZZBs) on time delay estimation (TDE) for known frequency hopping or multicarrier waveforms over random wideband (frequency selective) Gaussian channels, with a uniform prior on the delay. The channel model incorporates correlation, for example between subcarriers. A unified signal model is applicable to several cases of interest. The receiver does not have channel state information to estimate the time delay, but does have knowledge of the channel statistics. The ZZB provides a tight bound on the mean square error for Bayesian estimation in wideband random channels, revealing SNR threshold behavior indicating estimator breakdown.

To address the challenges inherent to a problem of practical interest - of Neyman-Pearson detection of fluctuating radar signals using wireless sensor networks, we propose in this paper a distributed and energy-efficient framework. Such framework is scalable with respect to the network size, and is able to greatly reduce the dependence on the central fusion center. It assumes a clustering infrastructure, and addresses signal processing and communications related issues arising from different layers. This framework includes a distributed scheduling protocol and a distributed routing protocol, which enable sensor nodes to make their own decisions about information transmissions, without requiring the knowledge of the network global information. In this framework, energy efficiency manifests itself at different network layers in a distributed fashion, and a balance between the detection performance and the energy efficiency is also attained.

Abstract The rapid proliferation of wireless systems makes interference management more and more important. This paper presents a novel cognitive coexistence framework, which enables an infrastructure system to reduce interference toad hocor peer‐to‐peer communication links in close proximity. Motivated by the superior resources of the infrastructure system, we study how its centralized resource allocation can accommodate thead hoclinks based on sensing and predicting their interference patterns. Based on an ON/OFF continuous‐time Markov chain model, the optimal allocation of power and transmission time is formulated as a convex optimization problem and structured solutions are derived. The optimal scheduling is extended to the case where the infrastructure channel is random and rate constraints need only be met in the long‐term average. Finally, the multi‐terminal case is addressed and the problem of optimal sub‐channel allocation is discussed. Numerical performance analysis illustrates that utilizing the superior flexibility of the infrastructure links can effectively mitigate interference. Copyright © 2009 John Wiley & Sons, Ltd.

Sampling is the bottleneck for ultra-wideband (UWB) communication. Our major contribution is to exploit the channel itself as part of compressed sensing, through waveform-based pre-coding at the transmitter. We also have demonstrated a UWB system baseband bandwidth (5 GHz) that would, if with the conventional sampling technology, take decades for the industry to reach. The concept has been demonstrated, through simulations, using real-world measurements. Realistic channel estimation is also considered.

A stochastic non-line-of-sight (NLOS) ultraviolet (UV) communication channel model is developed using a Monte Carlo simulation method based on photon tracing. The expected channel impulse response is obtained by computing photon arrival probabilities and associated propagation delay at the receiver. This method captures the multiple scattering effects of UV signal propagation in the atmosphere, and relaxes the assumptions of single scattering theory. The proposed model has a clear advantage in reliable prediction of NLOS path loss, as validated by outdoor experiments at small to medium elevation angles. A Gamma function is shown to agree well with the predicted impulse response, and this provides a simple means to determine the channel bandwidth. The developed model is employed to study the characteristics of NLOS UV scattering channels, including path loss and channel bandwidth, for a variety of scattering conditions, source wavelength, transmitter and receiver optical pointing geometries, and range.

The proliferation of wireless systems requires that the coexistence between heterogeneous technologies be addressed. This paper presents a cognitive framework in which sensing- based resource management of an infrastructure system effectively suppresses interference to close-by ad-hoc or peer-to- peer links. By utilizing its superior communication resources the infrastructure system estimates interference conditions and judiciously allocates transmission power such as to minimize interference. Despite adapting its transmission behavior, a rate constraint ensures that the infrastructure system continues to meet a specified quality-of-service level. The problem of optimal coexistence is formulated as a convex program. Structured solutions similar to classical water filling are derived and a solution method with guaranteed convergence is developed. An average-rate formulation extends the results to water filling across frequency and time. Numerical results corroborate our analysis and demonstrate a promising interference reduction.

Classification employing sensors connected by wireless networks is of great interest. As the sensor nodes are usually powered by batteries, saving transmissions is important. We demonstrate transmissions can be saved, without degradation in error probability, using an ordering approach. The average number of transmissions saved (ANTS) is lower bounded by a quantity proportional to the number of sensors employed provided a well-behaved distance measure between the sensor distributions is sufficiently large. For such cases, the ANTS over the optimum unconstrained energy approach is shown to be larger than half the number of sensors employed.

We study time delay estimation (TDE) on parallel channels with flat fading. Several models for the channel gains are considered, and for each case we present the the maximum likelihood estimator (MLE), the Cramer-Rao bound (CRB), and the Ziv-Zakai bound (ZZB). The bounds facilitate an analysis of the effects of fading and diversity on TDE accuracy over parallel channels. Computer simulations of the mean-squared error of the MLEs confirm the validity of the bounds.

