In this paper, we present a novel approach for studying Boolean function in a graph-theoretic perspective. In particular, we first transform a Boolean function f of n variables into an induced subgraph Hf of the n-dimensional hypercube, and then, we show the properties of linearly separable Boolean functions on the basis of the analysis of the structure of Hf . We define a new class of graphs, called hyperstar, and prove that the induced subgraph Hf of any linearly separable Boolean function f is a hyperstar. The proposal of hyperstar helps us uncover a number of fundamental properties of linearly separable Boolean functions in this paper.

We investigate the joint optimization of the group power allocation and prebeamformer for joint spatial division and multiplexing (JSDM) in massive MIMO downlink systems. In contrast with the approximated block diagonalization (ABD) prebeamformer which is derived by heuristic method in the original JSDM scheme and is restricted to semi-unitary matrix, general prebeamforming matrix together with group power is optimized in the framework of per-user ergodic rate balancing. Thus, both flexibility and optimality are integrated in our work. To ease the difficulty in optimizing the exact ergodic rate, the deterministic approximation is employed. Based on the uplink-downlink duality approach, an iterative algorithm alternatively updating group power and prebeamformers is designed. It is shown that the optimization subproblems of the algorithm can be solved efficiently. Compared with the classic signal-to-noise-and-interference ratio (SINR) balancing algorithm for MISO downlink, the proposed algorithm has similar properties of both convergence and optimality. Numerical experiments are conducted to validate the effectiveness of the proposed algorithm.

It is recognized that, for digital symbols taken from a discrete and finite alphabet, perfect transmultiplexing at a signaling rate larger than the Nyquist rate can be achieved by modulation with overcomplete frame pulses. By invoking the spectral distribution theory of large random matrix, the spectral efficiency of digital transmission scheme via overcomplete frames in band-limited additive white Gaussian noise (AWGN) channel with discrete, finite, and uniform alphabets is investigated in this paper. It is shown that the proposed digital signaling scheme can asymptotically achieve the maximum spectral efficiency dictated by the Shannon capacity theorem for reliable transmission without employing signal shaping techniques. The extension to the case of non-white Gaussian noise/spectrally shaped channels is also considered. It is shown that the employment of Weyl-Heisenberg frames facilitates the optimal “water-filling” power allocation and rate adaptation by adjusting the signal amplitudes and energy-dispersion factors. Some numerical results are provided to support the theoretical finding.

As a promising solution to the spectrum scarcity problem, cognitive radio (CR) has received much attention recently, The key component of CR is the spectrum sensing technique that can detect the idling spectrum of the authorized user. Currently, the accuracy of spectrum sensing remains a significant constraint that limits the practical application of CR. In this paper, we propose an enhanced spectrum sensing scheme based on decoding feedback strategy, where the secondary receiver decodes the desired signal and utilizes the remaining part for spectrum sensing. Specifically, we embed this concept into the underlay scheme with no silent slot in order to improve the throughput of the secondary user. The corresponding sensing performance is analyzed, based on which we formulate the optimization problems and derive the optimal transmission parameters. Simulation results show that, the proposed scheme can significantly improve the achievable throughput of the secondary system comparing to the traditional underlay scheme.

This paper studies robust beamforming design in a relay-assisted multiuser multi-antenna cognitive radio (CR) network that coexists with a primary radio (PR) system via opportunistic spectrum sharing (OSS). The robustness is with respect to imperfect channel knowledge at transmitter. Since the CR-to-PR interference needs to be carefully mitigated in OSS, the recently developed cognitive beamforming (CB) technique is employed by CR base station (BS), CR relay station (RS), and secondary receivers (SRs). Meanwhile, joint CR-BS and CR-RS transmit precoding is adopted to solve the weighted sum rate maximization (WSRM) problem. To cope with the non-convex problem, we apply the classic altering optimization process, namely the Blahut-Arimoto algorithm. Interestingly, it is shown that within each iterative step, we solely need to treat some semidefinite programming (SDP) subproblems with linear matrix inequalities (LMIs), which can be efficiently solved by standard convex optimization methods. Numerical results demonstrate significant performance gain of our approach over non-robust beamforming strategy.

We propose continuous power allocation strategies for secondary users (SUs) based on sensing the primary user (PU) channels in a multiband cognitive radio (CR) network. Unlike the conventional sensing-based spectrum sharing, where there are two transmit power levels corresponding to whether the PU is sensed present or not, in the proposed strategy, the power levels are continuous functions of the sensing statistics, and optimized with respect to the achievable rate of the SU. The power control process consists of two phases: in the first phase, the SU listens to the multiple bands licensed to the PU and obtains the received signal energies on these bands; in the second phase, the SU adjusts its transmit power levels on these bands based on the sensing results. Simulation results demonstrate that the proposed strategies can significantly improve the achievable throughput of the SU compared to the conventional methods.

