Invariant feature extraction is a critical challenge in intelligent image processing, particularly with the rapid advancement of multi-source/modal imaging. Cross-modal matching has attracted considerable attention, yet current studies primarily focus on targeted modalities rather than realizing a general approach. In this paper, cross-arbitrary-modal image invariant feature extraction and matching is studied. Inspired by human vision, a purely handcrafted invariant feature transform is proposed for universal cross-modal image matching, named Hierarchical Intrinsic Major Orientation (HIMO). Based on orientation information, a full-chain non-data-driven algorithm is designed that hinges on an Intrinsic Major Orientation (IMO) extraction. The HIMO incorporates a novel keypoint detector utilizing Difference-of-Feature Suppression (DoFS), a Polar-Pyramid descriptor (PolarP), and a Cascaded Dynamic Multi-scale Strategy (CDMS) to effectively address common challenges such as intensity distortion, rotation, scale differences, geometric deformation, and image noise. To validate the proposed method, two massive cross-modal datasets-General Cross-modal Zone (GCZ) and Wide-area Diverse Sources (WDS)-are introduced, alongside two practical evaluation metrics. Comprehensive experiments compared with 10 traditional and 15 deep-learning state-of-the-art algorithms on 5 datasets fully demonstrate that the proposed HIMO achieves superior performance in terms of robustness, stability, and generalization across diverse imaging conditions.

Hyperspectral camouflaged object tracking remains a significant challenge due to the high similarity between objects and replicas in texture and color. Despite recent progress, the bias present in the tracker and the embedding token hinders the model training. Specifically, most methods rely on false-color three-channel images to fine-tune RGB-based trackers. However, it introduces a confounding effect within the RGB domain, potentially leading to harmful biases that misguide the model toward spurious correlations while neglecting the critical spectral discrimination inherent in hyperspectral images. Furthermore, current token-type embedding methods overlook the key correlations between templates and searches, ultimately confusing correlation and impairing tracking performance. To address these challenges, this paper proposes a new unbiased tracking framework named Causal HyperPrompter. It first introduces a structural causal model to disentangle and control exclusive causal factors during tracking, and incorporates a counterfactual intervention strategy to eliminate confounding variables and mitigate the bias inherited from RGB-based models. In addition, we present a novel token-type embedding module that integrates local spectral angle modeling to enhance the semantic link between template and search tokens, thereby improving the model's sensitivity to object localization. Lastly, to overcome the difficulty of manually initializing the bounding box and addressing data scarcity, we introduce a large-scale hyperspectral camouflaged object detection and tracking dataset, BihoT-130 k, consisting of 1,30,750 annotated frames across various camouflage scenes. Extensive experiments on multiple large-scale datasets illustrate the effectiveness of our proposed methods.

We investigate a non-orthogonal pilot design for massive multiple-input multiple-output (MIMO) channel estimation with orthogonal frequency division multiplexing (OFDM) modulation. Leveraging two-dimensional (2D) beam based channel model, we formulate a signal model for channel estimation with a comb-type pilot structure. Then, we propose generalized phase shift pilots (GPSPs) and reveal that both GPSPs and their discrete Fourier transform (DFT) have constant modulus, and the DFT of GPSPs also has optimal autocorrelation property and beneficial crosscorrelation property. Subsequently, we prove that the inter-user interference with GPSPs is negligible when the weight sequence length of GPSPs is large enough or when channels can be differentiated in the angle domain, validating the feasibility of GPSPs, especially when the statistical channel state information (CSI) is unavailable. Further, by leveraging the correlation properties of GPSPs, an efficient implementation for GAMP based channel estimation is provided. Simulation results indicate that GPSPs enable low-complexity channel estimation while ensuring satisfactory performance.

In this paper, we investigate the signal detection for user-centric network (UCN) massive multi-input multi-output (mMIMO) system. We consider that the users are divided into multiple user groups (UGs). For each UG, leveraging the interference sparsity, we reveal that the performance of the minimum-mean-square-error (MMSE) detector can be guaranteed in the network mMIMO system by using the matched filtering (MF) outputs of the intra-group and interfering users. Then, with the base station (BS) connection sparsity, we reveal that the detection performance of each UG is primarily determined by a limited number of associated BSs. To facilitate practical application, we propose a straightforward user grouping method and outline the process for determining interfering users and associated BSs for each UG in UCN mMIMO systems. Then, we propose a user-centric detection method that decouples the detection process for each UG into two stages. In the first stage, local MF is performed at each associated BSs using local information. In the second stage, group-wise interference cancellation (IC) is carried out to obtain detection results at the primary serving BS (PSBS) of each UG, with information exchanged from auxiliary serving BSs (ASBSs). Simulation results confirm the effectiveness and computational efficiency of our proposed user-centric detection for the UCN mMIMO system.

In this paper, we investigate the downlink (DL) precoder design for massive multiple-input multiple-output (MIMO) multiple low earth orbit (LEO) satellite communication (SATCOM). We first establish the DL beam based channel model for massive MIMO multiple LEO SATCOM systems by using sampled array response vectors. We propose a closed-form statistical channel state information (sCSI) based space domain DL precoder design for multi-satellite systems by maximizing the average signal-to-leakage-plus-noise ratio (ASLNR). Then, with the proposed beam based channel model, we transform the design of space domain DL precoder into that of lower-dimensional beam domain vector and the resulting space domain precoder is beam structured. Moreover, by leveraging the properties of the beam matrix, we propose a low-complexity design and implementation for the beam structured DL precoder. Simulation results demonstrate that the proposed beam structured DL precoder can achieve near performance to the space domain approach with significantly reduced computational complexity.

Massive multiple-input multiple-output - orthogonal frequency division multiplexing (MIMO-OFDM) systems face the challenge of high channel acquisition overhead while providing significant spectral efficiency (SE). Adjustable phase shift pilots (APSPs) are an effective technique to acquire channels with low overhead by exploiting channel sparsity. In this paper, we extend it to multiple groups and propose multi-group adjustable phase shift pilots (MAPSPs) to improve SE further. We first introduce a massive MIMO-OFDM system model and transform the conventional channel model in the space-frequency domain to the angle-delay domain, obtaining a sparse channel matrix. Then, we propose a method of generating MAPSPs through multiple basic sequences and investigate channel estimation processes. By analyzing the components of pilot interference, we elucidate the underlying mechanism by which interference affects MMSE estimation. Building upon this foundation, we demonstrate the benefit of phase scheduling in MAPSP channel estimation and establish the optimal design condition tailored for scheduling. Furthermore, we propose an implementation scheme based on Zadoff-Chu sequences that includes received signal pre-processing and pilot scheduling methods to mitigate pilot interference. Simulation results indicate that the MAPSP method achieves a lower mean square error (MSE) of estimation than APSP and significantly enhances SE in mobility scenarios.

Hyperspectral imaging delivers high-resolution spectral-spatial information to support molecular tissue characterization, but its clinical utility is far from being fully realized. Existing segmentation techniques are constrained by fixed or suboptimal band selection strategies and insufficient frequency-domain modeling, which limit their ability to fully exploit discriminative spectral cues and subtle tissue structures. To address these challenges, we propose AMBS-SF2Net, a unified framework that enhances spectral representation and hierarchical frequency modeling for accurate and efficient segmentation. Specifically, the Adaptive Mask-based Band Selection (AMBS) module dynamically identifies informative spectral channels, the Adaptive Spectral-Frequency Integration (ASFI) module fuses multi-scale spatial edges and frequency-aware spectral features, and the Multi-Axis Frequency Enhanced (MAFE) module captures complementary spectral and spatial frequency patterns along different tensor dimensions. To rigorously evaluate the method’s generalizability, we conduct extensive experiments on datasets spanning distinct imaging scales, comprising a microscopic cholangiocarcinoma pathology dataset and two macroscopic tissue datasets of pig abdominal organs and human placenta. Results demonstrate that AMBS-SF2Net significantly outperforms state-of-the-art methods, exhibiting superior robustness across varying spectral resolutions and spatial modalities, thereby validating its strong potential for diverse clinical applications.

This paper presents an innovative movable antenna (MA)-enhanced multi-user multiple-input multiple-output (MIMO) downlink system. We aim to maximize the energy efficiency (EE) under statistical channel state information (S-CSI) through a joint optimization of the precoding matrix and the antenna position vectors (APVs). To solve the resulting stochastic problem, we first resort to deterministic equivalent (DE) tecnology to formulate the deterministic minorizing function of the system EE and the deterministic function of each user terminal (UT)’s average achievable rate w.r.t. the transmit variables (i.e., the precoding matrix and the transmit APV) and the corresponding receive APV, respectively. Then, we propose an alternating optimization (AO) algorithm to alternatively optimize the transmit variables and the receive APVs to maximize the formulated deterministic objective functions, respectively. Finally, the above AO algorithm is tailored for the single-user scenario. Our numerical results reveal that, the proposed MA-enhanced system can significantly improve the system EE compared to several benchmark schemes based on the S-CSI and the optimal performance can be achieved with a finite size of movement regions for MAs.

With advancements in wireless communication and localization technologies, cellular networks are evolving towards integrated sensing and communication (ISAC) capabilities. To address the challenges of sensing-assisted communication, we introduce the digital twin of channel (DToC). Specifically, locations of user terminals (UTs) and their statistical channel state information (sCSI) are treated as physical objects and virtual counterparts in the concept of digital twin (DT), respectively. In this work, we establish a probabilistic model that characterizes sCSI as a location-conditioned distribution. To enable precise sCSI generation, we enhance the latent diffusion model (LDM) and propose an improved latent diffusion model (ILDM) with deterministic sampling. We further propose an accelerated LDM method to speed up the generation process by skipping certain sampling steps. Simulation results demonstrate that the proposed ILDM achieves high accuracy in generating sCSI, while the accelerated LDM delivers significant speedups with minor performance degradation. Our results also validate that the DToC framework can effectively generate sCSI without pilot overhead.

Semantic communication is emerging as an effective means of facilitating intelligent and context-aware communication for next-generation communication systems. In this paper, we propose a novel metric called Age of Incorrect Semantics (AoIS) for the transmission of video frames over multiple-input multiple-output (MIMO) channels in a monitoring system. Different from the conventional age-based approaches, we jointly consider the information freshness and the semantic importance, and then formulate a time-averaged AoIS minimization problem by jointly optimizing the semantic actuation indicator, transceiver beamformer, and the semantic symbol design. We first transform the original problem into a low-complexity problem via the Lyapunov optimization. Then, we decompose the transformed problem into multiple subproblems and adopt the alternative optimization (AO) method to solve each subproblem. Specifically, we propose two efficient algorithms, i.e., the successive convex approximation (SCA) algorithm and the low-complexity zero-forcing (ZF) algorithm for optimizing transceiver beamformer. We adopt exhaustive search methods to solve the semantic actuation policy indicator optimization problem and the transmitted semantic symbol design problem. Experimental results demonstrate that our scheme can preserve more than 50% of the original information under the same AoIS compared to the constrained baselines.

Accurate channel state information (CSI) acquisition for massive multiple-input multiple-output (MIMO) systems is essential for future mobile communication networks. Channel fingerprint (CF), also referred to as channel knowledge map, is a key enabler for intelligent environment-aware communication and can facilitate CSI acquisition. However, due to the cost limitations of practical sensing nodes and test vehicles, the resulting CF is typically coarse-grained, making it insufficient for wireless transceiver design. In this work, we introduce the concept of CF twins and design aconditionalgenerative diffusion model (CGDM) with strong implicit prior learning capabilities as the computational core of the CF twin to establish the connection between coarse- and fine-grained CFs. Specifically, we employ a variational inference technique to derive the evidence lower bound (ELBO) for the log-marginal distribution of the observed fine-grained CFconditionedon the coarse-grained CF, enabling the CGDM to learn the complicated distribution of the target data. During the denoising neural network optimization, the coarse-grained CF is introduced asside informationto accurately guide the conditioned generation of the CGDM. To make the proposed CGDM lightweight, we further leverage the additivity of output distortion and introduce a one-shot pruning approach along with a multi-objective knowledge distillation technique. Experimental results show that the proposed approach exhibits significant improvement in reconstruction performance compared to the baselines. Additionally, zero-shot testing on reconstruction tasks with different magnification factors further demonstrates the scalability and generalization ability of the proposed approach.

Due to the crowded spectrum occupancy and dense user terminals (UTs), the conventional fixed antenna (FA)-based access points (APs) face challenges in realizing massive access and interference cancellation. To address this issue, in this paper we develop a six-dimensional movable antenna (6DMA) enhanced multi-AP coordination system to fully exploit its maximum spatial diversity for coverage enhancement and interference mitigation. First, we model the wireless channels between the APs and UTs to characterize their variation with respect to 6DMA movement, in terms of both the three-dimensional (3D) position and 3D orientation of each distributed AP’s antenna. Then, an optimization problem is formulated to maximize the weighted sum rate of multiple UTs for their uplink transmissions by jointly optimizing the antenna position vector (APV), the antenna orientation matrix (AOM), and the receive combining matrix over all coordinated APs, subject to the constraints on local antenna movement regions. To solve this challenging non-convex optimization problem, we first transform it into a more tractable Lagrangian dual problem. Then, an alternating optimization (AO)-based algorithm is developed by iteratively optimizing the APV and AOM, which are designed by applying the successive convex approximation (SCA) technique and Riemannian manifold optimization-based algorithm, respectively. Moreover, to further reduce the overhead of antenna movement, we propose an offline solution for APV and AOM design based on statistical channel state information (CSI). In addition, we further extend the proposed scheme from uni-polarized to dual-polarized modes for all antennas. Simulation results show that the proposed 6DMA-enhanced multi-AP coordination system can significantly enhance network capacity, and both of the online and offline 6DMA schemes can attain considerable performance improvement compared to the conventional FA-based schemes.

In this paper, we investigate receiver design for high frequency (HF) skywave massive multiple-input multiple-output (MIMO) communications. We first establish a modified beam based channel model (BBCM) by performing uniform sampling for directional cosine with deterministic sampling interval, where the beam matrix is constructed using a phase-shifted discrete Fourier transform (DFT) matrix. Based on the modified BBCM, we propose a beam structured turbo receiver (BSTR) involving low-dimensional beam domain signal detection for grouped user terminals (UTs), which is proved to be asymptotically optimal in terms of minimizing mean-squared error (MSE). Moreover, we extend it to windowed BSTR by introducing a windowing approach for interference suppression and complexity reduction, and propose a well-designed energy-focusing window. We also present an efficient implementation of the windowed BSTR by exploiting the structure properties of the beam matrix and the beam domain channel sparsity. Simulation results validate the superior performance of the proposed receivers but with remarkably low complexity.

In this paper, we propose a low complexity turbo receiver for high frequency (HF) skywave massive multiple-input multiple-output (MIMO) systems. We first introduce the beam based channel model (BBCM) with uniform sampling for directional cosine. By leveraging the BBCM, we reveal the interference sparsity of HF skywave massive MIMO systems, which is defined as the asymptotic sparsity of the channel Gram matrix. Exploiting the interference sparsity, we provide a condition of extracting sufficient observation for signal detection. Motivated by this condition, we construct the interference user terminal (UT) set (IUS) and extract the observation vector from the received signal after matched filtering (MF) for each UT. Then, a low-dimensional interference sparsity-aware detector (ISD) is separately designed for each UT by minimizing the mean-squared error (MSE), and the interference sparsity-aware turbo receiver (ISTR) is subsequently formulated using ISDs. Under a relaxed version of the condition for sufficient observation selection, we prove the optimality of the ISTR. Further, we develop an efficient implementation of the ISTR, involving approximate computation of the ISD, the signal reconstructed by ISD and the channel Gram matrix. Moreover, an efficient construction of IUS using the statistical channel state information (CSI) is also proposed. Simulation results confirm that the proposed ISTR achieves excellent performance with relatively low complexity.

The advancement of 6G mobile communication and positioning technologies has amplified the significance of location-aware tools, such as location-indexed channel fingerprints (CFs) and channel charting, which are becoming key enablers for massive MIMO-OFDM systems. In this paper, we propose a novel channel charting with physical CFs (PCFs) and demonstrate its effectiveness in channel state information (CSI) acquisition. First, we define the PCF based on a cluster-based geometric stochastic channel model (GBSM), enabling a comprehensive representation of physical channel characteristics using a compact set of parameters. We then develop a methodology for PCF acquisition in massive MIMO-OFDM systems. By exploiting the relationship between PCFs and the space-frequency-time (SFT) domain channel, the proposed method extracts PCFs from multi-location channel measurements and constructs a structured channel charting with location-indexed PCFs. Furthermore, we propose a low-complexity algorithm to acquire beam domain statistical CSI (sCSI) using the PCFs in the channel charting. The resulting sCSI can be directly employed as prior information for channel estimation. Simulation results show that the proposed method delivers sCSI performance comparable to traditional online probing techniques, and the generated sCSI can serve as reliable prior knowledge to significantly enhance the accuracy of channel estimation. These results validate the proposed PCF as a powerful and versatile tool for channel acquisition and system design of the next-generation mobile communication.

Orthogonal time frequency space (OTFS)/vector OFDM (VOFDM) is widely regarded as a promising waveform for next-generation mobile communications. However, its spectral characteristics are not yet fully understood. The bandwidth allocation scheme, which is crucial for OTFS’s integration into practical wireless standards, also remains unexplored. In this paper, we investigate the spectral characteristics of OTFS signals by analyzing their power spectral density (PSD). We demonstrate that the PSD of discrete-time OTFS signals is periodic with a period of 1/MTs, whereMis the size of the time/Doppler domain in OTFS, a.k.a., the vector size in VOFDM, andTsis the sampling interval length of digital to analog converter (DAC), resulting in M identical spectral components within the spectral range [− 1/2Ts, 1/2Ts) of the continuous-time OTFS signal. The periodicity makes bandwidth allocation for OTFS/VOFDM signaling substantially challenging. Furthermore, we establish a relationship between the PSD of OFDM signals and that of OTFS signals, revealing that, when the information symbols are independent, the PSD of OTFS signals is equal to the sum of the PSDs of the component-expanded OFDM (CEP-OFDM) signals. Lastly, we derive a relationship between the information symbols and the corresponding OTFS spectrum, and based on which, we propose a null-space-based linear precoding (NSLP) method for OTFS signals to enable flexible bandwidth allocation. Numerical results validate our analytical results regarding the PSD of OTFS signals and show the effectiveness of our proposed NSLP method in tailoring the spectrum of OTFS signals.

In this paper, we investigate the decoupled designs of precoders and receivers for both downlink (DL) and uplink (UL) in massive multiple-input multiple-output (MIMO) multiple low earth orbit (LEO) satellite communication systems. We first establish the beam based satellite channel model, where the beam matrix is constructed with sampled steering vectors. Then, we propose a decoupled precoder and receiver design for both DL and UL, which allows precoders and receivers to be designed independently at each satellite and user terminal (UT), respectively, with only local statistical channel state information (sCSI). Moreover, with the established beam based channel model, the design of space domain DL precoder and UL receiver can be converted into that of lower-dimensional beam domain vectors with only local sCSI, and the resulting space domain precoder and receiver are beam structured. Furthermore, we propose a low-complexity design and implementation for the beam structured DL precoder and UL receiver by exploiting properties of the beam matrix, significantly reducing the computational complexity. Simulation results validate the proposed approaches.

Movable antenna (MA) is an emerging technology that can significantly improve communication performance via the continuous adjustment of the antenna positions. To unleash the potential of MAs in wideband communication systems, acquiring accurate channel state information (CSI), i.e., the channel frequency responses (CFRs) between any position pair within the transmit (Tx) region and the receive (Rx) region across all subcarriers, is a crucial issue. In this paper, we study the channel estimation problem for wideband MA systems. To start with, we express the CFRs as a combination of the field-response vectors (FRVs), delay-response vector (DRV), and path-response tensor (PRT), which exhibit sparse characteristics and can be recovered by using a limited number of channel measurements at selected position pairs of Tx and Rx MAs over a few subcarriers. Specifically, we first formulate the recovery of the FRVs and DRV as a problem with multiple measurement vectors in compressed sensing (MMV-CS), which can be solved via a simultaneous orthogonal matching pursuit (SOMP) algorithm. Next, we estimate the PRT using the least-square (LS) method. Moreover, we also devise an alternating refinement approach to further improve the accuracy of the estimated FRVs, DRV, and PRT. This is achieved by minimizing the discrepancy between the received pilots and those constructed by the estimated CSI, which can be efficiently carried out by using the gradient descent algorithm. Finally, simulation results demonstrate that both the SOMP-based channel estimation method and alternating refinement method can reconstruct the complete wideband CSI with high accuracy, where the alternating refinement method performs better despite a higher complexity.

With the widespread application of location-based services, fingerprint-based localization has demonstrated advantages in environments with complex signal propagation. Deep learning has significantly improved the efficiency of both offline training and online matching in localization processes. However, existing fingerprints only contain terminal position information without capturing motion states, and neural network designs have not fully incorporated structural features such as fingerprint sparsity. In this paper, we propose a triple-beam fingerprint (TBF) incorporating Doppler information and design a Transformer-based localization and orientation awareness network (LOA-Net) to simultaneously estimate user position and motion direction in massive multiple-input multiple-output (MIMO) orthogonal frequency division multiplexing (OFDM) systems. We first show the correlation between TBF and multipath information, and investigate the collinearity of different TBFs, demonstrating that TBF is an effective small-size sparse fingerprint. Then, we propose LOA-Net containing a mask-augmented detection Transformer for regression (MaskDETR-Reg) module and a fusion-enhanced Transformer for direction classification (Fusion-TDC) module to process angle-delay domain information and Doppler domain information, respectively. Finally, in the simulation of indoor scenarios defined in 3GPP 38.901, the proposed method achieves significantly better localization accuracy than weighted$K$-nearest neighbors (WKNN), 2D and 3D convolutional neural networks (CNNs), and achieves satisfactory motion direction estimation accuracy.

We investigate the linear precoding for sum-rate maximization in network massive multiple-input multiple-output (MIMO) transmission, where the cooperative transmission by all base stations (BSs) enhances the capacity, reliability, and robustness. To address the growing complexity of traditional iterative algorithms in large-scale systems, we leverage the weighted minimum mean square error (WMMSE) solution and show that the precoding vectors can be fully reconstructed from a set of low-dimensional parameters. By exploiting the structure and relationship of these parameters, we reformulate the original problem in a reduced-dimensional space while preserving equivalence to the original solution. Deep learning techniques are employed to solve this reformulated problem, where equivalent scaling of the variables facilitates pre-processing for training and further reduces the dimension of the learning input. A neural network is trained on the refined low-dimensional objectives with a tailored loss, allowing the precoding vectors to be directly calculated from its output. As demonstrated by numerical results, the proposed deep learning-based precoder performs well with considerably reduced online processing complexity.

Existing semantic communications have exhibited satisfactory performance in many tasks, but secure image transmission has not been adequately investigated. In this paper, we propose a novel secure semantic image transmission (SSIT) framework over multiple-input single-output (MISO) wiretap channels. To enhance image transmission security for a legitimate semantic user (SU) while interfering with the eavesdropper (Eve), a type of beneficial semantic noise map, which is determined by both source and SU channel states, is produced by a semantic noise-aided conditional variational autoencoder (SN-CVAE) in an unsupervised manner. Furthermore, to improve the secure image reconstruction quality, we propose an efficient transmit beamformer optimization algorithm and leverage the constrained stochastic successive convex approximation (CSSCA) to solve the optimization problem. Numerical results demonstrate that our method effectively protects image information from eavesdroppers while ensuring high-fidelity image reconstruction at the legitimate receiver.

In this paper, we investigate receiver design for high frequency (HF) skywave massive multiple-input multipleoutput (MIMO) communications. We first establish a modified beam based channel model by performing uniform sampling for directional cosine with deterministic sampling interval, where the beam matrix is constructed as discrete Fourier transform (DFT)based structure. Rooted in the modified beam based channel model, we propose a beam structured turbo receiver (BSTR) involving low-dimensional beam structured signal detection for grouped user terminals (UTs). Then we present efficient implementation of the BSTR by exploiting the structure properties of the beam matrix and the beam domain channel sparsity. Simulation results validate the superior performance of the proposed BSTR with low complexity.

Vector OFDM (VOFDM) is equivalent to OTFS and is good for time-varying channels. However, due to its vector form, its signal spectrum is not as clear as that of the conventional OFDM. In this paper, we study the discrete spectrum of discrete VOFDM signals. We obtain a linear relationship between a vector of information symbols and a vector of the same size of components evenly distributed in the discrete VOFDM signal spectrum, and show that if a vector of information symbols is set to 0, then a corresponding vector of the same size of the discrete VOFDM signal spectrum is 0 as well, where the components of the 0 vector are not together but evenly distributed in the spectrum. With the linear relationship, the information symbol vectors can be locally precoded so that any of the discrete spectrum of VOFDM signals can be set to 0, similar to that of the conventional OFDM signals. These results are verified by simulations.

A wiretap channel is served as the fundamental model of physical layer security techniques, where the secrecy capacity of the Gaussian wiretap channel is proven to be achieved by Gaussian input. However, there remains a gap between the Gaussian secrecy capacity and the secrecy rate with conventional uniformly distributed discrete constellation input, e.g. amplitude shift keying (ASK) and quadrature amplitude modulation (QAM). In this paper, we propose a probabilistic shaped multilevel polar coding scheme to bridge the gap. Specifically, the input distribution optimization problem for maximizing the secrecy rate with ASK/QAM input is solved. Numerical results show that the resulting sub-optimal solution can still approach the Gaussian secrecy capacity. Then, we investigate the polarization of multilevel polar codes for the asymmetric discrete memoryless wiretap channel, and thus propose a multilevel polar coding scheme integration with probabilistic shaping. It is proved that the scheme can achieve the secrecy capacity of the Gaussian wiretap channel with discrete constellation input, and satisfies the reliability condition and weak security condition. A security-oriented polar code construction method to natively satisfies the leakage-based security condition is also investigated. Simulation results show that the proposed scheme achieves more efficient and secure transmission than the uniform constellation input case over both the Gaussian wiretap channel and the Rayleigh fading wiretap channel.

In this paper, we investigate the precoder design for user-centric network (UCN) massive multiple-input multiple-output (mMIMO) downlink with matrix manifold optimization. In UCN mMIMO systems, each user terminal (UT) is served by a subset of base stations (BSs) instead of all the BSs, facilitating the implementation of the system and lowering the dimension of the precoders to be designed. By proving that the precoder set satisfying the per-BS power constraints forms a Riemannian submanifold of a linear product manifold, we transform the constrained precoder design problem in Euclidean space to an unconstrained one on the Riemannian submanifold. Riemannian ingredients, including orthogonal projection, Riemannian gradient, retraction and vector transport, of the problem on the Riemannian submanifold are further derived, with which the Riemannian conjugate gradient (RCG) design method is proposed for solving the unconstrained problem. The proposed method avoids the inverses of large dimensional matrices, which is beneficial in practice. The complexity analyses show the high computational efficiency of RCG precoder design. Simulation results demonstrate the numerical superiority of the proposed precoder design and the high efficiency of the UCN mMIMO system.

Multi-band massive multiple-input multiple-output (MIMO) communication can promote the cooperation of licensed and unlicensed spectra, effectively enhancing spectrum efficiency for Wi-Fi and other wireless systems. As an enabler for multi-band transmission, channel fingerprints (CF), also known as the channel knowledge map or radio environment map, are used to assist channel state information (CSI) acquisition and reduce computational complexity. In this paper, we propose CF-CGN (Channel Fingerprints with Cycle-consistent Generative Networks) to extrapolate CF for multi-band massive MIMO transmission where licensed and unlicensed spectra cooperate to provide ubiquitous connectivity. Specifically, we first model CF as a multichannel image and transform the extrapolation problem into an image translation task, which converts CF from one frequency to another by exploring the shared characteristics of statistical CSI in the beam domain. Then, paired generative networks are designed and coupled by variable-weight cycle consistency losses to fit the reciprocal relationship at different bands. Matched with the coupled networks, a joint training strategy is developed accordingly, supporting synchronous optimization of all trainable parameters. During the inference process, we also introduce a refining scheme to improve the extrapolation accuracy based on the resolution of CF. Numerical results illustrate that our proposed CF-CGN can achieve bidirectional extrapolation with an error of 5 ∼ 17 dB lower than the benchmarks in different communication scenarios, demonstrating its excellent generalization ability. We further show that the sum rate performance assisted by CF-CGN-based CF is close to that with perfect CSI for multi-band massive MIMO transmission.

The acquisition of ground clutter data plays an important role in validating the feasibility of a spaceborne radar (SBR) system. Different from the airborne radar, the high production and maintenance costs of SBR systems make simulation a significant source for obtaining clutter data. However, high-precision clutter simulation typically demands significant computational resources and processing time. Moreover, the clutter range migration caused by high-speed motion of satellite platforms will make the clutter simulation and suppression problems more complicated. In this letter, we analyze the influence of clutter range migration on clutter-to-noise ratio (CNR) in an SBR system and conclude that whether or not the clutter range migration is considered does not affect the final CNR gain of the SBR system. The analytical results of this letter will significantly simplify the simulation process and provide a good reference for the engineering applications in the SBR system design.

This letter proposes a coherent integration approach for weak target detection with constant radial jerk in a low signal-to-noise (SNR) environment. In the proposed approach, the second-order Keystone transform (SoKT) is first employed to correct the quadratic range migration (QRM) induced by the radial acceleration of a target. Next, a velocity-jerk matched filter is constructed to eliminate the residual target radial velocity and jerk terms. Finally, the coherent integration is achieved through non-uniform fast Fourier transform (NuFFT) along the slow time. Compared with the conventional methods, the proposed approach can obtain comparable accumulation performance with a lower computational complexity. Simulation and real-measurement experiments are conducted to demonstrate the effectiveness and reliability of the proposed approach in low SNR environments.

Point cloud semantic segmentation can enhance the understanding of the production environment and is a crucial component of vision tasks. The efficacy and generalization prowess of deep learning-based segmentation models are inherently contingent upon the quality and nature of the data employed in their training. However, it is often challenging to obtain data with inter-class balance, and training an intelligent segmentation network with the imbalanced data may cause cognitive bias. In this paper, a network framework InvSpaceNet is proposed, which generates an inverse feature space to alleviate the cognitive bias caused by imbalanced data. Specifically, we design a dual-branch training architecture that combines the superior feature representations derived from instance-balanced sampling data with the cognitive corrections introduced by the proposed inverse sampling data. In the inverse feature space of the point cloud generated by the auxiliary branch, the central points aggregated by class are constrained by the contrastive loss. To refine the class cognition in the inverse feature space, features are used to generate point cloud class prototypes through momentum update. These class prototypes from the inverse space are utilized to generate feature maps and structure maps that are aligned with the positive feature space of the main branch segmentation network. The training of the main branch is dynamically guided through gradients back propagated from different losses. Extensive experiments conducted on four large benchmarks (i.e., S3DIS, ScanNet v2, Toronto-3D, and SemanticKITTI) demonstrate that the proposed method can effectively mitigate point cloud imbalance issues and improve segmentation performance.

In this letter, a new parameter estimation and multi-pulse coherent integration method for the high-speed and maneuvering target detection is proposed in a nonuniform sampling radar system. In the proposed method, the signal recovery with respect to the missing pulse data is firstly realized by using the iterative adaptive approach (IAA) in the range frequency domain. Then, the generalized nonuniform time-scaled transform (GNTST) is applied to decouple the complex coupling terms induced by the target maneuvering motion and irregular radar waveforms, where the velocity ambiguity caused by target high speed is considered. Finally, the well-focused target imaging result can be obtained after accomplishing the target signal recovery, reconstruction, and phase decoupling. Simulations and real-measured radar data experiments are conducted to validate the proposed method.

This letter investigates prime and co-prime integer matrices and their properties. It characterizes all pairwise co-prime integer matrices that are also prime integer matrices. This provides a simple way to construct families of pairwise co-prime integer matrices, that may have applications in multidimensional co-prime sensing and multidimensional Chinese remainder theorem.

Non-orthogonal multiple access (NOMA) is recognized as a pivotal technology for enhancing spectral efficiency and facilitating massive connectivity in wireless communications. In practice, users are often divided into groups that occupy different resource blocks, while the users in the same group share the same resource through power-domain multiplexing, leading to multi-group NOMA (MG-NOMA). Hence, the full benefit of MG-NOMA relies on optimum power allocation, which, unfortunately, leads to difficult optimization problems that have not been well solved so far. This work investigates the optimal power allocation to achieve the maximum spectral efficiency and energy efficiency with quality-of-service (QoS) requirements and arbitrary user weights in MG-NOMA. We show that the complicated MG-NOMA power allocation problems can be decomposed into two layers, the lower layer for power allocation within each group and the upper layer for the power budget allocation among groups. More importantly, we reveal that, although quite difficult, the two-layer problems both contain the hidden convexity, indicating the achievability of the globally optimal solution. Then, we derive the optimal power allocation in either closed or semi-closed form for the maximum spectral efficiency and energy efficiency in MG-NOMA, respectively. The simulation results demonstrate the superiority of the proposed optimal power allocation schemes.

In this paper, we investigate precoder design for high frequency (HF) skywave massive multiple-input multiple-output (MIMO) communications with orthogonal frequency division multiplexing (OFDM) modulation. We first reveal the effect of the precoder on the effective channel at receivers and formulate the precoder design for a group of subcarriers as a sum-rate maximization problem, where the delay spread of the effective channel is constrained to maintain its smoothness. Then with the beam based channel model and beam domain channel sparsity, the design of space domain precoders for a group of subcarriers are transformed into that of a space-frequency (SF) beam domain vector and the resulting space domain precoder at each subcarrier is beam structured. Efficient calculation for design and implementation of the beam structured precoder (BSP) is proposed. Moreover, effective channel estimation with the BSP is discussed. Simulation results show that the proposed BSP can enhance the effective channel estimation performance and significantly improve the system performance.

In this paper, we propose a channel acquisition approach with time-frequency phase-shifted pilots (TFPSPs) for massive multi-input multi-output orthogonal frequency division multiplexing (MIMO-OFDM) systems. We first present a triple-beam (TB) based channel tensor model, allowing for the representation of the space-frequency-time (SFT) domain channel as the product of beam matrices and the TB domain channel tensor. By leveraging the specific characteristics of TB domain channels, we develop TFPSPs, where distinct pilot signals are simultaneously transmitted in the frequency and time domains. Then, we present the optimal TFPSP design and provide the corresponding pilot scheduling algorithm. Further, we propose a tensor-based information geometry approach (IGA) to estimate the TB domain channel tensors. Leveraging the specific structure of beam matrices and the properties of TFPSPs, we propose a low-complexity implementation of the tensor-based IGA. We validate the efficiency of our proposed channel acquisition approach through extensive simulations. Simulation results demonstrate the superior performance of our approach. The proposed approach can effectively suppress inter-UT interference with low complexity and limited pilot overhead, thereby enhancing channel estimation performance. Particularly in scenarios with a large number of UTs, the channel acquisition method outperforms existing approaches by reducing the normalized mean square error (NMSE) by more than 8 dB.

