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This paper considers a method for improving physical layer security of wireless networks with full-duplex radio. In particular, fast algorithms are developed to compute power allocations in subcarriers, subject to power and rate constraints, to maximize the secrecy capacity of a three-node network consisting of a source, a full-duplex destination, and an eavesdropper. A residual level of radio self-interference channel is considered. The optimal power allocation at the destination is found to be significant especially when its power budget is high. Also studied in this paper are a network with multiple full-duplex destinations and another network with multiple sources. Using the algorithms developed in this paper, we are able to show that a multiuser strategy that optimizes the power distributions among the users (in terms of either the sources or the destinations) can yield a substantial gain of secrecy capacity over a single-user strategy. [ABSTRACT FROM PUBLISHER]

Consider the single-group multicast beamforming problem, where multiple users receive the same data stream simultaneously from a single transmitter. The problem is NP-hard and all existing algorithms for the problem either find suboptimal approximate or local stationary solutions. In this paper, we propose an efficient branch-and-bound algorithm for the problem that is guaranteed to find its global solution. To the best of our knowledge, our proposed algorithm is the first tailored global algorithm for the single-group multicast beamforming problem. Simulation results show that our proposed algorithm is computationally efficient (albeit its theoretical worst-case iteration complexity is exponential with respect to the number of receivers) and it significantly outperforms a state-of-the-art general-purpose global optimization solver called Baron. Our proposed algorithm provides an important benchmark for performance evaluation of existing algorithms for the same problem. By using it as the benchmark, we show that two state-of-the-art algorithms, semidefinite relaxation algorithm and successive linear approximation algorithm, work well when the problem dimension (i.e., the number of antennas at the transmitter and the number of receivers) is small but their performance deteriorates quickly as the problem dimension increases. [ABSTRACT FROM PUBLISHER]

This paper studies a multiple-input multiple-output (MIMO) broadcast relay channel (BRC), in which a multiple-antenna base station (BS) communicates with multiple-antenna users through an infrastructure-based multiple-antenna relay station (RS). Applying dirty paper coding (DPC) at the BS and linear processing at the RS, our aim is to find the input covariance matrices and the RS beamforming matrix that maximize the system sum-rate. To solve this non-convex problem, a more tractable dual multiple access relay channel (MARC) is investigated and an alternating-minimization algorithm is proposed. Furthermore, the mapping from the resulting covariance matrices for the MARC to the covariance matrices for the BRC is derived. Unlike other existing single-antenna-user schemes, our solution is applicable to a more general network with any number of antennas at the users. Compared with two such single-antenna-user schemes, simulations show that the proposed scheme outperforms the all-pass relay design and performs similarly to the SVD-relay design. Moreover, the proposed design performs close to the sum-rate upper bound with the performance gap decreasing with increasing number of antennas per user. It is also observed that having more antennas at the RS than at the BS is desirable for better system performance. [ABSTRACT FROM PUBLISHER]

Equal allocation of bandwidth and/or power may not be efficient for wireless multi-user networks with limited bandwidth and power resources. Optimal joint bandwidth and power allocation strategies for wireless multi-user networks with and without relaying are proposed in this paper for 1) the maximization of the sum capacity of all users; 2) the maximization of the worst user capacity; and 3) the minimization of the total power consumption of all users. It is shown that the proposed allocation problems are convex and, therefore, can be solved efficiently. Moreover, joint bandwidth and power allocation for admission control is considered. A suboptimal greedy search algorithm is developed to solve the admission control problem efficiently. Instructive analysis of the greedy search shows that it can achieve good performance, and the condition under which the greedy search is optimal is derived. The formal and in-depth analysis of the greedy search algorithm presented in this paper can serve as a benchmark for analyzing similar algorithms in other applications. The performance improvements offered by the proposed optimal joint bandwidth and power allocation are demonstrated by simulations. The advantages of the suboptimal greedy search algorithm for admission control are also shown in numerical results. [ABSTRACT FROM PUBLISHER]

Recently, the duality between multiple-input multiple-output (MIMO) multiple-access channels (MAC) and MIMO broadcast channels (BCs) has been established under a total power constraint. The same set of rates for MAC can be achieved in BC exploiting the MAC-BC duality formulas while preserving the total power constraint. In this paper, we describe the BC optimal power allocation applying this duality in a downstream x-digital subscriber lines (xDSLs) context under a total power constraint for all modems over all tones. Then, a new algorithm called BC-optimal spectrum balancing (BC-OSB) is devised for a more realistic power allocation under per-modem total power constraints. The capacity region of the primal BC problem under per-modem total power constraints is found by the dual optimization problem for the BC under per-modem total power constraints which can be rewritten as a dual optimization problem in the MAC by means of a precoder matrix based on the Lagrange multipliers. We show that the duality gap between the two problems is zero. The multiuser power allocation problem has been solved for interference channels and MAC using the OSB algorithm. In this paper we solve the problem of multiuser power allocation for the BC case using the OSB algorithm as well and we derive a computational efficient algorithm that will be referred to as BC-OSB. Simulation results are provided for two VDSL2 scenarios: the first one with differential-mode (DM) transmission only and the second one with both DM and phantom-mode (PM) transmissions. [ABSTRACT FROM AUTHOR]