The emerging theory of compressed sensing (CS) provides a univer sal signal detection approach for sparse signals at sub-Nyquist sampling rates. A small number of random projection measurements from the received analog signal would suffice to provide salient information for signal detection. However, the compressive measure ments are not efficient at gathering signal energy. In this paper, a set of detectors called subspace compressive detectors are proposed where a more efficient detection scheme can be constructed by exploiting the sparsity model of the underlying signal. Furthermore, we show that the signal sparsity model can be approximately estimated using reconstruction algorithms with very limited random measurements on the training signals. Based on the estimated signal sparsity model, an effective subspace random measurement matrix can be designed for unknown signal detection, which significantly reduces the necessary number of measurements. The performance of the subspace compressive detectors is analyzed. Simulation results show the effectiveness of the proposed subspace compressive detectors.

A parallel structure to do spectrum sensing in Cognitive Radio (CR) at sub-Nyquist rate is proposed. The structure is based on Compressed Sensing (CS) that exploits the sparsity of frequency utilization. Specifically, the received analog signal is segmented or time-windowed and CS is applied to each segment independently using an analog implementation of the inner product, then all the samples are processed together to reconstruct the signal. Applying the CS framework to the analog signal directly relaxes the requirements in wideband RF receiver front-ends. Moreover, the parallel structure provides a design flexibility and scalability on the sensing rate and system complexity. This paper also provides a joint reconstruction algorithm that optimally detects the information symbols from the sub-Nyquist analog projection coefficients. Simulations showing the efficiency of the proposed approach are also presented.

Authentication is the process where claims of identity are verified. Though authentication mechanisms typically exist above the physical layer, physical layer methods have recently been introduced that do not require extra bandwidth. In this paper we propose a multi-carrier extension to the work and consider the stealth and robustness tradeoffs. We conclude by discussing the power-reliability tradeoff and the applicability to cross-layer security.

In this paper we design a cognitive radio that can coexist with multiple parallel WLAN channels while abiding by an interference constraint. The interaction between both systems is characterized by measurement and coexistence is enhanced by predicting the WLAN's behavior based on a continuous-time Markov chain model. Cognitive medium access (CMA) is derived from this model by recasting the problem as one of constrained Markov decision processes. Solutions are obtained by linear programming. Furthermore, we show that optimal CMA admits structured solutions, simplifying practical implementations. Preliminary results for the partially observable case are presented. The performance of the proposed schemes is evaluated for a typical WLAN coexistence setup and shows a significant performance improvement.

We develop linear equations for low-complexity source localization, based on time difference of arrival (TDOA) measurements at a linear sensor array. The derivation comes from hyperbolic geometry in Cartesian coordinates, and the algorithm is valid for (possibly wideband) sources in the near or far field. An error analysis is used to predict bias and mean-square error, as well as provide optimal weighting for a low-complexity noniterative weighted least squares solution. A connection is made with an algorithm for arbitrary array geometry. Simulation results show that the proposed algorithm achieves maximum-likelihood performance and the CramrRao bound at medium to low TDOA measurement noise level.

Authentication is the process where claims of identity are verified. Most mechanisms of authentication (e.g., digital signatures and certificates) exist above the physical layer, though some (e.g., spread-spectrum communications) exist at the physical layer often with an additional cost in bandwidth. This paper introduces a general analysis and design framework for authentication at the physical layer where the authentication information is transmitted concurrently with the data. By superimposing a carefully designed secret modulation on the waveforms, authentication is added to the signal without requiring additional bandwidth, as do spread-spectrum methods. The authentication is designed to be stealthy to the uninformed user, robust to interference, and secure for identity verification. The tradeoffs between these three goals are identified and analyzed in block fading channels. The use of the authentication for channel estimation is also considered, and an improved bit-error rate is demonstrated for time-varying channels. Finally, simulation results are given that demonstrate the potential application of this authentication technique.

In this paper we propose cognitive medium access (CMA), a protocol aimed at improving coexistence with a set of independently evolving WLAN bands. A time-slotted physical layer for the cognitive radio is considered and CMA is derived based on experimental models. By recasting the problem as a constrained Markov decision process (CMDP), throughput is optimized while keeping interference below some given constraint. The optimal control policy is obtained via linear programming. In addition, we show that optimal CMA admits structured solutions which are computationally less expensive and allow further insight into the problem. Numerical results are presented for typical coexistence setups and show a significant performance improvement.

