In this work, we revisit the one- and two-sample testing problems: binary hypothesis testing in which one or both distributions are unknown. For the one-sample test, we provide a more streamlined proof of the asymptotic optimality of Hoeffding's likelihood ratio test, which is equivalent to the threshold test of the relative entropy between the empirical distribution and the nominal distribution. The new proof offers an intuitive interpretation and naturally extends to the two-sample test where we show that a similar form of Hoeffding's test, namely a threshold test of the relative entropy between the two empirical distributions is also asymptotically optimal. A strong converse for the two-sample test is also obtained.

This paper investigates the quickest change detection of quantum states in a universal setting: specifically, where the post-change quantum state is not known a priori. We establish the asymptotic optimality of a two-stage approach in terms of worst average delay to detection. The first stage employs block POVMs with classical outputs that preserve quantum relative entropy to arbitrary precision. The second stage leverages a recently proposed windowed-CUSUM algorithm that is known to be asymptotically optimal for quickest change detection with an unknown post-change distribution in the classical setting.

This paper proposes two linear projection approaches for supervised dimension reduction using only the first and second-order statistics for the binary classification problem. They are derived under the general Gaussian model by maximizing the Kullback-Leibler divergence between the two classes in the projected sample. They subsume existing linear projection approaches developed under simplifying assumptions of Gaussian distributions, i.e., when these distributions share an equal mean or covariance matrix. Experiments are conducted to validate the proposed solutions and demonstrate their effectiveness for supervised dimension reduction.

Hoeffding’s formulation and solution to the universal hypothesis testing (UHT) problem had a profound impact on many subsequent works dealing with asymmetric hypotheses. In this work, we introduce a quantum universal hypothesis testing framework that serves as a quantum analog to Hoeffding’s UHT. Motivated by Hoeffding’s approach, which estimates the empirical distribution and uses it to construct the test statistic, we employ quantum state tomography to reconstruct the unknown state prior to forming the test statistic. Leveraging the concentration properties of quantum state tomography, we establish the exponential consistency of the proposed test: the type II error probability decays exponentially quickly, with the exponent determined by the trace distance between the true state and the nominal state.

This paper proposes two linear projection methods for supervised dimension reduction using only the first and second-order statistics. The methods, each catering to a different parameter regime, are derived under the general Gaussian model by maximizing the Kullback-Leibler divergence between the two classes in the projected sample for a binary classification problem. They subsume existing linear projection approaches developed under simplifying assumptions of Gaussian distributions, such as these distributions might share an equal mean or covariance matrix. As a by-product, we establish that the multi-class linear discriminant analysis, a celebrated method for classification and supervised dimension reduction, is provably optimal for maximizing pairwise Kullback-Leibler divergence when the Gaussian populations share an identical covariance matrix. For the case when the Gaussian distributions share an equal mean, we establish conditions under which the optimal subspace remains invariant regardless of how the Kullback-Leibler divergence is defined, despite the asymmetry of the divergence measure itself. Such conditions encompass the classical case of signal plus noise, where both signal and noise have zero mean and arbitrary covariance matrices. Experiments are conducted to validate the proposed solutions, demonstrate their superior performance over existing alternatives, and illustrate the procedure for selecting the appropriate linear projection solution.

WiFi signals have been demonstrated to facilitate non-intrusive detection of a range of activities and behaviors in the physical environments they permeate. Different activities affect both phase and magnitude of channel state information (CSI) in$W$iFi networks in a complex yet predictable way, and machine learning models can be trained to classify activities from such information. While constructing such WiFi-sensing systems is generally convenient and cost-effective, acquiring labeled data for a particular task can be time and labor-intensive. In this paper, we seek to remedy this issue in the context of human motion detection using deep unsupervised learning. Our proposed method uses a deep clustering model trained on appropriately-preprocessed CSI magnitude-only data to detect human motion with over 99 % accuracy in the absence of any ground labels. Removing the need for labeled samples significantly reduces the training overhead, making it a promising alternative to existing methods for motion detection.

This article explores the use of ambient radio frequency (RF) signals for human presence detection through deep learning. Using Wi-Fi signal as an example, we demonstrate that the channel state information (CSI) obtained at the receiver contains rich information about the propagation environment. Through judicious preprocessing of the estimated CSI followed by deep learning, reliable presence detection can be achieved. Several challenges in passive RF sensing are addressed. With presence detection, how to collect training data with human presence can have a significant impact on the performance. This is in contrast to activity detection when a specific motion pattern is of interest. A second challenge is that RF signals are complex-valued. Handling complex-valued input in deep learning requires careful data representation and network architecture design. Finally, human presence affects CSI variation along multiple dimensions; such variation, however, is often masked by system impediments, such as timing or frequency offset. Addressing these challenges, the proposed learning system uses preprocessing to preserve human motion-induced channel variation while insulating against other impairments. A convolutional neural network (CNN) properly trained with both magnitude and phase information is then designed to achieve reliable presence detection. Extensive experiments are conducted. Using off-the-shelf Wi-Fi devices, the proposed deep-learning-based RF sensing achieves near-perfect presence detection during multiple extended periods of test and exhibits superior performance compared with leading edge passive infrared sensors. A comparison with existing RF-based human presence detection also demonstrates its robustness in performance, especially when deployed in a completely new environment. The learning-based passive RF sensing thus provides a viable and promising alternative for presence or occupancy detection.

We characterize the asymptotic performance of nonparametric one- and two-sample testing. The exponential decay rate or error exponent of the type-II error probability is used as the asymptotic performance metric, and an optimal test achieves the maximum rate subject to a constant level constraint on the type-I error probability. With Sanov's theorem, we derive a sufficient condition for one-sample tests to achieve the optimal error exponent in the universal setting, i.e., for any distribution defining the alternative hypothesis. We then show that two classes of Maximum Mean Discrepancy (MMD) based tests attain the optimal type-II error exponent on \mathbb Rd, while the quadratic-time Kernel Stein Discrepancy (KSD) based tests achieve this optimality with an asymptotic level constraint. For general two-sample testing, however, Sanov's theorem is insufficient to obtain a similar sufficient condition. We proceed to establish an extended version of Sanov's theorem and derive an exact error exponent for the quadratic-time MMD based two-sample tests. The obtained error exponent is further shown to be optimal among all two-sample tests satisfying a given level constraint. Our work hence provides an achievability result for optimal nonparametric one- and two-sample testing in the universal setting. Application to off-line change detection and related issues are also discussed.

This paper focuses on performance analysis of linkage-based hierarchical agglomerative clustering algorithms for sequence clustering using the Kolmogrov-Smirnov distance. Data sequences are assumed to be generated from unknown continuous distributions. The goal is to group the data sequences whose underlying generative distributions belong to one cluster without a priori knowledge of both the underlying distributions as well as the number of clusters. Upper bounds on the clustering error probability are derived. The upper bounds help establish the fact that the error probability decays exponentially fast as the sequence length goes to infinity and the obtained error exponent bound has a simple form. Tighter upper bounds on the error probability of single-linkage and complete-linkage algorithms are derived by taking advantage of the simplified metric updating for these two special cases. Simulation results are provided to validate the analysis.