This paper studies the two-hop relay broadcast channel (BC) for a relay-assisted wireless cellular network where the multiuser independent downlink signals from the base station (BS) are first transmitted to a fixed relay station (RS), and then forwarded by the RS to multiple mobile users. Assuming both the BS and RS are equipped with multiantennas, we study the joint optimization of linear beamforming and power control at the BS and RS so as to minimize their weighted sum-power consumption under the user minimum signal-to-interference-noise-ratio (SINR) - quality-of-service (QoS) - constraints. We apply two well-known criteria in the literature, namely, the "SINR balancing" and the "channel-inversion," for the design of linear precoding in the traditional nonrelay-assisted multiantenna BC to the relay-assisted multiantenna BC. First, a convergence-ensured iterative SINR-balancing algorithm is proposed to successively in turn optimize the transmit parameters at one station (BS or RS) with those at the other station being fixed. Second, a joint BS and RS channel-inversion algorithm is proposed together with a novel technique, termed "eigenmode switching," at the RS to reduce the power penalty of the channel inversion. Simulation results show that the proposed joint beamforming and power control schemes provide substantial power savings to achieve the assigned user QoS constraints. [ABSTRACT FROM AUTHOR]

In this paper, a new deterministic technique for blind equalization of multiple-input multiple-output (MIMO) channels is presented. The proposed method relies on the channel order diversity of the finite impulse response-MIMO channel, and it does not make any assumption about the spectra or the finite alphabet property of the sources. Assuming that the lengths of the single-input multiple-output channels are known or estimated a priori, it is able to extract the sources up to a scale and rotation matrix ambiguity, which only affects to those sources associated to single-input multiple-output (SIMO) channels with equal lengths. Unlike previously proposed techniques, the method described in this paper obtains the equalizers and the best linear combination of their outputs in a single step and, thus, optimally. The reformulation of the proposed method as a set of nested canonical correlation analysis problems is exploited to obtain efficient batch and adaptive equalization algorithms. Finally, the performance of the proposed algorithms is evaluated by means of some simulation examples. [ABSTRACT FROM AUTHOR]

Next generation wireless code division multiple access (CDMA) networks are required to support packet multimedia traffic. This paper addresses the connection admission control problem for multiservice packet traffic modeled as Markov modulated Poisson process (MMPP) with the quality of service (QoS) requirements on both physical layer signal-to-interference ratio (SIR) and network layer blocking probability. Optimal linear-programming-based algorithms are presented that take into account of SIR outage probability constraints. By exploiting the MMPP traffic models and introducing a small SIR outage probability, the proposed algorithms can dramatically improve the network utilization. In addition, we propose two reduced complexity algorithms that require less computation and can have satisfactory approximation to the optimal solutions. Numerical examples illustrating the performance of the proposed schemes are presented. [ABSTRACT FROM AUTHOR]

Channel estimation and tracking pose real problems in broadband single-carrier wireless communication systems employing multiple transmit and receive antennas. An alternative to estimating the channel is to adaptively equalize the received symbols. Several adaptive equalization solutions have been researched for systems operating in the time domain. However, these solutions tend to be computationally intensive. A low-complexity alternative is to adaptively equalize the received message in the frequency domain. In this paper, we present an adaptive frequency-domain equalization (FDE) algorithm for implementation in single-carrier (SC) multiple-input multiple-output (MIMO) systems. Furthermore, we outline a novel method of reducing the overhead required to train the proposed equalizer. Finally, we address the issues of complexity and training sequence design. Other computationally efficient adaptive FDE algorithms for use in SC systems employing single transmit and receive antennas, receive diversity, or space-lime block codes (STBC) can be found in the literature. However, the algorithm detailed in this paper can be implemented in STBC systems as well as in broadband spatial multiplexing systems, making it suitable for use in high data rate MIMO applications. [ABSTRACT FROM AUTHOR]

This paper investigates, under the zero-forcing (ZF) condition, the design of optimal precoders which minimize the bit error rate (BER) for the multiple-input/multiple-output (MIMO) downlink communication channels. It is shown that the optimal full information control can be employed to design the optimal ZF precoder. The closed-form solutions are derived, and an efficient design algorithm is proposed. Numerical simulations are provided to illustrate the proposed design technique and to demonstrate the performance of the optimal precoders. [ABSTRACT FROM AUTHOR]

In an interference limited network, joint power and admission control (JPAC) aims at supporting a maximum number of links at their specified signal-to-interference-plus-noise ratio (SINR) targets while using minimum total transmission power. Various convex approximation deflation approaches have been developed for the JPAC problem. In this paper, we propose an effective polynomial time non-convex approximation deflation approach for solving the problem. The approach is based on the non-convex \ell q~(0< q< 1) approximation of an equivalent sparse \ell 0 reformulation of the JPAC problem. We show that, for any instance of the JPAC problem, there exists a \bar q\in (0,1) such that it can be exactly solved by solving its \ell q approximation problem with any q\in (0, \bar q]. We also show that finding the global solution of the \ell q approximation problem is NP-hard. Then, we propose a potential reduction interior-point algorithm, which can return an \epsilon -KKT solution of the NP-hard \ell q approximation problem in polynomial time. The returned solution can be used to check the simultaneous supportability of all links in the network and to guide an iterative link removal procedure, resulting in the polynomial time non-convex approximation deflation approach for the JPAC problem. Numerical simulations show that the proposed approach outperforms the existing convex approximation approaches in terms of the number of supported links and the total transmission power, particularly exhibiting a quite good performance in selecting which subset of links to support. [ABSTRACT FROM PUBLISHER]

