Intelligent reflecting surface (IRS) is emerging as a transformative technology for next-generation wireless communication and sensing systems. In this letter, we consider the problem of adaptive beamforming (ABF) for a single-antenna receiver aided by a nearby IRS, where the receiver aims to extract signals from a desired direction in the presence of$K$strong,unknowninterferences. Specifically, we propose to maximize the signal-to-interference-plus-noise ratio (SINR) by optimizing the reflection coefficients of the IRS. Unlike conventional ABF methods, we do not have direct access to the signal-plus-interference covariance matrix. Instead, only a limited number of quadratic compressive measurements can be obtained. To close this gap, we present a sample-efficient analytical solution via implicit inference of the interference covariance matrix. Simulation results demonstrate that our method significantly improves the SINR over state-of-the-art approaches.

Millimeter wave/Terahertz (mmWave/THz) communication with extremely large-scale antenna arrays (ELAAs) offers a promising solution to meet the escalating demand for high data rates in next-generation communications. A large array aperture, along with the ever increasing carrier frequency over the mmWave/THz bands, leads to a large Rayleigh distance. As a result, the traditional planar-wave assumption may not hold valid for mmWave/THz systems featuring ELAAs. In this paper, we consider the problem of hybrid near/far-field channel estimation by taking spherical wave propagation into account. By analyzing the coherence properties of any two near-field steering vectors, we prove that the hybrid near/far-field channel admits a block-sparse representation on a specially designed unitary matrix. Specifically, the percentage of nonzero elements of such a block-sparse representation is in the order of 1/√N, which tends to zero as the number of antennas,N, grows. Such a block-sparse representation allows to convert channel estimation into a block-sparse signal recovery problem. Simulation results are provided to verify our theoretical results and illustrate the performance of the proposed channel estimation approach in comparison with existing state-of-the-art methods.

As spectrum congestion intensifies in wireless communication, efficient spectrum utilization through advanced sensing techniques has become increasingly important. This paper proposes a joint carrier frequency and two-dimensional (2-D) Direction of Arrival (DOA) estimation algorithm with signal recovery, utilizing identical-delay channels and sub-Nyquist sampling rates with a Uniform Rectangular Array (URA). Compared to multi-coset structures, the proposed method places identical-delay channels only along the edges of the URA, eliminating the need for additional ADCs and reducing hardware cost. Moreover, by leveraging tensor techniques, the spatial structure of the array is preserved, and the parameter pairing problem is avoided, leading to higher precision in estimation. Simulation results demonstrate the superior performance of the proposed method.

We study the problem of interference cancelation with the aid of an intelligent reflecting surface (IRS), where the objective is to determine the reflection coefficients at the IRS such that the interference signals are canceled at the receiver. Specifically, we are interested in a “blind” scenario where the channel state information (CSI) between the interference sources and the receiver is unknown. To tackle this challenging problem, we propose a sample-efficient blind approach which utilizes a small number of average received signal power measurements to automatically identify a reflection coefficient vector that is orthogonal to the cascaded interference channels and thus nullifies the interference signals at the receiver. Simulation results show that the proposed method can effectively cancel the interference signals and enhance the signal-to-interference-plus-noise ratio (SINR).

The increase in antenna apertures and transmission frequencies in next-generation wireless networks is catalyzing advancements in near-field communications (NFC). In this paper, we investigate secure transmission in near-field multi-user multiple-input single-output (MU-MISO) scenarios. Specifically, with the advent of extremely large-scale antenna arrays (ELAA) applied in the NFC regime, the spatial degrees of freedom in the channel matrix are significantly enhanced. This creates an expanded null space that can be exploited for designing secure communication schemes. Motivated by this observation, we propose a near-field dynamic hybrid beamforming architecture incorporating artificial noise, which effectively disrupts eavesdroppers at any undesired positions, even in the absence of their channel state information (CSI). Furthermore, we comprehensively analyze the dynamic precoder’s performance in terms of the average signal-to-interference-plus-noise ratio, achievable rate, secrecy capacity, secrecy outage probability, and the size of the secrecy zone. In contrast to far-field secure transmission techniques that only enhance security in the angular dimension, the proposed algorithm exploits the unique properties of spherical wave characteristics in NFC to achieve secure transmission in both the angular and distance dimensions. Remarkably, the proposed algorithm is applicable to arbitrary modulation types and array configurations. Numerical results demonstrate that the proposed method achieves approximately 20% higher rate capacity compared to zero-forcing and the weighted minimum mean squared error precoders.

In this article, we consider the problem of joint transceiver design for millimeter-wave (mmWave)/terahertz (THz) multiuser MIMO integrated sensing and communication (ISAC) systems. Such a problem is formulated into a nonconvex optimization problem, with the objective of maximizing a weighted sum of communication users’ rates and the passive radar’s signal-to-clutter-and-noise ratio (SCNR). By exploring a low-dimensional subspace property of the optimal precoder, a low-dimensional subspace property-inspired block-coordinate-descent (LS-BCD)-based algorithm is proposed with remarkably reduced computational complexity. Our analysis reveals that the hybrid analog/digital beamforming structure can attain the same performance as that of a fully digital precoder, provided that the number of radio frequency (RF) chains is no less than the number of resolvable signal paths. Also, through expressing the precoder as a sum of a communication-precoder and a sensing-precoder, we develop an analytical solution to the joint transceiver design problem by generalizing the idea of block diagonalization (BD) to the ISAC system. Simulation results show that with a proper tradeoff parameter, the proposed methods can achieve a decent compromise between communication and sensing, where the performance of each communication/sensing task experiences only a mild performance loss as compared with the performance attained by optimizing exclusively for a single task.

In this paper, we consider the problem of downlink beam training for extremely large-scale millimeter wave (mmWave)/Terahertz (THz) systems, where the far-field assumption which treats wavefronts as planar waves may not hold valid. For such hybrid far/near-field channels, beam training needs to identify the best beam alignment on a two-dimensional angle-range domain. An exhaustive search scheme sequentially scanning the entire angle-range space incurs a high training overhead. To address this issue, in this paper, we propose an efficient hybrid far/near-field beam training method. By utilizing the approximate orthogonality of near-field steering vectors of the same effective distance, we devise a multi-directional beam training sequence which can more efficiently scan the entire angle-range space. Based on the devised beam training sequence, we develop a simple estimation method at the receiver that can simultaneously identify the angle and the range associated with the dominant path. Simulation results show that the proposed method achieves better performance than the exhaustive search scheme, while with a much lower overhead cost. The proposed method also presents a clear advantage over other existing state-of-the-art hybrid far/near-field beam training methods in terms of performance and generality.

This paper tackles a notable security loophole in tag-based Physical-Layer Authentication (PLA) when faced with cooperative attacks, highlighting its significance due to two main reasons. First, while attackers may only observe the tag amid noise, they can reduce the noise effect through multiple observations. Furthermore, several cooperative attackers can orchestrate more sophisticated attacks, achieving goals beyond the reach of a single attacker. In this paper, we propose an enhanced PLA scheme, designated as the Enhanced Detection of Cooperative Attacks (EDCA) scheme, to counter these cooperative threats. The basic idea of the EDCA scheme involves utilizing a unique noise characteristic induced by replayed signals to identify and thwart cooperative attacks, preventing attackers from accumulating multiple observations of the same tag. We theoretically analyze the proposed scheme over fading channels and derive closed-form expressions for its performance. Implementation and rigorous evaluation of the EDCA scheme are carried out to compare its performance with prior schemes. Simulation results confirm the alignment of theoretical predictions with empirical outcomes. The proposed scheme not only can effectively detect cooperative attacks but also provide a better detection performance.

Tag-based physical-layer authentication (PLA) has gained significant research interest due to its high security and low complexity compared to traditional upper-layer authentication mechanisms. However, conventional tag-based PLA schemes often require strict synchronization between the tag and the source message before transmission, which may limit their practicality and effectiveness. To address this limitation, we propose an asynchronous tag-based PLA scheme, where the tag and the source message are transmitted asynchronously with an intentional time delay introduced at the transmitter. The proposed scheme enhances both compatibility and security without compromising robustness. We provide a theoretical analysis of the proposed scheme, and derive closed-form expressions to characterize its performance. Furthermore, theoretical comparisons with existing methods yield insightful conclusions regarding the advantages of the proposed approach. The scheme is implemented and extensively evaluated through simulations, where the theoretical results exhibit strong agreement with the simulation outcomes, thereby validating its effectiveness.

The Global Navigation Satellite System (GNSS) receiver has become indispensable in navigation applications due to its affordability and reliable accuracy. Despite its widespread use, GNSS is vulnerable to manipulation by malicious entities within the inherently insecure wireless landscape. This paper proposes innovative GNSS signal authentication strategies that employing multiple antennas to counteract spoofing attacks. Leveraging a multi-antenna framework enhances the distinguishability of spoofing signals over traditional single-antenna configurations. Importantly, the proposed approaches capitalize on spatial characteristics, which are independent of the GNSS signal’s intrinsic features, to accurately identify spoofing attacks. Specifically, we propose a Direction-of-Arrival (DOA)-based authentication scheme to address scenarios where authentic signals are blocked during spoofing attacks. Additionally, for situations where both authentic and spoofing signals coexist at the receiver, we propose a Spatial Filter (SF)-based authentication scheme. Through rigorous theoretical analysis and comprehensive simulations, we not only validate the proposed schemes but also demonstrate their robustness and effectiveness in enhancing GNSS signal security. The simulation results, aligning closely with theoretical predictions, underscore the superiority of the proposed schemes in safeguarding against spoofing threats.

This paper addresses key challenges caused by high sampling rates in wideband joint spectrum sensing applications. A joint Direction of Arrival (DOA) and frequency estimation algorithm is proposed by utilizing the Cross-Covariance Matrix (CCM) constructed from the outputs of an efficient undersampling array receiver with multiple elements, only one of which is connected with multiple time-delay branches. In contrast to previous autocorrelation-based methods, the proposed method reduces the impact of noise and doubles the maximum unit time-delay, resulting in improved estimation performance. Additionally, it does not impose restrictions on the number of array sensors and time-delay channels, which allows for more flexibility in the allocation of resources for the receiver. In the simulation, the proposed algorithm demonstrates outstanding performance.

In the context of perceiving spatial information, researchers extensively investigate the use of large-scale arrays due to their numerous advantages such as high precision and resolution, as well as increased degrees of freedom. However, large-scale arrays may be impractical in certain applications due to the prohibitive hardware cost. To address this bottleneck, a switch-element array structure composed of a switch network offers an appealing low-cost alternative by multiplexing the Radio Frequency (RF) chains. With this novel array architecture, we explore the direction-of-arrival (DOA) estimation problem and examine the inherent signal structures. Subsequently, two DOA estimation algorithms based on a dynamic-dictionary sparse representation are developed, namely the Jointly-Selected Orthogonal Matching Pursuit (JSOMP) algorithm and the Auxiliary Variable Joint Alternating Optimization (AVJAO) algorithm. The performance of the proposed algorithms is demonstrated through simulation results.

In this work, we consider a multi-server federated learning (FL) framework, referred to as Confederated Learning (CFL), in order to accommodate a larger number of users. To reduce the communication overhead of the CFL system, we propose a linearly convergent stochastic gradient method. The proposed algorithm incorporates a conditionally-triggered user selection (CTUS) mechanism as the central component. Simulation results show that it achieves advantageous communication efficiency over GT-SAGA.

In this paper, we consider state estimation in the Kalman filtering framework with unlimited sensing measurements (USMs), which are obtained from sensors equipped with a self-reset analog-to-digital (SR-ADC). SR-ADC was recently introduced to deal with the saturation issue frequently encountered in a conventional ADC. To tackle the nonlinearity of the USM, we present a unique decomposition property of the USM. Leveraging this property and a multiple model adaptive estimation strategy, we propose a novel USF-based Kalman filtering (KF-USM) algorithm. Numerical results reveal that the proposed KF-USM filter is an effective alternative to the conventional ADC-based KF to deal with high dynamic range input signals, offering more accurate state estimation in the presence of saturation.

This paper focuses on the wideband frequency invariant (FI) deterministic beamformer design problem for mitigating beam squint and presents a spatial response variation (SRV)-constrained array response control (ARC) synthesis approach. By regarding the SRV matrix as the covariance matrix of an extra virtual colored noise, we extend the ARC-based narrowband beampattern synthesis techniques to wide band FI scenarios. Furthermore, we introduce the FI maximum magnitude response (FI-MMR) based design principle, which maximizes the array magnitude response at the main-beam direction on the reference frequency. Based on this principle, we present an iterative FI beampattern synthesis algorithm under arbitrary array configurations. Simulation results show the effectiveness of the proposed algorithm in comparison with several popular FI beampattern synthesis techniques.

The user-centric cell-free network has emerged as an appealing technology to improve the wireless communication’s capacity of the Internet of Things (IoT) networks thanks to its ability to eliminate intercell interference effectively. However, the cell-free network inevitably brings in higher hardware cost and backhaul overhead as a larger number of base stations (BSs) are deployed. Additionally, severe channel fading in high-frequency bands constitutes another crucial issue that limits the practical application of the cell-free network. In order to address the above challenges, we amalgamate the cell-free system with another emerging technology, namely reconfigurable intelligent surface (RIS), which can provide high spectrum and energy efficiency with low hardware cost by reshaping the wireless propagation environment intelligently. To this end, we formulate a weighted sum-rate (WSR) maximization problem for RIS-assisted cell-free systems by jointly optimizing the BS precoding matrix and the RIS reflection coefficient vector. Subsequently, we transform the complicated WSR problem to a tractable optimization problem and propose a distributed cooperative alternating direction method of multipliers (ADMMs) to fully utilize parallel computing resources. Inspired by the model-based algorithm unrolling concept, we unroll our solver to a learning-based deep distributed ADMM (D2-ADMM) network framework. To improve the efficiency of the D2-ADMM in distributed BSs, we develop a monodirectional information exchange strategy with a small signaling overhead. In addition to benefiting from domain knowledge, D2-ADMM adaptively learns hyperparameters and nonconvex solvers of the intractable RIS design problem through data-driven end-to-end training. Finally, numerical results demonstrate that the proposed D2-ADMM achieves around 210% improvement in capacity compared with the distributed noncooperative algorithm and almost 96% compared with the centralized algorithm.