We characterize the asymptotic performance of nonparametric goodness of fit testing. The exponential decay rate of the type-II error probability is used as the asymptotic performance metric, and a test is optimal if it achieves the maximum rate subject to a constant level constraint on the type-I error probability. We show that two classes of Maximum Mean Discrepancy (MMD) based tests attain this optimality on $\mathbb R^d$, while the quadratic-time Kernel Stein Discrepancy (KSD) based tests achieve the maximum exponential decay rate under a relaxed level constraint. Under the same performance metric, we proceed to show that the quadratic-time MMD based two-sample tests are also optimal for general two-sample problems, provided that kernels are bounded continuous and characteristic. Key to our approach are Sanov’s theorem from large deviation theory and the weak metrizable properties of the MMD and KSD.

Universal hypothesis testing (UHT) refers to the problem of deciding whether samples come from a nominal distribution or an unknown distribution that is different from the nominal distribution. Hoeffding's test, whose test statistic is equivalent to the empirical Kullback-Leibler divergence (KL divergence), is known to be asymptotically optimal for distributions defined on finite alphabets. With continuous observations, however, the discontinuity of the KL divergence in the distribution functions results in significant complications for UHT. This paper introduces a robust version of the classical KL divergence, defined as the KL divergence from a distribution to the Lévy ball of a known distribution. This robust KL divergence is shown to be continuous in the underlying distribution function with respect to the weak convergence. The continuity property enables the development of an asymptotically optimal test for the university hypothesis testing problem with continuous observations. The optimality is in the same sense as that of the Hoeffding's test and stronger than that of Zeitouni and Gutman. Perhaps more importantly, the developed test statistic can be computed through convex programs, making it much more meaningful in practice. Numerical experiments are also conducted to evaluate its performance as compared with some kernel based goodness of fit test that has been proposed recently.

This paper studies clustering using a Kolmogorov-Smirnov based K-means algorithm. All data sequences are assumed to be generated by unknown continuous distributions. The pairwise KS distances of the distributions are assumed to be lower bounded by a certain positive constant. The convergence analysis of the proposed algorithms and upper bounds on the error probability are provided for both known and unknown number of clusters. More importantly, it is shown that the probability of error decays exponentially as the sample size of each data sequence goes to infinity, and the error exponent is only a function of the pairwise KS distances of the distributions. the analysis is validated by simulation results.

We study the asymptotic performance of distributed detection in large scale connected sensor networks. Contrasting to the canonical parallel network where a single node has access to local decisions from all other nodes, each node can only exchange information with its direct neighbors in the present setting. We establish that, with each node employing an identical one-bit quantizer for local information exchange, a novel consensus reaching approach can achieve the optimal asymptotic performance of centralized detection as the network size scales. The statement is true under three different detection frameworks: 1) the Bayesian criterion where the maximum a posteriori detector is optimal; 2) the Neyman-Pearson criterion with a constant type-I error probability constraint; and 3) the Neyman-Pearson criterion with an exponential type-I error probability constraint. Leveraging recent development in distributed consensus reaching using bounded quantizers with possibly unbounded data (which are log-likelihood ratios of local observations in the context of distributed detection), we design a one-bit deterministic quantizer with a controllable threshold that leads to desirable consensus error bounds. The obtained bounds are key to establishing the optimal asymptotic detection performance. In addition, we examine the non-asymptotic performance of the proposed approach and show that the type-I and type-II error probabilities at each node can be made arbitrarily close to the centralized ones simultaneously when a continuity condition is satisfied.

This paper considers the problem of constructing 2-D room shape. A mobile device with co-located microphone and loudspeaker is used and the distances between consecutive measurement points are assumed to be known. The uniqueness of the mapping between the first-order echoes and the room geometry is guaranteed for any convex polygons. A practical algorithm for room reconstruction in the presence of noise and higher order echoes is proposed. Experimental results are provided to demonstrate the effectiveness of our approach.

This paper considers multi-agent distributed optimization with quantized communication which is needed when inter-agent communications are subject to finite capacity and other practical constraints. To minimize the global objective formed by a sum of local convex functions, we develop a quantized distributed algorithm based on the alternating direction method of multipliers (ADMM). Under certain convexity assumptions, it is shown that the proposed algorithm converges to a consensus within log1+ηΩ iterations, where η > 0 depends on the network topology and the local objectives, and O is a polynomial fraction depending on the quantization resolution, the distance between initial and optimal variable values, the local objectives, and the network topology. We also obtain a tight upper bound on the consensus error which does not depend on the size of the network.

This paper considers distributed detection over large scale connected networks with arbitrary topology. Contrasting to the canonical parallel fusion network where a single node has access to the outputs from all other sensors, each node can only exchange one-bit information with its direct neighbors in the present setting. Our approach adopts a novel consensus reaching algorithm using asymmetric bounded quantizers that allow controllable consensus error. Under the Neyman-Pearson criterion, we show that, with each sensor employing an identical one-bit quantizer for local information exchange, this approach achieves the optimal error exponent of centralized detection provided that the algorithm converges. Simulations show that the algorithm converges when the network is large enough.

Wyner's common information was originally defined for a pair of dependent discrete random variables. Its significance is largely reflected in, and also confined to, several existing interpretations in various source coding problems. This paper attempts to expand its practical significance by providing a new operational interpretation. In the context of the Gray-Wyner network, it is established that Wyner's common information has a new lossy source coding interpretation. Specifically, it is established that, under suitable conditions, Wyner's common information equals to the smallest common message rate when the total rate is arbitrarily close to the rate distortion function with joint decoding for the Gray-Wyner network. A surprising observation is that such equality holds independent of the values of distortion constraints as long as the distortions are within some distortion region. The new lossy source coding interpretation provides the first meaningful justification for defining Wyner's common information for continuous random variables and the result can also be extended to that of multiple variables. Examples are given for characterizing the rate distortion region for the Gray-Wyner lossy source coding problem and for identifying conditions under which Wyner's common information equals that of the smallest common rate. As a by-product, the explicit expression for the common information between a pair of Gaussian random variables is obtained.

This letter examines a variation of the canonical distributed detection system: a sensor may overhear other sensors' transmissions and thus may choose to refine its output in the hope of achieving a better detection performance. We show that while this is indeed possible for the fixed sample size test, asymptotically (in the number of samples) there is no performance gain, as measured by the Kullback-Leibler distance achievable at the fusion center, provided that the observations are conditionally independent. For conditionally dependent observations, however, we demonstrate that asymptotic detection performance may indeed be improved when overhearing is utilized.