Synchronization of bio-inspired pulse-coupled oscillators is receiving increased attention due to its wide applications in sensor networks and wireless communications. However, most existing results are obtained in the absence of malicious attacks. Given the distributed and unattended nature of wireless sensor networks, it is imperative to enhance the resilience of pulse based synchronization against malicious attacks. To achieve this goal, we propose a new pulse based interaction mechanism to improve the resilience of pulse based synchronization. We rigorously characterize the condition for mounting stealthy attacks under the proposed pulse based interaction mechanism and prove analytically that synchronization of legitimate oscillators can be achieved in the presence of multiple stealthy attackers even when the initial phases are unrestricted, i.e., randomly distributed in the entire oscillation period. This is in distinct difference from most existing attack-resilient synchronization algorithms (including the seminal paper from L. Lamport and P. M. Melliar-Smith, “Synchronizing Clocks in the Presence of Faults,” J. ACM, vol. 32, no. 1, pp. 52–78, 1985), which require a priori (almost) synchronization among legitimate nodes. Numerical simulations are given to confirm the theoretical results. [ABSTRACT FROM AUTHOR]

One-bit quantization can significantly reduce the massive multiple-input and multiple-output (MIMO) system hardware complexity, but at the same time it also brings great challenges to the system algorithm design. Specifically, it is difficult to recover information from the highly distorted samples as well as to obtain accurate channel estimation without increasing the number of pilots. In this paper, a novel inference algorithm called variational approximate message passing (VAMP) for one-bit quantized massive MIMO receiver is developed, which attempts to exploit the advantages of both the variational Bayesian inference algorithm and the bilinear generalized approximated message passing algorithm to accomplish joint channel estimation and data detection in a closed form with first-order complexity. Asymptotic state evolution analysis indicates the fast convergence rate of VAMP and also provides a lower bound for the data detection error. Moreover, through extensive simulations, we show that VAMP can achieve excellent detection performance with low pilot overhead in a wide range of scenarios. [ABSTRACT FROM AUTHOR]

A precise model of the array response is required to maintain the performance of direction-of-arrival (DOA) estimation. When modeling errors are present or the sensor environment is time-varying, autocalibration becomes necessary. In this paper, the problem of phase autocalibration for uniform rectangular array (URA) geometries is considered. For the case with a single source, a simple and robust least-squares algorithm for joint 2-D DOA estimation and phase calibration is presented. When performing phase autocalibration with a URA, the phase and DOA parameters cannot be identified together without ambiguity. This problem is discussed and a suitable remedy is suggested. An approximate Cramér-Rao bound and analytical expressions for the mean squared error performance of the proposed estimator are presented. The proposed algorithm for phase autocalibration is extended for the case with multiple sources. The results are evaluated using a representative body of simulations. [ABSTRACT FROM PUBLISHER]

In this paper, we propose an iterative interference alignment (IA) algorithm for MIMO cellular networks with partial connectivity, which is induced by heterogeneous path losses and spatial correlation. Such systems impose several key technical challenges in the IA algorithm design, namely the overlapping between the direct and interfering links due to the MIMO cellular topology as well as how to exploit the partial connectivity. We shall address these challenges and propose a three stage IA algorithm. As illustration, we analyze the achievable degree of freedom (DoF) of the proposed algorithm for a symmetric partially connected MIMO cellular network. We show that there is significant DoF gain compared with conventional IA algorithms due to partial connectivity. The derived DoF bound is also backward compatible with that achieved on fully connected K-pair MIMO interference channels. [ABSTRACT FROM PUBLISHER]

In this paper, we consider a wireless amplify-and-forward relay network with one relay node and multiple source-destination pairs/users and propose a pricing framework that enables the relay to set prices to maximize either its revenue or any desirable system utility. Specifically, depending on the quality of the received signals, the relay sets prices and correspondingly charges the users utilizing its resources for their transmissions. The price is determined in such a way that the relay's revenue or system utility is maximized. Given the specified price, the users competitively employ the relay node to forward their signals. We model each user as a rational player, which aims at maximizing its own net utility through power allocation, and analyze the competition among the users within the framework of noncooperative game theory. It is shown that, in the game played by the users, there always exists a unique pure Nash equilibrium point that can be achieved through distributed iterations. Next, subject to the availability of complete information about the users at the relay, we propose a low-complexity uniform pricing algorithm and an optimal differentiated pricing algorithm, in which the relay either charges the users at a suboptimal uniform price or charges different users at different prices. We also show that, by applying the differentiated pricing algorithm that enforces the users to transmit at certain power levels, any system utility can be maximized. Extensive simulations are conducted to quantify the performance of the proposed methods. [ABSTRACT FROM AUTHOR]

Distributed network utility maximization (NUM) has received an increasing intensity of interest over the past few years. Distributed solutions (e.g., the primal-dual gradient method) have been intensively investigated under fading channels. As such distributed solutions involve iterative updating and explicit message passing, it is unrealistic to assume that the wireless channel remains unchanged during the iterations. Unfortunately, the behavior of those distributed solutions under time-varying channels is in general unknown. In this paper, we shall investigate the convergence behavior and tracking errors of the iterative primal-dual scaled gradient algorithm (PDSGA) with dynamic scaling matrices (DSC) for solving distributive NUM problems under time-varying fading channels. We shall also study a specific application example, namely the multicommodity flow control and multicarrier power allocation problem in multihop ad hoc networks. Our analysis shows that the PDSGA converges to a limit region rather than a single point under the finite state Markov chain (FSMC) fading channels. We also show that the order of growth of the tracking errors is given by \cal O\left(\bar T/\bar N\right), where \bar T and \bar N are the update interval and the average sojourn time of the FSMC, respectively. Based on this analysis, we derive a low complexity distributive adaptation algorithm for determining the adaptive scaling matrices, which can be implemented distributively at each transmitter. The numerical results show the superior performance of the proposed dynamic scaling matrix algorithm over several baseline schemes, such as the regular primal-dual gradient algorithm. [ABSTRACT FROM AUTHOR]