In this paper, spectrum-sensing based resource allocation for a cognitive radio network that contains multiple secondary users (SUs) and a primary user (PU) is investigated. Each SU firstly performs the individual spectrum sensing and forwards the result to a fusion center to make the global decision. If PU is determined absent, then SUs access the primary band with their regular transmit power. Otherwise SUs still access the licensed band but with a limited transmit power to avoid harmful interference to PU. We target at designing the optimal sensing time, bandwidth allocation, power allocation, and the detection threshold to maximize the total achievable rate of SUs subject to the constraints of their peak transmit powers as well as the interference constraint to PU. The original optimization is divided into several subproblems that can be solved separately with low computational complexity. Numerical results demonstrate the superiority of the proposed one over the existing candidates.

In this paper, three novel efficient power control algorithms are proposed for V-BLAST system with both total power constraint (TPC) and per-antenna power constraints (PAPC). Through analysis of the Karush-Kuhn-Tucker (KKT) conditions of the optimization problem, we find out that the antennas with the optimal power control can be divided into two sets. The optimal allocated power (OAP) of each antenna in the first set is its per-antenna power constraint, and the OAPs of the antennas in the second set can be obtained easily through a new optimization problem with TPC only. Based on the above analysis, three novel efficient power allocation algorithms are developed, which have much lower computational complexities compared with the existing dual update method. Simulation results are presented to demonstrate the efficiency of the proposed power control schemes.

The detection of orthogonal frequency division multiplexing (OFDM) for cognitive radio is considered in this paper. A frequency-selective fading channel is considered and the receiving process is modeled with timing and frequency offsets. Firstly, the authors propose a new decision statistic based on time-domain cross-correlation of the cyclic prefix (CP) embedded in OFDM signals. The probability distribution functions (PDFs) of the statistics under both hypotheses of primary signal absence and presence are derived. Estimation of the timing and frequency offset is obtained through the maximum likelihood method and the received signals are modified. Then another new decision statistic based on frequency-domain cross-correlation of the pilot tones (PTs) is proposed whose PDF is also analyzed. Then, through the likelihood ratio test, the authors utilize CP and PT jointly and propose a global test statistic. The theoretical probabilities of false alarm (PFA) and detection are derived, and the theoretical threshold for any given PFA is proposed. The simulation results show that the proposed spectrum-sensing scheme has excellent performance, especially under very low signal-to-noise ratio (SNR).

In this paper, we study resource scheduling in a relay-assisted cognitive radio network with the orthogonal frequency division multiple access (OFDMA) scheme adopted to cope with the multi-user configuration. We discuss the optimization over the relay assignment, subcarrier allocation, per-node power control, and heterogenous quality-of-service (QoS) provisioning. The discrete characters of relay assignment and subcarrier allocation lead to a mixed integer nonlinear program (MINLP) whose computational complexity grows exponentially with the number of subcarriers. In an attempt to treat this complication, an asymptotically optimal solution based on the dual-analysis framework is proposed, for which we investigate the optimality of the dual method in the two scenarios: with and without subcarrier pairing. For both cases, it is shown that zero-duality-gap is achievable and the joint scheduling problem can be solved through a series of subproblems whose closed-form solutions are found. Moreover, for the scenario without subcarrier pairing we prove that the relaxation of allocation variables does not affect global optimality, i.e., even if the allocation variables are relaxed into continuous ones, binary variables are always retrievable as optimal solutions. Numerical examples are provided to corroborate the efficiency of the proposed approach.

In this paper, we consider channel estimation for amplify-and-forward (AF) relay network with orthogonal frequency division multiplexing (OFDM) modulation. We propose a superimposed training strategy at relay that allows the destination node to obtain the separate channel information of the source-->;relay link and the relay-->;destination link. The proposed training strategy only requires two transmission phases and is thus compatible with the two-phase data transmission scheme, i.e., the training can be embedded into data transmission. Since the optimal minimum mean square error (MMSE) estimator and the maximum a posteriori (MAP) estimator cannot be expressed in close-forms, we propose to obtain the initial channel estimates from the low complexity suboptimal linear estimators, e.g., linear minimum mean-square error (LMMSE) or least square (LS), and then resort to iterative approaches to improve the estimation accuracy.

In this letter, we propose a novel pansharpening method which enhances the spatial resolution of low-spatial-resolution multispectral (MS) images through proportional detail injection (PDI) based on the generalized relative spectral response (GRSR). In the algorithm, the spatial details are extracted as the difference between the panchromatic (PAN) image and the resampled low-resolution PAN image. Then, the spatial details are further modulated and injected into each band of the resampled MS image according to the proportion of the intensity of the low-frequency component of each band to the offset-removed synthetic intensity component obtained according to the GRSR. Both quantitative and qualitative evaluations demonstrate the superiority of the proposed method in terms of spatial enhancement, spectral fidelity, and computational complexity.