The concept of Active Queue Management (AQM), broadly used in Internet Congestion Control, has been recently introduced to ad hoc wireless networks as a means to mitigate the severe TCP unfairness across flows. In particular, Neighborhood RED (NRED) has been proposed as an extension to the Random Early Detection (RED) mechanism with the goal of ensuring fair bandwidth allocation across flows in the networks. NRED provides improvements but suffers from limitations that can make its broad implementation difficult. This paper describes two fundamental principles governing neighborhood congestion among TCP flows in ad hoc wireless networks which are exploited to develop an improved control mechanism. The first principle indicates that the likelihood of the channel being captured by a node grows exponentially with the disparity between the node's channel utilization and the expected utilization level. The second principle indicates that traditional random packet marking in NRED leads to reduced fairness across flows, pointing to a simple dropping/marking strategy based on the concept of error diffusion where packet marks are spread apart as homogeneously as possible. The power of these fundamental principles is demonstrated in a Congestion Control scheme referred to as Neighborhood Diffusion Early Marking (NDEM) which results in a more efficient bandwidth distribution compared to NRED.

We present a general approach for multi-modal sensor fusion based on nonparametric probability density estimation and maximization of a mutual information criterion. We apply this approach to fusion of vector-magnetic and acoustic data for classification of vehicles. Linear features are used, although the approach may be applied more generally with other sensor modalities, nonlinear features, and other classification targets. For the magnetic data, we present a parametric model with computationally efficient parameter estimation. Experimental results are provided illustrating the effectiveness of a classifier that discriminates between cars and sport utility vehicles.

The Ziv-Zakai bound (ZZB) provides a general mean-square error analytical baseline to evaluate time delay estimation (TDE) techniques for a wide range of time-bandwidth products and signal-to-noise ratios, but generally can only be numerically evaluated. The Weiss-Weinstein bound (WWB) further improves characterization of the attainable system performance, for narrowband and wide-band signals with small to moderate fractional bandwidth. Similar to the WWB, here we find a simplified closed-form ZZB for TDE with ultra-wideband (UWB) signals. The resulting simplified bound is found over disjoint segments, separated by thresholds that characterize different regions of ambiguity. The closed-form simplified bound can be analytically studied, and approaches both the ZZB and the TDE performance of a maximum likelihood estimator.

The problem of dynamically accessing a set of parallel channels occupied by primary users is considered. The secondary user is allowed to sense and to transmit in a single channel. By exploiting idle periods between bursty transmissions of primary users, and by using a periodic sensing strategy, optimal dynamic access is achieved by maximizing the throughput of the secondary user while constraining collision probability with the primary user. The optimal dynamic spectrum access problem can then be formulated within the framework of constrained Markov decision processes (CMDPs). The optimal control policy is identified via a linear program, and its performance is analyzed numerically and through Monte Carlo simulations. Finally, we compare the optimal scheme to an ideal benchmark case when simultaneous sensing of all channels is assumed.

Stoica and Ng (1998) presented a simple expression for the constrained Cramer-Rao bound (CCRB) when the constraints are given by a differentiable function of the parameter to be estimated. This letter considers the parallel case in developing the CCRB when the parameters are locally fitted to a lower-dimensional parametric model, i.e., the parameters are locally assumed to be functions of a distinct reduced parameter vector. We employ classical elements of CRB theory on the locally fitted model to present a very simple derivation of the CCRB, conditions for attaining the bound, and a regularity condition. Examples illustrate the key ideas.

For four (or more) transmitters, a new design of differential space-time block code allowing symbol-wise decoding is presented in this letter. The new design not only has the minimum (symbol-wise) decoding complexity as that by Yuen, Guan and Tjhung (YGT) but also yields a lower error rate. While the YGT code uses a specially designed symbol constellation, the new code uses a conventional QAM with a rotation. At a high data rate such as 3 bps/Hz, the new design with symbol-wise decoding complexity can even yield a lower error rate than the code by Zhu and Jafarkhani that has the pair-wise decoding complexity.

This paper presents a reduced-complexity time reversal technique for ultra-wideband (UWB) communications. Time reversal takes advantage of rich scattering environments to achieve signal focusing via transmitter-side processing, which enables the use of simple receivers. The goal of this paper is to demonstrate a UWB time reversal system architecture based on experimental results and practical pulse waveform, taking into account some practical constraints, and to show feasibility of UWB time reversal. Pre-decorrelating in addition to time reversal processing is considered for a downlink multiuser configuration. Multiple transmit antennas are employed to improve the performance.

We consider a mixed-signal ultra wideband (UWB) direct-sequence spread-spectrum (DS-SS) receiver. The receiver expands the signal over a basis set, and then operates on the basis coefficients. An analog computation of the basis coefficients efficiently parallelizes the signal for digital processing, relaxing the sampling requirements and enabling parallel digital processing at a much lower rate. Bit error rate (BER) performance is evaluated, as well as distortion due to basis truncation. The parallel processing also facilitates efficient interference mitigation. Various models for interference including adjacent multicarrier interference are developed and analyzed. Several simulation results confirm the analysis and provide insight into quantization error, basis truncation distortion, and the impact of the UWB channel. We show that in the AWGN uncoded case for SNR < 10 dB, the receiver can be operated at almost half the Nyquist rate with only 1 dB of performance loss. Moreover, when a common convolutional code is employed, the aggregate sampling rate can be 37.5% lower than Nyquist rate with little loss in receiver performance.