Motivated by the primary user emulation attack in cognitive radios, this paper develops a physical layer user authentication scheme for wireless systems. The developed scheme consists of two stages: 1) one-way hash function for generating authentication tags and 2) physical layer tag embedding via controlled constellation perturbation. A detailed tradeoff analysis is provided that balances the performance of the user authentication for the secondary user and symbol detection for the primary user. In particular, we show that arbitrarily reliable user authentication can be achieved with an almost negligible performance degradation for the primary user under realistic system settings. Experimental results using the GNU Radio/Universal Software Radio Peripheral (USRP) platform are provided to validate the proposed scheme.

This paper studies the design of an estimation system where a remotely observed source sequence is to be communicated through a noisy channel to an estimator. The remote node is assumed to have the capability of harvesting, and, subject to a capacity limit, storing energy from its ambient environment. The focus is on various transmit power-allocation strategies that minimize the mean square error at the estimator for such an energy harvesting estimation system as the fluctuation of harvested energy presents a unique challenge compared with a traditional battery powered system. We first establish the optimality of uncoded transmission for such a system. Two types of side information (SI) at the transmitter are then considered in this paper: noncausal SI (energy harvested in the past, present, and future) and causal SI (energy harvested in the past). For the case where noncausal SI is available and battery storage is unlimited, it is shown that the optimal power allocation amounts to a simple “staircase-climbing” procedure, where the power level follows a nondecreasing staircase function. For the case where battery storage has a finite capacity, the optimal power-allocation policy can also be obtained via standard convex optimization techniques. Dynamic programming (DP) is used to optimize the allocation policy when only causal SI is available. The issue of unknown transmit power at the receiver is also addressed for both the causal and noncausal SI cases. Finally, to make the proposed solutions practically more meaningful, two heuristic schemes are proposed; these schemes are largely motivated by the structure of the solution to the DP formulation but with much reduced computational complexity. Numerical examples are provided to examine the complexity-performance tradeoff of various power-allocation strategies.

This paper considers the computation of Wyner's common information between outputs of additive Gaussian channels with a common input. The work is motivated by recent generalization of Wyner's common information to continuous random variables and the associated lossy source coding interpretation, as well as its application to statistical inference. It is shown that with independent and identically distributed Gaussian noises, Wyner's common information between channel outputs is precisely the same as the mutual information between the source input and the channel outputs regardless of the source distribution. The result extends the previous result when the source distribution is Gaussian. Generalization to additive channels with correlated noises and its application to statistical estimation are also presented.

We consider in this paper a system with multi-functioning radios: communication between nodes involve transmissions of both messages and source sequences. For point-to-point systems, this amounts to a simple trade-off between message transmission and source transmission: an optimal strategy is to split total capacity into two components, one for message transmission and one for source transmission as long as the message and the source sequence are independent of each other. For the multi-user case, we show that this is no longer the case by examining the simple problem of sending a common source sequence and two independent messages through a Gaussian broadcast channel.

This paper studies the impact of interactive fusion on detection performance in tandem fusion networks with conditionally independent observations. Within the Neyman-Pearson framework, two distinct regimes are considered: the fixed sample size test and the large sample test. For the former, it is established that interactive distributed detection may strictly outperform the one-way tandem fusion structure. However, for the large sample regime, it is shown that interactive fusion has no improvement on the asymptotic performance characterized by the Kullback-Leibler distance compared with the simple one-way tandem fusion. The results are then extended to interactive fusion systems where the fusion center and the sensor may undergo multiple steps of memoryless interactions or that involve multiple peripheral sensors, as well as to interactive fusion with soft sensor outputs.

This paper studies the capacity of a class of discrete memoryless interference channels (DMICs), where interference is defined analogous to that of a Gaussian interference channel with one-sided weak interference. The sum-rate capacity of this class of channels is determined. As with the Gaussian case, the sum-rate capacity is achieved by letting the transceiver pair subject to interference communicate at a rate such that its message can be decoded at the unintended receiver using single user detection. It is also established that this class of DMICs is equivalent in capacity region to certain degraded interference channels. This allows the construction of capacity outer-bounds using the capacity regions of associated degraded broadcast channels. The same technique is then used to determine the sum-rate capacity of DMICs with mixed interference as defined in this paper. The obtained capacity bounds and sum-rate capacities are used to resolve the capacities of several new DMICs.

The capacity of a network in which a multiple access channel generates interference to a single-user channel is studied. An achievable rate region based on superposition coding and joint decoding is established for the discrete case. If the interference is very strong, the capacity region is obtained for both the discrete memoryless channel and the Gaussian channel. For the strong interference case, the capacity region is established for the discrete memoryless channel; for the Gaussian case, a line segment on the boundary of the capacity region is attained. Moreover, the capacity region for the Gaussian channel is identified for the case in which one interference link is strong, and the other is very strong. For a subclass of Gaussian channels with mixed interference, a boundary point of the capacity region is determined. Finally, for the Gaussian channel with weak interference, sum capacities are obtained under various channel coefficient and power constraint conditions.

Within the Neyman-Pearson framework we investigate the effect of feedback in two-sensor tandem fusion networks with conditionally independent observations. While there is noticeable improvement in performance of the fixed sample size Neyman-Pearson (NP) test, it is shown that feedback has no effect on the asymptotic performance characterized by the Kullback-Leibler (KL) distance. The result can be extended to an interactive fusion system where the fusion center and the sensor may undergo multiple steps of interactions.

This paper develops a physical layer user authentication scheme for wireless systems. The approach can be used as an effective counter measure against the primary user emulation attack in cognitive radios. The developed scheme applies to general digital constellations and we establish its optimality in terms of error probability for user authentication. Trade-off analysis is provided that balances the performance of the user authentication for the secondary user and symbol detection for the primary user. In particular, we show that arbitrarily reliable user authentication can be achieved at the price of an almost negligible performance degradation for the primary user under realistic system settings.

In this paper, we consider the problem of quantizer design for distributed estimation under the Bayesian criterion. We derive general optimality conditions under the assumption of conditionally independent observations at the local sensors and show that for a conditionally unbiased and efficient estimator at the Fusion Center, identical quantizers are optimal when local observations have identical distributions. This results in an N-fold reduction in complexity where N is the number of sensors. We illustrate our approach by applying it to the location parameter estimation problem.

Optimal transmit power allocation strategies are proposed for an energy harvesting estimation system, where energy can be harvested from the environment and buffered in a battery for future use. With the aim of minimizing the mean squared error at the receiver, two types of side information (SI) available to the transmitter are considered: causal SI (energy harvested in the past) and non-causal SI (energy harvested in the past, present and future). For the case where non-causal SI is available and battery storage is unlimited, it is shown that the optimal power allocation can be attained by a simple water-filling-like procedure, where the water level follows a non-decreasing staircase function. Dynamic programming is used to optimize the allocation policy when causal SI is available. The issue of unknown transmit power at the receiver is also addressed.