In this paper, we consider the channel estimation for the classical three-node relay networks that employ the amplify-and-forward (AF) transmission scheme and the orthogonal frequency division multiplexing (OFDM) modulation. We propose a superimposed training strategy that allows the destination node to separately obtain the channel information of the source→relay link and the relay→destination link. Specifically, the relay superimposes its own training signal over the received one before forwarding it to the destination. The proposed training strategy can be implemented within two transmission phases and is thus compatible with the two-phase data transmission scheme, i.e., the training can be embedded into data transmission. We also derive the Cramér-Rao bound for the random channel parameters, from which we compute the optimal training sequence as well as the optimal power allocation. Since the optimal minimum mean square error (MMSE) estimator and the maximum a posteriori (MAP) estimator cannot be expressed in closed-form, we propose to first obtain the initial channel estimates from the low complexity linear estimators, e.g., linear minimum mean-square error (LMMSE) and least square (LS) estimators, and then resort to the iterative method to improve the estimation accuracy. Simulation results are provided to corroborate the proposed studies.

In this paper, the optimal analog network coding strategy (or relay beamforming) in MIMO two-way relay channels is studied. The weighted sum rate is used as the optimization criterion. Motivated by the classic Arimoto-Blahut algorithm, an iterative algorithm, which monotonically improves the cost function, is designed to update the relay beamformer. This relay beamforming algorithm is further extended to the case of joint source and relay beamforming. We show that in the iteration of the algorithms, the updates of the relay beamforming matrix and the source beamforming matrices are accomplished by solving a series of quadratic programs and quadratically constrained quadratic programs, which are of low complexity. Simulations are presented to compare the proposed algorithms with the performance upper bounds and the existing algorithms.

A low complexity algorithm is proposed for estimating the multipath time-of-arrival (TOA) and attenuation factors from a noisy received signal consisting of multiple overlapped echoes. Different from the conventional projection onto convex sets (POCS) method, the proposed approach harnesses the sparse property of multipath channel and the ℓ1-norm is adopted as the measurement of sparsity. The TOA estimation problem is solved by a series of projection onto convex sets, including hypersphere and hyper-polyhedra. The computational complexity of the modified POCS algorithm is O(N log N) per iteration with N being the length of the received signal. Simulation results confirm that the proposed approach provides better performance in terms of temporal resolution and robustness to noise compared with the matched filtering and the conventional POCS method.

In this paper, we consider a cognitive radio (CR) system in non-ideal fading wireless channels and propose a cooperative spectrum sensing scheme based on linear parallel access channel (PAC),serving as an alternative way to improve the cooperative spectrum sensing performance. We assume that the gains of the observation and transmission channels are all known which is available since we use standard preamble-aided channel estimation techniques to require channel state information. The key feature of the proposed scheme is that the observations are transmitted by amplify and forward transmissions from part of cognitive users (CUs) to a fusion center (FC) while the other CUs keep silent. The optimal weighting coefficients are obtained by maximizing the deflection coefficient under transmit power constraint and the optimal number and set of CUs in cooperation is searched by a sequential forward selection algorithm. Simulation results illustrate that the proposed optimal cooperative spectrum sensing scheme can significantly improve the spectrum sensing performance.

In this paper, cooperative beamforming in the context of wireless sensor networks with random mobile nodes is analyzed. In the first part, we use two dimensional Brownian motion to model mobile nodes, which are assumed to follow Gaussian distribution at every time. The concept of overall outage probability is introduced, and its lower and upper bounds are derived theoretically. In the second part, we extend the Brownian motion to any form and prove the beampattern with minimum mainlobe as well as maximum three-dB sidelobe region can be got simultaneously, then we design a low complexity algorithm to arrange sensor node locations to produce this optimum beampattern. Simulation results show that the overall outage probability and its lower and upper bounds are in good agreement, and compared with uniform and Gaussian distribution, the beampattern produced by the algorithm has sharpest mainbeam and significantly small sidelobe peaks.

In this paper, we study the linear transmission (or beamforming) techniques in MIMO broadcast channels (BC) and propose beamforming algorithms for rate optimization. Our method for algorithm design draws on ideas from the classic Arimoto-Blahut algorithm. Like Arimoto-Blahut algorithm, our algorithms operate iteratively in a two-stage manner, i.e., in one iteration, the first stage is for updating test conditional probabilities, the second for updating beamforming matrices, and both stages are composed of simple convex subproblems. Optimal transmit beamforming for weighted sum rate maximization, weighted rate balancing, and proportional fair rate allocation in MIMO BC are considered. We rediscover the weighted sum-rate beamforming weighted minimum mean square error (WSRBF-WMMSE) algorithm in [1] for MIMO BC. And we apply our method together with uplink-downlink duality to optimal beamforming for weighted rate balancing and proportional fair rate allocation in MIMO BC. We show experimentally that our algorithm for rate balancing requires less computation time to converge than existing algorithms without performance loss, and the algorithm for proportional fair rate allocation can achieve the balance between sum rate maximization and minimum rate maximization.

