We study uplink orthogonal frequency-division multiplexing (OFDM) pilot distortion to deliberately obfuscate time-of-arrival (ToA) estimation at a single base station while preserving communication performance. We design a complex persubcarrier distortion vector that increases sidelobes of the mismatched ambiguity function (MAF) relative to its mainlobe, using two objectives: the sidelobe-to-peak level ratio and the integrated sidelobe level. The design is subject to a transmit-power budget and a proximity (dissimilarity) constraint around the communication-optimal pilot. Communication impact is quantified by a capacity-motivated lower bound obtained from the linear minimum mean-squared error error covariance with a mismatched channel estimate. The resulting generalized fractional program is solved with Dinkelbach's transform and a difference-of-convex update that yields a closed-form Karush-Kuhn-Tucker step. Simulations on a single-input single-output OFDM link show that the optimized distortions raise MAF sidelobes and degrade delay estimation, as validated by a mismatched maximumlikelihood ToA estimator, while incurring only marginal capacity loss over a broad signal-to-noise ratio range. The method requires no protocol changes or artificial path injection and provides a signal-level mechanism to control ToA observability under communication constraints.

We investigate an indoor visible light positioning (VLP) system in which a number of light emitting diode (LED) transmitters emit signals over a certain number of time intervals for localizing a visible light communication (VLC) receiver. We formulate the problem of LED selection and orientation adaptation, with the aim of optimally determining the LED transmitter and its orientation vector to be employed in each time interval for minimizing the average Cramér-Rao lower bound on position estimation. Due to the complexity of the proposed problem, we take a discretization approach in which the orientation vector of each LED transmitter is confined to a finite set. Then, we obtain a convex problem via relaxation and propose a solution approach based on the relaxed solution and a swap algorithm. We also present numerical examples to illustrate the effectiveness of the proposed approach. In particular, it is shown that, in some cases, instead of employing multiple LEDs over a number of time intervals, using the same LED with different orientations can be more desirable for accurate localization.

We investigate a visible light positioning (VLP) system in which a number of light emitting diode (LED) transmitters emit signals over a certain number of time intervals for localizing a visible light communication (VLC) receiver. We formulate the problem of LED selection and orientation adaptation, with the aim of optimally determining the LED transmitter and its orientation vector to be employed in each time interval for minimizing the average Cramér-Rao lower bound (CRLB) on position estimation. Due to the complexity of the proposed problem, we take a discretization approach in which the orientation vector of each LED transmitter is confined to a finite set as in digital implementations. Then, we obtain a convex problem via relaxation and propose a solution approach based on the relaxed solution and a swap algorithm. We also formulate the problem of joint LED selection, orientation adaptation, and power allocation. For this problem, we extend the discretization approach by generating a power vector corresponding to each LED orientation. Then, we develop a convex formulation over all the design parameters via relaxation, and propose a low-complexity algorithm using the convex problem and a swap algorithm. We present numerical examples to illustrate the effectiveness of the proposed approaches. In particular, it is shown that, in some cases, instead of employing multiple LEDs over a number of time intervals, using the same LED with different orientations can be more desirable for accurate localization. Moreover, the proposed joint optimization approach can significantly reduce the average CRLB compared to the uniform power allocation approach.

We investigate the problem of reconfigurable intelligent surface (RIS)-aided monostatic sensing of a mobile target under line-of-sight (LoS) blockage considering a single-antenna, full-duplex, and dual-functional radar-communications base station (BS). For the purpose of target detection and delay/Doppler/angle estimation, we derive a detector based on the generalized likelihood ratio test (GLRT), which entails a high-dimensional parameter search and leads to angle-Doppler coupling. To tackle these challenges, we propose a two-step algorithm for solving the GLRT detector/estimator in a low-complexity manner, accompanied by a RIS phase profile design tailored to circumvent the angle-Doppler coupling effect. Simulation results verify the effectiveness of the proposed algorithm, demonstrating its convergence to theoretical bounds and its superiority over state-of-the-art mobility-agnostic benchmarks.

Reconfigurable intelligent surfaces (RISs) hold great potential as one of the key technological enablers for beyond-5G wireless networks, improving localization and communication performance under line-of-sight (LoS) blockage conditions. However, hardware imperfections might cause RIS elements to become faulty, a problem referred to aspixel failures, which can constitute a major showstopper especially for localization. In this paper, we investigate the problem of RIS-aided localization of a user equipment (UE) under LoS blockage in the presence of RIS pixel failures, considering the challenging single-input single-output (SISO) scenario. We first explore the impact of such failures on accuracy through misspecified Cramér-Rao bound (MCRB) analysis, which reveals severe performance loss with even a small percentage of pixel failures. To remedy this issue, we develop two strategies for joint localization and failure diagnosis (JLFD) to detect failing pixels while simultaneously locating the UE with high accuracy. The first strategy relies on ℓ1-regularization through exploitation of failure sparsity. The second strategy detects the failures one-by-one by solving a multiple hypothesis testing problem at each iteration, successively enhancing localization and diagnosis accuracy. Simulation results show significant performance improvements of the proposed JLFD algorithms over the conventional failure-agnostic benchmark, enabling successful recovery of failure-induced performance degradations.

The problem of optimal signal design for coherent detection of binary signals in Gaussian noise is revisited under power and secrecy constraints. In particular, the aim is to select the binary transmitted signals in an optimal manner so that the probability of error is minimized at an intended receiver while the probability of error at an eavesdropper is maintained above a threshold value and the signal powers are limited. It is shown that an optimal solution exists in the form of antipodal signaling along the eigenvector corresponding to the solution of a maximum (possibly generalized) eigenvalue problem, which is specified explicitly based on the channel coefficient matrices and the noise covariance matrices at the intended receiver and the eavesdropper. Furthermore, optimal signal design can be performed in an efficient manner by solving a semidefinite programming (SDP) relaxation followed by a matrix rank-one decomposition. Numerical examples are provided to illustrate optimal solutions for three different but exhaustive cases.

We consider a visible light positioning system in which a receiver performs position estimation based on signals emitted from a number of light emitting diode (LED) transmitters. Each LED transmitter can be malicious and transmit at an unknown power level with a certain probability. A maximum likelihood (ML) position estimator is derived based on the knowledge of probabilities that LED transmitters can be malicious. In addition, in the presence of training measurements, decision rules are designed for detection of malicious LED transmitters, and based on detection results, various ML based location estimators are proposed. To evaluate the performance of the proposed estimators, Cramér-Rao lower bounds (CRLBs) are derived for position estimation in scenarios with and without a training phase. Moreover, an ML estimator is derived when the probabilities that the LED transmitters can be malicious are unknown. The performances of all the proposed estimators are evaluated via numerical examples and compared against the CRLBs.

We investigate the problem of reconfigurable intelligent surface (RIS)-aided near-field localization of a user equipment (UE) served by a base station (BS) under phase-dependent amplitude variations at each RIS element. Through a misspecified Cramér-Rao bound (MCRB) analysis and a resulting lower bound (LB) on localization, we show that when the UE is unaware of amplitude variations (i.e., assumes unit-amplitude responses), severe performance penalties can arise, especially at high signal-to-noise ratios (SNRs). Leveraging Jacobi-Anger expansion to decouple range-azimuth-elevation dimensions, we develop a low-complexity approximated mismatched maximum likelihood (AMML) estimator, which is asymptotically tight to the LB. To mitigate performance loss due to model mismatch, we propose to jointly estimate the UE location and the RIS amplitude model parameters. The corresponding Cramér-Rao bound (CRB) is derived, as well as an iterative refinement algorithm, which employs the AMML method as a subroutine and alternatingly updates individual parameters of the RIS amplitude model. Simulation results indicate fast convergence and performance close to the CRB. The proposed method can successfully recover the performance loss of the AMML under a wide range of RIS parameters and effectively calibrate the RIS amplitude model online with the help of a user that has an a-priori unknown location.

