Targeted and deliberate exploration of state--action pairs is essential in reward-free Markov Decision Problems (MDPs). More precisely, different state-action pairs exhibit different degree of importance or difficulty which must be actively and explicitly built into a controlled exploration strategy. To this end, we propose a weighted and parameterized family of concave coverage objectives, denoted by $U_ρ$, defined directly over state--action occupancy measures. This family unifies several widely studied objectives within a single framework, including divergence-based marginal matching, weighted average coverage, and worst-case (minimax) coverage. While the concavity of $U_ρ$ captures the diminishing return associated with over-exploration, the simple closed form of the gradient of $U_ρ$ enables an explicit control to prioritize under-explored state--action pairs. Leveraging this structure, we develop a gradient-based algorithm that actively steers the induced occupancy toward a desired coverage pattern. Moreover, we show that as $ρ$ increases, the resulting exploration strategy increasingly emphasizes the least-explored state--action pairs, recovering worst-case coverage behavior in the limit.

The problem of model privacy against an eavesdropper (Eve) in a distributed learning environment is investigated. The solution is found via evaluating the Fisher Information Matrix (FIM) for the model learning problem for Eve. Through a model shift design process, the eavesdropper’s FIM can be driven to singularity, yielding a provably hard estimation problem for Eve. Both a one-shot and multi-shot solution are designed. These two approaches require the sharing of a modest amount of information with the central server learning the global model. The multi-shot solution has time-varying shifts that prevent Eve from using the temporal correlation of the gradients to learn the shifts. We design a convergence test for Eve to determine if model updates have been tampered with. However, our shift strategies pass the test and thus the shifts are not detectable. The single-shot and multi-shot methods are compared against a noise injection scheme and shown to offer superior performance.

Motivated by the constant modulus property of frequency shift keying (FSK) based waveforms and the stabilisation of its radar performance with an increase in the number of subpulses, in this paper an FSK-based dynamic subpulse number joint communications and radar waveform design is proposed. From a communications point of view, the system operates based on traditional FSK modulation. From a sensing point of view, although the subpulses are continuously generated and transmitted, radar waveforms are dynamically formed by monitoring the flatness of the spectrum which in return guarantees the accuracy of the delay estimation. Other constraints on the waveform length are used to ensure satisfactory values of the root mean square time duration, ambiguity function sidelobe levels and prevent overly long waveforms. To provide an estimation of the probability of generating extremely long waveforms, the distribution of the number of subpulses is approximated using a Brownian motion process and an existing result on its one-sided exit density. Numerical examples are provided to evaluate the accuracy of the approximate distribution, as well as the ambiguity function sidelobe levels and the delay and Doppler shift estimation performance of the transmitted waveforms.

Motivated by the ideal peak-to-average-power ratio and radar sensing capability of traditional frequency-coded radar waveforms, this paper considers the frequency shift keying (FSK) based waveform for joint communications and radar (JCR). An analysis of the probability distributions of its ambiguity function (AF) sidelobe levels (SLs) and peak sidelobe level (PSL) is conducted to study the radar sensing capability of random FSK. Numerical results show that the independent frequency modulation introduces uncontrollable AF PSLs. In order to address this problem, the initial phases of waveform sub-pulses are designed by solving a min-max optimisation problem. Numerical results indicate that the optimisation-based phase design can effectively reduce the AF PSL to a level close to well-designed radar waveforms while having no impact on the data rate and the receiver complexity. For large numbers of waveform sub-pulses and modulation orders, the impact on the error probability is also insignificant.

Q-learning is a powerful tool for network control and policy optimization in wireless networks, but it struggles with large state spaces. Recent advancements, like multi-environment mixed Q-learning (MEMQ), improves performance and reduces complexity by integrating multiple Q-learning algorithms across multiple related environments so-called digital cousins. However, MEMQ is designed for centralized single-agent networks and is not suitable for decentralized or multi-agent networks. To address this challenge, we propose a novel multi-agent MEMQ algorithm for partially decentralized wireless networks with multiple mobile transmitters (TXs) and base stations (BSs), where TXs do not have access to each other’s states and actions. In uncoordinated states, TXs act independently to minimize their individual costs. In coordinated states, TXs use a Bayesian approach to estimate the joint state based on local observations and share limited information with leader TX to minimize joint cost. The cost of information sharing scales linearly with the number of TXs and is independent of the joint state-action space size. The proposed scheme is 50% faster than centralized MEMQ with only a 20% increase in average policy error (APE) and is 25% faster than several advanced decentralized Q-learning algorithms with 40% less APE. The convergence of the algorithm is also demonstrated.

A novel Tucker decomposition based tensor approximation is considered herein: observations based on only a few lateral slices and side structural information of a true tensor are exploited. This work is motivated by quantum chemistry problems wherein full Hessian computation is expensive, but partial computation is available. The proposed method successfully estimates the quasi-polynomial and interpolative structure of frontal and lateral slices given a priori knowledge of the true tensor. A theoretical error bound is provided, which characterizes the impact due to errors in the side information. To the best of our knowledge, this work proposes the first tensor approximation with side information and interpolation.

In this paper, causal graph identification with natural observations as well as observations due to soft interventions is investigated. It is assumed that the graph is governed by linear structural equations; it is further assumed that both the causal topology and the distribution of interventions are unknown. The proposed causal graph learning approach is informed by prior work which proposed the decomposition of the problem into learning sub-graphs (all of the parents of a node) to learn the whole graph. A greedy algorithm that focuses on the reduction of the false negative rate (erroneously missing the presence of a causal relationship) is proposed. A sufficient condition is derived, under which the estimated graph is guaranteed to be free of false negatives, almost surely as the number of observations grows large. Numerical results indicate that the proposed scheme outperforms standard graph identification schemes by exploiting the sub-graph structure and by exploring a broader set of soft interventions. Compared to existing approaches, the proposed scheme achieves a 32% gain in false negative rate and a 62% gain in normalized Hamming distance.

In prior work, it was shown that high performance matrix approximation/completion was possible when only a few fully sampled columns were available of a ground truth matrix if there was appropriate side information on the rowspace of the matrix. Several applications from quantum chemistry, magnetic resonance imaging, etc. necessitate structured (versus random) sampling, but do have other domain-specific side information that can be exploited. Herein, it is shown that further complexity reduction is possible, with limited loss in performance, if one only has access to a sketch of the rowspace information. A spectral error bound is derived, which characterizes the needed dimension of the sketched side information. This bound directly considers the accuracy of the row-space information. Numerical results validate the computational efficiency and accuracy offered by the new algorithm.

In this paper, the causal bandit problem is investigated, in which the objective is to select an optimal sequence of interventions on nodes in a graph. By exploiting the causal relationships between the nodes whose signals contribute to the reward, interventions are optimized. First, a method to learn the directed acyclic graph is proposed that strongly reduces sample complexity relative to the prior art and adopts a novel edge detection method based on mutual information by learning sub-graphs. It is assumed that the graph is governed by linear structural equations; it is further assumed that the distribution of interventions is unknown. Under the assumption of Gaussian exogenous inputs and minimum-mean squared error weight estimation, a new uncertainty bound tailored to the causal bandit problem is derived. This uncertainty bound drives an upper confidence bound based intervention selection to optimize the reward. Numerical results compare the new methodology to existing schemes and show a substantial performance improvement.

We propose a framework to evaluate the random coding union bound with parameter$s$(RCUs) on the achievable error probability in the finite-blocklength regime for a pilot-assisted transmission scheme operating over an imperfectly synchronized and memoryless block-fading waveform channel. Unlike previous results, which disregard the effects of imperfect synchronization, our framework utilizes pilots for both synchronization and channel estimation. Additionally, we utilize the saddlepoint approximation to provide a numerically efficient method for evaluating the RCUs bound in this scenario. Our numerical experiments verify the accuracy of the proposed approximation. Moreover, when transmission blocks are received synchronously, numerical results indicate that the number of pilot symbols needed to estimate the fading channel gains to the level of accuracy required in ultra-reliable low-latency communication is also sufficient to acquire sufficiently good synchronization. However, when the blocks are received asynchronously, there can be a significant SNR penalty compared to the synchronous case.

In this paper, a delay-angle information spoofing (DAIS) strategy is proposed for location-privacy enhancement. By shifting the location-relevant delays and angles without the aid of channel state information (CSI) at the transmitter, the eavesdropper is obfuscated by a physical location that is distinct from the true one. A precoder is designed to preserve location-privacy while the legitimate localizer can remove the obfuscation with the securely shared information. Then, a lower bound on the localization error is derived via the analysis of the geometric mismatch caused by DAIS, validating the enhanced location-privacy. The statistical hardness for the estimation of the shared information is also investigated to assess the robustness to the potential leakage of the designed precoder structure. Numerical comparisons show that the proposed DAIS scheme results in more than 15 dB performance degradation for the illegitimate localizer at high signal-to-noise ratios, which is comparable to a recently proposed CSI-free location-privacy enhancement strategy and is less sensitive to the precoder structure leakage than the prior approach.

In this paper, an augmented analysis of a delay-angle information spoofing (DAIS) is provided for location-privacy preservation, where the location-relevant delays and angles are artificially shifted to obfuscate the eavesdropper with an incorrect physical location. A simplified misspecified Cramer-Rao bound (MCRB) is derived, which clearly manifests that not only estimation error, but also the geometric mismatch introduced by DAIS can lead to a significant increase in localization error for an eavesdropper. Given an assumption of the orthogonality among wireless paths, the simplified MCRB can be further expressed as a function of delay-angle shifts in a closed-form, which enables the more straightforward optimization of these design parameters for location-privacy enhancement. Numerical results are provided, validating the theoretical analysis and showing that the root-mean-square error for eavesdropper's localization can be more than 150 m with the ontimized delay-angle shifts for DATS.

Motivated by controlling the errors of individual edges in the causal graph discovery problem, we propose a novel framework for causal discovery inspired by the Neyman-Pearson formulation of hypothesis testing. In particular, our formulation requires that the false negative rate is minimized while simultaneously ensuring that the false positive rate is held below a specified tolerance level. This allows us to call on techniques from binary hypothesis testing. Specifically, we derive the optimal rule for our problem, which consists of a likelihood ratio test on the edges, and derive a series of matching upper and lower bounds on the false negative rate, characterized by the Renyi divergence, which can be used as benchmarks for current discovery algorithms.

Energy or time-efficient scheduling is of particular interest in wireless communications, with applications in sensor network design, cellular communications, and more. In many cases, wireless packets to be transmitted have deadlines that upper bound the times before their transmissions, to avoid staleness of transmitted data. In this paper, motivated by emerging applications in security-critical communications, age of information, and molecular communications, we expand the wireless packet scheduling framework to scenarios which involve strict limits on the timeaftertransmission, in addition to the conventional pre-transmission delay constraints. As a result, we introduce the scheduling problem under two-sided individual deadlines, which captures systems wherein transmitting too late (stale) and too early (fresh) are both undesired. Subject to said two-sided deadlines, we provably solve the optimal (energy-minimizing) offline packet scheduling problem. Leveraging this result and the inherent duality between rate and energy, we propose and solve the completion-time-optimal offline packet scheduling problem under the introduced two-sided framework. Overall, the developed theoretical framework can be utilized in applications wherein packets have finite lifetimes both before and after their transmission (e.g.,security-critical applications), or applications with joint strict constraints on packet delay and information freshness.

We propose a framework to evaluate the so-called random-coding union bound with parameter s (RCUs) on the achievable error probability in the finite-blocklength regime for a pilot-assisted transmission scheme operating over an imperfectly synchronized and memoryless block-fading waveform channel. Unlike previous results, which disregard the effects of imperfect synchronization, our framework utilizes pilots for both synchronization and channel estimation. Specifically, we provide an algorithm to perform joint synchronization and channel estimation, and verify its accuracy by observing its tightness in comparison with the Cramer-Rao bound. Then, we develop an RCUs bound on the error probability, which applies for a receiver that treats the estimates provided by the algorithm as accurate. Additionally, we utilize the saddlepoint approximation to provide a numerically efficient method for evaluating the RCUs bound in this scenario. Our numerical experiments verify the accuracy of the proposed approximation. Moreover, when the delays are modeled as fully dependent across fading blocks, numerical results indicate that the number of pilot symbols needed to estimate the fading channel gains to the level of accuracy required in ultra-reliable low-latency communication is also sufficient to acquire sufficiently good synchronization. However, when the delays are modeled as independent across blocks, synchronization becomes the bottleneck for the system performance.

Space-time scheduling for multi-user networks under fairness considerations is investigated. Scheduling is formulated as a sequential decision-making problem under the Markov Decision Processes (MDP) framework. Although the initial focus of the work is underwater acoustic networks, the proposed strategies are also validated for terrestrial radio frequency networks. If environment exploration is expensive, planning is more efficient than online learning. A challenge of the proportional fairness is that the additive structure between current and future rewards does not hold. An approximate reward function that is additive is proposed, enabling dynamic programming. Computational complexity is addressed through sample-based approximations. Error accumulation and error bounds are analyzed to show that error decays with time. As mobility induces model-shifts, a novel re-planning scheme is proposed to optimize the timings of policy updates. Numerical results show that the proposed scheme significantly improves network capacity while maintaining a high level of fairness. Furthermore, the proposed approach yields average capacity and fairness gains as high as 37% and 27%, respectively, compared to current approaches.

The optimization of large-scale networks such as finding the optimal control strategies through cost minimization is challenged by large state spaces. For networks that can be modeled via Markov Decision Processes (MDP), a previously proposed graph reduction strategy is used in conjunction with a novel ensemble learning method based on Q-learning algorithm for policy optimization in unknown environments. By exploiting the structural properties of the network, several structurally related Markov chains are created and these multiple chains are sampled to learn multiple policies which are fused. The convergence of the learning approach is analyzed and the ensemble learning strategy is shown to inherit the properties of classical Q-learning. Numerical results show that the proposed algorithm achieves a reduction of 60% with respect to the policy error and 80% for the runtime versus other state-of-the-art Q-learning algorithms.

A novel matrix completion problem is considered herein: observations based on fully sampled columns and quasi-polynomial side information is exploited. The framework is motivated by quantum chemistry problems wherein full matrix computation is expensive, but partial computations only lead to column information. The proposed algorithm successfully estimates the row-space of a true matrix given a priori knowledge of the true matrix. A theoretical error bound is provided, which captures the possible inaccuracies of the side information. This work designs the first provable matrix approximation algorithm using just column samples. The proposed algorithm is validated via simulations which enable the characterization of the amount of information provided by the quasi-polynomial side information.

In this paper, an artificial noise-aided strategy is presented for location-privacy preservation. A novel framework for the reduction of location-privacy leakage is introduced, where structured artificial noise is designed to degrade the structure of the illegitimate devices’ channel, without the aid of channel state information at the transmitter. Then, based on the location-privacy enhancement framework, a transmit beamformer is proposed to efficiently inject the structured artificial noise. Furthermore, the securely shared information is characterized to enable the legitimate devices to localize accurately. Numerical results show a 9dB degradation of illegitimate devices’ localization accuracy is achieved, and validate the efficacy of structured artificial noise versus unstructured Gaussian noise.

Achieving energy-efficiency is a hallmark of modern applications (multimedia communication, sensor networks, etc.) which exploit wireless communications. The classical approach to energy-efficient packet scheduling has presumed that wireless packets are subject to deadlines before which they need to be transmitted. Emerging applications such as security-critical communications, information freshness, molecular communications, and more, call for expanding the delay constraint to also include limits on the time after transmission. The current work thus strongly generalizes the energy-efficient scheduling problem to two-sided delay constraints that both strictly upper- and lower-bound departure times. The formulation for the new two-sided delay problem as well as a provably optimal offline algorithm are provided herein. The presented theoretical framework of two-sided constrained scheduling is general and can be leveraged in any scheduling application that involves packets with finite lifetimes both before and after their transmission.

The problem of distributed hypothesis testing with correlated observations is studied. Specifically, systems in which the behavior is governed by both the underlying hypothesis, as well as an underlying empirical distribution on the network state is considered. Thus, there is significant coupling between the interim decisions of the agents and the signals they transmit. The current model addresses increased coupling relative to prior work. The optimal decay rate for optimal detection is computed; key properties associated with this error rate are derived. The utility of the analysis is shown via the consideration of a multi-class problem wherein agents within each class have specific properties and interact with agents of other classes via signal enhancement or jamming. This multi-class case is studied numerically and it is shown that there is a optimal ratio between class populations that maximizes the decay rate of the error.

In this paper, a communication and control codesign framework is presented based on survival time, i.e., the time that a closed-loop wireless control system can continue without an anticipated message. The goal is to ensure the stability of wireless control systems with minimal resource usage. A novel interface between the controller and the scheduler is proposed, where the key communication and control parameters are analyzed for co-design, and jointly optimized. The proposed co-design framework leverages link adaptation for the communications system and sampling period adaptation for the closed-loop control system to preserve more resources. Our numerical example on closed-loop velocity control demonstrates a pronounced reduction of resources needed for control stability in contrast to the separate design paradigm that requires ultrahigh link reliability. An additional 52% reduction in resource utilization is achieved by further adapting the key parameters when the system is in survival mode.

Harnessing a block-sparse prior to recover signals through underdetermined linear measurements has been extensively shown to allow exact recovery in conditions where classical compressed sensing would provably fail. We exploit this result to propose a novel private communication framework where the secrecy is achieved by transmitting instances of an unidentifiable compressed sensing problem over a public channel. The legitimate receiver can attempt to overcome this ill-posedness by leveraging secret knowledge of a block structure that was used to encode the transmitter’s message. We study the privacy guarantees of this communication protocol to a single transmission, and to multiple transmissions without refreshing the shared secret. Additionally, we propose an algorithm for an eavesdropper to learn the block structure via the method of moments and highlight the privacy benefits of this framework through numerical experiments.

Herein, an atomic norm based method for accurately estimating the location and orientation of a target from millimeter-wave multi-input-multi-output (MIMO) orthogonal frequency-division multiplexing (OFDM) signals is presented. A novel virtual channel matrix is introduced and an algorithm to ex-tract localization-relevant channel parameters from its atomic norm decomposition is designed. Then, based on the extended invariance principle, a weighted least squares problem is pro-posed to accurately recover the location and orientation using both line-of-sight and non-line-of-sight channel information. Numerical results highlight performance improvements over a prior method and the resultant performance nearly achieves the Cramér-Rao lower bound.

Diffusion-based molecular communication (DBMC) between spatially separated bacterial colonies has limited range due to slow diffusive propagation. To this end, relay-aided DBMC with bacterial colonies as nodes is considered in this paper. A deterministic framework that governs the overall system behavior is provided for amplify-and-forward (AF) type relays. Motivated by real-life constraints in practical implementation, the framework is expanded to cover a maximum saturation limit on emission intensity, yielding the AF-with saturation (AFS) relay model. For n-hop bacterial DBMC with AFS relays, a trade-off between diffusion delay and relay processing time is investigated, which hints to an optimal number of relays that minimizes end-to-end delay. A tractable objective function for the end-to-end delay is provided by approximating the system as a cascade of n one-hop links. Numerical results show that the approximation is tight, and up to 50% decrease in end-to-end delay can be achieved by optimizing the number of relays.

Super-resolution estimation is the problem of recovering a stream of spikes (point sources) from the noisy observation of a few numbers of its first trigonometric moments. The performance of super-resolution is recognized to be intimately related to the separation between the spikes to recover. A novel notion of stability of the Fisher information matrix (FIM) of the super-resolution problem is introduced when the minimal eigenvalue of the FIM is not asymptotically vanishing. The regime where the minimal separation is inversely proportional to the number of acquired moments is considered. It is shown that there is a separation threshold above which the eigenvalues of the FIM can be bounded by a quantity that does not depend on the number of moments. The proof relies on characterizing the connection between the stability of the FIM and a generalization of the Beurling–Selberg box approximation problem.

Active, non-parametric peak detection is considered. As a use case, active source localization is examined and an uncertainty-based sampling scheme algorithm to effectively localize the peak from a few energy measurements is designed. It is shown that under very mild conditions, the source localization error with m actively chosen energy measurements scales as O(log2m/m). Numerically, it is shown that in low-sample regimes, the proposed method enjoys superior performance on several types of data and outperforms the state-of-the-art passive source localization approaches and in the low sample regime, can outperform greedy methods as well.

The problem of decentralized detection over a sensor network where each agent takes a state a priori is considered. It is assumed that the agents’ states have a impact the underlying hypothesis, resulting in correlated observations. The impact of fusion center knowledge is examined: with network state knowledge and without. In the limit of network size, the error exponent for both cases is computed and the relationship between both cases is characterized. A novel error exponent representation facilitates the asymptotic analysis.

Herein, design of false data injection attack on a distributed cyber-physical system is considered. A stochastic process with linear dynamics and Gaussian noise is measured by multiple agent nodes, each equipped with multiple sensors. The agent nodes form a multi-hop network among themselves. Each agent node computes an estimate of the process by using its sensor observation and messages obtained from neighboring nodes, via Kalman-consensus filtering. An external attacker, capable of arbitrarily manipulating the sensor observations of some or all agent nodes, injects errors into those sensor observations. The goal of the attacker is to steer the estimates at the agent nodes as close as possible to a pre-specified value, while respecting a constraint on the attack detection probability. To this end, a constrained optimization problem is formulated to find the optimal parameter values of a certain class of linear attacks. The parameters of linear attack are learnt on-line via a combination of stochastic approximation based update of a Lagrange multiplier, and an optimization technique involving either the Karush-Kuhn-Tucker (KKT) conditions or online stochastic gradient descent. The problem turns out to be convex for some special cases. Desired convergence of the proposed algorithms are proved by exploiting the convexity and properties of stochastic approximation algorithms. Finally, numerical results demonstrate the efficacy of the attack.