Multi-frequency massive multi-input multi-output (MIMO) communication is a promising strategy for both 5G and future 6G systems, ensuring reliable transmission while enhancing frequency resource utilization. Statistical channel state information (CSI) has been widely adopted in multi-frequency massive MIMO transmissions to reduce overhead and improve transmission performance. In this paper, we propose efficient and accurate methods for obtaining statistical CSI in multi-frequency massive MIMO systems. First, we introduce a multi-frequency massive MIMO channel model and analyze the mapping relationship between two types of statistical CSI, namely the angular power spectrum (APS) and the spatial covariance matrix, along with their correlation across different frequency bands. Next, we propose an autoregressive (AR) method to predict the spatial covariance matrix of any frequency band based on that of another frequency band. Furthermore, we emphasize that channels across different frequency bands share similar APS characteristics. Leveraging the maximum entropy (ME) criterion, we develop a low-complexity algorithm for high-resolution APS estimation. Simulation results validate the effectiveness of the AR-based covariance prediction method and demonstrate the highresolution estimation capability of the ME-based approach. Furthermore, we demonstrate the effectiveness of multi-frequency cooperative transmission by applying the proposed methods to obtain statistical CSI from low-frequency bands and utilizing it for high-frequency channel transmission. This approach significantly enhances high-frequency transmission performance while effectively reducing system overhead.

Accurate and comprehensive tree cover mapping plays a vital role in forest management and ecosystem assessment. However, the lack of spectral information and the low quality of available black and white (B&W) imagery makes it difficult to retroactively predict an historical tree cover map using common methods. Here, a network structure called B&WTreeNet, based on semantic segmentation, is proposed to make full use of the limited labeled training remote sensing data to obtain tree cover information from historical datasets. The proposed B&WTreeNet is capable of cross-temporal semantic segmentation, including various data augmentation methods, to address inconsistent tree cover features caused by variations in image quality. The luminance enhancer (LE) and other modules in B&WTreeNet can extract the characteristics of tree cover effectively, successfully compensating for the limited spectral information in B&W image datasets. The countrywide historical tree cover map in Switzerland generated for the 1980s using a limited training dataset from 2018 to 2019 agrees well with manual interpretation results.

It is well-known that for a maneuvering target, its inverse synthetic aperture radar (ISAR) imaging quality may significantly deteriorate using the classical range-Doppler (RD) algorithm. To address this issue, this article proposes a novel ISAR imaging algorithm based on the generalized second-order time-scaled transform (GSOTST). In the proposed method, the multicomponent cubic phase signal (CPS) modeling is adopted for the radar echo signal after translational motion compensation (TMC) to portray the phase change characteristics more accurately. First, the target signal is transformed into the slow-time-delay-time domain using a correlation kernel function (CKF). Subsequently, the nonstationary phase is eliminated in the slow-time-generalized delay-time-frequency (GDTF) domain, and the GSOTST is used to decouple the temporal variables. Finally, the generalized Fourier transform is performed to transform the signal into the 2-D frequency domain, where the energy of the target signal is integrated into a well-focused 2-D peak, enabling the high-precision target parameter estimation and finely focused ISAR imaging. The experimental results from both simulation and real-measured data validate the effectiveness of the proposed algorithm.

Ground moving target focusing performance and processing efficiency are two important metrics in the synthetic aperture radar (SAR) system. Owing to the complex nonstationary phases caused by target motions, ground maneuvering targets are commonly smeared and distorted in an SAR image. To realize the ground maneuvering target imaging, exhaustive high-order parameter searching and optimization processing are usually needed to achieve the optimal processing performance. However, the large computational resource consumption may limit their real-time processing capabilities. To deal with this issue, an efficient SAR imaging approach for ground maneuvering targets is proposed by adopting the mixed symmetric function (MSF) transform. After performing the multichannel SAR clutter rejection and target initial detection by applying the constant false alarm rate (CFAR) technique, the linear envelop shift of detected ground target is eliminated by using the well-known Keystone transform (KT). Then, an MSF is constructed to rectify the range curvature and relieve the Doppler broadening after carrying out the nonuniform fast Fourier transform (NUFFT) corresponding to the second-order time variable. After removing the second-order Doppler spreading influence, the quadratic chirp parameter is estimated via the NUFFT after the symmetrical correlation processing. Finally, after accomplishing the motion compensation, the ground maneuvering target can be well focused and relocated in the SAR image. Compared with the traditional approaches, the proposed approach not only realizes the high-precision Doppler parameter estimation, but is also computationally efficient without grid searching procedure. Numerical simulation and real-measured SAR experiment results are depicted to verify the correctness and effectiveness of the proposed approach.

To address the challenges posed by extensive modality differences in image matching tasks, this paper proposes a cascaded learning framework. It guides the optimization of an end-to-end matching model via a dynamic data engine, which can provide sufficient cross-modal training data to support the model’s full adaptation to cross-modal features. The data engine integrates a random homography transformation module and a lightweight image generation model, enabling the online synthesis of cross-modal image pairs with geometric variations and diverse styles. This provides the matching model with rich cross-modal stimulation. The matching model adopts a hybrid architecture combining a CNN backbone and Transformer attention mechanisms, which integrates multi-scale local feature extraction with global context modeling. By adopting the proposed stepwise aggregation strategy, the efficiency of feature extraction is well ensured. Subsequently, a coarse-to-fine matching strategy is employed to achieve high accuracy and robustness of feature alignment. Comprehensive experiments on both self-collected and public cross-modal image matching datasets demonstrate that the proposed DGIM outperforms existing state-of-the-art approaches in cross-modal matching performance while achieving a good balance between efficiency and effectiveness. It also exhibits broad practical potential across multiple fields and scenes. This work provides novel solutions and evaluation benchmarks for cross-modal image matching tasks. The code and testing dataset will be made publicly available at https://github.com/LotrL/DGIM.

In recent years, deep learning has shown immense promise in advancing medical hyperspectral imaging diagnostics at the microscopic level. Despite this progress, most existing research models remain constrained to single-task or single-scene applications, lacking robust collaborative interpretation of microscopic hyperspectral features and spatial information, thereby failing to fully explore the clinical value of hyperspectral data. In this paper, we propose a microscopic hyperspectral universal feature perception framework (UFPF), which extracts high-quality spatial-spectral features of hyperspectral data, providing a robust feature foundation for downstream tasks. Specifically, this innovative framework captures different sequential spatial nearest-neighbor relationships through a hierarchical corner-to-center mamba structure. It incorporates the concept of "progressive focus towards the center", starting by emphasizing edge information and gradually refining attention from the edges towards the center. This approach effectively integrates richer spatial-spectral information, boosting the model's feature extraction capability. On this basis, a dual-path spatial-spectral joint perception module is developed to achieve the complementarity of spatial and spectral information and fully explore the potential patterns in the data. In addition, a Mamba-attention Mix-alignment is designed to enhance the optimized alignment of deep semantic features. The experimental results on multiple datasets have shown that this framework significantly improves classification and segmentation performance, supporting the clinical application of medical hyperspectral data. The code is available at: https://github.com/Qugeryolo/UFPF.

Hyperspectral video (HSV) offers valuable spatial, spectral, and temporal information simultaneously, making it highly suitable for handling challenges such as background clutter and visual similarity in object tracking. However, existing methods primarily focus on band regrouping and rely on RGB trackers for feature extraction, resulting in limited exploration of spectral information and difficulties in achieving complementary representations of object features. In this paper, a spatial-spectral fusion network with spectral angle awareness (SSF-Net) is proposed for hyperspectral (HS) object tracking. Firstly, to address the issue of insufficient spectral feature extraction in existing networks, a spatial-spectral feature backbone ( $S^{2}$ FB) is designed. With the spatial and spectral extraction branch, a joint representation of texture and spectrum is obtained. Secondly, a spectral attention fusion module (SAFM) is presented to capture the intra- and inter-modality correlation to obtain the fused features from the HS and RGB modalities. It can incorporate the visual information into the HS context to form a robust representation. Thirdly, to ensure a more accurate response to the object position, a spectral angle awareness module (SAAM) is designed to investigate the region-level spectral similarity between the template and search images during the prediction stage. Furthermore, a novel spectral angle awareness loss (SAAL) is developed to offer guidance for the SAAM based on similar regions. Finally, to obtain the robust tracking results, a weighted prediction method is considered to combine the HS and RGB predicted motions of objects to leverage the strengths of each modality. Extensive experiments on the HOTC-2020, HOTC-2024, and BihoT datasets demonstrate the effectiveness of the proposed SSF-Net compared with state-of-the-art trackers. The source code will be available at https://github.com/hzwyhc/hsvt.

Recommendation system (RS) is an important information filtering tool in nowadays digital era. With the growing concern on privacy, deploying RSs in a federated learning (FL) manner emerges as a promising solution, which can train a high-quality model on the premise that the server does not directly access sensitive user data. Nevertheless, some malicious clients can deduce user data by analyzing the uploaded model parameters. Even worse, some Byzantine clients can also send contaminated data to the server, causing blockage or failure of model convergence. In addition, most existing researches on federated recommendation algorithms only focus on unimodality learning, ignoring the assistance of multiple modality data to promote recommendation accuracy. Therefore, this article designs an FL-based privacy-preserving multimodal RS framework. To distinguish various modality data, an attention mechanism is introduced, wherein different weight ratios are assigned to various modal features. To further strengthen the privacy, local differential privacy (LDP) and personalized FL strategies are designed to identify malicious clients and bolster the resilience against Byzantine attacks. Finally, two multimodal datasets are established to verify the effectiveness of the proposed algorithm. The superiority of our proposed techniques is confirmed by the simulation results.

Arbitrary-oriented object detection (AOOD) has been widely applied to locate and classify objects with diverse orientations in remote sensing images. However, the inconsistent features for the localization and classification tasks in AOOD models may lead to ambiguity and low-quality object predictions, which constrains the detection performance. In this article, an AOOD method called task-wise sampling convolutions (TS-Conv) is proposed. TS-Conv adaptively samples task-wise features from respective sensitive regions and maps these features together in alignment to guide a dynamic label assignment for better predictions. Specifically, sampling positions of the localization convolution in TS-Conv are supervised by the oriented bounding box (OBB) prediction associated with spatial coordinates, while sampling positions and convolutional kernel of the classification convolution are designed to be adaptively adjusted according to different orientations for improving the orientation robustness of features. Furthermore, a dynamic task-consistent-aware label assignment (DTLA) strategy is developed to select optimal candidate positions and assign labels dynamically according to ranked task-aware scores obtained from TS-Conv. Extensive experiments on several public datasets covering multiple scenes, multimodal images, and multiple categories of objects demonstrate the effectiveness, scalability, and superior performance of the proposed TS-Conv.

Hyperspectral object tracking (HOT) has many important applications, particularly in scenes where objects are camouflaged. The existing trackers can effectively retrieve objects via band regrouping because of the bias in the existing HOT datasets, where most objects tend to have distinguishing visual appearances rather than spectral characteristics. This bias allows a tracker to directly use the visual features obtained from the false-color images generated by hyperspectral images (HSIs) without extracting spectral features. To tackle this bias, the tracker should focus on the spectral information when object appearance is unreliable. Thus, we provide a new task called hyperspectral camouflaged object tracking (HCOT) and meticulously construct a large-scale HCOT dataset, BihoT, consisting of 41912 HSIs covering 49 video sequences. The dataset covers various artificial camouflage scenes, where objects have similar appearances, diverse spectrums, and frequent occlusion (OCC), making it a challenging dataset for HCOT. Besides, a simple but effective baseline model, named spectral prompt-based distractor-aware network (SPDAN), is proposed, comprising a spectral embedding network (SEN), a spectral prompt-based backbone network (SPBN), and a distractor-aware module (DAM). Specifically, the SEN extracts spectral-spatial features via 3-D and 2-D convolutions to form a refined prompt representation. Then, the SPBN fine-tunes powerful RGB trackers with spectral prompts and alleviates the insufficiency of training samples. Moreover, the DAM utilizes a novel statistic to capture the distractor caused by occlusion from objects and background and corrects the deterioration of the tracking performance via a novel motion predictor. Extensive experiments demonstrate that our proposed SPDAN achieves the state-of-the-art performance on the proposed BihoT and other HOT datasets.

Due to the limited energy and computing resources of Internet of Things (IoT) devices, the collaboration of IoT devices and edge servers is considered to handle the complex deep neural network (DNN) inference tasks. However, the heterogeneity of IoT devices and the various accuracy requirements of inference tasks make it difficult to deploy all the DNN models in edge servers. Moreover, a large-scale data transmission is engaged in collaborative inference, resulting in an increased demand on spectrum resource and energy consumption. To address these issues, in this paper, we first design an accuracy-aware multi-branch DNN inference model and quantify the relationship between branch selection and inference accuracy. Then, based on the multi-branch DNN model, we aim to minimize the energy consumption of devices by jointly optimizing the selection of DNN branches and partition layers, as well as the computing and communication resources allocation. The proposed problem is a mixed-integer nonlinear programming problem. We propose a hierarchical approach to decompose the problem, and then solve it with a proportional integral derivative based searching algorithm. Experimental results demonstrate our proposed scheme has better inference performance and can reduce the total energy consumption up to 65.3$\%$, compared to other collaboration schemes.

With the advancement of communication technology and the improvement of localization accuracy, cellular networks are gradually evolving from communication to perception-integrated networks. Addressing the research challenges of sensing-assisted communication, we propose, for the first time, the concept of Digital Twin of Channel (DToC). Specifically, we regard user terminal (UT) positions as physical objects, and statistical channel state information (CSI) as virtual digital objects. Observing the change trend of UTs’ statistical CSI caused by the changes of UT’s physical position enables predictive analytics for subsequent communication tasks. Then, we establish the relationship between physical and virtual digital objects using a Diffusion Model (DM) to achieve the DToC. Indeed, the DM can generate the desired objects by gradually denoising from noisy data using neural networks. Furthermore, we propose a conditional DM utilizing UTs’ positions, which completes the task of generating the corresponding statistical CSI under known user-specific position conditions, thus mapping UT positions to statistical CSI. Simulation results demonstrate that our DToC framework outperforms previous statistical CSI estimation methods. Without the need of pilots, our method can simultaneously generate statistical CSIs from a large number of UTs’ positions, achieving satisfactory results.

Channel knowledge map (CKM) has received widespread attention as an emerging enabling technology for environment-aware wireless communications. It involves the construction of databases containing location-specific channel knowledge, which are then leveraged to facilitate channel state information (CSI) acquisition and transceiver design. In this context, a fundamental challenge lies in efficiently constructing the CKM based on a given wireless propagation environment. Most existing methods are based on stochastic modeling and sequence prediction, which do not fully exploit the inherent physical characteristics of the propagation environment, resulting in low accuracy and high computational complexity. To address these limitations, we propose a Laplacian pyramid (LP)-based CKM construction scheme to predict the channel knowledge at arbitrary locations in a targeted area. Specifically, we first view the channel knowledge as a 2-D image and transform the CKM construction problem into an image-to-image (I2I) inpainting task, which predicts the channel knowledge at a specific location by recovering the corresponding pixel value in the image matrix. Then, inspired by the reversible and closed-form structure of the LP, we show its natural suitability for our task in designing a fast I2I mapping network. For different frequency components of LP decomposition, we design tailored networks accordingly. Besides, to encode the global structural information of the propagation environment, we introduce self-attention and cross-covariance attention mechanisms in different layers, respectively. Finally, experimental results demonstrate that the proposed scheme outperforms the benchmark, achieving higher reconstruction accuracy while with lower computational complexity. Moreover, the proposed approach has a strong generalization ability and can be implemented in different wireless communication scenarios.

This paper investigates asynchronous multiple-input multiple-output (MIMO) massive unsourced random access (URA) in an orthogonal frequency division multiplexing (OFDM) system over frequency-selective fading channels, with the presence of both timing and carrier frequency offsets (TO and CFO) and non-negligible codeword collisions. The proposed coding framework segregates the data into two components, namely, preamble and coding parts, with the former being tree-coded and the latter LDPC-coded. By leveraging the dual sparsity of the equivalent channel across both codeword and delay domains (CD and DD), we develop a message-passing-based sparse Bayesian learning algorithm, combined with belief propagation and mean field, to iteratively estimate DD channel responses, TO, and delay profiles. Furthermore, by jointly leveraging the observations among multiple slots, we establish a novel graph-based algorithm to iteratively separate the superimposed channels and compensate for the phase rotations. Additionally, the proposed algorithm is applied to the flat fading scenario to estimate both TO and CFO, where the channel and offset estimation is enhanced by leveraging the geometric characteristics of the signal constellation. Extensive simulations reveal that the proposed algorithm achieves superior performance and substantial complexity reduction in both channel and offset estimation compared to the codebook enlarging-based counterparts, and enhanced data recovery performances compared to state-of-the-art URA schemes.

In high-speed railway (HSR) communications, the channel suffers from severe Doppler and channel aging effects caused by the high mobility, making the channel outdated quickly. To address this issue, we investigate the robust precoder design against channel aging and prediction inaccuracy in massive multiple-input multiple-output (MIMO) systems with matrix manifold optimization. First of all, we introduce the concept of the quadruple beams (QBs), and establish a QB based channel model with sampled quadruple steering vectors. Then, the upcoming space domain channel of interest can achieve a higher accuracy by channel prediction with the estimated QB domain channel. To further improve the performance while save the pilot overhead, we predict the forthcoming QB domain channel and integrate the prediction inaccuracy within the a posterior QB domain statistical channel model. Then, we consider the robust precoder design aiming to maximize the upper bound of the ergodic weighted sum-rate (WSR) on the Riemannian submanifold formed by the precoders satisfying the total power constraint (TPC). Riemannian ingredients are derived for matrix manifold optimization, with which the Riemannian conjugate gradient (RCG) method is proposed to solve the unconstrained problem on the manifold. The RCG method mainly involves the matrix multiplication and avoids the need of matrix inversion of the transmit antenna dimension. The simulation results demonstrate the effectiveness of the proposed channel model and the superiority of the RCG method for robust precoder design against channel aging and prediction inaccuracy.

The low earth orbit (LEO) satellite communication network has attracted extensive attention owing to its advantages of seamless coverage and low propagation delays, which provides a promising solution to realize massive access for Internet of Things (IoT) devices. In this paper, we study cooperative grant free random access (GF-RA) in LEO satellite communication systems. Specifically, we investigate the joint activity detection and channel estimation (JADCE) problem for multi-input multi-output (MIMO) based massive connectivity. First, we analyze the channel characteristics, and reveal the low-rank and row-sparsity properties of the channel impulse response (CIR) matrix. Accordingly, we transfer the JADCE problem into a low-rank matrix completion problem and a compressive sensing problem, which are solved by a two-stage algorithm efficiently. In the first stage, we design the principal component analysis with adaptive signal space detection (PCA-AASD) algorithm to perform low-rank matrix completion. In the second stage, we employ a sequential sparse Bayesian learning with multiple measurement vector (MMV) algorithm to perform active terminal detection and channel estimation. Finally, a majority voting scheme is utilized to estimate the active terminals by aggregating the estimation of multiple satellites. Simulation results validate that the proposed method achieves lower activity detection error probability and better channel estimation performance than other baseline methods in the literature.

In this paper, we propose a novel semantic-aided image communication framework for supporting the compatibility with practical separation-based coding architectures. Particularly, the deep learning (DL)-based joint source-channel coding (JSCC) is integrated into the classical separate source-channel coding (SSCC) to transmit the images via the combination of semantic stream and image stream from DL networks and SSCC respectively, which we name as parallel-stream transmission. The positive coding gain stems from the sophisticated design of the JSCC encoder, which leverages the residual information neglected by the SSCC to enhance the learnable image features. Furthermore, a conditional rate adaptation mechanism is introduced to adjust the transmission rate of semantic stream according to residual, rendering the framework more flexible and efficient to bandwidth allocation. We also design a dynamic stream aggregation strategy at the receiver, which provides the composite framework with more robustness to signal-to-noise ratio (SNR) fluctuations in wireless systems compared to a single conventional codec. Finally, the proposed framework is verified to surpass the performance of both traditional and DL-based competitors in a large range of scenarios and meanwhile, maintains lightweight in terms of the transmission and computational complexity of semantic stream, which exhibits the potential to be applied in real systems.

In this paper, we investigate the near-field (NF) channel estimation (CE) for extremely large-scale multiple-input multiple-output (XL-MIMO) systems. Considering the pronounced NF effects in XL-MIMO communications, we first establish a joint angle-distance (AD) domain-based spherical-wavefront physical channel model that captures the inherent sparsity of XL-MIMO channels in the NF region. Leveraging the sparsity of the channel, the CE is approached as a task of reconstructing sparse signals. Anchored in this framework, we first propose a compressed sensing algorithm to acquire a preliminary channel estimation. Harnessing the powerful latent representation capability of generative artificial intelligence (GenAI), we further propose a GenAI-based approach to refine the estimated channel by employing advanced image denoising techniques. Specifically, we perceive the estimated channel as a noisy color image. Then, we derive the evidence lower bound (ELBO) of the design objective utilizing variational inference and reparameterization techniques, and propose a generative diffusion probabilistic model (GDM) dedicated to denoising. Experimental results indicate that the proposed GDM is capable of offering substantial performance gain in CE compared to existing benchmark approaches in NF XL-MIMO systems.

This paper investigates multi-user uplink transmission facilitated by holographic planar arrays (HPAs). It includes channel modeling and holographic precoding aimed at maximizing system spectral efficiency (SE) in scenarios where both users and base station are equipped with HPAs. First, we develop a holographic multiple-input multiple-output (MIMO) channel model utilizing electromagnetic field equations. Subsequently, we utilize Fourier space basis functions to discretize the continuous holographic MIMO model. Based on the discretized model, we formulate the problem of maximizing the system SE and propose a corresponding iterative water-filling algorithm to tackle it. Finally, we validate the effectiveness of the proposed scheme in enhancing the system SE and investigate the influence of physical size constraints of the transmitting and receiving arrays through simulation results.

In this paper, we investigate the precoder design for user-centric network (UCN) massive multiple-input multiple-output (mMIMO) downlink with matrix manifold optimization. In UCN mMIMO systems, each user terminal (UT) is served by a subset of the base stations (BSs) instead of all BSs, lowering the dimension of the precoders to be designed. Each BS in the system has a power constraint. By proving that the precoder set satisfying the constraints forms a Riemannian submanifold, we transform the constrained precoder design problem in Euclidean space as an unconstrained one on the Riemannian submanifold. Riemannian ingredients, including orthogonal projection, Riemannian gradient, retraction and vector transport, of the problem on the Riemannian submanifold are further derived, with which the Riemannian conjugate gradient (RCG) design method is proposed for solving the unconstrained problem. The proposed method avoids the inverses of large dimensional matrices. The complexity analyses show the high efficiency of RCG precoder design. Simulation results demonstrate the superiority of the proposed precoder design and the high efficiency of the UCN mMIMO system.

In this paper, we propose time-frequency phase-shifted pilots (TFPSPs) for massive multi-input multi-output orthogonal frequency division multiplexing (MIMO-OFDM) channel estimation. We first present a triple-beam (TB) based channel model, establishing the relationship between the space-frequency-time (SFT) domain channel and the TB domain channel. By leveraging the specific characteristics of TB domain channels, we develop TFPSPs, where distinct pilot signals are simultaneously transmitted in the frequency and time domains. Then, we present the optimal condition on TFPSP, indicating that the optimal channel estimation performance can be achieved if the TB domain channel power distributions of different UTs do not overlap with each other by scheduling TFPSPs properly. Based on this optimal condition, we propose a low-complexity pilot scheduling algorithm. Simulation results demonstrate that, compared with conventional pilot design approaches, the proposed TFPSP approach effectively improves the accuracy of channel estimation, particularly in scenarios involving a significant number of UTs.

Non-orthogonal multiple access (NOMA) has been regarded as a promising technology for next-generation wireless networks. Most of the existing NOMA schemes are designed with the assumption of infinite blocklength (IBL), which may lead to suboptimal performance in practice. This paper investigates the reliability-oriented downlink multiple-input multiple-output (MIMO)-NOMA design with finite blocklength (FBL) transmission. Specifically, we formulate the resource allocation problem to minimize the average block error rate (BLER) of FBL-MIMO-NOMA systems. We obtain the globally optimal solutions to the problem by proposing an efficient power, rate, and blocklength allocation scheme. The superiority of our proposed design is demonstrated via simulation results.

A reactive jamming attack, which performs spectrum jamming only during legal signal transmission based on the knowledge of user behaviors, poses a significant threat to wireless communications. In this paper, a novel deceiving approach is proposed for defending reactive jamming in unmanned aerial vehicle (UAV) assisted wireless communications. Specifically, the interaction of multiple legitimate users (LUs) and a malicious user (MU) is modelled as a Stackelberg game, where LUs are the leaders and MU is the follower. We first show that the equilibrium exists when the channel information is known. Then, we propose a multi-agent reinforcement learning (MARL) based method to address the reactive jamming. Finally, we perform simulations to demonstrate the superiority of our proposed method.

In this paper, we prove the convergence of the simplified information geometry approach (SIGA), which was proposed for massive MIMO-OFDM channel estimation. For a general Bayesian inference problem, we first show that the iteration of the common second-order natural parameter (SONP) is separated from that of the common first-order natural parameter (FONP). Hence, the convergence of the common SONP can be checked independently. We show that with the initialization satisfying a specific but large range, the common SONP is convergent regardless of the value of the damping factor. For the common FONP, we establish a sufficient condition of its convergence and prove that the convergence of the common FONP relies on the spectral radius of a particular matrix related to the damping factor. We give the range of the damping factor that guarantees the convergence in the worst case. Further, we determine the range of the damping factor for massive MIMO-OFDM channel estimation by using the specific properties of the measurement matrices. Simulation results are provided to confirm the theoretical results.

Channel knowledge map (CKM) has received widespread attention as an emerging enabling technology for environment-aware wireless communications. It involves the construction of databases containing location-specific channel knowledge, which are then leveraged to facilitate channel state information (CSI) acquisition and transceiver design. In this paper, we propose a Laplacian pyramid (LP)-based CKM construction scheme to predict the channel knowledge at arbitrary locations in a targeted area. Specifically, we first view the channel knowledge as a 2-D image and transform the CKM construction problem into an image to image (I2I) inpainting task, which predicts the channel knowledge at specific location by recovering the corresponding pixel value in the image matrix. Then, inspired by the reversible and closed-form frequency band decomposition structure of the LP, we design tailored subnetworks for different frequency components. In addition, to encode the global structural information of the propagation environment, we introduce self-attention and cross-covariance attention mechanisms in different layers, respectively. Experiments demonstrate that the proposed scheme can accurately reconstruct the CKM with low computational complexity. Moreover, the proposed method has a strong generalization ability to be implemented in different wireless communication scenarios.

We investigate the weighted sum-rate (WSR) max-imization linear precoder design under total power constraint (TPC) for massive MIMO downlink with matrix manifold optimization. Particularly, we prove that the precoders under TPC are on a Riemannian submanifold, and transform the constrained problem in Euclidean space to the unconstrained one on manifold. In accordance with this, Riemannian design methods using Riemannian steepest descent and Riemannian conjugate gradient are provided to design the WSR-maximization precoders under TPC. Riemannian methods are free of the inverse of large dimensional matrix, posing significant computational savings and potentially allowing to avoid ill numerical behavior in algorithms. Complexity analysis and performance simulations demonstrate the advantages of the proposed precoder design.

We investigate massive multiple-input multiple-output (MIMO) downlink (DL) transmission for multiple low-earth-orbit (LEO) satellite communication systems. We establish the signal and channel models, and reveal that the signals received by each user terminal (UT) are typically asynchronous in time and frequency. We propose a spatial linear receive processing for signal extraction and perform time and frequency compensations at each UT to achieve synchronized signal. We show that the single data stream transmission from each satellite to each UT is optimal to maximize the ergodic sum rate. Therefore, without loss of optimality, we can reduce the joint design of transmit covariance matrices and receive vectors for spatial linear processing to that of the precoding vectors and receive vectors, which we refer to as joint precoder and receiver design (JPRD). We devise a weighted minimum mean-square error (WMMSE) based JPRD algorithm by using the statistical channel state information. Simulation results validate the proposed approaches.

Wireless positioning is crucial for Internet of Things (IoT) landscape, enhancing precision and reliability in location-based services. This article addresses the challenges of existing massive multiple-input–multiple-output fingerprint positioning methods, which typically require accurate channel estimation and one-by-one labeled data sets. We propose a semisupervised representation contrastive learning technique that leverages a partially labeled received pilot signal data set readily available from the base station. Our approach employs data augmentation to generate a large number of positive and negative sample pairs, which are then used to pretrain an encoder with a contrastive loss function in the self-supervision way. During pretraining, the encoder learns to encode positive samples close to an anchor, while keeping negative samples far away in the representation space. A fully connected layer is added on top of the encoder for position regression, and the encoder and regression networks are fine-tuned with a small labeled subdataset for the downstream positioning task. Simulation results demonstrate that our pretraining and fine-tuning approach outperforms the previous methods, significantly improving positioning accuracy, avoiding exact channel estimation and achieving labeling efficiency.

Unmanned aerial vehicles (UAVs)-assisted wireless communication will play an important role in the next-generation mobile communication network. However, the inherent open nature of the signal propagation environment may cause illegal eavesdropping and surveillance from adversaries. In addition, the intergroup co-channel interference among different cells further degrades the system performance. Hence, we consider a generic scenario of multiple UAV base stations (UAV-BSs) and ground users, where multiple terrestrial wardens attempt to detect the transmissions from UAV-BSs to users and a UAV-mounted jammer is employed to generate artificial noise to assist the covert communications. To ensure fairness, we formulate an optimization problem to maximize the minimum of the average rate lower bounds of all users by jointly optimizing user association, bandwidth allocation, UAV transmit power control, and UAV 3-D deployment, subject to the constraints of the detection error probability of each warden. To solve this mixed-integer nonconvex problem, we propose a suboptimal algorithm by applying block coordinate descent (BCD) method to solve three subproblems iteratively. Specifically, in each iteration, the subproblem of user association and bandwidth allocation is solved by a customized genetic algorithm (GA) first, where a closed-form expression for bandwidth allocation is obtained. Second, the subproblem of UAV transmit power control is solved by using successive convex approximation (SCA) techniques. Finally, suboptimal 3-D positions of the UAVs are obtained through particle swarm optimization (PSO)-based algorithm. Extensive simulation results demonstrate the effectiveness and superiority of our proposed algorithm compared to benchmark schemes in terms of improving the minimum of the average rate lower bounds of all users.

The deployment of low earth orbit (LEO) satellites megaconstellations presents a promising way for achieving global coverage and service, attributed to their comparatively low round-trip latency and launch costs. However, this surge in LEO satellite launches exacerbates the scarcity of the limited spectrum resources. Spectrum sharing between satellite constellations and terrestrial networks and beam hopping (BH) technology emerge as viable strategies to mitigate this spectrum shortage. To enhance spectrum efficiency and avoid serious inter-system interference, we investigate the beam hopping scheduling of satellites for interference avoidance. The beam hopping scheduling of the integrated satellite-terrestrial wireless networks system is formulated as throughput-driven beam hopping (TDBH) problem and satisfaction-rate-driven beam hopping (SDBH) problem, respectively. In particular, we decompose the TDBH problem into two sub-problems by relaxation, and a genetic algorithm (GA) is introduced to handle the SDBH problem. The impact of channel conditions and traffic load intensity on the satellite system throughput is analyzed in TDBH simulation. As for SDBH optimization problem, the simulation results show that the proposed GA algorithm improves the average traffic satisfaction rate by 16.96% at least, compared with other benchmarks and suits to scenarios with different traffic demands and fading channel conditions.

This letter proposes a novel airborne synthetic aperture radar (SAR) motion compensation (MOCO) method based on the optimal weighting Doppler frequency modulation rate estimation. According to the traditional map-drift (MD) algorithm, the Doppler frequency modulation rate corresponding to the range units can be estimated through azimuth sub-aperture signals. The utilization rate for range units and the contributions of the strong scattering units can be improved by using the proposed optimal weighting on the signal amplitude, increasing the estimation accuracy of the Doppler frequency modulation rate. The platform motion error can be effectively estimated through fitting the Doppler frequency modulation rate and each initial slant range based on least square processing. Real-measured high-resolution airborne SAR data validate the effectiveness and feasibility of the proposed algorithm.

This letter proposes a fast Doppler parameter estimation and refocusing method for ground moving targets in a synthetic aperture radar (SAR) system. In the proposed method, after implementing the main-lobe clutter rejection by using the azimuth adaptive processing technique, the range-azimuth positions of smeared target scatterers can be obtained via the constant false alarm rate (CFAR) detection. Then, an autocorrelation function is constructed to transform a moving target signal into the time-frequency plane, where the target parameters can be precisely and efficiently estimated by applying the scaled fast Fourier transform (FFT). Finally, ground moving targets can be well refocused and relocated in a SAR imagery. Compared with the conventional methods, the target output SNR can be enlarged about 3 dB under the low SNR by using the proposed parameter estimation method.

For maneuvering targets with weak surface reflections, the conventional ISAR imaging algorithms based on the motion parameter estimation for each scatterer may have a poor performance under low signal-to-noise ratio (SNR) scenario. To tackle this problem, a novel ISAR imaging algorithm is proposed in this letter. The effects of translational motion, the high-order migration through resolution cells (MTRC), and the nonstationary phase distribution are simultaneously eliminated by constructing the compensation function and utilizing the time polynomial rescaling-nonuniform fast Fourier transform (TPRS-NUFFT). Then, the particle swarm optimization (PSO) algorithm is applied to accomplish the motion parameter estimation. Finally, a high-resolution ISAR image can be obtained after motion compensation. Both simulation and real-measured data processing results demonstrate that the proposed algorithm can obtain focused ISAR image and improve the SNR by 29.8 dB.