We investigate a reconfigurable intelligent surface (RIS)-aided near-field localization system with single-antenna user equipment (UE) and base station (BS) under hardware impairments by considering a practical phase-dependent RIS amplitude variations model. To analyze the localization performance under the mismatch between the practical model and the ideal model with unit-amplitude RIS elements, we employ the misspecified Cramér-Rao bound (MCRB). Based on the MCRB derivation, the lower bound (LB) on the mean-squared error for estimation of UE position is evaluated and shown to converge to the MCRB at low signal-to-noise ratios (SNRs). Simulation results indicate more severe performance degradation due to the model misspecification with increasing SNR. In addition, the mismatched maximum likelihood (MML) estimator is derived and found to be tight to the LB in the high SNR regime. Finally, we observe that the model mismatch can lead to an order-of-magnitude localization performance loss at high SNRs.

We investigate a privacy-signaling game problem in which a sender with privacy concerns observes a pair of correlated random vectors which are modeled as jointly Gaussian. The sender aims to hide one of these random vectors and convey the other one whereas the objective of the receiver is to accurately estimate both of the random vectors. We analyze these conflicting objectives in a game theoretic framework with quadratic costs where depending on the commitment conditions (of the sender), we consider Nash or Stackelberg (Bayesian persuasion) equilibria. We show that a payoff dominant Nash equilibrium among all admissible policies is attained by a set of explicitly characterized linear policies. We also show that a payoff dominant Nash equilibrium coincides with a Stackelberg equilibrium. We formulate the information bottleneck problem within our Stackelberg framework under the mean squared error distortion criterion where the information bottleneck setup has a further restriction that only one of the random variables is observed at the sender. We show that this MMSE Gaussian Information Bottleneck Problem admits a linear solution which is explicitly characterized in the paper. We provide explicit conditions on when the optimal solutions, or equilibrium solutions in the Nash setup, are informative or noninformative.

We investigate the equilibrium behavior for the decentralized cheap talk problem for real random variables and quadratic cost criteria in which an encoder and a decoder have misaligned objective functions. In prior work, it has been shown that the number of bins in any equilibrium has to be countable, generalizing a classical result due to Crawford and Sobel who considered sources with density supported on [0, 1]. In this paper, we first refine this result in the context of log-concave sources. For sources with two-sided unbounded support, we prove that, for any finite number of bins, there exists a unique equilibrium. In contrast, for sources with semi-unbounded support, there may be a finite upper bound on the number of bins in equilibrium depending on certain conditions stated explicitly. Moreover, we prove that for log-concave sources, the expected costs of the encoder and the decoder in equilibrium decrease as the number of bins increases. Furthermore, for strictly log-concave sources with two-sided unbounded support, we prove convergence to the unique equilibrium under best response dynamics which starts with a given number of bins, making a connection with the classical theory of optimal quantization and convergence results of Lloyd’s method. In addition, we consider more general sources which satisfy certain assumptions on the tail(s) of the distribution and we show that there exist equilibria with infinitely many bins for sources with two-sided unbounded support. Further explicit characterizations are provided for sources with exponential, Gaussian, and compactly-supported probability distributions.

Signaling game problems investigate communication scenarios where encoder(s) and decoder(s) have misaligned objectives due to the fact that they either employ different cost functions or have inconsistent priors. We investigate a signaling game problem where an encoder observes a multi-dimensional source and conveys a message to a decoder, and the quadratic objectives of the encoder and decoder are misaligned due to a bias vector. For the scalar case, Crawford and Sobel in their seminal paper, show that under certain technical assumptions an encoding policy must be a quantization policy at any Nash equilibrium. We first provide a set of geometry conditions that needs to be satisfied in equilibrium considering any multi-dimensional source. Then, we consider multi-dimensional sources with independent and identically distributed components and completely characterize conditions under which a Nash equilibrium with a linear encoder exists. In particular, we show that if the components of the bias vector are not equal in magnitude, then there exists a linear equilibrium if and only if the source distribution is Gaussian. On the other hand, for a linear equilibrium to exist in the case of equal bias components, it is required that the source density is symmetric around its mean. Moreover, in the case of Gaussian sources, our results have a rate-distortion theoretic implication that achievable rates and distortions in the considered game theoretic setup can be obtained from their team theoretic counterpart.

We consider a wireless source localization network in which eavesdropper nodes aim to estimate the position of a target node. We formulate the problem of selecting a set of NEpositions out of N possible positions for placing eavesdropper nodes in order to estimate the target node position as accurately as possible. The Cramér-Rao lower bound related to the estimation of the target node position by eavesdropper nodes is derived, and its monotonicity and convexity properties are investigated. Via relaxation of the integer constraints, the eavesdropper selection problem is approximated by a convex optimization problem, which is used to propose two algorithms for eavesdropper selection. Moreover, in the presence of parameter uncertainty, a robust version of the eavesdropper selection problem is investigated. Simulation results are presented to examine performance of the proposed algorithms.

We consider a privacy-signaling game problem in which a transmitter with privacy concerns and a receiver, which does not pay attention to these privacy concerns, communicate. In this communication scenario, the transmitter observes a pair of correlated random variables which are modeled as jointly Gaussian. The transmitter constructs its message based on these random variables with the aim to hide one of them and convey the other one. In contrast, the objective of the receiver is to accurately estimate both of the random variables so as to gather as much information as possible. These conflicting objectives are analyzed in a game theoretic framework where depending on the commitment conditions (of the sender), we consider Nash or Stackelberg equilibria. We show that a payoff dominant (i.e., most desirable for both players) Nash equilibrium is attained by affine policies and we explicitly characterize these policies. In addition, the strategies at the characterized Nash equilibrium is shown to form also a Stackelberg equilibrium. Furthermore, we show that there always exists an informative Stackelberg equilibrium for the multidimensional parameter setup. We also revisit the information bottleneck problem within our Stackelberg framework under the mean squared error distortion criterion where the information bottleneck setup has a further restriction that only one of the parameters is observed at the sender. We fully characterize the Stackelberg equilibria under certain conditions and when these conditions are not met we establish the existence of informative equilibria.

The problem of simple M-ary hypothesis testing under a generic performance criterion that depends on arbitrary functions of error probabilities is considered. Using results from convex analysis, it is proved that an optimal decision rule can be characterized as a randomization among at most two deterministic decision rules, each of the form reminiscent to Bayes rule, if the boundary points corresponding to each rule have zero probability under each hypothesis. Otherwise, a randomization among at most M(M-1)+1 deterministic decision rules is sufficient. The form of the deterministic decision rules are explicitly specified. Likelihood ratios are shown to be sufficient statistics. Classical performance measures including Bayesian, minimax, Neyman-Pearson, generalized Neyman-Pearson, restricted Bayesian, and prospect theory based approaches are all covered under the proposed formulation. A numerical example is presented for prospect theory based binary hypothesis testing.