In this paper, we are concerned about the problem of guaranteeing both privacy and security in a Location-Based Service (LBS) system, where a challenging scenario involving cooperative attack is considered. Since prior Physical-Layer Authentication (PLA) schemes do not consider cooperative attack, their security significantly declines under such attacks. We propose two privacy-preserving PLA schemes: the Privacy-Preserving Physical-Layer Authentication using Noise Variance (PPPLA-NV) scheme and the Privacy-Preserving Physical-Layer Authentication using Multiple Channel Responses (PPPLA-MCR) scheme, which significantly improve the privacy-preserving performance under a cooperative attack. Note that the proposed schemes protect not only user’s identity information but also data message. We theoretically analyze the performance of the proposed schemes, derive their closed-form expressions, and provide a theoretical comparison between both proposed schemes. We implement the proposed schemes and conduct extensive performance comparisons through simulations. Experimental results show a perfect match between the theoretical and simulation results. From the experimental results, we observe that if the overhead is not the priority, the PPPLA-MCR scheme is the best option; otherwise, the PPPLA-NV scheme may be a better option.

We investigate a twin-timescale joint beamforming problem for multiple intelligent reflecting surfaces (IRSs)-assisted multi-user mm Wave orthogonal frequency division multiplexing (OFDM) systems, where the base station (BS) employs a hybrid analog and digital precoder. To alleviate the burden of frequent channel state information (CSI) acquisition and reduce design complexity, we devise the passive beamforming vector and the analog precoder based on statistical CSI, while the digital precoder is designed based on low-dimensional instantaneous CSI. Specifically, the former long-term optimization can be formulated as a stochastic optimization problem. To address this problem, we propose two different solutions. The first method devises the passive beamforming vector and the analog precoder by maximizing the ergodic channel gain. We also propose a deep unrolling-based method to provide a unified framework for the stochastic optimization problem. Our simulation results demonstrate the effectiveness and computational efficiency of the proposed methods.

This letter examines line-of-sight (LOS) path identification for passive multi-target localization in multipath environments. We consider a system comprising multiple spatially distributed sensors, each transmitting a distinct waveform and using the echoes to measure the LOS and non-LOS (NLOS) delays (i.e., ranges) of the targets in the surveillance area. For simplicity, we assume a 2-D localization scenario, where each range measurement defines a circle, and measurements from different sensors create intersection points on the plane. The problem is to identify intersections that are created by LOS paths. To solve the problem, we classify the intersections into$N(N-1)/2$types, where$N$denotes the number of sensors. Then, an efficient clustering algorithm is proposed to efficiently identify the LOS intersections based on the type and other related attributes. Numerical results are presented to demonstrate the performance of the proposed technique in comparison with several peer methods.

Anomaly detection in hyperspectral images (HSIs) has attracted considerable interest in the remote-sensing domain, which aims to identify pixels with different spectral and spatial features from their surroundings. Most of the existing anomaly detection methods convert the 3-D data cube to a 2-D matrix composed of independent spectral vectors, which destroys the intrinsic spatial correlation between the pixels and their surrounding pixels, thus leading to considerable degradation in detection performance. In this article, we develop a tensor-based anomaly detection algorithm that can effectively preserve the spatial–spectral information of the original data. We first separate the 3-D HSI data into a background tensor and an anomaly tensor. Then the tensor nuclear norm based on the tensor singular value decomposition (SVD) is exploited to characterize the global low rank existing in both the spectral and spatial directions of the background tensor. In addition, the total variation (TV) regularization is incorporated due to the piecewise smoothness. For the anomaly component, the$l_{2.1}$norm is exploited to promote the group sparsity of anomalous pixels. In order to improve the ability of the algorithm to distinguish the anomaly from the background, we design a robust background dictionary. We first split the HSI data into local clusters by leveraging their spectral similarity and spatial distance. Then we develop a simple but effective way based on the SVD to select representative pixels as atoms. The constructed background dictionary can effectively represent the background materials and eliminate anomalies. Experimental results obtained using several real hyperspectral datasets demonstrate the superiority of the proposed method compared with some state-of-the-art anomaly detection algorithms.

Integrating sensing functionalities is envisioned as a distinguishing feature of next-generation mobile networks, which has given rise to the development of a novel enabling technology– Integrated Sensing and Communication (ISAC). Portraying the theoretical performance bounds of ISAC systems is fundamentally important to understand how sensing and communication functionalities interact (e.g., competitively or cooperatively) in terms of resource utilization, while revealing insights and guidelines for the development of effective physical-layer techniques. In this paper, we characterize the fundamental performance tradeoff between the detection probability for target monitoring and the user’s achievable rate in ISAC systems. To this end, we first discuss the achievable rate of the user under sensing-free and sensing-interfered communication scenarios. Furthermore, we derive closed-form expressions for the probability of false alarm (PFA) and the successful probability of detection (PD) for monitoring the target of interest, where we consider both communication-assisted and communication-interfered sensing scenarios. In addition, the effects of the unknown channel coefficient are also taken into account in our theoretical analysis. Based on our analytical results, we then carry out a comprehensive assessment of the performance tradeoff between sensing and communication functionalities. Specifically, we formulate a power allocation problem to minimize the transmit power at the base station (BS) under the constraints of ensuring a required PD for perception as well as the communication user’s quality of service requirement in terms of achievable rate. It indicates that, on the one hand, there exists an intrinsic tradeoff between sensing and communication performance under the mutual-interfered scenarios; On the other hand, with prior knowledge of the baseband waveform, these two functionalities might mutually assist each other to enhance the performance. Finally, simulation results corroborate the accuracy of our theoretical analysis and the effectiveness of the proposed power allocation solutions showing the advantages of the ISAC system over the conventional radar and communication coexistence counterpart.

We consider the problem of spatial channel covariance matrix (CCM) estimation for intelligent reflecting surface (IRS)-assisted millimeter wave (mmWave) communication systems. Spatial CCM is essential for two-timescale beamforming in IRS-assisted systems; however, estimating the spatial CCM is challenging due to the passive nature of reflecting elements and the large size of the CCM resulting from massive reflecting elements of the IRS. In this paper, we propose a CCM estimation method by exploiting the low-rankness as well as the positive semi-definite (PSD) 3-level Toeplitz structure of the CCM. Estimation of the CCM is formulated as a semidefinite programming (SDP) problem and an alternating direction method of multipliers (ADMM) algorithm is developed. Our analysis shows that the proposed method is theoretically guaranteed to attain a reliable CCM estimate with a sample complexity much smaller than the dimension of the CCM. Thus the proposed method can help achieve a significant training overhead reduction. Simulation results are presented to illustrate the effectiveness of our proposed method and the performance of two-timescale beamforming scheme based on the estimated CCM.

Intelligent reflecting surface (IRS) is a promising technology being considered for future wireless communications due to its ability to control signal propagation. This paper considers the joint active and passive beamforming problem for an IRS-assisted radar, where multiple IRSs are employed to assist the surveillance of multiple targets in cluttered environments. Specifically, we aim to maximize the minimum target illumination power at multiple target locations by jointly optimizing the active beamformer at the radar transmitter and the passive phase-shift matrices at the IRSs, subject to an upperbound on the clutter power at each clutter scatterer. The resulting optimization problem is nonconvex and solved with a sequential optimization procedure along with semidefinite relaxation (SDR). Simulation results show that additional line-of-sight (LOS) paths created by IRSs can substantially improve the radar robustness against target blockage.

Distributed multi-input multi-output (MIMO) radar with non-orthogonal waveforms has become a critical problem because waveform orthogonality may be lost as waveforms experience distinct delays and Doppler across different transmit-receive propagation paths. In such cases, the widely used matched filter (MF) cannot perfectly separate the waveforms, and it outputs the filtered echo of the desired waveform (auto term) as well as multiple undesired waveform residuals (cross terms). In this paper, a transmit delay compensation scheme is proposed by employing a set of transmit delay compensation variables to control the cross terms so that they can be utilized to enhance target detection. Specifically, the probability of detection is maximized by optimizing the delay parameters. To solve the resulting nonconvex problem, an optimum solution based on multi-dimensional search and a computationally efficient suboptimal method are proposed. Simulation results show that the proposed delay compensation approach can substantially improve the target detection performance.

We consider the problem of locating multiple targets using automotive radar by exploiting a pair of cooperative vehicles, which form a mono- and bi-static sensing system to provide spatial diversity for localization. Each of the two sub-systems can measure the target echoes. The problem is to determine the locations of multiple targets in the surrounding area. A conventional approach is to directly estimate the target locations from the joint distribution of the mono- and bi-static observations, which is computationally prohibitive. In this paper, we propose a efficient two-step method that first uses the delay and angle estimates from each individual system to determine initial target locations, which are subsequently refined via an association and fusion step. Specifically, we use a 2-dimensional (2-D) fast Fourier transform (FFT) based approach to obtain the delay and angle estimates of each target in a sequential manner. The delay/angle estimates obtained by mono-static and bi-static systems lead to two sets of initial target location estimates, which are then sorted and paired via a minimum distance criterion. Finally, the initial location estimates are fused/weighted according to the target strength observed by each system. Simulation results show that our cooperative approach yields significant improved performance over non-cooperative approaches using only the mono-static or bi-static sensing system.

In this article, anomaly detection is considered for hyperspectral imagery in the Gaussian background with an unknown covariance matrix. The anomaly to be detected occupies multiple pixels with an unknown pattern. Two adaptive detectors are proposed based on the generalized likelihood ratio test design procedure and ad hoc modification of it. Surprisingly, it turns out that the two proposed detectors are equivalent. Analytical expressions are derived for the probability of false alarm of the proposed detector, which exhibits a constant false alarm rate against the noise covariance matrix. Numerical examples using simulated data reveal how some system parameters (e.g., the background data size and pixel number) affect the performance of the proposed detector. Experiments are conducted on five real hyperspectral data sets, demonstrating that the proposed detector achieves better detection performance than its counterparts.

We study the problem of joint wideband spectrum sensing and recovery of multi-band signals in a multi-antenna-based sub-Nyquist sampling framework. Specifically, the multi-band signal is composed of a number of uncorrelated narrowband signals spreading over a wide frequency band. Unlike existing works which assume the source signals impinge on the receiver via a line-of-sight (LOS) path, we consider a more practical unknown MIMO channel which results from multipath propagation. A new sub-Nyquist sampling architecture is proposed, where each antenna output passes through two channels, namely, a direct path and a delayed path with a controlled amount of time delay. The signal at each channel is then sampled by a synchronized low-rate analog-to-digital converter (ADC). We utilize the collected data samples to build a set of cross-correlation matrices with different time lags and develop a CANDECOMP/PARAFAC (CP) decomposition-based method to recover the carrier frequencies, power spectra as well as the source signals themselves. Recovery conditions of the proposed method are analyzed, and Cramér-Rao bound (CRB) results for our estimation problem are derived. Simulation results are presented to illustrate the effectiveness of the proposed method.

Intelligent reflecting surface (IRS) has emerged as a competitive solution to address blockage issues in millimeter wave (mmWave) and Terahertz (THz) communications due to its capability of reshaping wireless transmission environments. Nevertheless, obtaining the channel state information of IRS-assisted systems is quite challenging because of the passive characteristics of the IRS. In this paper, we consider the problem of beam training/alignment for IRS-assisted downlink mmWave/THz systems, where a multi-antenna base station (BS) with a hybrid structure serves a single-antenna user aided by IRS. By exploiting the inherent sparse structure of the BS-IRS-user cascade channel, the beam training problem is formulated as a joint sparse sensing and phaseless estimation problem, which involves devising a sparse sensing matrix and developing an efficient estimation algorithm to identify the best beam alignment from compressive phaseless measurements. Theoretical analysis reveals that the proposed method can identify the best alignment with only a modest amount of training overhead. Simulation results show that, for both line-of-sight (LOS) and NLOS scenarios, the proposed method obtains a significant performance improvement over existing state-of-art methods. Notably, it can achieve performance close to that of the exhaustive beam search scheme, while reducing the training overhead by 95%.

We consider the joint optimization of multi-carrier radar and communication systems with shared spectrum. The systems operate in a cluttered environment, where the radar and communication receivers observe not only cross-interference but also multipath and/or clutter signals, which may arise from the system’s own transmission. We propose a non-alternating approach to jointly optimize the radar and communication transmission power allocated to each sub-carrier. Numerical results demonstrate the proposed joint designs offer significant performance gain over the conventional sub-carrier allocation-based approach.

We consider the problem of joint wideband spectrum sensing and carrier frequency estimation in a sub-Nyquist sampling framework. Specifically, a multi-antenna receiver is used to estimate the carrier frequencies and power spectra of multiple narrowband transmissions that spread over a wide frequency band. Unlike existing works that assume the source signals impinge on the receiver via a line-of-sight (LOS) path, we consider a more practical multiple-input multiple-output (MIMO) channel characterized by multipath propagation. A new sub-Nyquist sampling architecture is proposed, where each antenna output passes through two channels, namely, a direct path and a delayed path with a predetermined time delay. The signal at each channel is then sampled by a synchronized low-rate analog-to-digital converter (ADC). We utilize the collected data samples to build a set of cross-correlation matrices with different time lags and develop a CANDECOMP/PARAFAC (CP) decomposition-based method to recover the carrier frequencies and power spectra of the source signals. Simulation results are presented to illustrate the effectiveness of the proposed method.

