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In this paper, we devise a communication-efficient decentralized algorithm, named as communication-censored alternating direction method of multipliers (ADMM) (COCA), to solve a convex consensus optimization problem defined over a network. Similar to popular decentralized consensus optimization algorithms such as ADMM, at every iteration of COCA, a node exchanges its local variable with neighbors, and then updates its local variable according to the received neighboring variables and its local cost function. A different feature of COCA is that a node is not allowed to transmit its local variable to neighbors, if this variable is not sufficiently different to the previously transmitted one. The sufficiency of the difference is evaluated by a properly designed censoring function. Though this censoring strategy may slow down the optimization process, it effectively reduces the communication cost. We prove that when the censoring function is properly chosen, COCA converges to an optimal solution of the convex consensus optimization problem. Furthermore, if the local cost functions are strongly convex, COCA has a fast linear convergence rate. Numerical experiments demonstrate that, given a target solution accuracy, COCA is able to significantly reduce the overall communication cost compared to existing algorithms including ADMM, and hence fits for applications where network communication is a bottleneck. [ABSTRACT FROM AUTHOR]

Orthogonal Time Frequency Space (OTFS) modulation is a recently proposed scheme for time-varying narrowband channels in terrestrial radio-frequency communications. Underwater acoustic (UWA) and ultra-wideband (UWB) communication systems, on the other hand, confront wideband time-varying channels. Unlike narrowband channels, for which time contractions or dilations due to Doppler effect can be approximated by frequency-shifts, the Doppler effect in wideband channels results in frequency-dependent non-uniform shift of signal frequencies across the band. In this paper, we develop an OTFS-like modulation scheme – Orthogonal Delay Scale Space (ODSS) modulation – for handling wideband time-varying channels. We derive the ODSS transmission and reception schemes from first principles. In the process, we introduce the notion of $\omega$ -convolution in the delay-scale space that parallels the twisted convolution used in the time-frequency space. The preprocessing 2D transformation from the Fourier-Mellin domain to the delay-scale space in ODSS, which plays the role of inverse symplectic Fourier transform (ISFFT) in OTFS, improves the bit error rate performance compared to OTFS and Orthogonal Frequency Division Multiplexing (OFDM) in wideband time-varying channels. Furthermore, since the channel matrix is rendered near-diagonal, ODSS retains the advantage of OFDM in terms of its low-complexity receiver structure. [ABSTRACT FROM AUTHOR]

Fractional programming (FP) refers to a family of optimization problems that involve ratio term(s). This two-part paper explores the use of FP in the design and optimization of communication systems. Part I of this paper focuses on FP theory and on solving continuous problems. The main theoretical contribution is a novel quadratic transform technique for tackling the multiple-ratio concave–convex FP problem—in contrast to conventional FP techniques that mostly can only deal with the single-ratio or the max-min-ratio case. Multiple-ratio FP problems are important for the optimization of communication networks, because system-level design often involves multiple signal-to-interference-plus-noise ratio terms. This paper considers the applications of FP to solving continuous problems in communication system design, particularly for power control, beamforming, and energy efficiency maximization. These application cases illustrate that the proposed quadratic transform can greatly facilitate the optimization involving ratios by recasting the original nonconvex problem as a sequence of convex problems. This FP-based problem reformulation gives rise to an efficient iterative optimization algorithm with provable convergence to a stationary point. The paper further demonstrates close connections between the proposed FP approach and other well-known algorithms in the literature, such as the fixed-point iteration and the weighted minimum mean-square-error beamforming. The optimization of discrete problems is discussed in Part II of this paper. [ABSTRACT FROM AUTHOR]

This two-part paper develops novel methodologies for using fractional programming (FP) techniques to design and optimize communication systems. Part I of this paper proposes a new quadratic transform for FP and treats its application for continuous optimization problems. In this Part II of the paper, we study discrete problems, such as those involving user scheduling, which are considerably more difficult to solve. Unlike the continuous problems, discrete or mixed discrete-continuous problems normally cannot be recast as convex problems. In contrast to the common heuristic of relaxing the discrete variables, this work reformulates the original problem in an FP form amenable to distributed combinatorial optimization. The paper illustrates this methodology by tackling the important and challenging problem of uplink coordinated multicell user scheduling in wireless cellular systems. Uplink scheduling is more challenging than downlink scheduling, because uplink user scheduling decisions significantly affect the interference pattern in nearby cells. Furthermore, the discrete scheduling variable needs to be optimized jointly with continuous variables such as transmit power levels and beamformers. The main idea of the proposed FP approach is to decouple the interaction among the interfering links, thereby permitting a distributed and joint optimization of the discrete and continuous variables with provable convergence. The paper shows that the well-known weighted minimum mean-square-error (WMMSE) algorithm can also be derived from a particular use of FP; but our proposed FP-based method significantly outperforms WMMSE when discrete user scheduling variables are involved, both in term of run-time efficiency and optimizing results. [ABSTRACT FROM AUTHOR]

This paper proposes a novel evolution strategy for a genetic algorithm (GA). This new algorithm is then applied to design robust D(α, r)-stable infinite-impulse-response (IIR) filters. Unlike existing research on designing IIR filters by using GA, in which the stability of IIR filters is tested by trial and error after the evolution of each generation of a GA, the stability criterion in this paper is embedded within the evolution of each generation. Consequently, the stability of this system can be guaranteed without the need for any other checks of the stability criterion in the evolution of each generation. Numerical experimental results are discussed to illustrate the soundness of the proposed evolution strategy. The robustness of the IIR filters is achieved by ensuring that all poles of the filters are located inside a disk D (α, r) contained in the unit circle, in which α is the center, r is the radius of the disk and ∣α∣ + r < 1. So, in this paper, a D(α, r)-stability criterion will be first derived and then embedded in the GA for the design of robust IIR filters. Finally, two examples will be presented to show that the designed filters remain D(α, r)-stable during the evolution of the GA and will provide satisfactory results. [ABSTRACT FROM AUTHOR]