In this letter, estimation theoretic secure broadcast of a random parameter is investigated. In the considered setting, each receiver device employs a fixed estimator and carries a certain security risk such that its decision can be available to a malicious third party with a certain probability. The encoder at the transmitter is allowed to use a random mapping to minimize the weighted sum of the conditional Bayes risks of the estimators under secrecy and average power constraints. After formulating the optimal parameter design problem, it is shown that the optimization problem can be solved individually for each parameter value and the optimal mapping at the transmitter involves a randomization among at most three different signal levels. Sufficient conditions for improvability and non-improvability of the deterministic design via stochastic encoding are obtained. Numerical examples are provided to corroborate the theoretical results.

We investigate the equilibrium behavior for the decentralized quadratic cheap talk problem in which an encoder and a decoder, viewed as two decision makers, have misaligned objective functions. In prior work, we have shown that the number of bins under any equilibrium has to be at most countable, generalizing a classical result due to Crawford and Sobel who considered sources with density supported on [0, 1]. In this paper, we refine this result in the context of exponential and Gaussian sources. For exponential sources, a relation between the upper bound on the number of bins and the misalignment in the objective functions is derived, the equilibrium costs are compared, and it is shown that there also exist equilibria with infinitely many bins under certain parametric assumptions. For Gaussian sources, it is shown that there exist equilibria with infinitely many bins.

The distance estimation problem is investigated for visible light positioning (VLP) systems with red-green-blue (RGB) light emitting diodes (LEDs). The accuracy limits on distance estimation are calculated in terms of the Cramér-Rao lower bounds (CRLBs) for three different scenarios. Scenario 1 and Scenario 2 correspond to synchronous and asynchronous systems, respectively, with known channel attenuation formulas at the receiver. In Scenario 3, a synchronous systems is considered but channel attenuation formulas are not known at the receiver. The derived CRLB expressions reveal the relations among the distance estimation accuracies in the considered scenarios and provide intuitive explanations for the benefits of using RGB LEDs.

The problem of optimal power allocation among light emitting diode (LED) transmitters in a visible light positioning system is considered for the purpose of improving localization performance of visible light communication (VLC) receivers. Specifically, the aim is to minimize the Cramér-Rao lower bound (CRLB) on the localization error of a VLC receiver by optimizing LED transmission powers in the presence of practical constraints, such as individual and total power limitations and illuminance constraints. The formulated optimization problem is shown to be convex and thus can efficiently be solved via standard tools. We also investigate the case of imperfect knowledge of localization parameters and develop robust power allocation algorithms by taking into account both overall system uncertainty and individual parameter uncertainties related to the location and orientation of the VLC receiver. In addition, we address the total power minimization problem under predefined accuracy requirements to obtain the most energy-efficient power allocation vector for a given CRLB level. Numerical results illustrate the improvements in localization performance achieved by employing the proposed optimal and robust power allocation strategies over the conventional uniform and non-robust approaches.

In this paper, modulation classification and symbol decoding problems are jointly considered and optimal strategies are proposed under various settings. In the considered framework, there exist a number of candidate modulation formats and the aim is to decode a sequence of received signals with an unknown modulation scheme. To that aim, two different formulations are proposed. In the first formulation, the prior probabilities of the modulation schemes are assumed to be known and a formulation is proposed under the Bayesian framework. This formulation takes a constrained approach in which the objective function is related to symbol decoding performance whereas the constraint is related to modulation classification performance. The second formulation, on the other hand, addresses the case in which the prior probabilities of the modulation schemes are unknown, and provides a method under the minimax framework. In this case, a constrained approach is employed as well; however, the introduced performance metrics differ from those in the first formulation due to the absence of the prior probabilities of the modulation schemes. Finally, the performance of the proposed methods is illustrated through simulations. It is demonstrated that the proposed techniques improve the introduced symbol detection performance metrics via relaxing the constraint(s) on the modulation classification performance compared with the conventional techniques in a variety of system configurations.

Visible light communication (VLC) is an emerging paradigm that enables multiple functionalities to be accomplished concurrently, including illumination, high-speed data communications, and localization. Based on the VLC technology, visible light positioning (VLP) systems aim to estimate locations of VLC receivers by utilizing light-emitting diode (LED) transmitters at known locations. VLP presents a viable alternative to radio frequency (RF)-based positioning systems by providing inexpensive and accurate localization services. In this paper, we consider the problem of localization in visible light systems and provide an extensive survey of various location estimation techniques, accompanied by discussions of their relative merits and demerits within the context of accuracy and computational complexity. In addition, we investigate a cooperative VLP system architecture in which VLC receiver units are able to communicate with each other for the purpose of cooperation, and present a low-complexity, iterative localization algorithm to demonstrate the benefits of cooperation in VLP systems. Finally, we investigate optimal strategies for power allocation among LED transmitters to maximize the localization accuracy subject to power and illumination constraints.

In this letter, the optimality of the likelihood ratio test (LRT) is investigated for binary hypothesis testing problems in the presence of a behavioral decision-maker. By utilizing prospect theory, a behavioral decision-maker is modeled to cognitively distort probabilities and costs based on some weight and value functions, respectively. It is proved that the LRT may or may not be an optimal decision rule for prospect theory-based binary hypothesis testing, and conditions are derived to specify different scenarios. In addition, it is shown that when the LRT is an optimal decision rule, it corresponds to a randomized decision rule in some cases; i.e., nonrandomized LRTs may not be optimal. This is unlike Bayesian binary hypothesis testing, in which the optimal decision rule can always be expressed in the form of a nonrandomized LRT. Finally, it is proved that the optimal decision rule for prospect theory-based binary hypothesis testing can always be represented by a decision rule that randomizes at most two LRTs. Two examples are presented to corroborate the theoretical results.

The optimal stochastic signaling based on the joint design of prior distribution and signal constellation is investigated under an average bit rate and power constraints. First, an optimization problem is formulated to maximize the average probability of correct decision over the set of joint distribution functions for prior probabilities and the corresponding constellation symbols. Next, an alternative problem formulation, for which the optimal joint distribution is characterized by a randomization among at most three mass points, is provided, and it is shown that both formulations share the same solution. Three special cases of the problem are investigated in detail. First, in the absence of randomization, the optimal prior probability distribution is analyzed for a given signal constellation and a closed-form solution is provided. Second, the optimal deterministic pair of prior probabilities and the corresponding signal levels are considered. Third, a binary communication system with scalar observations is investigated in the presence of a zero-mean additive white Gaussian noise, and the optimal solution is obtained under practical assumptions. Finally, numerical examples are presented to illustrate the theoretical results. It is observed that the proposed approach can provide improvements in terms of average symbol error rate over the classical scheme for certain scenarios.

Visible light positioning (VLP) systems based on light emitting diodes can facilitate high accuracy localization services for indoor scenarios. In this paper, direct and two-step positioning approaches are investigated for both synchronous and asynchronous VLP systems. First, the Cramér-Rao lower bound (CRLB) and the direct positioning-based maximum likelihood estimator are derived for 3-D localization of a visible light communication receiver in a synchronous scenario by utilizing information from both time delay parameters and channel attenuation factors. Then, a two-step position estimator is designed for synchronous VLP systems by exploiting the asymptotic properties of time-of-arrival and received signal strength estimates. The proposed two-step estimator is shown to be asymptotically optimal, i.e., converges to the direct estimator at high signal-to-noise ratios. In addition, the CRLB and the direct and two-step estimators are obtained for positioning in asynchronous VLP systems. It is proved that the two-step position estimation is optimal in asynchronous VLP systems for practical pulse shapes. Various numerical examples are provided to illustrate the improved performance of the proposed estimators with respect to the current state-of-the-art and to investigate their robustness against model uncertainties in VLP systems.