We consider the problem of max-min power control for downlink cell-free (CF) massive MIMO systems. Under the bisection framework, solving this problem amounts to solving a sequence of convex conic feasibility checking problems (CCFCP). Unfortunately, the problem size of the CCFCP of the CF massive MIMO system grows rapidly as the number of users and access points (AP) increases. Existing feasibility checking methods become computationally intractable even when the system consists of only a moderate number of users and APs. To address this limitation, we propose to reformulate the CCFCP as a two-set feasibility problem, which is then solved by the averaged alternating reflection (AAR) algorithm. The proposed method outperforms existing methods in terms of computational efficiency.

We consider the joint transmit and receive design for multi-input multi-output radar with slow-time processing. The radar employs multiple transmit apertures to improve diversity. The design parameters include the spatial transmit code for each aperture, which varies from pulse to pulse to provide Doppler shaping, and the space-time receive filter, to jointly optimize the radar output signal-to-interference-and-noise ratio (SINR). To relieve the dependence on specific target parameters as required by some prior methods, we use the average SINR, averaged with respect to the target location/Doppler uncertainties, as the design metric. Simulation results show that our proposed multi-aperture solution outperforms a previous single-aperture based space-time transmit and receive design as well as the conventional phased-array radar.

In this article, we consider the problem of decentralized composite optimization over a connected and symmetric graph, in which each node holds its own agent-specific private convex functions, and communications are only allowed between nodes with direct links. A variety of algorithms has been proposed to solve such a problem in an alternating direction method of multiplier (ADMM) framework. Many of these algorithms, however, need to include some extra proximal term in the augmented Lagrangian function such that the resulting algorithm can be implemented in a decentralized manner. The use of the extra proximal term slows down the convergence speed because it forces the current solution to stay close to the solution obtained in the previous iteration. To address this issue, in this article, we first introduce the notion of simplest bipartite graph, which is defined as a bipartite graph that has a minimum number of edges to keep the graph connected. A simple two-step message passing-based procedure is proposed to find a simplest bipartite graph associated with the original graph. We show that the simplest bipartite graph has some interesting properties. By utilizing these properties, an ADMM without involving extra proximal terms can be developed to perform decentralized composite optimization over the simplest bipartite graph. The simulation results show that our proposed method achieves a much faster convergence speed than the existing state-of-the-art decentralized algorithms.

Large intelligent surface (LIS) has recently emerged as a potential low-cost solution to reshape the wireless propagation environment for improving the spectral efficiency. In this article, we consider a downlink millimeter-wave (mmWave) multiple-input-multiple-output (MIMO) system, where an LIS is deployed to assist the downlink data transmission from a base station (BS) to a user equipment (UE). Both the BS and the UE are equipped with a large number of antennas, and a hybrid analog/digital precoding/combining structure is used to reduce the hardware cost and energy consumption. We aim to maximize the spectral efficiency by jointly optimizing the LIS's reflection coefficients and the hybrid precoder (combiner) at the BS (UE). To tackle this non-convex problem, we reformulate the complex optimization problem into a much more friendly optimization problem by exploiting the inherent structure of the effective (cascade) mmWave channel. A manifold optimization (MO)-based algorithm is then developed. Simulation results show that by carefully devising LIS's reflection coefficients, our proposed method can help realize a favorable propagation environment with a small channel matrix condition number. Besides, it can achieve a performance comparable to those of state-of-the-art algorithms, while at a much lower computational complexity.

We study the problem of downlink beamforming training and channel estimation for millimeter wave (mmWave) OFDM systems, where a hybrid analog and digital beamforming structure is employed at the transmitter (i.e., base station) and an omni-directional antenna or an antenna array is used at the receiver (i.e., user). To efficiently probe the channel, we form multiple directional beams simultaneously at the transmitter and steer them towards different directions. The objective is to devise the beam training sequence and develop an efficient algorithm to estimate the channel. By exploiting the sparse scattering nature of mmWave channels, the above problem is formulated as one of sparse encoding and signal recovery, which involves finding a sparse sensing matrix to compress the sparse channel and an efficient channel estimation algorithm to recover the sparse channel from compressive measurements. In this article, we propose a sparse bipartite graph code-based algorithm, where a set of bipartite graphs are employed to encode the sparse channel and a simple decoding procedure that relies on the presence of a No-Multiton-graph (NM-graph) is used to reconstruct the sparse channel. Theoretical analysis shows that our proposed method can help achieve a substantial training overhead reduction. Simulations are provided to show the effectiveness of the proposed algorithm and its performance advantage over compressed sensing-based methods.

In this paper, we investigate the anomaly detection problem for multi-pixel targets in hyperspectral imagery when training data are available. We derive the generalized likelihood ratio test and obtain its analytical expressions of the probability of false alarm and probability of detection. The performance of the proposed detector is evaluated by using simulated and real data. The results demonstrate that this training data assisted detector outperforms its counterpart without training data.

In this letter, we consider channel estimation for intelligent reflecting surface (IRS)-assisted millimeter wave (mmWave) systems, where an IRS is deployed to assist the data transmission from the base station (BS) to a user. It is shown that for the purpose of joint active and passive beamforming, the knowledge of a large-size cascade channel matrix needs to be acquired. To reduce the training overhead, the inherent sparsity in mmWave channels is exploited. By utilizing properties of Katri-Rao and Kronecker products, we find a sparse representation of the cascade channel and convert cascade channel estimation into a sparse signal recovery problem. Simulation results show that our proposed method can provide an accurate channel estimate and achieve a substantial training overhead reduction.

We consider the joint design of transmit and receive beamforming for multiple-input multiple-output (MIMO) systems in the presence of multiple targets and interferences. The objective is to maximize the minimum receiver output signal-to-interference-plus-noise-ratio (SINR) of the targets with constraints on the total and, respectively, per-antenna transmission power. The problem can be solved by an iterative bisection search (IBS) based method, which is, however, computationally intensive since it requires solving a feasibility problem multiple times for each update of the transmit beamformer. We propose a new method that bypasses the iterative feasibility checking by employing an SINR approximation. An analytical proof, which ensures the convergence of the proposed method, is provided. Numerical results show that the proposed method attains a similar SINR performance as the IBS based method but at a significantly lower computational complexity.

The spread spectrum signal detection problem is examined in colored noise with an unknown covariance matrix. When the receiver is equipped with a symmetrically spaced linear array, persymmetry exists in the received data. We exploit the persymmetric structures to design adaptive detectors according to the principles of generalized likelihood ratio test (GLRT), Wald test, and Rao test. It turns out that the proposed GLRT has the same form as the proposed Wald test, and the Rao test does not exist. We prove that the proposed detector exhibits a constant false alarm rate against the unknown noise covariance matrix. Numerical examples demonstrate that the proposed detector has better performance than its non-persymmetric counterpart.

In this paper, we consider the problem of channel estimation for uplink multiuser massive MIMO systems, where, in order to significantly reduce the hardware cost and power consumption, one-bit analog-to-digital converters (ADCs) are used at the base station (BS) to quantize the received signal. We first extend the conventional Bussgang linear minimum mean square error (BLMMSE) estimator to the general nonzero threshold case. We then study the problem of one-bit quantization design, aiming at minimizing the mean squared error of the generalized BLMMSE estimator. A set partition scheme is proposed to devise the quantization thresholds. The rationale behind the proposed scheme is to divide each antenna's received samples into a number of disjoint subsets according to their pairwise correlation and assign diverse thresholds to those highly correlated data samples. In addition to the set partition scheme, a gradient descent scheme is developed to search for optimal quantization thresholds. The proposed schemes only require the statistical information of the received signals to devise the quantization thresholds, which can be calculated in advance before the training process begins. Simulation results show that the generalized BLMMSE estimator can achieve a significant performance improvement over the conventional Bussgang LMMSE estimator.

There has been a growing interest in wideband spectrum sensing due to its applications in cognitive radios and electronic surveillance. To overcome the sampling rate bottleneck for wideband spectrum sensing, in this paper, we study the problem of compressed power spectrum estimation whose objective is to reconstruct the power spectrum of a wide-sense stationary signal based on sub-Nyquist samples. By exploring the sampling structure inherent in the multicoset sampling scheme, we develop a computationally efficient method for power spectrum reconstruction. An important advantage of our proposed method over existing compressed power spectrum estimation methods is that our proposed method, whose primary computational task consists of fast Fourier transform (FFT), has a very low computational complexity. Such a merit makes it possible to efficiently implement the proposed algorithm in a practical field-programmable gate array (FPGA)-based system for real-time wideband spectrum sensing. Our proposed method also provides a new perspective on the power spectrum recovery condition, which leads to a result similar to what was reported in prior works. Simulation results are presented to show the computational efficiency and the effectiveness of the proposed method.

We consider the problem of channel estimation for uplink massive multiple-input multiple-output systems, where one-bit analog-to-digital converters (ADCs) are used at the base station (BS) to quantize the received signal. In this paper, we study the problem of one-bit quantizer design when a Bussgang linear minimum mean square error (BLMMSE) estimator is used for channel estimation. We first extend the conventional Bussgang LMMSE estimator \cite{LiTao17} to the general nonzero threshold case. We then analyze the estimation performance of the generalized Bussgang LMMSE estimator and investigate the design of quantization thresholds. A gradient descent scheme is developed to search for optimal quantization thresholds. Simulation results show that, with carefully devised quantization thresholds, the generalized Bussgang LMMSE estimator can achieve a substantial performance improvement over the conventional Bussgang LMMSE estimator.

The problem of beam alignment for millimeter wave (mm-Wave) communications is studied in this paper. We show that, by exploiting the sparse scattering nature of mmWave channels, the beam alignment problem can be formulated as a sparse encoding and phaseless decoding problem, which involves finding a sparse sensing matrix and an efficient recovery algorithm to recover the support and magnitude of the s-parse signal from compressive phaseless measurements. We develop a general function-Code (GF-Code) algorithm for s-parse encoding and phaseless decoding. Simulation results are provided to corroborate the effectiveness of the proposed GF-Code method.

Many well-known line spectral estimators may experience significant performance loss with noisy measurements. To address the problem, we propose a deep learning denoising based approach for line spectral estimation. The proposed approach utilizes a residual learning assisted denoising convolutional neural network (DnCNN) trained to recover the unstructured noise component, which is used to denoise the original measurements. Following the denoising step, we employ a popular model order selection method and a subspace line spectral estimator to the denoised measurements for line spectral estimation. Numerical results show that the proposed approach outperforms a recently introduced atomic norm minimization based denoising method and offers a substantial improvement compared with the line spectral estimation results obtained by directly applying the subspace estimator without denoising.

Co-existence of radar and communication systems based on cooperative spectrum sharing is of significant interest in recent years. This letter proposes two joint power allocation schemes for radar and communication spectrum sharing, where both systems use the same bandwidth with cross interference between them. The proposed joint design maximizes a performance metric of one system while meeting a service constraint requirement for the other, i.e., throughput for the communication system or signal-to-interference-plus-noise ratio (SINR) for the radar. For both designs, we show that either the radar or the communication system needs to transmit at peak power to achieve the optimum solution, depending on the relation among the peak power constraint, channel conditions, and service constraints.

We consider the joint design of the transmit and receive beamforming in transmit subaperturing multiple-input-multiple-output (TS-MIMO) systems by utilizing the prior knowledge about the locations and power of the target and inherent interferences. The joint design is solved by an iterative algorithm to maximize the output signal-to-interference-plus-noise-ratio (SINR). The proposed approach can benefit from the sum co-array and joint transmit and receive optimization. Simulation results show that the approach provides more directions of freedom (DOFs) and/or directional gain compared to the original TS-MIMO and omni-MIMO, which leads to better output SINR results and interference rejection.

We consider the nonlinear robust filtering problem where the measurements are partially disturbed by outliers. A new robust Kalman filter based on a detect-and-reject idea is developed. To identify and exclude outliers automatically, each measurement is assigned an indicator variable, which is modeled by a beta-Bernoulli prior. The mean-field variational Bayesian method is then utilized to estimate the state of interest as well as the indicator in an iterative manner at each time instant. Simulation results reveal that the proposed algorithm outperforms several recent robust solutions with higher computational efficiency and better accuracy.

This letter addresses the detection of a subspace distributed target signal obscured by disturbance. The disturbance consists of a clutter component with an unknown subspace structure and a white noise component with unknown noise power. A detection strategy is proposed based on the volume cross-correlation function, which provides a metric that measures the linear (in) dependency between two subspaces. Simulation results indicate that the proposed detector can achieve better performance than several peer methods, without resorting to secondary data and a priori knowledge about the clutter subspace including its rank.

In this paper, we propose a gridless method for estimating an unknown number of fundamental frequencies. Starting with a conventional dictionary matrix, containing sets of candidate fundamental frequencies and their corresponding harmonics, a nonconvex log-sum cost function is formed such that it imposes the harmonic structure and treats every fundamental frequency in the dictionary as a parameter. The cost function is iteratively decreased by minimizing a surrogate function, and, in each iteration, the fundamental frequencies are refined, whereas redundant parameters are omitted from the dictionary. The proposed method is tested on both real and simulated data, showing its preferred performance as compared to other state-of-the-art multipitch estimators.

We consider the problem of channel estimation for millimeter wave (mmWave) systems, where, to minimize the hardware complexity and power consumption, an analog transmit beamforming and receive combining structure with only one radio frequency chain at the base station and mobile station is employed. Most existing works for mmWave channel estimation exploit sparse scattering characteristics of the channel. In addition to sparsity, mmWave channels may exhibit angular spreads over the angle of arrival, angle of departure, and elevation domains. In this paper, we show that angular spreads give rise to a useful low-rank structure that, along with the sparsity, can be simultaneously utilized to reduce the sample complexity, i.e., the number of samples needed to successfully recover the mmWave channel. Specifically, to effectively leverage the joint sparse and low-rank structure, we develop a two-stage compressed sensing method for mmWave channel estimation, where the sparse and low-rank properties are respectively utilized in two consecutive stages, namely, a matrix completion stage and a sparse recovery stage. Our theoretical analysis reveals that the proposed two-stage scheme can achieve a lower sample complexity than a conventional compressed sensing method that exploits only the sparse structure of the mmWave channel. Simulation results are provided to corroborate our theoretical results and to show the superiority of the proposed two-stage method.