Distributed power control over interference limited network has received an increasing intensity of interest over the past few years. Distributed solutions (like the iterative water-filling, gradient projection, etc.) have been intensively investigated under quasi-static channels. However, as such distributed solutions involve iterative updating and explicit message passing, it is unrealistic to assume that the wireless channel remains unchanged during the iterations. Unfortunately, the behavior of those distributed solutions under time-varying channels is in general unknown. In this paper, we shall investigate the distributed scaled gradient projection algorithm (DSGPA) in a K pairs multicarrier interference network under a finite-state Markov channel (FSMC) model. We shall analyze the convergence property as well as tracking performance of the proposed DSGPA. Our analysis shows that the proposed DSGPA converges to a limit region rather than a single point under the FSMC model. We also show that the order of growth of the tracking errors is given by \cal O(1 / \barN), where \barN is the average sojourn time of the FSMC. Based on the analysis, we shall derive the tracking error optimal scaling matrices via Markov decision process modeling. We shall show that the tracking error optimal scaling matrices can be implemented distributively at each transmitter. The numerical results show the superior performance of the proposed DSGPA over three baseline schemes, such as the gradient projection algorithm with a constant stepsize. [ABSTRACT FROM PUBLISHER]

Node localization is essential to most applications of wireless sensor networks (WSNs). In this paper, we consider both range-based node localization and range-free node localization with uncertainties in range measurements, radio range, and anchor positions. First, a greedy optimization algorithm, named sequential greedy optimization (SGO) algorithm, is presented, which is more suitable for distributed optimization in networks than the classical nonlinear Gauss-Seidel algorithm. Then a unified optimization framework is proposed for both range-based localization and range-free localization, and two convex localization formulations are obtained based on semidefinite programming (SDP) relaxation techniques. By applying the SGO algorithm to the edge-based SDP relaxation formulation, we propose a second-order cone programming (SOCP)-based distributed node localization algorithm. Two distributed refinement algorithms are also proposed by using the SGO algorithm to nonconvex localization formulations. The proposed localization algorithms all can be implemented partially asynchronously in networks. Finally, extensive simulations are conducted to demonstrate the efficiency and accuracy of the proposed distributed localization algorithms. [ABSTRACT FROM AUTHOR]

Recently, lattice-reduction-aided detectors have been proposed for multiinput multioutput (MIMO) systems to achieve performance with full diversity like the maximum likelihood receiver. However, these lattice-reduction-aided detectors are based on the traditional Lenstra-Lenstra-Lovász (LLL) reduction algorithm that was originally introduced for reducing real lattice bases, in spite of the fact that the channel matrices are inherently complex-valued. In this paper, we introduce the complex LLL algorithm for direct application to reducing the basis of a complex lattice which is naturally defined by a complex-valued channel matrix. We derive an upper bound on proximity factors, which not only show the full diversity of complex LLL reduction-aided detectors, but also characterize the performance gap relative to the lattice decoder. Our analysis reveals that the complex LLL algorithm can reduce the complexity by nearly 50% compared to the traditional LLL algorithm, and this is confirmed by simulation. Interestingly, our simulation results suggest that the complex LLL algorithm has practically the same bit-error-rate performance as the traditional LLL algorithm, in spite of its lower complexity. [ABSTRACT FROM AUTHOR]

The PARAFAC decomposition of a higher-order tensor is a powerful multilinear algebra tool that becomes more and more popular in a number of disciplines. Existing PARAFAC algorithms are computationally demanding and operate in batch mode-both serious drawbacks for on-line applications. When the data are serially acquired, or the underlying model changes with time, adaptive PARAFAC algorithms that can track the sought decomposition at low complexity would be highly desirable. This is a challenging task that has not been addressed in the literature, and the topic of this paper. Given an estimate of the PARAFAC decomposition of a tensor at instant t, we propose two adaptive algorithms to update the decomposition at instant t + 1, the new tensor being obtained from the old one after appending' a new slice in the 'time' dimension. The proposed algorithms can yield estimation performance that is very close to that obtained via repeated application of state-of-art batch algorithms, at orders of magnitude lower complexity. The effectiveness of the proposed algorithms is illustrated using a MIMO radar application (tracking of directions of arrival and directions of departure) as an 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]

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, we propose a new maximum-likelihood (ML) target localization approach which uses quantized sensor data as well as wireless channel statistics in a wireless sensor network. The novelty of our approach comes from the fact that statistics of imperfect wireless channels between sensors and the fusion center along with some physical layer design parameters are incorporated in the localization algorithm. We call this approach "channel-aware target localization." ML target location estimators are derived for different wireless channel models and receiver architectures. Furthermore, we derive the Cramér-Rao lower bounds (CRLBs) for our proposed channel-aware ML location estimators. Simulation results are presented to show that the performance of the channel-aware ML location estimators are quite close to their theoretical performance bounds even with relatively small number of sensors and their performance is superior compared to that of the channel-unaware ML estimators. [ABSTRACT FROM AUTHOR]

Abstract-This paper discusses the channel capacity of multiantenna ultra-wideband (UWB) indoor wireless systems. It is shown first that for both single-input single-output (SISO) and multiple-input multiple-output (MIMO) cases, when the transmit SNR is lower than some value, say -20 dB, using optimal power spectrum allocation (OPSA) at the transmitter side can increase the reliable transmission rate considerably compared to the uniform power spectrum allocation scheme, while when the transmit signal-to-noise ratio (SNR) is higher than some value, say 10 dB, the benefit of OPSA is very limited. Therefore, measures such as the water-filling algorithm should be taken to make full use of the frequency-selectivity of UWB systems if the transmit SNR is low. Second, in the multiple-input single-output (MISO) case, the outage probability decreases with the number of transmit antennas when the communication rate is lower than some value, but increases instead when the rate is higher than another value. A critical transmission rate is identified. Third, in the cases of SIMO and MIMO, the communication rate supportable by the channel with a given outage probability increases approximately logarithmically and linearly, respectively, with the number of receive antennas. [ABSTRACT FROM AUTHOR]