In this letter, optimal deterministic encoding of a uniformly distributed scalar parameter is performed in the presence of an eavesdropper. The objective is to maximize the worst case Fisher information of the parameter at the intended receiver while keeping the mean-squared error (MSE) at the eavesdropper above a certain level. The eavesdropper is modeled to employ the linear minimum MSE estimator based on the encoded version of the parameter. First, the optimal encoding function is derived when there exist no secrecy constraints. Next, to obtain the solution of the problem in the presence of the secrecy constraint, the form of the encoding function that maximizes the MSE at the eavesdropper is explicitly derived for any given level of worst case Fisher information. Then, based on this result, a low-complexity algorithm is provided to calculate the optimal encoding function for the given secrecy constraint. Finally, numerical examples are presented.

In this study, dynamic and repeated quadratic cheap talk and signaling game problems are investigated. These involve encoder and decoders with mismatched performance objectives, where the encoder has a bias term in the quadratic cost functional. We consider both Nash equilibria and Stackelberg equilibria as our solution concepts, under a perfect Bayesian formulation. These two lead to drastically different characteristics for the equilibria. For the cheap talk problem under Nash equilibria, we show that fully revealing equilibria cannot exist and the final state equilibria have to be quantized for a large class of source models; whereas, for the Stackelberg case, the equilibria must be fully revealing regardless of the source model. In the dynamic signaling game where the transmission of a Gaussian source over a Gaussian channel is considered, the equilibrium policies are always linear for scalar sources under Stackelberg equilibria, and affine policies constitute an invariant subspace under best response maps for Nash equilibria.

In this study, optimal channel switching (time sharing) strategies are investigated under average power and cost constraints in order to maximize the average number of correctly received symbols between a transmitter and a receiver that are connected via multiple additive Gaussian noise channels. The optimal strategy is shown to perform channel switching either among at most three channels with full channel utilization (i.e., no idle periods), or between at most two channels with partial channel utilization. In addition, it is stated that the optimal solution must operate at the maximum average power and the maximum average cost, which facilitates low-complexity approaches for calculating the optimal strategy. For two-channel strategies, an upper bound in terms of the noise standard deviations of the employed channels is provided for the ratio between the optimal power levels. Furthermore, a simple condition depending solely on the systems parameters is derived, under which partial channel utilization cannot be optimal. Numerical examples are presented to demonstrate the validity of the theoretical results.

In this study, the optimal channel switching problem is investigated for average capacity maximization in the presence of channel switching delays. First, the optimal strategy is obtained and the corresponding average capacity is derived when channel switching is performed among a given number of channels. Then, it is proved that channel switching among more than two different channels is not optimal. Also, the maximum average capacity achieved by the optimal channel switching strategy is expressed as a function of the channel switching delay parameter and the average and peak power limits. Then, scenarios in which the optimal strategy corresponds to the use of a single channel or to channel switching between two channels are described. Numerical examples are presented for showing the effects of channel switching delays.

An optimal decision framework is proposed for joint detection and decoding when the prior information is available with some uncertainty. The proposed framework provides tradeoffs between the average inclusive error probability (computed using estimated prior probabilities) and the worst case inclusive error probability according to the amount of uncertainty while satisfying constraints on the probability of false alarm and the maximum probability of miss-detection. Theoretical results that characterize the structure of the optimal decision rule according to the proposed criterion are obtained. The proposed decision rule reduces to some well-known detectors in the case of perfect prior information or when the constraints on the probabilities of miss-detection and false alarm are relaxed. Numerical examples are provided to illustrate the theoretical results.

In this paper, the optimal jamming of wireless localization systems is investigated. Two optimal power allocation schemes are proposed for jammer nodes in the presence of total and peak power constraints. In the first scheme, power is allocated to jammer nodes in order to maximize the average Cramér-Rao lower bound (CRLB) of target nodes, whereas in the second scheme, the power allocation is performed for the aim of maximizing the minimum CRLB of target nodes. Both the schemes are formulated as linear programs, and a closed-form solution is obtained for the first scheme. For the second scheme, under certain conditions, the property of full total power utilization is specified, and a closed-form solution is obtained when the total power is lower than a specific threshold. In addition, it is shown that non-zero power is allocated to at most NTjammer nodes according to the second scheme in the absence of peak power constraints, where NTis the number of target nodes. In the presence of parameter uncertainty, robust versions of the power allocation schemes are proposed. Simulation results are presented to investigate the performance of the proposed schemes and to illustrate the theoretical results.

The optimal channel switching problem is studied for average capacity maximization in the presence of additive white Gaussian noise channels and channel switching delays. First, an optimization problem is formulated for the maximization of the average channel capacity, considering channel switching delays and constraints on average and peak powers. Then, an equivalent optimization problem is obtained to facilitate theoretical investigations. The optimal strategy is derived and the corresponding average capacity is specified when channel switching is performed among a given number of channels. Based on this result, it is shown that channel switching among more than two different channels is not optimal. In addition, the maximum average capacity achieved by the optimal channel switching strategy is formulated as a function of the channel switching delay parameter and the average and peak power limits. Then, scenarios under which the optimal strategy corresponds to the exclusive use of a single channel or to channel switching between two channels are described. Furthermore, sufficient conditions are obtained to determine when the optimal single channel strategy outperforms the optimal channel switching strategy. The numerical results are presented to provide the examples of the theoretical results and to illustrate the effects of channel switching delays.

In this study, an optimal channel switching strategy is proposed for average capacity maximization in the presence of average and peak power constraints. Necessary and sufficient conditions are derived to determine when the proposed optimal channel switching strategy can or cannot outperform the optimal single channel strategy, which performs no channel switching. Also, it is obtained that the optimal channel switching strategy can be realized by channel switching between, at most, two different channels. In addition, a low-complexity optimization problem is derived to obtain the optimal channel switching strategy. Furthermore, based on some necessary conditions that need to be satisfied by the optimal channel switching solution, an alternative approach is proposed for calculating the optimal channel switching strategy. Numerical examples are provided to exemplify the derived theoretical results and to provide intuitive explanations.

Optimal channel switching that provides the highest performance over a set of Gaussian channels with variable utilization costs is investigated in the presence of average power and average cost constraints. First, generic cost functions are considered, and it is shown that the optimal channel switching strategy performs channel switching (time sharing) among at most three different channels and always operates at the average power and average cost limits. Also, for channel switching between two channels, relations between the optimal power levels are obtained depending on the average power limit, and it is proved that the ratio of the optimal power levels is upper bounded by the ratio of the larger noise variance to the smaller one. In addition, for logarithmic cost functions, the convexity properties of the error probability are investigated as a function of power and cost, and the optimal channel switching strategy is shown to employ at most two channels, which can easily be determined based on specific formulas, when the average power limit is larger than a certain threshold. Numerical examples are presented to provide illustrations of the theoretical results.

Optimal channel switching is proposed for average capacity maximization in the presence of average and peak power constraints. A necessary and sufficient condition is derived in order to determine when the proposed optimal channel switching approach can or cannot outperform the optimal single channel approach, which performs no channel switching. Also, it is stated that the optimal channel switching solution can be realized by channel switching between at most two different channels. In addition, a low-complexity optimization problem is derived in order to obtain the optimal channel switching solution. Numerical examples are provided to exemplify the derived theoretical results.

In this study, we consider performance improvement of an array of fixed estimators by using stochastic design techniques. The optimal design is investigated both in the absence and presence of an average power constraint. Two different performance criteria are considered; the average Bayes risk and the maximum Bayes risk. It is shown that the optimal stochastic parameter design results in a randomization between different numbers of parameter values depending on the type of the performance criterion.