In this paper, we attack the estimation problem in Kalman filtering when the measurements are contaminated by outliers. We employ the Laplace distribution to model the underlying non-Gaussian measurement process. The maximum posterior estimation is solved by the majorization minimization (MM) approach. This yields an MM based robust filter, where the intractable ℓ1norm problem is converted into an ℓ2norm format. Furthermore, we implement the MM based robust filter in the Kalman filtering framework and develop a Laplace ℓ1robust Kalman filter. The proposed algorithm is tested by numerical simulations. The robustness of our algorithm has been borne out when compared with other robust filters, especially in scenarios of heavy outliers.

This paper concerns detecting the frequency components from a spectral sparse, undersampled signal. This problem is also called super-resolution line spectral estimation because the frequencies can take arbitrary continuous values. The prior knowledge of the frequency distribution is often available in many applications. To exploit the prior knowledge, a weighting function w(f) designed according to the frequency distribution p(f) is introduced. The prior information can be harnessed through minimizing the corresponding weighted log-sum penalty function. We solve the optimization problem through iteratively decreasing a surrogate function majorizing the original penalty function. Simulation results show that the proposed algorithm outperforms other methods both in noiseless and noisy case, and it also presents superior performance in resolving closely-spaced frequency components.

The paper presents the average signal-to-clutter loss (SCRL) analysis for polarimetric space-time adaptive processing by exploiting the Kronecker structure of the clutter covariance matrix (CM). An expression for the average SCRL as a function of the mean square error of the corresponding CM estimator is derived. Based on that expression, one can determine how many samples are required in order to achieve a desired SCRL. The proposed average SCRL analysis methodology can be extended to more general scenarios, where closedform CM estimates are not available. Simulations indicate that even in the non-asymptotic regime, the proposed method can provide a good prediction of the average SCRL.

In this paper, the downlink channel estimation for millimeter wave (mmWave) MIMO systems over frequency selective channels is considered, where both the base station (BS) and the mobile station (MS) are equipped with massive number of antennas. We assume hybrid analog and digital beamforming structures are employed at BS and MS. To overcome the frequency selective fading, we employ orthogonal frequencydivision multiplexing (OFDM) in transmission. By exploiting the sparse scattering nature of mmWave channels, we propose a CANDECOMP/PARAFAC (CP) decomposition-based method for downlink channel estimation. Our analysis reveals that the uniqueness of the CP decomposition can be guaranteed even when the size of the tensor is small. Hence the proposed method has the potential to achieve substantial training overhead reduction. Simulation results show that the proposed method presents a clear advantage over the compressed sensing-based method in terms of both estimation accuracy and computational complexity.

We consider the problem of downlink channel estimation for millimeter wave (mmWave) MIMO-OFDM systems, where both the base station (BS) and the mobile station (MS) employ large antenna arrays for directional precoding/beamforming. Hybrid analog and digital beamforming structures are employed in order to offer a compromise between hardware complexity and system performance. Different from most existing studies that are concerned with narrowband channels, we consider estimation of wideband mmWave channels with frequency selectivity, which is more appropriate for mmWave MIMO-OFDM systems. By exploiting the sparse scattering nature of mmWave channels, we propose a CANDECOMP/PARAFAC (CP) decomposition-based method for channel parameter estimation (including angles of arrival/departure, time delays, and fading coefficients). In our proposed method, the received signal at the MS is expressed as a third-order tensor. We show that the tensor has the form of a low-rank CP, and the channel parameters can be estimated from the associated factor matrices. Our analysis reveals that the uniqueness of the CP decomposition can be guaranteed even when the size of the tensor is small. Hence the proposed method has the potential to achieve substantial training overhead reduction. We also develop Cramér-Rao bound (CRB) results for channel parameters and compare our proposed method with a compressed sensing-based method. Simulation results show that the proposed method attains mean square errors that are very close to their associated CRBs and present a clear advantage over the compressed sensing-based method.

Sparse Beyesian learning is a popular approach for sparse signal recovery, and has demonstrated superior performance in a series of experiments. Nevertheless, the sparse Bayesian learning algorithm involves a matrix inverse at each iteration. Its associated computational complexity grows significantly with the problem size, which hinders its application to many practical problems even with moderately large datasets. To address this issue, in this letter, we develop a fast inverse-free sparse Bayesian learning method. Specifically, by invoking a fundamental property for smooth functions, we obtain a relaxed evidence lower bound (relaxed-ELBO) that is computationally more amiable than the conventional ELBO used by sparse Bayesian learning. A variational expectation-maximization (EM) scheme is then employed to maximize the relaxed-ELBO, which leads to a computationally efficient inverse-free sparse Bayesian learning algorithm. Simulation results show that the proposed algorithm has a fast convergence rate and achieves lower reconstruction errors than other state-of-the-art fast sparse recovery methods in the presence of noise.

We consider a new hybrid radar paradigm consisting of an active array and a passive array. In this hybrid system, the radar transmits a probing signal from its active array and receives two types of echoes: One is the return from the cooperative active transmission and the other is the target return due to transmission from noncooperative illuminators of opportunity. The motivation for this approach is to exploit not only the active signal but also any passive signals that are present in the surveillance area, thereby maximizing the signal-to-interference-plus-noise ratio (SINR). Numerical results demonstrate that the proposed concept can achieve a significant improvement of the output SINR, compared with conventional methods for active-only or passive-only radar systems.

This letter concentrates on the problem of spectral compressed sensing in impulsive noise, which aims to recover a spectrally sparse signal from its contaminated and undersampled measurements. We propose a robust formulation for joint sparse signal and frequency recovery, which includes the generalized ℓpnorm(02approach via majorizing the original objective function by a quadratic surrogate function. Simulation results illustrate that the proposed approach attains a significant performance improvement over the existing methods under impulsive noise.

Passive radar, which detects and tracks targets of interest by using noncooperative illuminators of opportunity (IOs), has become popular since qualified IO sources are widely accessible nowadays. This letter examines the target detection problem for a passive multistatic radar, where the receivers are contaminated by nonnegligible noise and direct-path interference (DPI). The signal transmitted from the IO is treated as a deterministic but unknown process. A generalized likelihood ratio test approach is proposed, where the H1estimation problem is solved using an iterative method. A clairvoyant matched filtering detector, which assumes the knowledge of the IO waveform, is provided as well to benchmark performance. Simulation results are presented to show the effectiveness of the proposed detector in the presence of DPI.

We consider the problem of downlink training and channel estimation in frequency division duplex (FDD) massive MIMO systems, where the base station (BS) equipped with a large number of antennas serves a number of single-antenna users simultaneously. To obtain the channel state information (CSI) at the BS in FDD systems, the downlink channel has to be estimated by users via downlink training and then fed back to the BS. For FDD large-scale MIMO systems, the overhead for downlink training and CSI uplink feedback could be prohibitively high, which presents a significant challenge. In this paper, we study the behavior of the minimum mean-squared error (MMSE) estimator when the channel covariance matrix has a low rank or an approximate low-rank structure. Our theoretical analysis reveals that the amount of training overhead can be substantially reduced by exploiting the low-rank property of the channel covariance matrix. In particular, we show that the MMSE estimator is able to achieve exact channel recovery in the asymptotic low-noise regime, provided that the number of pilot symbols in time is no less than the rank of the channel covariance matrix. We also present an optimal pilot design for the single-user case, and an asymptotic optimal pilot design for the multi-user scenario. Last, we develop a simple model-based scheme to estimate the channel covariance matrix, based on which the MMSE estimator can be employed to estimate the channel. The proposed scheme does not need any additional training overhead. Simulation results are provided to verify our theoretical results and illustrate the effectiveness of the proposed estimated covariance-assisted MMSE estimator.

This paper considers the sparse recovery problem of multiple measurement vector (MMV) model corrupted in impulsive noise. To ensure outlier-robust sparse recovery, we formulate an MMV problem that includes the generalized ℓp-norm (12,0joint sparsity-promoting regularizer. The joint sparse penalty, however, is non-continuous and hence non-differentiable, which inevitably raises difficulty in optimization when using a gradient-based method. To address this, we build a smooth approximation for the ℓ2,0-based sparse metric via the log-sum based sparse-encouraging surrogate function. Then, we propose a block successive upper-bound minimization algorithm for the smooth MMV problem by solving a series of subproblems based on the block coordinate descent (BCD) method. Furthermore, local convergence of the proposed algorithm to a stationary point of the smooth problem is proved. Experiments demonstrate its efficiency and robust recovery performance for suppressing impulsive noise.

This paper considers adaptive signal detection in stochastic homogeneous environments where the disturbance covariance matrix of both test and training signals, R, is assumed to be a random matrix with a priori knowledge of R. Unlike existing detectors assuming a known hyperparameter associated with R, a knowledge-aided detector with the capability of automatic weighting is considered by accounting for the uncertainty of the prior knowledge. Specifically, the generalized likelihood ratio test (GLRT) is utilized to develop the test statistic, along with the maximum marginal likelihood (MML) estimation of the hyperparameter. The proposed KA-MML-GLRT detector is evaluated by numerical simulations and the results show improved detection performance over conventional and knowledge-aided detectors, especially in the case of limited training signals and inaccurate prior knowledge.

We consider a dictionary learning problem aimed at designing a dictionary such that the signals admits a sparse or an approximate sparse representation over the learned dictionary. The problem finds a variety of applications including image denoising, feature extraction, etc. In this paper, we propose a new hierarchical Bayesian model for dictionary learning, in which a Gaussian-inverse Gamma hierarchical prior is used to promote the sparsity of the representation. Suitable non-informative priors are also placed on the dictionary and the noise variance such that they can be reliably estimated from the data. Based on the hierarchical model, a Gibbs sampling method is developed for Bayesian inference. The proposed method have the advantage that it does not require the knowledge of the noise variance a priori. Numerical results show that the proposed method is able to learn the dictionary with an accuracy better than existing methods.

In this paper, we present an iterative algorithm which jointly estimates the PA characteristics and the channel impulse response for communication systems. The proposed approach is motivated by the fact that the nonlinearly amplified communication signal carries more bandwidth, referred to as spectral regrowth, and therefore allows better probing of the channel. In contrast to existing conventional schemes, where the expansion of the bandwidth is treated as a distortion, we propose to exploit the spectral regrowth to enhance channel identification accuracy. Numerical results show that an improved performance is achieved by using the proposed approach over the ones that ignore the outband energy of the received signal.

Spectrum sharing and aggregation among authorized secondary users (A-SUs) are important tasks in operating effective cognitive radio networks. Furthermore, in protecting spectrum sharing/aggregation against unauthorized secondary users (UA-SUs), secondary user access control (SUAC) is needed, which is investigated in this paper. A jamming signal is injected to degrade the spectrum sensing performance of UA-SUs, while reliable spectrum sensing performance for A-SUs can be achieved through an oblique projection-based jamming cancellation method. An orthogonal frequency division multiplexing-based transmission model is considered in this paper. The generalized likelihood ratio test algorithm is used for both authorized and unauthorized SUs in spectrum sensing. Numerical results show the effectiveness of the proposed SUAC in degrading the spectrum sensing performance of the unauthorized SUs.

We consider the problem of recovering block-sparse signals with intrablock correlated entries. The block partition of the sparse signal is assumed unknown a priori. To exploit the block-sparse structure as well as the local smoothness of the sparse signal, consecutive coefficients of the sparse signal are organized into a number of 2×2 matrices, and the log-determinant function is used to promote the low rankness of these 2×2 matrices. We show that such a log-determinant function has the ability to promote the block-sparsity and local smoothness simultaneously. An iterative reweighted method is developed by iteratively minimizing a surrogate function of the original objective function. Simulation results show that our proposed method offers competitive performance for recovering block-sparse signals with intrablock correlated entries.

We consider the problem of recovering 2D block-sparse signals with unknown cluster patterns. The 2D block-sparse patterns arise naturally in many practical applications, such as foreground detection and inverse synthetic aperture radar imaging. To exploit the underlying block-sparse structure, we propose a 2D pattern-coupled hierarchical Gaussian prior model. The proposed pattern-coupled hierarchical Gaussian prior model imposes a soft coupling mechanism among neighboring coefficients through their shared hyperparameters. This coupling mechanism enables effective and automatic learning of the underlying irregular cluster patterns, without requiring any a priori knowledge of the block partition of sparse signals. We develop a computationally efficient Bayesian inference method, which integrates the generalized approximate message passing technique with the proposed prior model. Simulation results show that the proposed method offers competitive recovery performance for a range of 2D sparse signal recovery and image processing applications over the existing method, meanwhile achieving a significant reduction in the computational complexity.

We consider the problem of peak-to-average power ratio (PAPR) reduction in orthogonal frequency-division multiplexing (OFDM) based massive multiple-input multiple-output (MIMO) downlink systems. Specifically, given a set of symbol vectors to be transmitted to K users, the problem is to find an OFDM-modulated signal that has a low PAPR and meanwhile enables multiuser interference (MUI) cancellation. Unlike previous works that tackled the problem using convex optimization, we take a Bayesian approach and develop an efficient PAPR reduction method by exploiting the redundant degrees of freedom of the transmit array. The sought-after signal is treated as a random vector with a hierarchical truncated Gaussian mixture prior, which has the potential to encourage a low PAPR signal with most of its samples concentrated on the boundaries. A variational expectation-maximization (EM) strategy is developed to obtain estimates of the hyperparameters associated with the prior model, along with the signal. In addition, the generalized approximate message passing (GAMP) is embedded into the variational EM framework, which results in a significant reduction in computational complexity of the proposed algorithm. Simulation results show our proposed algorithm achieves a substantial performance improvement over existing methods in terms of both the PAPR reduction and computational complexity.