The optimum multiuser detection problem can be formulated as a maximum likelihood problem, which yields a binary quadratic programming problem to be solved. Generally this problem is .iVP-hard and is therefore hard to solve in real time. In this paper, a preprocessing algorithm is presented which makes it possible to detect some or all users optimally for a low computational cost if signature sequences with low cross correlation, e.g., Gold sequences, are used. The algorithm can be interpreted as, e.g., an adaptive tradeoff between parallel interference cancellation and successive interference cancellation. Simulations show that the preprocessing algorithm is able to optimally compute more than 94,% of the bits in the problem when the users are time-synchronous, even though the system is heavily loaded and affected by noise. Any remaining bits, not computed by the preprocessing algorithm, can either be computed by a suboptimal detector or an optimal detector. Simulations of the time-synchronous case show that if a suboptimal detector is chosen, the bit error rate (BER) rate is significantly reduced compared with using the suboptimal detector alone. [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]

In this paper, we study the problem of downlink transmission in multiple-input-single-output (MISO) wireless communication systems using multiuser diversity. There are M antennas at the base station (BS) and K single-antenna receivers. The performance of multiuser diversity depends on the number of users that have independent channel realizations. It is well known that increasing the number of users improves the performance at the expense of increasing feedback that is proportional to the number of users. For sufficiently large K, the capacity scales like M log log K. When K is large, increasing feedback limits practical applications of the multiuser diversity. Our approach is to reduce the feedback by selecting a threshold for the l∞-norm of the normalized cross-correlation (∥ zk∥∞) between the users' channel and the beamforming matrix based on the random unitary beamforming. The average amount of feedback per time slot is d real numbers and d integers which does not change with K. To improve fairness, an equal ratio scheduling algorithm which could serve the users with different rate requirements is developed. Monte Carlo simulation results is provided to verify the performance of the proposed algorithm. [ABSTRACT FROM AUTHOR]

In this paper, a new, efficient class of blind equalization algorithms is proposed for use in high-order, two-dimensional digital communication systems. We have called this family: the Constant Norm Algorithms (CNA). This family is derived in the context of Bussgang techniques. Therefore, the resulting algorithms are very simple. We show that some well-known blind algorithms such as "Sato's algorithm" or the Constant Modulus Algorithm (CMA) are particular cases in our CNA family. In addition, from this class, a new cost function, named Constant sQuare Algorithm (CQA), is derived, which is well designed for QAM. It results in a lower algorithm noise without increasing the complexity. Another advantage of this class lies in the possibility of creating new norms by combining several existing norms in order to benefit from the advantages of each original norm. For example, we present the norm resulting from the combination of the two algorithms, CMA and CQA. Moreover, we highlight that, with regard to the excess mean-square error performance, there is an optimal norm for each constellation, i.e., each modulation, in order to equalize it blindly. [ABSTRACT FROM AUTHOR]

Optimal receivers recovering signals transmitted across noisy communication channels employ a maximum-likelihood (ML) criterion to minimize the probability of error. The problem of finding the most likely transmitted symbol is often equivalent to finding the closest lattice point to a given point and is known to be NP-hard. In systems that employ error-correcting coding for data protection, the symbol space forms a sparse lattice, where the sparsity structure is determined by the code. In such systems, ML data recovery may be geometrically interpreted as a search for the closest point in the sparse lattice. In this paper, motivated by the idea of the "sphere decoding" algorithm of Fincke and Pohst, we propose an algorithm that finds the closest point in the sparse lattice to the given vector. This given vector is not arbitrary, but rather is an unknown sparse lattice point that has been perturbed by an additive noise vector whose statistical properties are known. The complexity of the proposed algorithm is thus a random variable. We study its expected value, averaged over the noise and over the lattice. For binary linear block codes, we find the expected complexity in closed form. Simulation results indicate significant performance gains over systems employing separate detection and decoding, yet are obtained at a complexity that is practically feasible over a wide range of system parameters. [ABSTRACT FROM AUTHOR]

In this paper, we consider the design of a full-rate linear space-time block code for coherent multiple-input multiple-output (MIMO) communication systems under a quasi-static Rayleigh flat-fading environment. Our design targets specifically at the use of a linear minimum mean-square error (MMSE) receiver that minimizes the asymptotic average bit error rate (BER) when the transmitted signal is selected from a 4-QAM constellation. This optimization problem is solved in two main stages: 1) a lower bound on the BER is first minimized, and 2) how this minimized lower bound can be achieved is then shown. By exploiting a rigorous convex optimization technique without any assumption on the code, we prove that individual unitary and trace-orthogonal structures are the necessary and sufficient conditions to assure the minimum asymptotic average BER with an MMSE detector. An algorithm is provided for an efficient generation of our codes, and simulation results confirm that our optimally designed codes are indeed superior in performance compared to some other commonly used codes. [ABSTRACT FROM AUTHOR]

A novel extension of the Multiple Signal Classification (MUSIC) algorithm for the frequency estimation problem is proposed in this work. It is shown that the MUSIC algorithm is a data dependent optimum notch filter design technique. When the input data passed through this optimum notch filter, the signal-to-noise ratio (SNR) of the output is minimized. The zeros of the optimum notch filter are then used to estimate the frequencies. In the classical MUSIC algorithm, this optimization is carried out over the set of all finite impulse response (FIR) filters having a fixed order. The framework of this paper allows the use of rational notch filters. The fact that the rational filters have sharper spectral characteristics is used to obtain improved estimates, particularly at low SNR. Some interesting properties of the proposed algorithm are derived, and further perspectives on the implementation aspects are provided. The analytical predictions are substantiated using numerical simulation results. [ABSTRACT FROM AUTHOR]