An ultra-wideband (UWB) transmitted reference (TR) system transmits an un-modulated pulse and a delayed modulated pulse pair. Then, a correlation receiver uses the former to demodulate the latter. Because of the long spread of a typical UWB channel, time delay between the two pulses is preferable to be larger than the channel delay spread for reduced noise at the receiver. However, for bandwidth efficiency, that delay should be made small, resulting in inter-pulse interference at the receiver. In this paper, digital receivers are constructed for TR-UWB systems including inter-pulse interference. A typical mean matching technique, appropriate for both PPM and PAM schemes, is implemented digitally to obtain a good template for symbol detection. Joint estimation and detection performance of this family of digital receivers, using finite number of bits in analog-to-digital conversion and finite noisy observations, is analyzed. Closed form results are derived and verified by computer simulations. In addition, the effect of time offset between the reference pulse and information carrying pulse is studied. Overlap of the two pulses does not incur noticeable performance degradation. The proposed analytical framework can be applied to study detection performance of other related digital receivers not covered in this paper

A conventional transmitted reference (TR) modulation scheme is an effective means to combat severe multipath distortion in an ultra-wideband (UWB) system, significantly relaxing the equalization requirements. However, it suffers from multiple-access interference and a data rate loss. In this paper, we propose a multiuser TR scheme to extend TR modulation to the multiuser case, while almost doubling the data rate by allowing arbitrarily small spacing of pulse pairs. The proposed scheme assigns a pair of pseudorandom sequences to each user to enable multiple access, modulating the amplitude of data and reference pulses, respectively. A waveform template is first estimated, followed by data demodulation. A time-hopping code can be employed to further minimize the effect of signal collisions. The method is appropriate for both pulse amplitude modulation and pulse position modulation. Waveform estimation and bit-error rate analysis are provided, and confirmed with simulations. Substantial detection improvements over conventional TR detectors are observed.

This letter examines the influence of low-bit quantization on moment estimators with special emphasis on the 1-bit case. Moment estimators are especially useful if no prior knowledge on the distribution of the observations is available or if an ML approach is analytically intractable or computationally infeasible. In order to arrive at analytical results for this very general case, we focus on a dithered quantization scheme that allows us to specify and analyze its asymptotic behavior. We show that consistency can be retained under mild conditions, and furthermore, we quantify the asymptotic variance. Additionally, we illustrate how to find an estimator that achieves the best performance possible in this scenario. Finally, we bolster our analytical results with simulations for the illustrative case of an AR(1) process and provide a comparison with undithered schemes. A conclusion summarizes this letter's contribution and explores possible areas of application

Energy constrained wireless sensor networks need to maintain network timing for coordinating event detection and processing, and to enable receiver duty cycling and communications rendezvous, in order to save energy. Motivated by this, we consider synchronization of a slow low-power local oscillator with an occasionally observed fast and highly accurate broadcast clock (such as GPS). Errors are modeled as quantization noise, incurred when comparing the slow and fast clocks. We consider estimation of the offset and skew between the clocks, detection of drift in the local clock, and prediction of the broadcast clock based on the locally observed times.

This paper introduces a multicarrier communication receiver for broadband applications based on analog to digital conversion (ADC) of the received signal in the frequency domain. The samples of the spectrum of the received signal are used in the digital receiver to estimate the transmitted symbols through a matched filter operation in the discrete frequency domain. The proposed receiver is aimed at the reception of high information rates in a multicarrier signal with very large bandwidth. Thus, the receiver architecture provides a solution to some of the challenging problems found in the implementation of conventional wideband multicarrier receivers based on time-domain ADC, since It efficiently parallelizes the A/D conversion reducing the sampling speed requirements. We show that the sampling rate requirements are relaxed as the number of frequency samples is increased, which introduces a trade-off between complexity and sampling rate. The new receiver possesses additional advantages, including scalability with increasing frequency samples, the possibility of optimally allocating the available number of bits for the ATD conversion across the frequency domain samples which potentially reduces the distortion introduced by the high-speed ADC, narrowband interference suppression that can be directly carried out in the frequency domain, and inherent robustness to frequency offset which makes it an attractive solution when compared with traditional multicarrier receivers. We also investigate how the proposed receiver responds to common multicarrier communication receiver problems such as phase noise and channel frequency selectivity.

We propose two methods for the estimation of sparse communication channels. In the first method, we consider the problem of channel estimation based on training symbols, and formulate it as an optimization problem. In this formulation, we combine the objective of fidelity to the received data with a non-quadratic constraint reflecting the prior information about the sparsity of the channel. This approach leads to accurate channel estimates with much shorter training sequences than conventional methods. The second method we propose is aimed at taking advantage of any available training-based data, as well as any "blind" data based on unknown, constant modulus symbols. We propose a semi-blind optimization framework making use of these two types of data, and enforcing the sparsity of the channel, as well as the constant modulus property of the symbols. This approach improves upon the channel estimates based only on training sequences, and also produces accurate estimates for the unknown symbols.