We consider the problem of uplink channel estimation for millimeter wave (mmWave) systems, where the base station (BS) and mobile stations (MSs) are equipped with large antenna arrays to provide sufficient beamforming gain for outdoor wireless communications. Hybrid analog and digital beamforming structures are employed by both the BS and the MS due to hardware constraints. We propose a layered pilot transmission scheme and a CANDECOMP/PARAFAC (CP) decomposition-based method for joint estimation of the channels from multiple users (i.e., MSs) to the BS. The proposed method exploits the intrinsic low-rank structure of the multiway data collected from multiple modes, where the low-rank structure is a result of the sparse scattering nature of the mmWave channel. The uniqueness of the CP decomposition is studied, and the sufficient conditions for essential uniqueness are obtained. The conditions shed light on the design of the beamforming matrix, the combining matrix, and the pilot sequences, and meanwhile provide general guidelines for choosing system parameters. Our analysis reveals that our proposed method can achieve a substantial training overhead reduction by leveraging the low-rank structure of the received signal. Simulation results show that the proposed method presents a clear advantage over a compressed sensing-based method in terms of both estimation accuracy and computational complexity.

In this paper, we study the problem of sparse signal recovery when partial but partly erroneous prior knowledge of the signal's support is available. Based on the conventional sparse Bayesian learning framework, we propose an improved hierarchical prior model. The proposed modeling constitutes a three-layer hierarchical form. The first two layers, similar to the conventional sparse Bayesian learning, place a Gaussian-inverse-Gamma prior on the signal, while the third layer is newly added, with a prior placed on the parameters {bi}, where {bi} are parameters characterizing the sparsity-controlling hyperparameters {αi}. Such a modeling enables to automatically learn the true support from partly erroneous information through learning the values of the parameters {bi}. A variational Bayesian inference algorithm is developed based on the proposed prior model. Numerical results are provided to illustrate the performance of the proposed algorithm.

This paper considers the problem of passive detection with a multistatic radar system involving a non-cooperative illuminator of opportunity (IO) and multiple receive platforms. An unknown source signal is transmitted by the IO, which illuminates a target of interest. These receive platforms are geographically dispersed, and collect independent target echoes due to the illumination by the same IO. We propose a generalized likelihood ratio test (GLRT) detector to deal with the passive detection problem in the case of unknown noise power. Moreover, a closed-form expression for the probability of false alarm of this GLRT detector is given. Numerical simulations demonstrate that the proposed GLRT detector generally outperforms its natural counterparts.

We propose a pattern-coupled sparse Bayesian learning method for inverse synthetic aperture radar (ISAR) imaging by exploiting a block-sparse structure inherent in ISAR target images. A two-dimensional pattern-coupled hierarchical Gaussian prior is proposed to model the pattern dependencies among neighboring scatterers on the target scene. An expectation-maximization (EM) algorithm is developed to infer the maximum a posterior (MAP) estimate of the hyperparameters, along with the posterior distribution of the sparse signal. Numerical results are provided to illustrate the effectiveness of the proposed algorithm.

We consider the problem of sparse signal recovery from one-bit measurements. Due to the noise present in the acquisition and transmission process, some quantized bits may be flipped to their opposite states. These bit-flip errors, also referred to as the sign-flip errors, may result in severe performance degradation. To address this issue, we introduce a robust Bayesian compressed sensing framework to account for sign flip errors. Specifically, sign-flip errors are considered as a result of a sparse noise-corrupted model in which original (unquantized) observations are corrupted by sparse (impulse) noise. A Gaussian-inverse Gamma hierarchical prior is assigned to the noise vector to promote sparsity. Based on the modified hierarchical model, we develop a variational expectation-maximization (EM) algorithm to identify the sign-flip errors and recover the sparse signal simultaneously. Numerical results are provided to illustrate the effectiveness and superiority of the proposed method.

Our aim in this work is to analyze the detection performance of a constant false alarm rata matched filter (CFAR-MF) which was developed for the detection problem in white Gaussian noise with unknown noise power. An exact expression for the detection probability of the CFAR-MF is derived in the mismatched case where mismatch exists between the actual signal steering vector and the nominal one. This theoretical expression can be used to facilitate the performance evaluation of the CFAR-MF in real-world scenarios when signal mismatch cannot be neglected.

It is known that the probabilities of false alarm (PFAs) of several celebrated adaptive detectors including the adaptive matched filter (AMF) and the adaptive coherence estimator (ACE) can be expressed as integral forms. Nevertheless, it is inconvenient to set the detection thresholds by using these integral expressions. Here, we propose two computationally efficient schemes to calculate the thresholds of the AMF and ACE. In the first method, approximate expressions, in forms of elementary functions, for the PFAs of the AMF and ACE are derived. The thresholds of the AMF and ACE can be numerically computed by using these elementary expressions instead of the integrals, for reducing computational complexity. In the second approach, further approximations are employed to lead to highly simple expressions for the thresholds of the AMF and ACE, which enable us to directly compute the thresholds for a given PFA. Compared to the first one, the second scheme is more computationally efficient, but at the cost of a slight loss in accuracy. Numerical results verify the effectiveness of the two proposed schemes.

We consider the problem of distributed detection of a mean parameter corrupted by Gaussian noise in wireless sensor networks, where a large number of sensor nodes jointly detect the presence of a weak unknown signal. To circumvent power/bandwidth constraints, a multilevel quantizer is employed in each sensor to quantize the original observation. The quantized data are transmitted through binary symmetric channels to a fusion center where a generalized likelihood ratio test (GLRT) detector is employed to perform a global decision. The asymptotic performance analysis of the multibit GLRT detector is provided, showing that the detection probability is monotonically increasing with respect to the Fisher information (FI) of the unknown signal parameter. We propose a quantizer design approach by maximizing the FI with respect to the quantization thresholds. Since the FI is a nonlinear and nonconvex function of the quantization thresholds, we employ the particle swarm optimization algorithm for FI maximization. Numerical results demonstrate that with 2- or 3-bit quantization, the GLRT detector can provide detection performance very close to that of the unquantized GLRT detector, which uses the original observations without quantization.

In this paper, we develop a new sparse Bayesian learning method for recovery of block-sparse signals with unknown cluster patterns. A pattern-coupled hierarchical Gaussian prior model is introduced to characterize the statistical dependencies among coefficients, where a set of hyperparameters are employed to control the sparsity of signal coefficients. Unlike the conventional sparse Bayesian learning framework in which each individual hyperparameter is associated independently with each coefficient, in this paper, the prior for each coefficient not only involves its own hyperparameter, but also the hyperparameters of its immediate neighbors. In doing this way, the sparsity patterns of neighboring coefficients are related to each other and the hierarchical model has the potential to encourage structured-sparse solutions. The hyperparameters, along with the sparse signal, are learned by maximizing their posterior probability via an expectation-maximization (EM) algorithm.

In this paper, a knowledge-aided parametric adaptive matched filter (KA-PAMF) is proposed that utilizing both observations (including the test and training signals) and a priori knowledge of the spatial co-variance matrix. Unlike existing KA-PAMF methods, the proposed KA-PAMF is able to automatically adjust the combining weight of a priori covariance matrix, thus gaining enhanced robustness against uncertainty in the prior knowledge. Meanwhile, the proposed KA-PAMF is significantly more efficient than its KA non-parametric counterparts when the amount of training signals is limited. One distinct feature of the proposed KA-PAMF is the inclusion of both the test and training signals for automatic determination of the combining weights for the prior spatial covariance matrix and observations. Numerical results are presented to demonstrate the effectiveness of the proposed KA-PAMF, especially in the limited training scenarios.

In modern communication systems, power amplifiers (PAs) are important components and inherently nonlinear. The nonlinearity of the PA causes bandwidth expansion of the communication signal, often referred to as spectral regrowth, at the PA output. Conventionally, spectral regrowth is treated as a distortion, and a range of compensation and filtering techniques have been considered to mitigate its effect. In this paper, we propose to exploit spectral regrowth to enhance channel identification accuracy. Our approach is motivated by the fact that the nonlinearly amplified communication signal carries more bandwidth and allows better probing of the channel. We introduce an iterative algorithm which jointly estimates the PA characteristics and the channel impulse response. The effectiveness of the proposed algorithm is illustrated by computer simulation.

We consider adaptive transmit and receive beampattern design for array radar systems. While adaptive processing is primarily employed for only receive beamforming in conventional design, we propose a fully adaptive approach involving jointly selecting the transmit correlation matrix and receive beamformer by maximizing the signal-to-interference-plus-noise ratio (SINR). The motivation of utilizing adaptive processing at the transmitter is that with imprecise knowledge of the interference (e.g., due to limited training data), only relying on adaptive receive beamforming may be inadequate for effective interference cancellation, whereas joint adaptive transmit and receive beamforming can afford a stronger ability to handle the interference. Simulations are provided to demonstrate the performance of the proposed joint beamforming approach.

In many practical applications such as direction-of- arrival (DOA) estimation and line spectral estimation, the sparsifying dictionary is usually characterized by a set of unknown parameters in a continuous domain. To apply the conventional compressed sensing to such applications, the continuous parameter space has to be discretized to a finite set of grid points. Discretization, however, incurs errors and leads to deteriorated recovery performance. To address this issue, we propose an iterative reweighted method which jointly estimates the unknown parameters and the sparse signals. Specifically, the proposed algorithm is developed by iteratively decreasing a surrogate function majorizing a given objective function, which results in a gradual and interweaved iterative process to refine the unknown parameters and the sparse signal. Numerical results show that the algorithm provides superior performance in resolving closely-spaced frequency components.

We jointly design the transmit and receive beamforming based on a-priori information on the locations of target and interferences in an active array, where each transmit element emits the same waveform up to a complex scalar. A sequential optimization algorithm is proposed to maximize the output signal-to-interference-plus-noise ratio (SINR). Numerical results demonstrate that a significant gain in the output SINR can be achieve in this active array, compared to the conventional phased-array radar and omnidirectional multiple-input-multiple-output (MIMO) radar.

In this paper, we consider a decentralized detection problem in which a number of sensor nodes collaborate to detect the presence of an unknown deterministic signal. Due to stringent power/bandwidth constraints, each sensor quantizes its local observation into one bit of information. The binary data are then sent to the fusion center (FC), where a generalized likelihood ratio test (GLRT) detector is employed to make a global decision. In this context, we study one-bit quantizer design and analyze the asymptotic performance of the one-bit GLRT detector for cases where the quantized data are sent to the FC via perfect or imperfect channels. Simulation results are carried out to corroborate our theoretical analysis and to illustrate the performance of the proposed scheme.

This paper considers the problem of reconstructing sparse or compressible signals from one-bit quantized measurements. We study a new method that uses a log-sum penalty function, also referred to as the Gaussian entropy, for sparse signal recovery. Additionally, in the proposed method, the sigmoid function is introduced to quantify the consistency between the measured one-bit quantized data and the reconstructed signal. A fast iterative algorithm is developed by iteratively minimizing a convex surrogate function that bounds the original objective function. This leads to an iterative reweighted process that alternates between estimating the sparse signal and refining the weights of the surrogate function. Connections between the proposed algorithm and other existing methods are discussed. Numerical results are provided to illustrate the effectiveness of the proposed algorithm.

In this letter, we consider a decentralized detection problem in which a number of sensor nodes collaborate to detect the presence of an unknown deterministic signal. Due to stringent power/bandwidth constraints, each sensor quantizes its local observation into one bit of information. The binary data are then sent to the fusion center (FC), where a generalized likelihood ratio test (GLRT) detector is employed to make a global decision. In this context, we study one-bit quantizer design and analyze the asymptotic performance of the one-bit GLRT detector for cases where the quantized data are sent to the FC via perfect or imperfect channels. Simulation results are carried out to corroborate our theoretical analysis and to illustrate the performance of the proposed scheme.

The fact that fewer measurements are needed by log-sum minimization for sparse signal recovery than the ℓ1-minimization has been observed by extensive experiments. Nevertheless, such a benefit brought by the use of the log-sum penalty function has not been rigorously proved. This paper provides a theoretical justification for adopting the log-sum as an alternative sparsity-encouraging function. We prove that minimizing the log-sum penalty function subject to Az = y is able to yield the exact solution, provided that a certain condition is satisfied. Specifically, our analysis suggests that, for a properly chosen regularization parameter, exact reconstruction can be attained when the restricted isometry constant δ3Kis smaller than one, which presents a less restrictive isometry condition than that required by the conventional ℓ1-type methods.

We consider the problem of transmitting multiple spatially distributed correlated sources to a common destination (e.g. a fusion center or an access point) in wireless sensor networks (WSNs). The correlated data from multiple sensors are jointly transmitted to the destination via orthogonal channels. We assume that the channel between each sensor and the receiver is multiple-input multiple-output (MIMO), with each sensor and the receiver equipped with multiple transmit/receive antennas. In this framework, we study the problem of joint linear precoder design for all sensors by assuming the knowledge of the instantaneous channel state information (CSI), aiming at maximizing the mutual information between the sources and the received signals at the destination. We propose a Gauss-Seidel iterative approach which successively optimizes the precoding matrix associated with each sensor, while fixing the other precoding matrices. Numerical results show that the proposed algorithm that takes into account the spatial correlation across sensors can achieve higher capacity than conventional methods that neglect the spatial correlation.