Fully decentralized online learning with multiple kernels (named FDOMKL) is studied, where each node in a network learns a sequence of global functions in an online fashion without the control of a central server. Every node finds the best global function only using information from its one-hop neighboring nodes via online alternating direction method of multipliers (ADMM) and the network-wise Hedge algorithm. The learning framework for an individual node is based on kernel learning and the proposed algorithm successfully harness multi-kernel method to find the best common function over the entire network. To the best of our knowledge, this is the first work that proposes a fully-decentralized online learning algorithm based on multiple kernels. The proposed FDOMKL preserves privacy by maintaining the local data at the edge nodes and exchanging model parameters only. We prove that FDOMKL achieves a sublinear regret bound compared with the best kernel function in hindsight under certain assumptions. In addition, numerical tests on real time-series datasets demonstrate the superiority of the proposed algorithm in terms of learning accuracy and network consistency compared to state-of-the-art single kernel methods.

We consider a new formulation of the decentralized detection problem with parallel agent configuration. In particular, each agent in the network exists in a set of pre-specified states that affects the distribution of their observations as well as the underlying hypothesis. As such, observations are conditionally dependent. Following a person-by-person design methodology, it is shown that the Bayes optimal detection rule for each agent is a likelihood ratio test with a state dependent threshold. Moreover, it is shown that even for statistically identical agents, the optimal rules for the agents may not be the same. Motivated by this, we turn our attention to large networks and find the error exponent, and show that as the number of agents increases there is no loss of asymptotic optimality if the agents use the same rule, dramatically reducing the complexity of computing the decision rules for each agent.

In this paper, improved channel gain delay estimation strategies are investigated when practical pulse shapes with finite block length and transmission bandwidth are employed. Pilot-aided channel estimation with an improved atomic norm based approach is proposed to promote the low rank structure of the channel. All the channel parameters, i.e., delays, Doppler shifts and channel gains are recovered. Design choices which ensure unique estimates of channel parameters for root-raised-cosine pulse shapes are examined. Furthermore, a perturbation analysis is conducted. Finally, numerical results verify the theoretical analysis and show performance improvements over the previously proposed method.

Several statistics-based detectors, based on unimodal matrix models, for determining the number of sources in a field are designed. A new variance-ratio statistic is proposed, and its asymptotic distribution is analyzed. The variance-ratio detector is shown to outperform the alternatives. It is shown that further improvements are achievable via optimally selected rotations. Numerical experiments demonstrate the performance gains of our detection methods over the baseline approach.

In this paper, improved channel gain delay estimation strategies are investigated when practical pulse shapes with finite block length and transmission bandwidth are employed. Pilot-aided channel estimation with an augmented atomic norm based approach is proposed to promote the low rank structure of the time-varying narrowband leaked channel. All the channel parameters, i.e., delays, Doppler shifts, and channel gains are recovered. Design choices which ensure unique estimates of channel parameters for rectangular, Gaussian, and root-raised-cosine pulse shapes are examined in the noiseless case, respectively. Furthermore, a perturbation analysis is conducted to measure the impact of noise and further design choices for parameters are proposed to mitigate the effects of noise. Finally, numerical results verify the theoretical analysis and show performance improvements over the previously proposed method.

Optimal unmanned aerial vehicle (UAV) placement in a 3-dimensional (3D) space to build a connection between a base station (BS) and a ground user is studied herein. A key challenge is to avoid signal propagation blockage due to obstacles. Much prior work uses probabilistic terrain models with model parameters learned from the statistics over a large area, and therefore, the optimization for a specific user in a small local area is poor. In contrast, this paper seeks the optimal UAV position over actual and fine-grained terrain, and develops efficient UAV positioning strategy adaptive to the degree of location-dependent line-of-sight (LOS) condition measured on the fly. It is proven that the globally optimal UAV position in 3D can be determined from the proposed search trajectory which has merely linear length in the diameter of the target area. Therefore, the proposed strategy can be practically implemented. Numerical experiments are performed over a real-world urban topology and demonstrate superior performance gain over existing strategies based on probabilistic models.

Significant inter-symbol interference (ISI) challenges the achievement of reliable high data-rate molecular communication (MC) links. Inspired by recent results showing the ISI mitigation capability of pre-processing received signals by differentiation, the impact of using higher order derivatives is studied herein. The trade-off between ISI mitigation and noise amplification with higher order derivatives is characterized. Optimal maximum-likelihood sequence detection (MLSD) is investigated as well as low complexity banded MLSD to exploit the pulse narrowing induced by differentiation. Furthermore, analysis suggests the existence of an optimal derivative order. The bit error ratio (BER) for a fixed threshold detector is tightly approximated and employed to find the optimal derivative order. Numerical results confirm the aforementioned trade-off and show that reliable communication can be established using symbol durations considerably smaller than the peak time.

A wireless sensing system with n sensors, observing independent and identically distributed continuous random variables with a symmetric probability density function, and one non-collocated estimator acting as a fusion center is considered. The sensors transmit information to the fusion center via a limited capacity communication medium modeled by a collision channel. It is assumed that there is no communication among the sensors prior to transmission, and the collision channel allows at most k < n simultaneous transmissions. Assuming that each sensor uses a symmetric threshold communication strategy, the problem of designing a threshold that minimizes a mean-squared error criterion is considered. Theoretical analysis shows the existence and uniqueness of the optimal threshold for this optimization problem.

Modulation design is a particularly interesting problem in the context of molecular communication via diffusion (MCvD), due to the heavy and signal dependent inter-symbol interference (ISI) imposed on the communication link. To tackle the modulation design issue in MCvD, a hybrid modulation family is proposed in this study. The proposed scheme operates by combining conventional concentration constellations with pulse position modulation symbols, and is able to encode more bits into a single joint symbol than traditional concentration or position-based schemes. Called molecular concentration-position modulation (MCPM), it is shown through theoretical and numerical results that the proposed scheme yields promising error performances, especially in the regime with high ISI and low transmission power. Furthermore, MCPM only utilizes a single type of molecule, which suggests an easier implementability for micro- or nano-scale machinery.

In this paper, we present new results on the achievable rate-distortion regions in networked scalable compression problems, based on a flexible codebook generation and binning method. First, we consider the problem of scalable coding in the presence of decoder side information, for which the prior work analyzed the two important cases the degraded side information where source X and the side information variables (Y1, Y2) form a Markov chain in the order of either X - Y1-Y2or X - Y2- Y1. First, we present an example non-Markov side information scenario where the proposed coding strategy achieves a strictly larger rate-distortion region compared to prior work. We then consider the problem of multi-user successive refinement where different users that are connected to a central server via links with different noiseless capacities strive to reconstruct the source in a progressive fashion. It is shown that a prior rate-distortion region is suboptimal in general, albeit its optimality for a Gaussian source with MSE distortion, and the proposed coding scheme achieves points beyond the achievable region of prior work.

The problem of verifying whether a multi-component system has anomalies or not is addressed. Each component can be probed over time in a data-driven manner to obtain noisy observations that indicate whether the selected component is anomalous or not. The aim is to minimize the probability of incorrectly declaring the system to be free of anomalies while ensuring that the probability of correctly declaring it to be safe is sufficiently large. This problem is modeled as an active hypothesis testing problem in the Neyman-Pearson setting. Component selection and inference strategies are designed and analyzed in the non-asymptotic regime. For a specific class of homogeneous problems, stronger (with respect to prior work) non-asymptotic converse and achievability bounds are provided.

Reinforcement learning is a classical tool to solve network control or policy optimization problems in unknown environments. In order to learn the optimal policy correctly, the classical Q-learning algorithm requires sufficient visits to all state-action pairs, resulting in the need for a large number of observations in the presence of a large state-action space. Nevertheless, complexity reduction can be achieved by exploiting the particular structure of the optimal policy. A sampled reinforcement learning algorithm is proposed, where the optimal policy is estimated only for a subset of states; a machine learning technique, as well as a graph signal processing approach, are applied for policy interpolation for unvisited states. A policy refinement algorithm is further proposed to improve the performance of policy interpolation. Performance analysis and bounds are also provided for the proposed policy sampling and interpolation algorithms. Numerical experiments on a single link wireless network with a large state space show that the sample Q-learning algorithm with policy interpolation achieves a much faster runtime with negligible performance loss compared to classical Q-learning.

In this paper, the Cramer Rao bound (CRB) on the time-varying narrowband leaked Multiple-Input Multiple- Output Orthogonal Frequency Division Multiplexing (MIMO OFDM) channel estimators is derived, under the assumption of deterministic sequences. The CRB is proven to be effectively decoupled in delay and Doppler domains. Fixed point equations for determining the optimal training sequences for the decoupled CRB are provided. Doppler- optimized training sequences are shown to be whiter. Optimized sequences exhibit a performance gain on the order of 5 dB and 2.5 dB over purely random sequences used for training for Doppler and delay, respectively.

Reinforcement learning can be applied to solve various types of control problems in wireless networks. While the classical Q-learning technique can learn an optimal policy without requiring the model of the environment, the Q function for all state-action pairs needs to be learned in order to obtain the optimal policy. To tackle the issue of the sample complexity associated with such learning, given the structural property of the optimal policy, a policy sampling algorithm adapted from classical Q-learning is proposed, where the optimal policy is estimated only for a subset of states. A graph signal processing approach is applied for policy interpolation. Numerical experiments on a wireless network with a large state space show that the sample Q- learning algorithm with policy interpolation achieves a much faster runtime with negligible performance loss compared to the classical Q-learning.

Unimodality-constrained matrix or tensor factorization has applications in various domains, such as non-parametric source localization and data clustering, where the signals of interest are unimodal. Such factorizations are challenged by the non-convex nature of unimodality constraints. This paper develops a modified Frank-Wolfe algorithm with a successive programming technique, which produces a sequence of linear subproblems with modified and adaptive constraints. The algorithm is proven to converge and the subproblems are shown to be solved easily. In an application example of solving unimodality-constrained tensor factorization problems, the proposed algorithm demonstrates substantial complexity reduction while achieving the same convergence performance as compared to a brute-force projected gradient algorithm.

Precise synchronisation of transmitters and receivers is particularly challenging in diffusive molecular communication environments. To this end, a point-to-point molecular communication system is examined wherein the design of the transceiver offers resilience to synchronisation errors. In particular, the development of a sequential probability ratio test-based detector, which allows for additional observations in the presence of uncertainty due to mis-synchronisation at the receiver, and a modulation design which is optimised for this receiver strategy, is considered. The structure of the probability of molecules hitting a receiver within a particular time slot is exploited. An approximate maximum log-likelihood estimator for the synchronisation error is derived and the Cramér-Rao bound (CRB) computed, to show that the performance of the proposed estimator is close to the CRB at low transmission rates. The proposed receiver and modulation designs achieve strongly improved asynchronous detection performance for the same data rate as a decision feedback based receiver by a factor of 3 to 5 on average.

An active hypothesis testing problem is formulated. In this problem, the agent can perform a fixed number of experiments and then decide on one of the hypotheses. The agent is also allowed to declare its experiments inconclusive if needed. The objective is to minimize the probability of making an incorrect inference (misclassification probability) while ensuring that the true hypothesis is declared conclusively with moderately high probability. For this problem, lower and upper bounds on the optimal misclassification probability are derived and these bounds are shown to be asymptotically tight. In the analysis, a sub-problem, which can be viewed as a generalization of the Chernoff-Stein lemma, is formulated and analyzed. A heuristic approach to strategy design is proposed and its relationship with existing heuristic strategies is discussed.

In recent years, communicating information using molecules via diffusion has attracted significant interest in bio-medical applications. To date, most of the studies have concentrated on point-to-point molecular communication (MC), whereas in a realistic environment, multiple MC transmitters are likely to transmit molecular messages simultaneously sharing the same propagation medium, resulting in significant performance variation of the MC system. In this type of large-scale MC system, the collective signal strength at the desired receiver can be impaired by the interference caused by other MC transmitters, which may degrade the system reliability and efficiency. This paper presents the first tractable analytical framework for the collective signal strength at a partially absorbing receiver due to the desired transmitter under the impact of a swarm of interfering transmitters in a 3D large-scale MC system using stochastic geometry. To combat the multi-user interference and the intersymbol interference (ISI) in the multi-user environment, we propose Reed-Solomon (RS) error correction coding, due to its high effectiveness in combating burst and random errors, as well as the two types of information molecule modulating scheme, where the transmitted bits are encoded using two types of information molecules at consecutive bit intervals. We derive analytical expressions for the bit error probability (BEP) of the large-scale MC system with the proposed two schemes to show their effectiveness. The results obtained using Monte Carlo simulations, match exactly with the analytical results, justifying the accuracy of the derivations. Results reveal that both schemes improve the BEP by a factor of 3-4 compared with that of a conventional MC system without using any ISI mitigation techniques. Due to the implementation simplicity, the two-type molecule encoding scheme is better than the RS error correction coding scheme, as the RS error correction coding scheme involves additional encoding and decoding process at both the transmitter and receiver nodes. Furthermore, the proposed analytical framework can be generalized to the analysis of other types of receiver designs and performance characterization in multi-user large-scale MC systems. Also, the two types of information molecule modulating scheme can be extended to M-type of information molecule modulating scheme without loss of generality.

In this paper, a point-to-point network transmission control problem is formulated as a Markov decision process (MDP). Classical dynamic programming techniques such as value iteration, policy iteration, and linear programming can be employed to solve the optimization problem, but they suffer from high-computational complexity in networks with large state space. To achieve complexity reduction, the structure of the optimal policy can be exploited and incorporated into standard algorithms. In addition, function approximation can also be applied, where the value function is approximated by the linear combination of some basis vectors in a lower dimensional subspace. The main challenge for function approximation lies in the absence of general guidelines for subspace construction. In this paper, a proper subspace for projection is first generated based on system information, and more general construction methods are proposed using tools from graph signal processing (GSP). Graph symmetrization methods are also used to tackle the directed nature of the probability transition graph so that the well-developed GSP theory for undirected graphs can be employed. The numerical results for a typical wireless system show that standard algorithms with structural information incorporated can achieve 50% complexity reduction without performance loss. The subspace generated from the system can achieve zero policy error with faster runtime, and the GSP approach can also provide a proper subspace for perfect reconstruction of the optimal policy. It is also shown that how the proposed method can be applied to other MDP problems.

Multi-scale spectrum sensing is proposed to overcome the cost of full network state information on the spectrum occupancy of primary users (PUs) in dense multi-cell cognitive networks. Secondary users (SUs) estimate the local spectrum occupancies and aggregate them hierarchically to estimate spectrum occupancy at multiple spatial scales. Thus, SUs obtain fine-grained estimates of spectrum occupancies of nearby cells, more relevant to scheduling tasks, and coarse-grained estimates of those of distant cells. An agglomerative clustering algorithm is proposed to design a cost-effective aggregation tree, matched to the structure of interference, robust to local estimation errors, and delays. Given these multi-scale estimates, the SU traffic is adapted in a decentralized fashion in each cell, to optimize the trade-off among SU cell throughput, interference caused to PUs, and mutual SU interference. Numerical evaluations demonstrate a small degradation in SU cell throughput (up to 15% for a 0 dB interference-to-noise ratio experienced at PUs) compared to a scheme with full network state information, using only one-third of the cost incurred in the exchange of spectrum estimates. The proposed interference-matched design is shown to significantly outperform a random tree design, by providing more relevant information for network control, and a state-of-the-art consensus-based algorithm, which does not leverage the spatio-temporal structure of interference across the network.

This paper studies the problem of localizing a source based on different types of signals measured at different sensing locations, where propagation models of the signals are not known. A tensor observation model is proposed to arrange such multimodal data into different layers to form a 3D data array. It is proven that the vectors extracted from the least squares rank-1 approximation of the tensor under the Tucker's model are location signature vectors of the source, where the vectors are unimodal and their peak locations correspond to the source location. Numerical experiments demonstrate that the proposed localization method based on tensor decomposition outperforms the baseline that heuristically averages the estimates individually from different types of data.

In this paper, target tracking constrained to short-term linear trajectories is explored. The problem is viewed as an extension of the matrix decomposition problem into low-rank and sparse components by incorporating an additional line constraint. The Cramér-Rao Bound (CRB) for the trajectory estimation is derived; numerical results show that an alternating algorithm which estimates the various components of the trajectory image is near optimal due to proximity to the computed CRB. In addition to the theoretical contribution of incorporating an additional constraint in the estimation problem, the alternating algorithm is applied to real video data and shown to be effective in estimating the trajectory despite it not being exactly linear.

In this paper, the problem of time-varying narrowband leaked Orthogonal Frequency Division multiplexing (OFDM) channel estimation is considered. The leakage effect results from practical constraints on communication systems: finite bandwidth and block length. These practical constraints effectively render a sparse channel into a non-sparse one. The inherent low-rank structure of the received signal, determined by the number of dominant paths of the channel, is exploited. The current work extends prior work on single carrier systems to OFDM; herein, it is also known that leaked OFDM channel is separable in delay and Doppler domains. A convex optimization approach, based on the atomic norm heuristic, is developed. Optimality and uniqueness of the proposed channel estimation method are shown. Simulation results show superiority over the classical l1element-wise sparse method with an average 8 dB improvement.

Quorum sensing plays a significant role in infection, biofilm production and potentially can impact the design of microbial fuel cells in the future. Herein, a production of public-goods interpretation is employed to introduce a novel optimization-based model for bacterial quorum sensing. In this model, each bacterium cell act as a decision-maker seeking to maximize a pay-off function under the uncertainty on the concentration of the colony population. First, the design of a socially optimal strategy profile is considered, where all the cells employ the same threshold strategy. Second, the probability of not activating while the quorum is being formed is analyzed; this phenomenon is known in the literature as cheating. Lastly, preliminary results are presented that establish a connection between the new decision-making model with experimental data.

Motivated by the Internet-of-things and sensor networks for cyberphysical systems, the problem of low complexity dynamic sensor activation for the tracking of a time-varying process is examined. The tradeoff is between energy efficiency and fidelity. The problem of minimizing the time-averaged mean-squared error over infinite horizon is examined under a constraint on the mean number of active sensors. The proposed method artfully combines two key ingredients: Gibbs sampling for sensor subset selection, and stochastic approximation for learning, in order to create a high performance, energy efficient tracking mechanism with active sensor selection. Tracking of an i.i.d. process with unknown parametric distribution is considered; the main challenge here is that the unknown parameter vector must be learned. The key theoretical result proves that the proposed algorithm converges to locally optimal solutions. Numerical results suggest that global optimality is in fact achieved in some cases.

Based on recent results on the challenges of identifiability in blind deconvolution and new methods for blind deconvolution with the knowledge of autocorrelation functions, a novel approach to secure communication over wireless channels is provided by using the Binary Modulation on Conjugated Zeros design. In particular, the blind deconvolution of a transmitted sequence via a wireless channel with Rayleigh fading is rendered impossible through the introduction of common zeros. A signal codebook design is provided as well as a decoding strategy with a shared secret key for the legitimate user. The probability of an eavesdropper guessing the correct key is computed and shown to converge to zero nearly exponentially with an increasing length of the key.

An energy efficient use of large scale sensor networks necessitates activating a subset of possible sensors for estimation at a fusion center. The problem is inherently com- binatorial; to this end, a set of iterative, randomized algorithms are developed for sensor subset selection by exploiting the underlying statistics. Gibbs sampling-based methods are designed to optimize the estimation error and the mean number of activated sensors. The optimality of the proposed strategy is proven, along with guarantees on their convergence speeds. Also, another new algorithm exploiting stochastic approximation in conjunction with Gibbs sampling is derived for a constrained version of the sensor selection problem. The methodology is extended to the scenario where the fusion center has access to only a parametric form of the joint statistics, but not the true underlying distribution. Therein, expectation-maximization is effectively employed to learn the distribution. Strategies for iid time- varying data are also outlined. Numerical results show that the proposed methods converge very fast to the respective optimal solutions, and therefore can be employed for optimal sensor subset selection in practical sensor networks.

In this paper, the estimation of a narrowband time-varying channel under the practical assumptions of finite block length and finite transmission bandwidth is investigated. It is shown that the signal after passing through a time-varying narrowband channel, under these assumptions, reveals a particular low-rank structure. The rank in this structure is governed by the number of dominant paths in the channel. Moreover, it is shown that this low-rank structure can be represented as a summation of few rank-one atoms (matrix) that are fully described by the channel and leakage key parameters. To estimated the channel, a novel approach based on minimization of atomic norm using measurements of signal at time domain is proposed. Numerical results show that the performance of proposed algorithm is independent of the leakage effect and the new method can achieve significant gains over previously proposed methods.

In this paper, a multi-scale approach to spectrum sensing in cognitive cellular networks is proposed. In order to overcome the huge cost incurred in the acquisition of full network state information, a hierarchical scheme is proposed, based on which local state estimates are aggregated up the hierarchy to obtain aggregate state information at multiple scales, which are then sent back to each cell for local decision making. Thus, each cell obtains fine-grained estimates of the channel occupancies of nearby cells, but coarse-grained estimates of those of distant cells. The performance of the aggregation scheme is studied in terms of the trade-off between the throughput achievable by secondary users and the interference generated by the activity of these secondary users to primary users. In order to account for the irregular structure of interference patterns arising from path loss, shadowing, and blockages, which are especially relevant in millimeter wave networks, a greedy algorithm is proposed to find a multi-scale aggregation tree to optimize the performance. It is shown numerically that this tailored hierarchy outperforms a regular tree construction by 60%.