Energy-constrained wireless sensor networks have conflicting requirements between the need to communicate, and the desire to avoid idle listening and thus save energy. Beneath this simple observation lies a complex design space that encompasses all layers of the radio through application. Broad design principles include the desire to exploit resources external to the network, such as beacons and (perhaps mobile) access points that do not have strong energy constraints. We consider synchronization, scheduled communications rendezvous, and packet acquisition. Synchronization accuracy worsens with prediction interval, saves energy by enabling scheduling, but costs energy to maintain. Acquisition is probabilistic due to noise, fading, and synchronization error, with operating points defining energy-performance tradeoffs.

We consider the problem of sensor node localization in a randomly deployed sensor network, using a mobile access point (AP). The mobile AP can be used to localize many sensors simultaneously in a broadcast mode, without a preestablished sensor network. We consider a multi-modal approach, combining radio and acoustics. The radio broadcasts timing, location information, and acoustic signal parameters. The acoustic emission may be used at the sensor to measure Doppler stretch, time delay, and angle of arrival. These measurements are individually sufficient to localize a sensor node, or they may be advantageously combined. We focus on the cases of Doppler and time delay. Sensor localization algorithms are developed, and performance analysis includes acoustic propagation effects caused by the turbulent atmosphere.

An ultra wideband (UWB) transmitted reference (TR) scheme transmits an un-modulated pulse and a delayed modulated pulse each time. Then a correlation receiver uses the former to demodulate the latter. However, to guarantee satisfactory detection performance in severe multipath distortion, two pulses have to be well separated by at least the channel spread, resulting in reduced data rate. In this paper, the restrictive assumption is relaxed which consequently permits interference from neighboring pulses (termed as inter-pulse interference). Instead of using instantaneous signal from the former pulse as a template, improved estimates by a mean matching technique under different modulation schemes are proposed and used for better detection performance. Besides low complexity approaches, joint maximum likelihood (ML) template estimators and detectors are also proposed. Their statistical performance is analyzed and compared.

In the UWB multicarrier receiver, after analog to digital conversion (ADC), the samples of the spectrum of the received signal are used in the digital receiver to estimate the transmitted symbols through a matched filter operation in the discrete frequency domain. The proposed receiver is aimed at the reception of high information rates in a multicarrier signal with very large bandwidth. Thus, the receiver architecture provides a solution to some of the challenging problems found in the implementation of conventional wideband multicarrier receivers based on time-domain ADC, since it parallelizes the A/D conversion, reducing the sampling rate. The receiver is also directly applicable to multicarrier ultra-wideband communication receivers. Additional advantages of the proposed receiver include the possibility of optimally allocating the available number of bits for the A/D conversion across the frequency domain samples, narrowband interference suppression that can be directly carried out in the frequency domain, and inherent robustness to frequency offset which makes it an attractive solution when compared with traditional multicarrier receivers.

Ultrawideband systems employ short low-power pulses. Analog receiver designs can accommodate the required bandwidths, but they come at a cost of reduced flexibility. Digital approaches, on the other hand, provide flexibility in receiver signal processing but are limited by analog-to-digital converter (ADC) resolution and power consumption. In this paper, we consider reduced complexity digital receivers, in which the ADC is limited to a single bit per sample. We study three one-bit ADC schemes: 1) fixed reference; 2) stochastic reference; and 3) sigma-delta modulation (SDM). These are compared for two types of receivers based on: 1) matched filtering; and 2) transmitted reference. Bit-error rate (BER) expressions are developed for these systems and compared to full-resolution implementations with negligible quantization error. The analysis includes the impact of quantization noise, filtering, and oversampling. In particular, for an additive white Gaussian noise channel, we show that the SDM scheme with oversampling can achieve the BER performance of a full-resolution digital receiver.

We investigate the impact of medium access control (MAC) design on the reconstruction performance of a 1D random signal field measured by a large scale sensor network. Assuming the sensor density goes to infinity, we show that MAC design affects the decay rate of reconstruction distortion, and thus the efficiency of reconstruction, as the number of received packets M increases. Using a deterministic MAC with uniform spatial sampling, i.e., scheduling sensor transmissions from uniformly spaced locations, results in a faster decay rate of distortion than that using an ALOHA-like random access MAC. In particular, the ratio of the excess reconstruction distortion under random access MACs to that under the MAC with uniform sampling grows as log M+O(log log M). We further show that in the high measurement SNR regime, the benefit from carefully scheduling transmission, instead of random access, is substantial. In the low SNR regime, however, using random access MACs results in little reconstruction performance loss.