In this paper, we consider the multiuser Gaussian broadcast channel with multiple transmit antennas at the base station and multiple receive antennas at each user. Assuming full knowledge of the channel state information at the transmitter and the different receivers, a new transmission scheme that employs partial interference cancellation at the transmitter with dirty-paper encoding and decoding is proposed. The maximal achievable throughput of this system is characterized, and it is shown that given any ordered set of users the proposed scheme is asymptotically optimal in the high signal-to-noise ratio (SNR) regime. In addition, with optimal user ordering, the proposed scheme is shown to be optimal in the low-SNR regime. We also consider a linear transmission scheme which employs only partial interuser interference cancellation at the base station without dirty-paper coding. Given a transmit power constraint at the base station, the sum-rate capacity of this scheme is characterized and a suboptimal precoding algorithm is proposed. In several cases, it is shown that, for all values of the SNR, the achievable throughput of this scheme is strictly larger than a system which employs full interference cancellation at the base station [21]. In addition, it is shown that, in some cases, the linear transmission scheme can support simultaneously an increased number of users while achieving a larger system throughput. [ABSTRACT FROM AUTHOR]

Integrating the wireless power transfer (WPT) technology into the wireless communication system has been important for operational cost saving and power-hungry problem solving of electronic devices. In this paper, we propose a resonant beam simultaneous wireless information and power transfer (RB-SWIPT) system, which utilizes a gain medium and two retro-reflecting surfaces to enhance and retro-reflect energy, and allows devices to recharge their batteries and exchange information from the resonant beam wirelessly. To reveal the SWIPT mechanism and evaluate the SWIPT performance, we establish an analytical end-to-end (E $\mathscr{2}$ E) transmission model based on a modular approach and the electromagnetic field propagation. Then, the intra-cavity power intensity distribution, transmission loss, output power, and E $\mathscr{2}$ E efficiency can be obtained. The numerical evaluation illustrates that the exemplary RB-SWIPT system can provide about 4.20 W electric power and 12.41 bps/Hz spectral efficiency, and shorter transmission distance or larger retro-reflecting surface size can lead to higher E $\mathscr{2}$ E efficiency. The RB-SWIPT presents a new way for high-power, long-range WPT, and high-rate communication. [ABSTRACT FROM AUTHOR]

Regularized zero-forcing beamforming (RZFB) is an interesting class of linear signal processing problems, which is very attractive for use in large-scale communication networks due its simple visualization as a straightforward extension of the well-accepted zero-forcing beamforming (ZFB). However, unlike ZFB, which is multi-user interference free, RZFB must manage multi-user interference to achieve its high throughput performance. Most existing works focus on the performance analysis of particular RZBF schemes such as the equip-power allocated RZBF under a fixed regularization parameter. This paper is the first work to consider the joint design of power allocation and regularization parameter for RZFB to maximize the worst users’ throughput or the quality-of-service awarded energy efficiency under a fixed transmit power constraint. Such designs pose very computationally challenging optimization problems, for which the paper proposes two-stage optimization algorithms of low computational complexity. Their computational and performance efficiencies are substantiated through numerical examples. [ABSTRACT FROM AUTHOR]

This paper considers the problem of co-existence of collocated multiple-input multiple-output (MIMO) radar and downlink multiple-input single-output (MISO) communication systems, which share the same frequency band. As the interference caused by the communication systems would degrade the accuracy of radar target localization, we formulate the co-design of radar waveform and communication transmit weights by minimizing the Cramér-Rao Bound (CRB) of direction-of-arrival (DOA) estimation, subject to a set of constraints accounting for the worst-case signal-to-noise-plus-interference ratio (SINR), similarity and energy. However, the objective is nonconvex and all variables are coupled together in the SINR constraints, the formulated problem is thus NP-hard. Towards that end, a decentralized block successive upper-bound minimization (D-BSUM) method based on the decomposition theory is developed. More specifically, at each iteration of this algorithm, the radar waveform is obtained with the aid of the alternating direction method of multipliers (ADMM) algorithm, and communication weights are obtained by exploiting the semidefinite programs (SDP). It is also proved that the proposed SDP-based method gives a rank-one solution. Numerical simulations are conducted to evaluate the effectiveness of the proposed algorithm. [ABSTRACT FROM AUTHOR]

This paper studies resilient distributed estimation under measurement attacks. A set of agents each makes successive local, linear, noisy measurements of an unknown vector field collected in a vector parameter. The local measurement models are heterogeneous across agents and may be locally unobservable for the unknown parameter. An adversary compromises some of the measurement streams and changes their values arbitrarily. The agents’ goal is to cooperate over a peer-to-peer communication network to process their (possibly compromised) local measurements and estimate the value of the unknown vector parameter. We present SAGE, the Saturating Adaptive Gain Estimator, a distributed, recursive, consensus + innovations estimator that is resilient to measurement attacks. We demonstrate that, as long as the number of compromised measurement streams is below a particular bound, then, SAGE guarantees that all of the agents’ local estimates converge almost surely to the value of the parameter. The resilience of the estimator – i.e., the number of compromised measurement streams it can tolerate – does not depend on the topology of the inter-agent communication network. Finally, we illustrate the performance of SAGE through numerical examples. [ABSTRACT FROM AUTHOR]

Time-of-arrival (TOA) based localization plays a central role in current and future localization systems. Such systems, exploiting the fine delay resolution properties of wideband and ultra-wideband (UWB) signals, are particularly attractive for ranging under harsh propagation conditions in which significant multipath may be present. While multipath has been traditionally considered detrimental in the design of TOA estimators, it can be exploited to benefit ranging. This paper investigates the impact of a priori multipath information on TOA estimation. To this end, bounds on the performance of TOA estimation in dense multipath channels are provided and discussed under different operating conditions. The effects of channel dispersion and transmission bandwidth on ranging systems are investigated showing that, contrary to communication systems, diversity behavior is only exhibited in the medium signal-to-noise ratio regime. [ABSTRACT FROM AUTHOR]

In this paper, we address the problem of state estimation and input recovery for a class of nonlinear systems in the presence of disturbances in both the state and output equations. Indeed one of the main difficulties, that arise when input recovery is considered, is how to cope with the problem of disturbance's derivative for the filter design. Our contribution lies in the use of Sobolev norms to develop a simple and useful observer. We provide first the state filtering and input recovery equations. After, based on the Lyapunov stability theory and some robustness criteria, new sufficient synthesis conditions are given in terms of linear matrix inequalities (LM15). To show performances of the proposed method, we considered the problem of simultaneous synchronization and decryption in chaotic communication systems with some experimental results. [ABSTRACT FROM AUTHOR]