The prevalent use of unmanned vehicles in military and civilian applications requires the existence of robust and high throughput communication with airborne platforms. Real channel measurements are conducted and the analysis supports the use of MIMO communications for such applications due to its potential throughput advantage. Unique challenges and the ways to address them are described in detail. In particular, the lack of scattering and the blockage of line of sight may lead to rank deficient channel matrices, which are exacerbated due to the absence of channel state information at the transmitter. A variable rate MIMO scheme is proposed to overcome these challenges in order to realize the promising throughput gain afforded by MIMO communication.

This paper deals with interactive distributed detection with conditionally independent observations where the fusion center may exchange information with a local sensor. Using a two sensor system, we demonstrate that this two-way interaction provides improvement in detection performance compared with the classical tandem detection system where only one-way communication is allowed. An important observation is that, contrary to that of the tandem network, the fusion rule is no longer a simple likelihood ratio test due to the correlation introduced in the initial feedback from the fusion center to the sensor.

This paper studies the problem of secure communication over a noiseless network from an information-theoretic perspective. A single-source single-sink acyclic planar network is considered, and the communication between the source and the sink is subject to noncooperative eavesdropping on each link. Using equivocation to measure the confidentiality of messages, we establish sufficient conditions, in terms of communication rates and network parameters, for provably secure communication. A constructive proof, which combines Shannon's key encryption and the Ford-Fulkerson algorithm, is provided and constitutes a readily implementable secure coding scheme. The derived achievable rate equivocation region is tight when specializing to several special cases. In particular, when the communication network decouples into nonoverlapping parallel paths, the proposed encoding scheme is optimal, i.e., it achieves the secure communication capacity for such networks.

Without the conditional independence assumption, the problem of distributed detection becomes intractable in general. A promising approach for this problem was proposed recently that utilizes a hierarchical conditional independence model. By inserting a hidden variable in the detection hierarchy that induces conditional independence among sensor observations, it is possible to identify certain conditions under which the detection problems become tractable. This paper generalizes the conditions thereby broadening the classes of distributed detection problems with dependent observations that can be readily solved. An example of distributed detection of a random signal is given to show that the proposed generalization can solve problems that were not possible using the original approach.

This paper develops the sufficiency principle suitable for data reduction in decentralized inference systems. Both parallel and tandem networks are studied and we focus on the cases where observations at decentralized nodes are conditionally dependent. For a parallel network, through the introduction of a hidden variable that induces conditional independence among the observations, the locally sufficient statistics, defined with respect to the hidden variable, are shown to be globally sufficient for the parameter of inference interest. For a tandem network, the notion of conditional sufficiency is introduced and the related theories and tools are developed. Finally, connections between the sufficiency principle and somedistributed source coding problemsare explored.

A simple encoding scheme based on Sato's non-naive frequency division is proposed for a class of Gaussian interference channels with mixed interference. The achievable region is shown to be equivalent to that of Costa's noiseberg region for the onesided Gaussian interference channel. This allows for an indirect proof that this simple achievable rate region is indeed equivalent to the Han-Kobayashi (HK) region with Gaussian input and with time sharing for this class of Gaussian interference channels with mixed interference.

The sum capacity of a class of discrete memoryless interference channels is determined. This class of channels is defined analogous to the Gaussian Z-interference channel with weak interference; as a result, the sum capacity is achieved by letting the transceiver pair subject to the interference communicates at a rate such that its message can be decoded at the unintended receiver using single user detection. Moreover, this class of discrete memoryless interference channels is equivalent in capacity region to certain discrete degraded interference channels. This allows the construction of a capacity outer-bound using the capacity region of associated degraded broadcast channels. The same technique is then used to determine the sum capacity of the discrete memoryless interference channel with mixed interference. The above results allow one to determine sum capacities or capacity regions of several new discrete memoryless interference channels.

The capacity of a network in which a multiple access channel (MAC) generates interference to a single-user channel is studied. An achievable rate region based on superposition coding and joint decoding is established for the discrete case. If the interference is strong, the capacity region is obtained for both the discrete memoryless channel and Gaussian channel. A boundary point of the capacity region is determined for a subclass of Gaussian channels with mixed interference.

It was shown that most of the radio frequency spectrum was inefficiently utilized. To fully use these spectrums, cognitive radio networks have been proposed. The idea is to allow secondary users to use a spectrum if the primary user (i.e., the legitimate owner of the spectrum) is not using it. To achieve this, secondary users should constantly monitor the usage of the spectrum to avoid interference with the primary user. However, achieving a trustworthy monitoring is not easy. A malicious secondary user who wants to gain an unfair use of a spectrum can emulate the primary user, and can thus trick the other secondary users into believing that the primary user is using the spectrum when it is not. This attack is called the Primary User Emulation (PUE) attack. To prevent this attack, there should be a way to authenticate primary users' spectrum usage.

Communicating arbitrarily correlated sources over interference channels is considered in this paper. A sufficient condition is found for lossless transmission of a pair of correlated sources over a discrete memoryless interference channel. With independent sources, the sufficient condition reduces to the Han-Kobayashi achievable rate region for interference channels. For sources with a special correlation structure, the proposed region reduces to the known achievable region for interference channels with common information. Moreover, the proposed coding scheme is optimal for transmitting such set of correlated sources over a class of deterministic interference channels as defined in El Gamal and Costa, 1982.

The sum-rate capacity of the parallel Gaussian interference channel is shown to be achieved by independent transmission across subchannels and treating interference as noise if the channel coefficients and power constraints satisfy a certain condition. The condition requires the interference to be weak, a situation commonly encountered, e.g., in digital subscriber line transmission. The optimal power allocation is characterized by using the concavity of the sum-rate capacity as a function of the power constraints.

For a multiuser interference channel with multiantenna transmitters and single-antenna receivers, by restricting each transmitter to a Gaussian input and each receiver to a single-user detector, computing the largest achievable rate region amounts to solving a family of nonconvex optimization problems. Recognizing the intrinsic connection between the signal power at the intended receiver and the interference power at the unintended receiver, the original family of nonconvex optimization problems is converted into a new family of convex optimization problems. It is shown that, for such interference channels with each receiver implementing single-user detection, transmitter beamforming can achieve all boundary points of the achievable rate region.

This paper revisits the interference channels under strong interference and very strong interference, for both discrete memoryless and Gaussian channels. In particular, it is shown that for the classical definitions, while the conditions of strong interference for discrete and Gaussian cases coincide with each other, those of very strong interference are not consistent with each other in terms of mutual information inequalities. Based on this observation, a new class of discrete memoryless interference channels under very strong interference is introduced whose capacity region is characterized. This new class of channels is consistent with the Gaussian interference channel under very strong interference. Moreover, this new class of interference channels is not a subset of any existing classes of discrete memoryless interference channels with known capacity region.