Two Newton-type algorithms using the generalized complex modulus (GCM) criterion for blind equalization and carrier phase recovery are proposed. First the partial Hessian and full Hessian of the real GCM loss function with complex valued arguments are calculated by second-order differential. Then an adaptive pseudo Newton learning algorithm and a full Newton learning algorithm are designed. By using the matrix inversion lemma, both Newton algorithms can be implemented with a computational complexity of O(L2) efficiently, where L is the length of equalizer. Simulation results demonstrate that the two Newton algorithms can achieve automatic carrier phase recovery and exhibit fast convergence rates.

A new signal-selective direction finding algorithm which exploits the property of the cyclostationarity of incoming signals is proposed. After dimensionality reducing by projecting the observed array data onto the signal subspace, the array manifold matrix is identified by the simultaneous diagonalization structure of the matrix pencil consisting of the cyclic correlation matrix and the cyclic conjugate correlation matrix. Then the direction-of-arrivals (DOAs) are obtained from the phase-differences of the estimated array manifold matrix. Simulation results demonstrate that the proposed algorithm is superior to the cyclic-MUSIC and cyclic-ESPRIT in terms of the root mean squares errors (RMSEs) of the DOA estimates.

This paper presents a new progressive distributed estimation scheme (DES) along with the power scheduling among sensors under AWGN channels. The progressive DES consists of a transmission bit allocation scheme and a quasi best linear unbiased estimate (BLUE) of the unknown parameter at each sensor. This scheme is shown to outperform the traditional progressive DES. Moreover, the power scheduling among sensors, which minimizes the total transmission power subject to the desired MSE performance tolerance, is formulated as a convex problem and the optimal solution is derived analytically.

A new algorithm for direction-of-arrival (DOA) estimation of non-Gaussian sources is proposed. Based on the joint diagonalization structure of multiple fourth-order cumulant matrices, a novel cost function is designed and a new spatial spectrum for direction finding at hand is derived. Unlike the subspace-based techniques, it is not necessary to determine the number of sources in advance for the proposed algorithm. Moreover, the proposed method is insensitive to the spatially correlated noise. Simulation results are provided to demonstrate the performance of the proposed approach.

The issue of multicarrier transmission with high spectral efficiency and low interference over time-frequency (TF) dispersive channels is addressed in this paper. Specifically, we propose a digital signal transmission scheme employing overcomplete Weyl-Heisenberg (W-H) frames as modulation pulses. The proposed W-H frame transmission scheme, which relaxes the conventional orthogonality/biorthogonality constraints for perfect transmultiplexing, can be viewed as a generalization of the orthogonal frequency division multiplexing (OFDM) and nonorthogonal (biorthogonal) frequency division multiplexing (NOFDM). With this new transmission scheme, one can not only achieve higher spectral efficiency effectively but also design the pulse shape more flexibly to combat the impact of the propagation channel. In order to optimally mitigate the intersymbol interference (ISI) and interchannel interference (ICI) caused by the wireless channel, the selections of the Gaussian pulse shape and T-F grid parameters are addressed jointly from the viewpoint of minimum energy perturbation for several typical channel scattering functions. Simulation results are presented to study the performance of the proposed system. It is shown that the proposed overcomplete W-H frame transmission system with a lower-order constellation is more robust against the TF dispersive channels than the incomplete Riesz basis system employing a higher-order constellation with the same spectral efficiency.

This letter proposes a blind carrier frequency offset (CFO) estimator for orthogonal frequency-division multiplexing (OFDM) systems based on the frequency analysis of the received signal, and derives a closed-form CFO estimate. Since only one OFDM symbol is utilized instead of multi-symbol averaging, the proposed method is effective even when the CFO is time varying. Finally, analysis and simulation results indicate the outstanding performance of the proposed estimator.

Carrier frequency offset (CFO) estimation is a critical problem in orthogonal frequency-division multiplexing (OFDM) systems. This letter proposes a blind CFO estimator based on smoothing the signal power spectrum. A closed-form CFO estimate is also presented, which greatly reduces the computational complexity of the proposed method. Analysis and simulation results show that the proposed estimator is very effective for OFDM systems.

We present a quantized precoding scheme for the V-BLAST system. The precoding matrix is decomposed into the multiplication of a beamforming matrix and a power allocation matrix. They are obtained by solving an optimization problem that maximizes the minimum post-detection SINR of the MMSE-SIC receiver under a total power constraint. The beamforming matrix is constrained to be in a predetermined codebook. We show that the precoder of UCD- VBLAST happens to be the optimal solution of the problem. Therefore, the proposed scheme can be regarded as a natural but nontrivial limited feedback version of UCD-VBLAST Simulation results are presented to demonstrate the effectiveness of the proposed scheme.

This paper, we introduce two new reduced-complexity algorithms on sphere decoding (SD). The two algorithms execute in different ways to avoid unnecessary recomputations. Compared with the existing modified SD, our algorithms save a considerable amount of unnecessary computation and also perform exact maximum-likelihood (ML) detection.