In this two-part paper, we address the problem of finding the optimal precoding/multiplexing scheme for a set of noncooperative links sharing the same physical resources, e.g., time and bandwidth. We consider two alternative optimization problems: P.1) the maximization of mutual information on each link, given constraints on the transmit power and spectral mask; and P.2) the maximization of the transmission rate on each link, using finite-order constellations, under the same constraints as in P.1, plus a constraint on the maximum average error probability on each link. Aiming at finding decentralized strategies, we adopted as optimality criterion the achievement of a Nash equilibrium and thus we formulated both problems P.1 and P.2 as strategic noncooperative (matrix-valued) games. In Part I of this two-part paper, after deriving the optimal structure of the linear transceivers for both games, we provided a unified set of sufficient conditions that guarantee the uniqueness of the Nash equilibrium. In this Part II of the paper, we focus on the achieve- ment of the equilibrium and propose alternative distributed iterative algorithms that solve both games. Specifically, the new proposed algorithms are the following: 1) the sequential and Simultaneous iterative waterfilling-based algorithms, incorporating spectral mask constraints and 2) the sequential and simultaneous gradient-projection-based algorithms, establishing an interesting link with variational inequality problems. Our main contribution is to provide sufficient conditions for the global convergence of all the proposed algorithms which, although derived under stronger constraints, incorporating for example spectral mask constraints, have a broader validity than the convergence conditions known in the current literature for the sequential iterative waterfilling algorithm. [ABSTRACT FROM AUTHOR]

This paper deals with the problem of medium access control (MAC)-layer fair power control in a wireless mesh network with an established network topology. The notion of MAC-layer fairness is defined along similar lines as end-to-end fairness for elastic traffic, except that instead of end-to-end flows, MAC-layer flows are considered, being that one hop flows between neigh- boring nodes. In this paper, we identify a class of utility functions of link rates that allows for a convex problem formulation. The convexity property is a key prerequisite for implementing power control algorithms in practice. We present a novel distributed algorithmic solution to the power control problem based on gradient-projection methods, prove its global convergence, and provide sufficient conditions for a geometric convergence rate. The main novelty of our scheme lies in the use of the so-called adjoint network in such a way that each transmitter can estimate its current update direction from the received signal power. This mitigates the problem of global coordination of the transmitters when carrying out gradient-projection algorithms in distributed wireless networks. The price for this are possible estimation errors so that the proposed scheme is analyzed within the framework of stochastic approximation. [ABSTRACT FROM AUTHOR]

This paper reports a solution to a linear blind equalizer used in communication systems under the effect of two factors: i) An unknown linear inter symbol interference and ii) the impulsive noise satisfying the $\alpha$ -stable distribution. First, a constellation matching error-based cost function associated with the linear equalizer is designed to effectively compensate for the inter symbol interference and suppress the influence of the impulsive noise. Second, an improved form of the proposed cost function is derived through the finite alphabet property of information symbols. The theoretical analysis shows that the improved cost function significantly reduces the local minima and converges more stably in high-order modulation systems. Furthermore, based on the special structure of the designed cost function, a modified Newton scheme is constructed to quickly minimize it, and then the corresponding optimal blind equalizer can be obtained. Finally, computer simulations are presented to demonstrate the robust equalization performance and fast convergence of the novel algorithm under both impulsive and Gaussian noise environments. [ABSTRACT FROM AUTHOR]

In this paper, we extend the bilinear generalized approximate message passing (BiG-AMP) approach, originally proposed for high-dimensional generalized bilinear regression, to the multi-layer case for the handling of cascaded problem such as matrix-factorization problem arising in relay communication among others. Assuming statistically independent matrix entries with known priors, the new algorithm called ML-BiGAMP could approximate the general sum-product loopy belief propagation (LBP) in the high-dimensional limit enjoying a substantial reduction in computational complexity. We demonstrate that, in large system limit, the asymptotic MSE performance of ML-BiGAMP could be fully characterized via a set of simple one-dimensional equations termed state evolution (SE). We establish that the asymptotic MSE predicted by ML-BiGAMP’ SE matches perfectly the exact MMSE predicted by the replica method, which is well-known to be Bayes-optimal but infeasible in practice. This consistency indicates that the ML-BiGAMP may still retain the same Bayes-optimal performance as the MMSE estimator in high-dimensional applications, although ML-BiGAMP's computational burden is far lower. As an illustrative example of the general ML-BiGAMP, we provide a detector design that could estimate the channel fading and the data symbols jointly with high precision for the two-hop amplify-and-forward relay communication systems. [ABSTRACT FROM AUTHOR]

For multiple-input multiple-output (MIMO) transceiver designs, sum power constraint is an elegant and ideal model. When various practical limitations are taken into account e.g., peak power constraints, per-antenna power constraints, etc., covariance shaping constraints will act as an effective and reasonable model. In this paper, we develop a framework for transceiver designs for multi-hop communications under covariance shaping constraints. Particularly, we focus on multi-hop amplify-and-forward (AF) MIMO relaying communications which are recognized as a key enabling technology for device-to-device (D2D) communications for next generation wireless systems such as 5G. The proposed framework includes a broad range of various linear and nonlinear transceiver designs as its special cases. It reveals an interesting fact that the relaying operation in each hop can be understood as a matrix version weighting operation. Furthermore, the nonlinear operations of Tomolision–Harashima Precoding (THP) and Decision Feedback Equalizer (DFE) also belong to the category of this kind of matrix version weighting operation. Furthermore, for both the cases with only pure shaping constraints or joint power constraints, the closed-form optimal solutions have been derived. At the end of this paper, the performance of the various designs is assessed by simulations. [ABSTRACT FROM PUBLISHER]

This paper considers the average-consensus problem within the same framework as the companion paper [K. Topley and V. Krishnamurthy, “Average-Consensus Algorithms in a Deterministic Framework—Part I: Strong Connectivity, IEEE Trans. Signal Process., vol. 60, no. 12, Dec. 2012]. Two distributed algorithms are proposed and shown to be analogous to the algorithms presented in the Part I of the paper with respect to the communication costs and conditions sufficient for average-consensus. We provide convergence proofs, as well as numerical examples that (i) illustrate the empirical convergence rate of all four algorithms, and (ii) show that consensus algorithms in the past literature can fail to achieve average-consensus within our framework. Three applications from the literature that motivate the proposed algorithms are discussed, and also we show how all four algorithms allow each node to compute an upper bound on the error of their current local consensus estimate. [ABSTRACT FROM PUBLISHER]