In this paper, we present a unifying framework for distributed detection with dependent or independent observations. This novel framework utilizes an expanded hierarchical model by introducing a hidden variable. Facilitated by this new framework, we identify several classes of distributed detection problems with conditionally dependent observations whose optimal sensor signaling structure resembles that of the independent case. These classes of problems exhibit a decoupling effect on the form of the optimal local decision rules, much in the same way as the conditionally independent case using both the Bayesian and the Neyman-Pearson criteria.

In this paper, we consider lossy transmission of two correlated sources over a two-user discrete memoryless interference channel (DMIC). An achievable distortion region is obtained and it is shown that it includes Salehi and Kurtas's result on lossless transmission of correlated sources over a DMIC as a special case. Sending correlated Gaussian sources over a Gaussian interference channel is also studied, and several achievable schemes as well as lower bounds are proposed.

The capacity regions of vector, or multiple-input multiple-output, Gaussian interference channels are established for very strong interference and aligned strong interference. Furthermore, the sum-rate capacities are established for Z interference, noisy interference, and mixed (aligned weak/intermediate and aligned strong) interference. These results generalize known results for scalar Gaussian interference channels.

This paper considers the so-called cognitive Z channel, where two users transmit two independent messages to their respective receivers through a Z interference channel. User 1 (primary user) interferes with receiver 2 while user 2 (secondary user) does not interfere with receiver 1. In addition, user 2 overhears the transmission of user 1 through a noisy channel, hence the term 'cognitive'. We proposed two nontrivial capacity outer bounds, which established a capacity region corner point for certain parameter regimes. These outer bounds, together with previously proposed achievable rate region, shed light on the usefulness of the causal cooperation under certain channel conditions. Numerical examples are also given.

In this paper, we consider transmission of two correlated Gaussian sources to two receivers through a two-user Gaussian Z interference channel (Z IC). To facilitate our study, we first investigate Gaussian multiple access channels with correlated Gaussian sources and a single distortion constraint at the receiver. Lower bounds on the distortion as well as several achievable schemes are proposed. In particular, a hybrid digital analog transmission scheme is proposed whose achievable region is resolved through coupling it with the quadratic Gaussian CEO problem. These lower bounds and achievable schemes are then applied to the source channel communication over a two-user Gaussian Z IC to derive inner and outer bounds for the distortion region. We conclude this paper by giving a sufficient condition for sending a correlated source over a general Z interference channel.

For a multi-user interference channel with multi-antenna transmitters and single-antenna receivers, by restricting each receiver to a single-user detector, computing the largest achievable rate region amounts to solving a family of nonconvex optimization problems. Recognizing the intrinsic connection between the signal power at the intended receiver and the interference power at the unintended receiver, the original family of non-convex optimization problems is converted into a new family of convex optimization problems. It is shown that, for such interference channels with each receiver implementing single-user detection, transmitter beamforming can achieve all boundary points of the achievable rate region.

Distributed detection with conditionally independent observations at local sensors is well understood. The problem becomes significantly more complicated when dependence is present among sensor observations. In this paper, we attempt to make progress in our understanding of the dependent observation case. Toward this end, we present a new hierarchical model by introducing a hidden or latent variable; this model attempts to present a unified framework for distributed detection with conditionally dependent or independent observations. By a close examination of this model, we identify a class of distributed detection problems with conditionally dependent observations whose optimal sensor signaling structure resembles that of the independent case. This class of problems exhibits a decoupling effect on the form of the optimal local decision rules, much in the same way as the conditionally independent case. Important cases of this class of problems include both the previously known Gaussian case under certain parameter regimes as well as several problems first introduced in this paper. An example is given to illustrate the proposed design approach.

The sum-rate capacity of the parallel Gaussian interference channel is studied. Sufficient conditions are derived in terms of channel coefficients and power constraints such that the sum-rate capacity can be achieved by: 1) independent transmission across sub-channels, and 2) treating interference as noise in each sub-channel. The optimal power allocation is characterized for such parallel channels.

In this paper we study the problem of secure communication over a network in which each link may be noisy or noiseless. A single-source single-sink acyclic planar network is considered, and the communication between the source and the sink is subject to non-cooperating eavesdropping on each link. Sufficient conditions, in terms of communication rates and network parameters, are found for provable secure communication, along with an intuitive and efficient coding scheme. The derived achievable rate equivocation region is tight when specializing to several special cases.

In this paper, we consider the design of local decision rules for distributed detection systems where decisions from peripheral detectors are transmitted over dependent nonideal channels. Under the conditional independence assumption among multiple sensor observations, we show that the optimal detection performance can be achieved by employing likelihood-ratio quantizers (LRQ) as local decision rules under both the Bayesian criterion and Neyman-Pearson (NP) criterion even for the cases where the channels between the fusion center and local sensors are dependent and noisy. This work generalizes the previous work where independence among such channels was assumed. A person-by-person optimization (PBPO) procedure to obtain the solution is presented along with an illustrative example.

A new outer bound on the capacity region of Gaussian interference channels is developed. The bound combines and improves existing genie-aided methods and is shown to give the sum-rate capacity for noisy interference as defined in this paper. Specifically, it is shown that if the channel crosstalk coefficient magnitudes lie below thresholds defined by the power constraints then single-user detection at each receiver is sum-rate optimal, i.e., treating the interference as noise incurs no loss in performance. This is the first capacity result for the Gaussian interference channel with weak to moderate interference. Furthermore, for certain mixed (weak and strong) interference scenarios, the new outer bounds give a corner point of the capacity region.

This paper studies capacity bounds for discrete memoryless broadcast channels with confidential messages. Two private messages as well as a common message are transmitted; the common message is to be decoded by both receivers, while each private message is only for its intended receiver. In addition, each private message is to be kept secret from the unintended receiver where secrecy is measured by equivocation. Both inner and outer bounds are proposed to the rate equivocation region for broadcast channels with confidential messages. The proposed inner bound generalizes Csiszar and Korner's rate equivocation region for broadcast channels with a single confidential message, Liu 's achievable rate region for broadcast channels with perfect secrecy, Marton's and Gel'fand-Pinsker's achievable rate region for general broadcast channels. The proposed outer bounds, together with the inner bound, help establish the rate equivocation region of several classes of discrete memoryless broadcast channels with confidential messages, including the less noisy, deterministic, and semideterministic broadcast channels. Furthermore, specializing to the general broadcast channel by removing the confidentiality constraint, the proposed outer bounds reduce to new capacity outer bounds for the discrete memory broadcast channel.

In this paper, we study the optimal transceiver structure for achieving sum-rate capacity of a parallel Gaussian symmetric interference channel. Specifically, we derive a set of channel and power constraint conditions such that the total sum-rate capacity can be achieved by treating interference as noise in each sub-channel. The property of the optimal power allocation is also characterized for such parallel channels.