This paper studies the symbol error rate performance of cognitive radio transmissions in the presence of imperfect sensing decisions. Two different transmission schemes, namely sensing-based spectrum sharing (SSS) and opportunistic spectrum access (OSA), are considered. In both schemes, secondary users first perform channel sensing, albeit with possible errors. In SSS, depending on the sensing decisions, they adapt the transmission power level and coexist with primary users in the channel. On the other hand, in OSA, secondary users are allowed to transmit only when the primary user activity is not detected. Initially, for both transmission schemes, general formulations for the optimal decision rule and error probabilities are provided for arbitrary modulation schemes under the assumptions that the receiver is equipped with the sensing decision and perfect knowledge of the channel fading, and the primary user's received faded signals at the secondary receiver has a Gaussian mixture distribution. Subsequently, the general approach is specialized to rectangular quadrature amplitude modulation (QAM). More specifically, the optimal decision rule is characterized for rectangular QAM, and closed-form expressions for the average symbol error probability attained with the optimal detector are derived under both transmit power and interference constraints. The effects of imperfect channel sensing decisions, interference from the primary user and its Gaussian mixture model, and the transmit power and interference constraints on the error rate performance of cognitive transmissions are analyzed.

We deal with the positioning problem based on two-way time-of-arrival (TW-TOA) measurements in asynchronous wireless sensor networks. The optimal estimator for this problem poses a difficult global optimization problem. To avoid the drawbacks in solving the optimal estimator, we use approximations and derive linear models, which facilitate efficient solutions. In particular, we employ the least squares method and solve a general trust region subproblem to find a coarse estimate. To further refine the estimate, we linearize the measurements and obtain a linear model which can be solved using regularized least squares. Simulation results illustrate that the proposed approaches asymptotically attain the Cramér-Rao lower bound.

Optimal channel switching and detector design is studied for M-ary communication systems in the presence of stochastic signaling, which facilitates randomization of signal values transmitted for each information symbol. Considering the presence of multiple additive noise channels (which can have non-Gaussian distributions in general) between a transmitter and a receiver, the joint optimization of the channel switching (timesharing) strategy, stochastic signals, and detectors is performed in order to achieve the minimum average probability of error. It is proved that the optimal solution to this problem corresponds to either (i) switching between at most two channels with deterministic signaling over each channel, or (ii) time-sharing between at most two different signals over a single channel (i.e., stochastic signaling over a single channel). For both cases, the optimal solutions are shown to employ corresponding maximum a posteriori probability (MAP) detectors at the receiver. Numerical results are presented to investigate the proposed approach.

In this paper, error rate performance of cognitive radio transmissions is studied in the presence of imperfect channel sensing decisions. It is assumed that cognitive users first perform channel sensing, albeit with possible errors. Then, depending on the sensing decisions, they select the transmission energy level and employ MI× MQrectangular quadrature amplitude modulation (QAM) for data transmission over a fading channel. In this setting, the optimal decision rule is formulated under the assumptions that the receiver is equipped with the sensing decision and perfect knowledge of the channel fading. It is shown that the thresholds for optimal detection at the receiver are the midpoints between the signals under any sensing decision. Subsequently, minimum average error probability expressions for M-ary pulse amplitude modulation (M-PAM) and MI× MQrectangular QAM transmissions attained with the optimal detector are derived. The effects of imperfect channel sensing decisions on the average symbol error probability are analyzed.

This paper studies the positioning problem of a single target node based on time-difference-of-arrival (TDOA) measurements in the presence of clock imperfections. Employing an affine model for the behaviour of a local clock, it is observed that TDOA based approaches suffer from a parameter of the model, called the clock skew. Modeling the clock skew as a nuisance parameter, this paper investigates joint clock skew and position estimation. The maximum likelihood estimator (MLE) is derived for this problem, which is highly nonconvex and difficult to solve. To avoid the difficulty in solving the MLE, we employ suitable approximations and relaxations and propose two suboptimal estimators based on semidefinite programming and linear estimation. To further improve the estimation accuracy, we also propose a refining step. In addition, the Cramér-Rao lower bound (CRLB) is derived for this problem as a benchmark. Simulation results show that the proposed suboptimal estimators can attain the CRLB for sufficiently high signal-to-noise ratios.

Optimal detector randomization is studied for the downlink of a multiuser communications system, in which users can perform time-sharing among multiple detectors. A formulation is provided to obtain optimal signal amplitudes, detectors, and detector randomization factors. It is shown that the solution of this joint optimization problem can be calculated in two steps, resulting in significant reduction in computational complexity. It is proved that the optimal solution is achieved via randomization among at most min{K,Nd} detector sets, where K is the number of users and \Nd is the number of detectors at each receiver. Lower and upper bounds are derived on the performance of optimal detector randomization, and it is proved that the optimal detector randomization approach can reduce the worst-case average probability of error of the optimal approach that employs a single detector for each user by up to K times. Various sufficient conditions are obtained for the improvability and nonimprovability via detector randomization. In the special case of equal crosscorrelations and noise powers, a simple solution is developed for the optimal detector randomization problem, and necessary and sufficient conditions are presented for the uniqueness of that solution. Numerical examples are provided to illustrate the improvements achieved via detector randomization.

The optimum power randomization problem is studied to minimize outage probability in flat block-fading Gaussian channels under an average transmit power constraint and in the presence of channel distribution information at the transmitter. When the probability density function of the channel power gain is continuously differentiable with a finite second moment, it is shown that the outage probability curve is a nonincreasing function of the normalized transmit power with at least one inflection point and the total number of inflection points is odd. Based on this result, it is proved that the optimum power transmission strategy involves randomization between at most two power levels. In the case of a single inflection point, the optimum strategy simplifies to on-off signaling for weak transmitters. Through analytical and numerical discussions, it is shown that the proposed framework can be adapted to a wide variety of scenarios including log-normal shadowing, diversity combining over Rayleigh fading channels, Nakagami-m fading, spectrum sharing, and jamming applications. We also show that power randomization does not necessarily improve the outage performance when the finite second moment assumption is violated by the power distribution of the fading.

In this study, the jointly optimal power control with signal constellation randomization is proposed for the downlink of a multiuser communications system. Unlike a conventional system in which a fixed signal constellation is employed for all the bits of a user (for given channel conditions and noise power), power control with signal constellation randomization involves randomization/time-sharing among different signal constellations for each user. A formulation is obtained for the problem of optimal power control with signal constellation randomization, and it is shown that the optimal solution can be represented by a randomization among (K+1) or fewer distinct signal constellations for each user, where K denotes the number of users. In addition to the original nonconvex formulation, an approximate solution based on convex relaxation is derived. Then, detailed performance analysis is presented when the receivers employ symmetric signaling and sign detectors. Specifically, the maximum asymptotical improvement ratio is shown to be equal to the number of users, and the conditions under which the maximum and minimum asymptotical improvement ratios are achieved are derived. Numerical examples are presented to investigate the theoretical results, and to illustrate performance improvements achieved via the proposed approach.