This paper considers the average-consensus problem in a network with arbitrary (but finite) communication delays. A novel Distributed-Averaging (DA) algorithm is presented and shown to achieve average-consensus if at any time t there exists a finite time interval [t, Tt] over which each node can communicate (via a time-respecting path) with all other nodes. For consensus variables with dimensions on the order of the network size, the DA algorithm requires an order of magnitude less data to be communicated and stored at each node as compared to an idealized algorithm that floods the initial data. In the companion paper refid="ref29"/ , practical applications of the DA algorithm are provided along with numerical examples. [ABSTRACT FROM PUBLISHER]

This paper introduces a novel method of spectrum sensing in communication systems that utilizes nonuniform sampling in conjunction with a suitable spectral analysis tool. It is referred to here as spectral analysis for randomized sampling (SARS). Owing to the deployment of nonuniform sampling, the proposed technique can accomplish the sensing task by using sampling rates well below the ones demanded by uniform-sampling-based digital signal processing (DSP). The effect of the cyclostationary nature of the incoming digital communication signal on the adequacy of the adopted periodogram-type estimator for the spectrum sensing operation is addressed. The statistical characteristics of the estimator are presented. General reliability conditions on the length of the required signal observation window, i.e., sensing time, for a chosen sampling rate or vice versa are provided amid a sought system performance. The impact of the presence of noise and processing transmissions with various power levels on the derived dependability recommendations is given. The analytical results are illustrated by numerical examples. This paper establishes a new framework for efficient spectrum sensing where considerable savings on the sampling rate and number of processed samples can be attained. [ABSTRACT FROM AUTHOR]

Blind recognition of communication parameters is a research topic of high importance for both military and civilian communication systems. Numerous studies about carrier frequency estimation, modulation recognition as well as channel identification are available in literature. This paper deals with the blind recognition of the space-time block coding (STBC) scheme used in multiple-input-multiple-output (MIMO) communication systems. Assuming there is perfect synchronization at the receiver side, this paper proposes three maximum-likelihood (ML)-based approaches for STBC classification: the optimal classifier, the second-order statistic (SOS) classifier, and the code parameter (CP) classifier. While the optimal and the SOS approaches require ideal conditions, the CP classifier is well suited for the blind context where the communication parameters are unknown at the receiver side. Our simulations show that this blind classifier is more easily implemented and yields better performance than those available in literature. [ABSTRACT FROM AUTHOR]

This paper considers a noncooperative game in which competing users sharing a frequency-selective interference channel selfishly optimize their power allocation in order to improve their achievable rates. Previously, it was shown that a user having the knowledge of its opponents' channel state information can make foresighted decisions and substantially improve its performance compared with the case in which it deploys the conventional iterative water-filling algorithm, which does not exploit such knowledge. This paper discusses how a foresighted user can acquire this knowledge by modeling its experienced interference as a function of its own power allocation. To characterize the outcome of the multiuser interaction, the conjectural equilibrium is introduced, and the existence of this equilibrium for the investigated water-filling game is proven. Importantly, we show that both the Nash equilibrium and the Stackelberg equilibrium are special cases of the conjectural equilibrium. We also develop practical algorithms to form accurate beliefs and select desirable power allocation strategies. Numerical simulations indicate that a foresighted user without any a priori knowledge of its competitors' private information can effectively learn how the other users will respond to its actions, and induce the entire system to an operating point that improves both its own achievable rate as well as the rates of the other participants in the water-filling game. [ABSTRACT FROM AUTHOR]

This paper is focused on the cross-layer design problem of joint multiuser detection and power control for energy-efficiency optimization in wireless data networks through a game-theoretic approach. Unlike previous studies in this area, which have considered the application of game theory to achieve energy-efficiency in idealized synchronous code division multiple access systems, more realistic assumptions including system asynchronism, the use of band-limited chip-pulses, and the multipath distortion induced by the wireless channel are explicitly incorporated in the development of this paper. Several noncooperative games are proposed wherein each user may vary its transmit power and uplink receiver in order to maximize its utility, which is defined here as the ratio of data throughput to transmit power. In particular, the case in which a linear multiuser detector is adopted at the receiver is considered first, and then, the more challenging case in which a nonlinear decision feedback multiuser detector is employed is considered. Via large system analysis (LSA), a decentralized implementation of the power allocation game requiring very little prior information on the interference background is proposed. LSA is also used to compare the energy efficiency of several linear multiuser detectors, and to obtain the optimal (i.e., utility-maximizing) length of the training sequence of each data frame. Numerical results show the effectiveness of the proposed solutions, as well as a very satisfactory agreement of the LSA-based analysis with simulation results obtained for systems with finite (and not so large) numbers of users. [ABSTRACT FROM AUTHOR]

Probabilistic Boolean networks (PBNs) have beer recently introduced as a paradigm for modeling genetic regulatory networks. One of the objectives of PBN modeling is to use the network for the design and analysis of intervention strategies aimed at moving the network out of undesirable states, such a those associated with disease, and into desirable ones. To date a number of intervention strategies have been proposed in the context of PBNs. However, all these techniques assume perfect knowledge of the transition probability matrix of the PBN. Such an assumption cannot be satisfied in practice since the presence of noise and the availability of limited number of samples will prevent the transition probabilities from being accurately deter mined. Moreover, even if the exact transition probabilities from be estimated from the data, mismatch between the PBN model am the actual genetic regulatory network will invariably be present Thus, it is important to study the effect of modeling errors on the final outcome of an intervention strategy and one of the goals of this paper is to do precisely that when the uncertainties are in the entries of the transition probability matrix. In addition, the paper develops a robust intervention strategy that is obtained by minimizing the worst-case cost over the uncertainty set. [ABSTRACT FROM AUTHOR]