We study the problem of recovering the sparsity pattern of block-sparse signals from noise-corrupted measurements. A simple, efficient recovery method, namely, a block-version of the orthogonal matching pursuit (OMP) method, is considered in this paper and its behavior for recovering the block-sparsity pattern is analyzed. We provide sufficient conditions under which the block-version of the OMP can successfully recover the block-sparse representations in the presence of noise. Our analysis reveals that exploiting block-sparsity can improve the recovery ability and lead to a guaranteed recovery for a higher sparsity level. Numerical results are presented to corroborate our theoretical claim.

This paper considers a parametric approach for adaptive multichannel signal detection, where the disturbance is modeled by a multichannel auto-regressive (AR) process. Motivated by the fact that a symmetric antenna geometry usually yields a persymmetric structure on the covariance matrix of disturbance, a new persymmetric AR (PAR) modeling for the disturbance is proposed and, accordingly, a persymmetric parametric adaptive matched filter (Per-PAMF) is developed. The developed Per-PAMF, while allowing a simple implementation like the traditional PAMF, extends the PAMF by developing the maximum likelihood (ML) estimation of unknown nuisance (disturbance-related) parameters under the persymmetric constraint. Numerical results show that the Per-PAMF provides significantly better detection performance than the conventional PAMF and other non-parametric detectors when the number of training signals is limited.

This study considers the problem of detecting a multi-channel signal of range-spread targets in a homogeneous environment, where the disturbances in both test signal and training signals share the same covariance matrix. To this end, a generalised parametric Rao (GP-Rao) test is developed by modelling the disturbance as a multi-channel auto-regressive process. The GP-Rao test uses less training data and is computationally more efficient, when compared with conventional covariance matrix-based solutions. The theoretical detection performance of the GP-Rao test is characterised in terms of the asymptotic distribution under both hypotheses. Numerical results indicate that the proposed GP-Rao test attains asymptotically the constant false alarm rate property. Numerical results show that the GP-Rao test achieves better detection performance and uses significantly less training signals than the covariance matrix-based approach.

In this letter, we consider the detection of a multichannel signal with an unknown amplitude in colored noise, when there is a covariance mismatch between the null and alternative hypotheses. Specifically, the covariance mismatch is caused by a target-induced subspace interference that is present only under the alternative hypothesis. According to the signal model, we propose a detector involving the following steps. The observation is first projected to the orthogonal complement of the signal to be detected, followed by a second projection to the interference subspace. Then, the energy of the doubly projected signal (residual) is computed. If the residual energy is small, the proposed detector reduces to the standard matched filter (MF), which ignores the subspace interference; otherwise, a modified test statistic is employed for additional interference cancellation. Simulation results are presented to demonstrate the effectiveness of the proposed detector.

This letter introduces a stochastic partially homogeneous model for adaptive signal detection. In this model, the disturbance covariance matrix of training signals,${\bf R}$, is assumed to be a random matrix with some a priori information, while the disturbance covariance matrix of the test signal,${\bf R}_{0}$, is assumed to be equal to$\lambda{\bf R}$, i.e.,${\bf R}_{0}=\lambda{\bf R}$. On one hand, this model extends the stochastic homogeneous model by introducing an unknown power scaling factor$\lambda$between the test and training signals. On the other hand, it can be considered as a generalization of the standard partially homogeneous model to the stochastic Bayesian framework, which treats the covariance matrix as a random matrix. According to the stochastic partially homogeneous model, a scale-invariant generalized likelihood ratio test (GLRT) for the adaptive signal detection is developed, which is a knowledge-aided version of the well-known adaptive coherence estimator (ACE). The resulting knowledge-aided ACE (KA-ACE) employs a colored loading step utilizing the a priori knowledge and the sample covariance matrix. Various simulation results and comparison with respect to other detectors confirm the scale-invariance and the effectiveness of the KA-ACE.

We consider the problem of distributed estimation of a vector parameter in wireless sensor networks (WSNs). Due to stringent power and bandwidth constraints, vector quantization is performed at each sensor to convert its local noisy vector observation into one bit of information. The one bit quantized data is then sent to the fusion center (FC), where a final estimate of the vector parameter is formed. The vector quantization problem is studied in such a distributed estimation context. Specifically, our study focuses on a class of hyperplane-based vector quantizers which linearly convert the observation vector into a scalar by using a compression vector and then carry out a scalar quantization. Under the framework of the Cramér-Rao bound (CRB) analysis, we study the choice of the quantization thresholds and the design of the compression vectors.

We consider the problem of distributed estimation in a power constrained collaborative wireless sensor network (WSN), where the network is divided into a set of sensor clusters, with collaboration allowed among sensors within the same cluster but not across clusters. Specifically, each cluster forms one or multiple local messages via sensor collaboration (in particular, linear operation is considered) and transmits the messages over noisy channels to a fusion center (FC). The final estimate is constructed at the FC based on the noisy data received from all clusters. In this collaborative setup, we study the following fundamental problems. Given a total transmit power constraint, shall we transmit the raw data or some low-dimensional local messages for each cluster? What is the optimal collaboration scheme for each cluster? How to optimally allocate the power among different clusters? These questions are addressed in this paper.

We consider the problem of distributed estimation of a Gauss-Markov random field using a wireless sensor network (WSN), where due to the stringent power and communication constraints, each sensor has to quantize its data before transmission. In this case, the convergence of conventional iterative matrix-splitting algorithms is hindered by the quantization errors. To address this issue, we propose a one-bit adaptive quantization approach which leads to decaying quantization errors. Numerical results show that even with one bit quantization, the proposed approach achieves a superior mean square deviation performance (with respect to the global linear minimum mean-square error estimate) within a moderate number of iterations.

This paper considers the problem of knowledge-aided space-time adaptive processing (STAP) in nonhomogeneous environments, where the covariance matrices of the training and test signals are assumed random and different from each other. A Bayesian detector is proposed by incorporating somea prioriknowledge of the disturbance covariance matrices, and exploring their inherent block-Toeplitz structure. Specifically, the block-Toeplitz structure of the covariance matrix allows us to model the training signals as a multichannel auto-regressive (AR) process. The resulting detector is referred to as the Bayesian parametric adaptive matched filter (B-PAMF) which, compared with nonparametric Bayesian detectors, entails a lower training requirement and alleviates the computational complexity. Numerical results show that the proposed B-PAMF detector outperforms the standard PAMF test in nonhomogeneous environments.

The problem of distributed average consensus with quantized data is considered in this paper. We firstly propose a simple modification to the classical consensus protocol. Under a condition that the quantization noise variance converges to zero, the proposed protocol achieves a consensus in a mean squared sense and the consensus value is equal to the average of the initial state. Based on this result, we develop an adaptive quantization scheme which can adaptively adjust its quantization threshold and step-size by learning from previous runs, in a way such that the quantization noise variance at each sensor decreases to zero. Simulation results are presented to illustrate the effectiveness of the proposed algorithm.

For a high-order polynomial-phase signal (PPS), instantaneous frequency rate (IFR), which is defined as the second derivative of the phase, is estimated by using an estimator with only a second-order nonlinearity. Compared to high-order phase function (HPF), the proposed IFR estimator presents improved performance including smaller mean-squared error (MSE) and lower SNR threshold. Statistical analysis via a multivariate first-order perturbation analysis is derived for the estimate bias and MSE. Numerical results verify our analytical results.

Instantaneous frequency rate (IFR) estimation for high-order polynomial phase signals (PPSs) is considered. Specifically, an IFR estimator with only a second-order nonlinearity is proposed. The asymptotic mean-squared error (MSE) of the proposed IFR estimator is obtained via a multivariate first-order perturbation analysis. Our results show that the proposed estimator yields a smaller MSE and a lower signal-to-noise ratio (SNR) threshold than a popular IFR estimator involving higher nonlinearity. The proposed IFR estimator is also extended to estimate the phase parameters of a PPS. Numerical studies are presented to illustrate the performance of the proposed estimator.

This paper considers distributed estimation of a vector parameter in the presence of zero-mean additive multivariate Gaussian noise in wireless sensor networks. Due to stringent power and bandwidth constraints, vector quantization is performed at each sensor to convert its local noisy vector observation into one bit of information, which is then forwarded to a fusion center where a final estimate of the vector parameter is obtained. Within such a context, this paper focuses on a class of hyperplane-based vector quantizers which linearly convert the observation vector into a scalar by using a compression vector and then carry out a scalar quantization. It is shown that the key of the vector quantization design is to find a compression vector for each sensor. Under the framework of the Cramer-Rao bound (CRB) analysis, the compression vector design problem is formulated as an optimization problem that minimizes the trace of the CRB matrix. Such an optimization problem is extensively studied. In particular, an efficient iterative algorithm is developed for the general case, along with optimal and near-optimal solutions for some specific but important noise scenarios. Performance analysis and simulation results are carried out to illustrate the effectiveness of the proposed scheme.

We consider the problem of distributed estimation in a power constrained collaborative wireless sensor network (WSN), where the network is divided into a set of sensor clusters, with collaboration allowed among sensors within the same cluster but not across clusters. Specifically, each cluster forms one or multiple local messages via sensor collaboration (in particular, linear operation is considered) and transmits the messages over noisy channels to a fusion center (FC). The final estimate is constructed at the FC based on the noisy data received from all clusters. In this collaborative setup, we study the following fundamental problems. Given a total transmit power constraint, shall we transmit the raw data or some low-dimensional local messages for each cluster? What is the optimal collaboration scheme for each cluster? How to optimally allocate the power among different clusters? These questions are addressed in this paper. We will show that the optimum collaboration strategy is to compress the data into one local message which, depending on the channel characteristics, is transmitted using one or multiple available channels to the FC. The optimal power allocation among the clusters is also investigated, which yields a water- filling type of scheme.

We consider distributed estimation of a random vector parameter by a wireless sensor network (WSN). To meet stringent power and bandwidth budgets in WSN, local data compression is performed at each sensor to reduce the number of messages sent to a fusion center (FC). Under the constraint of a given total number of messages, our problem is to jointly determine the number of messages sent by each senor (a.k.a. dimension assignment) and design the corresponding compression matrix. The problem is formulated as a constrained optimization problem that minimizes the estimation mean-square error (MSE) at the FC. We analyze the problem using a subspace projection technique, which yields an efficient iterative solution. Numerical results are presented to illustrate the effectiveness of the proposed algorithm.

We consider the problem of distributed parameter estimation in wireless sensor networks (WSNs), where due to bandwidth/power constraints, each sensor quantizes its local observation into one bit of information that is sent to a fusion center (FC) to form a global estimate. Conventional fixed quantization (FQ) approaches, which utilize a fixed threshold for all sensors, incurs an estimation error growing exponentially with the difference between the threshold and the unknown parameter to be estimated. To overcome this difficulty, we propose a distributed adaptive quantization (AQ) approach, where, under the condition that sensors successively broadcast their quantized data, each sensor adaptively adjusts its quantization threshold using prior transmissions from other sensors. Specifically, our strategy here is to let each sensor choose its quantization threshold as the maximum likelihood (ML) estimate of the unknown parameter based on the quantized data sent from other sensors. The Cramér-Rao bound (CRB) analysis shows that our proposed one-bit AQ approach asymptotically attains an estimation variance that is only π/2 times that of the clairvoyant sample-mean estimator using unquantized observations.

Block transmission techniques have received much research interest recently for their ability to eliminate the inter-symbol interference problem associated with continuous transmission. Numerous detection schemes for single carrier and multi-carrier have been proposed. While these schemes may be derived from different principles, they usually rely on some prior estimate of the channel. However, channel estimation is usually affected by errors and most of existing detection schemes are known to be sensitive to such errors. In this paper, we develop a robust detection scheme that explicitly accounts for channel estimation error by optimizing the worst-case performance over a properly selected bounded uncertainty set. Although the prior channel estimation error is generally not bounded, we show that it is beneficial to refine the channel estimate over a properly chosen bounded uncertainty set of the channel centered on the prior channel estimate. Numerical results show an improved performance using the proposed robust approach over the one that ignores the prior estimation errors.

We consider distributed estimation of a random vector parameter by a wireless sensor network (WSN). To meet stringent power and bandwidth budgets in WSN, local data compression is performed at each sensor to reduce the number of messages sent to a fusion center (FC). Under the constraint of a given total number of messages, our problem is to jointly determine the number of messages sent by each senor (a.k.a. dimension assignment) and design the corresponding compression matrix. The problem is formulated as a constrained optimization problem that minimizes the estimation mean-square error (MSE) at the FC. We analyze the problem using a subspace projection technique, which yields an efficient iterative solution. Numerical results are presented to illustrate the effectiveness of the proposed algorithm.

We consider distributed parameter estimation in a wireless sensor network, where due to bandwidth constraint, all sensor nodes have to quantize their observations and send quantized data to a fusion center. We consider the case where each sensor can send only one bit of information. In such a case, the achievable estimation performance is critically dependent on the choice of the one-bit quantizer used at the sensor nodes to perform quantization; it is also known that a fixed quantizer does not perform well, in particular when the quantization threshold is away from the unknown parameter to be estimated. In this paper, we propose a new distributed adaptive quantization scheme by which each individual sensor node dynamically adjusts the threshold of its quantizer based on earlier transmissions from other sensor nodes. We develop the maximum likelihood estimator (MLE) and derive the Cramer-Rao bound (CRB) associated with our distributed adaptive quantization scheme. Numerical results show that our approach does not suffer from the drawback of the fixed quantization approach and outperforms the latter.

In this paper, the problem of distributed parameter estimation in a wireless sensor network is considered, where due to bandwidth constraint, each sensor node sends only one bit of information per sample to a fusion center. We propose a new distributed adaptive quantization scheme by which each individual sensor node dynamically adjusts the threshold of its quantizer based on earlier transmissions from other sensor nodes. The maximum likelihood estimator (MLE) and the Cramer-Rao bound (CRB) associated with our distributed adaptive quantization scheme are derived. Numerical results depicting the performance and advantages of our approach over a fixed quantization scheme are presented.