The paper introduces techniques to perform analog-to-digital (A/D) conversion, based on the quantization of the coefficients obtained by the projection of a continuous-time signal over an orthogonal space. This framework for A/D conversion is motivated by the sampling of an input signal in domains which may lead to lower levels of signal distortion and significantly less demanding A/D conversion characteristics. The A/D conversion distortion is reduced by assigning optimal bit rates according to the variance distribution of the coefficients. Moreover, since the quantization of the coefficients is realized at the end of a time window during which the signal is projected, the speed of the quantizers can be much lower than the one needed in conventional time-domain ADCs. In particular, we study ADC in the frequency domain which overcomes some of the difficulties encountered with conventional time-domain A/D conversion of signals with very large bandwidths, such as ultra-wideband (UWB) signals.

A statistical analysis of differential Doppler estimation is presented for acoustic sources with a harmonic spectrum. Our model for the sensor measurements includes a physics-based statistical model for the scattering of the wavefronts by the atmosphere. We derive the Cramer-Rao bound (CRB) on differential Doppler estimation as a function of the atmospheric conditions, the frequency of the source, and the range of the source. We apply the CRB result for several cases of interest with simulated and measured data.

The effect of medium access control (MAC) for information retrieval on signal field reconstruction in large-scale sensor networks with finite density is analyzed. Two MAC schemes are compared: the deterministic scheduling and random access. For fixed sensor density, we show that there is a critical threshold of e/sup -/spl lambda/(1+o(1))/, where /spl lambda/ is the throughput of the random access protocol, on the sensor outage probability P/sub out/ beyond which the reconstruction performance of deterministic central scheduling is inferior to that of distributed random access.

We consider a binary pulsed communication system, with possibly episodic transmission, i.e., the system transmits n pulses per information bit and allows for off-time separation between pulses. In the ultra-wideband (UWB) regime, such systems are motivated for overlay applications, as well as low probability of intercept and low probability of detection scenarios. Processing gain enables low-power transmission, and the UWB pulsing limits the interference effects into narrowband systems. We introduce a random ternary sequence model and use this to study multiuser system performance. We consider the issues of processing gain, jamming margin, coding gain, and multiuser interference (MUI) for a single-user matched filter receiver. The introduction of episodic transmission, with a corresponding reduction in bit rate, provides system flexibility with respect to both MUI rejection and handling multipath channels. Highly episodic transmission provides nearly orthogonal low-rate users, even with system asynchrony and no power control. Performance of a single-user RAKE receiver is evaluated via a Chernoff bound on bit error rate, in the presence of MUI. The analysis includes the impact of fading as well as pilot-aided channel estimation.

Given a noisy sequence of (possibly shifted) integer multiples of a certain period, it is often of interest to estimate the period (and offset). With known integer regressors, the problem is classical linear regression. In many applications, however, the actual regressors are unknown; only categorical information (i.e., the regressors are integers) and, perhaps, loose bounds are available. Examples include hop timing estimation, pulse repetition interval (PRI) analysis, and passive rotating-beam radio scanning. With unknown regressors, this seemingly simple problem exhibits many surprising twists. Even for small sample sizes, a proposed quasi-maximum likelihood approach essentially meets the clairvoyant CRB at moderately high SNR - the latter assumes knowledge of the unknown regressors. This is quite unusual, and it holds despite the fact that our algorithm ignores noise color. We outline analogies and differences between our problem and classical linear regression and harmonic retrieval, and corroborate our findings with careful simulations.

The problem of training symbol placement in data packets for channel tracking is considered, where the channel is time-varying Rayleigh flat fading. We use the minimum mean-square error (MMSE) estimator for channel tracking. A minmax approach is considered. We optimize the placement by minimizing the maximum MSE over a packet. It is shown that training symbols should be scattered throughout the packet with equal space to achieve the optimal performance.

Cramér-Rao bounds (CRBs) are developed for narrow band source separation, when the sources are constrained to have constant modulus (CM). The bounds are appropriate for multi path CM sources, in blind, semi-blind, or fully known cases. Source separation bounds are contrasted for calibrated and uncalibrated arrays. It is shown that, from the CRB perspective, calibration adds no additional information. Closed-form CRBs are given for a single source, and two-source examples are also presented. Optimality of the analytical constant modulus algorithm (ACMA) for blind CM-source separation is demonstrated, achieving the appropriate constrained CRBs over a wide SNR range in challenging multipath scenarios.

We present constrained Cramer-Rao bounds for multi-input multi-output (MIMO) channel and source estimation. We find the MIMO Fisher information matrix (FIM) and consider its properties, including the maximum rank of the unconstrained FIM, and develop necessary conditions for the FIM to achieve full rank. Equality constraints provide a means to study the potential value of side information, such as training (semi-blind case), constant modulus (CM) sources, or source non-Gaussianity. Non-redundant constraints may be combined in an arbitrary fashion, so that side information may be different for different sources. The bounds are useful for evaluating various MIMO source and channel estimation algorithms. We present an example using the constant modulus blind equalization algorithm.