In this two-part paper, we propose a decentralized strategy, based on a game-theoretic formulation, to find out the optimal precoding/multiplexing matrices for a multipoint-to-multipoint communication system composed of a set of wideband links sharing the same physical resources, i.e., time and bandwidth. We assume, as optimality criterion, the achievement of a Nash equilibrium and consider two alternative optimization problems: 1) the competitive maximization of mutual information on each link, given constraints on the transmit power and on the spectral mask imposedby the radio spectrum regulatory bodies; and 2) the competitive maximization of the transmission rate, using finite order constellations, under the same constraints as above, plus a constraint on the average error probability. In this first part of the paper, we start by showing that the solution set of both noncooperative games is always nonempty and contains only pure strategies. Then, we prove that the optimal precoding/multiplexing scheme for both games leads to a channel diagonalizing structure, so that both matrix-valued problems. can be recast in a simpler unified vector power control game; with no performance penalty. Thus, we study this simpler game and derive sufficient conditions ensuring the uniqueness of the Nash equilibrium. Interestingly, although derived under stronger constraints, incorporating for example spectral mask constraints, our uniqueness conditions have broader validity than previously known conditions. Finally, we assess the goodness of the proposed decentralized strategy by comparing its performance with the performance of a Pareto-optimal centralized scheme. To reach the Nash equilibria of the game, in Part II, we propose alternative distributed algorithms, along with their convergence conditions. [ABSTRACT FROM AUTHOR]

This paper addresses the optimal power and rate allocation control in multiple-input multiple-output (MIMO) wireless systems over Markovian fading channels. The problem is posed as an infinite horizon average-cost constrained Markov decision process (CMDP) with the goal of minimizing the average transmission power subject to delay constraints. By using a Lagrangian formulation of the CMDP, we use the concepts of stochastic dominance, submodularity, and multimodularity to prove that the optimal randomized policies are monotone. Three important structural results on the nature of the optimal randomized policies are derived. First, we show that the action space can be exponentially reduced by decomposing the rate allocation problem into bit-loading problem across individual antennas and the total rate allocation based on the current buffer occupancy and channel state. Second, we show that the optimal rate allocation policy is a randomized mixture of two pure policies that are monotonically increasing in the buffer occupancy. Finally, we show that the optimal power allocation is piecewise linear in the delay constraint. These three structural results can be exploited to devise efficient online reinforcement learning algorithms for optimal rate allocation. [ABSTRACT FROM AUTHOR]

Walsh codes are perfectly orthogonal binary (antipodal) block codes that found their use in many popular applications over several decades including synchronous multiuser communications. It is well known in the literature that they perform poorly for the case of asynchronous multiuser communications. Therefore, the near-orthogonal and nonlinear phase Gold codes with better performance are the preferred user codes in asynchronous direct sequence code division multiple access (DS-CDMA) communications standards. In this paper, we relaxed linear phase requirements and new sets of Walsh-like nonlinear phase binary orthogonal user codes (transforms) are obtained for asynchronous and synchronous spread spectrum multiuser communications. It is shown that the proposed binary user code family outperforms the Walsh codes significantly and they match in performance with the popular, nearly orthogonal Gold codes closely for asynchronous multiuser communications in additive white Gaussian noise (AWGN) channels. It is also shown that all of the binary code families considered in this paper performed comparable for Rayleigh flat-fading channels. We present in this paper that there are a good number of such desirable code sets available in the binary sample space with different transform sizes. These new binary sets with good performance and flexible code lengths might help us to improve the spread spectrum multiplexing capabilities of future wireline and wireless CDMA communications systems. [ABSTRACT FROM AUTHOR]

This papers considers the design of a mutiple-input multiple-output (MIMO) communication system with channel knowledge at the transmitter and receiver. The design methods available in the literature have addressed the following two aspects of the problem: a) choice of the symbol constellations for a given transmission scheme or b) choice of the optimal (linear) precoder and equalizer for a given choice of the constellations. More specifically, the choice of the constellations has been made enforcing a diagonal, or parallel, transmission. However, in practice, the two problems of choosing the constellations and the linear precoder/equalizer are clearly coupled, and the diagonal structure may not be necessarily the best. This paper attempts to provide a global view of the problem) by bridging the gap between the existing results on the selection of the constellations and on the design of the signal processing in the form of a linear transceiver (i.e., precoding at the transmitter and equalization at the receiver). [ABSTRACT FROM AUTHOR]

This paper considers the employment of linear transceivers for communication through multiple-input multiple-output (MIMO) channels with channel state information (CSI) at both sides of the link. The design of linear MIMO transceivers has been studied since the 1970s by optimizing simple measures of the quality of the system, such as the trace of the mean-square error matrix, subject to a power constraint. Recent results showed how to solve the problem in an optimal way for the family of Schur-concave and Schur-convex cost functions. In particular, when the constellations used on the different transmit dimensions are equal, the bit-error rate (BER) averaged over these dimensions happens to be a Schur-convex function, and therefore, it can be optimally solved. In a more general case, however, when different constellations are used, the average BER is not a Schur-convex function, and the optimal design in terms of minimum BER is an open problem. This paper solves the minimum BER problem with arbitrary constellations by first reformulating the problem in convex form and then proposing two solutions. One is a heuristic and suboptimal solution, which performs remarkably well in practice. The other one is the optimal solution obtained by decomposing the convex problem into several subproblems controlled by a master problem (a technique borrowed from optimization theory), for which extremely simple algorithms exist. Thus, the minimum BER problem can be optimally solved in practice with very simple algorithms. [ABSTRACT FROM AUTHOR]

In this paper, we aim at developing a robust H2 filtering approach to the design of robust equalizers for communication systems. The contents of this paper mainly include two parts. First, we present a robust H2 filter design method for general multi-input multioutput linear systems with norm-bounded uncertainties in the system matrices based on the linear matrix inequality technique. The characteristics of the equalization problem are taken into account in the filtering model considered here. The advantage of the proposed method is that we can find the optimal solution to robust H2 filtering problem at a reasonable computational burden. Second, we apply the above method to the design of robust equalizers. Two generic examples are studied, one for a single transmit and receive antenna system and another for a multiple transmit and receive antenna system. Both analytical and simulation results show that robust H2 equalizer outperforms the zero-forcing equalizer in both bit-error rate and mean-square errors in the whole simulated range of the signal-to-noise ratio (SNR) when the channel is perturbed from its nominal value, while for the nominal channel, the robust H2 equalizer performs better than the zero-forcing equalizer when the SNR is lower, but it performs worse than the zero-forcing equalizer when the SNR is higher. [ABSTRACT FROM AUTHOR]