In this paper, the problem of blind spatial signature estimation using the parallel factor (PARAFAC) analysis model is addressed in application to wireless communications. A time-varying user power loading in the uplink mode is proposed to make the model identifiable and to enable application of PARAFAC analysis. Then, identifiability issues are studied in detail and closed-form expressions for the corresponding modified Cramér-Rao bound (CRB) are obtained. Furthermore, two blind spatial signature estimation algorithms are developed. The first technique is based on the PARAFAC fitting trilinear alternating least squares (TALS) regression procedure, whereas the second one makes use of the joint approximate diagonalization algorithm. These techniques do not require any knowledge of the propagation channel and/or sensor array manifold and are applicable to a more general class of scenarios than earlier approaches to blind spatial signature estimation. [ABSTRACT FROM AUTHOR]

This paper looks at the linear reception of spatially multiplexed signals across MIMO channels. We address the problem of robustness in the presence of detrimental effects such as correlation and Ricean components. We consider the error-rate performance of MIMO linear filters as these can be used in purely linear receivers or as part of each stage in successive interference canceling (SIC) receivers. We know from multiuser detection theory that minimum error-rate (MER) linear receivers significantly outperform minimum mean-square error (MMSE) receivers when correlation is high; however, no direct method exist to design the MER receiver simply. We derive a scheme allowing a closed-form approximate solution to this problem. The solution is a good approximation to the true MER receiver upon fulfillment of a certain, easily checkable, channel-related condition. The algorithms are derived first for the two-input many-output case. A generalized scheme is provided for the case of arbitrary number of inputs and outputs. The performance gain compared with that MMSE is significant and evaluated for various correlated and Ricean channels and transmit power allocation strategies. [ABSTRACT FROM AUTHOR]

Detection of the number of transmit antennas plays an important role in the design of adaptive multiantenna wireless communication systems. This paper develops a detection algorithm based on second- and fourth-order statistics of the received signal and time-varying nature of the wireless channel. In contrast to existing feature-based algorithms, the proposed algorithm does not require a priori information about the transmitted signals, such as coding scheme, modulation type, and pilot patterns. Unlike information-theoretic algorithms, it does not require the number of receive antennas to be larger than that of transmit antennas. In fact, the proposed algorithm is capable of operating with a single receive antenna even for large numbers of transmit antennas. The performance analysis of the proposed algorithm is provided, and the results are verified through simulations. The algorithm exhibits a good performance over a wide range of signal-to-noise ratios, and is robust to the carrier frequency and timing offsets. [ABSTRACT FROM PUBLISHER]

The multiple-input multiple-output (MIMO) system makes efficient use of spectrum and increases the transmission throughput in wireless communications. The sphere decoding (SD) is an efficient algorithm that enables the maximum-likelihood (ML) detection for the MIMO system. However, the SD algorithm has variable complexity, and its complexity increases rapidly with decreasing signal-to-noise ratio (SNR). In this paper, we propose a novel ML detection algorithm for the MIMO system based on differential metrics. We define the differential metrics and derive the associated recursive calculation. We then give the indicative functions, which can be used to possibly find some ML-detected bits of the initial sequence. The indicative functions are further applied to implement an efficient tree search for ML detection. The proposed algorithm does not need QR decomposition and matrix inversion. The tree search process needs only the additive operation, while the number of multiplications before the tree search is constant. Our algorithm can achieve the exact ML detection as the SD algorithm. Unlike the SD algorithm, the complexity of our algorithm reduces with decreasing SNR, whereas at high SNR, the complexity is nearly constant. We also give the convergence analysis for the SD and proposed algorithms, and the simulation verifies our analysis. For the proposed algorithm, the number of necessary memory is constant during the tree search, and the implementation by parallel processing is possible. The soft output of ML-detected bits can also be generated in our algorithm. [ABSTRACT FROM PUBLISHER]

Identification of systems with sparse impulse response encounters large applicability. Numerous techniques have been proposed to identify such systems efficiently. One strategy that leads to specially good results is to detect the active (nonzero) response samples and update only the corresponding adaptive coefficients. Wavelet-based approaches have been shown to be specially effective to this end. This paper proposes a new region-based wavelet-packet (RBWP) algorithm for efficient identification of systems with sparse impulse responses with arbitrary frequency spectra and with any delay of the effective response. The discrete wavelet packet transform (DWPT) is adaptively tailored to the energy distribution of the unknown system's response spectrum. The new algorithm leads to a reduced number of active weights and to a reduced computational complexity, when compared with competing wavelet-based algorithms. Monte Carlo simulation results show good performances regarding convergence speed and robustness to design parameter choice. [ABSTRACT FROM PUBLISHER]

In this paper, the iterative water-filling (IWF) algorithm is considered when the noise-plus-interference levels fed back from the receivers are quantized. A sufficient condition for this algorithm to have a unique Nash equilibrium is derived. This condition is shown to approach the corresponding condition for standard IWF when the quantization resolution or the transmission power are sufficiently high. [ABSTRACT FROM AUTHOR]