A successive interference cancellation (SIC) algorithm based on breadth-first search (BFS) is developed to achieve a soft-input soft-output detector via the tree structure of the MIMO system model in this paper. Instead of visiting all nodes of the tree, the proposed BFS-SIC algorithm only browses and extends those paths with large metrics. If paths are enough, the performance of BFS-SIC algorithm can approach that of sphere decoding but is much more flexible due to its providing a good tradeoff between complexity and performance. Moreover, the BFS-SIC algorithm possesses path metrics including only scalar operations rather than matrix operations. Simulation results demonstrate the effectiveness of the proposed algorithm.

A new algorithm for estimating the frequencies of the two- dimensional (2-D) exponentials is presented. Via joint diagonalization of a set of matrices, the estimation of the automatically paired 2-D frequency parameters are obtained. Hence the proposed algorithm can avoid the computationally expensive 2-D parameters pairing procedure. Simulation results validate the high performance of the new algorithm.

This letter analyzes the bit error probability (BEP) performance of multiple-input/multiple-output (MIMO) systems employing maximal ratio transmission/maximal ratio combining (MRT/MRC) diversity in Rayleigh fading channels, where the channels are known at the both transmitter and receiver. An exact closed-from expression for the BEP performances of square and rectangular quadrature amplitude modulation (QAM) is derived for systems with arbitrary number of transmit antennas and two receive antennas. Moreover, by approximating the probability density function of output signal-to-noise ratio (SNR) and using the close approximation of BEP performance with QAM in additive white Gaussian noise (AWGN), we obtain a simple expression for arbitrary number of transmitter antennas and receive antennas. Simulations show that our approximation is very close to the exact one.

A novel and unified framework is presented to evaluate the exact bit error probability (BEP) ofM-ary pulse amplitude modulation (PAM) and quadrature amplitude modulation (QAM) over additive white Gaussian noise (AWGN) channel for any bit-mapping techniques. Tight approximations are also provided for both of them. Using these results, we obtain the exact and tight approximate BEP expressions for maximal-ratio combining (MRC) and selection combining (SC) in Rayleigh fading.

This paper proposes a distributed incremental quantization and estimation scheme by which each sensor can make a maximum likelihood estimation (MLE) of the unknown parameter based on its local observations and the quantized messages transmitted by the previous sensor. We derive the upper bound of the estimation mean squared error (MSE) of the proposed scheme, and propose the bit allocation algorithms, which minimize the total required bandwidth while ensuring a given MSE performance. Simulation results demonstrate that the proposed scheme requires much less bandwidth, but achieves around 40% MSE reduction compared with the distributed adaptive quantization and estimation scheme.

In this paper we present a quantized preceding scheme for the spatial multiplexing system equipped with MMSE-SIC receiver. The preceding matrix, consisting of a beamforming matrix and a power allocation matrix, is selected to maximize the minimum post-detection SINR of the MMSE-SIC receiver. Rather than the whole beamforming matrix, the beamforming vector for each data stream is selected sequentially from a predetermined codebook. The advantage of the sequential selection is that the computational burden and storage requirements are significantly reduced while the performance degradation is marginal. This is especially appealing for mobile terminals due to their physical limitation. The codebook is constructed by the Grassmannian beamforming criterion. Moreover, the proposed scheme is flexible enough to allow for a change in the number of data streams without redesigning the codebook. Simulation results are presented to demonstrate the effectiveness of the proposed scheme.

In this paper, we introduce two novel successive interference cancellation (SIC) algorithms for multiuser detection (MUD) in direct-sequence code-division multiple-access (DS-CDMA) systems. One is proposed for receivers in the downlink and the other for receivers in the uplink. The downlink SIC algorithm is a blind MUD which consists of two parts: estimating the signature sequences and amplitudes of interference users first, and then implementing the SIC detection. The uplink SIC algorithm is implemented by accurately estimating the amplitudes of each active user first and then performing the QR-decomposition based SIC. Simulation results show that the proposed algorithms are near-far resistant and outperform the conventional well-known SIC algorithms significantly.

This paper presents a distributed estimation scheme (DES) along with the bit allocation algorithm, which minimizes the total required bandwidth while ensuing a given average estimation mean-squared error (MSE). The DES comprises of a probabilistic Lloyd-Max quantization rule, and a best linear unbiased estimation (BLUE). Since the optimum bit allocation problem does not have a closed form, we present a suboptimal solution utilizing the alternating minimization (AM) algorithm. Finally, simulation results are provided to show the effectiveness of the proposed DES along with the bit allocation algorithm.

Carrier frequency offset (CFO) is an important problem in multi-input multi-output orthogonal frequency-division multiplexing (MIMO-OFDM) systems. This letter proposes a blind CFO estimator for MIMO-OFDM systems with constant modulus constellations in the frequency-selective fading channels. A closed-form CFO estimate is also provided, which greatly reduces the computational complexity of the proposed method. Analysis and simulation results demonstrate that the proposed estimator is very effective for MIMO-OFDM systems.