We investigate massive multiple-input multiple-output (MIMO) downlink (DL) transmission for multiple low-earth-orbit satellite communication systems. We establish the signal and channel models, and reveal that the signals received by each user terminal (UT) are typically asynchronous in time and frequency. We propose a spatial linear receive processing for signal extraction and perform time and frequency compensations at each UT to achieve synchronized signal. We prove that the single data stream transmission from each satellite to each UT is optimal to maximize the ergodic sum rate. Therefore, without loss of optimality, we can reduce the joint design of the transmit covariance matrices and receive vectors for spatial linear processing to that of the precoding vectors and receive vectors, which we refer to as joint precoder and receiver design (JPRD). We devise a weighted minimum mean-square error (WMMSE) based JPRD algorithm by using the statistical channel state information. Further, we approximate the optimal design with an ergodic sum rate upper bound, for which the optimality of single data stream transmission still holds. We derive a condition under which the inter-satellite interference can be eliminated, and develop a low-complexity WMMSE based JPRD algorithm with the upper bound. Simulation results validate the proposed approaches.

This paper considers an unmanned aerial vehicle (UAV)-assisted communication system for data collection in urban areas, where multiple UAVs are dispatched to harvest data from multiple ground user equipments (UEs). We adopt a blockage-aware channel model to characterize the practical blockage effects for air-to-ground (A2G) links caused by buildings. Aiming to minimize the mission completion time while satisfying the data collection requirements of UEs, we formulate a problem by jointly optimizing the UAV three-dimensional (3-D) trajectory and resource allocation, including the UE scheduling and subcarrier assignment. To solve the formulated non-convex combinatorial programming problem, we propose a suboptimal algorithm that solves two subproblems iteratively. Specifically, in each iteration, the trajectory design subproblem jointly optimizes the UAVs’ waypoints and time slot length to decrease the mission completion time, which is solved by employing block successive convex approximation (BSCA). For the resource allocation subproblem, we develop a heuristic algorithm for UE scheduling and subcarrier assignment to increase the collected data volume for a given time duration. Simulation results demonstrate the superior performance of the proposed algorithm in terms of mission completion time compared to benchmark schemes.

Hyperspectral videos (HSVs) offer not only spatial information but also diagnostic spectral features. Due to the fact that spectral features are only related to the material of the object, this advantage can address the issue of RGB video tracking failure when the object and background are visually similar. However, the effectiveness of deep learning models is limited due to insufficient HSV training data. Existing methods tend to divide a hyperspectral image (HSI) into several three-channel false-color images to leverage the existing RGB trackers for transfer learning. Nonetheless, these methods lack adequate exploration of band interrelations and overlook correlation among objects prior to similarity calculation. In this article, a transformer-based band regrouping and feature refinement network (TBR-Net) is introduced, which is specifically tailored for hyperspectral object tracking. To maximize the potential of the RGB tracker and enhance the use of available training data, we propose a transformer-based band regrouping (TBR) method. By modeling long-range spectral dependencies, the inherent context information among bands is captured, which is subsequently utilized to reorganize bands into several false-color images. Furthermore, to combine the relationship of the template and the search (T & S) frames into a correlation calculation, a feature refinement module (FRM) is designed. The cross-attention mechanism enables mutual relation modeling, allowing similar regions to be perceived and form discriminative feature representation. As a result, a hyperspectral tracker can be efficiently trained via transfer learning to address the data insufficiency challenge, while the mutual perception between objects further enhances the tracking performance. Its effectiveness is validated by extensive benchmark experiments, which demonstrate that the TBR-Net surpasses state-of-the-art methods.

Interferometric synthetic aperture radar (InSAR) images are severely corrupted by noise in both magnitude and phase. It is significantly essential to recover the true interferometric phase during InSAR signal processing. Usually, traditional phase denoising methods are to find homogeneous samples for filtering with the need to balance noise reduction and phase preservation, which may be a problem in dealing with topography scenes. In this article, a novel algorithm of robust nonlocal tensor decomposition (RNLTD) for InSAR phase denoising is proposed. In the scheme, a nonlocal tensor (NLT) model of the interferogram is constructed by selecting and stacking similar image patches in a nonlocal region. Benefiting from the simultaneous use of nonlocal and tensor tools, superior low-rank properties of this NLT can be acquired, which is also confirmed by numerical analysis. Then, a robust tensor decomposition algorithm is proposed to formulate the low-rank recovery of the interferogram and constrain the sparse outliers for noise reduction. Next, an alternating direction method of multipliers (ADMM) solution is applied to robustly and accurately restore the noise-reduced interferometric phase. As a result, the proposed RNLTD algorithm takes advantage of effectively capturing the phase structure in a high-dimension manner, which is helpful in phase preservation with the achievement of excellent noise reduction. Lastly, the experimental analysis using one set of simulated and two sets of measured InSAR data is performed to show the promising performance of the proposed algorithm.

A spaceborne bistatic surveillance radar system, which may increase the target radar cross section through a large bistatic angle, can effectively improve the performance of small air moving target indication. However, due to the separation of the transmitter and the receiver, the clutter exhibits severe range dependence in both Doppler and spatial-angle domains. This may significantly decrease the number of independent identically distributed samples and, thus, cause the degradation of clutter suppression performance. In addition, when an air moving target flies above the sea, the broadened spectrum of sea clutter caused by the random intrinsic motion will further influence the clutter suppression performance. To deal with these problems, this article establishes a multichannel sea clutter model for a spaceborne bistatic surveillance radar system. The calculation of the positions of the iso-range contours is analyzed first. Then, the bistatic sea clutter model is established based on the directional wave spectrum, the monostatic–bistatic equivalence theorem, and the two-scale scattering model, where the Earth rotation and the range ambiguity of clutter are considered. Finally, the range dependence and the spectrum characteristics of bistatic sea clutter are discussed, and the clutter suppression performances under different azimuth bistatic angles, different elevation angles, and different sea states are analyzed. In addition, the influence of clutter range ambiguity is analyzed based on the simulations.

Hyperspectral images (HSIs), with hundreds of narrow spectral bands, are increasingly used for ground object classification in remote sensing. However, many HSI classification models operate pixel-by-pixel, limiting the utilization of spatial information and resulting in increased inference time for the whole image. This paper proposes SegHSI, an effective and efficient end-to-end HSI segmentation model, alongside a novel training strategy. SegHSI adopts a head-free structure with cluster attention modules and spatial-aware feedforward networks (SA-FFN) for multiscale spatial encoding. Cluster attention encodes pixels through constructed clusters within the HSI, while SA-FFN integrates depth-wise convolution to enhance spatial context. Our training strategy utilizes a student-teacher model framework that combines labeled pixel class information with consistency learning on unlabeled pixels. Experiments on three public HSI datasets demonstrate that SegHSI not only surpasses other state-of-the-art models in segmentation accuracy but also achieves inference time at the scale of seconds, even reaching sub-second speeds for full-image classification. Code is available at https://github.com/huanliu233/SegHSI.

Reconfigurable intelligent surface (RIS) has been recently regarded as a disruptive candidate technology for enabling next generation wireless communication. It can establish favorable propagation environment to facilitate low-power and spectrally efficient data transmission, possessing attractive potential to support massive access. However, the required activity detection and channel estimation for RIS-assisted massive access is quite challenging due to the passive nature of the conventional reflecting elements. To this end, this paper considers massive access for RIS-assisted communication systems with semi-passive elements, which can operate in sensing mode for receiving signals. Then, by exploiting the sparsity of the RIS-BS channel in the virtual angular domain as well as the sporadic transmission of massive connectivity, we formulate the joint activity detection and channel estimation as a special bilinear recovery problem, which is a combination of sparse matrix factorization, compressed sensing (CS)-based generalized multiple measurement vector (GMMV) problem and matrix completion. Furthermore, we propose a novel hierarchical message passing-based algorithm to address the problem, in which approximate message passing (AMP)-based approximations are adopted to reduce the computational complexity. Simulation results demonstrate the effectiveness of the proposed algorithm and its superior performance compared with state-of-the-art baseline schemes.

With antenna spacing much less than half a wavelength in confined space, holographic multiple-input multiple-output (HMIMO) technology presents a promising frontier in next-generation mobile communication. We delve into the research of the multi-user uplink transmission with both the base station and the users equipped with holographic planar arrays. To begin, we construct an HMIMO channel model utilizing electromagnetic field equations, accompanied by a colored noise model that accounts for both electromagnetic interference and hardware noise. Since this model is continuous, we approximate it within a finite-dimensional space spanned by Fourier space series, which can be defined as the communication mode functions. We show that this channel model samples Green’s function in the wavenumber domain in different communication modes. Subsequently, we tackle the challenging task of maximizing the spectral efficiency (SE) of the system, which involves optimizing the continuous current density function (CDF) for each user. Using the aforementioned approximation model, we transform the optimization variables into expansion coefficients of the CDFs on a finite-dimensional space, for which we propose an iterative water-filling algorithm. Simulation results illustrate the efficacy of the proposed algorithm in enhancing the system SE and show the influence of the colored noise and the system parameters on the SE.

In this paper, we investigate high frequency (HF) skywave massive multiple-input multiple-output (MIMO) communications with orthogonal frequency division multiplexing (OFDM) modulation. Based on the triple-beam (TB) based channel model and the channel sparsity in the TB domain, we propose a beam structured channel estimation (BSCE) approach. Specifically, we show that the space-frequency-time (SFT) domain estimator design for each TB domain channel element can be transformed into that of a low-dimensional TB domain estimator and the resulting SFT domain estimator is beam structured. We also present a method to select the TBs used for BSCE. Then we generalize the proposed BSCE by introducing window functions and a turbo principle to achieve a superior trade-off between complexity and performance. Furthermore, we present a low-complexity design and implementation of BSCE by exploiting the characteristics of the TB matrix. Simulation results validate the proposed theory and methods.

Movable antenna (MA) is a new technology with great potential to improve communication performance by enabling local movement of antennas for pursuing better channel conditions. In particular, the acquisition of complete channel state information (CSI) between the transmitter (Tx) and receiver (Rx) regions is an essential problem for MA systems to reap performance gains. In this paper, we propose a general channel estimation framework for MA systems by exploiting the multi-path field response channel structure. Specifically, the angles of departure (AoDs), angles of arrival (AoAs), and complex coefficients of the multi-path components (MPCs) are jointly estimated by employing the compressed sensing method, based on multiple channel measurements at designated positions of the Tx-MA and Rx-MA. Under this framework, the Tx-MA and Rx-MA measurement positions fundamentally determine the measurement matrix for compressed sensing, of which the mutual coherence is analyzed from the perspective of Fourier transform. Moreover, two criteria for MA measurement positions are provided to guarantee the successful recovery of MPCs. Then, we propose several MA measurement position setups and compare their performance. Finally, comprehensive simulation results show that the proposed framework is able to estimate the complete CSI between the Tx and Rx regions with a high accuracy.

Movable antenna (MA) is an innovative technology that facilitates the repositioning of antennas within the transmitter/receiver area to enhance channel conditions and communication performance. This paper proposes a new base station (BS) architecture employing multiple MAs for improving the multiuser network performance. First, the uplink multiple access channel (MAC) is modeled to capture the characteristics of the variation of wireless channels caused by the movement of MAs at the BS. Subsequently, we propose to maximize the minimum achievable rate among multiple users for MA-aided multiuser uplink transmissions by joint optimization of the MAs’ positions, their receive combining at the BS, and the transmit power of users, subject to the MAs’ positions-related constraints and the maximum transmit power of each user. To tackle this highly non-convex max-min fairness problem, we propose a two-loop iterative algorithm based on the particle swarm optimization (PSO). Specifically, the outer-loop updates the positions of a set of particles, where each particle’s position corresponds to one realization of the antenna position vector (APV) of all MAs. The inner-loop conducts the fitness evaluation for each particle, determining the max-min achievable rate for multiple users based on the current APV. Therein, for given APV, the receive combining matrix at the BS and the transmit power for each user are optimized using the block coordinate descent (BCD) technique. To further reduce the computational complexity, we develop an alternating optimization (AO)-based algorithm via iteratively updating the APV, combining matrix, and transmit power. Finally, extensive simulations demonstrate that the antenna position optimization for MAs-aided BSs can significantly improve the rate performance as compared to conventional BSs with fixed-position antennas (FPAs).

In this paper, we propose to deploy multiple unmanned aerial vehicle (UAV) mounted base stations to serve ground users in outdoor environments with obstacles. In particular, the geographic information is employed to capture the blockage effects for air-to-ground (A2G) links caused by buildings, and a realistic blockage-aware A2G channel model is proposed to characterize the continuous variation of the channels at different locations. Based on the proposed channel model, we formulate the joint optimization problem of UAV three-dimensional (3-D) positioning and resource allocation, by power allocation, user association, and subcarrier allocation, to maximize the minimum achievable rate among users. To solve this non-convex combinatorial programming problem, we introduce a penalty term to relax it and develop a suboptimal solution via a penalty-based double-loop iterative optimization framework. The inner loop solves the penalized problem by employing the block successive convex approximation (BSCA) technique, where the UAV positioning and resource allocation are alternately optimized in each iteration. The outer loop aims to obtain proper penalty multipliers to ensure the solution of the penalized problem converges to that of the original problem. Simulation results demonstrate the superiority of the proposed algorithm over other benchmark schemes in terms of the minimum achievable rate.

Non-orthogonal multiple access (NOMA) has been considered as a promising technology for enabling massive connectivity and achieving high spectral efficiency in future wireless communication. In the literature, most of the existing NOMA schemes are designed with the assumption of infinite blocklength, which may lead to suboptimal performance in practical communication systems. Thus, this paper investigates the downlink NOMA system with finite blocklength (FBL) transmission, namely the downlink FBL-NOMA system. We focus on developing a joint resource allocation scheme from single-channel to multi-channel systems, aiming at minimizing the average block error rate (BLER) for the downlink FBL-NOMA. Specifically, for single-channel systems, the optimal rate and power allocation scheme are derived in semi-closed form. For multi-channel systems, we conduct the convexity analysis of the minimum BLER problem and provide the optimal solution in waterfilling form for convex cases, as well as an efficient majorization-minimization (MM)-based algorithm for general cases. We also propose an efficient method to jointly optimize channel assignment, rate allocation, and power allocation for multi-channel FBL-NOMA systems. Simulation results demonstrate the superiority of the proposed designs over the existing NOMA and orthogonal multiple access (OMA) schemes in terms of reliability and efficiency.

This paper investigates the multiple-input-multiple-output (MIMO) massive unsourced random access in an asynchronous orthogonal frequency division multiplexing (OFDM) system, with both timing and frequency offsets (TFO) and non-negligible user collisions. The proposed coding framework splits the data into two parts encoded by sparse regression code (SPARC) and low-density parity check (LDPC) code. Multistage orthogonal pilots are transmitted in the first part to reduce collision density. Unlike existing schemes requiring a quantization codebook with a large size for estimating TFO, we establish a graph-based channel reconstruction and collision resolution (GB-CR2) algorithm to iteratively reconstruct channels, resolve collisions, and compensate for TFO rotations on the formulated graph jointly among multiple stages. We further propose to leverage the geometric characteristics of signal constellations to correct TFO estimations. Exhaustive simulations demonstrate remarkable performance superiority in channel estimation and data recovery with substantial complexity reduction compared to state-of-the-art schemes.

A reactive jamming attack, which performs spec-trum jamming only during legal signal transmission based on the knowledge of sensor behaviors, poses a significant threat to wireless sensing networks (WSNs). In this paper, a novel deceiving approach is proposed for defending reactive jamming in dynamic WSNs. Specifically, when the maximum transmission power is given, we first formulate the anti-jamming process as an optimization problem to maximize the average received power while eliminating the effects of the jamming attack. Then the interaction between reactive jamming and legitimate sensors is modeled with the Markov decision process (MDP). Finally, a deep Q network (DQN) based jamming deceiving method is proposed to solve the formulated optimization problem. Simulation results show that the proposed anti-jamming scheme can converge quickly and is superior to the classical counterparts in terms of the mean of received signal power.

This paper proposes to deploy multiple unmanned aerial vehicle (UAV) mounted base stations to serve ground users collaboratively in outdoor environments with obstacles. In particular, the geographic information is employed to capture the blockage effects for air-to-ground (A2G) links caused by buildings, and a realistic blockage-aware A2G channel model is proposed to characterize the continuous variation of the channel at different locations. Based on the proposed channel model, we formulate a joint design problem of UAV three-dimensional (3-D) positioning and resource allocation, including the user association and subcarrier allocation, to maximize the minimum achievable rate among users. We propose a suboptimal iterative algorithm to solve the mixed-integer non-convex optimization problem. Specifically, the UAV positioning and resource allocation are alternately optimized in each iteration by employing the successive convex approximation (SCA) and matching theory, respectively. Simulation results reveal that the proposed algorithm outperforms several benchmark schemes in terms of the minimum achievable rate.

In this paper, we investigate the channel estimation for massive multiple-input multiple-output orthogonal frequency division multiplexing (MIMO-OFDM) systems. We revisit the information geometry approach (IGA) for massive MIMO-OFDM channel estimation. By using the constant magnitude property of the entries of the measurement matrix and the asymptotic analysis, we find that the second-order natural parameters (SONPs) of the distributions on all the auxiliary manifolds (AMs) are equivalent to each other at each iteration of IGA, and the first-order natural parameters (FONPs) of the distributions on all the AMs are asymptotically equivalent to each other at the fixed point. Motivated by these results, we simplify the iterative process of IGA and propose a simplified IGA for massive MIMO-OFDM channel estimation. It is proved that at the fixed point, the a posteriori mean obtained by the simplified IGA is asymptotically optimal. The simplified IGA allows efficient implementation with fast Fourier transformation (FFT). Simulations confirm that the simplified IGA can achieve near the optimal performance with low complexity in a limited number of iterations.

We propose a cost efficient cross-subcarrier pre-coder design (CSPD) for massive multiple-input multiple-output orthogonal frequency division multiplexing (MIMO-OFDM) downlink with imperfect channel state information (CSI). To reduce the high computational complexity caused by individual precoder design for each subcarrier, we design transform domain precoding vectors (TDPVs), from which the precoders for a set of subcarriers can be obtained through a transform. The number of TDPVs is much less than that of subcarriers, and the number of total parameters to be designed can be reduced significantly. The main objective is to maximize an upper bound of the ergodic sum-rate by exploiting the a posteriori beam-based statistical channel model. We provide a concave minorizing function of the upper bound of the ergodic sum-rate and then derive the stationary points of a concave quadratic optimization problem with this minorizing function. To reduce the dimension of the matrix inversion in the stationary points, we propose an algorithm by using block coordinate descent (BCD) method with power allocation.

To reduce the delay and network congestion for content delivery in wireless networks, proactive caching scheme has attracted lots of attentions from both academia and industry. However, traditional caching prediction methods require to collect user data in a centralized server, which is becoming unreliable and impractical due to regulatory restrictions. To circumvent this issue, deploying caching prediction system in a federated learning (FL) fashion becomes a promising solution. However, there still exist privacy risks, and even worse, the FL is vulnerable to low-cost attacks. To solve this problem, a novel federated prediction system (FPS) is studied to provide high robustness and privacy. Firstly, to keep a balance between further enhancing privacy protection and alleviating the performance degradation caused by additional protection schemes, we propose an edge-assisted, robust and privacy-preserving FPS framework based on the local differential privacy (LDP) scheme. Secondly, to mitigate the impact of heterogeneous data, we add a regularization term to the local loss function. Furthermore, an attention-based aggregation scheme is proposed to defend against Byzantine attacks during the training process. Finally, the experiment results are provided to show the superiority of our proposed algorithm in terms of prediction accuracy and robustness.

Multi-frequency communication is a potential strategy to overcome the limitations caused by frequency scarcity. This paper investigates the acquisition of statistical channel state information (CSI) in multi-frequency systems. We first analyze the multi-frequency channel model and reveal the relationship between spatial covariance matrices of different frequency bands. Based on the relationship, we propose a linear autoregressive (AR) method, directly establishing the mapping relationship of covariance elements between different frequency bands. In addition, with the acquired spatial covariance, we estimate APS with the maximum entropy (ME) criterion and use it to benefit downlink transmission. Simulation results verify the accuracy of the AR spatial covariance extrapolation method and show that the ME method can estimate APS with high resolution. Meanwhile, the results validate that the estimated statistical CSI can aid the realization of multi-frequency cooperative robust transmission.

Deep learning enhanced Internet of Things (IoT) is advancing the transformation toward smart manufacturing. Intelligent robot guidance is one of the most potential deep learning + IoT applications in the manufacturing industry. However, low costs, efficient computing, and extremely high localization accuracy are mandatory requirements for vision robot guidance, particularly in operational factories. Therefore, in this work, a low-cost edge computing-based IoT system is developed based on an innovative fine-grained attention model (FGAM). FGAM integrates a deep-learning-based attention model to detect the region of interest (ROI) and an optimized conventional computer vision model to perform fine-grained localization concentrating on the ROI. Trained with only 100 images collected from real production line, the proposed FGAM has shown superior performance over multiple benchmark models when validated using operational data. Eventually, the FGAM-based edge computing system has been deployed on a welding robot in a real-world factory for mass production. After the assembly of about 6000 products, the deployed system has achieved averaged overall process and transmission time down to 200 ms and overall localization accuracy up to 99.998%.

Indoor positioning system (IPS) plays an important role in the applications of Internet of Things (IoT), including intelligent hospital, logistics, and warehousing. Ultrawideband (UWB)-based IPS has shown superior performance due to its strong multipath resistance and high temporal resolution. However, the non-line-of-sight (NLOS) situations noticeably degrade both the positioning accuracy and the communication reliability. To address this issue, we first propose a support vector machine (SVM)-based channel detection method to distinguish the line-of-sight (LOS) and NLOS conditions. Then, one base station (BS)-based distance and angle positioning algorithm with extended Kalman filter (DAPA-EKF) in NLOS environment is proposed. For the LOS environment, least squares (LSs) with EKF processing of acceleration (LS-AEKF) and velocity (LS-VEKF) are developed. To further improve the performance, the combination of time difference of arrival (TDOA) and KF in LOS environment is proposed. Simulation results show that the positioning accuracy of the proposed algorithm is improved in various environments. Finally, validated using more than 1000 testing positions, the positioning accuracy of LS-AEKF is 73.8%–74.1% higher than that of LS-VEKF among the two proposed algorithms in terms of three or four BSs metrics.

In this letter, a high-resolution SAR imaging algorithm based on a modified omega-K algorithm is proposed. The proposed algorithm first multiplies the reference signal. Then, the azimuth non-stationary phase error (ANSPE) is considered after performing the interpolation process based on the generalized frequency scale transformation. Finally, a well-focused SAR image can be obtained after compensating the ANSPE and correcting the residual spatial variant range migration by utilizing the range segmentation technique. The simulation results of point targets and the real-measured airborne high-resolution SAR data simultaneously verify the effectiveness and feasibility of the proposed algorithm.

In this letter, the clutter range dependence issue caused by crab angle is investigated in an airborne multi-channel radar system. A new method is proposed to accomplish the crab angle estimation and the non-stationary error compensation. Firstly, from the center-biased and spectrum-broadened characteristics of the clutter space-time spectrum in the case of non-neglected crab angle, a 1-D cost function based on space-time spectrum broadening degree is constructed to achieve the accurate crab angle estimation. Then, the clutter non-stationary errors can be effectively compensated in the post-Doppler domain, significantly improving the subsequent space time adaptive processing (STAP) performance. Simulation results are presented to validate the feasibility and effectiveness of the proposed method.

For air moving target indication in nadir region, due to the fact that a spaceborne radar beam can illuminate the top of fuselage, the target radar cross section is usually high, which is beneficial for the detection of a low-observable target. However, due to the short slant range, specular reflection effect, and relatively low radar ground resolution, the power of clutter component from nadir region is comparatively high, leading to the insufficient clutter suppression and the degradation of target detection performance. Fortunately, when an air moving target is adequately high, the target echo is able to be separated from the main clutter echoes due to a shorter time delay, making it possible to be only mixed with low-power ambiguous clutter echoes. Based on these considerations, this paper analyzes the performance of air moving target indication in nadir region for a spaceborne surveillance radar system. It analyzes the target minimum detectable velocities with different target heights and beam center elevation angles. Also, an effective sample selection method based on adaptive range segmentation is proposed to solve the power heterogeneity issue between the main clutter area and the range ambiguous clutter area. As a conclusion, the larger the elevation angle of an air moving target is, the higher the minimum target detectable height is.

Stream media content caching is a key enabling technology to promote the value chain of future urban vehicular networks. Nevertheless, the high mobility of vehicles, intermittency of information transmissions, high dynamics of user requests, limited caching capacities and extreme complexity of business scenarios pose an enormous challenge to content caching and distribution in vehicular networks. To tackle this problem, this paper aims to design a novel edge-computing-enabled hierarchical cooperative caching framework. Firstly, we profoundly analyze the spatio-temporal correlation between the historical vehicle trajectory of user requests and construct the system model to predict the vehicle trajectory and content popularity, which lays a foundation for mobility-aware content caching and dispatching. Meanwhile, we probe into privacy protection strategies to realize privacy-preserved prediction model. Furthermore, based on trajectory and popular content prediction results, content caching strategy is studied, and adaptive and dynamic resource management schemes are proposed for hierarchical cooperative caching networks. Finally, simulations are provided to verify the superiority of our proposed scheme and algorithms. It shows that the proposed algorithms effectively improve the performance of the considered system in terms of hit ratio and average delay, and narrow the gap to the optimal caching scheme comparing with the traditional schemes.

We deploy an unmanned aerial vehicle (UAV) equipped with a large-scale uniform planar array (UPA) to serve multiple ground users in millimeter-wave band. Particularly, the practical UAV jitter is carefully considered, which may affect the beam gains and impact the communication quality. To provide a stable service, we first model the attitude change of the UPA caused by UAV jitter. Then, an optimization problem is formulated to maximize the minimum achievable rate of the users by optimizing the position and robust hybrid beamforming of the UAV. To solve the non-convex problem with highly coupled variables, a two-stage optimization strategy is developed. The first stage aims to decouple the beamforming from the original problem and design the UAV deployment under the assumption of an ideal beam pattern. The second stage aims to design robust hybrid beamforming with the obtained UAV position. Specifically, we first design analog beamforming for wide beams to cover the potential jitter angle range for each user via a chirp sequence-inspired method. Then, with equivalent channel estimation, we design digital beamforming by combining zero-forcing and water-filling power allocation algorithms. Extensive simulation results show the performance superiority of the proposed solution compared to the benchmark algorithms.

In hyperspectral image (HSI) classification, objects corresponding to pixels of different classes exhibit varying size characteristics, which causes a challenge for effective pixelwise feature extraction and classification. In this article, we propose a novel multiscale model, called multiarea target attention (MATA). The proposed MATA uses an architecture that includes a shared feature extractor (FE) and classifier to capture multiscale spectral–spatial information effectively and efficiently. The FE uses a multiscale target attention module (MSTAM) to extract spectral–spatial information from target pixels and their similar pixels across multiscale areas, while$L_{2}$-normalization is used to address discrepancies between features of different scales. The classifier adopts a classwise decision weighting strategy to account for the varying sizes of different classes and the different contributions of semantic features at each scale to each class. Experimental results on five public HSI datasets demonstrate that the proposed MATA outperforms existing state-of-the-art single- and multiscale models, confirming its effectiveness and efficiency in HSI classification. Code is available athttps://github.com/huanliu233/MATA.

Multiple-input–multiple-output (MIMO) technique has recently received great attention due to its promising prospect for ground moving target indication (GMTI) applications in a synthetic aperture radar (SAR) system. In this article, an SAR-GMTI processing technique in an MIMO-SAR system with dual-channel configuration is proposed. In the proposed method, up and down chirps are adopted as the transmitted waveforms. Then, the baseline estimation and clutter rejection are simultaneously accomplished in the raw Doppler data domain, after which two separated range-compressed echo signals corresponding to two transmitters can be obtained without the influence of cross-correlated interferences of the clutters. Subsequently, a ground moving target can be detected, clustered, finely refocused based on the Doppler chirp parameter estimation, and accurately relocated by using the along-track interferometry (ATI) processing technique. Simulated experiments are implemented to validate and evaluate the feasibility of the proposed method.

Sequence set design with good correlation properties and Doppler tolerance has been a classic and important problem in many multichannel systems, including but not limited to the multiple-input multiple-output (MIMO) and simultaneous polarimetric radar systems. In this article, we first consider the problem of jointly designing Doppler resilient unimodular sequences and receiving filters to minimize the metric weighted integrated sidelobe level (WISL), which can be used to construct sequence sets with “thumbtack” co-channel and zero cross-channel ambiguity functions (AFs). To control the signal-to-noise ratio (SNR) loss caused by the mismatched filter, a peak constraint function is added to the objective function based on the penalty function method. An algorithm based on the alternatively iterative scheme and general majorization-minimization (MM) method is developed to tackle the constrained joint design problem. Moreover, the proposed algorithm is then extended to optimize the${l_{p}}$-norm of sidelobes of AFs, which gives a way to optimize the weighted peak sidelobe level (WPSL) metric. Due to the use of the fast Fourier transform (FFT) algorithm and a general acceleration scheme, the proposed algorithm can be realized efficiently. A number of simulations are provided to demonstrate the excellent performance of the proposed sequence set synthesis algorithms. Besides, an application of using the designed Doppler resilient sequence set on the simultaneous polarimetric radar to detect multiple moving targets in a strong clutter scene is given via simulation, which further verifies the practicability of the proposed algorithm.

In this article, the intrinsic properties of hyperspectral imagery (HSI) are analyzed, and two principles for spectral-spatial feature extraction of HSI are built, including the foundation of pixel-level HSI classification and the definition of spatial information. Based on the two principles, scaled dot-product central attention (SDPCA) tailored for HSI is designed to extract spectral-spatial information from a central pixel (i.e., a query pixel to be classified) and pixels that are similar to the central pixel on an HSI patch. Then, employed with the HSI-tailored SDPCA module, a central attention network (CAN) is proposed by combining HSI-tailored dense connections of the features of the hidden layers and the spectral information of the query pixel. MiniCAN as a simplified version of CAN is also investigated. Superior classification performance of CAN and miniCAN on three datasets of different scenarios demonstrates their effectiveness and benefits compared with state-of-the-art methods.

Computation offloading via device-to-device communications can improve the performance of mobile edge computing by exploiting the computing resources of user devices. However, most proposed optimization-based computation offloading schemes lack self-adaptive abilities in dynamic environments due to time-varying wireless environment, continuous-discrete mixed actions, and coordination among devices. The conventional reinforcement learning based approaches are not effective for solving an optimal sequential decision problem with continuous-discrete mixed actions. In this paper, we propose a hierarchical deep reinforcement learning (HDRL) framework to solve the joint computation offloading and resource allocation problem. The proposed HDRL framework has a hierarchical actor-critic architecture with a meta critic, multiple basic critics and actors. Specifically, a combination of deep Q-network (DQN) and deep deterministic policy gradient (DDPG) is exploited to cope with the continuous-discrete mixed action spaces. Furthermore, to handle the coordination among devices, the meta critic acts as a DQN to output the joint discrete action of all devices and each basic critic acts as the critic part of DDPG to evaluate the output of the corresponding actor. Simulation results show that the proposed HDRL algorithm can significantly reduce the task computation latency compared with baseline offloading schemes.

Due to high bandwidth and small antenna size, millimeter-wave (mmWave) integrated line-of-sight (LOS) multiple-input-multiple-output (MIMO) systems have attracted much attention. Reconfigurable intelligent surfaces (RISs), which have the potential to change the characteristics of incident electromagnetic waves with low power cost, can improve the performance for the MIMO mmWave wireless communications. Uniform circular array (UCA) is an effective antenna structure with low complexity transceiver. In this paper, UCA based RIS-assisted MIMO mmWave wireless communications with transmit UCA, the RIS UCAs, and receive UCA are investigated. Since the rotation angles between the transceiver make the channel matrix noncirculant, an algorithm is developed to derive the ranges of the rotation angles based on an acceptable error and reduce the impact of rotation angles on channel matrix. Then, we propose a low-complexity precoding scheme at the transmitter, phase designs at the RIS UCAs, and a phase compensation scheme at the receiver, which can convert the channel matrix into an equivalent circulant channel matrix with a small error. Then, a fast symbol-wise maximum likelihood (ML) detection scheme is proposed to recover the signals with low computational complexity. Simulation results are presented to illustrate the theory.

In this paper, we investigate the massive multiple-input multiple-output orthogonal frequency division multiplexing channel estimation for low-earth-orbit satellite communication systems. First, we use the angle-delay domain channel to characterize the space-frequency domain channel. Then, we show that the asymptotic minimum mean square error (MMSE) of the channel estimation can be minimized if the array response vectors of the user terminals (UTs) that use the same pilot are orthogonal. Inspired by this, we design an efficient graph-based pilot allocation strategy to enhance the channel estimation performance. In addition, we devise a novel two-stage channel estimation (TSCE) approach, in which the received signals at the satellite are manipulated with per-subcarrier space domain processing followed by per-user frequency domain processing. Moreover, the space domain processing of each UT is shown to be identical for all the subcarriers, and an asymptotically optimal vector for the per-subcarrier space domain linear processing is derived. The frequency domain processing can be efficiently implemented by means of the fast Toeplitz system solver. Simulation results show that the proposed TSCE approach can achieve a near performance to the MMSE estimation with much lower complexity.

Due to the advantage of achieving scalable connectivity and low-latency communications, air-ground integrated mesh networks (AGIMNs) will play an important role in the next generation wireless communication systems. However, due to the heterogeneous character of AGIMNs, it is challenging to manage the network and optimize the communication resources for improving the end-to-end (E2E) performance. Therefore, in this paper, we study a joint routing and time-frequency resource scheduling problem aiming at minimizing the total weighted E2E delay for heterogeneous AGIMNs. To capture the features of this complex system, we mathematically model the network constraints and formulate an optimization problem. To solve the original nonconvex problem, a suboptimal solution is proposed. First, we propose an optimal minimum-weight routing method, in which the hop count, conflict delay, and contention delay are taken into consideration. Then, we transform the time-frequency resource scheduling subproblem into a series of tractable problems for maximizing the number of active links through channel assignment in successive time slots. Finally, the successive convex approximation (SCA) technique is utilized to solve the channel assignment problem per time slot. Extensive simulation results show the performance superiority of the proposed solution compared to the benchmarks in terms of the total E2E delay.

In this paper, the analysis of excess distortion exponent for joint source-channel coding (JSCC) in semantic-aware communication systems is presented. By introducing an unobservable semantic source, we extend the classical results by Csiszar to semantic-aware communication systems. Both upper and lower bounds of the exponent for the discrete memoryless source-channel pair are established. Moreover, an extended achievable bound of the excess distortion exponent for MIMO systems is derived. Further analysis explores how the block fading and numbers of antennas influence the exponent of semantic-aware MIMO systems. Our results offer some theoretical bounds of error decay performance and can be used to guide future semantic communications with joint source-channel coding scheme.