Consider a remote sensing system consisting of two sensors, a scheduler and a non-collocated fusion center. Each sensor observes a distinct component of a bivariate Gaussian source. The fusion center and the sensors are separated by a noiseless channel that can support the transmission of only one of the measurements at a time. The scheduler must decide which of the measurements will be revealed to the fusion center based on both of the observations. Finally, the fusion center forms an estimate of the entire source based on the observation chosen by the scheduler. Our goal is to design scheduling and estimation policies that jointly minimize a mean squared error criterion. We establish the person-by-person optimality between a scheduling policy where the observation with the largest magnitude is transmitted and its corresponding conditional expectation estimation policy in two scenarios: when the state is distributed according to a bivariate Gaussian density with independent components; and according to a symmetrically correlated Gaussian density with unit variances.

A novel low-complexity robust-to-noise iterative algorithm named compression-based gradient descent (C-GD) algorithm is proposed. C-GD is a generic compressed sensing recovery algorithm, that at its core, employs compression codes, such as JPEG2000 and MPEG4. Through using compression codes, C-GD strongly generalizes the scope of structures used by compressed sensing recovery algorithms beyond sparsity or low-rankness. The squared error of the proposed method and its associated convergence is characterized and predicts the strong performance of C-GD. Numerical results suggest that C-GD, when combined with state-of-the-art compression codes, either outperforms or performs comparably to modern compressed sensing recovery methods.

In this paper, the incorporation of a line constraint is considered for structured estimation. In particular, multiple forms of structure on matrices are extended from low-rank and sparsity. The line constraint is introduced via a rotation that yields a secondary low rank condition. The proposed method is applied to single object tracking in video wherein the trajectory can be parameterized as a line. The optimization is solved via the Augmented Lagrange Multiplier method. Measurable performance improvement is observed over previous background subtraction methods that do not exploit the line structure. An aggregated error is proven to converge to zero and a boundedness analysis is conducted which suggests that the iterative algorithm is convergent.

Communication over channels with state is a classical problem extensively studied. Herein, the problem statement is extended to consider a multihop channel where the channel state information in hops, unavailable to neither the source, the destination, nor the relay(s), is to be estimated at the destination, along with reliable information transmission. Each relay in the multihop channel forwards the source's transmitted signal, estimates the preceding hops' channel states, and assists in the destination's reliable decoding of the transmitted message and reconstruction of the channel states. For a two-hop channel with independent states over the two hops, it is shown that a decode-(indirectly)-compress-and-forward strategy achieves the capacity-distortion function.

A joint radar/communication system is considered, where the radar adaptively designs the transmitted waveform such that the interference caused to the cellular systems is strictly controlled. In this paper, different Mutual Information based criteria for radar waveform optimization are proposed and the corresponding waveform optimization problems are formulated and solved analytically. Radar performance trade-offs for the considered Mutual Information based criteria are presented and, using simulation results, it is shown that a larger maximized Mutual Information does not guarantee an optimal detection performance. It is also emphasized the importance of exploiting the communication signals scattered off the target for the detection task when dealing with weak radar returns.

The estimation of a narrowband time-varying channel under finite block length and transmission bandwidth is investigated. A novel method is proposed for estimation in the delay-Doppler domain by exploiting structural constraints on low-rank matrix recovery. The proposed algorithm uses Gauss-Seidel iterations on the low-rank parameterization under noisy training signal measurements. Theoretical global identifiability results for the channel leakage (due to finite block length and transmission bandwidth) are stated and the necessity of considering Doppler shift induced structure is demonstrated. Justification is provided for the choice of simulation parameters and initialization strategies to achieve good convergence rates and some ill-posed scenarios are also described. It is further shown that simple sparsity-based algorithms like basis pursuit/nuclear norm minimization do not perform well on the said constraint set for measurement operators arising out of training sequences.

The problem of active target detection using low rank methods is explored. In prior work, a strategy was proposed based on matrix completion for randomly sampling a field combined with binary search to localize a target. Herein, two innovations are explored: the consideration of tensor-completion in order to exploit multi-modal data and the examination of the costs associated with communication. In particular, the random samples are collected in neighborhoods wherein the quality of the observation is a function of the distance of the sampling point to the centroid of the neighborhood. Due to the tradeoff between communication quality and sampling quality, there is an optimal neighborhood size.

Active sensing refers to the process of choosing or tuning a set of sensors in order to track an underlying system in an efficient and accurate way. In a wireless environment, among the several kinds of features extracted by traditional sensors, the information carried by the communication channel about the state of the system can be used to further boost the tracking performance and save energy. A joint tracking problem which considers sensor measurements and communication channel together for tracking purposes is set up and solved. The system is modeled as a partially observable Markov decision problem and the properties of the cost-to-go function are used to reduce the problem complexity. Numerical results show the advantages of our proposal.

Herein, we analyze the sample complexity of general weighted ℓ1minimization in terms of support recovery from noisy underdetermined measurements. This analysis generalizes prior work for standard ℓ1minimization by considering the weighting effect. We state explicit relationship between the weights and the sample complexity such that i.i.d random Gaussian measurement matrices used with weighted ℓ1minimization recovers the support of the underlying signal with high probability as the problem dimension increases. This result provides a measure that is predictive of relative performance of different algorithms. Motivated by the analysis, a new iterative weighted strategy is proposed. In the Reweighted Partial Support (RePS) algorithm, a sequence of weighted ℓ1minimization problems are solved where partial support recovery is used to prune the optimization; furthermore, the weights used for the next iteration are updated by the current estimate. RePS is compared to other weighted algorithms through the proposed measure and numerical results, which demonstrate its superior performance for a spectrum occupancy estimation problem motivated by cognitive radio.

Microbial communities play a significant role in bioremediation, plant growth, human and animal digestion, global elemental cycles including the carbon-cycle, and water treatment. They are also posed to be the engines of renewable energy via microbial fuel cells, which can reverse the process of electrosynthesis. Microbial communication regulates many virulence mechanisms used by bacteria. Thus, it is of fundamental importance to understand interactions in microbial communities and to develop predictive tools that help control them, in order to aid the design of systems exploiting bacterial capabilities. This position paper explores how abstractions from communications, networking and information theory can play a role in understanding and modeling bacterial interactions. In particular, two forms of interactions in bacterial systems will be examined: electron transfer and quorum sensing. While the diffusion of chemical signals has been heavily studied, electron transfer occurring in living cells and its role in cell-cell interaction is less understood. Recent experimental observations open up new frontiers in the design of microbial systems based on electron transfer, which may coexist with the more well-known interaction strategies based on molecular diffusion. In quorum sensing, the concentration of certain signature chemical compounds emitted by the bacteria is used to estimate the bacterial population size, so as to activate collective behaviors. In this position paper, queuing models for electron transfer are summarized and adapted to provide new models for quorum sensing. These models are stochastic, and thus capture the inherent randomness exhibited by cell colonies in nature. It is shown that queuing models allow the characterization of the state of a single cell as a function of interactions with other cells and the environment, thus enabling the construction of an information theoretic framework, while being amenable to complexity reduction using methods based on statistical physics and wireless network design.

Path planning is an important component of autonomous mobile sensing systems. This paper studies upper and lower bounds of communication performance over Gaussian sensor networks, to drive power-distortion metrics for path planning problems. The Gaussian multiple-access channel is employed as a channel model and two source models are considered. In the first setting, the underlying source is estimated with minimum mean-squared-error, while in the second, reconstruction of a random spatial field is considered. For both problem settings, the upper and the lower bounds of sensor power-distortion curve are derived. For both settings, the upper bounds follow from the amplify-and-forward scheme and the lower bounds admit a unified derivation based on data processing inequality and tensorization property of the maximal correlation measure. Next, closed-form solutions of the optimal power allocation problems are obtained under a weighted sum-power constraint. The gap between the upper and the lower bounds is analyzed for both weighted sum and individual power constrained settings. Finally, these metrics are used to drive a path planning algorithm and the effects of power-distortion metrics, network parameters, and power optimization on the optimized path selection are analyzed.

In this paper, a simple memory limited transmitter for molecular communication is proposed, in which information is encoded in the emission rate of the molecules. Taking advantage of memory, the proposed transmitter reduces the ISI problem by properly adjusting its emission rate, which can be interpreted as water-filling on the expected interference. The error probability of the proposed scheme is derived and the result is compared with the error probability of the optimal transmitter obtained by dynamic programming methods. Furthermore, for the special case of channel with one symbol memory, a tight lower bound on error probability is derived. Numerical results show that the performance of introduced transmitter is near optimal. Simplicity is the key feature of the presented communication system: the transmitter follows a simple rule, the receiver is a simple threshold decoder, and only one type of molecule is used to convey the information.

The ever increasing demand for spectrum, due to services with data rate requirements and ongoing exponential increase in the number of wireless devices, has pushed for new methods that allow for a flexible and shared use of spectrum among different wireless and radar systems. Different systems need to sense the spectrum and adaptively design their transmitted waveforms in a manner such that they do not cause harmful interference to other systems. In this paper we consider a scenario where radar and wireless communication systems are operated jointly. The opportunistic radar constantly senses the spectrum and adapts its waveform based on the occupancy and maximum allowed power. A radar waveform is optimized for the target detection task such that it does not cause significant performance loss to the communication system. We show that the waveform optimized based on the Neyman-Pearson detector provides a very similar detection performance with the one optimized based on Mutual Information maximization. We also demonstrate that the detection performance improves if, at the radar receiver, the reflections off the target due to the communication signals are considered.

In this paper, a multiscale approach to spectrum sensing in cognitive cellular networks is analyzed. Observing that wireless interference decays with distance, and that estimating the entire spectrum occupancy across the network entails substantial energy cost and communication overhead, a protocol for distributed spectrum estimation is defined by which secondary users maintain fine-grained estimates of the spectrum occupancy of nearby cells, but coarse-grained estimates of that of distant cells. This is accomplished by arranging the cellular network into a hierarchy of increasingly coarser macro-cells and having secondary users fuse local spectrum estimates up the hierarchy. The spectrum occupancy is modeled as a Markov process, and the system is optimized by defining a probabilistic framework for spectrum sensing and information exchange that balances improvements in spectrum estimation against energy costs. The performance of the multiscale scheme is evaluated numerically, showing that it offers substantial improvements in energy efficiency over local estimation. On the other hand, it is shown that schemes that attempt to estimate the state of the whole network perform poorly, due to the excessive cost of performing information exchange with far away cells, and to the fact that, knowing the spectrum occupancy of distant cells, which experience low interference levels, results in a small increase in reward.

Multi-scale, multi-lag (MSML) models are adopted for time-varying (ultra)-wideband channels that are relevant for underwater acoustic, radar and ultrawideband radio applications. MSML channels are characterized by a limited number of paths, each parameterized by a delay, Doppler scale, and attenuation factor. Herein, a novel MSML channel estimator is proposed. First, in the Fourier domain, it is shown that there is an approximately linear relationship between the received signal and the Doppler scales that enables the recasting of channel estimation into a convex optimization problem. Second, the inherent sparsity of many MSML channels is exploited resulting in a further improvement in estimation performance of about 5 dB in low SNR relative to an unstructured estimation method. Finally, the resultant estimation strategy has very low implementation complexity.

A problem of broad interest is the detection and localization of a target or object from its generated field. In this paper, a detection and localization strategy which exploits the structure of target fields is designed and analyzed. In taking advantage of this structure, one is able to reduce sample complexity requirements while maintaining good performance. In particular, an exploration-exploitation approach to target detection is proposed utilizing the theory of low-rank matrix completion for a decaying separable target field. The assumptions on the field are fairly generic and are applicable to many decay profiles. Our approach does not require specific knowledge of the field, only that it admits a rank-one representation. A performance analysis for localization is presented that characterizes a trade-off with sample complexity in the presence of noise.

The LTI-Poisson model is a natural extension of the conventional memoryless Poisson channel to include memory, and can model the ISI effect in diffusion based molecular communication networks. In this paper, we exploit prior art on linear ISI channels to provide a computable finite-letter characterization of the capacity of single-hop LTI-Poisson networks. Then we find more explicit single-letter lower and upper bounds on the capacity in the point to point case. Further, an approach for bounding mutual information in the low SNR regime using the symmetrized KL divergence is introduced and its applicability for Poisson channels is demonstrated. This leads to a non-trivial upper bound on the capacity of Poisson channel with a maximum transmission constraint in the low SNR regime, which to best of our knowledge is the first such bound.

Motivated by recent discoveries of multi-cellular microbial communities that transfer electrons across centimeter-length scales, this paper studies the information capacity of bacterial cables via electron transfer, which may coexist with the more well-known communication strategies based on molecular diffusion. The bacterial cable is modeled as an electron queue, which transports electrons from the encoder to the decoder located at the two ends of the cable. The encoder controls the desired input electron intensity, whereas the decoder attempts to decode the transmitted message based on the measured output electron process. Clogging of the cable, induced by local ATP saturation and resulting in a loss of electron transport efficiency along the cable, is modeled. The case where both the encoder and the decoder have full causal channel state information (CSI) with binary inputs is studied. A discrete-time version of the system is considered, enabling the computation of an achievable rate for the continuous-time system, based on known results on the capacity of finite-state Markov channels. The regime of asymptotically small time-slot duration is studied, and it is shown that the capacity optimization problem can be recast as a Markov decision process, which enables the use of standard optimization algorithms, e.g., policy iteration, to compute the capacity and the optimal expected desired input electron intensity, which generates the binary signal.

In this paper, a simple memory limited transmitter for molecular communication is proposed, in which information is encoded in the diffusion rate of the molecules. Taking advantage of memory, the proposed transmitter reduces the ISI problem by properly adjusting its diffusion rate. The error probability of the proposed scheme is derived and the result is compared with the lower bound on error probability of the optimum transmitter. It is shown that the performance of introduced transmitter is near optimal (under certain simplifications). Simplicity is the key feature of the presented communication system: the transmitter follows a simple rule, the receiver is a simple threshold decoder and only one type of molecule is used to convey the information.

In this paper, a cross-layer framework to perform spectrum sensing and scheduling in agile wireless networks under resource constraints is presented. A network of secondary users (SUs) opportunistically accesses portions of the spectrum left unused by a network of licensed primary users (PUs). A central controller (CC) schedules the traffic of the SUs over the spectrum bands, based on distributed compressed spectrum sensing performed by the SUs. Both sensing and scheduling are controlled based on the current spectrum occupancy belief, with the goal to maximize the SU throughput, under constraints on the PU throughput degradation and the sensing-transmission cost incurred by the SUs. The high optimization complexity is reduced by proposing a partially myopic scheduling strategy, where the total traffic of the SUs is determined optimally via dynamic programming, whereas the allocation of the resulting total traffic across frequency bands is determined via a myopic maximization of the instantaneous trade-off between PU and SU throughputs, which can be solved efficiently using convex optimization tools. Structural results of the partially myopic scheduling strategy are proved. Simulation results demonstrate how the proposed framework allows to balance optimally the cost of acquisition of state information via distributed spectrum sensing and the cost of data transmission incurred by the SUs, while achieving the best trade-off between PU and SU throughput under the resource constraints available.

Recent discoveries of bacterial cables that transfer electrons across centimeter-length scales motivate the study of their information capacity. The bacterial cable is modeled as an electron queue that transfers electrons from the encoder at the electron donor source to the decoder at the electron acceptor sink. The model allows to capture the coupling between the electron signal and the energetic state of the cells via clogging due to local ATP saturation along the cable. Based on the analysis of a discrete-time scheme with asymptotically small time-slot duration, and assuming full causal channel state information (CSI), the optimality of binary input distributions is proved, i.e., the encoder transmits at either maximum or minimum intensity, as dictated by the physical constraints of the cable. It is proved that the optimal binary signal can be determined via dynamic programming, and that it has smaller intensity than that given by the myopic policy, which greedily maximizes the instantaneous information rate but neglects its effect on the steady-state distribution of the cable. This work represents a first contribution towards the design of electron signaling schemes in more complex microbial systems, e.g., biofilms, where the tension between maximizing the transfer of information and guaranteeing the well-being of the overall bacterial community arises, and motivates further research on the design of more practical schemes, where CSI is only partially available.

This paper presents novel data fusion methods that enable teams of vehicles to perform target search tasks without guaranteed communication. Techniques are introduced for merging estimates of a target's position from vehicles that regain contact after long periods of time, and a fully distributed team-planning algorithm is proposed, which utilizes limited shared information as it becomes available. The proposed data fusion techniques are shown to avoid overcounting information, which ensures that combining data from different vehicles will not decrease the performance of the search. Motivated by the underwater search domain, a realistic underwater acoustic communication channel is used to determine the probability of successful data transfer between two locations. The channel model is integrated into a simulation of multiple autonomous vehicles in both open water and harbor environments. The results demonstrate that the proposed distributed coordination techniques provide performance competitive with full communication.

A limited energy budget is a major obstacle to the practical, wide deployment of sensor networks and hence necessitates the judicious optimization of available resources. In this paper, joint optimization of sensing and communication resources to minimize total energy spent within a sensor network is considered. A particular sensor network model with one Gaussian source observed by many sensors, subject to additive independent Gaussian observation noise, is examined. Sensors communicate with the receiver over an additive Gaussian multiple access channel. The aim of the receiver is to reconstruct the underlying source with minimum mean squared error. The fundamental tradeoff between communication and sensing over this sensor network model is characterized. Under symmetric conditions, for a single sensor, power is shared equally between communication and sensing. As the number of sensors increases, the sensing error dominates the overall error expression, hence sensing takes almost all power. The optimal power scheduling among sensors in the asymmetric case is determined, and it is shown that the power allocation schedule admits a simple decentralized implementation. Numerical results show that joint optimization of communication and sensing power yields significant power savings compared to the conventional approach of optimization of only communication power allocation.

In this paper, we consider the estimation of a signal that has both group- and element-wise sparsity (joint sparsity); motivated by channel estimation in vehicle-to-vehicle channels. A general approach for the design of separable regularizing functions is proposed to adaptively induce sparsity in the estimation. A joint sparse signal estimation problem is formulated via these regularizers and its optimal solution is computed based on proximity operations. Our optimization results are quite general and they can be applied in the context of hierarchical sparsity models as well. The proposed recovery algorithm is a nested iterative method based on the alternating direction method of multipliers (ADMM). Due to regularizer separability, key operations can be performed in parallel. V2V channels are estimated by exploiting the joint sparsity (group/element-wise) exhibited in the delay-Doppler domain. Simulation results reveal that the proposed method can achieve as much as a 10 dB gain over previously examined methods.

This paper considers the problem of state tracking with observation control for a particular class of dynamical systems. The system state evolution is described by a discrete-time, finite-state Markov chain, while the measurement process is characterized by a controlled multi-variate Gaussian observation model. The computational complexity of the optimal control strategy proposed in our prior work proves to be prohibitive. A suboptimal, lower complexity algorithm based on the Fisher information measure is proposed. Toward this end, the preceding measure is generalized to account for multi-valued discrete parameters and control inputs. A closed-form formula for our system model is also derived. Numerical simulations are provided for a physical activity tracking application showing the near-optimal performance of the proposed algorithm.

Underwater acoustic channels are wideband time-varying channels, which can be well-described by a multi-scale multi-lag channel model. In this paper, a robust method to estimate the channel parameters from noisy measurements is proposed. The proposed method computes the multiple Doppler scales, delays, and channel attenuation gains corresponding to different propagation paths. In this work, we adapt a spectral line estimation algorithm with a low number of measurements and modest complexity to compute the unknown channel parameters. The performance of the proposed estimation strategy is investigated via numerical simulation and shows that our method has at least 5 to 10 dB improvement in signal-to-noise ratio over previously proposed methods.

In this paper, a geometry-based stochastic channel model (GSCM) for vehicle-to-vehicle (V2V) wireless communication is developed. The channel model reveals that the channel representation in delay-Doppler domain can be divided into four regions. In each region, the V2V channel can be modeled using a hybrid sparse/diffuse (HSD) model. Prior art on hybrid channel estimation for linear time-invariant channels is extended to the time-varying case. Furthermore, the effects of pulse shape leakage are explicitly determined and compensated. Simulation results shows that exploiting the V2V channel properties in the delay-Doppler domain, yields significantly improved channel estimates over unstructured approaches (more than 10dB gain in SNR).

A strategy for active target detection suitable for the use of mobile agents in a field is presented. In particular, there is an interest in autonomous underwater vehicles. By exploiting notions from group testing, the proposed algorithm decides when to collect new samples depending on whether the mobile agent perceives the sensor measurements correspond to noise or a target pattern. Under suitable assumptions about the field emanated by the target, i.e. the target signature is locally low rank in the field, one can efficiently sample the field to locate the target using O(m log m log n) samples on an n × n grid where m ≪ n is a parameter specifying the group size.