A new undersampling-based dual-rate signal acquisition technique for measuring a wideband sparse signal (i.e., a multiband signal) is presented in this paper. The proposed architecture employs a combination of dual-rate time-interleaved undersampling hardware and associated multicoset back-end signal processing algorithms. In dual-rate sampling hardware, a pair of uniform samplers is used to acquire a common incoming wideband sparse signal while the operation frequencies of the two samplers have a small frequency offset. Due to the sampling frequency offset, the time grids of the samples obtained from the two samplers are irregularly spaced. These nonuniform periodic samples are then digitally re-sequenced and applied as input to a multicoset signal reconstruction algorithm. The multicoset signal reconstruction algorithm uses the re-sequenced nonuniform periodic samples to achieve a perfect reconstruction of the original wideband signal with an enhanced time resolution beyond the sampling hardware’s capability. Compared to the conventional multi-channel sampling approach commonly used with multicoset algorithms, the proposed method uses fewer sampling channels and does not require their accurate clock phase adjustment. [ABSTRACT FROM AUTHOR]

Complete complementary codes (CCC) with equal sequence lengths have found numerous applications in wireless communications owing to their ideal correlation properties. In modern multi-user multi-rate communication systems, CCCs with various sequence lengths for different complementary codes are desirable. In this paper, we propose a new construction of polyphase CCC with various lengths by using unimodular paraunitary (PU) matrices. Our key idea is to decompose a large square PU matrix into direct sum of matrices with smaller sizes (which is equivalent to block diagonalization of matrices) in order to construct CCC with various lengths. We show that our proposed constructions not only generalize several previously known methods but also lead us to new CCCs with both equal and various lengths. [ABSTRACT FROM AUTHOR]

Designing the radar waveform, which ensures spectral compatibility with the communication systems, is known to be challenging. In addition to having the desirable transmitter efficiency, it is also anticipated to share the good pulse compression property of the reference waveform. In this paper, we investigate the problem of waveform design for multiple-input multiple-output radar with good transmit beampattern under certain practical constraints (e.g., peak to average power ratio and similarity constraints) in coexistence with communication systems. The waveform is designed by minimizing a weighted summation of the beampattern integrated sidelobe-to-mainlobe ratio and waveform energy over the space-frequency bands. Since the resulting problem is NP-hard, two approximated primal-dual algorithms based on the alternating direction method of multipliers algorithm are proposed. Concretely, the proposed algorithms update the primal variable by minimizing the linearized and quadratic approximations of the augmented Lagrangian function of the original problem, respectively. The closed-form solution of each primal variable is provided. Moreover, both the convergence and computational complexities of these two algorithms are discussed. Numerical simulations are provided to demonstrate the effectiveness of the proposed approaches. [ABSTRACT FROM AUTHOR]

Despite that underwater acoustic channels are well known to contain various interferences, research on interference mitigation in underwater acoustic communications has been very limited. In this paper, we deal with a wideband orthogonal frequency division multiplexing (OFDM) transmission in the presence of an external interference which occupies partially the signal band and whose time duration is shorter than the OFDM block. We parameterize the unknown interference waveform by a number of parameters assuming prior knowledge of the frequency band and time duration of the interference, and develop an iterative receiver, which couples interference detection via a generalized likelihood-ratio-test (GLRT), interference reconstruction and cancellation, channel estimation, and data detection. In addition to simulation results, we verify the receiver performance using data sets collected from two experiments. In both time-invariant and time-varying channels, the proposed iterative receiver achieves robust performance in the presence of unknown interference. [ABSTRACT FROM PUBLISHER]

In this paper, we derive the joint (amplitude, phase) distribution of the product of two independent non-zero-mean Complex Gaussian random variables. We call this new distribution the complex Double Gaussian distribution. This probability distribution function (PDF) is a doubly infinite summation over modified Bessel functions of the first and second kind. We analyze the behavior of this sum and show that the number of terms needed for accuracy is dependent upon the Rician k-factors of the two input variables. We derive an upper bound on the truncation error and use this to present an adaptive computational approach that selects the minimum number of terms required for accuracy. We also present the PDF for the special case where either one or both of the input complex Gaussian random variables is zero-mean. We demonstrate the relevance of our results by deriving the optimal Neyman–Pearson detector for a time reversal detection scheme and computing the receiver operating characteristics through Monte Carlo simulations, and by computing the symbol error probability (SEP) for a single-channel M-ary phase-shift-keying (M-PSK) communication system. [ABSTRACT FROM PUBLISHER]

In this paper, joint channel estimation and data detection in orthogonal frequency division multiplexing (OFDM) amplify-and-forward (AF) cooperative systems under high mobility is investigated. Unlike previous works on cooperative systems in which a number of subcarriers are solely occupied by pilots, partial data-dependent superimposed training (PDDST) is considered here, thus preserving the spectral efficiency. First, a closed-form channel estimator is developed based on the least squares (LS) method with Tikhonov regularization and a corresponding data detection algorithm is proposed using the linear minimum mean square error (LMMSE) criterion. In the derived channel estimator, the unknown data is treated as part of the noise and the resulting data detection may not meet the required performance. To address this issue, an iterative method based on the variational inference approach is derived to improve performance. Simulation results show that the data detection performance of the proposed iterative algorithm initialized by the LMMSE data detector is close to the ideal case with perfect channel state information. [ABSTRACT FROM PUBLISHER]

The response of an array processing system has been widely modelled using the concept of the array manifold. The conventional array manifold is a function of the geometry of the array, the carrier frequency and the directions of arrival (DOAs) of the sources only. However, the emergence of more sophisticated array systems, for instance antenna array communication systems, dictates the extension of this array manifold into various new types of manifolds that incorporate additional system and channel parameters, such as the code-division multiple access (CDMA) (spreading and scrambling) codes, the lack of synchronization, Doppler effects, polarization parameters, subcarriers, etc. This paper is concerned with the definition and geometric investigation of a generic model for these new manifolds that can be expressed as functions of the conventional array manifolds and, hence, are defined herein as extended array manifolds. Thus, initially, the concept of the extended array manifold is introduced and is shown to accommodate both existing and newly defined extensions of the widely employed in the literature conventional (or spatial) array manifold. Next, the geometric properties and parameters of the extended array manifold are studied and linked with the properties of the associated spatial array manifold in an attempt to facilitate the geometric study of the former by taking advantage of the well understood study of the latter. [ABSTRACT FROM PUBLISHER]