Image compression and bit transmission are con-ducted separately in most existing methods for image trans-mission, leading to possible transmission failure or a waste of communication resource for a time-varying channel condition. This paper proposes a neural network-based image transmission system trained by generative adversarial networks (GANs) aiming to achieve robust transmission. Specifically, the deep semantic of an input image is extracted and represented as bit streams at the transmitter, and the receiver reconstructs the original image based on possible bit error and the same background knowledge as the transmitter. Experimental results show that the proposed robust transmission system trained by GAN can adapt to the current communication condition, and achieve a high-quality reconstruction even with a high transmission error rate and a smaller transmission data size than engineered codecs such as JPEG.

Deep learning-enhanced Internet of Things (IoT) plays a pivot role in advancing the transformation toward smart manufacturing, and an essential component in many smart manufacturing IoT systems is the quality inspection. However, challenges, such as expensive data labeling, innumerable types of defects, and high costs for iterative optimization, hinder the industrial applicability of previous visual surface quality inspection methods. In this article, we present an edge-computing-enabled IoT system based on an innovative hybrid learning method for visual surface quality inspection using only few labeled data and minimum iterative optimization efforts. Our hybrid learning method first employs a deep neural network to synthesize global representations of real-world industrial images, which are subsequently analyzed via an unsupervised clustering algorithm for anomaly detection. Besides, enhancement strategies, such as fine-tuning and data augmentation, are proposed to improve the robustness against the noisy data set and support low-cost inference in multiple edge devices for manufacturing operation. On a holdout data set collected from real-world factories, our method achieves classification accuracies between 90% and 98%, outperforming the benchmark method by 7%–12%. Moreover, this hybrid learning method demonstrates the effectiveness in detecting new types of surface defects and achieves test recalls between 86% and 97%, outperforming the benchmark method by 11%–34%.

In this paper, we investigate a joint device activity detection (DAD), channel estimation (CE), and data decoding (DD) algorithm for multiple-input multiple-output (MIMO) massive unsourced random access (URA). Different from the state-of-the-art slotted transmission scheme, the data in the proposed framework is split into only two parts. A portion of the data is coded by compressed sensing (CS) and the rest is low-density-parity-check (LDPC) coded. In addition to being part of the data, information bits in the CS phase also undertake the task of interleaving pattern design and CE. The principle of interleave-division multiple access (IDMA) is exploited to reduce the interference among devices in the LDPC phase. Based on the belief propagation (BP) algorithm, a low-complexity iterative message passing (MP) algorithm is utilized to decode the data embedded in these two phases separately. Moreover, combined with successive interference cancellation (SIC), the proposed joint DAD-CE-DD algorithm is performed to further improve performance by utilizing the belief of each other. Additionally, based on the energy detection (ED) and sliding window protocol (SWP), we develop a collision resolution protocol to handle the codeword collision, a common issue in the URA system. In addition to the complexity reduction, the proposed algorithm exhibits a substantial performance enhancement compared to the state-of-the-art in terms of efficiency and accuracy.

Sea clutter magnitude distribution exhibits important guiding significance for the design of marine target detection algorithms and the selection of the constant false alarm rate (CFAR) detection threshold. In this letter, to deal with the limitation of the traditional generalized-K (GK) distribution in highly heterogeneous clutter scene, a modified GK (MGK) distribution is proposed for sea clutter magnitude. By combining the traditional GK and generalized Pareto distributions, the value range of power parameter and the applicable range of the distribution model are extended. The applicability of the proposed distribution model is verified by real-measured sea clutter data.

Squint ultrahigh-resolution (UHR) synthetic aperture radar (SAR) generally uses the extended Omega-K algorithm (EOK) for range cell migration correction (RCMC). However, the extra range migration and defocusing (ERMD) will be introduced by the EOK when there are motion errors. This problem will be severe as the squint angle increases, which may sharply reduce the signal-to-noise ratio (SNR) and lead to the failure of the existing autofocus methods. In this letter, a UHR autofocus algorithm based on cascaded map-drift (MD)-PGA is proposed. The MD is used for the rough estimation to improve the SNR and the phase gradient algorithm (PGA) is used to accurately estimate the residual error based on a relatively high SNR. The new algorithm combines the advantages of MD and PGA, which can solve the problem of severe defocusing. The processing of airborne real data validates the effectiveness of the proposed algorithm.

In this letter, we propose a novel range and Doppler sampling mismatch compensation method for moving target detection, which can effectively improve the output signal-to-noise ratio (SNR) of a moving target. In the proposed method, after performing the target coherent integration by using the well-known Keystone transform (KT), the range and Doppler sampling mismatch errors (SMEs) are estimated and compensated based on the constructed optimization model with the consideration of the change rate of a moving target peak amplitude. In order to improve the computational efficiency, the Hooke–Jeeves method is applied to achieve the optimal solution of the constructed optimization problem, thus efficiently solving the target energy diffusion problem caused by the SMEs. Simulated experiment is presented to verify the effectiveness and feasibility of the proposed method.

In this paper, we propose a novel long-time coherent integration detection method to detect an uncooperative moving target in a frequency and pulse repetition interval randomly jittering radar system based on signum coding (SC). In the proposed algorithm, an additional reference waveform is applied to eliminate the third-order harmonic influence induced by SC. Then, a generalized Keystone transform (GKT) is proposed to resolve the complex coupling among the range frequency, jittered carrier frequency, and nonuniformly sampled time. Simulation results are presented to validate the effectiveness and feasibility of the proposed method.

Due to the limited size, carrying capacity, power-aperture product, and high hardware cost of satellite platform, the traditional single-platform spaceborne radar system encounters the problems of poor target minimum detectable velocity (MDV) performance, considerably deteriorating the moving target detection performance. To improve the air moving target indication (AMTI) performance, especially for a weak target, distributed space-based radar system (DSBR) becomes a good candidate due to the longer along-track baseline (ATB) and spatial power synthesis. However, due to the sparse configuration of radar baseline distribution, the detection performance of air moving targets (AMTs) will be restricted by many practical factors in an actual DSBR system. In this paper, multi-channel signal models of an observed moving target and ground clutter are accurately established in a DSBR framework, where the error influences of cross-track baseline (CTB), terrain fluctuation, and channel inconsistency response are considered. Then, the influence of the non-ideal factors, including the channel noise, long-intersatellite ATB, long-intersatellite CTB, synchronization errors, and interchannel amplitude and phase inconsistency errors, on the AMTI performance is analyzed term by term. The simulation results provide the useful guidance for the system design of a DSBR with the AMTI tasks.

Semantic segmentation of the remote sensing images (RSIs) has attracted increasing interest in recent years. However, large-area segmentation of the woodland presents challenges. The wide distribution and diverse tree species of the woodland make feature extraction difficult. For this reason, an infrared attention network (InfAttNet) is proposed to extract woodland from multispectral RSIs. InfAttNet has an extra infrared spectral encoder which makes use of the sensitivity of vegetation to near infrared and red edge spectrums. This extra encoder applies learning about vegetation to improve woodland segmentation. Several attention blocks are designed to enhance learning about vegetation features and so improve the performance. In addition, a new dataset is built, containing a large number of woodland RSIs and covering several typical woodland distribution regions in China. The experimental results demonstrate that, compared with other networks, InfAttNet has the highest accuracy and is capable of rapid extraction of the woodland in RSIs.

As a wideband radar system, a synthetic aperture radar (SAR) may conflict with several electromagnetic systems, such as frequency modulation (FM), TV, and other communication systems. These signals, termed as radio frequency interference (RFI), may severely interfere SAR systems from generating a high-resolution image. Numerous previous researches focused on the RFI suppression problem, among which the semiparametric methods have been verified to have the state-of-the-art performance. However, most of the semiparametric methods are computationally expensive and can hardly be used on wide-swath SAR imaging processing. In this article, an efficient semiparametric algorithm is proposed to suppress RFIs via alternating projections. It has comparable performance as the other methods but significantly improves the computational efficiency a lot. It is able to remove both narrowband and wideband RFIs and can be used directly on the Level-1 SAR data. Finally, multiple real SAR data are provided to demonstrate the effectiveness and efficiency of the proposed algorithm.

For a spaceborne high-resolution and wide-swath synthetic aperture radar (HRWS-SAR) system, it usually uses the digital beamforming technology. However, in practice, because of the influences of temperature, antenna pattern, receiving antenna, and other error factors, there may exist the range synchronization time errors, amplitude errors, and phase errors among different spatial channels. These nonideal factors will significantly degrade the multichannel data reconstruction performance, resulting in a smeared SAR image. To address this issue, in this article we propose a novel channel error correction algorithm based on the orthogonal projection theory. First, the optimal weight of each Doppler ambiguity component is calculated by the orthogonal projection. Then, the cost function is constructed based on the power maximization criterion, from which the channel phase errors can be obtained. Finally, the HRWS-SAR imaging can be finely realized after performing the channel balancing. Compared with the conventional phase error estimation method, the proposed algorithm does not require to perform the matrix eigenvalue decomposition, avoiding the signal leakage phenomenon under low SNR case. The effectiveness of the proposed algorithm is validated by both airborne and space-borne real SAR data.

Object detection (OD) is an important task of computer vision and has been widely used in many fields, including remote sensing (RS). However, the complex scenes, large-scale variation, and dense instances of RS bring huge challenges to OD. To meet these challenges, a novel Nonlocal-aware Pyramid and Multiscale Multitask Refinement Detector (NPMMR-Det) is proposed. Specifically, nonlocal-aware pyramid attention (NP-Attention) is designed for guiding a neural network model to focus more on efficient features and suppress background noise. Then a multiscale refinement feature pyramid network (MSR-FPN) is proposed to fuse the multiscale context features extracted by the NP-Attention guided neural network and adjust the optimal receptive field. In order to use these features more effectively, a multitask refinement head called MTR-Head, with offset sharing and a modulation mechanism, is developed to refine the feature misalignment between the localization task and the classification task. Extensive experiments performed on two public RS data sets demonstrate that the proposed NPMMR-Det achieves competitive performance compared with state-of-the-art methods.

A few lightweight convolutional neural network (CNN) models have been recently designed for remote sensing object detection (RSOD). However, most of them simply replace vanilla convolutions with stacked separable convolutions (SConvs), which may not be efficient due to a lot of precision losses and may not be able to detect oriented bounding boxes (OBBs). Also, the existing OBB detection methods are difficult to constrain the shape of objects predicted by CNNs accurately. In this article, we propose an effective lightweight oriented object detector (LO-Det). Specifically, a channel separation-aggregation (CSA) structure is designed to simplify the complexity of SConvs, and a dynamic receptive field (DRF) mechanism is developed to maintain high accuracy by customizing the convolution kernel and its perception range dynamically when reducing the network complexity. The CSA-DRF component optimizes efficiency while maintaining high accuracy. Then, a diagonal support constraint head (DSC-Head) component is designed to detect OBBs and constrain their shapes more accurately and stably. Extensive experiments on public data sets demonstrate that the proposed LO-Det can run very fast even on embedded devices with the competitive accuracy of detecting oriented objects.

In a multichannel synthetic aperture radar (SAR) system, because of the target uncooperative motion, a ground moving target (GMT) is usually smeared, distorted, and shifted in an SAR image. In this article, a novel approach for multichannel SAR-GMT indication (GMTI) processing is proposed. The main innovations of this method are that a GMT can be well refocused and relocated since the target high-order Doppler parameters can be precisely estimated based on a high-order polynomial phase signal (PPS) model, and the target statistical amplitude and phase information is jointly applied to improve the radial velocity estimation robustness. Compared with the current SAR-GMTI algorithms, the improvements of this method over the existing methods are: 1) the topography interferometric phase can be effectively compensated by applying an adaptive 2-D spectrum filtering technique via iterative processing; 2) a GMT can be well imaged since the target Doppler chirp rate and the quadratic chirp rate can be well estimated via the 2-D coherent integration in the time–frequency plane; and 3) a GMT can be precisely relocated into its original position by applying the generalized amplitude and phase weighting technique. Real-measured SAR data processing results are presented to validate the effectiveness and feasibility of the proposed method.

In inverse synthetic aperture radar (ISAR) imaging for a uniformly moving rigid-body target, a finely focused ISAR image can be obtained by using the conventional range-Doppler algorithm. However, the ISAR image quality may significantly deteriorate when the time-vary Doppler phases in virtue of target maneuvering motions are present, such as an airplane with nonuniformly rotation and a ship with fluctuation. This has become a challenging task, especially under nonhigh signal-to-noise ratio (SNR) environment. In this article, a novel ISAR imaging algorithm for a maneuvering target with moderate reflection intensity is proposed. After motion compensation, the radar echo signal in a range cell is modeled as a multicomponent cubic phase signal (CPS), in which the chirp rate and the quadratic chirp rate are two important physical quantities that may determine the target ISAR focusing quality. Based on a symmetrical instantaneous autocorrelation function, the received CPSs are transformed into the time and lag-time plane, and then a 2-D coherent integration can be realized after the generalized time-scaled transform and 1-D maximization. This forms a high-quality ISAR image. The effectiveness and superiority of the proposed algorithm are validated by the ISAR imaging results of simulated and real measured data.

Due to the high-speed movement of a spaceborne radar (SBR) platform, the geographic clutter spectrum expands severely, resulting in the useful moving target signal submerged by the main-lobe clutter background. To deal with this issue, the equipped multichannel arrays in an SBR system provide sufficient spatial degrees, and as a consequence, the space-time adaptive processing (STAP) technology is often preferred to achieve the moving target detection, even in the main-lobe clutter regions. However, for the moving target detection under the sea scene, due to the complex internal motion of sea clutter, the clutter signal received by an SBR system may possess the space- and time-varying characteristics, worsening the multichannel clutter rejection performance using the traditional STAP techniques. In this article, a novel sea clutter suppression method based on the joint space-time-frequency adaptive filtering is proposed. In the proposed algorithm, according to the coherent time analysis of sea clutter, the subaperture time-domain sliding window is employed to alleviate the clutter decorrelation effect, and then, a modified subspace projection technique is applied to accomplish the first-stage clutter rejection. After realizing the effective signal recovery with respect to these residual subaperture clutter data, the second-stage spatial filtering method is applied to realize the final clutter suppression with respect to the relatively high Doppler resolution clutter returns. The effectiveness of the proposed algorithm is verified by both simulated multichannel sea clutter data and real-measured sea clutter data.

When an early warning radar installed in a spaceborne platform works in a down-looking mode to detect a low-altitude flying target, the severely broadened main-lobe clutter cannot be ignored, which will cause the deterioration of the moving target detection capability. To deal with this problem, a space–time adaptive processing (STAP) technique is proposed for effective clutter suppression based on the spatial–temporal 2-D joint filtering. However, the full-dimensional optimal STAP encounters the challenges of high computational complexity and large training sample requirement. Therefore, the dimension-reduced STAP technique becomes necessary. This article proposes a novel dimension-reduced STAP algorithm based on spatial–temporal 2-D sliding window processing. First, several sets of spatial–temporal data are obtained by using spatial–temporal 2-D sliding window. Then, for each set of data, the 2-D discrete Fourier transform is performed to transform the echo data into the angle-Doppler domain. Finally, jointly adaptive processing is performed to realize the clutter suppression. Compared with the conventional STAP algorithms, the improvements of this method over the existing methods are: 1) the proposed method requires fewer training samples due to the 2-D localization processing and 2) the proposed method can obtain the better clutter suppression performance with lower computational complexity. The feasibility and effectiveness of the proposed algorithm are verified by both simulated and real-measured multichannel surveillance radar data.

For the problem of target localization under the multipath propagation environment, the existing methods are mainly restricted to the limited prior information of complex reflections, especially when the target is embedded in a mixed interference environment. They may suffer from performance degradation due to the shortage of target classification ability. To address this problem, we propose a target localization method based on iterative implementation with semiunitary constraint and eigen-decomposition technique, where a practical propagation scenario based on the spherical Earth model is considered. Compared to the previous works, the proposed method can automatically distinguish a real target from the mixed interference environment with improved localization accuracy. Neither additional decorrelation preprocessing nor prior information of the dynamic scenario is required. Both simulations and real data experiments validate the effectiveness and robustness of the proposed method.

Recently, many arbitrary-oriented object detection (AOOD) methods have been proposed and attracted widespread attention in many fields. However, most of them are based on anchor-boxes or standard Gaussian heatmaps. Such label assignment strategy may not only fail to reflect the shape and direction characteristics of arbitrary-oriented objects, but also have high parameter-tuning efforts. In this paper, a novel AOOD method called General Gaussian Heatmap Label Assignment (GGHL) is proposed. Specifically, an anchor-free object-adaptation label assignment (OLA) strategy is presented to define the positive candidates based on two-dimensional (2D) oriented Gaussian heatmaps, which reflect the shape and direction features of arbitrary-oriented objects. Based on OLA, an oriented-bounding-box (OBB) representation component (ORC) is developed to indicate OBBs and adjust the Gaussian center prior weights to fit the characteristics of different objects adaptively through neural network learning. Moreover, a joint-optimization loss (JOL) with area normalization and dynamic confidence weighting is designed to refine the misalign optimal results of different subtasks. Extensive experiments on public datasets demonstrate that the proposed GGHL improves the AOOD performance with low parameter-tuning and time costs. Furthermore, it is generally applicable to most AOOD methods to improve their performance including lightweight models on embedded platforms.

This short note connects OTFS and single transmit antenna VOFDM systems and explains why VOFDM is good to time-varying channels with Doppler spread, which also explains why OTFS is so as well.

In this paper, we study to employ geographic information to address the blockage problem of air-to-ground links between UAV and terrestrial nodes. In particular, a UAV relay is deployed to establish communication links from a ground base station to multiple ground users. To improve communication capacity, we first model the blockage effect caused by buildings according to the three-dimensional (3-D) geographic information. Then, an optimization problem is formulated to maximize the minimum capacity among users by jointly optimizing the 3-D position and power allocation of the UAV relay, under the constraints of link capacity, maximum transmit power, and blockage. To solve this complex non-convex problem, a two-loop optimization framework is developed based on Lagrangian relaxation. The outer-loop aims to obtain proper Lagrangian multipliers to ensure the solution of the Lagrangian problem converge to the tightest upper bound on the original problem. The inner-loop solves the Lagrangian problem by applying the block coordinate descent (BCD) and successive convex approximation (SCA) techniques, where UAV 3-D positioning and power allocation are alternately optimized in each iteration. Simulation results confirm that the proposed solution significantly outperforms three benchmark schemes and achieves a performance close to the upper bound on the UAV relay system.

In this paper, we propose to employ multiple unmanned aerial vehicle (UAV) base stations to serve ground users in the millimeter-wave (mmWave) frequency bands. To improve the spectrum efficiency, uniform planar arrays are equipped at the UAVs and users for compensation of the high path loss and for mitigation of interference. We formulate a problem to jointly optimize the UAV positioning, user clustering, and hybrid analog-digital beamforming (BF) for the maximization of user achievable sum rate (ASR), subject to a minimum rate constraint for each user. Since the problem is highly non-convex and involves high-dimensional variable matrices and combinatorial programming variables, we develop a suboptimal solution via alternating optimization, successive convex optimization, and combinatorial optimization. First, we design the UAV positioning and user clustering under the assumption of ideal beam patterns, which significantly decouples the UAV positioning and directional BF. Then, the transmit and receive BF variables are successively optimized to approach the ideal beam patterns. Our simulation results verify the convergence and superiority of the proposed algorithm. Significant performance gains can be obtained compared to some benchmark schemes in terms of the ASR, and the proposed hybrid BF solution closely approaches a performance bound given by fully-digital BF.

As the commercial launch of the fifth-generation (5G) wireless communications gets near, the trend from the Internet of Things (IoT) to the Internet of Everything (IoE) is emerging. Due to the advantages of the high mobility, high Line-of-Sight (LoS) probability and low labor cost, unmanned aerial vehicles (UAVs) may play an important role in the future IoT communication networks, e.g., data collection in remote areas. In this article, we study the 3-D placement and resource allocation of multiple UAV-mounted base stations (BSs) in an uplink IoT network, where the balanced task for the UAV-BSs, the limited channel resource, and the signal interference are taken into consideration. In the considered system, the total transmission power of IoT devices is minimized, subject to a signal-to-interference-and-noise ratio (SINR) threshold for each device. First, aiming to balance the task of each UAV, we propose a clustering algorithm based on an improved$K$-means method to divide IoT devices into several groups so that the number of devices in each group is roughly the same. Then, based on matching theory, a modified-Hungarian-based dynamic many–many matching (HD4M) algorithm is designed for assigning subchannels to IoT devices, which can efficiently mitigate the interference. Finally, we jointly optimize the transmission power of IoT devices and the altitudes of UAVs via an alternating iterative method. The simulation results show that the total transmission power decreases significantly after applying the proposed algorithms.

Mobile-edge computing (MEC) is considered as a promising technology to reduce the energy consumption (EC) and task accomplishment latency of smart mobile user equipments (UEs) by offloading computation-intensive tasks to the nearby MEC servers. However, the Quality of Experience (QoE) for computation highly depends on the wireless channel conditions when computation tasks are offloaded to MEC servers. In this article, by considering the imperfect channel-state information (CSI), we study the joint offloading decision, transmit power, and computation resources to minimize the weighted sum of EC of all UEs while guaranteeing the probabilistic constraint in multiuser MEC-enabled Internet-of-Things (IoT) networks. This formulated optimization problem is a stochastic mixed-integer nonconvex problem and challenging to solve. To deal with it, we develop a low-complexity two-stage algorithm. In the first stage, we solve the relaxed version of the original problem to obtain offloading priorities of all UEs. In the second stage, we solve an iterative optimization problem to obtain a suboptimal offloading decision. As both stages include solving a series of nonconvex stochastic problems, we present a constrained stochastic successive convex approximation-based algorithm to obtain a near-optimal solution with low complexity. The numerical results demonstrate that the proposed algorithm provides comparable performance to existing approaches.

Hybrid precoding architecture, as a cost-effective approach for millimeter-wave (mmWave) communications, can achieve an excellent tradeoff between spectrum efficiency and hardware implementation complexity. However, the design of a robust hybrid precoding for improving the physical layer security (PLS), which is insensitive to the uncertainty of eavesdropper's channel state information (CSI), has not been well studied. This article for the first time designs a probabilistically robust hybrid precoding scheme for securing broadcast communications in Internet of Things (IoT) with eavesdropper's imperfect CSI. Specifically, considering the Gaussian CSI error model, we maximize the minimum secrecy rate of multiple IoT devices (IoDs) by jointly designing analog and digital precoders under the constraints in terms of secrecy outage probability and per IoD's information rate. The optimization problem is challenging due to the coupling of the analog and digital precoders, and the secrecy outage constraint. To handle these challenges, we first employ a conservative probability inequality to transform the secrecy outage probability constraint into a deterministic one. Then, by employing the penalty dual decomposition (PDD) method, we develop a novel iterative algorithm to convert the resultant nonconvex problem into a sequence of convex problems, which can guarantee the convergence to its Karush-Kuhn-Tucker (KKT) solution. Simulation results show that the proposed algorithm can achieve significant secrecy performance gains compared with the benchmark algorithm.

A ground moving target usually shifts its initial azimuth location in the synthetic aperture radar (SAR) image due to the radial motion. To address this problem, a new azimuth relocation method is proposed for a multichannel SAR in this letter. Based on the signal analysis in the range-compressed domain, it is found that the zeroth-order term of the interferometric phase without coregistration is determined by the moving target's initial azimuth location. A new azimuth relocation method utilizing the noncoregistrated interferometric phase is then proposed. First, the moving target is detected after clutter suppression in the range-compressed domain. Then, the interferometric phase along the slow time without coregistration can be extracted for parameter estimation via the least-squares method. Compared with the existing radial velocity-based relocation methods, the proposed method has higher accuracy, and there is no azimuth location ambiguity problem even when the target has a large radial velocity. Finally, both the simulated and real data are provided to demonstrate the effectiveness of the proposed method.

In this paper, we investigate the massive multi-input multi-output (MIMO) transmission for the satellite communication systems equipped with a uniform rectangular array (URA) and aim to design the precoder with broad coverage radiation power pattern to improve the performance of time and frequency synchronizations. The modified Cramér-Rao vector bounds (MCRVB) are chosen as the benchmark of symbol timing offset and frequency offset estimation problem, which can be viewed as the time and frequency synchronization performance metric for the broad coverage precoder design. By considering the minimax MCRVB criterion and the per-antenna equal power constraint, the precoder design approach is formulated as the non-convex constrained minimax problem over the discrete radiation power pattern. This optimization problem is solved by the smoothing techniques combined with the manifold optimization method. To reduce the calculation complexity, the nonmonotone conjugate gradient method is applied. Simulation results show that the average and the minimum received powers in the coverage area of the proposed scheme are higher than those of the scheme based on fairness criterion. Compared with the existing omnidirectional and broad coverage schemes, the proposed scheme has the best synchronization performance among all the contrast solutions.

Recently, unmanned aerial vehicles (UAVs) have attracted lots of attention because of their high mobility and low cost. This article investigates a communication system assisted by multiple UAV-mounted base stations (BSs), aiming to minimize the number of required UAVs and to improve the coverage rate by optimizing the three-dimensional (3D) positions of UAVs, user clustering, and frequency band allocation. Compared with the existing works, the constraints of the required quality of service (QoS) and the service ability of each UAV are considered, which makes the problem more challenging. A three-step method is developed to solve the formulated mixed-integer programming problem. First, to ensure that each UAV can serve more number of users, the maximum service radius of UAVs is derived according to the required minimum power of the received signals for the users. Second, an algorithm based on artificial bee colony (ABC) algorithm is proposed to minimize the number of required UAVs. Third, the 3D position and the frequency band of each UAV are designed to increase the power of the target signals and to reduce the interference. Finally, simulation results are presented to demonstrate the superiority of the proposed solution for UAV-assisted communication systems.

A nonlinear trajectory of a radar platform in synthetic aperture radar (SAR) may lead to severe coupling between the range and the azimuth, which may make the ambiguity function (AF) analysis complicated. The numerical algorithm-based AF analysis may be computationally expensive, while the existing analytical algorithm-based AF analysis may cause large errors because it does not consider the coupling between the range and the azimuth. By observing that the singular value decomposition (SVD) is good to deal with the coupling problem, in this article, we propose an effective AF analysis based on SVD. The key idea is to first use a small amount of sampling points for SVD of the coupled term in the AF and then the decoupled vectors are fitted to high-order polynomials for the analytical AF calculation. It converts the double integral into the product of two single integrals in the calculation. From the proposed SVD-based AF analysis, three parameters, namely, 3-dB resolution, peak sidelobe ratio (PSLR), and integrated sidelobe ratio (ISLR), are then effectively computed. The simulated results verify the good performance of the proposed SVD-based AF analysis.

In this article, we propose a multichannel sea clutter model in a spaceborne early warning radar system and analyze the influence of the sea clutter motion characteristics on the space-time adaptive processing (STAP) performance. To establish a multichannel sea clutter model, the 3-D Gerstner wave model is applied to construct the sea surface. Then the Pierson–Moskowitz wave spectrum and the stereo wave observation project (SWOP) directional spectrum are combined to describe the amplitude distribution of waves in different frequencies and directions. At the same time, the two-scale model is applied to obtain the specific backscattering coefficients of sea clutter at different time and positions. In addition, breaking waves are added in sea clutter returns with the form of false targets. Finally, the space-time distribution characteristics of sea clutter in a spaceborne multichannel array system and the influences of sea clutter under different wind speeds and directions on STAP performance are analyzed based on the simulation processing results. Processing results of some real-measured radar data are also exhibited to verify the theoretical analyses.

Ship motions during long coherent processing interval (CPI) have six degrees of freedom, and the oscillatory motions are roughly periodical. The traditional ship imaging methods usually use a short time interval to form an image, while the image quality may suffer from low resolution, poor signal-to-noise ratio (SNR), and scatter scintillation. Using a longer CPI to generate an image may improve the quality but, however, largely increase the focusing difficulty. In this article, we investigate the focusing challenges of oscillatory ships with long CPI. Through analyzing the relative motion between the radar and the ship, the properties of wavenumber domain support (WDS) and point spreading function (PSF) of oscillatory ship imaging are studied. It is illustrated that the WDS is a 3-D sparse curved surface generated by the complex relative motion, with a time-variant energy density, nonparallel spectrum boundaries, and a complex structure. The PSF of an oscillatory ship may have a 3-D resolution but also multiple high-level sidelobes. The relationship between the WDS and the nonideal PSF is illustrated with the projection slice theorem (PST). Moreover, it is discussed that the scatterers distributed on a 3-D ship cannot be focused uniformly on a 2-D imaging plane (IP) due to the variation of the slant-range plane (SRP). The projection relationships of the resolutions and focusing positions between the SRP and the IP are also derived. Simulation results are presented to validate the analyses throughout this article.

In this paper, we investigate broad coverage pre-coder design for 3D massive multi-input multi-output (MIMO) systems. We focus on the synchronization performance in the cell coverage. The log-distance path loss model in the line-of-sight (LoS) scenario is formulated. The synchronization performance can be characterized by the missed detection (MD) probability. By considering the equal MD probability in the cell, we formulate a criterion for the precoder design. Moreover, we consider the equal transmit power constraint on each antenna to efficiently utilize the power amplifier (PA) capacity of the BS. By using the manifold optimization framework, we design the precoder under the aforementioned criterion and constraint. Simulation results show that the fairness among all the users in this cell can be ensured. Compared with the precoders designed by half power beam-width (HPBW), the proposed scheme causes much less inter-cell interference, and has a better synchronization performance.

Low earth orbit (LEO) satellite communications are expected to be incorporated in future wireless networks to provide global wireless access with enhanced data rates. Massive multiple-input multiple-output (MIMO) techniques, though widely used in terrestrial communication systems, have not been applied to LEO satellite communication systems. In this paper, we propose a massive MIMO downlink (DL) transmission scheme with full frequency reuse (FFR) for LEO satellite communication systems by exploiting statistical channel state information (sCSI) at the transmitter. We first establish a massive MIMO channel model for LEO satellite communications and propose Doppler and time delay compensation techniques at user terminals (UTs). Then, we develop a closed-form low-complexity sCSI based DL precoder by maximizing the average signal-to-leakage-plus-noise ratio (ASLNR). Motivated by the DL ASLNR upper bound, we further propose a space angle based user grouping algorithm to schedule the served UTs into different groups, where each group of UTs use the same time and frequency resource. Numerical results demonstrate that the proposed massive MIMO transmission scheme with FFR significantly enhances the data rate of LEO satellite communication systems.

The emerging Internet of Things (IoT) applications, such as smart manufacturing and smart home, lead to a huge demand on the provisioning of low-cost and high-accuracy positioning and navigation solutions. Inertial measurement unit (IMU) can provide an accurate inertial navigation solution in a short time but its positioning error increases fast with time due to the cumulative error of accelerometer measurement. On the other hand, ultrawideband (UWB) positioning and navigation accuracy will be affected by the actual environment and may lead to uncertain jumps even under line-of-sight (LOS) conditions. Therefore, it is hard to use a standalone positioning and navigation system to achieve high accuracy in indoor environments. In this article, we propose an integrated indoor positioning system (IPS) combining IMU and UWB through the extended Kalman filter (EKF) and unscented Kalman filter (UKF) to improve the robustness and accuracy. We also discuss the relationship between the geometric distribution of the base stations (BSs) and the dilution of precision (DOP) to reasonably deploy the BSs. The simulation results show that the prior information provided by IMU can significantly suppress the observation error of UWB. It is also shown that the integrated positioning and navigation accuracy of IPS significantly improves that of the least squares (LSs) algorithm, which only depends on UWB measurements. Moreover, the proposed algorithm has high computational efficiency and can realize real-time computation on general embedded devices. In addition, two random motion approximation model algorithms are proposed and evaluated in the real environment. The experimental results show that the two algorithms can achieve certain robustness and continuous tracking ability in the actual IPS.

Unmanned aerial vehicle (UAV) with flexible mobility and low cost has been a promising technology for wireless communication. Thus, it can be used for wireless data collection in Internet of Things (IoT). In this article, we consider millimeter-wave (mmWave) communication on a UAV platform, where the UAV base station (UAV-BS) serves multiple ground users, which generate big sensor data. Both the deployment of the UAV-BS and the beamforming design have essential impact on the throughput of the system. Thus, we formulate a problem to maximize the achievable sum rate of all the users, subject to a minimum rate constraint for each user, a position constraint of the UAV-BS, and a constant-modulus (CM) constraint for the beamforming vector. We solve the nonconvex problem with two steps. First, by introducing the approximate beam pattern, we solve the deployment and beam gain allocation subproblem. Then, we utilize the artificial bee colony (ABC) algorithm to solve the beamforming subproblem. For the global optimization problem, we find the near-optimal position of the UAV-BS and the beamforming vector to steer toward each user, subject to an analog beamforming structure. The simulation results demonstrate that the proposed solution can achieve a more superior performance than the present random steering beamforming strategy in terms of achievable sum rate.

Low earth orbit (LEO) satellite communications are expected to be incorporated in future wireless networks, in particular 5G and beyond networks, to provide global wireless access with enhanced data rates. Massive multiple-input multiple-output (MIMO) techniques, though widely used in terrestrial communication systems, have not been applied to LEO satellite communication systems. In this paper, we propose a massive MIMO transmission scheme with full frequency reuse (FFR) for LEO satellite communication systems and exploit statistical channel state information (sCSI) to address the difficulty of obtaining instantaneous CSI (iCSI) at the transmitter. We first establish the massive MIMO channel model for LEO satellite communications and simplify the transmission designs via performing Doppler and delay compensations at user terminals (UTs). Then, we develop the low-complexity sCSI based downlink (DL) precoder and uplink (UL) receiver in closed-form, aiming to maximize the average signal-to-leakage-plus-noise ratio (ASLNR) and the average signal-to-interference-plus-noise ratio (ASINR), respectively. It is shown that the DL ASLNRs and UL ASINRs of all UTs reach their upper bounds under some channel condition. Motivated by this, we propose a space angle based user grouping (SAUG) algorithm to schedule the served UTs into different groups, where each group of UTs use the same time and frequency resource. The proposed algorithm is asymptotically optimal in the sense that the lower and upper bounds of the achievable rate coincide when the number of satellite antennas or UT groups is sufficiently large. Numerical results demonstrate that the proposed massive MIMO transmission scheme with FFR significantly enhances the data rate of LEO satellite communication systems. Notably, the proposed sCSI based precoder and receiver achieve the similar performance with the iCSI based ones that are often infeasible in practice.