In this paper, a new coding scheme for the multiple access channel (MAC) with noisy cooperative links is proposed. The cooperation cost is modelled by powers spent on exchanging common information at transmitters. The optimal power allocation policy is derived to explore the tradeoff between cooperation and transmission. For some important cases, optimal power allocation that maximizes weighted sum rate, is found analytically. The sufficient and necessary condition for which the sum and the individual rates are simultaneously maximized, is identified. Analytical and numerical results suggest that the transmitter, whose power budget is dominated by that of the other, acts purely as a relay. The cooperation gain becomes more significant when the difference between the power budgets is smaller.

In this paper, a framework for dynamic high-dimensional hypothesis testing in wireless sensor networks is presented. The sensor nodes (SNs) collect and transmit to a fusion center (FC), in a distributed fashion, compressed measurements of a time-correlated hypothesis vector. The FC, based on the measurements collected, tracks the hypothesis vector, and feeds back minimal information about the uncertainty in the current estimate, which enables adaptation of the SNs' data collection and transmission strategy. The policy of the SNs is optimized with the overall objective of minimizing the detection error probability, under sensing and transmission cost constraints incurred by each SN. A Bernoulli approximation on the detection error is employed, which enables a significant reduction in the optimization complexity and the design of scalable estimators based on sparse approximation recovery algorithms. Simulation results demonstrate that, for a target 5% detection error, the adaptive scheme attains 90% and 50% cost savings with respect to a memoryless scheme which does not exploit the time-correlation and a non-adaptive one, respectively.

The problem of scalable coding while exploiting the decoder side information is considered. Prior work considered the two important cases concerning the degraded side information where source X and the side information variables (Y1, Y2) form a Markov chain in the order of either X − Y1− Y2or X − Y2− Y1. While the encoding schemes for these settings differ considerably, they are both based on the combination of conditional codebook encoding, a standard tool in scalable coding, and random binning, conventionally used in decoder side information problems. In this paper, an encoding scheme is proposed solely on the basis of random binning, which essentially performs scalable and Wyner-Ziv coding simultaneously. Proposed scheme achieves the rate-distortion regions of prior results. A practical advantage of the unifying scheme is the fact that random binning can be realized via practical tools such as nested lattice codes and channel codes. Finally, motivated by the proposed encoding scheme, a network interpretation of scalable coding is considered. An achievable region is derived for this problem setting and the potential benefits of networked scalable coding are shown.

Exploration versus exploitation in a sensor field with a mobile agent is examined in the context of source coding. The encoder is the low complexity data gathering agent. The decoder is a high complexity fusion center. The encoder first sends a coarse description of the random field, then transmits a refined description of a region of interest, i.e., a subset of the correlated sources in the first stage and so on. The main source coding challenge is that the receiver wants to refine a subset of the correlated sources that is unknown to the encoder a priori. The conventional approach of scalable coding via conditional codebook encoding (CCE) requires a codebook that is exponential in size with respect to the number of sources and also the number of refinement stages. This paper studies an alternative approach, using random binning (RB), in lieu of CCE. The universality of RB plays a key role, as the encoder does not know a priori which sources the decoder wants to refine. It is shown that RB does not introduce any loss and can effectively replace CCE while providing significant storage reduction in terms of the number of codewords stored. Achievable rate regions are derived for the single and the multi-terminal encoding settings.

Identifiability is a key concern in ill-posed blind deconvolution problems arising in wireless communications and image processing. The single channel version of the problem is the most challenging and there have been efforts to use sparse models for regularizing the problem. Identifiability of the sparse blind deconvolution problem is analyzed and it is established that a simple sparsity assumption in the canonical basis is insufficient for unique recovery; a surprising negative result. The proof technique involves lifting the deconvolution problem into a rank one matrix recovery problem and analyzing the rank two null-space of the resultant linear operator. A DoF (degrees of freedom) wise tight parametrized subset of this rank two null-space is constructed to establish the results.

In this paper, a cross-layer framework for joint control and distributed sensing in agile wireless networks is presented, where an agent schedules actions to control a partially observable Markov decision process, whose state is inferred by collecting measurements from nearby assistant wireless nodes with cognitive and sensing capabilities (ANs). The framework makes it possible to model practical constraints of wireless networks, such as the cost incurred by the ANs to sense and transmit to the agent and the shared wireless channel, as well as to jointly optimize the acquisition of state information at the agent via distributed sensing, and the scheduling policy, under sensing-transmission cost constraints for the ANs. The optimality of a two-stage decomposition is proved, which enables decoupling of the optimization of action scheduling and distributed sensing. This scheme is applied to spectrum sensing, where the activity of licensed (PU, primary) users is measured by distributed wireless assisting receivers, based on which an agile (SU, secondary) user adapts its transmissions over time. Simulation results demonstrate that the proposed adaptive joint sensing-scheduling policy improves the SU throughput up to 50% over a scheme employing non-adaptive sensing, for a given constraint on the throughput degradation to the PU pair and cost incurred by the ANs, and up to a three-fold increase over a scheme where sensing is performed only locally by the SU.

The problem of sensing strategy design for active state tracking is considered. The system state is modeled by a discrete-time, finite-state Markov chain, which is observed through Gaussian measurement vectors that are dynamically selected by a controller. To overcome the computational complexity associated with the optimal sensing strategy derived in our prior work, a sensing strategy based on the sequential Weiss-Weinstein lower bound (WWLB) is proposed. To this end, closed-form WWLB formulae for our system model are obtained, while accommodating for multi-valued discrete parameters and control inputs. Numerical results validating the success of the proposed strategy on real data from a physical activity tracking application are provided.

Biological systems are known to communicate by diffusing chemical signals in the surrounding medium. However, most of the recent literature has neglected theelectron transfermechanism occurring among living cells, and its role in cell-cell communication. Each cell relies on a continuous flow of electrons from its electron donor to its electron acceptor through the electron transport chain to produce energy in the form of the molecule adenosine triphosphate, and to sustain the cell's vital operations and functions. While the importance of biological electron transfer is well-known for individual cells, the past decade has also brought about remarkable discoveries of multi-cellular microbial communities that transfer electrons between cells and across centimeter length scales, e.g., biofilms and multi-cellular bacterial cables. These experimental observations open up new frontiers in the design of electron-based communications networks in microbial communities, which may coexist with the more well-known communication strategies based on molecular diffusion, while benefiting from a much shorter communication delay. This paper develops a stochastic model that links the electron transfer mechanism to the energetic state of the cell. The model is also extensible to larger communities, by allowing for electron exchange between neighboring cells. Moreover, the parameters of the stochastic model are fit to experimental data available in the literature, and are shown to provide a good fit.

In this paper, a diffusion-based molecular communication channel between two nano-machines is considered. The effect of the amount of memory on performance is characterized, and a simple memory-limited decoder is proposed; its performance is shown to be close to that of the best possible decoder (without any restrictions on the computational complexity or its functional form), using genie-aided upper bounds. This effect is adapted to the case of Molecular Concentration Shift Keying; it is shown that a four-bit memory achieves nearly the same performance as infinite memory for all of the examples considered. A general class of threshold decoders is considered and shown to be suboptimal for a Poisson channel with memory, unless the SNR is higher than a computed threshold. During each symbol duration (symbol period), the probability that a released molecule hits the receiver changes over the duration of the period; thus, we also consider a receiver that samples at a rate higher than the transmission rate (a multi-read system). A multi-read system improves performance. The associated decision rule for this system is shown to be a weighted sum of the samples during each symbol interval. The performance of the system is analyzed using the saddle point approximation. The best performance gains are achieved for an oversampling factor of three for the examples considered.

The model of a side information vending machine (VM) accounts for scenarios in which the measurement of side information sequences can be controlled via the selection of cost-constrained actions. In this paper, the three-node cascade source coding problem is studied under the assumption that a side information VM is available at the intermediate and/or end node of the cascade. A single-letter characterization of the achievable tradeoff among the transmission rates, distortions in the reconstructions at the intermediate and end node, and cost for acquiring the side information is derived for a number of relevant special cases. It is shown that a joint design of the description, source, and control signals used to guide the selection of the actions at downstream nodes is generally necessary for an efficient use of the available communication links. In particular, for all the considered models, layered coding strategies prove to be optimal, whereby the base layer fulfills two network objectives: 1) determining the actions of downstream nodes and 2) simultaneously providing a coarse description of the source. Design of the optimal coding strategy is shown via examples to depend on both the network topology and action costs. Examples also illustrate the involved performance tradeoffs across the network.

The capacity of a relay channel with intersymbol interference (ISI) and additive colored Gaussian noise is examined under an input power constraint. Prior results are used to show that the capacity of this channel can be computed by examining the circular degraded relay channel in the limit of infinite block length. The current work provides single letter expressions for the achievable rates with decode-and-forward (DF) and compress-and-forward (CF) processing employed at the relay. Additionally, the cut-set bound for the relay channel is generalized for the ISI/colored Gaussian noise scenario. All results hinge on showing the optimality of the decomposition of the relay channel with ISI/colored Gaussian noise into an equivalent collection of coupled parallel, scalar, memoryless relay channels. The region of optimality of the DF and CF achievable rates is also discussed. The resulting rates are illustrated through the computation of numerical examples.

This paper considers identifiability and recoverability in bilinear inverse problems which is relevant to blind deconvolution and matrix factorization. It is shown that bilinear inverse problems can be posed as rank-1 matrix recovery problems subject to linear constraints. Sufficient conditions for identifiability are developed for the cases when rank-2 matrices are present in the null space of the linear operator. Signal recovery using the nuclear norm heuristic for rank-1 matrix recovery is considered and simple conditions for success are provided.

We consider a spectrum sharing protocol wherein the primary and secondary transmitters cooperatively relay each other's message. Transmission is done in two phases, with each transmitter attempting to decode messages from the other system transmission in a first phase. The second phase transmission consists of the decoded message superposed onto its own message. Priority is given to the primary system transmissions by having the primary message always transmitted over the two phases, while the secondary message is transmitted depending on successful decoding. We consider the scenario where the primary and secondary receivers are co-located, forming a virtual two-antenna receiver. We assess the performance of the system in terms of outage probability and characterize performance corresponding to each state of the Markov chain that governs the proposed transmission protocol. We show that joint decoding offers a 20 dB performance improvement over separate decoding for the primary user and 1.8 dB for the secondary user.

In this paper, the problem of state tracking with controlled observations is considered for a system modeled by a discrete-time, finite-state Markov chain. The system state is `hidden' and observed via conditionally Gaussian measurements that are shaped by the underlying state and an exogenous control input. Following an innovations approach, a Kalman-like filter is derived to estimate the Markov chain system state. To optimize the control strategy, the associated mean-squared error is used as an optimization criterion for a partially observable Markov Decision Process (POMDP). The optimal solution is determined via stochastic dynamic programming. Numerical results are presented for the application of physical activity detection in heterogeneous, wireless body area networks.

The problem of enhancing the quality of system state estimates is considered for a special class of dynamical systems. Specifically, a system characterized by a discrete-time, finite-state Markov chain state and observed via conditionally Gaussian measurements is assumed. The associated mean vectors and covariance matrices are tightly intertwined with the system state and a control input selected by a controller. Exploiting an innovations approach, finite-dimensional, non-linear approximate MMSE smoothing estimators are derived for the Markov chain system state. The resulting smoothers are driven by a control policy determined by a stochastic dynamic programming algorithm, which minimizes the MSE filtering error, and was proposed in our earlier work. An application of the smoothers derived in this paper is presented for the problem of physical activity detection in wireless body sensing networks, which illustrates the performance enhancement due to smoothing.

Underwater acoustic (UWA) communication channels are ultra-wideband in nature and experience long delay spreads and significant Doppler effects. The typical UWA channel distortion can be described by a multi-scale, multi-lag (MSML) channel model. Many UWA communication systems employ resampling by a single-scale at the front-end to compensate for the scale effects of UWA channels. In this letter, the optimal resampling parameter for OFDM signaling over MSML channels is investigated from a Bayesian perspective. The resampling parameter is selected to minimize the inter-carrier interference (ICI) resulting from the MSML channel for OFDM signaling. The exact interference energy is computed, but is intractable for optimization, thus, an upper bound is employed for optimization. Numerical results verify the tightness of the bound and quantify the performance of the Bayesian approach. As expected, the proposed method outperforms previous deterministic methods for resampling in MSML channels, which in turn outperform the classical packet-length-based approach, and is more effective in ICI mitigation.

The problem of state communication over a discrete memoryless channel with discrete memoryless state is studied when the state information is available strictly causally at the encoder. It is shown that block Markov encoding, in which the encoder communicates a description of the state sequence in the previous block by incorporating side information about the state sequence at the decoder, yields the minimum state estimation error. When the same channel is used to send additional independent information at the expense of a higher channel state estimation error, the optimal tradeoff between the rate of the independent information and the state estimation error is characterized via the capacity-distortion function. It is shown that any optimal tradeoff pair can be achieved via rate-splitting. These coding theorems are then extended optimally to the case of causal channel state information at the encoder using the Shannon strategy.

Channels with action-dependent states, as defined in [1], are considered. While [1] investigated the scenario of message dependent non-adaptive action sequences, this work focuses on adaptive action sequences, where the action is a strictly causal function of the message and the state sequences. The capacity of such a channel is characterized for the case when the state information is available non-causally at the channel encoder and it is shown that the adaptive action is not useful in increasing the capacity. The capacity of the action-dependent additive Gaussian channel, which was left open in [1], is then characterized by showing the equivalence of the current setting to the problem of the cooperative multiple access channel (MAC) with asymmetric state information at the encoders [2]. The problem setting is then extended to characterize the rate-distortion region of the source coding dual of the adaptive action-dependent channel coding setup, where a source is to be communicated with some fidelity to a decoder with adaptive action-dependent side information. In this setting adaptivity offers no help in enlarging the rate-distortion region.

A novel framework for the analysis and optimization of wireless networks operations is proposed. The temporal evolution of the state of the network is modeled as the trajectory of the state of a Finite State Machine (FSM). The state space of the FSM and the statistics of state transition are represented as a directed graph. Graph reduction and transform techniques are proposed to reduce the dimension of the graph associated with the FSM and analyze the properties of functions defined on its state space. The proposed methodology is based on the intrinsic multi-dimensional/multi-scale structure of the state space of the FSM and enables the analysis and minimization of cost-to-go functions, i.e., functions measuring the expected long-term cost associated with a control strategy, on coarser versions of the original graph.

The analog imperfections in RF direct conversion receiver, of which I/Q imbalance is a major detriment, are examined. Existing literatures on I/Q imbalance compensation try to compensate for the imbalance by estimating the mismatches. In this work, xampling-based decoding algorithm is examined. This algorithm is shown to be very efficient in handling the impairments in the analog components. Simulation results are also presented to illustrate some of the benefits of the proposed approach.

OFDM suffers from inter-carrier interferences in the presence of the time variation. This paper seeks to quantify the amount of interferences resulting from wideband channels which assumed to follow the multi-scale/multi-lag (MSML) model. Due to the fact that the mobility in wideband channels induces scale effects, Doppler is revealed in a manner distinct from the frequency shifts experienced in narrowband systems. The MSML channel model results in full channel matrices both in the frequency and time domains. However, banded approximations are still possible, leading to significant reduction in the equalization complexity. Herein, measures for determining whether time-domain or frequency-domain should be undertaken are provided based on the amount of the resulting interference.

Wireless Body Area Networks (WBANs) refer to a class of wireless sensor networks that are expected to support a wide variety of applications ranging from healthcare and emergency response to entertainment and sports. A WBAN can be characterized by a small number of heterogeneous sensors and an energy-constrained fusion center e.g. cellphone. A key goal is to maximize the lifetime of such a unique sensor network and based on an actual implementation of a prototype WBAN, the limited energy budget of the fusion center is a critical impediment. To overcome this issue, the stochastic control framework introduced in our earlier work is extended to account for less number of samples and sensor heterogeneity is redefined in terms of worst-case detection error probability. A Maximum Likelihood detector based on the belief state is also introduced to increase the detection performance. To account for the energy-constrained fusion center, our initial optimization problem is reformulated to two alternative, but distinct constrained versions and two completely new algorithms, E2MBADP and GME2PS2, are devised. Simulations on real-world data are provided to validate the schemes' performance. Energy gains on the order of 64% while achieving the same detection accuracy (99%) as an equal allocation scheme across sensors are observed.

We discuss the problem of inspecting an underwater structure, such as a submerged ship hull, with an autonomous underwater vehicle (AUV). In such scenarios, the goal is to construct an accurate 3D model of the structure and to detect any anomalies (e.g., foreign objects or deformations). We propose a method for constructing 3D meshes from sonar-derived point clouds that provides watertight surfaces, and we introduce uncertainty modeling through non-parametric Bayesian regression. Uncertainty modeling provides novel cost functions for planning the path of the AUV to minimize a metric of inspection performance. We draw connections between the resulting cost functions and submodular optimization, which provides insight into the formal properties of active perception problems. In addition, we present experimental trials that utilize profiling sonar data from ship hull inspection.

Wireless body area networks (WBANs) are emerging as a powerful tool for health management, emergency response, military personnel wellness as well as sports and entertainment. In contrast to traditional sensor networks for, say, environmental sensing, WBANs are often characterized by a modest number of heterogeneous sensors wirelessly coupled to a fusion center such as a mobile phone. Based on an actual implementation of a prototype WBAN, energy efficiency at the fusion center has proven to be one of the critical roadblocks to long-term deployment of WBANs. To this end, a novel formulation based on stochastic control tools is devised to model the sensor selection process. Sensors are heterogeneous both in their discrimination capabilities as well as their energy cost, further challenging sensor selection. The goal is to maximize the WBAN's lifetime while optimizing the performance of a physical state detection application. To this end, an optimal dynamic programming algorithm is derived. However, due to the prohibitive complexity of the optimal method, a low-cost approximation scheme, T3S, is designed. The low complexity design is based on several key properties of the cost functional. The proposed T3S scheme is evaluated on real-world data collected from an implemented WBAN and observed to offer near optimal performance with significantly lower complexity.

Channels with action-dependent states are considered: given the message to be communicated, the transmitter chooses an action sequence that affects the formation of the channel states, and then creates the channel input sequence based on the observed state sequence. The capacity - distortion tradeoff of such a channel is characterized for the case when the state information is available strictly causally at the channel encoder. The problem setting extends the action dependent framework of [1] and as a special case recovers the results of few previously considered joint communication and estimation scenarios in [2], [3], [4]. The scenario when the action is also allowed to depend on the past observed states is also considered and it has been shown that such adaptive action helps in achiveing a better capacity - distortion function.

The strategic behavior of receivers (players) in a multiple-input multiple-output Gaussian broadcast channel is investigated using the framework of non-cooperative game theory. In contrast to the non-cooperative Gaussian multiple access channel game in which each player's feasible set of actions is independent of the actions of other players, the action space of receivers in the Gaussian broadcast channel is mutually coupled, usually by a sum power or joint covariance constraint, and hence cannot be treated using traditional Nash equilibrium solution concepts. To characterize the strategic behavior of receivers in a broadcast channel game, this paper treats the broadcast channel power allocation (or covariance matrix selection) as a generalized Nash equilibrium problem with common constraints. The concept of normalized equilibrium (NoE) is used to characterize the equilibria and the existence and uniqueness of NoEs are proven for key scenarios.

The model of a side information “vending machine” accounts for scenarios in which acquiring side information is costly and thus should be done efficiently. In this paper, the three-node cascade source coding problem is studied under the assumption that a side information vending machine is available either at the intermediate or at the end node. In both cases, a single-letter characterization of the available trade-offs among the rate, the distortions in the reconstructions at the intermediate and at the end node, and the cost in acquiring the side information are derived under given conditions.

Motivated by the presence of an implicit communication channel in the asymptotic version of Witsenhausen's counterexample, implicit discrete memoryless channels (IDMC) with discrete memoryless (DM) states are considered. Information-theoretic lower and upper bounds (based respectively on the ideas from rate-distortion theory and hybrid-coding) are derived on the optimal distortion in estimating the input of the implicit channel. The intuition gained from the DMIC with DM state model is then used to evaluate the optimal distortion for the asymptotic version of the Witsenhausen counterexample. The minimum distortion is characterized for the counterexample and it is shown that a combination of linear coding and dirty-paper coding (DPC) proposed in [1] achieves the minimum distortion.

We examine the problem of utilizing an autonomous underwater vehicle (AUV) to collect data from an underwater sensor network. The sensors in the network are equipped with acoustic modems that provide noisy, range-limited communication. The AUV must plan a path that maximizes the information collected while minimizing travel time or fuel expenditure. We propose AUV path planning methods that extend algorithms for variants of the Traveling Salesperson Problem (TSP). While executing a path, the AUV can improve performance by communicating with multiple nodes in the network at once. Such multi-node communication requires a scheduling protocol that is robust to channel variations and interference. To this end, we examine two multiple access protocols for the underwater data collection scenario, one based on deterministic access and another based on random access. We compare the proposed algorithms to baseline strategies through simulated experiments that utilize models derived from experimental test data. Our results demonstrate that properly designed communication models and scheduling protocols are essential for choosing the appropriate path planning algorithms for data collection.