This letter analyzes the performance of several typical one-bit universal distributed estimation schemes (DESs) in bandwidth-constrained sensor networks. We theoretically obtain the mean and mean-square error (MSE) of the DES based on binary form (BF) of the observation along with the MSE upper bound. It is proved that in some cases, the BF-based DES has larger MSE than the DES based on the complementary cumulative distribution function (CCDF) of the observation. Moreover, performance analysis is presented for the CCDF-based and BF-based DESs employing probabilistic quantization, respectively.

Linear precoding is a simple method to improve the performance of V-BLAST architecture. However, it requires perfect channel state information at the transmitter, which necessitates a great amount of feedback. In this paper, we propose a linear precoding scheme for the V-BLAST architecture with an MMSE-SIC receiver. The whole scheme includes the design of an unquantized precoder and its finite rate feedback. The unquantized precoder, consisting of a beamforming matrix and a power allocation matrix, is designed by maximizing the minimum post-detection SINR of the MMSE-SIC receiver. In addition to the optimal precoder, we give a suboptimal solution which is shown to be asymptotically optimal at low SNR. The advantage of this suboptimal solution is that its extension to finite rate feedback is straightforward. Specifically, beamforming vectors are selected sequentially from a predetermined codebook and the indices are fed back. Besides, elements of the power allocation matrix are scalar quantized and fed back. Theoretical analysis reveals that the unquantized beamforming vectors are uniformly distributed on the unit sphere, leading to a very natural codebook design criterion - the Grassmannian criterion. Moreover, the proposed scheme is flexible enough to allow for a change in the number of data streams without redesigning the codebook. Simulation results are presented to demonstrate the effectiveness of the proposed scheme.

Based on an energy conservation relation, the tracking performance of a family of generalized constant modulus algorithm (GCMA) is analyzed in this paper. Both theoretical analysis and simulation results validate the excellent tracking performance of the extended constant modulus algorithm (ECMA) in some situations.

Carrier frequency offset (CFO) estimation is critical in practical orthogonal frequency-division multiplexing (OFDM) systems. In this paper, we propose two novel blind CFO estimators named the variance estimator (VE) and the approximate variance estimator (AVE) respectively with high bandwidth efficiency. Both of them are based on minimization of the variance of the received symbols on each subcarrier, but the AVE only utilizes a half of OFDM symbols needed in the VE at the cost of a little degradation in performance. Simulation results show that the proposed estimators are attractively efficient for the future wireless communications.

A novel oblique projection based detection scheme is proposed to reduce the computational cost of the vertical Bell Laboratories Layered Space-Time (V-BLAST) receivers. It constructs the ordinary zero-forcing (ZF) and minimum mean- square error (MMSE) detectors by oblique projector implementation, and has a computationally efficient way via projection matrix recursion. Different from the existing ones, we design a so-called "inverse" detection order to facilitate this new approach, whose complexity compares favorably with the competing alternatives. Theoretical analysis and simulation results show the efficiency of the proposed algorithm.

For code division multiple access (CDMA) systems operating over dispersive channels, the nested recursive least-squares (RLS)-type algorithm, based on minimum output energy (MOE) criterion, attracts much attention as an efficient interference suppression scheme for its simple adaptive recursion. In this letter, we investigate its performance analysis. Special attention is paid to theoretical issues concerning the global convergence and convergence dynamics performance. It is shown that this algorithm exhibits a global minimum attained, and the convergence is immune to the eigenvalue spread of the correlation matrix of received signal vector. Simulation results demonstrate that the nested RLS-type algorithm has indeed the expected convergence performance

This letter proposes a novel second-order statistics-based sequential blind extraction algorithm via approximate joint diagonalization of autocorrelation matrices. The proposed algorithm is computationally simple and efficient and can extract specific sources if some a priori knowledge is available. An experiment on real-world ECG data validates the good performance of the proposed algorithm

The Bayesian beamformer under minimum mean square error (MMSE) criterion has been reported to perform well against uncertainty in source direction of arrival (DOA), but its performance depends on the location of phase reference. This letter proposes a phase-invariant MMSE criterion under which the Bayesian beamformer is independent of the location of phase reference. An iterative algorithm is developed for designing such a new Bayesian beamformer. Numerical examples demonstrate that the new Bayesian beamformer can provide better performance than the existing Bayesian MMSE beamformer for either uniform or non-uniform linear array.

In this letter, we propose a new optimal filtering algorithm for systems with multiplicative noise process under correlated additive noises. Different from the traditional derivation method of constructing innovation process, we derive the new algorithm based on the well-known projection theorem, which is simple in principle, convenient for recursive estimation and flexible for application. The new recursive filter is superior to the conventional Kalman filter in flexibility and applicability.