Number theoretic methods have been used in the study of harmonic analysis to extend existing theories and develop new approaches to both theoretical and applied problems. A specific example of this approach is in the development of sampling theory on non-commensurate lattices. We will construct examples of these lattices, and use a generalization of B.Ya. Levin's (1996) sine-type functions to develop interpolating formulae on these lattices. We then consider application of these new sampling results to multi-rate A-D conversion.

Subspace methods are common in array processing, but standard schemes typically perform poorly when the noise is non-Gaussian and/or impulsive. Zero-memory nonlinear (ZMNL) functions may be applied to limit the influence of impulsive noise, but ZMNL pre-processing generally destroys the low-rank signal subspace. We develop a robust covariance matrix estimate that suppresses impulsive noise while also performing a model-based interpolation to restore the signal subspace. The approach is based on modeling the noise with a finite Gaussian mixture distribution, and an expectation-maximization (EM) algorithm is used for parameter estimation. The method is robust to noise model mismatch and works well with infinite-variance noise. Simulation results are included that illustrate the improved performance in detecting the number of sources and estimating the angles of arrival.

In this paper we develop Cramer-Rao bounds (CRBs) for bearing, symbol, and phase estimation of communications signals in flat fading channels. We do this using the constrained CRB formulation of Gorman and Hero (1999), and Stoica and Ng (1998). This provides a general framework for a large variety of cases, including semi-blind, constant modulus (CM), known cumulants, and others. These may be combined arbitrarily, e.g., we may develop CRBs for bearing estimation of constant modulus signals when a subset of the symbols are known (semi-blind, CM case). The results establish the value of side information in a large variety of communications scenarios, and may be used to compare performance of various blind and semi-blind algorithms.

The maximum likelihood (ML) multi-user detector is developed for fast frequency hopping/multiple frequency shift keying (FFH/MFSK) multiple access (MA) systems. The channels are modeled with Rayleigh fading and additive, white Gaussian noise (AWGN). The FFH system has L chips per symbol, and each user has a unique L-chip hopping pattern over 2/sup K/ frequencies. The M users are assumed to be chip-synchronized at the receiver, and the receiver performs non-coherent detection. The ML multiuser detector simultaneously performs diversity combining and joint symbol detection, but the computational requirements are prohibitive if the 2/sup KM/ symbol combinations are searched exhaustively. We propose a reduced-complexity multiuser detection algorithm for FFH/MFSK MA systems, and we demonstrate through simulations that the new algorithm performs better than existing multiuser detection algorithms on channels with AWGN and Rayleigh fading.

A simple method, based on elementary fourth-order cumulants, is proposed for the classification of digital modulation schemes. These statistics are natural in this setting as they characterize the shape of the distribution of the noisy baseband I and Q samples. It is shown that cumulant-based classification is particularly effective when used in a hierarchical scheme, enabling separation into subclasses at low signal-to-noise ratio with small sample size. Thus, the method can be used as a preliminary classifier if desired. Computational complexity is order N, where N is the number of complex baseband data samples. This method is robust in the presence of carrier phase and frequency offsets and can be implemented recursively. Theoretical arguments are verified via extensive simulations and comparisons with existing approaches.

Solutions to the analytic Bezout equation associated with certain multichannel deconvolution problems are interpolation problems on unions of non-commensurate lattices. These solutions provide insight into how one can develop general sampling schemes on properly chosen non-commensurate lattices. We give specific examples of non-commensurate lattices and use a generalization of B.Ya. Levin's (1996) sine-type functions to develop interpolating formulae on these lattices.

We consider narrowband angle of arrival estimation in non-Gaussian (NG) noise channels, such as arises in some indoor and outdoor mobile communications channels. We develop a general expression for the Cramer-Rao bound (CRB) for direction finding using arrays for deterministic signals plus i.i.d. non-Gaussian noise, generalizing the Gaussian CRB. The CRBs for the noise and direction parameters decouple. The CRB for direction finding is expressed as a product of two terms that depend on the noise distribution, and the signal, respectively. We illustrate the results for a Gaussian mixture PDF, and present simulation results comparing five direction finding algorithms. An approach based on the expectation-maximization (EM) algorithm, that simultaneously estimates the noise parameters, the signal directions, and the signal waveforms, is shown to achieve the CRB over a wide SNR range.