The use of biometric sensors for monitoring an individual’s health and related behaviors, continuously and in real time, promises to revolutionize healthcare in the near future. In an effort to better understand the complex interplay between one’s medical condition and social, environmental, and metabolic parameters, this article presents the KNOWME platform, a complete, end-to-end, body area sensing system that integrates off-the-shelf biometric sensors with a Nokia N95 mobile phone to continuously monitor the metabolic signals of a subject. With a current focus on pediatric obesity, KNOWME employs metabolic signals to monitor and evaluate physical activity. KNOWME development and in-lab deployment studies have revealed three major challenges: (1) the need for robustness to highly varying operating environments due to subject-induced variability, such as mobility or sensor placement; (2) balancing the tension between achieving high fidelity data collection and minimizing network energy consumption; and (3) accurate physical activity detection using a modest number of sensors. The KNOWME platform described herein directly addresses these three challenges. Design robustness is achieved by creating a three-tiered sensor data collection architecture. The system architecture is designed to provide robust, continuous, multichannel data collection and scales without compromising normal mobile device operation. Novel physical activity detection methods which exploit new representations of sensor signals provide accurate and efficient physical activity detection. The physical activity detection method employs personalized training phases and accounts for intersession variability. Finally, exploiting the features of the hardware implementation, a low-complexity sensor sampling algorithm is developed, resulting in significant energy savings without loss of performance.

Cognitive radio methodologies have the potential to dramatically increase the throughput of wireless systems. Herein, control strategies which enable the superposition in time and frequency of primary and secondary user transmissions are explored in contrast to more traditional sensing approaches which only allow the secondary user to transmit when the primary user is idle. In this paper, the optimal transmission policy for the secondary user when the primary user adopts a retransmission-based error control scheme is investigated. The policy aims to maximize the secondary users' throughput, with a constraint on the throughput loss and failure probability of the primary user. Due to the constraint, the optimal policy is randomized, and determines how often the secondary user transmits according to the retransmission state of the packet being served by the primary user. The resulting optimal strategy of the secondary user is proven to have a unique structure. In particular, the optimal throughput is achieved by the secondary user by concentrating its transmission, and thus its interference to the primary user, in the first transmissions of a primary user packet. The rather simple framework considered in this paper highlights two fundamental aspects of cognitive networks that have not been covered so far: 1) the networking mechanisms implemented by the primary users (error control by means of retransmissions in the considered model) react to secondary users' activity; 2) if networking mechanisms are considered, then their state must be taken into account when optimizing secondary users' strategy, i.e., a strategy based on a binary active/idle perception of the primary users' state is suboptimal.

Compressed sensing (CS) has renewed interest in sparse channel estimation. Herein, a semi-blind, iterative, sparse channel estimation method is proposed. The new method is based on minimizing Csiszar's I-divergence using Schulz & Snyder's iterative deautocorrelation algorithm. First, it is shown that the desired methods can be adapted to the problem of interest. The proposed semi-blind method accurately estimates the significant tap locations of a sparse channel, and their corresponding magnitudes. A method for determining the channel coefficients up to a phase ambiguity is presented. The simulation results show that although limited pilots are used, the proposed semi-blind iterative algorithm achieves performance comparable to that of training-based compressed sensing methods.

Block transmission of multi-scale orthogonal wavelet division multiplexing (OWDM) is proposed for signaling over wideband linear time-varying channels (LTV). Such channels are best modeled by multi-scale, multi-lag (MSML) models and the proposed OWDM designs are tailored to such channels. Given this signaling, the effective channel matrix for the received signal is banded, allowing for the modification of prior methods of equalization for orthogonal frequency division multiplexing over narrowband LTV channels. Performance of such equalizers and signaling is provided via simulation and shown to offer good performance coupled with high spectral efficiency over previously proposed designs.

We study optimal transmission strategies in interfering wireless networks, under Quality of Service constraints. A buffered, dynamic network with multiple sources is considered, and sources use a retransmission strategy in order to improve packet delivery probability. The optimization problem is formulated as a Markov Decision Process, where constraints and objective functions are ratios of time-averaged cost functions. The optimal strategy is found as the solution of a Linear Fractional Program, where the optimization variables are the steady-state probability of state-action pairs. Numerical results illustrate the dependence of optimal transmission/interference strategies on the constraints imposed on the network.

This paper presents coordination and data fusion methods for teams of vehicles performing target search tasks without guaranteed communication. A fully distributed team planning algorithm is proposed that utilizes limited shared information as it becomes available, and data fusion techniques are introduced for merging estimates of the target's position from vehicles that regain contact after long periods of time. The proposed data fusion techniques are shown to avoid overcounting information, which ensures that combining data from different vehicles will not decrease the performance of the search. Motivated by the underwater search domain, a realistic underwater acoustic communication channel is used to determine the probability of successful data transfer between two locations. The channel model is integrated into a simulation of multiple autonomous vehicles in both open ocean and harbor search scenarios. The simulated experiments demonstrate that distributed coordination with limited communication significantly improves team performance versus prior techniques that continually maintain connectivity.

This paper considers mission design strategies for mobile robots whose task is to perform spatial sampling of a static environmental field, in the framework of compressive sensing. According to this theory, we can reconstruct compressible fields using O(log n) nonadaptive measurements (where n is the number of sites of the spatial domain), in a basis that is "in coherent" to the representation basis [1]; random uncorrelated measurements satisfy this incoherence requirement. Because an autonomous vehicle is kinematically constrained and has finite energy and communication resources, it is an open question how to best design missions for CS reconstruction. We compare a two-dimensional random walk, a TSP approximation to pass through random points, and a randomized boustrophedon (lawnmower) strategy. Not unexpectedly, all three approaches can yield comparable reconstruction performance if the planning horizons are long enough; if planning occurs only over short time scales, the random walk will have an advantage.

We examine the problem of planning paths for an autonomous underwater vehicle (AUV) to collect data from an underwater sensor network. The sensors in the network are equipped with acoustic modems that provide noisy, range-limited communication. The AUV must plan a path that maximizes the information collected while minimizing travel time or fuel expenditure. This problem is closely related to the classical Traveling Salesperson Problem (TSP), but differs in that data from a particular sensor has a probability of being collected depending on the quality of communication. We propose methods for solving this problem by extending approximation algorithms for variants of TSP, and we compare our proposed algorithms to baseline strategies through simulated experiments with varying levels of communication quality. Our simulations utilize a realistic model of acoustic communication to determine the probability of acquiring data from each sensor. The results demonstrate that planning the tour for the entire network while exploiting the communication model during planning improves performance versus myopic methods.

A problem of state information transmission over a state-dependent discrete memoryless channel (DMC) with independent and identically distributed (i.i.d.) states, known causally at the transmitter is investigated. It is shown that block-Markov encoding coupled with channel state estimation conditioned on treating the decoded message and received channel output as side information at the decoder yields the minimum state estimation error. This same channel can also be used to send additional independent information at the expense of a higher channel state estimation error. It is shown that any optimal tradeoff pair can be achieved via a simple rate-splitting technique, whereby the transmitter appropriately allocates its rate between pure information transmission and state estimation.

Cooperation between rational users has emerged as a new networking paradigm to improve the performance of wireless networks. In this paper, transmitter cooperation between wireless nodes in a Gaussian multiple access channel is studied under the framework of coalitional game theory. The stability of the grand coalition, the coalition of all users, is studied by modeling the game in partition form, in contrast to previous approaches using characteristic form games, in scenarios with infinite and finite cooperation capacity between transmitters. In both cases, irrespective of the channel gains, the grand coalition is shown to be the sum rate optimal and stable, in the sense that users do not have any incentive to leave the coalition.

In this paper, frequency-domain relay processing in a two-hop transmission system is investigated. The relay is constrained to be "non-regenerative"; that is, the relay is only allowed to perform a symbol-by-symbol memoryless transformation of its received signals. Multicarrier modulation, e.g., orthogonal frequency division multiplexing (OFDM), is utilized to convert each hop into a collection of non-interfering parallel subcarriers. In contrast to conventional scalar amplify-and-forward (AF) relays that scale all the subcarriers uniformly, it is possible to suppress relay noise and to exploit frequency-domain diversity by optimizing the relay scaling coefficients of different subcarriers jointly with subcarrier power allocation at the source transmitter. This type of scheme is denoted by multicarrier amplify-and-forward (MCAF). Although the end-to-end achievable rate of MCAF is a non-concave function of the power allocation vectors, its optimization is accomplished with an algorithm (O-MCAF) whose computational complexity grows only quadratically with the number of subcarriers, by utilizing a structural property of the problem. Further motivated by the problem structure, a suboptimal algorithm (WF-MCAF) with a linear complexity is also proposed, in which each hop performs waterfilling separately over a selected subset of subcarriers. For hops with a frequency-flat channel response, the maximum achievable rate is explicitly derived from the associated optimization. For hops with Rayleigh fading frequency-domain channel responses, numerical results are presented and it is illustrated that the proposed low-complexity WF-MCAF algorithm usually achieves near-optimal performance.

A scenario involving a source, a channel, and a destination, where the destination is interested in both reliably reconstructing the message transmitted by the source and estimating with a fidelity criterion the state of the channel, is considered. The source knows the channel statistics but is oblivious to the actual channel state realization. Herein, it is established that a distortion constraint for channel state estimation can be reduced to an additional cost constraint on the source input distribution, in the limit of large coding block length. A newly defined capacity-distortion function thus characterizes the fundamental tradeoff between transmission rate and state estimation distortion. It is also shown that noncoherent communication coupled with channel state estimation conditioned on treating the decoded message as training symbols achieves the capacity-distortion function. Among the various examples considered, the capacity-distortion function for a memoryless Rayleigh fading channel is characterized to within 1.443 bits at high signal-to-noise ratio. The constrained channel coding approach is also extended to multiple access channels, leading to a coupled cost constraint on the input distributions for the transmitting sources.

This paper develops parametric methods to detect network anomalies using only aggregate traffic statistics, in contrast to other works requiring flow separation, even when the anomaly is a small fraction of the total traffic. By adopting simple statistical models for anomalous and background traffic in the time domain, one can estimate model parameters in real time, thus obviating the need for a long training phase or manual parameter tuning. The proposed bivariate parametric detection mechanism (bPDM) uses a sequential probability ratio test, allowing for control over the false positive rate while examining the tradeoff between detection time and the strength of an anomaly. Additionally, it uses both traffic-rate and packet-size statistics, yielding a bivariate model that eliminates most false positives. The method is analyzed using the bit-rate signal-to-noise ratio (SNR) metric, which is shown to be an effective metric for anomaly detection. The performance of the bPDM is evaluated in three ways. First, synthetically generated traffic provides for a controlled comparison of detection time as a function of the anomalous level of traffic. Second, the approach is shown to be able to detect controlled artificial attacks over the University of Southern California (USC), Los Angeles, campus network in varying real traffic mixes. Third, the proposed algorithm achieves rapid detection of real denial-of-service attacks as determined by the replay of previously captured network traces. The method developed in this paper is able to detect all attacks in these scenarios in a few seconds or less.

The achievable rate of a relay channel with inter-symbol interference and additive colored Gaussian noise is examined under an input power constraint and the compress-and-forward (CF) strategy at the relay. Using classical techniques, it is shown that the circular degraded relay channels is equivalent to that of the linear one in the limit of infinite block lengths. The achievable rate for CF for a circular Gaussian relay channel is equivalent to that of coupled collection of parallel, memoryless relay channels. Additionally, this parallel channel decomposition is shown to be information lossless for the evaluation of the CF rate.

Equalizer designs for digital communications over wireless channels exhibiting both multi-lag and multi-scale are investigated. Such channel models are well-suited for underwater acoustic communications and may have impact on the design of systems for vehicle-to-vehicle communications. First, the implications of the multi-scale, multi-lag model on equalizer design are highlighted. In particular, equalizers are time-varying as a function of symbol index. Three suboptimal, low complexity block equalizers (partial, truncated, and path combining) are compared to that of the full block equalizer and shown to offer a good tradeoff between complexity and performance. These four equalizers significantly outperform a simple matched filter which performs no equalization.

Fast and accurate Carrier Frequency Offset (CFO) estimation is a problem of significance in many multi-carrier modulation based systems, especially in uplink Orthogonal Frequency Division Multiple Access (OFDMA) where the presence of multiple users exacerbates the inter-carrier interference (ICI) and results in multi-user interference (MUI). In this paper, a new technique called partial FFT demodulation is proposed. Estimators for the CFO are derived by considering an approximated matched filter for each user, implemented efficiently using several FFTs operating on sub-intervals of an OFDM block. Through simulations, the feasibility and performance of the proposed estimators are demonstrated. Associated trade-offs are discussed.

Underwater acoustic channels are well modeled by different Doppler scaling per path, a generalization of the commonly employed model with equal Doppler scaling on all paths. The path dependent Doppler shifts and wideband channel, destroy carrier orthogonality and yield severe inter-carrier interference in an Orthogonal Frequency Division Multiplexing system. Resampling is typical front-end processing for signals over a common Doppler shift channel and this work examines the choice of resampling factor for the distinct Doppler per path scenario. Two criteria are derived to evaluate the optimal resampling parameter, one using sufficient statistics and the second using a matched filtering interpretation of resampling. The filtering interpretation is then used to derive a blind estimator for the optimal resampling parameter. Simulation results show the for small to moderate Doppler spreads, the blind estimator significantly outperforms the classical packet length based Doppler scaling estimator in many operating regimes.

A novel framework for the analysis and optimization of cognitive wireless networks with unslotted time operations and reactive primary users is proposed. In the considered network setting, primary users' channel access is regulated by a carrier sense-based contention mechanism. As the sensing mechanism cannot distinguish between primary and secondary signals, secondary users' activity may interfere with primary users' channel contention, thus biasing the statistics of the stochastic process modeling primary users' transmissions. In fact, a primary user which wakes up during a transmission from a secondary user may sense a busy channel and enter backoff or generate a collision. The proposed framework considers these effects and optimizes the fraction of time a secondary user is allowed to transmit according to a constraint on the minimum throughput achieved by the primary users. Numerical results are presented which illustrate fundamental behaviors and tradeoffs in a network with one primary and one secondary user. Extension to more general scenarios is also discussed.

A new approach to cognitive radio based on the premise that the legacy link is not fully loaded by the legacy service is presented. The assumption implies that there is a non-negligible margin to accommodate a cognitive transmission; this accommodation is achieved by spectrum shaping of the cognitive user. Much prior work on cognitive systems captures the effect of interference by the interference power level. In contrast, the current work characterizes interference by its induced degradation on the legacy user. Despite ignorance of the legacy user's message, the spectrum shaped cognitive user can always operate at its full available power. As a result, logarithmic growth of the cognitive transmission rate is achievable. In Part I of this two-part paper, analysis is provided for both scalar and vector system models where the legacy system employs coded transmission (Part II examines analog legacy users). The logarithmic growth rates, i.e., the prelog coefficients, of cognitive transmission rates in the high-power regime, are established for both the scalar and vector system models considered.

Spectrum shaping is examined for cognitive users to enable graceful coexistence with pre-existing legacy services. In Part I of this two-part paper series, the case of a coded legacy system was presented. In Part II, the case of an uncoded, or analog, legacy system is treated. Optimal power spectral densities are characterized for the cognitive user so that the analog legacy user achieves a prescribed distortion constraint as measured by a mean-squared estimation error. As in Part I, it is demonstrated that through spectrum shaping, the cognitive user can transmit at its full available power and thereby achieve logarithmic growth of the cognitive transmission rate. Necessary conditions for the optimality of the spectrum shaping problem are investigated and under special conditions, low-complexity optimization algorithms are developed. A low-complexity sub-optimal solution corresponding to an on-off cognitive power spectral density is analyzed, and shown to be sufficient for attaining the logarithmic growth of cognitive transmission rates. To summarize this two-part paper, a series of general considerations for system implementation of spectrum shaping are discussed.

The problem of time-varying channel estimation in multihop sensor networks is examined. Two relay processing methods are explored: amplify-and-forward and encode-and-forward. Bounds on the end-to-end distortion for all internode channel estimates are computed for these two relay processing schemes. Performance is analyzed via the asymptotic limit of the decay rate of the end-to-end distortion with respect to SNR at high SNR. It is also established that asymptotically in SNR, amplify-and-forward can outperform encode-and-forward and in fact can achieve the maximum possible distortion exponent (distortion decay rate) order of unity. Linear and many-to-one topologies are then examined and it is shown that orthogonal access in the many-to-one network is optimal.

In resource limited, large scale sensor networks, cooperative communication over multiple hops offers opportunities to save power: intermediate nodes between source and destination act as cooperative relays. In order to exploit spatial diversity, protocols coupled with space-time coding strategies are proposed herein and analyzed for distributed cooperative communication. In contrast to prior work, multi-hop (versus two-hop) schemes are developed and analyzed for amplify-and forward type of communication protocols. First, the Alamouti based two-hop scheme proposed by Hua et al and analyzed by Jing & Hassibi is generalized to an arbitrary number of hops L, and a general approximation for the pairwise error probability (PEP) at high SNR is obtained. This expression is used to provide a close approximation to the achievable diversity gain of the scheme. It is further shown that the diversity decreases with L, for large, but finite signal-to-noise ratio (SNR). This motivates the subsequent development of new distributed multihop protocols to mitigate the diversity losses and, hence, yield improved performance. This work presents two such strategies as well as their diversity characterization, which are analyzed for the specific case of L = 3 hops and shown to exhibit improved performance at high SNR. These schemes are based on the structure of the rate-half codes proposed by Tarokh and the square-matrix embeddable codes of Tirkkonen & Hottinen.

We present a novel optimization framework based on stochastic control and Markov theory for wireless networks where users concurrently access the channel and implement retransmission-based error control. In order to let users transmit at the same time, we consider an interference mitigation, rather than a collision avoidance approach. Our focus is on the interaction between the stochastic processes modeling the various individual sources of the network. Due to retransmissions, transmission by a user does not only instantaneously interfere with other simultaneous communications, but also biases the future evolution of the stochastic processes describing the other users. We, thus, define a novel interference measure called process distortion, that takes this effect into account. We investigate the optimization of access and power control for a network with two groups of users where transmission by the second group is constrained by the process distortion generated to the first group. We present algorithms to solve the unconstrained and constrained infinite horizon average cost per stage problems modeling this scenario. We discuss in detail the application of this framework to cognitive networks.

The capacity of a degraded relay channel with inter-symbol interference and additive colored Gaussian noise is examined under an input power constraint. Using the classical methodology, a related channel and signal model is developed - the circular degraded relay channel - the capacity of which is shown to be equivalent to that of the channel of interest in the limit of infinite block length. The capacity of circular relay channel is, in turn, determined by the decomposition into parallel degraded relay channels whose individual capacities are given by prior results. Two case studies based on the degraded relay channel with memory and white noise are examined: Common transmission bandwidths on each link and different effective transmission bandwidth over each link.

Channel estimation of coded multiple-input multiple-output (MIMO) orthogonal frequency division multiplexing (OFDM) in fast time-varying channels are considered. Maintaining high performance with manageable complexity relies on iterative soft-in soft-out equalization and decoding. Conventional frequency domain channel estimation methods have an irreducible error floor due to unaccounted intercarrier interference (ICI). This paper proposes an efficient and high performance time domain pilot symbol assisted modulation (PSAM) system for channel estimation. Simulation results are reported for the iterative receiver with application to the mobile worldwide interoperability for microwave access (WiMAX).

We consider block-based transmissions over doubly-selective wireless channels. It is well-known that orthogonal frequency division multiplexing decomposes linear time-invariant channels into a set of parallel (non-interfering) channels via a Fourier basis. In contrast, for time-varying channels, inter-channel interference occurs and may have a dramatic impact on the performance. Following a mean square error criterion, we propose an approximate eigenmode decomposition for linear time-varying channels, defined directly in the discrete-time domain. We show the effectiveness of the proposed waveform design by evaluating the mutual information (with Gaussian inputs) for doubly-selective terrestrial Rayleigh wireless channels.

This paper examines structured methods to perform multichannel estimation for underwater acoustic communication networks. Much of the receiver/protocol design for cooperative communications requires channel state information at the receiver. The proposed multichannel estimation algorithm exploits relationships between the multipath channels of cooperating transmitting nodes to a destination node. A simplified channel model is proposed from a geometry-based ray-tracing model. From an approximation of the channels between the relays and the destination, an iterative scheme is derived. The proposed method exploits the sparse nature of underwater acoustic channels and in so doing improves performance over unstructured methods. The efficacy of the proposed method is evaluated via simulations, i.e. comparing the mean-square error of the estimated channel with its Cramer-Rao bound.

Conventionally, a memoryless analog repeater at the relay of a multihop transmission system amplifies the signal received from its incoming link, and retransmits the amplified signal to its outcoming link. In the frequency domain, such an amplification essentially is ideal bandpass filtering, treating all the frequency components uniformly. For multicarrier systems like orthogonal frequency division multiplexing (OFDM) over frequency-selective channels, such a frequency-flat amplification is inadequate to exploit the benefits of adaptive processing at the relay. This paper analyzes the potential performance gain of non-uniform frequency-domain relay amplification, in which the gain coefficients for subcarriers are adapted from the frequency responses of both the incoming and outcoming links. An end-to-end achievable rate optimization problem is formulated. A simple heuristic power allocation algorithm is proposed. Numerical results indicate that the heuristic algorithm achieves considerable performance gains compared to conventional amplify-and-forward relay processing.

The cooperative dirty paper coding (DPC) rate region is investigated in a two-transmitter two-receiver network with full channel state information available at all terminals. The transmitters cooperate by first exchanging messages over an orthogonal cooperation channel, then they mimic a broadcast channel (BC) and jointly perform DPC to send to the two independent receivers. The allocation of network power and bandwidth between the data and the cooperation channel is studied to characterize the cooperative DPC rate region. First, the optimal sum power allocation for a multiple access channel (MAC) is presented. Then through an application of the MAC-BC capacity duality, the cooperative DPC rate region is evaluated under different bandwidth allocation assumptions. Cooperative DPC outperforms non-cooperative time-division (TD) only when the cooperation channel is strong, since the joint-encoding capacity gain is negated by the overhead of message exchanges in a weak cooperation channel. Moreover, the cooperative capacity advantage over TD is more pronounced at the maximum sum rate point than when the rate vector is skewed toward one of the users.