ABSTRACT In this study, optimal and suboptimal receivers are investigated for code‐multiplexed transmitted‐reference (CM‐TR) ultra‐wideband systems. First, a single‐user scenario is considered, and a CM‐TR system is modeled as a generalized noncoherent pulse‐position modulated system. Based on that model, the optimal receiver that minimizes the bit error probability is derived. Then, it is shown that the conventional CM‐TR receiver converges to the optimal receiver under certain conditions and achieves close‐to‐optimal performance in practical cases. Next, multi‐user systems are considered, and the conventional receiver, blinking receiver, and chip discriminator are investigated. Also, the linear minimum mean‐squared error (MMSE) receiver is derived for the downlink of a multi‐user CM‐TR system. In addition, the maximum likelihood receiver is obtained as a performance benchmark. The practicality and the computational complexity of the receivers are discussed, and their performance is evaluated via simulations. The linear MMSE receiver is observed to provide the best trade‐off between performance and complexity/practicality. Copyright © 2011 John Wiley & Sons, Ltd.

Power constrained on-off keying communications systems are investigated in the presence of stochastic signaling and detector randomization. The joint optimal design of decision rules, stochastic signals, and detector randomization factors is performed. It is shown that the solution to the most generic optimization problem that employs both stochastic signaling and detector randomization can be obtained as the randomization among no more than three Neyman-Pearson (NP) decision rules corresponding to three deterministic signal vectors. Numerical examples are also presented.

Optimal receiver design is studied for a communications system in which both detector randomization and stochastic signaling can be performed. First, it is proven that stochastic signaling without detector randomization cannot achieve a smaller average probability of error than detector randomization with deterministic signaling for the same average power constraint and noise statistics. Then, it is shown that the optimal receiver design results in a randomization between at most two maximum a-posteriori probability (MAP) detectors corresponding to two deterministic signal vectors. Numerical examples are provided to explain the results.

ABSTRACT In this paper, theoretical lower bounds on performance of linear least‐squares (LLS) position estimators are obtained, and performance differences between LLS and nonlinear least‐squares (NLS) position estimators are quantified. In addition, two techniques are proposed in order to improve the performance of the LLS approach. First, a reference selection algorithm is proposed to optimally select the measurement that is used for linearizing the other measurements in an LLS estimator. Then, a maximum likelihood approach is proposed, which takes correlations between different measurements into account in order to reduce average position estimation errors. Simulations are performed to evaluate the theoretical limits and to compare performance of various LLS estimators. Copyright © 2010 John Wiley & Sons, Ltd.

In this paper, stochastic signaling is studied for power-constrained scalar valued binary communications systems in the presence of uncertainties in channel state information (CSI). First, it is shown that, for a given decision rule at the receiver, stochastic signaling based on the available CSI at the transmitter results in a randomization between at most two different signal levels for each symbol. Then, the performance of stochastic signaling and conventional deterministic signaling is compared, and sufficient conditions are derived for improvability and nonimprovability of deterministic signaling via stochastic signaling in the presence of CSI uncertainty. Finally a numerical example is presented to explore the theoretical results.

Cognitive radio transmissions in the presence of channel uncertainty are considered. In practical scenarios, cognitive secondary users need to perform both channel sensing in order to identify whether the channel is being occupied by the primary users or not, and also channel estimation in order to learn the channel fading coefficients. Generally, errors occur in both channel sensing and estimation, and this leads to a coupling between the two. More specifically, imperfect sensing affects both the structure and the performance of channel estimation schemes. With this motivation, the interactions between channel sensing and estimation are studied in this paper. In particular, different channel estimation schemes including minimum mean-square error (MMSE), linear MMSE, and mismatched MMSE estimations are analyzed, and their dependence on sensing decisions and their performances are investigated.

The problem of positioning an unknown target is studied for a cooperative wireless sensor network using hybrid two-way time-of-arrival and time-difference-of-arrival measurements. A maximum likelihood estimator (MLE) can be employed to solve the problem. Due to the non-linear nature of the cost function in the MLE, a numerical method, e.g., an iterative search algorithm with a good initial point, should be taken to accurately estimate the target. To avoid drawbacks in a numerical method, we instead linearize the measurements and obtain a new two-step estimator that has a closed-form solution in each step. Simulation results confirm that the proposed linear estimator can attain Cramer-Rao lower bound for sufficiently high SNR.

Theoretical limits on time-of-arrival (equivalently, range) estimation are derived for multicarrier systems in the presence of interference. Specifically, closed-form expressions are obtained for Cramer-Rao bounds (CRBs) in various scenarios. In addition, based on CRB expressions, an optimal power allocation (or, spectrum shaping) strategy is proposed. This strategy considers the constraints not only from the sensed interference level but also from the regulatory emission mask. Numerical results are presented to illustrate the improvements achievable with the optimal power allocation scheme, and a maximum likelihood time-of-arrival estimation algorithm is studied to assess the effects of the proposed approach in practical estimators.

Effects of additive independent noise are investigated for suboptimal detectors according to the restricted Bayes criterion. The statistics of optimal additive noise are characterized. Also, sufficient conditions for improvability or nonimprovability of detection via additive noise are obtained. A detection example is presented to study the theoretical results.

Two-way time-of-arrival (TW-ToA) is a widely used ranging protocol that can provide the distance between two devices without time synchronization. One drawback of the TW-ToA is poor positioning accuracy in the absence of a sufficient number of reference ranging devices. Also, for a self- positioning system with a limited battery life, it might be necessary to limit the number of transmissions while satisfying accuracy constraints. In this paper, a cooperative positioning protocol is studied, which can improve positioning accuracy compared to the conventional TW-ToA based positioning systems and also facilitate positioning with fewer packet transmissions; hence, it can prolong battery life on average. The maximum likelihood estimator is obtained for the cooperative technique and the limits on the positioning accuracy are quantified in terms of the Cramer-Rao lower bound (CRLB). Simulation results are provided in order to show performance improvements.

The problem of positioning a target node is studied for wireless sensor networks with cooperative active and passive sensors. Two-way time-of-arrival and time-difference-of-arrival measurements made by both active and passive nodes are used to estimate the position of the target node. A maximum likelihood estimator (MLE) can be employed to solve the problem. Due to the nonlinear nature of the cost function in the MLE, an iterative search might converge to local minima which often results in large estimation errors. To avoid this drawback, we instead formulate the problem of positioning as finding the intersection of a number of convex sets derived from measurements. To obtain this intersection, we apply the projection onto convex sets approach, which is robust and can be implemented in a distributed manner. Simulations are performed to compare the performance of the MLE and the proposed method.

In this paper, the time-delay estimation problem is studied for multiple-input single-output (MISO) systems. First, a theoretical analysis is carried out by deriving the Cramer-Rao lower bound (CRLB) for time-delay estimation in a MISO system. Then, the maximum likelihood (ML) estimator for the time-delay parameter is obtained, which results in a complex optimization problem in general. In order to provide a solution of the ML estimator with low computational complexity, ML estimation based on a genetic global optimization algorithm, namely, differential evolution (DE), is proposed. Simulation studies for various fading scenarios are performed to investigate the performance of the proposed algorithm.

In this letter, optimal additive noise is characterized for parameter estimation based on quantized observations. First, optimal probability distribution of noise that should be added to observations is formulated in terms of a Cramer-Rao lower bound (CRLB) minimization problem. Then, it is proven that optimal additive ¿noise¿ can be represented by a constant signal level, which means that randomization of additive signal levels is not needed for CRLB minimization. In addition, the results are extended to the cases in which there exists prior information about the unknown parameter and the aim is to minimize the Bayesian CRLB (BCRLB). Finally, a numerical example is presented to explain the theoretical results.