Weber's law suggests a logarithmic relationship between perceptual stimuli and human perception. The Weber sampler is an adaptive, nonuniform sampling mechanism that exploits Weber's law to sample the signal at a minimum rate without significant perceptual degradation. In this paper, we introduce and analyze a regularized Weber sampler for smooth deterministic signals as well as smooth random processes. While analysis for a fixed Weber constant \delta is analytically intractable except for some special cases, we exploit the fact that the Weber constant is usually small. Under suitable assumptions, for deterministic signals, we provide analytical approximations to the number of samples in a unit time interval and the intersample times. For a random signal, we give analytical approximations to the expectation and probability distribution function of the respective quantities. Our class of deterministic as well as random signals is quite wide, and in particular covers bandlimited signals and signals satisfying second order ordinary differential equations. We also present a number of simulations to demonstrate that our approximations are good up to a Weber constant of 0.2, which is the regime of practical interest. Our results provide a source model for the Weber sampler, which can be used in the study of transmission of perceptual stimuli signals over communication links. [ABSTRACT FROM AUTHOR]

Compressive sensing (CS) is a new technique for the efficient acquisition of signals, images and other data that have a sparse representation in some basis, frame, or dictionary. By sparse we mean that the N-dimensional basis representation has just K\ll N significant coefficients; in this case, the CS theory maintains that just M = \cal O\left ( K \log N\right ) random linear signal measurements will both preserve all of the signal information and enable robust signal reconstruction in polynomial time. In this paper, we extend the CS theory to pulse stream data, which correspond to S-sparse signals/images that are convolved with an unknown F-sparse pulse shape. Ignoring their convolutional structure, a pulse stream signal is K=SF sparse. Such signals figure prominently in a number of applications, from neuroscience to astronomy. Our specific contributions are threefold. First, we propose a pulse stream signal model and show that it is equivalent to an infinite union of subspaces. Second, we derive a lower bound on the number of measurements M required to preserve the essential information present in pulse streams. The bound is linear in the total number of degrees of freedom S + F, which is significantly smaller than the naïve bound based on the total signal sparsity K=SF. Third, we develop an efficient signal recovery algorithm that infers both the shape of the impulse response as well as the locations and amplitudes of the pulses. The algorithm alternatively estimates the pulse locations and the pulse shape in a manner reminiscent of classical deconvolution algorithms. Numerical experiments on synthetic and real data demonstrate the advantages of our approach over standard CS. [ABSTRACT FROM AUTHOR]

Spectrum sensing is a crucial issue in cognitive radio networks. To improve the sensing performance, cooperation among the secondary users can be utilized to collect space diversity. In this paper, we quantify the diversity order for various cooperative spectrum sensing strategies. We first notice that, in a spectrum sensing problem, diversity should reflect both the false alarm and missed detection behaviors since they respectively capture the efficiency and reliability of the overall cognitive system. Hence, we consider both false alarm and missed detection probabilities individually and jointly via the average error probability. With the knowledge of both the noise strength and the primary user signal strength, the threshold of the Neyman-Pearson detector can be varied to control the system performance in desirable ways. Interesting tradeoffs between system efficiency and reliability are found in various cooperative strategies and analytical results are presented to guide practical system designs with different preferences. [ABSTRACT FROM AUTHOR]

This paper proposes a general framework to effectively estimate the unknown timing and channel parameters, as well as design efficient timing resynchronization algorithms for asynchronous amplify-and-forward (AF) cooperative communication systems. In order to obtain reliable timing and channel parameters, a least squares (LS) estimator is proposed for initial estimation and an iterative maximum-likelihood (ML) estimator is derived to refine the LS estimates. Furthermore, a timing and channel uncertainty analysis based on the Cramér-Rao bounds (CRB) is presented to provide insights into the system uncertainties resulted from estimation. Using the parameter estimates and uncertainty information in our analysis, timing resynchronization algorithms that are robust to estimation errors are designed jointly at the relays and the destination. The proposed framework is developed for different AF systems with varying degrees of timing misalignment and channel uncertainties and is numerically shown to provide excellent performances that approach the synchronized case with perfect channel information. [ABSTRACT FROM AUTHOR]

Consider a downlink communication system where multiantenna base stations transmit independent data streams to decentralized single-antenna users over a common frequency band. The goal of the base stations is to jointly adjust the beamforming vectors to minimize the transmission powers while ensuring the signal-to-interference-noise ratio requirement of each user within the system. At the same time, it may be necessary to keep the interference generated on other coexisting systems under a certain tolerable level. In addition, one may want to include general individual shaping constraints on the beamforming vectors. This beamforming problem is a separable homogeneous quadratically constrained quadratic program, and it is difficult to solve in general. In this paper, we give conditions under which strong duality holds and propose efficient algorithms for the optimal beamforming problem. First, we study rank-constrained solutions of general separable semidefinite programs (SDPs) and propose rank reduction procedures to achieve a lower rank solution. Then we show that the SDP relaxation of three classes of optimal beamforming problem always has a rank-one solution, which can be obtained by invoking the rank reduction procedures. [ABSTRACT FROM AUTHOR]

In this paper, we consider robust transmit strategies, against the imperfectness of the channel state information at the transmitter (CSIT), for multi-input multi-output (MIMO) communication systems. Following a worst-case deterministic model, the actual channel is assumed to be inside an ellipsoid centered at a nominal channel. The objective is to maximize the worst-case received signal-to-noise ratio (SNR), or to minimize the worst-case Chernoff bound of the error probability, thus leading to a max- imin problem. Moreover, we also consider the QoS problem, as a complement of the maximin design, which minimizes the transmit power consumption and meanwhile keeps the received SNR above a given threshold for any channel realization in the ellipsoid. It is shown that, for a general class of power constraints, both the maximin and QoS problems can be equivalently transformed into convex problems, or even further into semidefinite programs (SDPs), thus efficiently solvable by the numerical methods. The most interesting result is that the optimal transmit directions, i.e., the eigenvectors of the transmit covariance, are just the right singular vectors of the nominal channel under some mild conditions. This result leads to a channel-diagonalizing structure, as in the cases Of perfect CSIT and statistical CSIT with mean or covariance feedback, and reduces the complicated matrix-valued problem to a scalar power allocation problem. Then we provide the closed-form solution to the resulting power allocation problem. [ABSTRACT FROM AUTHOR]