In this paper, a full-duplex unmanned aerial vehicle (FD-UAV) relay is employed to increase the communication capacity of millimeter-wave (mmWave) networks. Large antenna arrays are equipped at the source node (SN), destination node (DN), and FD-UAV relay to overcome the high path loss of mmWave channels and to help mitigate the self-interference at the FD-UAV relay. Specifically, we formulate a problem for maximization of the achievable rate from the SN to the DN, where the UAV position, analog beamforming, and power control are jointly optimized. Since the problem is highly non-convex and involves high-dimensional, highly coupled variable vectors, we first obtain the conditional optimal position of the FD-UAV relay for maximization of an approximate upper bound on the achievable rate in closed form, under the assumption of a line-of-sight (LoS) environment and ideal beamforming. Then, the UAV is deployed to the position which is closest to the conditional optimal position and yields LoS paths for both air-to-ground links. Subsequently, we propose an alternating interference suppression (AIS) algorithm for the joint design of the beamforming vectors and the power control variables. In each iteration, the beamforming vectors are optimized for maximization of the beamforming gains of the target signals and the successive reduction of the interference, where the optimal power control variables are obtained in closed form. Our simulation results confirm the superiority of the proposed positioning, beamforming, and power control method compared to three benchmark schemes. Furthermore, our results show that the proposed solution closely approaches a performance upper bound for mmWave FD-UAV systems.

In this paper, we investigate broad coverage precoder design for 3D massive multi-input multi-output (MIMO) systems, where the base station (BS) is equipped with a uniform rectangular array (URA). We focus on the synchronization performance in the cell coverage. The flat fading channel model is formulated. The synchronization performance can be characterized by the missed detection (MD) probability. By considering the MD probability fairness in the cell, we formulate a criterion for the precoder design. Moreover, we consider the equal transmit power constraint on each antenna to efficiently utilize the power amplifier (PA) capacity of the BS. By using the manifold optimization framework, we design the precoder under the aforementioned criterion and constraint. Simulation results show that the fairness among all the users in this cell can be ensured. Compared with the precoders designed by half power beam-width (HPBW), the proposed scheme causes much less inter-cell interference and has a better synchronization performance.

In this paper, we investigate the omnidirectional precoding for three dimensional (3D) massive multi-input multi-output (MIMO) with uniform planar arrays (UPAs). The omnidirectional precoder is designed for public information transmission, where the channel state information (CSI) is usually not available at the base station (BS) and we would like to guarantee a performance to all users regardless of their angular positions. Thus, the first design objective of the precoding matrices is to satisfy the omnidirectional property, which means that the received mean power is constant at any angle. To ensure the power efficiency of each antenna, the per-antenna constant power constraint is also used in the design. Furthermore, the vectorized precoding matrices need to be mutually orthogonal to guarantee the spectral efficiency. The first two constraints can be satisfied by two dimensional Welti codes or two dimensional Golay arrays, whereas the third constraint is not necessarily satisfied by them. Furthermore, the methods to construct the Welti codes and Golay arrays are only given for certain array sizes. In this paper, we propose a novel and simple array design to construct the precoding matrices that satisfy the three properties. Based on the proposed array design, the design of the precoding matrices reduces to the design of a pair of complementary vectors having special structure, and the design of two sets of complementary orthonormal vectors with their aperiodic cross-correlation being zero. Finally, several examples of the omnidirectional precoding matrices for UPA generated by the proposed method are provided to verify the analytic results.

In this paper, we investigate channel estimation and robust signal detection for uplink massive multi-input multioutput orthogonal frequency division multiplexing systems with in-phase and quadrature-phase imbalances. By processing the real and imaginary parts of the received signal individually, we perform minimum mean square error (MMSE) estimation for the effective channel. Adjustable phase shift pilots (APSPs) are used for reducing the pilot overhead. Motivated by the optimal conditions to achieve the lower bound of the MSE of the effective channel estimation, a pilot scheduling algorithm is provided. We further propose an MMSE criterion based detection scheme which is robust to the channel estimation error. An analytical expression for the asymptotic achievable sum rate of the proposed detection is derived via using operator valued free probability theory. The performance of the channel estimation with APSPs and the robust MMSE detection are demonstrated by numerical results.

In this paper, a power allocation space-time coding scheme based on self-coding of residual loop interference (RLI) for two-way full-duplex (FD) amplify-and-forward (AF) relaying networks is proposed. In the proposed scheme, the relay first realizes a distributed space-time code by performing self-coding using the RLI at relay node after implementing some self-interference cancellation techniques. Then power allocation is performed between the terminals. With the proposed power allocation scheme, both amplifying factor and transmit powers of terminal nodes are jointly optimized under a total power constraint when estimated instantaneous channel state information is available. Simulation results show that the proposed scheme achieves performance improvement in an FD relaying system.

In this paper, we consider the design of joint resource blocks (RBs) and power allocation for dual-mode base stations operating over millimeter wave (mmW) band and microwave (μW) band. The resource allocation design aims to minimize the system energy consumption while taking into account the channel state information, maximum delay, load, and different types of user applications (UAs). To facilitate the design, we first propose a group-based algorithm to assign UAs to multiple groups. Within each group, low-power UAs, which often appear in short distance and experience less obstacles, are inclined to be served over mmW band. The allocation problem over mmW band can be solved by a greedy algorithm. Over μW band, we propose an estimation-optimal-descent algorithm. The rate of each UA at all RBs is estimated to initialize the allocation. Then, we keep altering RB's ownership until any altering makes power increases. Simulation results show that our proposed algorithm offers an excellent tradeoff between low energy consumption and fair transmission.

In this letter, a novel method is proposed to estimate the along-track baseline for a multichannel high-resolution and wide-swath (HRSW) synthetic aperture radar (SAR) system. First, a spatial correlation function is constructed to remove the range cell migration and Doppler broadening of ground static targets. Then, the iterative adaptive approach (IAA) is applied to iteratively estimate the along-track baseline with high precision. Finally, a high-resolution SAR image can be obtained based on the estimated baseline. The effectiveness of the proposed algorithm is validated by both simulated and real SAR data.

This paper presents a novel massive multiple-input multiple-output (MIMO) transmission in beam domain for optical wireless communications. The optical base station equipped with massive optical transmitters communicates with a number of user terminals (UTs) through a transmit lens. Focusing on LED transmitters, we analyze light refraction of the lens and establish a channel model for optical massive MIMO transmissions. For a large number of LEDs, channel vectors of different UTs become asymptotically orthogonal. We investigate the maximum ratio transmission and regularized zero-forcing precoding in the optical massive MIMO system and propose a linear precoding design to maximize the sum rate. We further design the precoding when the number of transmitters grows asymptotically large and show that beam division multiple access (BDMA) transmission achieves the asymptotically optimal performance for sum rate maximization. Unlike optical MIMO without a transmit lens, BDMA can increase the sum rate proportionally to$2K$and$K$under the total and per transmitter power constraints, respectively, where$K$is the number of UTs. In the non-asymptotic case, we prove the orthogonality conditions of the optimal power allocation in beam domain and propose efficient beam allocation algorithms. Numerical results confirm the significantly improved performance of our proposed beam-domain optical massive MIMO communication approaches.

In this paper, we investigate the combination of non-orthogonal multiple access and millimeter-wave communications (mmWave-NOMA). A downlink cellular system is considered, where an analog phased array is equipped at both the base station and users. A joint Tx-Rx beamforming and power allocation problem is formulated to maximize the achievable sum rate (ASR) subject to a minimum rate constraint for each user. As the problem is non-convex, we propose a sub-optimal solution with three stages. In the first stage, the optimal power allocation with a closed form is obtained for an arbitrary fixed Tx-Rx beamforming. In the second stage, the optimal Rx beamforming with a closed form is designed for an arbitrary fixed Tx beamforming. In the third stage, the original joint Tx-Rx beamforming and power allocation problem is reduced to a Tx beamforming problem by using the previous results, and a boundary-compressed particle swarm optimization (BC-PSO) algorithm is proposed to obtain a sub-optimal solution. Extensive performance evaluations are conducted to verify the rational of the proposed solution, and the results show that the proposed sub-optimal solution can achieve a significantly better performance in terms of ASR compared with those of the state-of-the-art schemes and the conventional mmWave orthogonal multiple access (mmWave-OMA) system.

This paper investigates the imaging problems in squint beam-steering synthetic aperture radar (SBS-SAR) mounted on high-speed platforms with constant acceleration. The cross-range-dependent range cell migration (RCM) is compensated by keystone transform (KT) and time domain RCM correction (RCMC). By derotation and phase compensation, the KT of Doppler folded signal is achieved without zero-padding. For azimuth processing, the signal is reconstructed by the nonlinear phase and range-dependent derotation. Then, the space-variant (SV) Doppler chirp rate is corrected by time domain azimuth nonlinear chirp scaling (ANCS). After frequency domain matched filtering, the full aperture signal is focused in the 2-D time domain. The algorithm is validated by simulated SAR data, including the evaluation of RCMC with KT, geometric correction, and the focusing performance.

In this paper, a new algorithm is presented to image ground moving targets in a synthetic aperture radar (SAR) system based on range frequency reversal transform-fractional Fourier transform (RFRT-FrFT). In this algorithm, a range compressed signal is initially transformed into the range frequency domain and then RFRT is proposed to directly compensate the range migration via multiplying the signal in the range frequency domain by its reversed data according to the equal interval sampling of range frequency variable, which can significantly decrease the computational complexity in target envelope migration elimination. Then, FrFT is applied to accomplish the target motion parameter estimation after range migration alignment. Finally, a ground moving target is well focused after motion compensation. The effectiveness of the proposed algorithm is validated by both simulated and real SAR data.

In this paper, we propose a long-time coherent integration method for a maneuvering target with the first-, second-, and third-order range migrations, and the complex Doppler frequency migration. In this method, after range compression, the echo signal is first transformed into the range-frequency and Doppler domain based on series reversion, and then an azimuth matched filtering procedure is implemented in the 2-D frequency domain. It can eliminate the coupling effects between range and azimuth jointly caused by the radial velocity, radial acceleration, and radial acceleration rate of a moving target. Due to the linear transform property, the proposed method can work well under low signal-to-clutter and noise ratio. In addition, the Doppler ambiguities, when target azimuth spectrum either is within a pulse repetition frequency (PRF) or spans over neighboring PRF bands, can be well solved. Both simulated and real synthetic aperture radar data processing results are provided to validate the effectiveness of the proposed algorithm.

A squinted observation geometry along with long integration time significantly aggravates the range walk and spatial variation of a medium-earth-orbit (MEO) synthetic aperture radar (SAR) signal. Variable pulse repeating frequency (PRF) is recommended to avoid the blockage in echo recording and save storage space. The existing wavenumber algorithms cannot handle the nonlinear and range-azimuth-coupled spatial variation (RACSP) over a large scene. In this paper, we propose a modified Stolt mapping method along with a modified joint time and Doppler resampling (JTDR) for highly squinted MEO SAR data processing. An azimuth timescale transformation is used to deal with the nonlinear spatial variation of the azimuth frequency-modulation (FM) rate. An extended Omega-K is used to linearize the range frequency and achieve range cell migration correction (RCMC). To address the RACSP, the Doppler is linearized in the range-Doppler domain using a range-dependent Doppler scale transformation. The computational complexity and geometry distortion correction (GDC) are also discussed. Simulation results are shown to verify the effectiveness of the developed focusing approaches.

Since the width of range swath of synthetic aperture radar (SAR) is restricted by the pulse repetition frequency, there exists a tradeoff between the azimuth resolution and the range swath width. As a result, conventional SAR imaging methods based on the Nyquist sampling theorem can hardly achieve the high resolution and wide swath simultaneously. In this paper, we propose an algorithm of high-resolution and wide-swath SAR imaging based on the combination of Poisson disk sampling and iterative shrinkage thresholding. Poisson disk sampling adopted in the azimuth direction can ensure that the interval between any two adjacent pulses is longer than the Nyquist sampling interval, which provides the potential to widen SAR imaging swath in the range direction. The imaging formation is carried out by performing the inverse operator of the chirp scaling algorithm and the shrinkage thresholding in an iterative fashion. Compared with the existing SAR imaging methods, the proposed method can realize high-resolution and wide-swath SAR imaging simultaneously with affordable computational cost. Simulations and experiments on real SAR data demonstrate the effectiveness of the proposed method.

The integration of non-orthogonal multiple access in millimeter-Wave communications (mm Wave-NOMA) can significantly improve the spectrum efficiency and increase the number of users in the fifth-generation (5G) mobile communication and beyond. In this paper, we consider a downlink mm Wave-NOMA cellular system, where the base station is mounted with an analog beamforming phased array, and multiple users are served in the same time-frequency resource block. To guarantee user fairness, we formulate joint beamforming and power allocation problem to maximize the minimal achievable rate among the users, i.e., we adopt the max–min fairness. As the problem is difficult to solve due to the non-convex formulation and high dimension of the optimization variables, we propose a sub-optimal solution, which makes use of the spatial sparsity in the angle domain of the mm Wave channel. In the solution, the closed-form optimal power allocation is obtained first, which reduces the joint optimization problem into an equivalent beamforming problem. Then, an appropriate beamforming vector is designed. The simulation results show that the proposed solution can achieve a near-upper-bound performance in terms of achievable rate, which is significantly better than that of the conventional mm Wave orthogonal multiple access (mm Wave-OMA) system.

This paper investigates the application of non-orthogonal multiple access in millimeter-Wave communications (mmWave-NOMA). Particularly, we consider downlink transmission with a hybrid beamforming structure. A user grouping algorithm is first proposed according to the channel correlations of the users. Whereafter, a joint hybrid beamforming and power allocation problem is formulated to maximize the achievable sum rate, subject to a minimum rate constraint for each user. To solve this non-convex problem with high-dimensional variables, we first obtain the solution of power allocation under arbitrary fixed hybrid beamforming, which is divided into intra-group power allocation and inter-group power allocation. Then, given arbitrary fixed analog beamforming, we utilize the approximate zero-forcing method to design the digital beamforming to minimize the inter-group interference. Finally, the analog beamforming problem with the constant-modulus constraint is solved with a proposed boundary-compressed particle swarm optimization algorithm. The simulation results show that the proposed joint approach, including user grouping, hybrid beamforming and power allocation, outperforms the state-of-the-art schemes and the conventional mmWave orthogonal multiple access system in terms of achievable sum rate, and energy efficiency.

The plane wave based wireless communications have becoming more and more matured, along with the well utilization of the traditional resources such as time and frequency. To further increase the capacity for rapidly increasing capacity demand of wireless communications, it is potential to use the twist wave, which has the orbital angular momentum (OAM). In this paper, we discuss the OAM based wireless communications in the aspect of orthogonality, degree of freedom (DoF), and capacity, where both the transmitter and the receiver use uniform circular array (UCA) antennas. In particular, we compare OAM based wireless communications with multiple-input-multiple-output (MIMO) based wireless communications in terms of DoF and capacity. Numerical results are presented to validate and evaluate that the DoF of OAM based wireless communications is greater than or equal to that of correlated MIMO based wireless communications when the transmitter and the receiver antennas are aligned well. The OAM based wireless communications can achieve larger capacity than the correlated MIMO in high signal-to-noise ratio (SNR) region under line-of-sight scenario.

Focusing moving targets with frequency-domain algorithms may suffer from azimuth spectrum not entirely contained within a pulse-repetition frequency band, which may lead to degraded detection performance due to distributing the energy to the artifacts. In order to avoid this problem, a refocusing algorithm after back-projection integral is proposed. The main idea is first to uniformly and coarsely focus moving targets for detection, and then extract the detected targets for refocusing. By deriving the exact analytic expression of the wavenumber spectrum, motion parameter estimation and motion compensation are directly carried out on the 2-D wavenumber domain of the small-sized extracted data, which involves fast Fourier transform and Inverse Fast Fourier Transform operations only with no interpolation, thus reduces the computational complexity. Then, the final refocused image of the moving target is achieved. Refocusing results of both airborne and spaceborne synthetic aperture radar data are shown to validate the effectiveness of the proposed method.

In this paper, we design the precoding matrices at the base station side and the combining matrices at the user terminal side for initial downlink synchronization in millimeter wave massive multiple-input multiple-output systems. First, we demonstrate two basic requirements for the precoding and combining matrices, including that all the entries therein should have constant amplitude under the implementation architecture constraint, and the average transmission power over the total K time slots taking for synchronization should be constant for any spatial direction. Then, we derive the optimal synchronization detector based on generalized likelihood ratio test. By utilizing this detector, we analyze the effect of the precoding and combining matrices to the missed detection probability and the false alarm probability, respectively, and present the corresponding conditions that should be satisfied. It is shown that, both of the precoding and combining matrices should guarantee the perfect omnidirectional coverage at each time slot, i.e., the average transmission power at each time slot is constant for any spatial direction, which is stricter than the second basic requirement mentioned earlier. We also show that such omnidirectional precoding matrices and omnidirectional combining matrices exist only when both of the number of transmit streams and the number of receive streams are equal to or greater than two. In this case, we propose to utilize Golay complementary pairs and Golay-Hadamard matrices to design the precoding and combining matrices. Simulation results verify the effectiveness of the propose approach.

Accurate range modeling, cross-range-dependent range migration, and space-variant Doppler parameter are main issues to be solved in processing high-squint synthetic aperture radar (SAR) data acquired from maneuvering platforms. A frequency domain backprojection algorithm, based on local Cartesian coordinate (LCC) and subregion range cell migration correction, is proposed to deal with these problems. With the proposed algorithm, the range model is built in an LCC system to accurately match the signal characteristics after range walk correction. Then, the compensation of cross-range-dependent range migration is implemented based on properly divided subregions after azimuth spectrum filtering. Finally, the space-variant Doppler parameter and higher order phase terms are coherently integrated in range-Doppler domain to get the focused subregion images with full resolution of the synthetic aperture. The final image of the entire scene is obtained by directly connecting all subregion images. The results of simulated and real SAR data validate the proposed algorithm.

In this paper, a new method is proposed to refocus a ground moving target in synthetic aperture radar imagery. In this method, range migration is compensated in the 2-D frequency domain, which can easily be implemented by using the complex multiplications, the fast Fourier transform (FFT), and the inverse FFT operations. Then, the received target signal in a range gate is characterized as a quadratic frequency-modulated (QFM) signal. Finally, a novel parameter estimation method, i.e., scaled generalized high-order ambiguity function (HAF), is proposed to transform the target signal into a signal on 2-D time-frequency plane and realize the 2-D coherent integration, where the peak position accurately determines the second- and third-order parameters of a QFM signal. Compared with our previously proposed generalized Hough-HAF method, the proposed method can obtain a better target focusing performance, since it can eliminate the incoherent operations in both range and azimuth directions. In addition, the proposed method is computationally efficient, since it is free of searching in the whole target focusing procedure. Both simulated and real data processing results are provided to validate the effectiveness of the proposed algorithm.

Image formation of large scenes is still challenging in medium-earth-orbit (MEO) synthetic aperture radar (SAR) due to the existence of severe 2-D space variance. In this paper, the properties of space variance are analyzed in detail, and then a variable-coefficient fourth-order range model is adopted to model the space-variant range history of every target in a large scene accurately. A method integrating a modified chirp scaling algorithm with joint time-Doppler resampling is proposed to address the range-variant range cell migration, as well as the azimuth-variant frequency-modulation rate and higher order Doppler parameters. The computational burden and alternative implementation approaches are also discussed. Finally, processing of simulated data for MEO SAR with 2-m resolution is presented to validate the proposed algorithm.

In this paper, with respect to multichannel synthetic aperture radar (SAR), we first formulate the problems of Doppler ambiguities on the radial velocity (RV) estimation of a ground moving target in the range-compressed domain, the range-Doppler domain, and the image domain, respectively. It is revealed that in these problems, the cascaded time–space Doppler ambiguity (CTSDA) may arise; that is, the time domain Doppler ambiguity in each channel arises first and then the spatial domain Doppler ambiguity among multichannels arises second. Accordingly, the multichannel SAR systems with different parameters are investigated in three cases with different Doppler ambiguity properties. Then, a multifrequency SAR is proposed for the RV estimation by solving the ambiguity problem based on the Chinese remainder theorem (CRT). In the first two cases, the ambiguity problem can be solved by the existing closed-form robust CRT. In the third case, it is found that the problem is different from the conventional CRT problem and we call it a double remaindering problem in this paper. We then propose a sufficient condition under which the double remaindering problem, i.e., the CTSDA, can also be solved by the closed-form robust CRT. When the sufficient condition is not satisfied, a searching-based method is proposed. Finally, some results of numerical experiments are provided to demonstrate the effectiveness of the proposed methods.

For a single-channel synthetic aperture radar (SAR), finite-pulse repetition frequency and nonideal antenna pattern cause azimuth ambiguities, i.e., ghosts in image domain. In this paper, a novel algorithm of locating processing weighted group lasso SAR image formation for maritime targets is proposed to effectively mitigate the ambiguities, which can work on a single-look complex SAR image. In the scheme, the ambiguous signal model using the conventional SAR focusing processor is first explicitly derived, showing the analytical expression of SAR image formulation. The weighted sparse group lasso algorithm is then employed to group-sparsely reconstruct the subimages of nonambiguous and ambiguous Doppler components. In particular, we introduce adaptively weighted sparsity constraint, obtained from a priori azimuth antenna pattern, and clutter clustering during sparse imaging. It should be emphasized that the proposed algorithm can effectively improve the azimuth resolution by coherently integrating the ambiguous signal components, which greatly helps the target detection and recognition in maritime surveillance. Finally, experiments based on simulated and measured data are performed to confirm the effectiveness of the proposed algorithm.

In this paper, we design space-time block codes (STBCs) to broadcast the common information omnidirectionally in a massive multiple-input multiple-output downlink. To reduce the burden of the downlink channel estimation and achieve partial spatial diversity from base station (BS) transmit antennas, we propose channel-independently precoded low-dimensional STBC. The precoding matrix and the signal constellation in the low-dimensional STBC are jointly designed to guarantee omnidirectional coverage at any instant time and sufficiently utilize the power amplifier capacities of BS transmit antennas, and at the same time, achieve the full diversity of the low-dimensional STBC. Under this framework, several designs are presented. To provide transmit diversity order of two, a precoded Alamouti code is proposed, which has a fast symbolwise maximum-likelihood (ML) decoding. To provide transmit diversity order of four, a precoded quasi-orthogonal STBC is proposed, which has a pairwise ML decoding. Moreover, a precoded no-zero-entry Toeplitz code and a precoded no-zero-entry overlapped Alamouti code are also proposed. These two codes can achieve a higher diversity order with linear receivers.

In this paper, we explore non-orthogonal multiple access (NOMA) in millimeter-wave (mm-wave) communications (mm-wave-NOMA). In particular, we consider a typical problem, i.e., maximization of the sum rate of a 2-user mm-wave-NOMA system. In this problem, we need to find the beamforming vector to steer towards the two users simultaneously subject to an analog beamforming structure, while allocating appropriate power to them. As the problem is non-convex and may not be converted to a convex problem with simple manipulations, we propose a suboptimal solution to this problem. The basic idea is to decompose the original joint beamforming and power allocation problem into two sub-problems which are relatively easy to solve: one is a power and beam gain allocation problem, and the other is a beamforming problem under a constant-modulus constraint. Extension of the proposed solution from 2-user mm-wave-NOMA to more-user mm-wave-NOMA is also discussed. Extensive performance evaluations are conducted to verify the rational of the proposed solution, and the results also show that the proposed sub-optimal solution achieves close-to-bound sum-rate performance, which is significantly better than that of time-division multiple access.

In this paper, distributed linear convolutional spacetime coding (DLC-STC) for two relay full-duplex (FD) 2 × 2 × 2 cooperative communication networks is presented. A network topology model which consists of two sources, two destinations and one layer of two relays, is considered. In this communication model, two sources may transmit signals to two different destinations with the help of two relays simultaneously, which will cause inter-user interference. As a result, one has to deal with the interuser interference in addition to the self-loop interference at the relays. Most part of the self-loop interference can be cancelled largely with the self-loop interference techniques at the relays, but the residual part is still a problem that requires proper processing. We consider that the two relays work in FD mode with an amplify-and-forward (AF) protocol, and the relay self-loop interference may not be cancelled completely from the received signals. Distributed space-time coding for this model is studied, for which the residual part of relay self-loop signal is applied to serve as a self-coding signal. With the self-coding property of the AF FD system, a distributed linear convolutional space time code is formed. Simulation experiments are conducted to evaluate the proposed DLC-STC for the considered FD relay communication network model, and the results illustrate the effectiveness of the presented approach.

In this paper, we investigate the combination of two key enabling technologies for the fifth generation wireless mobile communication, namely millimeter-wave (mm-wave) communications and non-orthogonal multiple access (NOMA). In particular, we consider a typical two-user uplink mm-wave-NOMA system, where the base station equips an analog beamforming structure with a single radio-frequency chain and serves two NOMA users. An optimization problem is formulated to maximize the achievable sum rate of the two users while ensuring a minimal rate constraint for each user. The problem turns to be a joint power control and beamforming problem, i.e., we need to find the beamforming vectors to steer to the two users simultaneously subject to an analog beamforming structure, and meanwhile control appropriate power on them. As direct search for the optimal solution of the non-convex problem is too complicated, we propose decomposing the original problem into two sub-problems that are relatively easy to solve: one is a power control and beam gain allocation problem, and the other is an analog beamforming problem under a constant-modulus constraint. The rationale of the proposed solution is verified by extensive simulations, and the performance evaluation results show that the proposed sub-optimal solution achieves a close-to-bound uplink sum-rate performance.

Due to radio-frequency (RF) circuit mismatch, the channel reciprocity of time-division duplex massive multiple-input multiple-output system is impaired. Under this condition, there exist several different approaches for base station (BS) to obtain the downlink (DL) channel estimate based on the minimum mean-square-error (MMSE) estimation method. We show that with the RF mismatch parameters BS will obtain the same DL channel estimates via these different approaches. As the DL channel estimate is usually imperfect, we propose an MMSE based multiuser precoding technique, which is robust to the estimation error. Furthermore, we derive an asymptotic approximation of the ergodic sum rate for the robust MMSE precoding using the large dimensional random matrix theory, which is tight as the numbers of both antennas at BS and user terminals tend to infinity with a fixed non-zero and finite ratio. Our results are verified by the simulations.

In multiple-input multiple-output (MIMO) two-way relay networks (TWRN), linear physical-layer network coding (LPNC) was proposed to boost the throughput by using spatial multiplexing at source nodes. How to design optimal LPNC for full-rate full-diversity MIMO TWRN is still an open problem. In this paper, we propose a full-rate full-diversity (FRFD) LPNC scheme. In this scheme, two source nodes, each with two antennas, transmit full-rate universal space-time codes to a two-antenna relay simultaneously. Then, the relay applies LPNC to compute multiple network-coded (NC) messages. In particular, we explicitly solve the optimal LPNC mapping to minimize decoding errors of NC messages in the FRFD LPNC scheme. Our analytical results verify that the optimal FRFD LPNC scheme guarantees the full-diversity and full-rate transmission at the same time. Simulation results are consistent with the analytical results and further demonstrate that our optimal FRFD LPNC scheme outperforms the conventional MIMO LPNC scheme.

We propose beam division multiple access (BDMA) with per-beam synchronization (PBS) in time and frequency for wideband massive multiple-input multiple-output (MIMO) transmission over millimeter-wave (mmW)/Terahertz (THz) channels. Based on a physically motivated beam domain channel model, we first show that the envelopes of the beam domain channel elements tend to be independent of time and frequency when both the numbers of antennas at base station and user terminals (UTs) tend to infinity. Motivated by this, we then propose PBS for massive MIMO. We show that both the effective delay and Doppler frequency spreads of massive MIMO channels with PBS are reduced by a factor of the number of UT antennas compared with the conventional synchronization approaches. Subsequently, we apply PBS to BDMA and investigate beam scheduling to maximize the achievable ergodic rates for BDMA. Simulation results verify the effectiveness of BDMA with PBS for mmW/THz massive MIMO in typical mobility scenarios.

We propose beam division multiple access (BDMA) with per-beam synchronization (PBS) in time and frequency for wideband massive multiple-input multiple-output (MIMO) transmission over millimeter-wave (mmW)/Terahertz (THz) bands. We first introduce a physically motivated beam domain channel model for massive MIMO and demonstrate that the envelopes of the beam domain channel elements tend to be independent of time and frequency when both the numbers of antennas at base station and user terminals (UTs) tend to infinity. Motivated by the derived beam domain channel properties, we then propose PBS for mmW/THz massive MIMO. We show that both the effective delay and Doppler frequency spreads of wideband massive MIMO channels with PBS are reduced by a factor of the number of UT antennas compared with the conventional synchronization approaches. Subsequently, we apply PBS to BDMA, investigate beam scheduling to maximize the ergodic achievable rates for both uplink and downlink BDMA, and develop a greedy beam scheduling algorithm. Simulation results verify the effectiveness of BDMA with PBS for mmW/THz wideband massive MIMO systems in typical mobility scenarios.

For a multiple-input and multiple-output synthetic aperture radar, stepped frequency chirps can be used to generate high-resolution range profiles (HRRPs) by using spectrum synthesis. However, the presence of channel phase errors may degrade the performance of HRRP synthesis. This letter presents a channel error estimation method to address this problem. First, to obtain a focused subband image, a range phase adjustment by contrast enhancement algorithm is proposed to estimate inner-channel high-order phase errors. Second, a sidelobe balanced model is established to estimate constant phase error from the relationship between the balanced sidelobe and constant phase; the constant phase error can be directly obtained in an efficient manner. Experimental analysis using real data demonstrates the effectiveness of the proposed method.

Phase autofocus is a key step in translational motion compensation for inverse synthetic aperture radar. From the eigenvalue decomposition (EVD) of the covariance matrix generated by the aligned range-compressed signal, eigenvectors can be obtained for effective phase autofocus. However, as the number of pulse samples is increased to improve the cross-range resolution, the high computational complexity of the EVD may become burdensome. To address this problem, we propose a novel EVD-based method in this letter. When the number of range units is larger than the number of pulse samples, the conventional method is used. Otherwise, the transpose of the envelope-aligned data matrix is used to generate a lower dimensional covariance matrix and to perform successive autofocus processing. Since many real targets exist in limited range units, a one- or two-order-higher computational efficiency can be obtained in some typical scenarios with the proposed method, compared with existing EVD-based approaches. Furthermore, the equivalence between the above two methods has been proven in this letter. Finally, the results for real measured data are provided to demonstrate the effectiveness of the proposed method.

In this paper, a delay diversity orthogonal frequency-division multiplexing (DD OFDM) transmission scheme in amplify-and-forward (AF) full-duplex relay systems is investigated. One direct source-to-destination link, one relay forwarding link, and residual self-interference are considered in the system. The necessary cyclic prefix length is investigated and a suitable AF relay protocol in the full-duplex relay OFDM system is proposed. This paper demonstrates that the AF relay link and the direct source-to-destination link can be combined to provide spatial diversity. The key is that the DD OFDM scheme is used to transform the spatial diversity into increased channel frequency diversity that is further exploited by using the bit-interleaved coding. The bit error rate performance of the proposed system is verified by simulation results.

A method of motion status estimation of airborne synthetic aperture radar (SAR) platform in short subapertures via parametric sparse representation is proposed for high-resolution SAR image autofocusing. The SAR echo is formulated as a jointly sparse signal through a parametric dictionary matrix, which converts the problem of SAR motion status estimation into a problem of dynamic representation of jointly sparse signals. A full synthetic aperture is decomposed into several subapertures to estimate the dynamic motion parameters of a platform, and SAR motion compensation is achieved by refining the estimation of the equivalent platform motion parameters, i.e., the azimuth velocity and the radial acceleration of the radar platform, at each subaperture in an iterative fashion. Experimental results based on both simulated and real data demonstrate that: 1) the proposed algorithm outperforms the map-drift algorithm and the phase gradient autofocus algorithm in terms of the imaging quality and 2) compared to the iterative minimum-entropy autofocus, the proposed algorithm produces the comparative imaging quality with less computational complexity in complex motion environment.

This paper proposes a new method to focus a ground moving target with complex motions and estimate its motion parameters in a synthetic aperture radar (SAR) system. In this method, the second-order Keystone transform is applied to correct the range curvature. Then, the Hough transform is applied to estimate the slope of the range walk trajectory, from which the target cross-track velocity is obtained. Finally, a generalized Hough-high-order ambiguity function (GHHAF) transform is applied to transform the target signal into a 2-D time-frequency plane and estimate its slope associated with the third-order Doppler parameter. Compared with the conventional SAR imaging methods using the second-order phase model, the proposed method can obtain better imaging quality since the third-order Doppler frequency migration is effectively eliminated. Both simulated and real data processing results are provided to validate the effectiveness of the proposed algorithm.

In synthetic aperture radar (SAR), long integration time may induce range migration and Doppler frequency migration of a received signal, which may degrade the SAR imaging performance of ground moving targets. Most of the conventional algorithms deal with the problems of range migration and Doppler frequency migration based on parameter searching. However, the exhaustive searching of target motion parameters may result in heavy computational burden. To avoid this problem, this paper proposes a new imaging method for ground moving targets without target motion parameter estimation. First, Keystone transform is applied to correct the range walk. Second, range curvature is compensated by the matched filtering function. Third, Doppler frequency migration is compensated via multiplying the data in range- and azimuth-time domains by its reversed conjugate data according to the equal interval sampling of the azimuth slow time, which avoids the searching procedure for target motion parameter estimation. Finally, the signal energy will be well accumulated in the range-Doppler domain, and thus, the moving targets can be efficiently recognized in the focused image. The major advantage of the proposed method is that it can obtain well-focused images of all targets in one processing step without target motion parameter estimation; thus, it is computationally efficient. Both simulated and real data processing results are used to validate the effectiveness of the proposed method.

In this paper, we study hierarchical codebook design for channel estimation in millimeter-wave (mmWave) communications with a hybrid precoding structure. Due to the limited saturation power of the mmWave power amplifier, we consider the per-antenna power constraint (PAPC). We first propose a metric, termed generalized detection probability (GDP), to evaluate the quality of an arbitrary codeword. This metric not only enables an optimization approach for mmWave codebook design, but also can be used to compare the performance of two different codewords/codebooks. To the best of our knowledge, GDP is the first such metric, particularly for mmWave codebook design. We then propose a heuristic approach to design a hierarchical codebook exploiting beam widening with the multi-RF-chain sub-array (BMW-MS) technique. To obtain crucial parameters of BMW-MS, we provide two solutions, namely, a low-complexity search (LCS) solution to optimize the GDP metric and a closed-form (CF) solution to pursue a flat beam pattern. Performance comparisons show that BMW-MS/LCS and BMW-MS/CF achieve very close performances, and they outperform the existing alternatives under the PAPC.