Block transmission has recently been considered as an alternative to the conventional continuous transmission technique. In particular, block transmission techniques with zero padding (ZP) and cyclic prefix (CP) are becoming attractive procedures for their ability to eliminate both inter-symbol interference (ISI) and inter-block interference (IBI). In this paper, we present a unified approach to blind channel estimation and interference suppression for block transmission using ZP or CP in both single-carrier (SC) and multicarrier (MC) systems. Our approach uses a generalized multichannel minimum variance principle to design an equalizing filterbank. Channel estimate is then obtained from an asymptotically tight lower bound of the filterbank output power. Numerical examples show that the proposed schemes approach the CRB as the signal-to-noise ratio (SNR) increases. Additionally, they exhibit low sensitivity to unknown narrowband interference and compare favorably with subspace blind channel estimators.

A blind channel estimation and equalisation scheme is given for orthogonal frequency-division multiplexing systems with unmodelled interference. The approach uses a generalised multichannel minimum variance principle to design an equalising filterbank that preserves the desired signal components and suppresses the overall interference. A channel estimate is then obtained by deriving an asymptotically tight lower bound of the filterbank output power, which reduces the problem to a quadratic minimisation. By imposing a special structure on the received signal, the performance is shown to be significantly increased. To assess the performance of the proposed scheme, an unconditional Cramér–Rao bound (CRB) is derived which, similar to the proposed blind channel estimator, does not assume knowledge of the transmitted information symbols. The CRB serves as a lower bound for all unbiased blind channel estimation schemes. Numerical examples show that the proposed scheme approaches the CRB as the SNR increases. It also exhibits low sensitivity to unknown narrowband interference and compares favourably with a subspace blind channel estimator.

In this letter, the problem of distributed parameter estimation in a wireless sensor network is considered, where due to bandwidth constraint, each sensor node sends only one bit of information to a fusion center. We propose a new distributed adaptive quantization scheme by which each individual sensor node dynamically adjusts the threshold of its quantizer based on earlier transmissions from other sensor nodes. The maximum likelihood estimator (MLE) and the Cramer-Rao bound (CRB) associated with our distributed adaptive quantization scheme are derived. Numerical results depicting the performance and advantages of our approach over a fixed quantization scheme are presented.

The parametric Rao test for a multichannel adaptive signal detection problem is derived by modeling the disturbance signal as a multichannel autoregressive (AR) process. Interestingly, the parametric Rao test takes a form identical to that of the recently introduced parametric adaptive matched filter (PAMF) detector. The equivalence offers new insights into the performance and implementation of the PAMF detector. Specifically, the Rao/PAMF detector is asymptotically (for large samples) a parametric generalized likelihood ratio test (GLRT), due to an asymptotic equivalence between the Rao test and the GLRT. The asymptotic distribution of the Rao test statistic is obtained in closed-form, which follows an exponential distribution under H0and, respectively, a non-central Chi-squared distribution with two degrees of freedom under H1. The non-centrality parameter of the non-central Chi-squared distribution is determined by the output signal-to-interference-plus-noise ratio (SINR) of a temporal whitening filter. Since the asymptotic distribution under H0is independent of the unknown parameters, the Rao/ PAMF asymptotically achieves constant false alarm rate (CFAR). Numerical results show that these results are accurate in predicting the performance of the parametric Rao/PAMF detector even with moderate data support.

We consider a regenerative differential modulation scheme for wireless networks with relays to seek cooperative diversity. We examine the performance of a differential binary phase shift keying (BPSK) modulation scheme, referred to as the differential decode-and-forward (DDF), for wireless relay networks composed of one source, one relay and one destination node in Nakagami-m fading channels. A regenerative relay differentially decodes/encodes the received signal and forwards it to the destination. A closed form bit error rate (BER) expression is presented for the piece-wise linear (PL) detector. Both analytical and simulation results show that the proposed DDF scheme is capable of providing diversity gain in Nakagami-m fading channels.

Multiuser detection (MUD) for code-division multiple-access (CDMA) systems usually relies on some a priori channel estimates, which are obtained either blindly or by using training sequences, and the covariance matrix of the received signal, usually replaced by the sample covariance matrix. However, such prior estimates are often affected by errors that are typically ignored in subsequent detection. In this paper, we present robust channel estimation and MUD techniques for multicarrier (MC) CDMA by explicitly taking into account such estimation errors. The proposed techniques are obtained by optimizing the worst case performance over two bounded uncertainty sets pertaining to the two types of estimation errors. We show that although the estimation errors associated with the prior channel estimate and the sample covariance matrix are generally not bounded, it is beneficial to optimize the worst case performance over properly chosen bounded uncertainty sets determined by a parameter called bounding probability. At a slightly higher computational complexity, our proposed robust detectors are shown to yield improved performance over the standard detectors that ignore the prior estimation errors.

A new differential modulation scheme is presented for coded orthogonal frequency-division multiplexing (OFDM) systems with multiple transmit antennas in frequency- and time-selective channels. In contrast to an earlier scheme that involves differential modulation in space and time (ST) across two code matrices, the new scheme performs it in space and frequency (SF) within only one code matrix. As such, the shortest coherence time of the channel that can be handled is reduced, which makes the differential SF scheme more resistant to fast fading. Along with a suitable spectral encoder and interleaver, both the differential ST and SF schemes offer joint spatio-spectral diversity and coding gain. Numerical simulation confirms that SF is indeed more resistant to time-selective fading than ST; however, the former also suffers some performance loss caused by frequency-selective fading and is preferred only when fast fading is prevalent.

In this paper, we present a blind code-timing estimator for asynchronous code-division multiple-access (CDMA) systems that use bandlimited chip waveforms. The proposed estimator first converts the received signal to the frequency domain, followed by a frequency deconvolution to remove the convolving chip waveform, and then calculates the code-timing estimate from the output of a narrowband filter with a sweeping center frequency, which is designed to suppress the overall interference in the frequency domain. The proposed estimator is near-far resistant, and can deal with time- and frequency-selective channel fading. It uses only the spreading code of the desired user, and can be adaptively implemented for both code acquisition and tracking. We also derive an unconditional Crame/spl acute/r-Rao bound (CRB) that is not conditioned on the fading coefficients or the information symbols. It is a more suitable lower bound than a conditional CRB for blind code-timing estimators which do not assume knowledge of the channel or information symbols. We present numerical examples to evaluate and compare the proposed and several other code-timing estimators for bandlimited CDMA systems.

The paper presents blind joint channel estimation and interference suppression for orthogonal frequency-division multiplexing (OFDM) systems. Our approach uses a generalized multichannel minimum variance principle to design an equalizing filterbank that preserves the desired signal components and suppresses the overall interference. The channel estimate is then obtained by deriving an asymptotically tight lower bound of the filterbank output power, which reduces the problem to a quadratic minimization. While a channel estimate may be obtained by directly maximizing the filterbank output power through multidimensional nonlinear searches, such an approach is computationally prohibitive and suffers local convergence. Numerical examples show that the proposed scheme approaches the Cramer-Rao bound (CRB) as the SNR increases. It also exhibits low sensitivity to unknown narrowband interference and compares favorably with a subspace blind channel estimator.

In this paper, we develop a differential binary phase shift keying (BPSK) modulation scheme for wireless relay networks composed of one source, one relay and one destination node. The proposed scheme, referred to as the differential decode-and-forward (DDF), utilizes the relay to assist data transmission from the source to the destination. We derive a maximum likelihood (ML) detector and a piece-wise linear (PL) detector for the proposed DDF scheme. A closed-form bit error rate (BER) expression is presented for the proposed PL detector. Both analytical and simulation results show that the proposed DDF scheme is capable of providing diversity gain at the destination node over Rayleigh fading channels.

This paper considers the problem of joint carrier offset and code timing estimation for code-division multiple-access (CDMA) systems. In contrast to existing schemes that require nonlinear iterative searches over the multidimensional parameter space, this paper proposes a blind estimator that provides an algebraic solution to the joint parameter estimation problem. By exploiting the subspace structure of the observed signal, the multiuser estimation is first decoupled into a series of single-user estimation problems, and then analytical tools of polynomial matrices are invoked for joint carrier and code timing estimation of a single user. The proposed estimator is near-far resistant. It can deal with frequency-selective and time-varying channels. The performance of the proposed scheme is examined analytically by a first-order perturbation analysis. The authors also derive an unconditional Crame/spl acute/r-Rao bound (CRB) that is conditioned neither on fading coefficients nor information symbols; as such, the CRB is considered a suitable lower bound for blind methods. Numerical examples are presented to evaluate and compare the proposed and a multidimensional search (MD-search)-based scheme.

We present a new differential space-time coding scheme based on generalized multi-channel amplitude and phase modulation. Each code matrix employed by our scheme consists of an amplitude and a phase component, and can be thought of as a space-time multi-channel generalization of the scalar amplitude and phase shift keying (APSK) constellation. The amplitude component takes a scalar coefficient that controls the total transmission power, while the phase component is a unitary matrix formed from PSK symbols. Both the amplitude and phase components are differentially encoded and admit efficient differential decoding. We show that the maximum likelihood (ML) decoding of the amplitude coefficient and phase matrix is decoupled. Moreover, the phase matrix, when constructed from orthogonal designs, is amenable to decoupled differential decoding of the phase entries, which further simplifies the decoding complexity significantly. Simulation results show that the proposed amplitude-phase differential space-time modulation scheme achieves a performance very close to its phase-only counterpart, while providing the higher spectral efficiency offered by amplitude modulation.

In this paper, we present a deterministic multiuser code-timing estimator for asynchronous direct-sequence (DS) code-division multiple-access (CDMA) systems with aperiodic long spreading codes and bandlimited chip waveforms. A key feature of the proposed estimator is that it captures and capitalizes on a deterministic structure of the overall interference, namely multi-access interference (MAI) and inter-symbol interference (ISI), in the frequency domain. This allows complete interference elimination in a deterministic manner, which is in general more effective and data-efficient than stochastic approaches. Numerical results show that the proposed estimator can achieve fast acquisition; it is also near-far resistant, providing accurate code acquisition for even overloaded systems (i.e., systems with more users than the processing gain) in multipath fading environments.

In this paper, we present a novel code-timing estimator for uplink asynchronous direct-sequence code-division multiple-access systems utilizing bandlimited chip waveforms. The proposed estimator requires only the spreading code and training of the desired user. We start from a maximum likelihood (ML) approach that models the intersymbol interference and multiple-access interference as a colored Gaussian process with unknown covariance matrix in the frequency domain. The exact ML estimator is highly nonlinear and requires iterative searches over multi-dimensional parameter space that is impractical to implement. To deal with this difficulty, we invoke asymptotic (large-sample) approximations of the ML criterion and reparameterization techniques, which lead to an asymptotic ML estimator that yields code-timing and channel estimates via efficient noniterative quadratic optimizations. To benchmark the proposed estimator, we provide Crame/spl acute/r-Rao bound analysis for the code-timing estimation problem. Numerical simulation results are presented, which show that the proposed scheme is resistant to interference, fading, and modeling errors (e.g., sampling position errors), and compares favorably to several competing schemes in multipath fading channels.

We consider the problem of joint carrier offset and code timing estimation for CDMA (code division multiple access) systems. In contrast to most existing schemes which require a multi-dimensional search over the parameter space, we propose a blind estimator that solves the joint estimation problem algebraically. By exploiting the noise subspace of the covariance matrix of the received data, the multiuser estimation is decoupled into parallel estimations of individual users, which makes computations efficient. The proposed estimator is non-iterative and near-far resistant. It can deal with frequency-selective and time-varying channels. The performance of the proposed scheme is illustrated by some computer simulations.

We present a differential space-time-frequency (DSTF) modulation scheme for systems with an arbitrary number of transmit antennas over frequency-selective fading channels. The proposed DSTF scheme employs a concatenation of a specially designed spectral encoder and a differential encoder/mapper that yield the maximum spatio-spectral diversity advantage and significant coding gain. To reduce the decoding complexity, the differential encoder is designed with a unitary structure that decouples the maximum likelihood (ML) detection in space and time; meanwhile, the spectral encoder utilizes a new linear constellation decimation (LCD) coding scheme that encodes across minimally required subchannels and, as a result, has the least decoding complexity among all full-diversity codes. Numerical results show that the proposed DSTF scheme compares favorably with several existing differential space-time schemes for frequency-selective channels.

In this paper, we present a code-timing estimation scheme for asynchronous direct-sequence (DS) code-division multiple-access (CDMA) systems with aperiodic (long) spreading codes and bandlimited chip waveforms. The proposed scheme first converts the observed signal to the frequency domain by fast Fourier transform (FFT). Then, a nonlinear least-squares (NLS) criterion is invoked to fit the unknown parameters to the frequency-domain data. While the exact minimizer of the NLS criterion requires computationally prohibitive searches over a multidimensional parameter space, we propose an efficient approach that iteratively estimates one user/path at a time via simple linear processing and, furthermore, combines successive interference suppression (SIC) with parameter re-estimation for improved code acquisition performance. Simulation results show that the proposed scheme achieves a significantly larger user capacity and faster acquisition time than the SIC and standard matched-filter based code acquisition techniques in time-varying fading channels.

In this paper, we present a differential space-time-frequency (DSTF) modulation scheme for systems with two transmit antennas over frequency-selective fading channels. The proposed DSTF scheme employs a concatenation of a spectral encoder and a differential encoder/mapper, which are designed to yield the maximum spatio-spectral diversity and significant coding gain. To reduce the decoding complexity, the differential encoder is designed with a unitary structure that decouples the maximum likelihood (ML) detection in space and time; meanwhile, the spectral encoder utilizes a linear constellation decimation (LCD) coding scheme that encodes across a minimally required set of subchannels for full diversity and, hence, incurs the least decoding complexity among all full-diversity codes. Numerical results are presented to illustrate the performance of the proposed DSTF modulation and coding scheme, which compares favorably with several existing differential space-time schemes for frequency-selective channels.