We analyze discrete wavelet transform (DWT) multiscale products for detection and estimation of steps. Here the DWT is an over complete approximation to smoothed gradient estimation, with smoothing varied over dyadic scale, as developed by Mallat and Zhong (1992). The multiscale product approach was first proposed by Rosenfeld (1970) for edge detection. We develop statistics of the multiscale products, and characterize the resulting non-Gaussian heavy tailed densities. The results may be applied to edge detection with a false-alarm constraint. The response to impulses, steps, and pulses is also characterized. To facilitate the analysis, we employ a new general closed-form expression for the Cramer-Rao bound (CRB) for discrete-time step-change location estimation. The CRB can incorporate any underlying continuous and differentiable edge model, including an arbitrary number of steps. The CRB analysis also includes sampling phase offset effects and is valid in both additive correlated Gaussian and independent and identically distributed (i.i.d.) non-Gaussian noise. We consider location estimation using multiscale products, and compare results to the appropriate CRB.

A central problem in sensor array processing is the localization of multiple sources and the reception of the signals emitted by those sources. Many approaches have been studied for this problem when the additive noise in the sensor array data is modeled with a Gaussian distribution. However, the schemes designed for Gaussian noise typically perform very poorly when the noise is non-Gaussian. An algorithm is presented for array processing in non-Gaussian noise. The algorithm is based on modeling the noise with a Gaussian mixture distribution. The expectation-maximization (EM) algorithm is then used to derive an iterative processing structure that estimates the source locations, estimates the source waveforms, and adapts the processing to match the characteristics of the noise. Simulation examples are presented to illustrate the performance of the algorithm.

We consider step detection and estimation using a multiscale wavelet analysis, based on the ability of a certain discrete wavelet transform (DWT) to characterize signal steps and edges. This DWT, developed by Mallat and Zhong (1992), estimates the gradient at various smoothing levels without downsampling in time. As first proposed by Rosenfeld (1970) for edge sharpening, multiple scales are combined by forming the pointwise product across scales. We show that this approach is a non-linear whitening transformation, and characterize the non-Gaussian PDF of the output. Detection curves are shown for parameterized sigmoidal step change signals. Step location estimation performance is also shown, with comparison to the Cramer-Rao bound in additive white Gaussian noise.

Although alpha-stable processes have infinite variance, one can define and consistently estimate the normalized correlations and cumulants of linear processes with stable innovations. Hence, conventional techniques can be used to estimate the parameters of nonminimum phase alpha-stable processes.

Cyclostationary processes are an important class of nonstationary processes. We consider nonparametric estimation of the cyclic cross spectrum. A smoothed periodogram-based estimator is studied and its asymptotic behavior characterized, extending univariate work to the multivariate case. Application to cyclic coherence measurements is discussed. The results are useful in a variety of multisensor cyclostationary signal processing scenarios such as time delay and bearing estimation.

We consider detection and estimation of aeroacoustic shockwaves generated by supersonic projectiles. The shockwave is an N-shaped acoustic wave. The optimal detection/estimation scheme is considered based on an additive white Gaussian noise model. The introduction of an invertible linear transformation, such as the Fourier transform or the wavelet transform, does not improve detection performance under this model. However, if unknown interference and/or model mismatch is present, linear transforms may be of use. In addition, they may significantly reduce complexity at the cost of sub-optimality. We consider the use of the wavelet transform as a means of detecting the very fast rise and fall times of the shockwave, resulting in a l-D edge detection problem. This method is effective at moderate to high SNR and is robust with respect to unknown environmental interference that will generally not exhibit singularities as sharp as the N-wave edges.

We consider estimation of the frequency of a single sinusoid in Gaussian noise at high SNR using zero crossing times with (perhaps very many) missing observations. A period estimator is developed based on a modified Euclidean algorithm (MEA). The MEA is a computationally simple method for estimating the greatest common divisor (GCD) of a noisy contaminated data set. The approach is motivated by the fact that in the noise-free case the GCD of a set of the first differences of the zero crossing times is, with high probability, the half-period of the sinusoid. Simulation results demonstrate period estimation with 75% of the zero crossing times missing, and the data set contaminated with outliers.

A modified Euclidean algorithm is presented for determining the period from a sparse set of noisy measurements. The set may arise from measuring the occurrence time of noisy zero-crossings of a sinusoid with very many missing observations. The procedure is computationally simple, stable with respect to noise, and converges quickly. Its use is justified by a theorem that shows that, for a set of randomly chosen positive integers, the probability that they do not all share a common prime factor approaches one quickly as the cardinality of the set increases. Simulations are presented to demonstrate the proposed algorithm.

The binary hypothesis testing problem is formulated in the higher order statistics (HOS) domain, with the advantages that the test is insensitive to additive Gaussian noise of unknown covariance and insensitive to signal shifts. Unlike detection with a matched filter, the asymptotically maximum likelihood HOS-based test requires no prewhitening and no synchronization. The asymptotic normality of the test statistic is exploited to formulate three sequential detection tests: a fixed sample size test, a sequential probability ratio test (SPRT), and a truncated sequential test. The SPRT performance is examined through the average sample number by comparing a large sample analytical expression with a Monte Carlo experiment. The results validate the test assumptions.>