In millimeter-wave (mmWave) MIMO systems, while a hybrid digital/analog precoding structure offers the potential to increase the achievable rate, it also faces the challenge to lower channel estimation time due to a large number of antennas at both Tx/Rx sides. In this paper, the channel estimation is realized via multi-beam search, and a new hierarchical multi-beam search scheme, which uses a pre- designed analog hierarchical codebook, is proposed. Performance evaluations show that, compared with a state-of- the-art scheme, the proposed scheme not only achieves a higher success rate to acquired multiple beams under typical system settings, but also greatly reduces the channel estimation time.

A new Doppler ambiguity resolver (DAR) is proposed for ground moving targets of synthetic aperture radar (SAR) in the image domain. Based on the range-Doppler imaging of a static scene, the moving target's response is analyzed in the image domain, and the target's response slope is jointly determined by three motion parameters, i.e., azimuth velocity, ambiguous range velocity, and Doppler ambiguity number. A new DAR utilizing these three parameters is then proposed via the following steps. First, the moving target is detected after ground clutter cancelation between dual-channel images. Second, the ambiguous range velocity is estimated, and the road and its slope are extracted from the SAR image to establish the relationship between the 2-D velocities. Third, the Doppler ambiguity number is determined based on the response slope, the road slope, and the estimated ambiguous range velocity. Compared with the existing DARs in the 2-D time domain, the proposed algorithm works better in the most common signal-to-clutter-noise ratio scenarios. Finally, the results of the numerical experiments are provided to demonstrate the effectiveness of the proposed method.

We study a beamformed Alamouti coding scheme for a multigroup multicast downlink system. In this letter, we take into account imperfect channel state information (CSI) at the base station, where the CSI uncertainty region is formed by the intersection of multiple ellipsoids. This unified CSI uncertainty model leads to a challenging robust rank-two multicast beamforming problem. We first develop a conservative approximation method to handle the intractable constraints in the formulated robust beamforming problem under the considered CSI uncertainty region. Then, we apply the semidefinite relaxation (SDR) technique to approximately solve this problem. Our rank-profile analysis shows that the SDR is tight when there are at most two users in each multicast group and the CSI uncertainty region is not too large. Finally, we carry out simulations to validate the effectiveness of the proposed conservative approximation method. The simulation results coincide with the rank-profile analysis.

Common signals in public channels of cellular systems should be transmitted omnidirectionally from the base station (BS) to ensure cell-wide coverage. In this paper, we propose an omnidirectional precoding (OP) based transmission for public channels in massive MIMO systems, which leads to a significant reduction in the downlink pilot overhead while providing omnidirectional signaling. For this OP based transmission, we present three necessary conditions that the OP matrix should satisfy, to meet the requirements for omnidirectional transmission for reliable cell-wide coverage, equal average power on each antenna to sufficiently utilize all the available power amplifier capacities of BS antennas, and achievable ergodic rate maximization for the i.i.d. channel, respectively. Then, several examples of the OP matrix satisfying the three necessary conditions simultaneously are designed by utilizing the Zadoff-Chu sequence and its properties. We also analyze the system performance in terms of achievable ergodic rate, outage probability, and peak-to-average power ratio (PAPR) for these designs. It is shown that a dedicated design of the OP matrix has the following advantages: 1) it asymptotically maximizes the achievable ergodic rate, maximizes the achievable diversity order, and minimizes the outage probability in the large-scale array regime, not only for the i.i.d. channel, but also for spatially correlated channels; 2) it preserves the PAPR of the transmitted signal after precoding.

Generalized frequency division multiplexing (GFDM) is a recent multicarrier 5G waveform candidate with the flexibility of pulse shaping filters. However, the flexibility of choosing a pulse shaping filter may result in intercarrier interference (ICI) and intersymbol interference (ISI), which becomes more severe in a broadband channel. In order to eliminate the ISI and ICI, based on discrete Gabor transform (DGT), in this paper, a transmit GFDM signal is first treated as an inverse DGT, and then a frequency-domain DGT is formulated to recover (as a receiver) the GFDM signal. Furthermore, to reduce the complexity, a suboptimal frequency-domain DGT called local DGT is developed. Some analyses are also given for the proposed DGT-based receivers.

In synthetic aperture radar (SAR) imaging of a ground moving target, long-time coherent integration may effectively improve the imaging quality, whereas the imaging performance may severely degrade due to the range migration and the Doppler frequency migration. In this paper, a novel motion parameter estimation method named second-order Wigner-Ville distribution (SoWVD) transform is proposed, and then, a new SAR imaging method based on the SoWVD for a ground moving target is developed. As a modified Wigner-Ville distribution method, the SoWVD method can estimate the motion parameter without the search procedure, which achieves motion parameter estimation by Fourier transform operations in the 2-D frequency plane with respect to the slow time and the delay time. In addition, it can effectively recognize the cross terms based on multiple symmetrical properties of the peaks in the 2-D frequency domain. Both simulated and real data processing results are presented to validate the proposed imaging method.

In synthetic aperture radar (SAR) images, moving targets are usually smeared and/or imaged at incorrect positions due to the target motions during the SAR integration time. Moreover, since a high-resolution wide-swath SAR system is operated with a rather low pulse repetition frequency, a moving target will cause multiple ghost targets in the reconstructed SAR image. A new space-time adaptive processing framework is proposed in this paper for removing moving target artifacts in SAR images. In this new framework, the dynamic steering vector concept is proposed. In addition, this paper develops a moving target processing scheme for clutter suppression and moving target imaging and location for a high-resolution wide-swath SAR system. Finally, we locate the well-focused moving targets at the stationary scene image without any disturbing artifacts. The simulated and real data are used to validate the effectiveness of our proposed method.

In this paper, for a multichannel synthetic aperture radar-ground moving target indication (SAR-GMTI) system, a new high-accuracy focusing and relocating method using instantaneous interferometry, i.e., carrying out interferometry operation in the azimuth time domain before focusing, is proposed. One of the key steps of this method is to perform instantaneous interferometry to get accurate equivalent cross-track velocity (ECV) estimation for cross-track motion compensation. After that, the signal from a moving target is concentrated in range, and along-track motion compensation becomes convenient. Motion compensation transforms a moving target into a stationary one; thus, the conventional SAR imaging algorithm can be applied to focus the moving target. Finally, a strategy for accurately relocating a moving target is presented. The processing results of simulated and measured data illustrate the effectiveness of the proposed method.

In the past, two main approaches for the purpose of authentication, including information-theoretic authentication codes and complexity-theoretic message authentication codes (MACs), were almost independently developed.In this paper, we propose a new cryptographic primitive, namely, artificial-noiseaided MACs (ANA-MACs), which can be considered as both computationally secure and information-theoretically secure.For ANA-MACs, we introduce artificial noise to interfere with the complexity-theoretic MACs and quantization is further employed to facilitate packet-based transmission.With a channel coding formulation of key recovery in the MACs, the generation of standard authentication tags can be seen as an encoding process for the ensemble of codes, where the shared key between Alice and Bob is considered as the input and the message is used to specify a code from the ensemble of codes.Then, we show that the introduction of artificial noise in ANA-MACs can be well employed to resist the key recovery attack even if the opponent has an unlimited computing power.Finally, a pragmatic approach for the analysis of ANA-MACs is provided, and we show how to balance the three performance metrics, including the completeness error, the false acceptance probability, and the conditional equivocation about the key.The analysis can be well applied to a class of ANA-MACs, where MACs with Rijndael cipher are employed.

In the current scenario of high-resolution inverse synthetic aperture radar (ISAR) imaging, the non-cooperative targets may have strong maneuverability, which tends to cause time-variant Doppler modulation and imaging plane in the echoed data. Furthermore, it is still a challenge to realize ISAR imaging of maneuvering targets from sparse aperture (SA) data. In this paper, we focus on the problem of 3D geometry and motion estimations of maneuvering targets for interferometric ISAR (InISAR) with SA. For a target of uniformly accelerated rotation, the rotational modulation in echo is formulated as chirp sensing code under a chirp-Fourier dictionary to represent the maneuverability. In particular, a joint multi-channel imaging approach is developed to incorporate the multi-channel data and treat the multi-channel ISAR image formation as a joint-sparsity constraint optimization. Then, a modified orthogonal matching pursuit (OMP) algorithm is employed to solve the optimization problem to produce high-resolution range-Doppler (RD) images and chirp parameter estimation. The 3D target geometry and the motion estimations are followed by using the acquired RD images and chirp parameters. Herein, a joint estimation approach of 3D geometry and rotation motion is presented to realize outlier removing and error reduction. In comparison with independent single-channel processing, the proposed joint multi-channel imaging approach performs better in 2D imaging, 3D imaging, and motion estimation. Finally, experiments using both simulated and measured data are performed to confirm the effectiveness of the proposed algorithm.

A generalized Chinese remainder theorem (CRT) for multiple integers from residue sets has been studied recently, where the correspondence between the remainders and the integers in each residue set modulo several moduli is not known. A robust CRT has also been proposed lately to robustly reconstruct a single integer from its erroneous remainders. In this paper, we consider the reconstruction problem of two integers from their residue sets, where the remainders not only are out of order but also may have errors. We prove that two integers can be robustly reconstructed if their remainder errors are less than M/8, where M is the greatest common divisor of all the moduli. We also propose an efficient reconstruction algorithm. Finally, we present some simulations to verify the efficiency of the proposed algorithm. This paper is motivated from and has applications in the determination of multiple frequencies from multiple undersampled waveforms.

In this paper, we address differential non-coherence detections in uplink massive multiple input multiple output (MIMO) systems. Utilizing the channel statistics information, a special 16 quadrature amplitude modulation (QAM) can be adopted to achieve differential detection in uplink massive MIMO systems, which avoids costs of pilots or channel estimation. In addition, a higher-order extension of this differential 16-QAM is also presented in this paper. Moreover, it is found that users in different cells can transmit symbols simultaneously without causing interference to each other by jointly processing signals from all base stations if the number of receive antennas goes to infinity. For comparison, we also provide the performances of the differential amplitude phase shift keying (DAPSK) and coherence detection with pilot contamination in uplink massive MIMO systems, respectively. Simulation results show that the proposed differential 16-QAM scheme exhibits significant advantage over 16-DAPSK, and exhibits a large performance gain compared with the coherence detection with pilot contamination.

In this paper, we propose a novel Artificial-Noise-Aided PHYsical layer Phase Challenge-Response Authentication Scheme (ANA-PHY-PCRAS) for practical orthogonal frequency division multiplexing (OFDM) transmission. In this new scheme, Tikhonov-distributed artificial noise is introduced to interfere with the phase-modulated key for resisting potential key-recovery attacks. Then, we address various practical issues for ANA-PHY-PCRAS with OFDM transmission, including correlation among subchannels, imperfect carrier, and timing recoveries. Among them, we show that the effect of sampling offset is significant and a search procedure in the frequency domain should be incorporated for verification. With practical OFDM transmission, the number of uncorrelated subchannels is often insufficient. Hence, we employ a time-separated approach for allocating enough subchannels, and a modified ANA-PHY-PCRAS is proposed to alleviate the discontinuity of channel phase at far-separated time slots. Finally, the key equivocation is derived for the worst case scenario. We conclude that the enhanced security of ANA-PHY-PCRAS comes from the uncertainties of both the wireless channel and introduced artificial noise, compared with the traditional challenge-response authentication scheme implemented at the upper layer.

In millimeter-wave communication, large antenna arrays are required to achieve high power gain by steering toward each other with narrow beams, which poses the problem to efficiently search the best beam direction in the angle domain at both Tx and Rx sides. As the exhaustive search is time consuming, hierarchical search has been widely accepted to reduce the complexity, and its performance is highly dependent on the codebook design. In this paper, we propose two basic criteria for the hierarchical codebook design, and devise an efficient hierarchical codebook by jointly exploiting sub-array and deactivation (turning-off) antenna processing techniques, where closed-form expressions are provided to generate the codebook. Performance evaluations are conducted under different system and channel models. Results show superiority of the proposed codebook over the existing alternatives.

In this paper, we consider two-way full-duplex (FD) relay networks, which allow relaying communications in both directions at the same time and over the same frequency band. We propose a distributed space-time coding scheme, which we call SC-DSTC (Self-Coded Distributed Space-Time Coding), where the main idea is to perform convolutional encoding at the relay node and form a distributed linear convolutional space-time code (DLC-STC) together with the direct link. The convolutional code at the relay is realized by using part of the residual loop interference (RLI), which produces an automatic convolutional encoder. The amplifying factor is optimized when the loop channel information is imperfect. Simulation results show that the proposed SC-DSTC scheme can achieve asynchronous full cooperative diversity and is robust to the error of the loop channel information.

Common signals in public channels of cellular systems should be transmitted omnidirectionally from the base station (BS) to ensure cell-wide coverage. In this paper, we propose two omnidirectional space-time block codes (STBCs), being referred to as precoded Alamouti code and precoded quasi-orthogonal STBC (QOSTBC), respectively, to provide omnidirectional signaling with spatial diversity for public channels in massive MIMO systems. Both of these two schemes are with low downlink pilot overhead and can guarantee omnidirectional transmission and equal-power transmission per antenna simultaneously, to ensure reliable cell-wide coverage and sufficiently utilize all the available power amplifier capacities at BS antennas, respectively. The precoded Alamouti code provides diversity order 2 and has fast single-symbol maximum-likelihood (ML) decoding. The precoded QOSTBC provides a higher diversity order 4, but at the expense of a higher decoding complexity, since the ML decoding must be done on each pair of the modulation symbols.

This paper investigates polynomial remainder codes with non-pairwise coprime moduli. We first propose a robust reconstruction for polynomials from erroneous residues when the degrees of all residue errors are small, namely robust Chinese Remainder Theorem (CRT) for polynomials. It basically says that a polynomial can be reconstructed from erroneous residues such that the degree of the reconstruction error is upper bounded by τ whenever the degrees of all residue errors are upper bounded by τ, where a sufficient condition for τ and a reconstruction algorithm are obtained. By relaxing the constraint that all residue errors have small degrees, another robust reconstruction is then presented when there are multiple unrestricted errors and an arbitrary number of errors with small degrees in the residues. By making full use of redundancy in moduli, we obtain a stronger residue error correction capability in the sense that apart from the number of errors that can be corrected in the previous existing result, some errors with small degrees can be also corrected in the residues. With this newly obtained result, improvements in uncorrected error probability and burst error correction capability in a data transmission are illustrated.

Cyclic prefix (CP)-based multiple-input multiple-output (MIMO) orthogonal frequency-division multiplexing (OFDM) radar has been recently proposed for distributed transmit antennas, where there is no inter-range-cell interference (IRCI). However, it needs to transmit multiple OFDM pulses consecutively to obtain range profiles for a single swath, which may be too long in time for a reasonable swath width. In this letter, we propose a CP-based MIMO-OFDM synthetic aperture radar system, where each transmitter transmits only a single OFDM pulse to obtain range profiles and has the same frequency band; thus, the range resolution is not reduced. It is IRCI free and can collect full spatial diversity if the transmit antennas are distributed. Our main idea is to use circularly shifted Zadoff-Chu sequences as the weighting coefficients in the OFDM waveforms for different transmit antennas and apply spatial filters with multiple receive antennas to divide the whole swath into multiple subswaths, and then, each subswath is reconstructed/imaged using our proposed IRCI-free range reconstruction method.

It is commonly known that the ionosphere has significant effects on a low-frequency (particularly P-band) radar signal. It causes the degradation of the image quality in synthetic aperture radar (SAR) and inverse SAR (ISAR) imaging systems. In this letter, we analyze the ionospheric effects on radar signals and find that the total electron content (TEC) is a key to the ionospheric effects. A method is proposed to evaluate the TEC from a received ISAR signal and to correct the ionospheric effects. Some real experimental results, using a ground-based P-band ISAR system to observe a space target in the ionosphere, are used to validate the proposed method.

A DFT-based frequency estimator with low computational complexity is proposed, which is derived by extracting the phase difference from the peak DFT bins of two sub-segments from input samples. Some of its statistical properties, such as asymptotical unbiasedness and error variance, are derived. Furthermore, an iterative procedure of frequency estimate is also presented. Simulation results show that the proposed estimator's accuracy is close to those of AM estimator and CO estimator.

In this letter we determine the largest dynamic range within which any two nonnegative integers can be uniquely determined from their residues modulo a set of moduli. We also propose an efficient determination algorithm that extends a recent known result.

Chinese remainder theorem (CRT) provides an undersampling method to detect the frequency of a complex sinusoid. The detection of the multiple frequencies in a signal formed by the superposition of multiple complex sinusoids is a task frequently encountered in several applications such as phase unwrapping in radar signal processing and multiwavelength optical interferometry. A generalized CRT for multiple integers has recently been studied. In this letter, we complement it by giving two new conditions that ensure the maximal possible dynamic range for the multiple integers, i.e., the least common multiple (lcm) of all the moduli. Then, two corresponding determination algorithms are also proposed.

This paper investigates polynomial remainder codes with non-pairwise coprime moduli. We first consider a robust reconstruction problem for polynomials from erroneous residues when the degrees of all residue errors are assumed small, namely, the robust Chinese Remainder Theorem (CRT) for polynomials. It basically says that a polynomial can be reconstructed from its erroneous residues such that the degree of the reconstruction error is upper bounded by$\tau$whenever the degrees of all residue errors are upper bounded by$\tau$, where a sufficient condition for$\tau$and a reconstruction algorithm are obtained. By relaxing the constraint that all residue errors have small degrees, another robust reconstruction is then presented when there are multiple unrestricted errors and an arbitrary number of errors with small degrees in the residues. We finally obtain a stronger residue error correction capability in the sense that apart from the number of errors that can be corrected in the previous existing result, some errors with small degrees can be also corrected in the residues. With this newly obtained result, improvements in uncorrected error probability and burst error correction capability in data transmission are illustrated.

Interferometric synthetic aperture radar (InSAR) images are corrupted by strong noise, including interferometric phase and speckle noises. In general, the scenes in homogeneous areas are characterized by continuous-variation heights and stationary backscattered coefficients, exhibiting a locally spatial stationarity. The stationarity provides a rational of sparse representation of amplitude and interferometric phase to perform noise reduction. In this paper, we develop a novel algorithm of InSAR image formation from Bayesian perspective to perform interferometric phase noise reduction and despeckling. In the scheme, the InSAR image formation is constructed via maximum a posteriori estimation, which is formulated as a sparse regularization of amplitude and interferometric phase in the wavelet domain. Furthermore, the statistics of the wavelet-transformed image is modeled as complex Laplace distribution to enforce a sparse prior. Then, multichannel imaging is realized using a modified quasi-Newton method in a sequential and iterative manner, where both the interferometric phase and speckle noises are reduced step by step. Due to the simultaneously sparse regularized reconstruction of amplitude and interferometric phase, the performance of noise reduction can be effectively improved. Then, we extend it to joint sparse constraint on multichannel data by considering the joint statistics of multichannel data. Finally, experimental results based on simulated and measured data confirm the effectiveness of the proposed algorithm.

The existing linear-frequency-modulated (or step frequency) and random noise synthetic aperture radar (SAR) systems may correspond to the frequency-hopping and direct-sequence spread spectrum systems in the past second- and third-generation wireless communications. Similar to the current and future wireless communications generations, in this paper, we propose the orthogonal frequency-division multiplexing (OFDM) SAR imaging, where a sufficient cyclic prefix (CP) is added to each OFDM pulse. The sufficient CP insertion converts an intersymbol interference (ISI) channel from multipaths into multiple ISI-free subchannels as the key in a wireless communications system, and analogously, it provides an inter-range-cell interference (IRCI)-free (high range resolution) SAR image in a SAR system. The sufficient CP insertion along with our newly proposed SAR imaging algorithm, particularly for the OFDM signals, also differentiates this paper from all the existing studies in the literature on OFDM radar signal processing. Simulation results are presented to illustrate the high-range-resolution performance of our proposed CP-based OFDM SAR imaging algorithm.

This paper describes a clutter suppression approach and the corresponding moving target imaging algorithm for a multichannel in azimuth high-resolution and wide-swath (MC-HRWS) synthetic aperture radar (SAR) system. Incorporated with digital beamforming processing, MC-HRWS SAR systems are able to suppress the Doppler ambiguities to allow for HRWS SAR imaging and null the clutter directions to suppress clutter for ground moving target indication. In this paper, the degrees of freedom in azimuth for the multichannel SAR systems are employed to implement clutter suppression. First, the clutter and moving target echoes are transformed into the range compression and azimuth chirp Fourier transform frequency domain, i.e., coarse-focused images formation, when the clutter echoes are with azimuth Doppler ambiguity. Considering that moving targets are sparse in the imaging scene and that there is a difference between clutter and a moving target in the spatial domain, a series of spatial domain filters are constructed to extract moving target echoes. Then, using an extracted moving target echo, two groups of signals are formed, and slant-range velocity of a moving target can be estimated based on baseband Doppler centroid estimation algorithm and multilook cross-correlation Doppler centroid ambiguity number resolving approach. After the linear range cell migration correction and azimuth focus processing, a well-focused moving target image can be obtained. In addition, the proposed clutter suppression and imaging approach is not only adapted for uniformly displaced phase center sampling but also for the nonuniform sampling cases. Some simulation experiments are taken to demonstrate our proposed algorithms. Finally, some real measured data results are presented to validate the theoretical investigations and the proposed approaches.

This paper investigates the physical-layer security of a multiuser peer-to-peer (MUP2P) relay network for amplify-and-forward (AF) protocol, where a secure user and other unclassified users coexist with a multi-antenna eavesdropper and the eavesdropper can wiretap the confidential information in both two cooperative phases. Our goal is to optimize the transmit power of the source and the beamforming weights of the relays jointly for secrecy rate maximization subject to the minimum signal-to-interference-noise-ratio (SINR) constraint at each user, and the individual and total power constraints. Mathematically, the optimization problem is non-linear and non-convex, which does not facilitate an efficient resource allocation algorithm design. As an alternative, a null space beamforming scheme is adopted at the relays for simplifying the joint optimization and eliminating the confidential information leakage in the second cooperative phase, where the relay beamforming vector lies in the null space of the equivalent channel of the relay to eavesdropper links. Although the null space beamforming scheme simplifies the design of resource allocation algorithm, the considered problem is still non-convex and obtaining the global optimum is very difficult, if not impossible. Employing a sequential parametric convex approximation (SPCA) method, we propose an iterative algorithm to obtain an efficient solution of the non-convex problem. Besides, the proposed joint design algorithm requires a feasible starting point, we also propose a low complexity feasible initial points searching algorithm. Simulations demonstrate the validity of the proposed strategy.

This paper studies the cooperative transmission for securing a decode-and-forward (DF) two-hop network where multiple cooperative nodes coexist with a potential eavesdropper. Under the more practical assumption that only the channel distribution information (CDI) of the eavesdropper is known, we propose an opportunistic relaying with artificial jamming secrecy scheme, where a “best” cooperative node is chosen among a collection of N possible candidates to forward the confidential signal and the others send jamming signals to confuse the eavesdroppers. We first investigate the ergodic secrecy rate (ESR) maximization problem by optimizing the power allocation between the confidential signal and jamming signals. In particular, we exploit the limiting distribution technique of extreme order statistics to build an asymptotic closed-form expression of the achievable ESR and the power allocation is optimized to maximize the ESR lower bound. Although the optimization problems are non-convex, we propose a sequential parametric convex approximation (SPCA) algorithm to locate the Karush-Kuhn-Tucker (KKT) solutions. Furthermore, taking the time variance of the legitimate links' CSIs into consideration, we address the impacts of the outdated CSIs to the proposed secrecy scheme, and derive an asymptotic ESR. Finally, we generalize the analysis to the scenario with multiple eavesdroppers, and give the asymptotic analytical results of the achievable ESR. Simulation results confirm our analytical results.

This paper studies a single-input multi-output multi-eavesdropper (SIMOME) wiretap channel with multiple friendly single-antenna jammers. We consider random networks where the jammers and the eavesdroppers are distributed according to independent two-dimensional homogeneous Poisson point processes (PPP). We propose an opportunistic jammer selection scheme for the physical layer security enhancement, where the jammers whose channels are nearly orthogonal to the channel direction information (CDI) of the legitimate channel are selected to transmit independent and identically distributed (i.i.d.) Gaussian jamming signals to confound the eavesdroppers. The proposed scheme does not require a centralized design and signal coordinations among multiple jammers are not needed anymore. Furthermore, we analyze both the achievable secrecy throughput and the ergodic secrecy rate of the proposed jammer selection scheme. Based on the analysis results, we optimize the selection threshold for the secrecy throughput maximization and ergodic secrecy rate maximization. Simulation results show that the proposed jammer selection scheme can achieve a substantial performance gain.

The artificial noise (AN) scheme is an efficient strategy for enhancing the secrecy rate of a multiple-input-single-output channel in the presence of a passive eavesdropper, whose channel state information is unavailable. Recently, a randomized beamforming scheme has been proposed for deteriorating the eavesdropper's bit-error-rate performance via corrupting its receiving signal by time-varying multiplicative noise. However, the secrecy rate of such a scheme has not been well addressed yet. In this paper, we name it the artificial fast fading (AFF) scheme and provide a comprehensive secrecy rate analysis for it. We show that with this scheme, the eavesdropper will face a noncoherent Ricean fading single-input-multiple-output channel. Although the closed-form secrecy rate is difficult to obtain, we derive an exact expression for the single-antenna-eavesdropper case and a lower bound for the multiantenna-eavesdropper case, both of which can be numerically calculated conveniently. Furthermore, we compare the AFF scheme with the AN scheme and show that their respective superiorities to each other depend on the number of antennas that the transmitter and the eavesdropper possessed, i.e., when the eavesdropper has more antennas than the transmitter does, the AFF scheme achieves a larger secrecy rate; otherwise, the AN scheme outperforms. Motivated by this observation, we propose a hybrid AN-AFF scheme and investigate the power allocation problem, which achieves better secrecy performance further.

We investigate Tx/Rx joint beamforming in millimeter-wave communications (MMWC). As the multipath components (MPCs) have different steering angles and independent fadings, beamforming aims at achieving array gain and diversity gain in this scenario. A sub-optimal beamforming scheme is proposed to find the antenna weight vectors (AWVs) at Tx/Rx via iterative eigenvalue decomposition (EVD), provided that full channel state information (CSI) is available at both the transmitter and receiver. To make this scheme practically feasible in MMWC, a corresponding training approach is suggested to avoid the channel estimation and iterative EVD computation. As in fast fading scenario, the training approach may be time-consuming due to frequent training; another beamforming scheme, which exploits the quasi-static steering angles in MMWC, is proposed to reduce the overhead and increase the system reliability by multipath grouping (MPG). The scheme first groups the MPCs and then concurrently beamforms toward multiple steering angles of the grouped MPCs, so that both array gain and diversity gain are achieved. Performance comparisons show that, compared with the corresponding state-of-the-art schemes, the iterative EVD scheme with the training approach achieves the same performance with a reduced overhead and complexity, whereas the MPG scheme achieves better performance with approximately equivalent complexity.

We propose pilot reuse (PR) in single cell for massive multiuser multiple-input multiple-output (MIMO) transmission to reduce the pilot overhead. For spatially correlated Rayleigh fading channels, we establish a relationship between channel spatial correlations and channel power angle spectrum when the base station antenna number tends to infinity. With this channel model, we show that sum mean square error (MSE) of channel estimation can be minimized provided that channel angle of arrival intervals of the user terminals reusing the pilots are non-overlapping, which shows feasibility of PR over spatially correlated massive MIMO channels with constrained channel angular spreads. Since channel estimation performance might degrade due to PR, we also develop the closed-form robust multiuser uplink receiver and downlink precoder that minimize sum MSE of signal detection, and reveal a duality between them. Subsequently, we investigate pilot scheduling, which determines the PR pattern, under two minimum MSE related criteria, and propose a low complexity pilot scheduling algorithm, which relies on the channel statistics only. Simulation results show that the proposed PR scheme provides significant performance gains over the conventional orthogonal training scheme in terms of net spectral efficiency.

The common signals in public channels of cellular networks should be transmitted omni-directionally from the base station to guarantee reliable coverage. In this paper, we propose a transmission scheme for public channels in massive MIMO systems based on the precoding-extended Alamouti code. This proposed space-time code has the following four features: 1) the transmitted signal is omni-directional, i.e., its DFT has constant-envelope; 2) the transmitted signal on each antenna has constant-envelope to achieve the highest power-efficiency; 3) the maximum diversity order 2 can be obtained; 4) it is easy for downlink channel estimation at the user side. Furthermore, we propose two different constellation designs for the two symbols in this space-time code, i.e., these two symbols can be modulated independently or jointly. The independent design has the advantage in fast decoding while the joint design has the advantage in superior performance. Simulations verify the effectiveness of our scheme.

We propose pilot reuse (PR) in single cell for massive multiuser multiple-input multiple-output (MIMO) transmission to reduce the pilot overhead. For the spatially correlated Rayleigh fading channels, we establish a relationship between the channel spatial correlations and the channel power angle spectrum when the base station antenna number tends to infinity. With this channel model, we first show that the sum mean square error of the channel estimation (MSE-CE) can be minimized if the channel angle of arrival intervals of the user terminals reusing the pilots are non-overlapping, which shows the feasibility of PR in massive MIMO channels with constrained channel angular spreads. Then we design the pilot scheduler under the minimum MSE-CE criterion. With the channel estimation error due to PR taken into account, we also develop the closed-form robust uplink receiver and downlink precoder that minimize the sum MSE of the signal detection. Simulation results show that the proposed PR scheme provides significant performance gains over the conventional orthogonal training scheme in terms of the net spectral efficiency.

This letter proposes a novel deramp space-time adaptive processing (Deramp-STAP) method for synthetic aperture radar (SAR) systems to achieve effective clutter suppression. Compared with the traditional STAP, the proposed method can overcome the spectral wrapping problem of a moving target from a Doppler shift. Furthermore, in the case of signal undersampling, the ambiguously focused position in azimuth for a moving target can be avoided by the proposed method. Moreover, the computational complexity can be drastically reduced because we only need to consider the baseband velocity of the moving target; here, the range of the baseband velocity is much smaller than that of the real target velocity, for clutter suppression in this method. Simulation results validate the effectiveness of the proposed algorithm.

For the squinted terrain observation by progressive scans (TOPS) imaging mode, three problems need to be considered: azimuth spectrum aliasing, serious range-azimuth coupling, and azimuth time aliasing after range cell migration correction (RCMC). For these problems, a subaperture imaging algorithm based on the modified range migration algorithm (RMA) combined with spectral analysis (SPECAN) is proposed in this letter. Echo data are properly divided into subapertues so that the 2-D spectrum of each subaperture without aliasing can be obtained; then, the modified RMA is used to perform RCMC; finally, the signal is focused in the Doppler domain by SPECAN and deramping after subaperture recombination. Both simulated and real SAR data in the squinted TOPS mode are used to validate the proposed algorithm.

A generalized Chinese remainder theorem (CRT) for the determination of two integers is studied in this letter, where the correspondence between the remainders and the two integers in each residue set is not known. A better range than the existing known ones of two integers that can be uniquely determined from their residue sets is first obtained. Then, a closed-form and simple determination algorithm is proposed. Finally, a better sufficient condition on the range of determinable two integers is obtained when the number of erroneous residue sets is given. The study is motivated and has applications in the determination of multiple frequencies from multiple undersampled waveforms.

Progressive edge-growth (PEG) algorithm construction builds a Tanner graph, or equivalently a parity-check matrix, for an LDPC code by establishing edges between the symbol nodes and the check nodes in an edge-by-edge manner and maximizing the girth in a greedy fashion. This approach is simple but the complexity of the PEG algorithm scale is O(nm), where n is the number of symbol nodes and m is the number of check nodes. We deal with this problem by construct a base matrix Hbof size mb× nbwith the PEG algorithm and simultaneously expand this base matrix into a parity-check matrix H of size mx n via the the Chinese remainder theorem (CRT), where m ≫ mband n ≥ nb. The size of the base matrix is expanded without decreasing the girth. For convenience, the PEG and CRT combined algorithm is referred to as the PEG-CRT algorithm in this paper. Since a smaller matrix is constructed with the PEG algorithm and the complexity of the CRT computation is negligible compared to the PEG algorithm, the complexity of the whole code construction process is reduced. Furthermore, the proposed algorithm has a potential advantage of saving storage space by storing a smaller matrix Hband expanding it to H "on-the-fly" in hardware. The expanded matrix H preserves the important properties of base matrix such as large girth, flexible code rate and low density. The complexity analysis shows that the complexity of the PEG-CRT algorithm does not grow with the code length n. Simulation results show that compared with the PEG LDPC codes of length nb, the expanded PEG-CRT LDPC codes have better bit error rate (BER) performance with the iterative decoding. It is also shown that compared with PEG LDPC codes of length n, which constructed with higher complexities, the PEG-CRT codes have similar BER performance.

In this paper, we study the precoder design problem for MIMO-OFDM systems with insufficient cyclic prefix (CP) using statistical channel state information (CSI) at the transmitter. In our previously proposed channel independent precoding scheme, an interference nulling based precoding structure is studied, which can assist to either cancel interblock interference (IBI) or separate the subspace occupied by IBI from the subspace used for information symbols. In this work, we consider a robust precoder design which combines statistical CSI at the transmitter and the interference nulling based precoding structure to gain a better performance compared to the channel independent precoding scheme. Statistical channel state information at the transmitter in the form of covariance matrix of the MIMO-OFDM channel matrix is utilized to perform the robust precoding. The design criterion for the precoder design is to minimize the maximum average mean squared error (MSE) of all the transmitted symbols during one OFDM block interval. To achieve this goal, the precoder design exhibits a similar procedure to the optimized design framework for MIMO transceivers for either perfect or statistical CSI with no interference effect, but with a distinct equivalent channel. Since the average MSEs are set equal using this precoder, the BER performance is better than our previously proposed channel independent precoder and the simulation validates this result.