A new perspective on cognitive radio is presented for the case where the primary transmission is in uncoded analog form. The basic idea is to exploit signal-to-noise ratio margins in primary receivers, and to optimize cognitive signals by appropriately shaping their spectrum. It is shown that coexistence of primary and cognitive users is possible even without message-sharing, and furthermore, the cognitive user is no longer interference-limited, but can always transmit at its full available power thus achieving logarithmic growth of its information rate as its average power constraint grows large.

A joint communication and channel state estimation problem is investigated, in which reliable information transmission over a noisy channel, and high-fidelity estimation of the channel state, are simultaneously sought. The tradeoff between the achievable information rate and the estimation distortion is quantified by formulating the problem as a constrained channel coding problem, and the resulting capacity-distortion function characterizes the fundamental limit of the joint communication and channel estimation problem. The analytical results are illustrated through case studies, and further issues such as multiple cost constraints, channel uncertainty, and capacity per unit distortion are also briefly discussed.

The thirteen papers in this special issue focus on multiuser detection for advanced communication systems and networks. The papers can be divided into three thematic groups: Multiuser detection (MUD) in i) CDMA; ii) MIMO and Multicarrier CDMA/OFDM; and iii) Cooperative Communications.

The 13 papers in this special issue focus on underwater wireless communication networks.

This paper focuses on energy-efficient packet transmission with individual packet delay constraints. The solution presented herein is a generalization of Uysal-Biyikoglu (2002), which considered energy-efficient transmissions for a group of M packets subject to a single transmission deadline. First, the optimal offline scheduler (vis-À-vis total transmission energy) for packet transmissions with individual packet delay constraints is developed. It is shown that when packet inter-arrival times are independent and identically distributed (i.i.d.), the optimal transmission durations of packet $m$ and packet M-m+1, m ∈ [1,...,M, M ≥ 1, are identically distributed. This symmetry property leads to a simple and exact solution of the average packet delay for any i.i.d. inter-arrival times under the optimal offline scheduling. In addition, the packet delay performance for the single transmission deadline model is analyzed and shown to grow monotonically with $M$ and at a rate proportional to √M. A heuristic online scheduler, which assumes no future arrival information, is also studied and shown to achieve a comparable energy performance to the optimal offline scheduler in a wide range of scenarios. The flexible energy and delay tradeoff provided by the individual delay constraint model is further illustrated via simulations.

Orthogonal coding schemes, known to asymptotically achieve the capacity per unit cost (CPUC) for single-user ergodic memoryless channels with a zero-cost input symbol, are investigated for single-user compound memoryless channels, which exhibit uncertainties in their input-output statistical relationships. A minimax formulation is adopted to attain robustness. First, a class of achievable rates per unit cost (ARPUC) is derived, and its utility is demonstrated through several representative case studies. Second, when the uncertainty set of channel transition statistics satisfies a convexity property, optimization is performed over the class of ARPUC through utilizing results of minimax robustness. The resulting CPUC lower bound indicates the ultimate performance of the orthogonal coding scheme, and coincides with the CPUC under certain restrictive conditions. Finally, still under the convexity property, it is shown that the CPUC can generally be achieved, through utilizing a so-called mixed strategy in which an orthogonal code contains an appropriate composition of different nonzero-cost input symbols.

This paper focuses on delay-constrained energy-efficient packet transmission over a static multihop link. Optimal offline scheduling (vis-à-vis total transmission energy), assuming information of all packet arrivals before scheduling, is derived. The optimal offline schedule relies on a simple delay budget allocation scheme, which allocates the delay budget to the first hop (from source to the first relaying node) as much as possible. All the relaying nodes simply perform buffer-clearing during any transmission opportunities. The total transmission energy and average packet delay are analyzed and characterized. It is demonstrated that energy savings via multihopping are possible, but depend heavily on factors such as multihop resource orthogonalization mode, delay constraints, and SNR operating regimes.

Multihop channels are believed to provide benefits in performance because the reduction in transmission distance per hop directly translates to increased channel gains. However, a potential concern of multihop transmission is that the incurred delay also increases with the number of hops, and hence may pose challenges for delay-sensitive applications. This paper investigates the issue of delay from the perspective of error exponents, motivated by the fact that delay is tightly coupled with reliability. Two multihopping strategies are compared. The concatenated coding strategy, in which relay nodes perform inner coding and the source-destination pair further performs outer coding, is shown to provide near-optimal performance at low rates. In contrast, the pass-or-decode strategy, where relay nodes can either decode-and-forward or simply pass the received symbols, is shown to outperform the concatenated coding strategy as the achievable rate increases. The comparison suggests different system architectures for high and low rate applications,respectively.

Space-time block codes (STBCs) have attracted recent interest due to their ability to take advantage of both space and time diversity to reliably transmit data over a wireless fading channel. In many cases, their design is based on asymptotically tight performance criteria, such as the worst-case pairwise error probability (PEP) or the union bound. However, these quantities fail to give an accurate performance picture, especially at low signal-to-noise ratio, because the classical union bound is known to be loose in this case. This paper develops tighter performance criteria for STBCs which yield considerably better bounds. First, the union bound is developed as the average of the exact PEPs. By noting that some of the terms in the bound are redundant, a second bound is obtained by expurgation. Since this still yields a loose bound, a tighter bound, denoted as the progressive union bound (PUB), is obtained. Because the PUB cannot be computed in closed form, in its most general case, and to avoid computing a high-dimensional numerical integration, its saddlepoint approximation is developed. In addition to the significant improvement of the PUB analysis over other bounding methods, it is also shown that codes designed to optimize the PUB can perform better than those obtained by the looser criteria

Capacity improvement from transmitter and receiver cooperation is investigated in a two-transmitter, two-receiver network with phase fading and full channel state information (CSI) available at all terminals. The transmitters cooperate by first exchanging messages over an orthogonal transmitter cooperation channel, then encoding jointly with dirty-paper coding. The receivers cooperate by using Wyner-Ziv compress-and-forward over an analogous orthogonal receiver cooperation channel. To account for the cost of cooperation, the allocation of network power and bandwidth among the data and cooperation channels is studied. It is shown that transmitter cooperation outperforms receiver cooperation and improves capacity over noncooperative transmission under most operating conditions when the cooperation channel is strong. However, a weak cooperation channel limits the transmitter cooperation rate; in this case, receiver cooperation is more advantageous. Transmitter-and-receiver cooperation offers sizable additional capacity gain over transmitter-only cooperation at low signal-to-noise ratio (SNR), whereas at high SNR transmitter cooperation alone captures most of the cooperative capacity improvement.

In this correspondence, a relay rate-constrained variation of the three-node relay channel is introduced and studied. In the rate-constrained relay channel, the relay cannot reliably decode or encode beyond a rate constraint R macr. A cut-set upper bound, and rate-constrained variations of decode-and-forward and estimate-and-forward protocol are derived. It is observed that relative performance of the unconstrained protocols does not predict relative performance of the constrained protocols; that is, estimate-and-forward almost always offers superior performance for small rate constraints. A new metric, denoted as the relay efficiency, measures the slope at which the relay channel rate increases over the relay-less channel. While the cut-set upper bound has a relay efficiency of one for the Gaussian relay channel at R macr = 0, the two new protocols do not. Finally, it is shown that an effective relay rate constraint can be computed for general relay protocols. To demonstrate this concept, a Markovian amplify-and-forward protocol, in which the relay does not perform any explicit encoding or decoding, is examined and compared to the introduced rate-constrained protocols.

Due to their extremely large transmission bandwidth, ultrawideband (UWB) communications systems have the potential for significant multipath capture. However, such capture can be dependent on the accuracy of channel estimation, which is the problem considered herein. Strategies that exploit properties of the UWB channel can offer performance improvement over schemes which are less parametric. To this end, recent UWB propagation studies and associated channel modelling suggest that clustering occurs in both space and time for indoor wireless office/laboratory environments. Motivated by this study, a two-stage channel estimation scheme is proposed. First, coarse channel estimation is conducted which estimates the location and dispersion of a cluster around this location. Then, channel estimation efforts are localized around the nominal location to reduce the dimensionality of the estimation of the parameters associated with the different multipath components. For cluster estimation, methods based on maximum-likelihood and correlation matrix fitting are considered. Different strategies are presented for multipath estimation, based on the expectation maximization algorithm and correlator output maximization. The bit error rate of a Rake type receiver is simulated with the estimated channel parameters for a multipath scenario based on clusters. Simulation results indicate that the clustered approach offers solid performance gains over conventional methods

The multipath capture of ultrawideband (UWB) communications systems can be dependent on the accuracy of channel estimation. Furthermore, inefficient modeling of the channel often leads to over-parametrization and increased channel estimation error. Relying on a channel model based on clusters, this letter proposes a maximum likelihood estimation strategy which exploits the properties of the UWB channel and offers performance improvement of about 2 dB over less parametric schemes. The robustness of the proposed algorithm in the presence of pulse distortion is investigated and the corresponding BER degradation is observed to be small even when experimental data are employed.

Algorithms for the estimation of a channel whose impulse response is characterized by a large number of zero tap coefficients are developed and compared. Estimation is conducted in a two-stage fashion where an estimate of the non-zero taps is followed by channel estimation. Tap detection is transformed into an equivalent on-off keying detection problem. Several tap detection algorithms are investigated which tradeoff between complexity and performance. The proposed methods are compared to an unstructured least squares channel estimate as well as a structured approach based on matching pursuit. Three schemes in particular are developed: a sphere decoder based scheme, a Viterbi algorithm based method and a simpler iterative approach. The latter offers a better tradeoff between estimation accuracy and computational cost. A joint estimation and zero tap detection scheme is also considered. All solutions exhibit a significant gain in terms of mean-squared error and bit error rate over conventional schemes which do not exploit the sparse nature of the channel, as well as the matching pursuit approach which does endeavor to exploit the sparsity

Synchronization is one of the most critical issues in ultra-wideband communications due to the short duration of the pulses. In this work, a semi-blind synchronization scheme based on maximum-likelihood techniques to recover both symbol and frame timing is developed. The proposed algorithm performs joint timing and channel estimation and is thus robust against moderate timing errors. The probability of acquisition is computed based on correlated Gaussian random variables and analytical results are shown to be accurate for moderate to high SNR values relative to simulated performance. The results reveal that the proposed algorithm achieves a significant performance gain in terms of mean-squared error and bit-error-rate in comparison to proposed frame rate (FR) based algorithms. The impact of a low resolution analog-to-digital converter is shown to be marginal

Digital implementation of ultra-wideband receivers requires analog-to-digital conversion (ADC) at an extremely high speed, thereby limiting the available bit resolution. Herein, the effect of low bit resolution quantization on the performance of UWB transmitted reference receivers is investigated. It is verified that the gain of the automatic-gain-control (AGC) has a significant effect on the achievable performance. Because of the considerable performance loss of conventional transmitted reference receivers in the presence of a low resolution ADC a new family of receiver structures optimized and tailored to quantized observations is presented. In particular, the generalized- likelihood ratio test (GLRT) based on the quantized samples is derived and shown to provide modest performance gains relative to the infinite resolution GLRT rule employed on the quantized received signal suggesting that conventional receiver structures can also be employed in the presence of a low resolution ADC. Results reveal that four bits of resolution in combination with an optimal choice for the AGC gain are sufficient to closely approach the performance of an infinite resolution receiver.

The achievable rate of training-based UWB systems is investigated. Assuming a block-fading model and the IEEE 802.15.3a channel model it is found that training-based UWB systems, that restrict the transmitted signals in their power- spectral density, are able to utilize the available bandwidth efficiently. This is in contrast to recent results for power limited systems which predict a diminishing mutual information with increasing bandwidth if white-like signals are used. The bounds developed are independent of the particular channel estimator used. An optimization of the training sequence length shows that a training sequence length of about 2% of the coherence interval is optimal independent of the signal bandwidth.

In resource limited, large scale underwater sensor networks, cooperative communication over multiple hops offers opportunities to save power when intermediate nodes between source and destination act as cooperative relays. Herein, protocols coupled with space-time block code (STBC) strategies are analyzed for distributed cooperative communication in underwater channels. Amplify-and-forward type protocols are considered, in which the relays do not attempt to decode the information. The Alamouti-based cooperative scheme proposed by Hua et al (2003) for flat-fading channels is modified in order work in the presence of multipath. A time-reversal distributed space-time block code (TR-DSTBC) is employed, which extends the classical TR-STBC approach from Lindskog and Paulraj (2000) to a cooperative communication scenario. Furthermore, the performance of the scheme employing a DFE equalizer at the destination is analytically investigated in terms of bit error rate (BER) bounds and achievable spatial diversity.

Abstract — This paper focuses on energy-efficient packet transmission with individual packet delay constraints. The optimal offline scheduler (vis-à-vis total transmission energy), assuming information of all packet arrivals before scheduling, was developed by Zafer, et al. (2005) and Chen et al. (2006). This paper shows that when packet inter-arrival times are identically and independently distributed (i.i.d.), the resulting optimal transmission durations of packets m and M − m + 1, m ∈ [1, · · · , M], M ≥ 1, are identically distributed. This symmetry property leads to a simple and exact solution of the average packet delay under the optimal offline schedule. Two heuristic online scheduling algorithms, which assume no future arrival information, are then studied. These online schedulers are compared with the optimal offline scheduler in terms of delay and energy performance via analysis and simulations. While both online schedulers are inherently inferior, one online scheduler is shown to achieve a comparable energy performance to the optimal offline scheduler in a wide range of scenarios. I.

A new class of abstract sensor networks is introduced and analyzed. The object of the sensing is the inter-node channel. Examples of systems which seek to sense the channel include: underwater sonar, radar and optics based atmospheric sensor networks. In these networks, the sensing and communication tasks share the bandwidth resource in addition to the sharing of power resources as has been conventionally studied in the sensor network framework. Bounds on distortion tradeoffs are developed for various protocols, such as source coding analogues of decode-and-forward and amplify-and-forward and simple topologies such as two-hop networks (three node system).

This paper investigates proactive packet dropping to achieve transmission energy savings. Such a scheme can be employed for applications which can tolerate a small fraction of packet losses. For a group of packets subject to a single transmission deadline, the optimal dropping scheme (vis-` a-vis total transmission energy) is derived. For packets subject to individual delay constraints, the optimal scheme depends on the energy function and packet sizes. Thus, asymptotically optimal dropping schemes, i. e., when packet size grows large, are pursued. The asymptotically optimal dropping scheme for a single dropped packet is obtained. For dropping more than one packet, two suboptimal, recursive schemes are proposed. These schemes achieve performance very close to the asymptotically optimal schemes as determined by an exhaustive search. Additionally, two performance bounds are derived. It is observed via simulations that significant energy savings are possible via intelligent packet dropping schemes.

The acquisition, or synchronization, of a multipath channel profile for ultra-wideband pulse position modulation (PPM) communication systems is considered. The rate of increase of the number of paths as the bandwidth grows determines whether acquisition can occur. If the number of independent Gaussian paths increases without bound, but slower than the bandwidth, then the system cannot acquire in the limit. Acquisition is not possible on multipath channels with deterministic path amplitudes, if the number of paths diverges but not too fast. These results hold for exponential or uniform power delay profiles

A joint sensing/communication problem is considered for sensor networks. Herein, the channel(s) between a source and destination are the parameters to be sensed and communicated over the network. Lower bounds on the end-to-end distortion are developed for a multihop, linear network. Internode communication is assumed to be done via an encode-and-forward approach. For a many-to-one topology with two hops, data aggregation and time-division communication approaches are compared. Asymptotic in the SNR, it is shown that a time-division approach is superior

Maximum-likelihood (ML) and generalized likelihood ratio test (GLRT)-based data detection schemes for an ultra-wideband communication system are proposed and compared with the training-based (TB) transmitted reference (TR) receiver. The exact probability of error for the TB receiver and a lower bound for the ML and the GLRT receiver are provided in terms of a doubly noncentral F-variate. An optimization with respect to the integration interval length and the energy allocation between the training and the data symbols is investigated. Analytical and simulation results reveal that the proposed detection schemes provide significant performance improvements in terms of bit-error rate over the TB receiver structure.

The performance criteria of multiple-input multiple-output (MIMO) fading channels, diversity gain and coding gain, demand quite different coding techniques than those for Gaussian channels. This paper proposes a new approach to space-time block code (STBC) construction for coherent systems different from algebraic constructions (unitary, orthogonal) by explicitly manipulating the relationship between codewords rather than properties of the individual codeword. Algebraic constructions may incur performance losses of several decibels, relative to code sets determined by exhaustive search. By characterizing the structure of exhaustively searched code sets, a set of isometries between code words is determined. These isometries, in turn, suggest a greedy code construction method: nonlinear hierarchical codes (NHCs). NHCs of arbitrary block sizes/constellations can be constructed bottom-up by optimizing the coding gain layer-by-layer and reusing the optimized structure. The strong symmetry and layered structure make NHCs ideal constituent codes for regular multiple trellis-coded modulation (RMTCM). New RMTCM design procedures are proposed for various rates/block sizes/constellation sizes. The match between the distance spectrum of NHCs to the regular trellis structure optimizes coding gain directly, and full diversity is naturally maintained given the full rank of the constituent NHCs. Several factors which affect performance are analyzed and exploited to improve the overall performance. Set expansion is used to improve high-rate MTCM designs. In each design, the performance/rate/complexity tradeoffs are gracefully balanced and optimized. With limited growth in complexity, the proposed designs can achieve more than 2 dB gain over current MTCM designs.

The maximum-likelihood (ML) sequence detection problem for channels with memory is investigated. The Viterbi algorithm (VA) provides an exact solution. Its computational complexity is linear in the length of the transmitted sequence, but exponential in the channel memory length. On the other hand, the sphere decoding (SD) algorithm also solves the ML detection problem exactly, and has expected complexity which is a low-degree polynomial (often cubic) in the length of the transmitted sequence over a wide range of signal-to-noise ratios. We combine the sphere-constrained search strategy of SD with the dynamic programming principles of the VA. The resulting algorithm has the worst-case complexity determined by the VA, but often significantly lower expected complexity

In this correspondence, a new bound on the performance of multiuser receivers in fading channels is presented. The bound is based on a novel criterion for decomposability of error sequences. A constructive method to find the set of indecomposable error sequences under the new criterion is presented. Although the presentation is in the context of a narrow-band multiuser system, the improved bounds can be applied directly to code-division multiple-access (CDMA) multiuser systems over flat-fading channels. A modified bound is also developed for multipath-fading channels. Several examples demonstrate the improved tightness of the new bound, relative to previous results

A preponderance of ultrawideband radio communication systems under current study employ the use of pulse position modulation (PPM). However, there are significant issues related to the synchronization of PPM systems in multipath. If the multipath scattering is rich i.e. the number of reflections (paths) increases without bound as the bandwidth increases, then synchronization is impaired. In particular, it is shown that a threshold-type detector fails in synchronizing, for any possible threshold, in the limit of large bandwidth. The maximum likelihood detector can synchronize under some severely constrained scenarios which do not appear to reflect reality in light of recent propagation measurements

In this paper, the impact of limited resources on achievable rates in relay channels is investigated. Resource limitation is modeled (as previously proposed) by (Rmacr) which constrains the rate at which a relay can reliably decode or encode data. With this constraint in hand, the achievable rates for two commonly studied protocols, decode-and-forward and estimate-and-forward are derived and_compared. In particular, the case of severe resource limitation (Rmacr rarr 0) is considered. While decode-and-forward is the superior protocol for most relay locations (topologies) under no rate constraints, estimate-and forward almost always offers superior performance when relaying with strong rate constraints

Multistage (MS) implementation of the minimum mean-square error (MMSE), minimum output energy (MOE), best linear unbiased estimation (BLUE), and maximum-likelihood (ML) filter banks (FBs) is developed based on the concept of the MS Wiener filtering (MSWF) introduced by Goldstein et al. These FBs are shown to share a common MS structure for interference suppression, modulo a distinctive scaling matrix at each filter's output. Based on this finding, a framework is proposed for joint channel estimation and multiuser detection (MUD) in frequency-selective fading channels. Adaptive reduced-rank equal gain combining (EGC) schemes for this family of FBs (MMSE, MOE, BLUE, and ML) are proposed for noncoherent blind MUD of direct-sequence code-division multiple-access systems, and contrasted with the maximal ratio combining counterparts that are also formed with the proposed common structure under the assumption of known channel-state information. The bit-error rate, steady-state output signal-to-interference plus noise ratio (SINR), and convergence of the output SINRs are investigated via computer simulation. Simulation results indicate that the output SINRs attain full-rank performance with much lower rank for a highly loaded system, and that the adaptive reduced-rank EGC BLUE/ML FBs outperform the EGC MMSE/MOE FBs, due to the unbiased nature of the implicit BLUE channel estimators employed in the EGC BLUE/ML schemes.