For joint blind channel estimation and multiuser detection in direct-sequence code division multiple access (DS-CDMA) systems, a combined scheme is proposed, which couples the recursive least-squares (RLS) adaptations and the PASTd subspace tracking algorithm. A major advantage of the RLS-PASTd algorithm is that it admits an adaptive implementation with low computational complexity instead of singular value decomposition. Simulation results demonstrate the fast tracking capability and high steady-state estimation accuracy of the proposed algorithm in both the time-invariant and time-variant environments

A new blind channel estimator for multi-carrier PSK transmission is developed. By exploiting the structure of PSK signals, the proposed channel estimator can identify non-minumum phase channel using only the power spectrum of received signals. Unlike existing subspace-based channel estimators, our scheme is based on symbol-block-by-symbol-block approach, and, thus, the channel tracking ability is improved. Simulation results demonstrate that the proposed blind receiver exhibits satisfactory performance.

In this letter, the problem of blind separation of n independent sources from their m linear instantaneous mixtures is considered. First, a generalized contrast function is defined as a valuable extension of the existing classical and nonsymmetrical contrast functions. It is applicable to the overdetermined blind separation (m > n) with an unknown number of sources, because not only independent components but also redundant ones are allowed in the outputs of a separation system. Second, a natural gradient learning algorithm developed primarily for the complete case (m = n) is shown to work as well with an n × m or m × m separating matrix, for each optimizes a certain mutual information contrast function. Finally, we present stability analysis for a newly proposed generalized orthogonal natural gradient algorithm (which can perform the overdetermined blind separation when n is unknown), obtaining an expectable result that its local stability conditions are slightly stricter than those of the conventional natural gradient algorithm using an invertible mixing matrix (m = n).

A totally blind ZP-OFDM receiver is derived in this letter. Unlike existing blind receivers that are based on the scheme that channels are estimated first and then equalizers (MMSE or ZF) are adopted to detect information symbols, our scheme is based on the symbol-block-by-symbol-block direct symbol estimation approach. The proposed receiver is also robust in rapid channel variation cases as long as channel responses remain almost constant during one symbol block. Simulation results show that the BER performance of our scheme and linear MMSE (and ZF) equalizers with perfect CSI are indistinguishable in SNR scenarios of interests, despite the fact that our scheme is totally blind.

Blind source separation (BSS) is of high interest in practical applications. Some approaches are proposed for the unknown number of sources of the BSS problem. However, they only consider the overdetermined case with the number of sensors more than the number of sources. To implement the practical BSS without prior assumption on the number of sources, we propose a new BSS method. It uses the unsupervised robust C prototypes algorithm to estimate the mixing matrix and then makes the estimation of sources. Simulations using speech signals confirm the validity of the proposed method.

For synchronous direct-sequence code-division multiple access (DS-CDMA) systems, a new criterion based on minimum probability of error (MPE) is proposed for designing decision feedback detectors (DFDs) instead of the conventional minimum mean-squared error criterion. The MPE criterion-based DFD (MPEDFD) tries to minimize the average/conditional probability of error adaptively. Study of bit-error rate and convergence performance shows that the MPEDFDs work pretty well after a short training sequence. Moreover, the modified MPEDFD is applicable for the asynchronous DS-CDMA systems with multipath.

By generalizing the definition of complex modulus, this letter presents a family of generalized constant modulus algorithms whose special examples includes not only the well-known constant modulus algorithm, but also the recently proposed sign Godard algorithm, square contour algorithm (SCA), generalized SCA, and sign SCA

The paper presents a Kalman filtering algorithm based on nonlinear principal component analysis (PCA) with prewhitening for blind source separation (BSS), and compares the new algorithm with other algorithms. Simulations show that, for BSS, the Kalman filtering algorithm has a faster convergence rate and a much better tracking capability, compared with the existing natural gradient algorithm for independent component analysis (ICA), the RLS algorithm and the natural gradient based RLS-type algorithm for nonlinear PCA.

In this letter, we introduce a novel blind multiuser detector based on the affine projection algorithm (APA) for direct-sequence code-division multiple-access (DS-CDMA) systems. Computational complexities of least mean-squares (LMS), recursive least squares (RLS), Kalman, and APA-based blind multiuser detection algorithms are compared. The proposed technique has similar convergence and tracking properties as the RLS algorithm with much reduced computational cost. Simulation results are presented to compare our APA-based algorithm with the blind LMS, RLS, and Kalman schemes.

This letter proposes a recursive oblique projector. To understand better the recursive oblique projector, we provide a geometrical interpretation of recursive computation and present a brief numerical example.

The blind source separation (BSS) problem with an unknown number of sources is an important practical issue that is usually skipped by assuming that the source number n is known and equal to the number m of sensors. This letter studies the general BSS problem satisfying m ≥ n. First, it is shown that the mutual information of outputs of the separation network is a cost function for BSS, provided that the mixing matrix is of full column rank and the m×m separating matrix is nonsingular. The mutual information reaches its local minima at the separation points, where the m outputs consist of n desired source signals and m−n redundant signals. Second, it is proved that the natural gradient algorithm proposed primarily for complete BSS (m n) can be generalized to deal with the overdetermined BSS problem (m>n), but it would diverge inevitably due to lack of a stationary point. To overcome this shortcoming, we present a modified algorithm, which can perform BSS steadily and provide the desired source signals at specified channels if some matrix is designed properly. Finally, the validity of the proposed algorithm is confirmed by computer simulations on artificially synthesized data.