In this paper we study a generalization of Csiszar and Korner's broadcast channel with confidential messages. Specifically, we consider a two-user broadcast channel with one common message and two confidential messages, one for each receiver. We establish outer bounds to the rate equivocation region of this channel. Our proposed outer bounds, together with a recently proposed achievable region, help establish the rate equivocation region of several classes of discrete memoryless broadcast channels with confidential messages. Furthermore, specializing to the general broad.cast channel by removing the secrecy constraint, our proposed outer bounds reduce to new capacity outer bounds for the discrete memory broadcast channel.

We consider decentralized detection for resource-constrained wireless sensor networks where local sensor decisions need to go through a multi-hop relay network before reaching the fusion center. Our objective is to collectively design sensor decision rules and relay rules for optimum detection performance. Under the Bayesian criterion, we establish the necessary conditions for an optimal system and derive the form of the optimal fusion rule at the fusion center. Under some conditional independence assumptions, we derive the forms of the optimal local decision rules and the optimal relay rules and show that the optimal set of decision rules for the entire system can be specified by a set of parameters. We demonstrate the advantages of our proposed systematic approach against more conventional design approaches through a numerical example.

In this paper, we consider non-degraded discrete memoryless broadcast channels where two private messages as well as a common message are transmitted at rate R1, R2and Rorespectively. The common message is for both receivers to decode; the private message is only for its intended receiver of which the other receiver shall be kept as ignorant as possible. Measuring ignorance by equivocation, we propose an achievable rate region (R1, R2, Ro, Re1, Re2) for this channel where Re1and Re2are the equivocation rates for the two receivers respectively. This result generalizes Csiszar and Kornerpsilas capacity region for broadcast channels with single private message, Liu et alpsilas result for broadcast channels with perfect secrecy, Gelpsilafand and Pinskerpsilas achievable rate region for broadcast channels with common message, and Martonpsilas achievable rate region for general broadcast channels.

An outer bound on the capacity region of the two-user Gaussian interference channel is derived. The bound shows that for low power and small crosstalk coefficients the sum-rate capacity is achieved by treating interference as noise. The results are generalized to multiuser channels.

We study in this paper the sum capacity achievability of orthogonal transmissions in vector Gaussian multiple access channels (MAC). Specifically, we derive sufficient and necessary conditions, in terms of channel matrices and transmitter power constraints, for orthogonal transmissions to achieve the sum capacity of a vector Gaussian MAC. The obtained conditions provide a unified framework that helps explain many intuitive and known results as well as explore cases that have not been addressed. In the cases when these conditions are violated, our results enable us to quantify the suboptimality of orthogonal transmission when the sum capacity can only be achieved by overlay transmission.

Four outer bounds are given for the weak and mixed interference cases, all derived from existing outer bounds for the classic interference channel: a) Carleial's outer bound, b) Kramer's genie aided approach that gives both receivers enough information to decode all the messages, c) Kramer's Z-channel based outer bound, d) a recent outer bound proposed in [1]. We show that b) unified and improves a) and the existing outer bound by Tan (1980); c) further improves upon b); c) and d) do not have a subset relation.

We address the design of binary local sensor quantizers for decentralized detection over multiple access channels. Our goal is to minimize the error probability at the fusion center using a single snapshot of local observations. Considering both the synchronized and asynchronous transmissions among sensors, we establish the optimality of a likelihood ratio test for both cases. For the case of asynchronous transmissions, to compensate for the unknown fading channel parameters and transmission delays, we propose a structure consisting of a RAKE receiver and a square-law detector. Simulations results are presented to demonstrate effectiveness of the design procedure.

We study in this paper the sum capacity achievability of orthogonal transmissions in vector Gaussian multiple access channels (MAC). Specifically, we derive the sufficient and necessary conditions, in terms of channel matrices and transmitter power constraints, for orthogonal transmission to achieve the sum capacity of a vector Gaussian MAC. The obtained conditions provide a unified framework in explaining many of the results that are intuitively true. They also enable us to explore cases that have not been addressed to determine the (sub)optimality of orthogonal transmissions compared with the overlay transmission.

We consider in this paper a likelihood principle based approach for the range dependent problem in space time adaptive processing. The proposed generalized likelihood ratio test (GLRT) addresses the range dependent issue by directly applying the likelihood principle to the range dependent signal model. Using the knowledge of platform geometry, we develop maximum likelihood estimators that facilitate the GLRT. This differs from existing methods that rely on data transformations in dealing with the range dependence issue. Numerical examples show that the new GLRT approach exhibits significant performance gain over existing approaches.

In this paper, we propose an achievable rate region for discrete memoryless interference channels with conferencing at the transmitter side. We employ superposition block Markov encoding, combined with simultaneous superposition coding, dirty paper coding, and random binning to obtain the achievable rate region. We show that, under respective conditions, the proposed achievable region reduces to Han and Kobayashi's achievable region for interference channels, the capacity region for degraded relay channels, and the capacity region for the Gaussian vector broadcast channel. Numerical examples for the Gaussian case are given.

A modified FDM/TDM method was first used by Sato to study the achievable rate region of degraded Gaussian interference channel. Recently Sason used this method to get an achievable rate region of Gaussian interference channel. In this paper, we generalize this method in terms of the transmission mode and frequency band allocation, and obtain an achievable rate region for Gaussian interference channel, which improves the existing computable rate regions for both the weak and moderate interferences. We note that this modified FDM/TDM is different to time sharing and the difference depends on the activeness and concavity of the constraints. The achievable sum rate of symmetric Gaussian interference channel is derived for a subregion of the classical Han-Kobayashi region, and subsequently, for the proposed new achievable rate region.

In this paper, a new achievable rate region for general interference channels with common information is presented. Our result improves upon by applying simultaneous superposition coding over sequential superposition coding. A detailed computation and comparison of the achievable rate region for the Gaussian case is conducted. The proposed achievable rate region is shown to coincide with the capacity region of the strong interference case.

An inner bound of capacity region for the Gaussian interference channel is derived using Sato's modified frequency division multiplexing idea (Sato, 1978) and a special case of Han and Kobayashi's rate region (denoted by G' in Han and Kobayashi (1981)). We show that the new inner bound includes G', Sason's rate region D (Sason, 2004), as well as the achievable region via TDM/FDM (Carleial, 1978), as its subsets. The advantage of this improved inner bound over G' arises due to its inherent ability to utilize the whole transmit power range on the real line without violating the power constraint. We also provide analysis to show that the new achievable region strictly extends G' if max(R1, R2)isinG'{R1+ kR2} is nonconcave for some k isin 0, +infin, where R1and R2are the rates of the two users.