Feedback of quantized channel state information (CS!), called limited feedback, enables transmit beamforming in multiple-input-multiple-output (MIMO) wireless systems with a small amount of overhead. Due to its efficiency, beamforming with limited feedback has been adopted in several wireless communication standards. Prior work on limited feedback commonly adopts the block fading channel model where temporal correlation in wireless channels is neglected. In this paper, we consider temporally correlated channels and design single-user transmit beamforming with limited feedback. Analytical results concerning CSI feedback are derived by modeling quantized CSI as a first-order finite-state Markov chain. These results include the information rate of the CS! quantizer output, the bit rate a CS! feedback channel is required to support, and the effect of feedback delay on throughput. In particular, based on the theory of Markov chain convergence rate, feedback delay is proved to reduce the throughput gain due to CS! feedback at least exponentially. Furthermore, an algorithm is proposed for CS! feedback compression in time. Combining the results in this work leads to a new method for designing limited feedback beamforming as demonstrated by a design example. [ABSTRACT FROM AUTHOR]

In this paper, a new framework for target tracking in a wireless sensor network using particle filters is proposed. Under this framework, the imperfect nature of the wireless i communication channels between sensors and the fusion center along with some physical layer design parameters of the network are incorporated in the tracking algorithm based on particle filters. We call this approach "channel-aware particle filtering." Channel-aware particle filtering schemes are derived for different wireless channel models and receiver architectures. Furthermore, we derive the posterior Cramér-Rao lower bounds (PCRLBs) for our proposed channel-aware particle filters. Simulation results are presented to demonstrate that the tracking performance of the channel-aware particle filters can reach their theoretical performance bounds even with relatively small number of sensors and they have superior performance compared to channel-unaware particle filters. [ABSTRACT FROM AUTHOR]

The performance of a mobile multiple-input multiple-output orthogonal-frequency-division multiplexing (MIMO-OFDM) system depends on the ability of the system to accurately account for the effects of the frequency-selective time-varying channel at every symbol time and at every frequency subcarrier. Typically, pilot symbols are strategically placed at various times over various subcarriers in order to calculate sample channel estimates, and then these estimates are interpolated or extrapolated forward to provide channel estimates where no pilot data was transmitted. Performance is highly dependent on the distribution of the pilots with respect to the coherence time and coherence bandwidth of the channel, and on the chosen channel parameterization. In this paper, a vector formulation of the Cramér-Rao bound (CRB) for biased estimators and for functions of parameters is used to derive a lower bound on the channel estimation and prediction error of such a system. Numerical calculations using the bound demonstrate the benefits of multiple antennas for channel estimation and prediction and illustrate the impact of modeling errors on estimation performance when using channel models based on calibrated arrays. [ABSTRACT FROM AUTHOR]

Cooperative communication systems have attracted much attention recently due to their desirable performance gain while using single antenna terminals. This paper addresses the joint timing and channel estimation problem, and furthermore the resynchronization of multiple timing offsets in a cooperative relay system. The estimations of timing and channel are conducted in two phases and the associated Cramér-Rao bounds (CRB) are derived for both phases. It is demonstrated that the conventional CRB is not valid for timing parameters under fading conditions, and a new bound called Weighted Bayesian CRB is proposed. With the timing and channel estimates, a general framework of the resynchronization filter design is developed in order to compensate the multiple timing offsets at the destination. The proposed methods are applied to different scenarios with varying degrees of timing misalignment and are numerically shown to provide excellent performances that approach the perfectly synchronized case. [ABSTRACT FROM AUTHOR]

In this paper, a novel nonlinear Volterra equalizer is presented. We define a framework for nonlinear second-order Volterra models, which is applicable to different applications in engineering. We use this framework to define the channel and to model the equalizer. We then solve the minimum mean-square error (MMSE) problem explicitly for the tandem connection of the two second-order Volterra systems. Optimal solutions for a simplified, linear version of the MMSE equalizer are also presented. The novel equalizer was tested when applied to a nonlinear ultra-wide- band transmitted reference receiver front end. As a comparison, a least mean squares (LMS) equalizer with a training sequence has been used to verify the performance of the newly proposed equalizer. The simulation results show that the LMS equalizer is only able to attain the proposed MMSE equalizer after very long training, which might not be desirable in a communication system. [ABSTRACT FROM AUTHOR]

Cooperative diversity systems are wireless communication systems designed to exploit cooperation among users to mitigate the effects of multipath fading. In fairly general conditions, it has been shown that these systems can achieve the diversity order of an equivalent multiple-input multiple-output (MIMO) channel and, if the node geometry permits, virtually the same outage probability can be achieved as that of the equivalent MIMO channel for a wide range of applicable signal-to-noise ratio (SNR). However, much of the prior analysis has been performed' under the assumption of perfect timing and frequency offset synchronization. In this paper, we derive the estimation bounds and associated maximum a posteriori estimators for frequency offset estimation in a cooperative communication system. We show the benefit of adaptively tuning the frequency of the relay node in order to reduce estimation error at the destination. We also derive an efficient estimation algorithm, based on the correlation sequence of the data, which has mean squared error close to the Cramér-Rao Bound (CRB). [ABSTRACT FROM AUTHOR]

Multiple-input/multiple-output (MIMO) frequency selective fading channels introduce both the intersymbol interference (ISI) and the inter-(sub)channel interference (ICI) that are the major performance limiting factors for MIMO wireless data communications. This paper focuses on joint suppression of such two interferences in receiver design under the criterion of the minimum mean-squared error (MMSE). The well-known Kalman filtering arises as a natural design tool to achieve the optimum joint suppression of the ISI and ICI in the sense of MMSE that results in a two-stage design method that suppresses the ISI and ICI in series. Specifically, a state-space solution is derived for the optimum decision-feedback equalizer (DEE) based on temporal Kalman filtering, and is shown to be equivalent to a conventional finite length DEE for ISI suppression. A novel spatial Kalman filtering scheme i's proposed as the second stage to successively cancel the ICI. It is shown that the proposed two-stage method yields the realizable and optimum joint ISI and ICI suppression, while it admits lower computational complexity than the conventional finite length DFE for ISI suppression and the MMSE V-BLAST receiver for ICI suppression. The results are applicable to any linear channels that admit state-space realizations, including MIMO frequency selective time-varying channels, and are thus the most general. Simulation results show the effectiveness of the proposed two-stage method and its robustness to channel estimation uncertainties that may arise in practical systems. [ABSTRACT FROM AUTHOR]