This paper proposes a novel cross-correlation neural network (CNN) model for finding the principal singular subspace of a cross-correlation matrix between two high-dimensional data streams. We introduce a novel nonquadratic criterion (NQC) for searching the optimum weights of two linear neural networks (LNN). The NQC exhibits a single global minimum attained if and only if the weight matrices of the left and right neural networks span the left and right principal singular subspace of a cross-correlation matrix, respectively. The other stationary points of the NQC are (unstable) saddle points. We develop an adaptive algorithm based on the NQC for tracking the principal singular subspace of a cross-correlation matrix between two high-dimensional vector sequences. The NQC algorithm provides a fast online learning of the optimum weights for two LNN. The global asymptotic stability of the NQC algorithm is analyzed. The NQC algorithm has several key advantages such as faster convergence, which is illustrated through simulations.

This paper focuses on a multicarrier direct-sequence code-division multiple-access system using frequency spread coding, which was proposed by Matsutani and Nakagawa in 1999. Although this system is attractive due to its inherent frequency diversity gain without time or frequency redundancy, it is disadvantaged by its exponential complexity in the maximum likelihood sequence decoding (MLSD). To cope with such a bottleneck, this paper proposes a fast MLSD method which consists of a searching process along the eigenvectors of a symmetric matrix and a refining process. The fast MLSD method is compared with the normal MLSD method via theoretical analysis and simulation results.

Many independent component analysis (ICA) algorithms have been proposed for blind source separation. These algorithms belong to the LMS-type algorithm in natural. Hence, the choice of the step-size reflects a tradeoff between misadjustment and the speed of convergence. Based on the separation state of outputs of the neural network for ICA, the paper develops a fuzzy inference-based step-size selection algorithm. The fuzzy inference system consists of two inputs (the second- and higher order correlation coefficients of output components) and one output (the fuzzy learning rate). In this way, the ICA algorithms become more efficient, which is verified by simulation results.

By using the natural gradient on the Stiefel manifold to minimize a nonlinear principle component analysis criterion, this letter proposes a new adaptive recursive-least-squares (RLS) algorithm with prewhitening for blind source separation (BSS), which makes full use of the orthogonality constraint of the separating matrix. Simulations show that the new natural-gradient-based RLS algorithm has faster convergence than the existing least-mean-square algorithms and RLS algorithm for BSS.

This paper proposes a cross-associative neural network (CANN) for singular value decomposition (SVD) of a non-squared data matrix in signal processing, in order to improve the convergence speed and avoid the potential instability of the deterministic networks associated with the cross-correlation neural-network models. We study the global asymptotic stability of the network for tracking all the singular components, and show that the selection of its learning rate in the iterative algorithm is independent of the singular value distribution of a non-squared matrix. The performances of CANN are shown via simulations.

In recent years, some blind adaptive multiuser detectors have been presented to implement multiuser detection with only the knowledge of the desired user's spreading code and propagation delay. We find, in this paper, that with the aid of the antenna array, multiuser detection can be achieved even without the desired user's spreading code and propagation delay, provided that the direction of arrival of every user is different from each other. Propagation delay and array response estimation methods are also proposed in this paper.

The paper addresses the harmonic retrieval problem in colored noise. As contrasted to the reported studies in which Gaussian noise was assumed, the present paper concentrates on additive non-Gaussian ARMA noise. The authors propose a unified prefiltering-based approach to this problem. The approach is hybrid in the sense that 3rd-order cumulants are first used to identify the AR part of the non-Gaussian noise process, and then correlation-based high resolution methods may be used for the filtered output process to estimate the parameters of harmonics. Simulation examples are presented to demonstrate the high resolution of this approach.

This paper addresses the problem of multiple source signals separation in noise. As contrasted to the reported studies in which white noise in different sensors with same noise covariance was assumed, the additive noise sensors considered in this paper have different noise covariance. An extended algorithm for multiple sources extraction (EAMUSE) is proposed. The effectiveness of our approach is demonstrated through standard simulation examples.

Addresses the harmonic retrieval problem in colored noise. As contrasted to the reported studies in which Gaussian noise was assumed, this paper focuses on additive non-Gaussian ARMA noise. Our approach is hybrid in the sense that third-order cumulants are first used to identify the AR part of the non-Gaussian noise process, and then correlation-based high-resolution methods are used for the filtered process to estimate the number of harmonics and their frequencies. Simulation examples are presented to demonstrate the high resolution of this approach.>

The two-dimensional harmonic retrieval is examined in theory by confirming that the 2-D sinusoids in white noise are modeled as a special 2-D autoregressive moving average (ARMA) process whose AR parameters are identical to the MA ones. A new analysis technique for resolving 2-D sinusoids in white noise is proposed.>