For high-resolution wide-swath (HRWS) SAR based on multiple receive apertures in azimuth, this paper proposes a novel imaging approach for moving targets. This approach utilizes the wide bandwidth characteristics of the transmitted signal (multiple wavelengths) to estimate the moving target velocity. First, this paper explains that there is a phase mismatch (PM) between azimuth channels for the echo of a moving target, which depends on range frequency. In order to correct the PM, an algorithm based on local maximum-likelihood minimum entropy is proposed. The linear dependence of the PM on range frequency is employed to estimate the target velocity. Second, after the signal reconstruction in Doppler frequency and the compensation of the PM for a moving target, the estimated target velocity is utilized to implement the linear range cell migration correction and the Doppler centroid shifting. Then, the quadratic range cell migration is corrected by the keystone processing. After that, the focused moving target image can be obtained using the existing azimuth focusing approaches. Theoretical analysis shows that no interpolation is needed. The effectiveness of the imaging algorithm for moving targets is demonstrated via simulated and real measured ship HRWS ScanSAR data.

In order to obtain high-resolution wide-swath (HRWS) images, the multichannel in azimuth synthetic aperture radar (SAR) system has been adopted to deal with the contradiction problem between high resolution and low pulse repetition frequency (PRF). In this paper, a novel channel-calibration method is proposed for the multichannel in azimuth HRWS SAR imaging system. During the channel calibration, the mismatch between the channels, which results from the gain-phase error and the range sampling time error, is first corrected by the coarse-calibration processing in the range frequency domain. Then, the along azimuth baseline measurement error is estimated. Considering the range variance in the residual phase error, the data are processed in blocks along the range time domain, and the error of every subblock data is estimated. After that, a fitting and filtering is implemented along the range to the estimated values of the phase error of all subblocks. The range-variant phase error is then compensated using their estimated values. After channel calibration, this paper also presents a new Doppler ambiguity suppression algorithm which nulls the ambiguity components in the Doppler domain. The newly proposed algorithm outperforms the post-Doppler ambiguity suppression algorithm. The airborne real measured scan synthetic aperture radar data, which are acquired by a seven-channel in azimuth SAR imaging system with the system working at X-band, are utilized to demonstrate the performance of the newly proposed channel-calibration method and the new Doppler ambiguity suppression algorithm.

In this paper, we investigate the security issue of a two-hop amplify-and-forward multiple-input multiple-output wireless relay network in the existence of a multiantenna eavesdropper. The optimal scheme to achieve the secrecy capacity involves a nonconvex optimization and is still an open problem. Aiming to find an efficient way to enhance the secrecy rate with a tractable complexity, we propose a suboptimal joint source and relay linear precoding and power allocation scheme. In the scheme, the source node adopts a generalized singular value decomposition (SVD)-based precoding to transmit the signal in the first phase, and the relay node forwards the received signal based on the SVD precoding in the null-space of the wiretap channel in the second phase. Power allocations in both phases are optimized to maximize the secrecy rate by an alternating iterative optimization algorithm. Each iteration involves two subproblems. One has a water-filling solution and the other has a closed-form solution or a water-filling-like solution as well, both of which are computationally very efficient. The iteration converges fast and we prove that it guarantees to find a stationary optimum. Furthermore, we show that when the eavesdropper has equal or more antennas than the source does, the secrecy rate is a quasi-concave function of the source power so that allocating all the source power is generally not optimal. Numerical evaluation results are provided to show the effectiveness of the iterative algorithm and the proposed secrecy scheme.

Gomadam recently proposed two distributed interference alignment algorithms, namely the zero-forcing and the maximal signal to interference plus noise ratio (max-SINR) algorithms. Both of them only require local channel state information and no symbol extension is needed. Then, Ning showed that when only one stream of information symbols is sent by each user, interference alignment may achieve receive diversity using the max-SINR algorithm. This result was, however, derived only based on an assumption. In this paper, using a different approach, we prove that interference alignment using the max-SINR algorithm indeed achieves receive diversity without the assumption used by Ning The result in this paper not only completes the proof of the result by Ning , but also generalizes it by allowing more than one stream of information symbols to be sent by each user.

In this paper, two-relay full-duplex asynchronous cooperative networks with cross-talks are considered. We show that the cross-talk interference at relays cannot be removed well even when the relays know all the accurate channel state information, for which a partial distributed linear convolutional space-time coding scheme (partial DLC-STC) is proposed by using the two-relay cross-talks instead of removing them. Simulation results show that the partial DLC-STC for two-relay full-duplex asynchronous cooperative networks can achieve full asynchronous diversity.

Vector Orthogonal Frequency Division Multiplexing (V-OFDM) for single transmit antenna systems is a generalization of OFDM where Single-Carrier Frequency Domain Equalization (SC-FDE) and OFDM are two special cases. It has been shown to be able to collect multipath diversity and thus generally outperforms OFDM. The performance of OFDM under phase noise has been extensively studied in literature. These effects can be summarized as introducing a Common Phase Error (CPE) and Inter Carrier Interference (ICI) resulting in poor performance. The performance of V-OFDM under phase noise still remains uninvestigated and in this paper it is shown that phase noise in V-OFDM systems leads to a Common Vector Block Phase Error (CVBPE) as well as an Inter Vector Block Carrier Interference (IVBCI) effect. Both of these effects are detrimental to the performance of a V-OFDM system. An exact expression for the Signal to Interference Noise Ratio (SINR) for a V-OFDM system is derived and found to agree closely with simulation results.

In millimeter-wave communications (MMWC), multipath components (MPCs) have different steering angles and independent fadings. Park and Pan recently proposed a simple scheme for both transmitter and receiver to concurrently beamform towards multiple steering angles of MPCs to achieve both array gain and diversity gain. However, when the number of MPCs is greater than that of the transmit or receive antennas, a solution of antenna weight vector (AWV) does not exist. To cope with this problem, two multipath grouping (MPG) schemes, namely MPG-steering vector grouping and MPG-channel vector grouping, are proposed. These schemes group the MPCs that have close steering angles, and define an equivalent MPC for each non-empty group in both the transmitter and receiver. As the number of non-empty groups is always no larger than that of antennas in both the transmitter and receiver, a solution of AWV is guaranteed. Moreover, performance comparisons show that the two proposed MPG schemes achieve not only full diversity, but also an even better array gain than that of the scheme proposed by Park and Pan.

Cooperative beamforming and jamming are two efficient schemes to improve the physical-layer security of a wireless transmission in the presence of a passive eavesdropper. However, in most works they are discussed separately. In this paper, we propose a joint cooperative beamforming and jamming scheme to enhance the security of a cooperative relay network, where some intermediate nodes adopt distributed beamforming while the others jam the eavesdropper, simultaneously. Subjected to the more practical individual power constraint of each node, we propose a null-space beamforming based secrecy strategy. The beamformer design can be optimized by a bisection method and second-order convex cone programming (SOCP). Simulations show the joint scheme greatly improves the security.

In this paper, we study space-time block code (STBC) designs for two-user MIMO X channels to achieve full diversity when the inter-user interference is cancelled by the group zero-forcing receiver. To do so, we first propose a design criterion and then propose a systematic design to satisfy the criterion so that it achieves the full diversity. The code rate approaches one when encoding length gets large. We prove that full diversity can be achieved by the proposed STBC without the channel information at the transmitters. Simulation results are provided to demonstrate the theory.

Exploiting the idea of cooperative communications is an efficient way to improve the physical-layer security of a wireless transmission in the presence of passive eavesdroppers. In this paper, we propose a hybrid cooperative relaying and jamming scheme to enhance the security of a two-way relay network, where some intermediate nodes help to relay the signal to the legitimate terminals via distributed beamforming and the others jam the eavesdropper simultaneously. In such a way, both two cooperative phases of the data transmission are protected. Subjected to the more practical per-node power constraint of each node and without the channel state information (CSI) of the eavesdropper, we propose a scheme to enhance the secrecy of the two terminals. It is shown that the problem can be transformed into a semidefinite programming (SDP) problem with an additional rank-1 constraint. We then develop a penalty function method and an iterative algorithm to solve such a problem efficiently, instead of the popular semi-definite relaxation (SDR) and randomization techniques proposed in the previous literatures. Simulations show that the proposed hybrid scheme greatly improves the security of the two-way relay networks.

In this paper, the problem of semi-distributed resource allocation is considered in an orthogonal frequency division multiple access (OFDMA) downlink transmission of wireless relay networks, where subcarriers are allocated to the relay stations at the base station (BS) only with a small part of channel state information (CSI), and afterwards subcarrier and power are allocated to the users at the relay stations with perfect CSI. A high-efficiency semi-distributed algorithm of linear complexity is proposed to address the joint resource allocation, which observably reduces the computational complexity of the subcarrier allocation at the BS and alleviates the uplink transmission of CSI feedback from the relay stations. It is shown through numerical evaluation that the performance of the proposed semi-distributed algorithm is close to the optimal joint subcarrier and power allocation, which is of exponential complexity and requires entire CSI at the BS.

The articles in this special issue focus on the technology and applications supported by virtual multiple antennas, or VMIMOs. The impetus for this has been spurred by the strong desire to understand VMIMO, which is a rapidly growing research area. VMIMO is believed to be a key technology for beyond 4th generation mobile communications technologies (B4G). It enables one to make use of all the neighboring terminals and amortize the cost of multiple antennas; hence, a large MIMO channel can be created to increase capacity significantly as well as improve error rate performance. Nevertheless, fundamental roadblocks need to be addressed in order to take full advantage of VMIMO.

Doppler rate is an important parameter in synthetic aperture radar (SAR) signal processing since it affects the SAR image focusing. There are many approaches to estimate the Doppler rate from SAR data; however, some approaches are not appropriate for spotlight SAR, which is focused with the two-step algorithm, since, after azimuth preprocessing, the signal is aliased in the azimuth time domain. Although the shift-and-correlation (SAC) algorithm may be suitable for such signals, it is proposed for the stripmap imaging mode; and when it is used to estimate the Doppler rate for spotlight SAR, some problems, such as the high computational load from zero padding and the constraint of the focus depth, may occur. In this letter, an improved Doppler rate estimation approach, which is called the Keystone-SAC algorithm, is proposed. An iterative scheme is presented to estimate the ambiguity number, and a special case when the ambiguity number splits into two numbers is analyzed. The real spotlight SAR data processing results are used to validate the effectiveness of the proposed algorithm.

In this letter, we discuss the problem that linear range cell walk correction in the azimuth time domain may cause space variation along the azimuth not only to the quadratic phase but also to the quadratic range cell migration (QRCM) under the conditions of high resolution and large scene along the azimuth. Moreover, an algorithm is proposed to deal with this problem. The proposed algorithm adopts the azimuth space variation filtering in the range frequency domain. In addition, the range-dependence component of QRCM is corrected by linear chirp scaling, and the unified QRCM can be corrected in the 2-D frequency domain. The proposed algorithm, without interpolation, can be easily implemented by integrating with motion compensation for image processing. Simulation and airborne strip-map real data show the accuracy and efficiency of the proposed algorithm.

In airborne/stationary bistatic-synthetic-aperture-radar imaging, translational invariance was no longer valid. After range cell migration correction, the range-compressed signal under the same range gate exhibited azimuth-dependent FM rates that made the motion-induced phase error difficult to separate from the echoes. To solve this problem, an azimuth-dependent phase gradient autofocus (PGA) algorithm was proposed. Different from the conventional PGA, the residual quadratic phase arising from the azimuth-dependent FM rates was additionally estimated and compensated. As the influence of the azimuth-dependent FM rates was greatly reduced, a phase gradient estimator was subsequently applied for accurate phase error retrieval. Acquired raw data were analyzed to verify the proposed algorithm.

In the above paper (ibid., vol. 10, no, 6, pp. 1290-1294, Nov. 2013), there is an error in equation (5). The correction is presented here.

Cooperative beamforming and jamming are two efficient schemes to improve the physical-layer security of a wireless relay system in the presence of passive eavesdroppers. However, in most works these two techniques are adopted separately. In this letter, we propose a joint cooperative beamforming and jamming scheme to enhance the security of a cooperative relay network, where a part of intermediate nodes adopt distributed beamforming while others jam the eavesdropper, simultaneously. Since the instantaneous channel state information (CSI) of the eavesdropper may not be known, we propose a cooperative artificial noise transmission based secrecy strategy, subjected to the individual power constraint of each node. The beamformer weights and power allocation can be obtained by solving a second-order convex cone programming (SOCP) together with a linear programming problem. Simulations show the joint scheme greatly improves the security.

In this paper, a new interference nulling based channel independent precoding for MIMO-OFDM systems of nttransmit and nrreceive antennas with insufficient cyclic prefix (CP) is proposed. By employing the notion of interference nulling, we show that our proposed channel independent precoding scheme can eliminate the inter-block interference (IBI) caused by the insufficient CP with higher bandwidth efficiency than the conventional zero-padded or a sufficient CP added block transmission system when nr≤ nt. It is also shown that when nr>; nt, the IBI can be eliminated without the need of any zero-padding or adding CP or precoding when the OFDM block length is not too small.

Partial interference cancellation (PIC) group decoding is an attractive decoding alternative for multiple-input multiple-output (MIMO) wireless communications. It can well deal with the tradeoff among rate, diversity and decoding complexity of space-time block codes. In this paper, a design criterion of full-diversity space-frequency codes (SFC) is proposed for MIMO-OFDM systems with the PIC group decoding. Based on the criterion, a systematic design of full-diversity SFC is proposed that can achieve full diversity under the PIC group decoding. Simulation results of the newly proposed codes demonstrate the theory.

In this paper, we study space-time block code (STBC) designs for two-user MIMO X channels to achieve full diversity when the inter-user interference is cancelled by the group zero-forcing receiver. To do so, we first propose a design criterion and then propose a systematic design to satisfy the criterion so that it achieves full diversity. The code rate approaches one when encoding length gets large. We prove that full diversity can be achieved by the proposed STBC without the channel information at the transmitters. Simulation results are provided to demonstrate the theory.

Range cell migration (RCM) correction and azimuth spectrum being contained entirely in baseband are critical for ground moving-target imaging (GMTIm). Without the azimuth spectrum entirely contained within baseband and a proper RCM correction, the image will be defocused, or artifacts may appear in the image. An instantaneous-range-Doppler algorithm of GMTIm based on deramp-keystone processing is proposed. The main idea is to focus all the targets in the scene at an arbitrarily chosen azimuth time. With our proposed algorithm, RCMs of all targets in the scene are removed without a priori knowledge of their accurate motion parameters. The targets with azimuth spectrum not entirely in baseband, i.e., azimuth spectrum within an ambiguous pulse repeating frequency (PRF) band or spanning neighboring PRF bands, can also be effectively dealt with simultaneously. Theoretical analysis shows that no interpolation is needed. The simulated and real data are used to validate the effectiveness of this method.

For different applications with different requirements, many synthetic aperture radar (SAR) modes have been developed in the literature, such as, Terrain Observation by Progressive Scans (TOPS) SAR and sliding spotlight SAR. In this paper, we call TOPS SAR, sliding spotlight SAR, and spotlight SAR as beam steering SAR (BS-SAR for short). Comparing with stripmap SAR, BS-SAR can obtain a wide diversity of resolutions by increasing or reducing the azimuth synthetic time. Traditional polar formation algorithm (PFA) is an efficient algorithm which is mainly developed for spotlight SAR. The PFA has been validated to obtain well-focused results of raw data. In this paper, we extend the traditional PFA to process sliding spotlight SAR and TOPS SAR data, and we call it generalized PFA (GPFA). Comparing with the traditional PFA, GPFA contains a different azimuth deramping function and an additional azimuth scaling operation. The simulated and real data are used to validate the effectiveness of this method.

Terrain Observation by Progressive Scans (TOPS) synthetic aperture radar (SAR) and spotlight SAR are advanced SAR imaging modes for wide range swath and high resolution. In order to obtain a wider range coverage, azimuth multichannel is introduced in the literature. Since the azimuth bandwidth of beam steering SAR (BS-SAR; spotlight SAR, sliding spotlight SAR, or TOPS SAR) is much greater than that of a stripmap SAR, a signal reconstruction algorithm used for multichannel stripmap SAR may not be effective for multichannel BS-SAR. In this paper, a multichannel full-aperture azimuth processing algorithm is proposed for a BS-SAR. The key of this algorithm lies in the beam and the azimuth bandwidth compressions of multichannel signals in the Doppler-array and slow time-angle planes, respectively. Through compression processing, the beamwidth and the azimuth bandwidth are smaller than the available angle and equivalent pulse repeating frequency , respectively. Then, an improved post-Doppler STAP method is proposed to recover a 2-D spectrum. With the recovered signal, further processing can be utilized to focus the multichannel signal. Simulation and real data results show the effectiveness of the proposed algorithm.

Many imaging algorithms for different modes, such as, stripmap synthetic aperture radar (SAR), spotlight SAR, sliding spotlight SAR, and terrain observation by progressive scans (TOPS) SAR, of SAR have been studied. This paper is to obtain a unified focusing algorithm (UFA) for these SAR modes based on fractional Fourier transform. By defining the rotation-center range, the stripmap SAR and spotlight SAR can be treated as special cases of sliding spotlight SAR or TOPS SAR. Then, a parameterized focusing algorithm determined by the rotation-center range is presented. Data of each mode can be focused by utilizing UFA and selecting parameters or rotation angles. Some application aspects of UFA are also analyzed. Simulation and real data results are presented to validate the analysis and the proposed method.

A novel method to obtain the formulations of the return signals in the 2-D frequency domain for both monostatic and bistatic synthetic aperture radar (SAR) is proposed. In this study, the squinted effective wavelength (SEW) is firstly used, so that the 2-D spectrums can be derived directly from their imaging geometries. For monostatic SAR (MoSAR), the 2-D spectrum is obtained without a lengthy derivation by using the widely-used principle of stationary phase. For the bistatic SAR (BiSAR), based on the assumption that the azimuth time durations of the transmitter and the receiver are the same, two individual SEWs can be derived, as well as the 2-D spectrums that are both concise and of high accuracy. Then, two modified omega-K algorithms based on the two 2-D spectrums are developed to process MoSAR and translational-invariant BiSAR data. Furthermore, as important processing steps of the proposed omega-K algorithms, a modified reference function multiplication and a modified Stolt mapping, which are much more suitable for SAR data processing than the conventional ones, are proposed. Simulations under a wide range of MoSAR and BiSAR system parameters are conducted. Finally, the proposed algorithms are applied to the analysis of acquired data and the results confirm not only the validity of the derived 2-D spectrums for both MoSAR and BiSAR but also the effectiveness of the proposed method.

In this paper, we propose a hybrid cooperative beamforming and jamming scheme to enhance the physical-layer security of a single-antenna-equipped two-way relay network in the presence of an eavesdropper. The basic idea is that in both cooperative transmission phases, some intermediate nodes help to relay signals to the legitimate destination adopting distributed beamforming, while the remaining nodes jam the eavesdropper, simultaneously, which takes the data transmissions in both phases under protection. Two different schemes are proposed, with and without the instantaneous channel state information of the eavesdropper, respectively, and both are subjected to the more practical individual power constraint of each cooperative node. Under the general channel model, it is shown that both problems can be transformed into a semi-definite programming (SDP) problem with an additional rank-1 constraint. A current state of the art technique for handling such a problem is the semi-definite relaxation (SDR) and randomization techniques. In this paper, however, we propose a penalty function method incorporating the rank-1 constraint into the objective function. Although the so-obtained problem is not convex, we develop an efficient iterative algorithm to solve it. Each iteration is a convex SDP problem, thus it can be efficiently solved using the interior point method. When the channels are reciprocal such as in TDD mode, we show that the problems become second-order convex cone programming ones. Numerical evaluation results are provided and analyzed to show the properties and efficiency of the proposed hybrid security scheme, and also demonstrate that our optimization algorithms outperform the SDR technique.

High-resolution and wide-swath (HRWS) synthetic aperture radar (SAR) is an essential tool for modern remote sensing. To effectively deal with the contradiction problem between high-resolution and low pulse repetition frequency and obtain an HRWS SAR image, a multi-channel in azimuth SAR system has been adopted in the literature. However, the performance of the Doppler ambiguity suppression via digital beam forming processing suffers the losses from the channel mismatch. In this paper, a robust channel-calibration algorithm based on weighted minimum entropy is proposed for the multi-channel in azimuth HRWS SAR imaging. The proposed algorithm is implemented by a two-step process. 1) The timing uncertainty in each channel and most of the range-invariant channel mismatches in amplitude and phase have been corrected in the pre-processing of the coarse-compensation. 2) After the pre-processing, there is only residual range-dependent channel mismatch in phase. Then, the retrieval of the range-dependent channel mismatch in phase is achieved by a local maximum-likelihood weighted minimum entropy algorithm. The simulated multi-channel in azimuth HRWS SAR data experiment is adopted to evaluate the performance of the proposed algorithm. Then, some real measured airborne multi-channel in azimuth HRWS Scan-SAR data is used to demonstrate the effectiveness of the proposed approach.

In this paper, a two-relay full-duplex asynchronous cooperative network with the amplify-and-forward (AF) protocol is considered. We propose two distributed space-time coding schemes for the cases with and without cross-talks, respectively. In the first case, each relay can receive the signal sent by the other through the cross-talk link. We first study the feasibility of cross-talk cancellation in this network and show that the cross-talk interference cannot be removed well. For this reason, we design space-time codes by utilizing the cross-talk signals instead of removing them. In the other case, the self-coding is realized individually through the loop channel at each relay node and the signals from the two relay nodes form a space-time code. The achievable cooperative diversity of both cases is investigated and the conditions to achieve full cooperative diversity are presented. Simulation results verify the theoretical analysis.

This paper proposes an interference alignment (IA) based precoder design for OFDM with insufficient cyclic prefix (CP) when only the statistics of channel coefficients is known at the transmitter. This precoding not only performs the inter-block interference (IBI) separation and elimination as in our previously proposed channel independent IA based precoding schemes, but also minimizes the MSE in a linear MMSE receiver and therefore improves the BER performance.

A partial interference cancellation (PIC) group decoding as well as a full diversity design criterion were recently proposed for space-time block code. The PIC group decoding complexity is between the lowest linear decoding complexity and the highest ML decoding complexity, and so is the code rate. Based on the criterion for the PIC group decoding, families of space-time block codes were also proposed recently. However, the interference cancellation operator is hard to determine and with the direct zero-forcing (ZF) PIC group decoding, these codes may not achieve the full diversity. In this paper, we propose a new PIC group decoding method for these existing codes, which has a similar decoding complexity to the ZF PIC group decoding, but achieves full diversity (or nearly).

In this paper, the error rate performance and diversity gain of amplify-and-forward (AF) wireless relay networks are studied. The distributed space-time block code (DSTBC) in a synchronous relay network with multiple relay nodes is considered. The distributed linear dispersion code is used at the relays. The symbol (or codeword) error rate is studied in terms of upper and lower bounds through rigorous derivations, and the diversity gain of the DSTBC is derived, for any number of relay nodes. As a corollary, a necessary and sufficient condition is obtained for achieving full cooperative diversity. Arbitrary coding matrices at the relays, path loss and general linear power allocation are considered.

In this paper, the error rate performance and diversity gain of amplify-and-forward (AF) wireless relay networks are studied. The distributed space-time block code (DSTBC) in a synchronous relay network with multiple relay nodes is considered. The distributed linear dispersion code is used at the relays. The symbol (or codeword) error rate is studied in terms of upper and lower bounds through rigorous derivations, and the diversity gain of the DSTBC is derived, for any number of relay nodes. As a corollary, a necessary and sufficient condition is obtained for achieving full cooperative diversity. Arbitrary coding matrices at the relays, path loss and general linear power allocation are considered. Finally, the orthogonal frequency division multiplexing (OFDM)-based space-time transmission scheme in asynchronous cooperative networks is also discussed.

Group zero-forcing (GZF) detection is an efficient and powerful detection tool that has flexible detection complexity and performance gain between the optimum detection and the ZF detection. In this paper, we give diversity and multiplexing tradeoff (DMT) analysis for a 2-layer D-BLAST transmission under GZF detection over Rayleigh MIMO channels. With M transmit antennas and N receive antennas, the DMT of such a system is shown as d_{out}(r)=(N-1)(M-\frac{r(M+1)}{2})^+ for \frac{2}{M+1}≤ r≤ \frac{2M}{M+1} and d_{out}(r)=(MN-M+1)-\frac{rN(M+1)}{2} for 0≤ r< \frac{2}{M+1}, where d_{out} denotes the diversity gain and r denotes the multiplexing gain.

For along-track multichannel synthetic aperture radar, this paper proposes a novel ground moving target signal model in the high-resolution complex image domain. Based on the range-Doppler imaging of static scene, the 2-D complex response of an isolated rectilinearly moving target is derived via the stationary phase principle (SPP) approximations. It is shown that moving targets in complex domain can be divided into three types according to the 2-D motion distribution and the SPP approximation conditions. Different from the known peaklike response of a static target, different amplitude and phase modulations will appear for different types of moving targets. Moreover, a single target can be split into two targets in the image when its Doppler spectrum spreads over two ambiguous Doppler zones. All types of targets will have the same Doppler interferometric effect along multichannel images, which is decided by the target's ambiguous Doppler frequency. Furthermore, with the proposed signal model, the complex image properties can be completely described and analyzed. Finally, some numerical experiments are also provided to demonstrate the effectiveness of the proposed signal model and analysis.

A partial interference cancellation (PIC) group decoding based space-time block code (STBC) design criterion was recently proposed by Guo and Xia, where the decoding complexity and the code rate traeoff is dealt when the full diversity is achieved. In this paper, two designs of STBC are proposed for any number of transmit antennas that can obtain full diversity when a PIC group decoding (with a particular grouping scheme) is applied at receiver. With the PIC group decoding and an appropriate grouping scheme for the decoding, the proposed STBC are shown to obtain the same diversity gain as the ML decoding, but have a low decoding complexity. The first proposed STBC is designed with multiple diagonal layers and it can obtain the full diversity for two-layer design with the PIC group decoding and the rate is up to 2 symbols per channel use. With PIC-SIC group decoding, the first proposed STBC can obtain full diversity for any number of layers and the rate can be full. The second proposed STBC can obtain full diversity and a rate up to 9/4 with the PIC group decoding. Some code design examples are given and simulation results show that the newly proposed STBC can well address the rate-performance-complexity tradeoff of the MIMO systems.

As a space- and cost-efficient antenna configuration, dual-polarized antennas are widely deployed in real field MIMO wireless communication systems. The Quasi-Orthogonal Space-Time Block Code (QOSTBC), due to its advantages in the transmission rate and the decoding complexity, is an important transmit diversity scheme for more than 2 transmit antennas. In this paper, we investigate the performance of QOSTBC for dual-polarized MIMO channels. We show that when QOSTBC is used with dual-polarized antennas, we need to connect the power amplifiers (PAs) to the differently polarized antennas carefully, in order to avoid more than 1dB performance loss at high cross-polar discrimination (XPD). Also, we show that, if the QOSTBC with constellation rotations, which achieves full diversity in uni-polarized MIMO channels, is used, the performance loss due to the bad PA-to-antenna connections is mitigated. However, when the XPD is high, the QOSTBC without full diversity, if connected properly, can achieve better performance than the QOSTBC with full diversity when connected badly, which shows the importance of a proper connection at high XPD. So, to guarantee robust performance of QOSTBC at any XPD, besides designing codes to have full diversity and good diversity product, connecting the PAs and the antennas properly is also important.

In this paper, we propose two distributed linear convolutional space-time coding (DLC-STC) schemes for full-duplex (FD) asynchronous cooperative communications. The DLC-STC Scheme 1 is for the case of the complete loop channel cancellation, which achieves the full asynchronous cooperative diversity. The DLC-STC Scheme 2 is for the case of the partial loop channel cancellation and amplifying, where some loop signals are used as the self-coding instead of treated as interference to be directly cancelled. We show this scheme can achieve full asynchronous cooperative diversity. We then evaluate the performance of the two schemes when loop channel information is not accurate and present an amplifying factor control method for the DLC-STC Scheme 2 to improve its performance with inaccurate loop channel information. Simulation results show that the DLC-STC Scheme 1 outperforms the DLC-STC Scheme 2 and the delay diversity scheme if perfect or high quality loop channel information is available at the relay, while the DLC-STC Scheme 2 achieves better performance if the loop channel information is imperfect.

In this paper, a design criterion for space-time block codes (STBC) is proposed for two-user MIMO interference channels when a group zero-forcing (ZF) algorithm is applied at each receiver to eliminate the inter-user interference. Based on the design criterion, a design of STBC for two-user interference channels is proposed that can achieve full diversity for each user with the group ZF receiver. The code rate approaches one when the time delay in the encoding (or code block size) gets large. Performance results demonstrate that the full diversity can be guaranteed by our proposed STBC with the group ZF receiver.

ABSTRACT Proportional fairness (PF) scheduling achieves a balanced tradeoff between throughput and fairness and has attracted great attention recently. However, most previous work on PF only considers the single cell scenario. This paper focuses on the problem of achieving network‐wide PF in a generalized multiple base station multiple user network. The problem is formulated as a maximization model and solved using the dual method. By decomposing the dual objective function, we get a distributed pricing based algorithm. Optimality of this algorithm is presented. Although the algorithm is derived using fixed link rate assumption, it can still apply in the presence of time‐varying rates. The proposed algorithm is suitable for distributed systems in the sense that it does not need any inter base station communication at all. Simulations illustrate that the proposed distributed network‐wide PF scheduling algorithm achieves almost the same performance as the centralized one. Compared with traditional local PF (LPF) scheduling, the network‐wide PF scheduling achieves higher throughput, lower throughput oscillation, and greater fairness. Copyright © 2010 John Wiley & Sons, Ltd.

In this paper, a channel independent precoding for MIMO-OFDM systems of nttransmit and nrreceive antennas with insufficient cyclic prefix (CP) is proposed. By empolying the interference alignment (IA) idea, we show that our proposed channel independent precoding scheme can eliminate the interblock interference (IBI) caused by the insufficient CP with a higher bandwidth efficiency than the conventional zero-padding or adding a sufficient CP when nr≤ nt. It is also shown that when nr>; nt, the IBI can be eliminated without any zero-padding or adding CP or precoding when the OFDM block length is not small.

Partial interference cancellation (PIC) group decoding is an attractive method to reduce the maximum-likelihood (ML) decoding complexity of a layered Alamouti code. However, different grouping schemes for the PIC group decoding may have different performances. In this paper, by fully taking advantage of the Alamouti code structure, we analytically calculate the power gains of the information symbols after the PIC for a grouping scheme. We then propose an optimal grouping scheme for the PIC group decoding by proposing a criterion to choose the grouping schemes adaptively so that the power gains of the information symbols are maximized after the PIC.

Partial interference cancellation (PIC) group decoding proposed by Guo and Xia is an attractive decoding alternative for multiple-input multiple- output (MIMO) wireless communications. It can well deal with the tradeoff among rate, diversity and decoding complexity of space-time block codes. In this paper, two design criteria of full diversity space-frequency codes (SFC) are proposed for MIMO- OFDM systems with the PIC group decoding. Based on the criteria, a systematic design of full-diversity SFC with the PIC group decoding is proposed that can achieve full diversity and a rate of $2M_t/(M_t+1)$ for $M_t$ transmit antennas. Performance results demonstrate that the full diversity is offered by the newly proposed SFC with the PIC group decoding.

In this paper, we address the physical-layer security of two-way (bi-directional) transmissions of two terminals with the help of multiple relay nodes in the presence of an eavesdropper, where each node in the network is only equipped with single antenna. The secrecy sum rate is used as the metric of security, which is the rate difference between the legitimate information exchange between the terminals and the information leakage to the eavesdropper. We propose two-phase analog network coding (ANC) and power allocation scheme for the relay nodes and terminals to enhance the security of the data exchange. Three different schemes are proposed. The first scheme is the optimal ANC to achieve the maximum secrecy sum rate, i.e. secrecy capacity, which is found to be mathematically difficult to solve. Two suboptimal schemes are proposed with and without the instantaneous channel state information (CSI) of the eavesdropper, respectively. The solutions of both schemes are shown to be unique and globally optimum, which are solved by convex optimization techniques. Numerical evaluation results of the obtained secrecy sum rate and transmit power are provided and analyzed to show the properties and efficiency of the proposed ANCs.

Traditional channel quantization based methods for encryption key generation usually suffer from the quantization error which may decrease the key agreement ratio between authorized users. In this paper, we make use of the reciprocity of the wireless channel in time-division duplex (TDD) mode, and adopt a closed-form robust Chinese remainder theorem (CRT) to extract the encryption key from the noisy channel estimations. The channel coefficients are regarded as the remainders in robust CRT, and the key is generated from the quotient output of the robust CRT. In addition, a remainder selection procedure is proposed to further improve its performance. Simulations demonstrate the effectiveness of the proposed method.

In wireless communication systems, signal space diversity techniques are usually adopted to combat channel fading by exploiting time diversity, frequency diversity, spatial diversity or a combination of them. Most existing schemes to achieve signal space diversity are based on linear constellation spreading. In this paper, we propose a novel nonlinear signal space diversity technique based on maximum distance separable (MDS) codes. The new technique provides a design flexibility for almost any number of diversity channels and desired diversity orders. We also propose a simple and suboptimal diversity channel selection (DCS) decoding for our new scheme. DCS decoding can greatly reduce the decoding complexity at a cost of marginal performance loss relative to the optimal detection while keeping the diversity order. Simulation results show that with the same throughput but a lower decoding and implementation complexity, our scheme can have superior performance than the optimal linear spreading schemes over either independent fading or additive white Gaussian noise (AWGN) channels.

Time asynchronism is a practical issue needs to be addressed for a general distributed two-way relay network, where two terminal nodes exchange information through multiple spatial-separated relay nodes. In this paper, we propose an analog network coding (ANC) scheme for a time asynchronous two-way relay network. In the proposed scheme, each relay node linearly transforms the received mixed asynchronous signals in the first time-slot by a Toeplitz matrix, and then broadcasts them back to the terminals in the second time-slot. A sufficient condition is derived for the proposed ANC to achieve full cooperative diversity using only linear receivers at the terminal nodes, such as zero-forcing (ZF), or minimum mean square error (MMSE) receivers, with any delay profiles of the timing errors. The design of the coefficients in the Toeplitz matrix to satisfy the sufficient condition is also proposed, which is shown coincides with the design we proposed in the previous work. Simulation results verify our analysis of the diversity order.