In this paper, probability of error performance of single-threshold detectors is studied for binary communications systems in the presence of Gaussian mixture noise. First, sufficient conditions are proposed to specify when the sign detector is (not) an optimal detector among all the single-threshold detectors. Then, a monotonicity property of the error probability is derived for the optimal single-threshold detector. In addition, a theoretical limit is obtained on the maximum ratio between the average probabilities of error for the sign detector and the optimal single-threshold detector. Finally, numerical examples are presented to investigate the theoretical results.

Optimal stochastic signaling is studied under second and fourth moment constraints for the detection of scalar-valued binary signals in additive noise channels. Sufficient conditions are obtained to specify when the use of stochastic signals instead of deterministic ones can or cannot improve the error performance of a given binary communications system. Also, statistical characterization of optimal signals is presented, and it is shown that an optimal stochastic signal can be represented by a randomization of at most three different signal levels. In addition, the power constraints achieved by optimal stochastic signals are specified under various conditions. Furthermore, two approaches for solving the optimal stochastic signaling problem are proposed; one based on particle swarm optimization (PSO) and the other based on convex relaxation of the original optimization problem. Finally, simulations are performed to investigate the theoretical results, and extensions of the results to M-ary communications systems and to other criteria than the average probability of error are discussed.

The high time resolution of ultra-wide-band (UWB) signals facilitates very precise position estimation in many scenarios, which makes a variety applications possible. This paper reviews the problem of position estimation in UWB systems, beginning with an overview of the basic structure of UWB signals and their positioning applications. This overview is followed by a discussion of various position estimation techniques, with an emphasis on time-based approaches, which are particularly suitable for UWB positioning systems. Practical issues arising in UWB signal design and hardware implementation are also discussed.

Addition of independent noise to measurements can improve performance of some suboptimal detectors under certain conditions. In this letter, sufficient conditions under which the performance of a suboptimal detector cannot be enhanced by additional independent noise are derived according to the Neyman-Pearson criterion. Also, sufficient conditions are obtained to specify when the detector performance can be improved. In addition to a generic condition, various explicit sufficient conditions are proposed for easy evaluation of improvability. Finally, a numerical example is presented and the practicality of the proposed conditions is discussed.

In this paper, mean acquisition time (MAT) analysis of fixed-step serial search (FSSS) algorithms is presented. First, it is shown that the MAT of an FSSS algorithm can be obtained from that of a conventional serial search (CSS) algorithm after a certain mapping of the uncertainty region. Then, a generic formula for the MAT of FSSS algorithms is derived, which is valid for both dense and sparse channel environments. In addition, MAT formulas for high signal-to-noise ratio scenarios, for large uncertainty regions, and for dense channels are obtained as special cases of the generic solution. Finally, simulation results are presented to verify the analysis and to investigate the factors that affect the optimal step size for FSSS algorithms.

In this paper, fundamental limits on time delay estimation are studied for cognitive radio systems, which facilitate opportunistic use of spectral resources. First, a generic Cramer-Rao lower bound (CRLB) expression is obtained in the case of unknown channel coefficients and carrier-frequency offsets (CFOs) for cognitive radio systems with dispersed spectrum utilization. Then, various modulation schemes are considered, and the effects of unknown channel coefficients and CFOs on the accuracy of time delay estimation are quantified. Finally, numerical studies are performed in order to verify the theoretical analysis.

A common technique for wireless positioning is to estimate time-of-arrivals (TOAs) of signals traveling between a target node and a number of reference nodes, and then to determine the position of the target node based on those TOA parameters. In determining the position of the target node from TOA parameters, linear or nonlinear least-squares (LS) estimation techniques can be employed. Although the linear LS techniques are suboptimal in general, they facilitate low- complexity position estimation. In this paper, performance of various linear LS techniques are compared, and suboptimality of the linear approach is quantified in terms of the Cramer-Rao lower bound (CRLB). Simulations are performed to compare the performance of the linear LS approaches versus the CRLBs for linear and nonlinear techniques.

Linear least squares (LLS) estimation is a low complexity but sub-optimum method for estimating the location of a mobile terminal (MT) from some distance measurements. It requires selecting one of the fixed terminals (FTs) as a reference FT for obtaining a linear set of expressions. However, selection of the reference FT is commonly performed arbitrarily in the literature. In this paper, a method for selection of the reference FT is proposed, which improves the location accuracy compared to a fixed selection of the reference FT. Moreover, a covariance- matrix based LLS estimator is proposed in line of sight (LOS) and non-LOS (NLOS) environments which further improves accuracy since the correlations between the observations are exploited. Simulation results prove the effectiveness of the proposed techniques.

In recent years, there has been a growing interest in multiple access communication systems that spread their transmitted energy over very large bandwidths. These systems, which are referred to as ultra wide-band (UWB) systems, have various advantages over narrow-band and conventional wide-band systems. The importance of multiuser detection for achieving high data or low bit error rates in these systems has already been established in several studies. This paper presents iterative ("turbo") multiuser detection for impulse radio (IR) UWB systems over multipath channels. While this approach is demonstrated for UWB signals, it can also be used in other systems that use similar types of signaling. When applied to the type of signals used by UWB systems, the complexity of the proposed detector can be quite low. Also, two very low complexity implementations of the iterative multiuser detection scheme are proposed based on Gaussian approximation and soft interference cancellation. The performance of these detectors is assessed using simulations that demonstrate their favorable properties.

In this paper, Cramer-Rao lower bounds (CRLBs) for estimation of vital signal parameters, such as respiration and heart-beat rates, using ultra-wideband (UWB) pulses are derived. In addition, a simple closed-form CRLB expression is obtained for sinusoidal displacement functions under certain conditions. Moreover, a two-step suboptimal solution is proposed, which is based on time-delay estimation via matched filtering followed by least-squares (LS) estimation. It is shown that the proposed solution is asymptotically optimal in the limit of certain system parameters. Simulation studies are performed to evaluate the lower bounds and performance of the proposed solution for realistic system parameters.

Low power consumption is a critical issue in many UWB systems. This paper investigates the application of dynamic voltage scaling (DVS) and other low power design techniques to a multiband-OFDM UWB transceiver baseband circuit design in order to reduce average power consumption of the chip. The results show significant power savings over the conventional approach.

Ultrawideband impulse radio systems offer two kinds of processing gains that can be adapted based on the interference level in the system so that quality of service requirements are fulfilled. An adaptive assignment scheme for two types of multiple-access parameters in cluster-based wireless sensor networks is investigated. A mathematical framework is developed for asynchronous communications using a Gaussian approximation method to model the multiple-access interference in two cases: one with fixed frame duration, where the goal is to increase the average throughput, and the other with fixed symbol duration, where the goal is to increase the network lifetime. Extension of the analysis to multipath channels is carried out, and the validity of the Gaussian approximation is investigated using the Kullback-Leibler distance.

In time hopping impulse radio, Nfpulses of duration Tcare transmitted for each information symbol. This gives rise to two types of processing gains: (i) pulse combining gain, which is a factor Nf, and (ii) pulse spreading gain, which is Nc= Tf/Tc, where Tfis the mean interval between two subsequent pulses. This paper investigates the tradeoff between these two types of processing gains in the presence of timing jitter. First, an additive white Gaussian noise (AWGN) channel is considered, and approximate closed-form expressions for bit error probability (BEP) are derived for impulse radio systems with and without pulse-based polarity randomization. Both symbol-synchronous and chip-synchronous scenarios are considered. The effects of multiple-access interference (MAI) and timing jitter on the selection of optimal system parameters are explained through theoretical analysis. Finally, a multipath scenario is considered, and the tradeoff between processing gains of a synchronous impulse radio system with pulse-based polarity randomization is analyzed. The effects of the timing jitter, MAI, and interframe interference (IFI) are investigated. Simulation studies support the theoretical results.