We consider robust signal processing techniques for inference-centric distributed sensor networks operating in the presence of possible sensor and/or communication failures. Motivated by the multiple description (MD) principle, we develop robust distributed quantization schemes for a decentralized detection system. Specifically, focusing on a two-sensor system, our design criterion mirrors that of MD principle: if one of the two transmissions fails, we can guarantee an acceptable performance, while enhanced performance can be achieved if both transmissions are successful. Different from the conventional MD problem is the distributed nature of the problem as well as the use of error probability as the performance measure. Two different optimization criteria are used in the distributed quantizer design, the first a constrained optimization problem, and the second using an erasure channel model. We demonstrate that these two formulations are intrinsically related to each other. Further, using a person-by-person optimization approach, we propose an iterative algorithm to find the optimal local quantization thresholds. A design example is provided to illustrate the validity of the iterative algorithm and the improved robustness compared to the classical distributed detection approach that disregards the possible transmission losses.

We consider in this paper local sensor quantizer design for large-scale bandwidth and/or energy constrained wireless sensor networks (WSNs) operating in fading channels. In particular, under the Neyman-Pears on framework, we address the design of binary local sensor quantizers for a binary hypothesis problem in the asymptotic regime where the number of sensors is large. Motivated by the sensor censoring idea for reduced communication rate, each sensor either transmits `1' to a fusion center or remains silent. By adopting energy detector as the fusion rule, we develop a procedure to obtain local sensor threshold that maximizes the Kullback-Leibler distance of the distributions of the fusion statistic under the two hypotheses. The proposed quantizer design is well suited for the emerging large scale resource-constrained WSNs applications. Numerical results based on Gaussian and exponential observations are presented to demonstrate the design procedure

Existing channel aware signal processing design for decentralized detection in wireless sensor networks typically assumes the clairvoyant case, i.e., global information regarding the transmission channels is known at the design stage. In this paper, we consider the distributed detection problem where only the channel fading statistics, instead of the instant channel state information (CSI), is available to the designer. We investigate the design of local decision rules for the following two cases: 1. Fusion center has the instant CSI; 2. Fusion center does not have the instant CSI. We show that, for both cases, the optimal local decision rules that minimize the error probability at the fusion center amount to a likelihood-ratio test (LRT), as in the previous work with known CSI. The proposed approach enables distributed design for a decentralized detection problem.

We develop a procedure to compute a lower bound of the sum capacity for the vector Gaussian interference channel (IFC). The obtained lower bound is shown to be tight for the very strong interference case and the procedure can be used to construct an inner bound for the vector Gaussian IFC. Alternatively, orthogonal transmission via frequency division multiplexing is considered and we establish the concavity of the sum rate as a function of the bandwidth allocation factor for the vector channel case. Numerical examples indicate that the achievable sum rate via the superposition code compares favorably with orthogonal transmission. This improvement holds for all interference power levels, a sharp contrast to that of the scalar counterpart

Motivated by the delay and resource constraints omnipresent in most wireless sensor network applications, we design channel-optimized scalar quantizers for a canonical decentralized detection system. Aimed at minimizing the error probability of the fusion center output, we first establish the optimality of monotone likelihood ratio partition of the observation space for the local quantizer design. We then devise an iterative algorithm to construct distributed quantizers that are person-by-person optimal. The channel-optimized approach is shown to offer better performance compared with various alternatives. It also exhibits inherent adaptivity in resource (bit) allocation in response to varying channel conditions.

In this correspondence, we study some information theoretical characteristics of vector Gaussian interference channels. Resorting to the superposition code technique, a lower bound of the sum capacity for the vector Gaussian interference channel is obtained. Alternatively, orthogonal transmission via frequency division multiplexing is considered and we establish the concavity of sum rate as the bandwidth allocation factor for the vector channel case. Numerical examples indicate that the achievable sum rate via the superposition code compares favorably with orthogonal transmission: the lower bound obtained via the superposition code dominates the best achievable sum rate through orthogonal transmission. This improvement holds for all interference power levels, a sharp contrast to that of the scalar counterpart

Existing studies on the classical distributed detection problem typically assume idealized transmissions between local sensors and a fusion center. This is not guaranteed in the emerging wireless sensor networks with low-cost sensors and stringent power/delay constraints. By focusing on discrete transmission channels, we study the performance limits, in both asymptotic and non-asymptotic regimes, of a distributed detection system as a function of channel characteristics. For asymptotic analysis, we compute the error exponents of the underlying hypothesis testing problem; while for cases with a finite number of sensors, we determine channel conditions under which the distributed detection systems become useless - observing the channel outputs cannot help reduce the error probability at the fusion center. We demonstrate that as the number of sensors or the quantization levels at local sensors increase, the requirements on channel quality can be relaxed

In this paper, we consider the distributed classification problem in wireless sensor networks. Local decisions made by local sensors, possibly in the presence of faults, are transmitted to a fusion center through fading channels. Classification performance could be degraded due to the errors caused by both sensor faults and fading channels. Integrating channel decoding into the distributed fault-tolerant classification fusion algorithm, we obtain a new fusion rule that combines both soft-decision decoding and local decision rules without introducing any redundancy. The soft decoding scheme is utilized to combat channel fading, while the distributed classification fusion structure using error correcting codes provides good sensor fault-tolerance capability. Asymptotic performance of the proposed approach is also investigated. Performance evaluation of the proposed approach with both sensor faults and fading channel impairments is carried out. These results show that the proposed approach outperforms the system employing the MAP fusion rule designed without regard to sensor faults and the multiclass equal gain combining fusion rule

We consider, in this paper, the design of a cooperative relay strategy by exploiting the finite-alphabet property of the source. Assuming a single source-sink pair with L relay nodes all communicating in orthogonal channels, we derive necessary conditions for optimal relay signaling that minimizes the error probability at the sink node. The derived conditions allow us to construct an iterative algorithm to find the distributed relay signaling that is at least locally optimal. As a byproduct, one can show that the so-called decode-and-forward (DF) relay scheme does not satisfy the necessary condition hence is not optimal in its error probability performance. Indeed, numerical examples show that the proposed scheme provides substantial performance improvement over both DF and the amplify-and-forward approach.

We study the throughput limits of a MIMO (multiple-input multiple output) ad hoc network with K simultaneous communicating transceiver pairs. Assume that each transmitter is equipped with t antennas and the receivers with r antennas, we show that in the absence of channel state information (CSI) at the transmitters, the asymptotic network throughput is limited by r nats/s/Hz as K/spl rarr//spl infin/. With CSI corresponding to the desired receiver available at the transmitter, we demonstrate that an asymptotic throughput of t+r+2/spl radic/tr nats/s/Hz can be achieved using a simple beamforming approach. Further, we show that the asymptotically optimal transmission scheme with CSI amounts to a single-user waterfilling for a properly scaled channel.