The performance of a set of linear reduced-rank multistage filter banks is studied in the context of multiuser detection for direct-sequence (DS) code-division multiple-access (CDMA) systems. The set of filter banks under consideration is comprised of the minimum mean-square error (MMSE), the minimum output energy (MOE), the best linear unbiased estimator (BLUE), and the maximum-likelihood (ML) detector. Based on a common framework for the multistage implementations of the aforementioned filter banks, the signal-to-interference plus noise ratios (SINRs) and bit-error rates (BERs) of these reduced-rank filter banks are studied for multipath Rayleigh-fading channels. A generic BER formula is provided for coherent detection and noncoherent differential detection schemes constructed under this common framework. Analysis shows that all of these performance measures are characterized by a kernel matrix K/sub mmse/ whose trace forms the output SINR of the MMSE filter bank. Through investigating the recursive structure of K/sub mmse/, the output SINRs are proven to be monotonically increasing with the number of stages and upper-bounded by a number equal to the paths of the desired user's channel. The condition for asymptotically achieving this upper bound is also provided, which leads to the notion of effective user capacity of linear reduced-rank multiuser detection as well as serves as a test for the existence of a BER floor for coherent detection. In addition, the channel mismatch due to differential detection is also shown to yield a BER floor for noncoherent detection. Based on this analysis, a simple yet effective rule for choosing the number of stages is provided for both coherent and noncoherent linear multistage multiuser detection.

Controlling transmitted power in a wireless network is critical for maintaining quality of service, maximizing channel utilization and minimizing near-far effect for suboptimal receivers. In this paper, a general proportional-integral-derivative (PID) type algorithm for controlling transmitted powers in wireless networks is studied and a systematic way to adapt or tune the parameters of the controller in a distributed fashion is suggested. The proposed algorithm utilizes multiple candidate PID gains. Depending on the prevailing channel conditions, it selects an optimal PID gain from the candidate gain set at each instant and places it in the feedback loop. The algorithm is data driven and can distinguish between stabilizing and destabilizing controller gains as well as rank the stabilizing controllers based on their performance. Simulation results indicate that the proposed scheme performs better than several candidate controllers, including a well known distributed power control (DPC) algorithm.

A unified structure is proposed for joint channel tracking and symbol detection in multipath fading channels. Based on the expectation maximization (EM) algorithm, a group of recursive stochastic filters is derived for blind channel estimation, using the maximum likelihood and minimum mean squared error criteria. In conjunction with the BCJR algorithm, it is shown that the recursive procedure for joint channel estimation and maximum a posteriori symbol detection can be represented as a message-passing scheme on a factor graph. The graphical model not only provides a generalized implementation architecture for existing training-based schemes, it also greatly reduces the complexity of existing algorithms for blind channel tracking.

Channel estimation for ultra-wideband (UWB) signals is considered herein. The UWB propagation study and associated channel modelling of R. J. -M Cramer et al., (May 2002) suggest clustering occurs in both space and time for indoor wireless office/laboratory environments. Motivated by this study, a two-stage channel estimation scheme is proposed. First, coarse channel estimation is conducted to estimate the nominal location and dispersion of a cluster. Then, channel estimation efforts are concentrated around the nominal parameters to reduce the dimensionality of the estimation of the parameters associated with the different multipath components. Different strategies based on maximum-likelihood, least squares and expectation maximization are presented and compared in terms of estimation accuracy and complexity. The bit error rate of a Rake type receiver is simulated with the estimated channel parameters for a clustered multipath scenario.

Algorithms for the estimation of a channel whose impulse response is characterized by a large number of negligible tap coefficients are developed and compared. Exploiting this sparsity, the estimation problem is transformed into an equivalent on-off keying detection problem, whose solution gives an indication on the position of the zero taps. The proposed schemes are compared to the standard least squares estimate via simulations in terms of mean square error and bit error rate. A scheme based on sphere decoding appears to give the best performance while maintaining moderate complexity.

Space-time block codes have attracted recent interest due to their ability to take advantage of available diversity to transmit data over a wireless fading channel. Since they were first introduced, several strategies have been proposed for their design. In most cases, these designs were optimized for high SNR through a worst case pairwise error probability analysis and either placed strict constraints on the structure of codewords or were limited to codesets of low cardinality. This work addresses the issue of code design specifically for low SNR environments using union bound-based performance criteria which are SNR dependent. A more accurate union bound-based measure for performance evaluation of codes is developed and a variation of a locally optimum algorithm used to systematically construct good codeword sets is presented. New found codes are compared via simulation with other existing codes and are shown to exhibit improved performance.

The problem of joint channel estimation and multiple access interference suppression for an asynchronous code-division multiple-access system is studied. A low-complexity sliding-window scheme is proposed for channel tracking based on the expectation maximization (EM) algorithm and a noncoherent maximum a posteriori probability (MAP) multiuser detector. By exploiting the features of the discrete random phase ambiguities of channel estimates using the EM algorithm, the proposed scheme can track channel phase even if the channel is in a deep fade. In conjunction with iterative soft interference cancellation, the proposed detector can achieve a near single user system performance with polynomial complexity in the number of users.

Motivated by limited computational resources in sensor nodes, the impact of complexity constraints on the communication efficiency of sensor networks is studied. A single-parameter characterization of processing limitation of nodes in sensor networks is invoked. Specifically, the relaying nodes are assumed to donate only a small part of their total processor time to relay other nodes information. The amount of donated processor time is modelled by the node's ability to decode a channel code reliably at given rate R. Focusing on a four node network, with two relays, prior work for a complexity constrained single relay network is built upon. In the proposed coding scheme, the transmitter sends a broadcast code such that the relays decode only the coarse information, and assist the receiver in removing ambiguity only in that information. Via numerical examples, the impact of different power constraints in the system, ranging from per node power bound to network wide power constraint is explored. As the complexity bound R increases, the proposed scheme becomes identical to the recently proposed achievable rate by Gupta & Kumar (2003). Both discrete memoryless and Gaussian channels are considered.

This paper examines communication between a cluster of closely-packed nodes with another cluster of closely-packed nodes. The nodes within each cluster are separated by small distances, relative to the distance between the two clusters. The effect on capacity of cooperation between nodes in the transmitting cluster and cooperation between nodes in the receiving cluster is investigated.

A new criterion for performance evaluation of space-time block codes, based on the progressive union bound (PUB) is proposed. The PUB is not analytically tractable, in general, but can be well approximated by a saddlepoint approximation. The improvements provided by this new approach are illustrated by comparison with other bounds. New code sets, offering improved performance at low SNR, can also be found via the new bounds.

Sensor networks have emerged as a fundamentally new tool for monitoring spatial phenomena. This paper describes a theory and methodology for estimating inhomogeneous, two-dimensional fields using wireless sensor networks. Inhomogeneous fields are composed of two or more homogeneous (smoothly varying) regions separated by boundaries. The boundaries, which correspond to abrupt spatial changes in the field, are nonparametric one-dimensional curves. The sensors make noisy measurements of the field, and the goal is to obtain an accurate estimate of the field at some desired destination (typically remote from the sensor network). The presence of boundaries makes this problem especially challenging. There are two key questions: 1) Given n sensors, how accurately can the field be estimated? 2) How much energy will be consumed by the communications required to obtain an accurate estimate at the destination? Theoretical upper and lower bounds on the estimation error and energy consumption are given. A practical strategy for estimation and communication is presented. The strategy, based on a hierarchical data-handling and communication architecture, provides a near-optimal balance of accuracy and energy consumption.

Semiblind channel estimation combines the methods of channel estimation based on a pilot signal and blind channel estimation based on a data-only conveying signal. Maximum-likelihood (ML)-based semiblind estimators with Gaussian assumptions can provide improvement in performance, compared with channel-estimation schemes using the pilot signal only. This improvement can be even larger when the pilot and the data signals are sent simultaneously, as is the case in the third-generation wideband code-division multiple-access standards. However, the Gaussian ML approach results in very large complexity. Previously proposed semiblind methods with low complexity have been derived for serial pilot and data transmission, and are not suitable for the parallel transmission case. In this paper, algorithms for semiblind channel estimation for the parallel data and training signal case are developed. Approximations which reduce the computational complexity of the Gaussian ML method significantly are proposed. Solutions with iterations with very low attendant complexity are provided. The mean squared error analysis of the proposed method is obtained and compared with that of a method with no approximations. The approximations are justified through simulations, and the performance improvement over estimation schemes using the pilot signal solely is verified.

Synchronization and channel acquisition for ultra-wideband signals are investigated. The channel impulse response is estimated via two approaches: a least-squares method which ignores channel structure, and a subspace technique which finds channel sparseness and then exploits this structure for final channel estimation. Symbol and frame synchronization is also accomplished using least squares methods. Performance is investigated through the evaluation of mean-squared channel estimation error and probability of error with a RAKE receiver employing the channel estimates for an indoor wireless channel. It is observed that the subspace method exploiting the clustered property of the channel yields the best performance at the expense of complexity.

Transmitted reference (TR) modulation schemes, initially proposed for spread-spectrum systems in the 1920's have regained popularity in the context of ultra-wideband (UWB) communications, where accurate channel estimation is a challenging task. In the conventional TR approach, a reference signal (without data modulation) is received and employed in a correlator receiver for data modulated signals. By exploiting the statistics of the received signals, optimal and suboptimal data detection schemes for a single-user UWB communication system employing antipodal modulation with TR are investigated and compared to the conventional TR receiver. The proposed schemes can cope with a variable number of reference and data modulated pulses. By construction, the modulation and demodulation methods work for arbitrary channels. The efficacy of the new methods is investigated via simulations emulating an indoor multipath channel. These simulation results reveal that the proposed detection schemes provide significant performance improvements in terms of bit error rate over the conventional TR receiver structure.

Recent designs for space-time block codes (STBCs) have exploited the structure of the codes (unitary, orthogonal) to enable systematic construction. Code sets found by an exhaustive search to optimize cost functions can often achieve performance gains of several dB; however, such searches are not feasible for large block or constellation sizes. By exploiting isometries between codewords, properties of the searched codes are determined which suggest a systematic code construction method. The results are nonlinear hierarchical codes (NHCs), where coding gain is optimized at each layer of the hierarchy and codes of arbitrary block-sizes/constellations can be designed. A fundamental difference between the structured codes (unitary, orthogonal) and NHCs is that in NHCs, the relationship between codewords is manipulated, rather than properties of the individual codeword. The proposed scheme is essentially a generalization of D. Slepian's group codes for the Gaussian channel (see Bell System Tech. J., vol.47, p.575-602, 1968) to the multiple-input multiple-output quasi-static fading channel.

In many wireless communication systems, space-time block codes (STBC) alone are insufficient to provide the needed protection from the effects of a fading channel, and hence are more suitable as an inner code for a serially concatenated system. Serial concatenation introduces memory between STBCs indirectly. A direct approach, multiple trellis coded modulation (MTCM), can match the distance spectrum of constituent codes to the trellis structure and optimize coding gain. Full diversity is naturally maintained given the full rank of the constituent STBCs. Previous MTCM designs of STBC relied heavily on orthogonal codes. In this work, nonlinear hierarchical codes (NHC), with no constraint on the size and structure of each block code, serve as a natural candidate for set partitioning and expansion in MTCM design. Regular MTCM design procedures are proposed to exploit the layered structure of NHC which leads to optimized designs for various rates/block sizes/constellation sizes. Several factors which affect performance are analyzed and exploited to improve the overall performance. Set expansion is used to improve high rate MTCM designs. In each design, the performance/rate/complexity tradeoffs are gracefully balanced and optimized. With limited growth in complexity, the proposed designs can achieve more than 3.5 dB of gain over current MTCM designs.

The multistage (MS) Wiener filter proposed in J.S. Goldstein et al. (1998) is extended to blind maximum likelihood (ML) multiuser detection for direct-sequence (DS) code-division multiple access (CDMA) systems. This multistage ML detector is shown to share the same structure with the MS minimum-mean squared error (MMSE), minimum output energy (MOE) and best linear unbiased estimator (BLUE) filter banks (FBs) derived in S.-H. Wu et al. (2002) and S.-H. Wu et al. (2003), each with a distinctive output scaling matrix. Based on this result, a common framework is proposed for the implementation and analysis of coherent maximum ratio combining (MRC) and non-coherent differential equal gain combining (EGC) of these receivers. A generic bit error rate (BER) formula is provided for receivers constructed within this common framework. The BER floors for EGC receivers are analyzed and compared to those of the MRC schemes presented in S.-H. Wu et al. (2003). Based on simulation results, the heterogeneous EGC BLUE-ML receiver exhibits the best performance due to the fact that BLUE-FB is essentially the ML channel estimator and ML-FB is the ML symbol detector for the linear Gaussian system models of DS-CDMA systems.

Maximum-likelihood (ML) detection problem for channels with memory is investigated. The Viterbi algorithm provides an elegant solution, but is computationally inefficient when employed for detection on long channels. On the other hand, sphere decoding solves the ML detection problem in polynomial expected time over a wide range of SNRs. The sphere-constrained search strategy of sphere decoding is combined with the dynamic programming principles of the Viterbi algorithm. The resulting algorithm has the worst-case complexity of the Viterbi algorithm, but significantly lower expected complexity.

Blind equalization and decoding of unitary space-time codes is considered for inter-symbol interference (ISI) channels. Previously, an adaptive equalization method that can equalize channel blindly was proposed, by exploiting the structure of the codes proposed by Hochwald et al. In this paper, the performance of the decoder followed by this equalizer is investigated. After giving a modified version of the algorithm in order to avoid undesired convergence, a generalized likelihood ratio test based non-coherent decoder is presented. Semi-analytic methods are developed to investigate the performance of non-coherent decoder followed by zero-forcing and minimum-mean squared-error equalizers. Simulation results indicate that the performance of the algorithm is in between ZF and MMSE equalizers.

The problem of joint blind channel estimation and multiple access interference (MAI) suppression for an asynchronous code-division multiple-access (CDMA) system is studied. A low-complexity sliding-window scheme based on the expectation maximization (EM) algorithm is developed for joint blind maximum a posteriori probability (MAP) multi-user detection (MUD) and stochastic maximum likelihood (ML) channel estimation in a dispersive fading channel. With multi-stage soft interference cancellation, MAI can be efficiently suppressed. Together with an initial value prediction method for algorithm initialization at each window, the proposed scheme can track fading channels on the fly with no phase ambiguities even when channel gains are close to zero.

The multistage (MS) Wiener filter proposed in [J.S. Goldstein et al., Nov. 1998] is extended to meet the best linear unbiased estimator (BLUE) criterion in this research. The MS BLUE filter is shown to share the same structure of the MS minimum-mean squared error (MMSE)/minimum output energy (MOE) filter bank proposed in S. H. Wu et al. [Nov. 2002] for multipath channels modulo a scaling matrix. The limiting performance and the relationship between the output SINRs of MS-BLUE, MS-MMSE and MS-MOE multiuser receivers in a multipath fading channel are derived and linked to the resultant bit rate error (BER). It is shown in our analysis the BERs and SINRs of the MMSE, MOE and BLUE receivers are, respectively in flat Rayleigh fading channels.

Power control and handoff are two significant problems for cellular wireless systems. While both problems have received considerable attention of late, the problems are not often treated in a joint manner. Combined downlink power control and handoff design for cellular communication systems using a hybrid system framework is considered herein. Two new algorithms are proposed. The first one is a hard handoff/power control algorithm that endeavors a tradeoff between three performance criteria: transmitted power, number of handoffs and call quality. The second algorithm is a joint soft handoff/power control algorithm that takes into account the effect of the number of base stations in the active set in addition to the above performance criteria. The significance of the algorithms is that they incorporate the effects of channel fading and mobility, and achieve a tradeoff between the satisfaction levels of the mobile user and the network operator, thereby provide satisfactory service for the user while reducing the burden on the network such as undesired switching between base stations. The tradeoffs involved in both algorithms are verified through simulations.

Performance of the output signal-to-interference-plus-noise ratios (SINRs) and bit error rates (BERs) of the maximum ratio combining (MRC) multistage minimum-mean squared error (MMSE), minimum output energy (MOE), best linear unbiased estimator (BLUE) and maximum likelihood (ML) receivers are analyzed for direct-sequence code-division multiple access (DS-CDMA) systems in multipath Rayleigh fading channels, based on the common multistage structure we proposed previously (Sau-Hsuan Wu et al., Proc. IEEE Globecom, 2002, 2003; Proc. IEEE ICC, 2003). The SINRs of these receivers are proved to be monotonically increasing with the number of applied stages. The upper bound of output SINRs and its achievability conditions are also provided, which are shown to form the condition for the occurrence of BER floors of the multistage MRC MMSE/MOE/BLUE/ML multiuser receivers. A rule for selecting the number of stages is thus provided to take full advantage of the multistage structure of these receivers.

Sensor networks have emerged as a fundamentally new tool for monitoring inaccessible environments. They are distinguished from traditional sensors by strict limitations on system bandwidth and sensor energy resources. These constraints motivate the use of data compression at each sensor. Location finding is an important application of sensor networks, and estimation of the time delay between data from different sensors is a key step in localization. In this work, new quantizer designs specific to the time-delay estimation problem in sensor networks are presented. The goal for these new application-specific encoders is to achieve the best time delay estimate at a given bandwidth budget or latency bound, or minimize the rate required to reach an estimate with desired accuracy.

Single-user channel estimation in multiuser DS-CDMA systems for the case of sparse channels with large delay spreads is addressed. In addition, practical pulse shapes are considered. In sparse channels, the efficient way to estimate the parameters is to estimate the continuous delays of each path, instead of using the typical discrete tapped delay-line model. Due to the facts that the desired delays are not drawn from a simple finite set and that band-limited pulse shapes are employed, the resulting methods require numerical optimization techniques. To facilitate estimation, it is proposed to optimize the spreading code employed during the training, or estimation, phase. The optimal single-path spreading code is derived and extended for estimation in the multipath scenario. Both single-path and multipath channel estimation are considered. The proposed algorithms are evaluated through simulation and via the determination of the Cramer-Rao lower bound on the estimation variance. Analytical approximations of key performance measures are also derived and are seen to be tight for a variety of scenarios.

This paper discusses the design of soft handoff algorithms for cellular communication systems. The handoff process is modeled as a hybrid system and handoff design is cast as an optimization problem based on such a model. Performance is evaluated in terms of call quality, average number of active base stations, average number of active set updates, and average amount of interference. A soft handoff algorithm, which achieves a tradeoff between these performance criteria, is obtained using principles of dynamic programming. One key feature of the algorithm is that it incorporates the effects of mobility and shadow fading in the handoff decision. Different diversity combining schemes are considered including selective combining, equal gain combining (EGC), and various optimized combining (OC) methods in the soft handoff mode. For EGC and OC, Wilkinson's and Schwartz and Yeh's methods are used to compute the statistics for the power sum of the signals. Simulation results indicate that the performance of the handoff algorithm is a function of the different combining schemes and of the different methods used to compute the statistics of the power sum. Moreover, it is observed that interference cancellation is important in order for the algorithm to be viable for cellular systems which experience interference due to using nonorthogonal multiple access.

A multi-stage minimum mean square error filter bank (MS-MMSE-FB) and a minimum output energy filter bank (MS-MOE-FB) for the detection of DS-CDMA (Direct-Sequence Code-Division Multi-Access) signals under a dynamic multi-path fading environment are presented. The output signal-to-interference plus noise ratios (SINRs) for both MS-MMSE-FB and MS-MOE-FB are derived. The analytical results reveal how the output SINRs of reduced-rank MS-MMSE-FB and MS-MOE-FB vary with the rank of filters. The findings coincide with results of Honig and Goldstein (see IEEE Trans. on Communications, 2000), namely, the rank needed to achieve a target performance does not scale with the system loading in an additive white Gaussian noise (AWGN) channel. The derivation shows that, with the aid of desired user's channel information, the rank of the receivers can be drastically dropped without affecting the steady state SINR, an important factor for interference suppression in a dynamic fading channel with adaptive MS-MMSE-FB/MS-MOE-FB detections.

An improved correlation matrix estimation scheme for blind adaptive MMSE receivers for DS-CDMA communications was proposed by Chen and Mitra (see IEEE Journal on Selected Areas on Communications, vol.19, p.1531-43, 2001). Blind adaptive MMSE receivers with the estimation scheme not only maintain the merit of robustness against fading, but achieve performance comparable to the training-sequence based adaptive MMSE receivers. This paper presents an enhanced version of the improved correlation matrix estimation scheme for blind adaptive MMSE detection, which results in a better convergence and tracking property and noticeably improved performance than the original scheme of Chen and Mitra. Theoretical analysis is performed for the flat fading case which predicts the advantages of the new scheme. Detailed computer simulations are also carried out to verify the performance of blind adaptive MMSE receivers with the new estimation scheme.

This correspondence shows the equivalence of three previously proposed reduced-rank detection schemes for direct-sequence code-division multiple-access (DS-CDMA) communication systems. The auxiliary vector filtering (AVF) algorithm is simplified through a key observation on the construction of the auxiliary vectors. After simplification, it is shown that the AVF algorithm is equivalent to the multistage Wiener filtering (MWF) algorithm of Honig and Goldstein (2002). Furthermore, these schemes can be shown to be equivalent to the multistage linear receiver scheme based on the Cayley-Hamilton (CH) theorem when the minimum mean-square error (MMSE) criterion is applied to the reduced dimensional space of the received signal.