Due to the fine delay resolution of typical ultra wideband (UWB) systems, Rake receivers need to combine samples from a large number of multipath components (MPCs) in order to collect sufficient signal energy for reliable decisions. In addition, these samples need to be combined optimally in order to minimize bit error probability. The optimal linear minimum mean square error (MMSE) combining scheme might require inversion of a large matrix depending on channel and system parameters. Therefore, suboptimal algorithms with lower computational complexity but close-to-optimal performance are desirable. In this paper, a low-complexity combining scheme is proposed for that purpose, which divides samples into a number of groups and performs MMSE combining in two steps. Performance of this two-step combining scheme is investigated theoretically and by simulations.

In an ultra-wideband (UWB) impulse radio (IR) system, a number of pulses, each transmitted in an interval called a "frame," is employed to represent one information symbol. Conventionally, a single type of UWB pulse is used in all frames of all users. In this paper, IR systems with multiple types of UWB pulses are considered, where different types of pulses can be used in different frames by different users. Both stored-reference (SR) and transmitted-reference (TR) systems are considered. First, the spectral properties of a multipulse IR system with polarity randomization is investigated. It is shown that the average power spectral density is the average of the spectral contents of different pulse shapes. Then, approximate closed-form expressions for the bit-error probability of a multipulse SR-IR system are derived for Rake receivers in asynchronous multiuser environments. The effects of both interframe interference (IFI) and multiple-access interference (MAI) are analyzed. The theoretical and simulation results indicate that SR-IR systems that are more robust against IFI and MAI than a "conventional" SR-IR system can be designed with multiple types of ultra-wideband pulses. Finally, extensions to multipulse TR-IR systems are briefly described.

Transmitted-reference (TR) schemes for time-hopping impulse radio (TH-IR) ultra-wideband (UWB) communications allow the use of simple receiver structures that are able to combine energy from different multipath components without channel estimation. A conventional TR receiver consists of a simple delay-and-multiply operation combined with an integrator. On the downside, it shows a performance loss due to non-linear operations on noise terms (generation of noise-noise cross-terms) when forming the decision variable. This paper describes a hybrid receiver structure for UWB communications that reduces these noise-noise cross-terms by first performing a "matched filtering" operation matched to the time-hopping sequence of pulses. The receiver retains most of the simplicity of the conventional TR receiver, but requires an analog correlator for the time-hopping sequence of pulses. The performance the proposed receiver is analyzed in both AWGN and multipath channels. For the AWGN case, the exact expression for the bit error probability is obtained, which takes into account the nonGaussian nature of the noise-noise cross-terms arising in the correlators. For the multipath case, both inter-frame interference and multipath interference from the reference pulse to the data pulse are considered, and approximate closed-form expressions are derived based on the assumption of a large integration interval. Also approximate criteria for optimal integration interval are obtained for the best receiver performance. Simulation studies are presented to analyze the performance of the proposed receiver structure and to confirm the theoretical analysis

Convex relaxations of the optimal finger selection algorithm are proposed for a minimum mean square error (MMSE) RAKE receiver in an impulse radio ultra-wideband system. First, the optimal finger selection problem is formulated as an integer programming problem with a non-convex objective function. Then, the objective function is approximated by a convex function and the integer programming problem is solved by means of constraint relaxation techniques. The proposed algorithms are suboptimal due to the approximate objective function and the constraint relaxation steps. However, they can be used in conjunction with the conventional finger selection algorithm, which is suboptimal on its own since it ignores the correlation between multipath components, to obtain performances reasonably close to that of the optimal scheme that cannot be implemented in practice due to its complexity. The proposed algorithms leverage convexity of the optimization problem formulations, which is the watershed between 'easy' and 'difficult' optimization problems.

We consider a time-hopping impulse-radio system that uses transmitted-reference pulses for implicit channel estimation and equalization. A hybrid receiver structure first performs a filtering matched to the hopping sequence, and a subsequent correlation of the data pulses with the reference pulses. We analyze the performance of such a system both in AWGN and in multipath. For the AWGN case, we give exact expressions for the bit error probability that take into account the non-Gaussian nature of the noise-noise crossterms arising in the correlators. For the multipath case, we analyze inter-frame interference, as well as multipath interference from the reference pulse to the data pulse, providing dosed-form equations in the limit of a large number of multipath components.

In time hopping impulse radio, N/sub f/ pulses of duration T/sub c/ are transmitted for each symbol. This gives rise to two types of processing gain: (i) pulse combining gain, which is a factor N/sub f/, and (ii) pulse spreading gain, which is N/sub c/ = T/sub f//T/sub c/, where T/sub f/ is the mean interval between two subsequent pulses. This paper investigates the trade-off between these two types of processing gain with and without random polarity codes in the presence of timing jitter. Approximate expressions for bit error probability are derived for both coded and uncoded systems over additive white Gaussian noise channels and are used as the criterion to choose optimal N/sub f/ and N/sub c/ values. The effects of timing jitter and multiple access interference on the selection of optimal system parameters are explained through theoretical analysis. Simulation studies support the theoretical results.

Ultrawideband (UWB) is an attractive physical layer technology for wireless sensor networks due to its unique characteristics. Flexibility in adjusting the processing gain of UWB systems makes it possible to tune the data rate and transmission range to fulfill the requirements of specific applications. Conventional systems assign identical multiple access parameters to all users regardless of the signal-to-interference plus noise ratio of the received signal. An adaptive assignment scheme for multiple access parameters in cluster based wireless sensor networks is investigated. First, an orthogonal time hopping sequence construction is proposed for synchronous communications (downlink), where the number of pulses per symbol are adjusted to meet the bit error rate requirement of an application. Then, adaptation of multiple access parameters in asynchronous scenarios (uplink) is evaluated using a Gaussian approximation method to model the multiple access interference in two cases: one with fixed frame duration, where the goal is to increase the average throughput, and the other with fixed symbol duration, where the goal is to increase the network lifetime. Finally, a mathematical framework is developed for approximating the interference when the number of pulses per symbol and the frame duration vary.

The performance of a binary phase shift keyed random time-hopping impulse radio system with pulse-based polarity randomization is analyzed. The effects of multiple access interference are investigated for both chip-synchronous and asynchronous systems. It is shown that the performance of a chip-synchronous system is the same as that for the symbol-synchronous case studied in E. Fisher and H. V. Poor (Oct. 2-4, 2002). The asynchronous system is modelled as a chip-synchronous system with uniformly distributed timing jitter on the transmitted pulses of interfering users. This extends the analytical technique developed for the chip-synchronous case to the asynchronous case. An approximate closed-form expression for the probability of error, expressed in terms of the autocorrelation function of the transmitted pulse, is derived for the asynchronous case. The analysis shows that the chip-synchronous assumption can result in over-estimating the error probability, and hence that the system design based on this approximation will he on the safe side. The degree of over-estimation mainly depends on the autocorrelation function of the UWB pulse and signal-to-interference-plus-noise-ratio (SIR) of the system. Simulations studies support this approximate analysis.

We present a new image denoising method based on adaptive subband decomposition (or adaptive wavelet transform) in which the filter coefficients are updated according to a least mean square (LMS) type algorithm. Adaptive subband decomposition filter banks have the perfect reconstruction property. Since the adaptive filter bank adjusts itself to the changing input environment, denoising is more effective compared to fixed filter banks. Simulation examples are presented.