Randomization has emerged as a useful technique for data disguising in privacy-preserving data mining. Its privacy properties have been studied in a number of papers. Kargupta et al. challenged the randomization schemes, and they pointed out that randomization might not be able to preserve privacy. However, it is still unclear what factors cause such a security breach, how they affect the privacy preserving property of the randomization, and what kinds of data have higher risk of disclosing their private contents even though they are randomized.We believe that the key factor is the correlations among attributes. We propose two data reconstruction methods that are based on data correlations. One method uses the Principal Component Analysis (PCA) technique, and the other method uses the Bayes Estimate (BE) technique. We have conducted theoretical and experimental analysis on the relationship between data correlations and the amount of private information that can be disclosed based our proposed data reconstructions schemes. Our studies have shown that when the correlations are high, the original data can be reconstructed more accurately, i.e., more private information can be disclosed.To improve privacy, we propose a modified randomization scheme, in which we let the correlation of random noises "similar" to the original data. Our results have shown that the reconstruction accuracy of both PCA-based and BE-based schemes become worse as the similarity increases.

Distributed detection has been intensively studied in the past. In this correspondence, we consider the design of local decision rules in the presence of nonideal transmission channels between the sensors and the fusion center. Under the conditional independence assumption among multiple sensor observations, we show that the optimal local decisions that minimize the error probability at the fusion center amount to a likelihood-ratio test (LRT) given a particular constraint on the fusion rule. This constraint turns out to be quite general and is easily satisfied for most sensible fusion rules. A design example using a parallel sensor fusion structure with binary-symmetric channels (BSCs) between local sensors and the fusion center is given to illustrate the usefulness of the result in obtaining optimal thresholds for local sensor observations. The study that incorporates the transmission channel in the sensor system design may have potential applications in the emerging field of wireless sensor networks.

Sensor censoring has been introduced for reduced communication rate in a decentralized detection system where decisions made at peripheral nodes need to be communicated to a fusion center. In this letter, the fusion of decisions from censoring sensors transmitted over wireless fading channels is investigated. The knowledge of fading channels, either in the form of instantaneous channel envelopes or the fading statistics, is integrated in the optimum and suboptimum fusion rule design. The sensor censoring and the ensuing fusion rule design have two major advantages compared with the previous work. 1) Communication overhead is dramatically reduced. 2) It allows incoherent detection, hence, the phase information of transmission channels is no longer required. As such, it is particularly suitable for wireless sensor network applications with severe resource constraints.

Distributed binary quantizer design for sensor nets tasked with a hypothesis testing problem is considered in this paper. Allowing for non-ideal transmission channels, we show that under the conditional independence assumption, the optimum binary quantizer, in the sense of minimizing the error probability, should operate on the likelihood ratio (LR) of the local sensor observations. Necessary conditions for optimality are derived to facilitate finding of optimal LRT thresholds through an iterative algorithm. A design example with binary symmetric channels between local sensors and the fusion center is given to illustrate how the results can be applied in sensor signaling design.

In this paper we investigate the distribution of the peak-to-average power ratio for non-circularly modulated OFDM system. In contrast to circularly modulated OFDM systems, we find the following characteristics for non-circularly modulated OFDM symbols: i) they do not converge to complex Gaussian sequence asymptotically; and ii) the correlation among OFDM symbol powers does not diminish as the number of subcarriers goes to infinity. We further derive, based on the above two observations, an expression for the distribution of peak-to-average power ratio for non-circular constellation. While not in closed form, the obtained expression allows easy numerical evaluation. For the special case of BPSK, the expression can be simplified to a form that admits closed-form approximation. We show that the analytical prediction matches well with empirical result obtained through simulation.

Blind estimation of the OFDM carrier frequency offset (CFO) is studied in this paper. Maximum likelihood estimation is developed in the presence of virtual carriers. It turned out that the resulting estimator has an identical form to that of a previously proposed blind estimation scheme by Liu and Tureli (see IEEE Communications Letters, vol.2, p.104-6, April 1998). We explain, using the projection argument, why these two estimators are equivalent. For improved CFO estimation performance, multiple OFDM blocks can be utilized. Alternatively receiver diversity may be used in the CFO estimation. We show that in both cases, the estimator again reduces to the form similar to that of the MUSIC-like algorithm.. Performance improvement is shown using both the Cramer Rao lower bound and numerical examples.

We develop a maximum likelihood estimate for orthogonal frequency division multiplexing (OFDM) carrier frequency offset in the presence of virtual carriers. It is found that the resulting estimate has an identical form to that of a previously proposed blind OFDM carrier frequency offset estimate by Liu and Tureli (see IEEE Commun. Lett., vol.2, p.104-106, 1998). Insights are provided as to why these two algorithms are equivalent.

Traffic burstiness results in the predictability of user activity at the individual source level, and the exploitation of such predictability in the receiver design for wireless packet switching random access/CDMA networks is investigated. It is shown that the conventional approach of assuming all users are active results in substantial performance loss when a linear multiuser detector, and in particular, a decorrelating detector is implemented. A two-stage receiver is proposed where the first stage tracks active users and prior to the second stage symbol detection. Two different user trackers are presented and it is demonstrated that, with the help of traffic predictability, accurate estimate of active set of users is possible, even with a simple matched filter bank implementation at the first stage.

A simple yet effective statistic is proposed for detecting a transient buried in partially unknown ambient noise. The transient model is frequency scattered increased variance observations. We pose the transient detection problem as a homogeneity test and the statistic is derived as the (generalized) likelihood ratio test of overdispersion when the underlying observation sequence follows a double exponential distribution. Numerical testing focuses on the comparison of this scheme with the CFAR power-law detector.

We develop an expression for the minimum extra bandwidth needed for a frequency-division multiple-access (FDMA) system to out perform its code-division (CDMA) counterpart uniformly (that is, for all rate n-tuples) in a Gaussian multiple-access channel (MAC). For equal-power sources, the behavior of this factor is as an iterated logarithm of the number of users; hence it increases slowly yet is unbounded. Asymmetric power cases are also studied and it is shown that the equal power scenario provides the least bandwidth expansion factor assuming constant constraint on total power.

For quickest detection of a permanent change in distribution of otherwise i.i.d. observations Page's test provides the optimal processor. Page's test has also been applied to the detection of transient (i.e. temporary) changes in distribution: it is easy to implement and has reliable performance, but as applied to the transient problem its optimality is questionable. In this paper we offer an alternative to the Page procedure which we call the iterated generalized sequential probability ratio test, or IGSPRT. While Page's test is itself an IGSPRT, its form and performance are constrained by its reliance on constant thresholds and biases. We demonstrate that with these time-varying, markedly increased detection probabilities are possible. The IGSPRT is easiest to understand and motivate in the Gaussian shift-in-mean problem, and we discuss this in detail, but since that problem is of limited practical interest, we also examine the effect of the IGSPRT in a more realistic situation.

Page's (1954) test is optimal for detecting a permanent change in distribution, in the sense that it minimizes the worst case average delay to detection given an average distance between false alarms. When used to detect transient signals, however, it in fact becomes the generalized likelihood ratio test (GLRT). Since a GLRT is in almost all cases ad hoc, Page's test used as such cannot be said to be optimal in any explicit sense. This article discusses the development of the min-max test, via the new ideas of Baygun and Hero (see ibid., vol.41, p.688-703, 1995) for the detection of a transient.