Maximum-likelihood (ML) based semi-blind estimators with Gaussian assumptions can provide improvement in performance compared to channel estimation schemes using the pilot signal only. In the third generation UTRA-FDD standard context this improvement can be even larger due to fact that the pilot and the data signals are sent simultaneously. However, the Gaussian ML approach results in very large complexity. In this paper, assumptions which reduce the computational complexity of the method significantly are proposed, without causing performance degradation. The assumptions are justified through simulations and the performance improvement over estimation schemes using pilot signal solely is verified.

In this paper, an improved correlation matrix estimation scheme for blind adaptive MMSE receivers is provided. The new scheme takes advantage of the fact that the desired linear receiver can be expressed as a function of the interference correlation matrix only, rather than the total data correlation matrix. A theoretical analysis is performed for the flat fading case which predicts that the new estimation scheme will result in significant performance improvement. Blind adaptive MMSE receivers with the new estimation scheme appear to achieve performance comparable to the training-sequence based adaptive MMSE receivers. Detailed computer simulations for the fast multipath fading environment verify that the proposed scheme yields strong performance gain over previous methods.

A new bound on the performance of multiuser receivers in fading channels is presented. The bound is based on a novel criterion for decomposability of error sequences. Although the bound development is in the context of a narrowband multiuser system, the improved bounds can be applied directly to CDMA multiuser systems over a fading channels. Several examples demonstrate the tightness of the new bound, relative to previous results from the literature.

Decentralized single user multipath channel estimation for the downlink direct-sequence code-division multiple-access (DS-CDMA) is addressed. Sparse channels and bandwidth efficient signaling are considered. Under the assumption of sparse channels, it is more efficient to estimate the continuous path delays, and associated complex coefficients, rather than adopting the typical equally spaced tapped delay line model, for which the number of parameters to be estimated can be much larger. On the other hand, estimation of the continuous delays is rather challenging for bandlimited pulse shapes. A pilot aided scheme for the estimation of the continuous delays is proposed, which reduced the interval over which the delay is searched. Performance improvement can be achieved by exploiting the fact that all users share the same channel: however no knowledge of the interfering users is assumed. Finally, for the single path channel, a closed form solution is obtained. The estimation algorithms for evaluated through simulations.

The problems of blind timing acquisition and channel estimation for DS-CDMA signals in multipath fading channels are investigated. Methods based on QR decompositions are proposed. These methods perform comparable or even better than subspace based methods with an order lower complexity. Furthermore, the methods exhibit significantly more robustness to channel order mismatch. Based on the acquired timing information, channel estimation algorithms are also developed which are competitive with the previously proposed subspace based channel estimation algorithms. In addition, a channel order estimation algorithm is proposed for the scenario where the order is unknown. Performance of the proposed algorithms is evaluated through simulation and comparison to Cramer-Rao lower bounds.

The problem of blind data rate detection for a multi-rate direct-sequence code-division multiple-access system is addressed. The system model is based on the time-division duplex mode of the UMTS standard. A host of blind algorithms based on or inspired by maximum likelihood principles are investigated. Rate detectors with implicit or explicit multiple access interference (MAI) suppression are considered. Based on the MAI suppression filter, additional rate detectors are devised. It is seen through simulation results that the best complexity and performance tradeoff is achieved by evaluating the "energy" at the output of the MAI suppression filter associated with the candidate rate.

The design of handoff algorithms for cellular communication systems based on signal-strength measurements is addressed. The system is modeled using a hybrid framework: a mixture of continuous state and discrete event systems. The handoff problem is formulated as an optimization problem to control the switchings within the discrete event system. Performance is evaluated as a function of the expected number of handoffs, the expected handoff delay, and the expected number of signal degradations. A signal degradation occurs when the signal level falls below a threshold. The cost of handoff delay is explicitly specified, in contrast to prior work. Various optimization problems are posed to trade off between these quantities. Based on the optimal solutions which are obtained through dynamic programming, suboptimal versions are proposed for ease of implementation. The performance of the suboptimal algorithm which trades off between the expected number of handoffs and the expected number of signal degradations is improved through the use of signal averaging; however, this algorithm suffers from excessive handoff delay. Therefore, the tradeoff between handoff delay and number of handoffs is considered. The corresponding suboptimal algorithm provides nearly one handoff and almost no delay, which is ideal if call quality is also good. Finally, an algorithm which is a combination of the two previous algorithms is explored.

Blind adaptive minimum mean-squared errors (MMSE) receivers for multiuser direct-sequence code-division multiple access (DS-CDMA) systems that assume knowledge of the steering vector, i.e., the cross-correlation between the desired output and the input signal, are known for their robustness against channel fading as they do not attempt to explicitly track the channel of the user of interest. However, these receivers often have higher excess mean squared error and, hence, poorer performance than training-sequence based adaptive MMSE receivers. In this paper, an improved correlation matrix estimation scheme for blind adaptive MMSE receivers is provided. The new scheme takes advantage of the fact that the desired linear receiver can be expressed as a function of the interference correlation matrix only, rather than the total data correlation matrix. A theoretical analysis is performed for the flat fading case which predicts that the new estimation scheme will result in significant performance improvement. Blind adaptive MMSE receivers with the new estimation scheme appear to achieve performance comparable to the training-sequence based adaptive MMSE receivers. Detailed computer simulations for the fast multipath fading environment verify that the proposed scheme yields strong performance gains over previous methods.

Uplink channel estimation for a block-synchronous chip-asynchronous DS/CDMA system as proposed for the time-division duplex option of third-generation cellular systems is considered. Training midambles are employed for joint channel estimation of all users. The standard unstructured approach based on modeling the effective user channels as unknown FIR filters is compared with two structured methods that exploit a priori knowledge about the user channels such as the maximum delay-spread, the transmit chip-shaping pulse and the path delays. Since these are usually unknown, a low-complexity estimator for the path delays of all users is derived from a maximum-likelihood approach. For all channel estimators, optimal sets of training sequences based on perfect root-of-unity sequences are found. For these optimal sets, it is shown that the reduction in channel estimation mean-squared error of the structured estimator versus the unstructured estimator is exactly the ratio of the number of structured parameters to unstructured parameters. Simulation results show that structured channel estimation provide advantages up to 4 dB in terms of output signal-to-interference plus noise ratio with respect to unstructured estimation, for linear RINSE detection. In contrast, for conventional single-user matched filtering, unstructured estimation proves to be sufficiently good.

The problem of estimating the channel parameters of a new user in a multiuser code-division multiple-access (CDMA) communication system is addressed. It is assumed that the new user transmits training data over a slowly fading multipath channel. The proposed algorithm is based on maximum-likelihood estimation of the channel parameters. First, an asymptotic expression for the likelihood function of channel parameters is derived and a re-parametrization of this likelihood function is proposed. In this re-parametrization, the channel parameters are combined into a discrete time channel filter of symbol period length. Then, expectation-maximization algorithm and alternating projection algorithm-based techniques are considered to extract channel parameters from the estimated discrete channel filter, to maximize the derived asymptotic likelihood function. The performance of the proposed algorithms is evaluated through simulation studies. In addition, the proposed algorithms are compared to previously suggested subspace techniques for multipath channel estimation.

Minimum-mean squared error (MMSE) receivers are designed and analyzed for multiple data rate direct-sequence code-division multiple-access (DS-CDMA) systems. The inherent cyclostationarity of the DS-CDMA signal is exploited to construct receivers for asynchronous multipath channels. Multiple- and single-bandwidth access are treated for both single and multicarrier scenarios. In general, the optimal receiver is periodically time-varying. When the period of the optimal receiver is large, suboptimal receivers are proposed to achieve a lower complexity implementation; the receivers are designed as a function of the cyclic statistics of the signals. In multiple chipping rate systems, the complexity of receivers for smaller bandwidth users can also be controlled by changing their front-end filter bandwidth. The effect of front-end filter bandwidth on receiver performance and system capacity is quantified for a variable chipping rate system. Analysis and simulation show that significant performance gains are realized by the periodically time-varying MMSE receivers over their time-invariant counterparts.

Various suboptimal handoff algorithms for cellular communication systems based on signal strength measurements are proposed and implemented. Performance of the algorithms is evaluated as a function of the expected number of handoffs, the expected handoff delay, and the expected number of signal degradations. Some of the algorithms require information about the relative distance between the mobile and the base stations, which must be estimated from signal strength measurements. Various location estimators are proposed, and the quality of these estimates are assessed by computing the Cramer-Rao bounds on the estimation error. Simulation results indicate that the suboptimal delay-handoff algorithm using the proposed distance estimator provides the best handoff-delay tradeoff curve among the available handoff algorithms.

Two previously proposed training sequence optimization techniques for channel estimation are compared. One method is based on a frequency domain based channel estimation method (FD) and the other is based on a time domain channel estimation technique (TD). The FD method produces a lower complexity search strategy but does not always result in the optimal training sequences in terms of the mean-squared channel estimation error. A proof of the superiority of the TD method over the FD method is presented in this paper. Based on the proof an alternative search criterion is proposed, which generally performs better than the FD method while still enjoying the low search complexity.

The design of handoff algorithms for cellular communication systems based on signal-strength and distance measurements is studied within a hybrid system framework. It is shown that a previously proposed algorithm which trades off between the expected number of handoffs and the expected number of signal degradations suffers from excessive handoff delay. Therefore, the optimal tradeoff between handoff delay and number of handoffs is investigated. The corresponding suboptimal algorithm provides near ideal performance: exactly one handoff and no delay.

Direct-sequence code-division multiple access is emerging as a potential multiple-access communication scheme for future digital wireless communications systems. Such wide-band systems usually operate in a frequency-selective fading channel that introduces intersymbol interference and thus potential performance degradation. Previously proposed subspace-based blind channel identification algorithms, which provide estimates of channel parameters for effective equalization, suffer from high numerical complexity for systems with large spreading gains. In this paper, it is shown that, through the use of matched filter outputs, reduction in numerical complexity can be obtained. The complexity reduction is considerable when the channel length is small and the system is moderately loaded. The results show that the new algorithm suffers a slight performance loss. Although the employed matched filter outputs do not form a set of sufficient statistics for the unknown channels, the difference between the matched filter outputs and the sufficient statistics becomes negligible for large observation lengths and the asymptotic normalized Fisher information does not change. Performance is evaluated through simulations, the derivation of a tight approximation of the mean-squared channel estimation error, and through comparisons to the Cramer-Rao bound for the estimation error variance. It is shown that the approximation of the mean-squared error can be obtained in terms of the correlation of the spreading codes and the channels. This representation of the error supplies a tool for investigating the relationship between performance and spreading sequence correlations.

Two previously proposed training sequence optimization techniques for channel estimation are compared. One method is based on a frequency-domain (FD) based channel estimation method and the other is based on a time-domain (TD) channel estimation technique. The FD method produces a lower complexity search strategy, but does not always result in the optimal training sequences in terms of the mean-squared channel estimation error. A proof of the superiority of the TD method over the FD method is presented. Based on the proof, an alternative search criterion is proposed, which, in general, provides equivalent or better performance than the FD method while still enjoying the low search complexity.

A weighted recursive least-squares algorithm for pilot-signal aided channel estimation in direct-sequence/code-division multiple-access (DS/CDMA) is proposed. Centralized and decentralized versions of the basic algorithm are considered. Since the algorithm tracks both the channel of the user of interest and the inverse covariance matrix of interference, it can be coupled with an adaptive linear MMSE receiver without additional complexity. The resulting receiver automatically performs the cancellation of the pilot-signals before data detection. Therefore, fairly significant power can be devoted to the pilot signals without affecting the overall interference level and without increasing the dimensionality of the desired signal space.

We consider a dual rate synchronous DS/CDMA system in which the length of each signature sequence is inversely proportional to the corresponding data rate. Two types of decorrelators that employ the difference in processing gains have been proposed: a high-rate decorrelator (HRD) matched to the data rate of the high-rate users, and a low-rate decorrelator (LRD) matched to the data rate of the low-rate users. It has been observed that the LRD provides better performance than the HRD for all the users at the expense of higher complexity and longer demodulation delay for high-rate users. To improve the performance and reduce the demodulation delay, a decorrelating decision-feedback detector is proposed and its asymptotic multiuser efficiency (AME) is analyzed. Because of the high computational complexity in calculating the exact AME, both upper and lower bounds of the AME are developed. In some special cases, the complexity of these bounds is linear in the number of users. It is shown that this detector incurs little demodulation delay for high-rate users and provides better performance for low-rate users than that of the LRD when the powers of the interfering users are comparable to that of the desired user.

The problem of multi-user detection in direct-sequence code-division multiple-access systems with channel state mismatch is addressed. An approximate maximum-likelihood criterion is derived based on knowledge of the statistical properties of the residual channel estimation errors. The time of arrival is assumed to be a constant during the time frame of interest. Errors in both timing information and fading coefficient information are assumed. A Viterbi-like recursive demodulation scheme is derived and analyzed. The performance of the new algorithm is studied via simulation and the development of performance bounds.

Detection methods for multiple rate direct sequence code division multiple access (DS-CDMA) signalling are addressed. Attention is focused on the development of receivers based on the minimum mean squared error (MMSE). Due to the cyclostationarity of the multirate signal, a representation of the multirate signal in terms of the Fourier basis is possible. This expansion facilitates construction of the MMSE receivers. Both variable spreading gain as well as variable chipping rate DS-CDMA access schemes are considered. For the multiple chipping rate system, the effect of the front-end filter bandwidth on the receiver performance is studied. Simulation results are provided to compare the performance of the proposed receivers.

As multimedia applications proliferate, there is a desire to provide wireless transport to information streams with inherently different data rates. Direct-sequence code division multiple access (DS-CDMA) is a natural multiple-access strategy for multiple data-rate systems. Previous work on multirate DS-CDMA receivers has focused on signal-processing techniques, which detect all users of all rates simultaneously. In the current work, multirate users have multiple bandwidths. Thus, it is proposed to exploit bandwidth differences to achieve frequency-based rate separation followed by single-rate detection schemes. Such a methodology enables a tradeoff between receiver complexity and performance. The performance of the proposed filters and receivers are derived for both a modified matched filter and modified decorrelator employing rate separation. The performance of a multirate CDMA overlay system is evaluated. In addition, chip pulse shaping for wide-band users is developed to improve performance for narrow-band users for the overlay system.

Most receiver designs for asynchronous direct-sequence code-division multiple-access (DS-CDMA) systems exploit timing information to simultaneously detect the desired signal and suppress the interference. In this paper, a previously proposed MUSIC delay estimation algorithm is considered which requires no initial information and no exhaustive search of the delays. Based on a Taylor series expansion, approximations of the first and second moments of the delay estimation error are derived for this MUSIC algorithm. The analysis is alternative and further to that previously performed.

Future wireless systems will need to accommodate information sources with different data rates. Direct-sequence code-division multiple-access (DS/CDMA) is a multiple access technique that is well suited to provide multirate access. Thus, in this paper, multirate communication systems are considered for the transmission of DS/CDMA wireless signals. Performance for maximum-likelihood-based detection is studied in the context of two multirate access methodologies: multicode access, where high data rate users multiplex their information streams onto multiple codes; and variable spreading length access where signature sequences of different lengths are assigned to users with different data rates. Various maximum-likelihood-based detection schemes for the variable spreading length system are considered as they can achieve near-optimal performance and thus provide reference points for comparison with suboptimal schemes. In addition, asymptotic multiuser performance measures are calculated and bounded to compare performance of the two systems.

Optimum near-far resistance is studied for synchronous dual-rate DS/CDMA systems. Three multirate access schemes are considered: multicode (MC) access where high-rate users multiplex their data bits onto multiple codes and form a single-rate system; variable spreading length (VSL) access where the spreading lengths of signature sequences are inversely proportional to users' data rates; and variable chipping rate (VCR) access where the chipping rates of the signature sequences are proportional to users' data rates. In order to remove the influence of signature sequences in the comparison of the three schemes, random signature sequences are assumed. Optimum mar-far resistance is then averaged over all possible realizations. Two types of code sets are considered for the VSL system: general random codes and random repetition codes. Bounds and approximations are provided for the average optimum near-far resistance. Analytical results show that the performance depends on the access schemes and the data rate of the users. The results for the VSL scheme with general random codes are extended for performance evaluation of systems with signature sequences which span many symbol intervals.

Blind identification of digital communication channels has attracted a significant amount of attention. In this work, subspace-based methods are considered specifically for direct-sequence/code-division multiple-access (DS/CDMA) signals in an asynchronous, multipath, multi-user environment. The objective is to reduce the complexity of previously proposed methods by exploiting the properties of DS/CDMA waveforms. In particular, knowledge of the DS/CDMA spreading codes is employed, via matched filtering, to reduce the dimensionality. The resulting algorithm shows negligible difference in performance with respect to a previously proposed algorithm by Torlak and Xu (see IEEE Trans. on Signal Processing, vol.45, no.1, p.137-47, 1997). However, the reduction in complexity can be significant. The Cramer-Rao lower bounds on the estimation variance are determined and provide further justification for the matched filtering scheme.

Motivated by the fact that time delays in a practical direct-sequence code-division multiple-access (DS-CDMA) system can never be perfectly estimated, an improved minimum-mean squared-error (MMSE)-based receiver is proposed and analyzed. Via the simple assumption of a probability distribution for the delay estimation errors, the proposed receiver can achieve a performance superior to that of the conventional MMSE (CMMSE) receiver. The performances of this improved receiver and the CMMSE receiver are compared in terms of the mean squared error (MSE), probability of error, and asymptotic multiuser efficiency (AME). As the original definition of AME does not consider mismatched channels, the behavior of three single-user receivers bearing imperfect delay estimation is also investigated. These single-user receivers are employed to define a more appropriate AME. Finally, an efficient update mechanism to accommodate dynamic channel statistics, and thus practical implementation, is proposed.

Multiuser detection allows for the efficient use of bandwidth in code-division multiple-access (CDMA) channels through mitigation of near-far effects and multiple-access noise limitations. The decorrelating detector, developed by Lupas and Verdu (1989), is a linear multiuser detector that is asymptotically optimal in terms of near-far resistance when certain communication parameters are completely known to the detector. In this paper, a simple adaptive decorrelating detector is developed by placing constraints on the set of spreading codes to be used by the active users. This adaptive detector has two modules: it first decorrelates the existing users, and then it determines the spreading code of a new user entering the network with or without the use of a training sequence. Maximum likelihood detection is proposed for determining the new user's spreading code. The performance of this algorithm is studied by investigating the probability of making an error in determining the new user's spreading code as a function of the number of samples used to make the determination, the number of users transmitting, and the signal to noise ratio of the new user with respect to the ambient Gaussian noise.

Code-division multiple-access (CDMA) is emerging as a desirable protocol by which multiple users can simultaneously share a communication channel. Motivated by a previous study of adaptive multi-user demodulators for direct-sequence spread-spectrum multiple access, detectors for spread-spectrum signals are investigated. Due to the prohibitive complexity of the locally optimum detector for such a stochastic multi-variate signal in impulsive channel noise, moderate complexity distribution-free detectors are pursued. In particular, the differential SNRs (processing gain) of correlator based structures are determined. This performance measure is apt given the relatively low signal strength of spread digital signals. The numerical results (in the context of the prior investigation of adaptive multi-user demodulators) impel the development of a hybrid detector which is composed of linear and nonlinear structures. The asymptotic normality of the test statistics under study is also examined.

Adaptive receiver algorithms are considered for the demodulation of code-division multiple-access (CDMA) signals. These algorithms include neural-network based algorithms and algorithms adapted from linear channel equalization techniques. Convergence issues are treated, and the performance of various algorithms is compared via computer simulations.>

Adaptive methods for performing multiuser demodulation in a direct-sequence spread-spectrum multiple-access (DS/SSMA) communication environment are investigated. In this scenario, the noise is characterized as being the sum of the interfering users' signals and additive Gaussian noise. The optimal receiver for DS/SSMA systems has a complexity that is exponential in the number of users. This prohibitive complexity has spawned the area of research on suboptimal receivers with moderate complexity. Adaptive algorithms for detection allow for reception when the communication environment is either unknown or changing. Motivated by previous work with radial basis functions (RBF's) for performing equalization, RBF networks that operate with knowledge of only a subset of the system parameters are studied. Although this form of detection has been previously studied (group detection) when the system parameters are known, in this work, neural network techniques are employed to adaptively determine unknown system parameters. This approach is further bolstered by the fact that the optimal detector in the synchronous case can be implemented by a RBF network when all of the system parameters are known. The RBF network's performance (with estimated parameters) is compared with the optimal synchronous detector, the decorrelating detector and the single layer perceptron detector. Clustering techniques and adaptive least mean squares methods are investigated to determine the unknown system parameters. This work shows that the adaptive radial basis function network attains near optimal performance and is robust in realistic communication environments.>

Adaptive receiver algorithms are considered for the demodulation of code-division multiple-access (CDMA) signals. These algorithms include neural-network based algorithms and algorithms adapted from linear channel equalization techniques. Convergence issues are treated, and the performance of various algorithms is compared via computer simulations.>

FIR (finite-impulse response) median hybrid (FMH)-filter-based algorithms for real-time detection are developed. The algorithms are designed so that trends are gradually refined as data become available. Special attention is paid to the detection of sharp edges in trends. The algorithms are based on five- or three-point median operations taken over the outputs of linear subfilters or some other auxiliary outputs. The noise attenuation and the edge preserving abilities of several FMH trend-detection filters are analyzed. The results of the detection show that the in-place growing FMH-filter-based trend detector has significant advantages over the other methods. The trend filtering concept can also be successfully applied to the filtering of the beginning and end of a finite-length data sequence.>