This paper considers applying the minimum mean square error (MMSE) criterion on the detection for space time coded multicarrier CDMA (STC-MC-CDMA) systems in frequency selective environments. In particular, we consider the Alamouti's space-time coding scheme that involves two transmit antennas. To acquire the channel information needed by the MMSE detector, a subspace-based blind channel identification algorithm, which utilizes only the second-order statistics of the received signal to perform the channel estimation, is employed. The performances of the MMSE detector are evaluated with computer simulations.

In this paper, we present a novel code-timing estimation for asynchronous direct-sequence (DS) code-division multiple-access (CDMA) systems utilizing bandlimited chip waveforms. We start from a maximum likelihood (ML) approach that model the inter-symbol interference (ISI) and multiple-access interference (MAI) as a colored Gaussian process with unknown covariance matrix in the frequency domain. The exact ML estimator is highly nonlinear and requires iterative searches over multidimensional parameter space that is impractical to implement. To deal with this difficulty, we invoke asymptotic approximations of the ML criterion and reparameterization techniques, which lead to an asymptotic ML estimator that yields code-timing and channel estimates via efficient non-iterative quadratic optimizations. To benchmark the proposed estimator, we provide Cramer-Rao bound (CRB) analysis for the code-timing estimation problem. Numerical simulation results are presented to illustrate the performance of the proposed code-timing estimator for bandlimited CDMA in multipath fading channels.

In this paper we consider the problem of joint carrier offset and code timing estimation for CDMA (code division multiple access) systems. In contrast to most existing schemes which require multi-dimensional search over the parameter space, we propose a blind estimator that solves the joint estimation problem algebraically. By exploiting the noise subspace of the covariance matrix of the received data, the multiuser estimation is decoupled into parallel estimations of individual users, which makes computations efficient. The proposed estimator is non-iterative and near- far resistant. It can deal with frequency-selective and time-varying channels. The performance of the proposed scheme is illustrated by some computer simulations.

In [1], we introduced a linear blind multiuser receiver, referred to as the Capon receiver, for space-time (ST) coded COMA (code division multiple access) systems. The blind Capon receiver was shown to yield a similar performance to that of the optimum linear MMSE (minimum mean squared error) receiver which requires the CSI (channel state information). The proposed blind Capon receiver, however, needs to resolve a scalar ambiguity (with its blind channel estimate) that is inherent to all blind schemes. Furthermore, all blind receivers, including Capon, are relatively slow in convergence. To address these issues, we present herein a semi-blind Capon receiver by capitalizing on periodically inserted training symbols. We show via numerical examples that the semi-blind Capon receiver achieves a better performance than the training-assisted MMSE receiver in (slowly) time-varying channels, even though the latter incurs a much higher training overhead.

In this paper, we present a blind code-timing estimator for asynchronous DS (direct-sequence) CDMA (code-division multiple-access) systems. that utilize bandlimited chip waveforms. The received signal is first Fourier-transformed to the frequency domain, followed by a frequency deconvolution to remove the effect of the chip waveform. The code-timing is next derived from the output of a narrowband filter with a sweeping center frequency, which is designed to suppress interference of various sources, including, e.g., MUI (multiuser interference), ISI (inter-symbol interference), CCI (co-channel interference), and narrowband interference. The proposed estimator requires only the spreading code of the desired user. It can be used in frequency-flat or frequency-selective, time-invariant or time-varying fading channels. Moreover, the proposed estimator can be readily implemented using standard adaptive schemes, making it appealing not only for acquisition but also for tracking. Numerical results are presented to illustrate the performance of the proposed scheme.

Uniting the strengths of multicarrier (MC) modulation and code division multiple access (CDMA), MC-CDMA systems are of great interest for future broadband transmissions. We consider the problem of channel identification and signal detection/combining schemes for MC-CDMA systems which are equipped with multiple transmit antennas and space-time (ST) coding to provide transmit diversity gain to the receiver. In particular, we present a subspace-based blind channel identification algorithm for such systems. We extend several signal combining schemes, including the maximum ratio combining (MRC) and the equal gain combining (EGC), which are often utilized in conventional single-transmit-antenna based MC-CDMA systems, to ST-coded MC-CDMA (STC-MC-CDMA) systems. Numerical examples are presented to evaluate and compare the proposed channel identification and signal combining techniques.

As a coding technique designed for use with multiple transmit antennas, space-time coding (STC) has been gaining more and more attention recently due to its attractive characteristics. One of these characteristics is to provide diversity at the receiver and coding gain over an uncoded system without sacrificing bandwidth. It is also attractive because it increases the effective transmission rate as well as the potential system capacity. This paper presents powerful and computationally efficient linear and decision feedback equalization schemes, considering both zero-forcing (ZF) and minimum mean-square error (MMSE) criteria to combat intersymbol interference (ISI) and obtain diversity gain in a system with a transmit diversity technique using symbol-level space-time block coding. General linear and decision feedback equalizers are derived for the transmit diversity case with two transmit antennas and M receive antennas. The conditions are investigated under which FIR channels can be equalized perfectly using linear FIR filters in noise-free environments. BPSK modulation scheme is assumed to simulate the proposed diversity and equalization methods. Numerical results show significant performance improvements compared to the cases without equalization.

This paper considers the problem of channel estimation for space-time coded multicarrier CDMA (ST-MC-CDMA) systems. In particular, we propose a subspace-based blind channel identification algorithm which exploits the second-order statistics of the received signals and, thus, leads to higher channel utilization. Sufficient conditions for the applying the subspace-based method are also provided. The performance evaluation of the proposed algorithm is evaluated with simulations.

Stoica, Li and Li, (see IEEE Trans. Signal Processing, vol.48, p.338-52, 2000) introduced an amplitude estimation based scheme for one-dimensional (1-D) system identification that overcomes several drawbacks (e.g., computational complexity, local convergence, and statistical inefficiency when spectrally colored noise is present) suffered by the conventional output error method (OEM). Along the same line, we herein propose a two-dimensional (2-D) system identification scheme that makes use of 2-D amplitude estimation. In particular, we consider the recently introduced 2-D amplitude and phase estimation (APES) amplitude estimator, which has been shown to yield superior performance over its competitors. To benchmark the proposed scheme, we also derive the Cramer-Rao bound (CRB) for the 2-D system identification problem.

Abstract It is of great interest to provide high data rate services in wireless communication systems. In order to support such services, it is desirable to extend space‐time (ST) coding, originally proposed for known, frequency‐nonselective fading channels, to unknown, multipath channels. In this paper, we consider the problem of interference suppression for wireless TDMA (time division multiple access) systems equipped with multiple transmit antennas and receive antennas in frequency‐selective fading channels. A novel scheme with space‐time block coding based transmit diversity (STTD) is presented to estimate the multipath channel, coherently demodulate information symbols, and meanwhile suppress radio interference. The proposed scheme is simple to implement and able to mitigate interference of various origins, including intersymbol interference (ISI), cochannel interference (CCI), and others. Numerical examples are presented to illustrate the performance of the proposed estimator and detector in multipath Rayleigh‐fading channels. Copyright © 2002 John Wiley & Sons, Ltd.

We present a blind code synchronization scheme, referred to as the Capon algorithm, for DS (direct sequence) CDMA (code division multiple access) systems. The only required knowledge of the proposed scheme is the spreading code of the desired user, so that it is appealing for a decentralized implementation. The Capon algorithm can be used in frequency-flat or frequency-selective, time-invariant or time-varying fading channels; it is able to deal with colored channel noise and unknown interference of various origins, such as inter-cell interference and interference in cellular overlay systems. The Capon algorithm can be readily implemented using standard adaptive schemes, making it amenable to not only acquisition but tracking as well. An additional advantage enjoyed by Capon is that it does not need to know the number of active transmissions, a knowledge usually indispensable for some blind algorithms to function properly. Numerical simulations are presented to illustrate the performance of the proposed Capon scheme.

As an effective technique to combat adverse effects of fading, provide diversity and increase the transmission rate, space-time coding (STC) has been gaining more and more attention. This paper presents efficient zero-forcing (ZF) and minimum mean-square error (MMSE) equalization schemes to combat intersymbol interference (ISI) and obtain diversity gain in a system using symbol-level space-time block coding. General linear and decision feedback equalizers are derived with two transmit antennas and M receive antennas. The conditions are explored under which FIR channels can be equalized perfectly in noise-free environments. In order to estimate the system performance, upper bounds of bit error rate (BER) are derived. A training-aided method are proposed as well to estimate the channel state information (CSI) utilizing training sequences. Numerical results of the proposed techniques show significant performance improvement compared to the case without equalization, and show the tightness of the upper bounds along with the effectiveness of the channel estimation scheme.

We previously studied amplitude estimation of one-dimensional (1D) sinusoidal signals from measurements corrupted by possibly colored observation noise (see Stoica, P. et al., IEEE Trans. on Sig. Proc., vol. 48, p.338-52, 2000). We extend those results for two-dimensional (2D) amplitude estimation. In particular, we investigate the 2D sinusoidal amplitude estimation within the general frameworks of least squares (LS), weighted least squares (WLS), and MAtched FIlterbank (MAFI) estimation. A variety of 2D amplitude estimators are presented, which are all asymptotically statistically efficient. The performances of these estimators in finite samples are compared numerically with one another. Making use of amplitude estimation techniques, we introduce a new scheme for 2D system identification, which is shown to be computationally simpler and statistically more accurate than the conventional output error method (OEM), when the observation noise is colored.

In this paper, we present a novel blind code synchronization algorithm, referred to as the Capon algorithm, for direct-sequence (DS) code division multiple access (CDMA) systems. The idea is to design a bank of filters, each of which passes one user signal of interest without distortion (unit-gain), meanwhile suppressing the overall interference as much as possible. The only required knowledge of the proposed scheme is the spreading code of the desired user; therefore, it is appealing to a decentralized implementation. The Capon algorithm is able to mitigate not only interference signals originated within the same cell (i.e., intra-cell interference), but also unknown interference of various origins, such as unmodeled inter-cell interference and narrowband interference in cellular overlay systems. It can be readily implemented using standard adaptive schemes, making it amenable to both acquisition and tracking. An additional advantage enjoyed by Capon is that it does not need to know the number of active transmissions, a knowledge usually indispensable for some blind algorithms to function properly. Numerical simulations are presented to illustrate the performance of the proposed Capon scheme.

As a coding technique designed for use with multiple transmit antennas, space-time coding (STC) has been gaining more and more attention due to its attractive characteristics to provide diversity at the receiver and coding gain over an uncoded system without sacrificing the bandwidth, and increase the effective transmission rate as well as the potential system capacity. This paper presents powerful and computationally linear intersymbol and cochannel interference suppression schemes, considering both zero-forcing (ZF) and minimum mean-square error (MMSE) criteria to combat intersymbol interference (ISI) and suppress cochannel interference (CCI) and obtain diversity gain in a system with the transmit diversity technique using symbol-level space-time block coding (STBC). General linear schemes are derived for the transmit diversity case with two transmit antennas and M receive antennas. The conditions are investigated under which interference can be suppressed perfectly using linear FIR filters in noise-free environments. The binary phase shift keying (BPSK) modulation scheme is assumed to simulate the proposed diversity and interference suppression methods. Numerical results show significant performance improvements compared to the cases without interference suppression.

We present herein a decoupled multiuser acquisition (DEMA) algorithm for code-timing estimation in asynchronous code-division multiple-access (CDMA) communication systems. The DEMA estimator is an asymptotic (for large data samples) maximum-likelihood method that models the channel parameters as deterministic unknowns. By evoking the mild assumption that the transmitted data bits for all users are independently and identically distributed, we show that the multiuser timing estimation problem that usually requires a search over a multidimensional parameter space decouples into a set of noniterative one-dimensional problems. Hence, the proposed algorithm is computationally efficient. DEMA has the desired property that, in the absence of noise, it obtains the exact parameter estimates even with a finite number of data samples which can be heavily correlated. Another important feature of DEMA is that it exploits the structure of the receiver vectors and, therefore, is near-far resistant. Numerical examples are included to demonstrate and compare the performances of DEMA and a few other standard code-timing estimators.

We investigate herein the problem of amplitude estimation of sinusoidal signals from observations corrupted by colored noise. A relatively large number of amplitude estimators are described which encompass least squares (LS) and weighted least squares (WLS) methods. Additionally, filterbank approaches, which are widely used for spectral analysis, are extended to amplitude estimation. Specifically, we consider the matched-filterbank (MAFI) approach and show that, by appropriately designing the prefilters, the MAFI approach includes the WLS approach. The amplitude estimation techniques discussed in this paper do not model the noise, and yet they are all asymptotically statistically efficient. It is their different finite-sample properties that are of particular interest to this study. Numerical examples are provided to illustrate the differences among the various estimators. Though amplitude estimation applications are numerous, we focus on system identification using sinusoidal probing signals.

We introduce a novel design criterion for data-dependent narrowband filters that are of interest in temporal or spatial spectral analysis applications. The solution to the design problem considered is shown to coincide with the previously introduced amplitude and phase estimation (APES) filter. The new derivation of APES in this article sheds more light on the properties of APES and provides some intuitive explanation of the performance superiority of the APES filter over the Capon filter.

A computationally efficient method for structured covariance matrix estimation is presented. The proposed method provides an asymptotic (for large samples) maximum likelihood estimate of a structured covariance matrix and is referred to as AML. A closed-form formula for estimating Hermitian Toeplitz covariance matrices is derived which makes AML computationally much simpler than most existing Hermitian Toeplitz matrix estimation algorithms. The AML covariance matrix estimator can be used in a variety of applications. We focus on array processing and show that AML enhances the performance of angle estimation algorithms, such as MUSIC, by making them attain the corresponding Cramer-Rao bound (CRB) for uncorrelated signals.