Multiple distributed nodes in cooperative networks generally are subject to multiple carrier frequency offsets (MCFOs) and multiple timing offsets (MTOs), which result in time varying channels and erroneous decoding. This paper seeks to develop estimation and detection algorithms that enable cooperative communications for both decode-and-forward (DF) and amplify-and-forward (AF) relaying networks in the presence of MCFOs, MTOs, and unknown channel gains. A novel transceiver structure at the relays for achieving synchronization in AF-relaying networks is proposed. New exact closed-form expressions for the Cramér–Rao lower bounds (CRLBs) for the multi-parameter estimation problem are derived. Next, two iterative algorithms based on the expectation conditional maximization (ECM) and space-alternating generalized expectation-maximization (SAGE) algorithms are proposed for jointly estimating MCFOs, MTOs, and channel gains at the destination. Though the global convergence of the proposed ECM and SAGE estimators cannot be shown analytically, numerical simulations indicate that through appropriate initialization the proposed algorithms can estimate channel and synchronization impairments in a few iterations. Finally, a maximum likelihood (ML) decoder is devised for decoding the received signal at the destination in the presence of MCFOs and MTOs. Simulation results show that through the application of the proposed estimation and decoding methods, cooperative systems result in significant performance gains even in presence of impairments. [ABSTRACT FROM PUBLISHER]

Real-time matrix inversion is a key enabling technology in multiple-input multiple-output (MIMO) communications systems, such as 802.11n. To date, however, no matrix inversion implementation has been devised which supports real-time operation for these standards. In this paper, we overcome this barrier by presenting a novel matrix inversion algorithm which is ideally suited to high performance floating-point implementation. We show how the resulting architecture offers fundamentally higher performance than currently published matrix inversion approaches and we use it to create the first reported architecture capable of supporting real-time 802.11n operation. Specifically, we present a matrix inversion approach based on modified squared Givens rotations (MSGR). This is a new QR decomposition algorithm which overcomes critical limitations in other QR algorithms that prohibits their application to MIMO systems. In addition, we present a novel modification that further reduces the complexity of MSGR by almost 20%. This enables real-time implementation with negligible reduction in the accuracy of the inversion operation, or the BER of a MIMO receiver based on this. [ABSTRACT FROM PUBLISHER]

Detection algorithms for multiple-input multiple-output (MIMO) wireless systems based on orthogonal frequency-division multiplexing (OFDM) typically require the computation of a QR decomposition for each of the data-carrying OFDM tones. The resulting computational complexity will, in general, be significant. Motivated by the fact that the channel matrices arising in MIMO-OFDM systems result from oversampling of a polynomial matrix, we formulate interpolation-based QR decomposition algorithms. An in-depth complexity analysis, based on a metric relevant for very large scale integration (VLSI) implementations, shows that the proposed algorithms, for a sufficiently large number of data-carrying tones and sufficiently small channel order, provably exhibit significantly smaller complexity than brute-force per-tone QR decomposition. [ABSTRACT FROM AUTHOR]

In a cellular wireless system, users located at cell edges often suffer significant out-of-cell interference. Assuming each base station is equipped with multiple antennas, we can model this scenario as a multiple-input single-output (MISO) interference channel. In this paper we consider a coordinated beamforming approach whereby multiple base stations jointly optimize their downlink beamforming vectors in order to simultaneously improve the data rates of a given group of cell edge users. Assuming perfect channel knowledge, we formulate this problem as the maximization of a system utility (which balances user fairness and average user rates), subject to individual power constraints at each base station. We show that, for the single-carrier case and when the number of antennas at each base station is at least two, the optimal coordinated beamforming problem is NP-hard for both the harmonic mean utility and the proportional fairness utility. For general utilities, we propose a cyclic coordinate descent algorithm, which enables each transmitter to update its beamformer locally with limited information exchange and establish its global convergence to a stationary point. We illustrate its effectiveness in computer simulations by using the space matched beamformer as the benchmark. [ABSTRACT FROM AUTHOR]

Network diversity multiple access protocols (NDMA) have been shown to considerably outperform previously proposed algorithms. However, several issues in their analysis remain unsolved, particularly under the assumption of imperfect collision-multiplicity detection in asymmetrical system configurations (i.e., configurations where the users have different channel and queuing statistics). To partially fill this gap, this paper presents a detailed study on stability, throughput and delay properties of asymmetrical nonblind NDMA protocols under such imperfect detection assumption. Additionally, the protocol formulation includes a carrier-sense mechanism which is found to improve system performance under finite-SNR environments and which can be useful in assessing the benefits of retransmission diversity on IEEE 802.11 wireless local area networks (WLANs). New expressions for the parameters and performance metrics of the NDMA protocol under different assumptions are here provided. These include the stability condition of a system without feedback to acknowledge correct packet reception and the derivation of the boundaries of the throughput region using a joint cross-layer optimization with respect to the probabilities of transmission and false alarm. Simulation results are used to further assess the performance of the protocol and to confirm the analytic results. [ABSTRACT FROM AUTHOR]

This paper studies the performance of multiple-input multiple-output (MIMO) systems employing multichannel beam-forming in the presence of unequal power cochannel interference and noise. Our results apply to a wide class of multichannel systems which transmit on the eigenmodes of the MIMO channel, allowing for transmission on any arbitrary number of eigensubchannels, with possibly different modulation formats, and with possibly unequal powers. New exact expressions are derived for the symbol error rate (SER) of each eigensubchannel, the global SER, and the outage probability. We also present simplified closed-form expressions for the high SNR regime, and show that cochannel interference has no effect on the diversity order, but leads to a loss in array gain. Our results are based on new closed-form exact and asymptotic expressions which we derive for the marginal ordered eigenvalue distributions of a certain class of finite-dimensional complex random matrices. [ABSTRACT FROM AUTHOR]

Adaptive algorithms based on in-network processing of distributed observations are well-motivated for online parameter estimation and tracking of (non)stationary signals using ad hoc wireless sensor networks (WSNs). To this end, a fully distributed least mean-square (D-LMS) algorithm is, developed in this paper, offering simplicity and flexibility while solely requiring single-hop communications among sensors. The resultant estimator minimizes a pertinent squared-error cost by resorting to i) the alternating-direction method of multipliers so as to gain the desired degree of parallelization and ii) a stochastic approximation iteration to cope with the time-varying statistics of the process under consideration. Information is efficiently percolated across the WSN using a subset of "bridge" sensors, which further tradeoff communication cost for robustness to sensor failures. For a linear data model and under mild assumptions aligned with those considered in the centralized LMS, stability of the novel D-LMS algorithm is established to guarantee that local sensor estimation error norms remain bounded most of the time. Interestingly, this weak stochastic stability result extends to the pragmatic setup where intersensor communications are corrupted by additive noise. In the absence of observation and communication noise, consensus is achieved almost surely as local estimates are shown exponentially convergent to the parameter of interest with probability one. Mean-square error performance of D-LMS is also assessed. Numerical simulations: i) illustrate that D-LMS outperforms existing alternatives that rely either on information diffusion among neighboring sensors, or, local sensor filtering; ii) highlight its tracking capabilities; and iii) corroborate the stability and performance analysis results. [ABSTRACT FROM AUTHOR]

Finding the positions of nodes in an ad hoc wireless sensor network (WSN) with the use of the incomplete and noisy distance measurements between nodes as well as anchor position information is currently an important and challenging research topic. However, most WSN localization studies have considered that the anchor positions and the signal propagation speed are perfectly known which is not a valid assumption in the underwater and underground scenarios. In this paper, semi-definite programming (SDP) algorithms are devised for node localization in the presence of these uncertainties. The corresponding Cramér-Rao lower bound (CRLB) is also produced. Computer simulations are included to contrast the performance of the proposed algorithms with the conventional SDP method and CRLB. [ABSTRACT FROM AUTHOR]

The code-constrained constant modulus algorithm (CCM) implemented with a stochastic gradient (SG) technique is a very effective and efficient blind approach for interference suppression when a communication channel is frequency-selective. In nonstationary wireless environments, users frequently enter and exit the system, making it very difficult for the receiver to compute a predetermined step-size. This suggests the deployment of mechanisms to automatically adjust the step-size in order to ensure good tracking of the interference and the channel. In this paper, the performance of blind CCM adaptive receivers for direct sequence code division multiple access (DS-CDMA) systems that employ stochastic gradient (SG) algorithms with variable step-size mechanisms is investigated. We propose a novel low-complexity variable step-size mechanism for blind CCM CDMA receivers. Convergence and tracking analyses of the proposed adaptation techniques are carried out for multipath channels. Finally, numerical experiments are presented for nonstationary environments, showing that the new mechanism achieves superior performance to previously reported methods at a reduced complexity. [ABSTRACT FROM AUTHOR]

In this paper, we consider the positioning problem in wireless sensor networks from a systems perspective, including aspects of the physical layer, channel, ranging algorithm and positioning algorithm. We argue that gains in positioning accuracy and robustness, can be achieved by extending the interface between the physical layer ranging algorithm and the higher layer positioning algorithm to include more information than just a single distance estimate per link. Towards this end, we propose and analyze a "soft" distance estimator. This estimator, differently from traditional distance estimators, outputs a list of likely distances for each link together with a list of weights similar to likelihoods. Hence, we broaden the interface between ranging and positioning algorithm, and provide additional information that the positioning algorithm can exploit. The soft distance estimator we propose is of comparably low complexity, and offers competitive performance while making no assumptions on a priori channel information or network synchronization. The analysis and simulations in this work are based on the channel models adopted by the IEEE 802.15.4a working group, and highlight the benefits and drawbacks of the proposed approach. [ABSTRACT FROM AUTHOR]

This paper addresses the problem of joint estimation of carrier frequency offset (CFO) and channel impulse response (CIR) in the uplink transmission of multicarrier code-division multiple-access (MC-CDMA) systems. A subspace-based blind estimator is proposed, which only uses the received signal and the desired user's spreading code to estimate the CFO and dR. The CFO is estimated using a one-dimensional linear search where the objective of the search is to minimize the determinant of a low dimensional matrix and the dIR is estimated as the unit-norm eigenvector corresponding to the minimum eigenvalue of this matrix. The identifiability of the CFO and dIR estimates is investigated by imposing asymptotic constraints on the spreading codes. Although the cost function used by the proposed estimator is nonlinear and nonconvex, it is locally convex in the neighbor- hood of the true CFO value. This property is used to formulate a simple two-stage CFO estimator. In the first stage, a coarse search is performed to lock into the local convexity region. Then, an adaptive algorithm is used in the second stage to finetune the CFO estimate. The "linearized" proposed estimator is shown to be unbiased. The simulation results show that the proposed estimator's performance is close to the Cramér-Rao lower bound (CRLB). [ABSTRACT FROM AUTHOR]

This paper examines the problem of target detection by a wireless sensor network. Sensors acquire measurements emitted from the target that are corrupted by noise, and initially make individual decisions about the presence/absence of the target. We propose the local vote decision fusion algorithm, in which sensors first correct their decisions using decisions of neighboring sensors, and then make a collective decision as a network. An explicit formula that approximates the system's decision threshold for a given false alarm rate is derived using limit theorems for random fields, which provides a theoretical performance guarantee for the algorithm. We examine both distance- and nearest-neighbor-based versions of the local vote algorithm for grid and random sensor deployments and show that, in many situations, for a fixed-system false alarm, the local vote correction achieves significantly higher target detection rate than decision fusion based on uncorrected decisions. The algorithm does not depend on the signal model and is shown to be robust to different types of signal decay. We also extend this framework to temporal fusion, where information becomes available over time. [ABSTRACT FROM AUTHOR]