Cooperative relay networks are inherently time- and frequency- asynchronous due to their distributed nature. The existence of multiple time and frequency offsets results in a time-varying and frequency-selective equivalent channel between relay nodes and destination node. In this paper, we propose a transceiver scheme to combat both time and frequency offsets for cooperative relay networks. At the relay nodes, the distributed linear convolutive space-time coding is adopted to achieve the time-asynchronous full cooperative diversity. This transmission scheme leads to an equivalent channel with special structure at the destination node. By taking full advantage of this special structure, fast Kalman equalizations based on linear minimum mean square error (LMMSE) and MMSE decision feedback equalizers (MMSE-DFE) are proposed for the receiver, where the estimation of the state vector (information symbols) can be operated recursively and computational efficiently. The proposed scheme can achieve considerable diversity gain with both time and frequency offsets, and also applies to frequency-selective fading channels.

Inverse synthetic aperture radar (ISAR) imaging of maneuvering targets via matching pursuit (MP) can produce high resolution and remove sidelobe effect, providing that the target rotation rate is already known. For non-cooperative uniformly rotating targets, the rotation rate is unknown and should be retrieved together with the ISAR image. In this paper, a computational efficient parametric MP is proposed to simultaneously retrieve the rotation rate and the high-resolution ISAR image. The joint estimation of ISAR image and rotation rate is converted into alternate independent estimations, i.e. alternately estimating the ISAR image and rotation rate in an iterative manner. With the iterations, the rotation rate estimate converges to the true value and meanwhile the clear ISAR image with high resolution and low sidelobes may be obtained. Numerical examples show that the approach is efficient in estimating the real target rotation rate and recovering the high resolution ISAR image.

In this paper, the error performances and diversity gains of two wireless relay networks are studied. The distributed space-time block coding in a synchronous relay network with multiple relay nodes is considered. The distributed linear dispersion code is used at the relays. The symbol (or codeword) error rate is studied in terms of upper and lower bounds through rigorous derivations, and sufficient conditions the system achieves full cooperative in two cases without and with a direct path from the source to the destination are also given. Different from the existing works, here the strict proof of the diversity gain through analyzing upper and lower bounds of SER performance is given. Finally, the orthogonal frequency division multiplexing (OFDM)-based space-time transmission scheme in asynchronous cooperative networks is also discussed.

Single-carrier frequency division multiple access (SC-FDMA) has appeared to be a promising technique for high data rate uplink communications. Aimed at SC-FDMA applications, a cyclic prefixed version of the offset quadrature amplitude modulation based OFDM (OQAM-OFDM) is first proposed in this paper. We show that cyclic prefixed OQAM-OFDM (CP-OQAM-OFDM) can be realized within the framework of the standard OFDM system, and perfect recovery condition in the ideal channel is derived. We then apply CP-OQAM-OFDM to SC-FDMA transmission in frequency selective fading channels. Signal model and joint widely linear minimum mean square error (WLMMSE) equalization using a prior information with low complexity are developed. Compared with the existing DFTS-OFDM based SC-FDMA, the proposed SC-FDMA can significantly reduce envelope fluctuation (EF) of the transmitted signal while maintaining the bandwidth efficiency. The inherent structure of CP-OQAM-OFDM enables low-complexity joint equalization in the frequency domain to combat both the multiple access interference and the intersymbol interference. The joint WLMMSE equalization using a prior information guarantees optimal MMSE performance and supports Turbo receiver for improved bit error rate (BER) perform BER) performance. Simulation results confirm the effectiveness of the proposed SC-FDMA in terms of EF (including peak-to-average power ratio, instantaneous-to-average power ratio and cubic metric) and BER performances.

The decoding complexity of the layered Alamouti code with the maximum-likelihood (ML) receiver is high. Partial interference cancellation (PIC) group decoding is an attractive method to reduce the ML decoding complexity. However, different grouping schemes for the PIC group decoding may have different performances. In this letter, by fully taking advantage of the Alamouti code structure, we analytically calculate the power gains of the information symbols after the PIC for a grouping scheme, where the zero-forcing (ZF) operator is used for the PIC. We then propose an optimal grouping scheme for the PIC group decoding by proposing a criterion to choose the grouping schemes adaptively so that the power gains of the information symbols are maximized after the PIC.

In this letter, we propose a systematic design of space-time block codes (STBC) which can achieve high rate and full diversity when the partial interference cancellation (PIC) group decoding is used at receivers. The proposed codes can be applied to any number of transmit antennas and admit a low decoding complexity while achieving full diversity. For M transmit antennas, in each codeword real and imaginary parts of PM complex information symbols are parsed into P diagonal layers and then encoded, respectively. With PIC group decoding, it is shown that the decoding complexity can be reduced to a joint decoding of M/2 real symbols. In particular, for 4 transmit antennas, the code has real symbol pairwise (i.e., single complex symbol) decoding that achieves full diversity and the code rate is 4/3. Simulation results demonstrate that the full diversity is offered by the newly proposed STBC with the PIC group decoding.

In this paper, we propose a family of distributed space-frequency codes (SFCs) for frequency asynchronous cooperative relay networks with multiple carrier frequency offsets (CFOs) and flat fading channels. The codes have the special structures that the columns of each code matrix can be divided into several orthogonal groups and each group is stacked by several sub-blocks with Toeplitz structure. These codes possess two interesting properties: a) Even under the frequency-asynchronous scenario, the codes can , namely, if R relay nodes participate the cooperation adopting the proposed SFCs, then at the destination node, the achievable diversity order is R; b) Only , such as zero-forcing (ZF) and minimum mean square error (MMSE) receivers, are required to collect the full cooperative diversity, instead of the maximum likelihood (ML) detector as utilized in most existing STC (SFC) schemes. The codes in this family have different symbol rates, orthogonality and performances for different numbers of relay nodes, which can be adjusted by choosing different design parameters. It is shown that frequency-reversal SFC (FR-SFC), and frequency-shift SFC (FS-SFC) proposed in previous papers are only two special members of the code family, and new SFCs outperforming both of them are proposed.

Two-path or successive relaying has recently emerged as a promising cooperative communication protocol to improve spectral efficiency in half-duplex relaying networks. In this paper, we consider decode-and-forward (DF) version of the two-path relaying protocol. We analyze the fundamental performance of this protocol in terms of the diversity-multiplexing tradeoff (DMT). We first derive the DMT for this protocol, where perfect decoding at the relays is assumed, and show that it approaches the 3 x 1 multiple-input single-output (MISO) DMT. We then remove the assumption of perfect decoding at the relays and derive the closed-form expression of the achievable DMT based on the relative distances between nodes. Specifically, we found that for sufficiently long transmission length, if the average source-relay SNR is at least 2.5 times (measured in dB) of other links, the 3 x 1 MISO DMT is achieved. Successive interference cancellation at the relays is also proposed to further improve the performance of the DMT.

Space-time code designs based on a partial interference cancellation (PIC) group decoding have been recently proposed. The PIC group decoding complexity depends on the group size and is between the lowest linear receiver complexity and the highest ML decoding complexity. The symbol rate for a space-time code achieving full diversity with the PIC group decoding is also between those for the linear receivers and the ML decoding. In this paper, we propose a new decoding, called conditional PIC group decoding, that is between the PIC group decoding and the ML decoding. With the proposed new decoding, we obtain a new design criterion for space-time codes to achieve full diversity, which is also between the one with the PIC group decoding and the one with the ML decoding. We then present some designs that satisfy the new criterion and in the meantime have higher symbol rates than that for the PIC group decoding.

Signal space diversity technique has been widely adopted in wireless communication systems to combat channel fading by exploiting time diversity, frequency diversity, spatial diversity or a combination of them. Most existing signal space diversity schemes are based on linear constellation spreading. In this paper, we propose a novel nonlinear signal space diversity technique based on maximum distance separable (MDS) codes. The new technique provides a design flexibility to support almost any number of diversity channels and desired diversity orders. We also propose a simple and suboptimal diversity channel selection (DCS) decoding for our new scheme. DCS decoding can significantly reduce the decoding complexity with a marginal performance loss relative to the optimal detection while keeping the diversity order. Simulation results show that with the same throughput but a lower decoding and implementation complexity, our scheme can achieve superior performance than the conventional linear spreading schemes over either independent fading or additive white Gaussian noise (AWGN) channels.

In this paper, we propose a systematic design of space-time block codes (STBC) which can achieve high rate and full diversity when the partial interference cancellation (PIC) group decoding is used at receivers. The proposed codes can be applied to any number of transmit antennas and admit a low decoding complexity while achieving full diversity. For $M$ transmit antennas, in each codeword real and imaginary parts of $PM$ complex information symbols are parsed into $P$ diagonal layers and then encoded, respectively. With PIC group decoding, it is shown that the decoding complexity can be reduced to a joint decoding of $M/2$ real symbols. In particular, for $4$ transmit antennas, the code has real symbol pairwise (i.e., single complex symbol) decoding that achieves full diversity and the code rate is $4/3$. Simulation results demonstrate that the full diversity is offered by the newly proposed STBC with the PIC group decoding.

Locations of moving targets in synthetic aperture radar (SAR) images are determined not only by their geometric locations but also by their velocities that cause their SAR images defocused, smeared, and mis-located. With a linear antenna array, velocity synthetic aperture radar (VSAR) can detect, focus, and locate slowly moving targets well. However, it may mis-locate fast moving targets in the azimuth (cross-range) direction, and sometimes even in the ground range direction if the targets are elevated above the ground. In this paper, we propose an antenna array approach with cross-track interferometry, in which multiple wavelength signals are transmitted. It is shown that our proposed multiple-frequency interferometric velocity SAR (MFIn-VSAR) can locate both slowly and fast moving elevated targets correctly.

Nowadays proportional fairness (PF) scheduling has attracted much attention in various wireless systems. But most previous work just considers the systems with only one base station (or data center), which just achieves local PF. In this paper we consider the problem of achieving global PF for the multiple base station multiple user scenario. Compared with previous works in the literature, the main contributions of this paper are threefold: (1) The PF rule is employed in the multiple base station multiple user case. Here we propose an approximate gradient projection based PF scheduling scheme, GP-PF, to approach the global PF optimality. And the convergence of the proposed algorithm is proved. (2) We study the communication and computation complexity of GP-PF and show that the developed GP-PF algorithm can be implemented either in a user selection mode, or in a random accessing way. And GP-PF applies to distributed systems in the sense that it does not need any inter base station cooperation at all. (3) By simulation, it is shown that global PF leads to higher throughput and greater fairness for users than local PF.

An algorithm based on matching pursuit (MP) technique is proposed for inverse synthetic aperture radar (ISAR) imaging of maneuvering targets. The received ISAR echo is decomposed into many basis sub-signals that are generated by discretizing the target spatial domain and synthesizing the ISAR data for every discretized spatial position, and then the ISAR imaging problem is converted into the sub-signal selection problem. The basis sub-signals that indeed contribute to the ISAR echo are selected by using the MP technique, and the projection coefficients of the ISAR echo on the selected basis sub-signals represent the ISAR image. In the case of unknown rotation rate of the target, the true rotation rate is obtained by combining the MP technique with the maximum contrast search. Numerical examples show that the proposed algorithm can produce high resolution and remove sidelobe artifacts.

Space-time code designs based on a partial interference cancellation (PIC) group decoding has been recently proposed. The PIC group decoding complexity depends on the group size and is between the lowest linear receiver complexity and the highest ML decoding complexity. The symbol rate for a space-time code achieving full diversity with the PIC group decoding is also between those for the linear receivers and the ML decoding. In this paper, we propose a new decoding that is between the PIC group decoding and the ML decoding. With the proposed new decoding, we obtain a new design criterion for space-time codes to achieve full diversity, which is also between the one with the PIC group decoding and the one with the ML decoding. We then present some designs that satisfy the new criterion and in the meantime have higher symbol rates than that for the PIC group decoding.

Recently, space-time block codes (STBC) with a partial interference cancellation (PIC) group decoding was proposed to deal with the tradeoff among rate, diversity and complexity by Guo and Xia. In this paper, we propose a systematic design of STBC with reduced-complexity PIC group decoding. The proposed STBC is featured as Alamouti-type block code in which each entry of Alamouti code matrix is replaced by a block of multi-layer coded symbols. With the PIC group decoding and a particular grouping scheme, the proposed STBC can achieve full diversity, rate- 2M/M+2 and a low-complexity decoding for M transmit antennas. Simulation results show that the proposed codes can achieve the full diversity with PIC group decoding while requiring half decoding complexity of the existing codes.

Proportional fairness (PF) scheduling achieves a balanced tradeoff between throughput and fairness and has attracted great attention recently. However, most previous works on PF only consider the single cell scenario. This paper focuses on the problem of achieving global PF in a generalized multiple base station multiple user network. The problem is formulated as a maximization model and solved using dual method. By decomposing the dual objective function, we get a pricing based PF algorithm. Optimality of this algorithm is presented. Although the algorithm is derived using fixed link rate assumption, it can still achieve network wide PF in the presence of time varying rates. We show that the proposed algorithm is suitable for distributed systems in the sense that it does not need any inter base station communication at all. Simulations illustrate that compared with traditional local PF scheduling, global PF scheduling achieves higher throughput, lower throughput oscillation and greater fairness.

In this paper, we propose a simple orthogonal space time transmission scheme for asynchronous cooperative systems. In the proposed scheme, OFDM is implemented at the source node, some very simple operations, namely time reversion and complex conjugation, are implemented at the relay nodes, and a two-step of cyclic prefix (CP) removal is performed at the destination. The CP at the source node is used for combating the frequency selective fading channels and the timing errors. In this scheme, the received signals at the destination node have the orthogonal code structure on each subcarrier and thus it has the fast symbol-wise ML decoding and can achieve full spatial diversity when SNR is large without the requirement of symbol level synchronization. It should be emphasized that since no Add/Remove CP or IFFT/FFT operation is needed at the relay nodes, the relay nodes do not have to know any information about the channels and the timing errors, and the complexity of the relay nodes is very low.

In wireless communication systems, signal space diversity techniques are usually adopted to combat channel fading by exploiting time diversity, frequency diversity, spatial diversity or a combination of them. Most existing schemes to achieve signal space diversity are based on linear constellation spreading. In this paper, we propose a novel nonlinear signal space diversity technique based on maximum distance separable (MDS) codes. The new technique provides a design flexibility for almost any number of diversity channels and desired diversity orders. We also propose a simple and suboptimal diversity channel selection (DCS) decoding for our new scheme. DCS decoding can greatly reduce the decoding complexity at a cost of marginal performance loss relative to the optimal detection while keeping the diversity order. Simulation results show that with the same throughput but a lower decoding and implementation complexity, our scheme can have superior performance than the optimal linear spreading schemes over either independent fading or additive white Gaussian noise (AWGN) channels.

In this paper, a correction is made to the criterion for a linear dispersion space-time block code to achieve full diversity with the partial interference cancellation (PIC) group decoding recently published in the above titled paper (ibid., vol. 55, no. 10, pp. 4366-4385).

The Alamouti orthogonal space-time block code for two transmit antennas was designed primarily for QAM and PSK modulations, and we have previously generalized it for the continuous phase modulation (CPM), denoted as OST-CPM, by maintaining the orthogonality (for the fast ML decoding/demodulation) and the phase continuity of two signals from two transmit antennas. In this paper, we design orthogonal-like space-time coded CPM systems for three and four transmit antennas based on orthogonal and quasi-orthogonal space-time codes. Although the signals from transmit antennas in the proposed orthogonal-like space-time coded CPM systems are not orthogonal, the fast decoding/demodulation is maintained like the two transmit antenna case. Simulation results show that the performance of the proposed orthogonal-like space-time coded CPM systems for four transmit antennas is much better than that of the OST-CPM systems for two transmit antennas.

Time asynchronism is a practical issue need to be addressed for a general distributed two-way relay network, where two terminal nodes exchange information through multiple spatial-separated relay nodes. In this letter, we propose an analog network coding (ANC) scheme for a time asynchronous two-way relay network. In the proposed scheme, each relay node linearly transforms the received mixed asynchronous signals in the first time-slot by a Toeplitz matrix, and then broadcasts them back to the terminals in the second time-slot. A sufficient condition is derived for the proposed ANC to achieve full cooperative diversity using only linear receivers at the terminal nodes, such as zero-forcing (ZF), or minimum mean square error (MMSE) receivers, with any delay profiles of the timing errors. The decoding of the proposed ANC scheme is computationally efficient and the symbol rate can approach 1, when the coding block length is sufficiently large compared to the number of relay nodes R and the timing errors.

In this paper, we propose a family of space-time block codes (STBC) that achieve full diversity when linear receivers, such as zero-forcing (ZF) or minimum mean square error (MMSE) receivers, are used. Our proposed STBC family is a combination/overlay between orthogonal STBC and Toeplitz codes, which can be viewed as a generalization of overlapped Alamouti codes (OAC) and Toeplitz codes recently proposed in the literature. Simulation results show that the newly proposed STBC may outperform the existing codes when linear receivers are used.

A partial interference cancellation (PIC) group decoding based space-time block code (STBC) design criterion was recently proposed by Guo and Xia, where the decoding complexity and the code rate trade-off is dealt when the full diversity is achieved. In this paper, we propose a systematic design of STBC that satisfy the criterion, i.e., achieve full diversity with the PIC group decoding for any number of transmit antennas. Moreover, the proposed full-diversity STBC can achieve a rate of 2 symbols per channel use for any number of transmit antennas when the code length is large. Some design examples are given. Simulation results show that the newly proposed STBC can obtain full diversity over flat Rayleigh fading channels and outperform some existing codes given the same bandwidth efficiency.

A cooperative system may have both timing errors and multiple carrier frequency offsets (CFOs). To combat timing errors, space-frequency (SF) coded OFDM systems have been recently proposed for cooperative communications to achieve both full cooperative and full multipath diversities without time synchronization requirement. In this paper, we study the effect of multiple CFOs from relay nodes on a family of rotation based SF codes. We find that they can still achieve full diversities under the condition that the absolute values of normalized CFOs are less than 0.5. We further show that this full diversity property still holds for a complexity-reduced two-stage zero forcing (ZF) aided maximum likelihood (ML) decoding method, for which a ZF method is used to equalize multiple CFOs before ML decoding.

Multiple frequency offsets (CFO) in cooperative communications are difficult, if not impossible, to compensate completely at the destination node. The multiple CFO make the channel time-varying, and thus the conventional space-time codes to collect diversity gain for co-located multi-input multi-output (MIMO) systems may not be applied directly. In a previous paper, we proposed a distributed space-frequency code (SFC), called frequency-reversal SFC (FR-SFC), for cooperative transmissions with multiple CFOs and flat fading channels. We have shown that this code can achieve the full cooperative diversity with only linear receivers, such as zero-forcing (ZF) and minimum mean square error (MMSE) receivers. However, the code is not bandwidth efficient when the relay nodes are more than 3. To have a higher bandwidth efficiency, in this paper we propose a new distributed SFC called frequency-domain linear convolutive SFC (FLC-SFC). The symbol rates of the new codes approach 1 when code block length is sufficiently larger than the number of relay nodes. Furthermore. It achieves the full cooperative diversity with linear receivers.

OFDM transmission has been proposed in the literature for relay nodes to combat the time delays from the relay nodes, where the paths from relay nodes to destination node are treated as multipaths and space-time (or frequency) coding is used to achieve the spatial (or multipath) diversity. With this approach, when the cyclic prefix (CP) length is less than the time delay length, inter-block interference occurs. In this paper, we consider Alamouti coded OFDM systems in cooperative communications where the CP length may be less than the time delay length. By taking the advantage of the Alamouti code structure, we propose a time domain interference cancellation algorithm. Simulation results show that the performance is significantly improved with our newly proposed time domain interference cancellation algorithm.

In this paper, we propose a simple analog network coding (ANC) scheme for asynchronous two-way relay networks where two sources exchange information through the relay nodes. In our proposed scheme, we consider a wireless scenario with frequency selective fading channels. Orthogonal frequencydivision multiplexing (OFDM) is implemented at the two sources and then the OFDM blocks are transmitted to the relay nodes in the first time slot. In the second time slot, at the relay nodes, the ANC that has a few simple operations are implemented on the received mixed signals and then the processed signals are broadcasted to the two sources to finish the information exchange. In this scheme, at each source, when recovering the designed information transmitted from another source, the received signals have the orthogonal space time block code (OSTBC) structure or the quasi-orthogonal space time block code (QOSTBC) structure on each subcarrier. By a proper power allocation, the two sources can achieve full spatial diversity and fast ML decoding without the requirement of symbol level synchronization. By the repetition across the subcarriers, multi-path diversity available in frequency selective fading channels can be also exploited. Moreover, the proposed ANC scheme is also valid for multi-way relay networks where multiple sources exchange information.

In this paper, we propose a design scheme for a family of space-time block codes (STBCs) that achieve full diversity when linear receivers, such as zero-forcing (ZF) or minimum mean square error (MMSE) receivers, are used. New STBCs obtained based on this design scheme are characterized by their special structure that combines/overlays orthogonal STBCs (OSTBCs) and Toeplitz codes together, which are called Group Orthogonal-Toeplitz Codes (GOTCs). By choosing different parameters, codes with different symbol rates and orthogonality can be obtained. It is indicated that the existing STBCs achieving full diversity with linear receivers, such as the Toeplitz codes and overlapped Alamouti codes (OACs) recently proposed in the literature and the OSTBCs are three members of the GOTCs with different parameters. It is also shown that the time-reversal space-time block codes (TR-STBCs) and delay diversity codes (DDCs) for frequency selective channels are equivalent to OACs and Toeplitz codes, respectively, and thus they both can achieve full space-frequency diversity with only linear receivers. Furthermore, simulations show that with linear receivers, GOTCs outperforming the above mentioned three codes can be obtained from the proposed design scheme.

Differential space-time modulation (DSTM) has been recently proposed by Hughes, and Hochwald and Sweldens when the channel information is not known at the receiver, where the demodulation is in fact the same as the coherent demodulation of space-time block coding by replacing the channel matrix with the previously received signal matrix. On the other hand, the DSTM also needs a recursive memory of a matrix block at the encoder and therefore provides a trellis structure when the channel information is known at the receiver, which is the interest of this paper. This recursive structure of the DSTM has been adopted lately by Schlegel and Grant in joint with a conventional binary code and joint iterative decoding/demodulation with a superior performance. The number of states of the trellis from the recursive structure depends on both the memory size, which is fixed in this case, and the unitary space-time code (USTC). When a USTC for the DSTM forms a group, the number of states is the same as the size of the USTC, otherwise the number of the states is the size of the semi-group generated by the USTC from all the multiplications of the matrices in the USTC. It is well known in the conventional convolutional coding (CC) or the trellis coded modulation (TCM), the free (Hamming or Euclidean) distance (or the performance) increases when the number of states increases by adding more memory with a properly designed CC or TCM. In this paper, we systematically study and design the USTC/DSTM for the recursive space-time trellis modulation and show that the diversity product increases when the number of states increases, which is not because of the memory size but because of the different USTC designs that generate different sizes of semi-groups. We propose a new USTC design criterion to ensure that the trellis structure improves the diversity product over the USTC as a block code. Based on the new criterion, we propose a new class of USTC design for an arbitrary number of transmit antennas that has an analytical diversity product formula for two transmit antennas. We then follow Schlegel and Grant's approach for joint encoding and iterative decoding of a binary coded DSTM (turbo space-time coding) and numerically show that our new USTC designs for the recursive space-time trellis modulation outperforms the group USTC used by Schlegel and Grant.

Cyclic division algebra (CDA) has recently become a major technique to construct space–time block codes with nonvanishing determinant (NVD). One of the key steps in this technique is the determination of non-norm elements and a sufficient condition for the determination has been given by Kiran and Rajan lately based on algebraic number theory. In this paper, based on Kiran and Rajan's condition, we present a more elementary condition for non-norm elements when signals are QAM or HEX, which is easier to check. With this elementary condition, non-norm elements with smaller absolute values than the existing ones can be found.

In this paper, we propose a partial interference cancellation (PIC) group decoding strategy/scheme for linear dispersive space-time block codes (STBC) and a design criterion for the codes to achieve full diversity when the PIC group decoding is used at the receiver. A PIC group decoding decodes the symbols embedded in an STBC by dividing them into several groups and decoding each group separately after a linear PIC operation is implemented. It can be viewed as an intermediate decoding between the maximum likelihood (ML) receiver that decodes all the embedded symbols together, i.e., all the embedded symbols are in a single group, and the zero-forcing (ZF) receiver that decodes all the embedded symbols separately and independently, i.e., each group has and only has one embedded symbol, after the ZF operation is implemented. The PIC group decoding provides a framework to adjust the complexity-performance tradeoff by choosing the sizes of the information symbol groups. Our proposed design criterion (group independence) for the PIC group decoding to achieve full diversity is an intermediate condition between the loosest ML full rank criterion of codewords and the strongest ZF linear independence condition of the column vectors in the equivalent channel matrix. We also propose asymptotic optimal (AO) group decoding algorithm which is an intermediate decoding between the MMSE decoding algorithm and the ML decoding algorithm. The design criterion for the PIC group decoding can be applied to the AO group decoding algorithm because of its asymptotic optimality. It is well-known that the symbol rate for a full rank linear STBC can be full, i.e.,nt, fornttransmit antennas. It has been recently shown that its rate is upper bounded by 1 if a code achieves full diversity with a linear receiver. The intermediate criterion proposed in this paper provides the possibility for codes of rates betweenntand 1 that achieve full diversity with the PIC group decoding. This therefore provides a complexity-performance-rate tradeoff. Some design examples are given.

In this paper, we first present two tight upper bounds for the normalized diversity products (or product distances) of2 × 2diagonal space-time block codes from quadratic extensions on\BBQ (i)and\BBQ ( \mmbzeta6), where i=radic{-1}and\mmbzeta6=exp(i2pi/6). Two such codes are shown to reach the tight upper bounds and therefore have the maximal normalized diversity products. We present two new diagonal space-time block codes from higher order algebraic extensions on\BBQ (i)and\BBQ ( \mmbzeta6)for three and four transmit antennas. We also present a nontight upper bound for normalized diversity products of2 × 2diagonal space-time block codes with QAM information symbols, i.e., in\BBZ [i], from general2 × 2complex-valued generating matrices. We then present ann×n-diagonal space-time code design method directly from2nreal integers based on extended complex lattices (of generating matrix sizen× 2n) that are shown to have better normalized diversity products than the optimal diagonal cyclotomic codes do. We finally use the optimal2 × 2diagonal space-time codes from the optimal quadratic extensions to construct two2 × 2full-rate space-time block codes and find that both of them have better normalized diversity products than the Golden code does.

In this paper, we show that the diversity products of the full transmit diversity space-time block codes proposed by Lu-Kumar (we call them Lu-Kumar's codes) with quadratic-amplitude modulation (QAM) constellations are lower bounded by 4. We present a sufficient condition on the minimum Hamming weight of the linear binary full-rank space-time code such that this lower bound is met. We show that the special Lu-Kumar codes does satisfy the sufficient condition, and therefore, the diversity products of the special Lu-Kumar codes are 4, where ldquospecialrdquo means that the linear binary full-rank space-time codes are not general but specially constructed by Lu-Kumar.

Orthogonal space-time block codes (OSTBC) from orthogonal designs have both advantages of complex symbol-wise maximum-likelihood (ML) decoding and full diversity. However, their symbol rates are upper bounded by 3/4 for more than two antennas for complex symbols. To increase the symbol rates, they have been generalized to quasi-orthogonal space-time block codes (QOSTBC) in the literature but the diversity order is reduced by half and the complex symbol-wise ML decoding is significantly increased to complex symbol pair-wise (pair of complex symbols) ML decoding. The QOSTBC has been modified by rotating half of the complex symbols for achieving the full diversity while maintaining the complex symbol pair-wise ML decoding. The optimal rotation angles for any signal constellation of any finite symbols located on both square lattices and equal-literal triangular lattices have been found by Su-Xia, where the optimality means the optimal diversity product (or product distance). QOSTBC has also been modified by Yuen-Guan-Tjhung by rotating information symbols in another way such that it has full diversity and in the meantime it has real symbol pair-wise ML decoding (the same complexity as complex symbol-wise decoding) and the optimal rotation angle for square and rectangular QAM constellations has been found. In this paper, we systematically study general linear transformations of information symbols for QOSTBC to have both full diversity and real symbol pair-wise ML decoding. We present the optimal transformation matrices (among all possible linear transformations not necessarily symbol rotations) of information symbols for QOSTBC with real symbol pair-wise ML decoding such that the optimal diversity product is achieved for both general square QAM and general rectangular QAM signal constellations. Furthermore, our newly proposed optimal linear transformations for QOSTBC also work for general QAM constellations in the sense that QOSTBC have full diversity with good diversity product property and real symbol pair-wise ML decoding. Interestingly, the optimal diversity products for square QAM constellations from the optimal linear transformations of information symbols found in this paper coincide with the ones presented by Yuen-Guan-Tjhung by using their optimal rotations. However, the optimal diversity products for (nonsquare) rectangular QAM constellations from the optimal linear transformations of information symbols found in this paper are better than the ones presented by Yuen-Guan-Tjhung by using their optimal rotations. In this paper, we also present the optimal transformations for the co-ordinate interleaved orthogonal designs (CIOD) proposed by Khan-Rajan for rectangular QAM constellations.

Recently, Li, Hwu and Ratazzi have proposed a physical-layer security design to guarantee low probability of interception (LPI) for MIMO systems without relying on upper layer data encryption. The proposed scheme utilizes antenna array redundancy to deliberately randomize the transmitted signals to prevent eavesdropping. Motivated by their idea, in this paper we design a physical-layer transmission scheme to achieve LPI in cooperative systems. There are two major differences in cooperative systems: (1) each relay node may have only one antenna that can not provide antenna array redundancy for signal randomization; (2) there may exist timing errors due to the asynchronous nature of cooperative systems. Considering the two differences, we propose a distributed differentially encoded OFDM transmission scheme with deliberate signal randomization to prevent eavesdropping and exploit the available spatial and frequency diversities in asynchronous cooperative systems. We use diagonal unitary codes to perform the differential encoding in the frequency domain over subcarriers within each OFDM block, or we use general (not necessarily diagonal) unitary codes to perform the differential encoding in the frequency domain across several OFDM blocks. By some deliberate signal randomization, the eavesdropper can not detect the transmitted symbols, while the authorized receiver can perform differential decoding successfully without the knowledge of the channels or the timing errors.

A fast recursive algorithm for the vertical Bell Laboratories layered space-time (V-BLAST) with the optimal ordered successive interference cancellation (SIC) detection has been proposed by Benesty-Huang-Chen and two improved algorithms have been also recently introduced by Szczecinski-Massicotte and Zhu-Lei-Chin independently. In this letter, our first contribution is to incorporate the existing improvements into the original fast recursive algorithm to provide the fastest known algorithm for the optimal ordered SIC detection of V-BLAST. Subsequently, we propose two new algorithms that result from one further improvement for the fast recursive algorithm. Compared with the fastest known algorithm built from the existing improvements, one new proposed algorithm has a speedup of 1.3 times in both the number of multiplications and the number of additions, and the other new proposed algorithm requires less intermediate variables and saves memories.

In cooperative communications, time and frequency synchronization is an important issue needed to be addressed in practice. Due to the nature of cooperative communications, multiple frequency offsets may occur and the traditional frequency offset compensations may not apply. For this problem, equalization for the time-varying channel has been used in the literature, where the equalization matrix inverse needs to be retaken every symbol. In this paper, we propose computationally efficient minimum mean square error (MMSE) and MMSE decision feedback equalizers (MMSE-DFE) when multiple frequency offsets are present, where the equalization matrix inverses do not need to be retaken every symbol. Our proposed equalization methods apply to linear convolutively coded cooperative systems, where linear convolutive space-time coding is used to achieve the full cooperative diversity when there are timing errors from the cooperative users or relay nodes, i.e., asynchronous cooperative communication systems.

In cooperative communications, due to the distributed nature, multiple different carrier frequency offsets (CFOs) may occur and make the channel time-varying. It is hard for the receiver to compensate multiple CFOs from multiple relay nodes simultaneously. Thus, the conventional space-time codes to collect cooperative diversity for co-located multi-input multi-output (MIMO) systems may not be applied directly. In this paper, we consider the cooperative transmission with multiple CFOs when the channels from relay nodes to destination node are flat (frequency-non-selective) fading. We approximate a flat fading channel with CFO as a block time-invariant intersymbol-interference (ISI) channel in the frequency domain. We then propose two distributed space-frequency codes (SFC) for such ISI channels in the frequency domain to achieve the cooperative full spatial diversity, where the space-frequency coding concept is different from the one in the literature and also has a different role. One is called frequency-reversal SFC and the other is called frequency-domain linear convolutive SFC. Furthermore, we show that, with only linear receivers, such as zero-forcing (ZF) and minimum mean square error (MMSE) receivers, our codes achieve the full cooperative diversity.

Recently, Li, Hwu and Ratazzi have proposed a physical-layer security design to guarantee low probability of interception (LPI) for MIMO systems without relying on upper- layer data encryption. The proposed scheme utilizes antenna array redundancy to deliberately randomize the transmitted signals to prevent eavesdropping. Motivated by their idea, in this paper we design a physical-layer transmission scheme to achieve LPI in cooperative systems. There are two major differences in cooperative systems: 1) each relay node may have only one antenna that can not provide antenna array redundancy for signal randomization; 2) there may exist timing errors due to the asynchronous nature of the cooperative systems. Considering the two differences, we propose a distributed differentially space- time-frequency coded OFDM transmission scheme with deliberate signal randomization to prevent eavesdropping and exploit the available spatial and frequency diversities in asynchronous cooperative systems. We use unitary codes to perform the differential encoding in the frequency domain across several OFDM blocks. By some deliberate signal randomization, the eavesdropper can not detect the transmitted symbols. However, the authorized receiver can perform differential decoding successfully without the knowledge of the channels or the timing errors.

In cooperative communications, due to the distributed nature, multiple different carrier frequency offsets (CFOs) may be present and make the channel time-varying. It is hard for the receiver to compensate multiple CFOs from multiple relay nodes simultaneously. Thus, the conventional space-time codes to collect cooperative diversity for co-located multi-input multi-output (MIMO) systems may not be applied directly. In this paper, we consider the cooperative transmission with multiple CFOs when the channels from relay nodes to destination node are flat fading. We approximate a flat fading channel with CFO as a block time-invariant intersymbol-interference (ISI) channel in the frequency domain. We then propose a distributed space- frequency code (SFC), called frequency-reversal SFC, for such ISI channels in the frequency domain to achieve the cooperative spatial diversity, where the space-frequency coding concept is different from the one in the literature and also has a different role. Furthermore, we show that, with only linear receivers, such as zero-forcing (ZF) and minimum mean square error (MMSE) receivers, our code achieves the full cooperative diversity.

A cooperative system may have both timing errors and carrier frequency offsets (CFOs) from relay nodes. To combat timing errors, space-frequency (SF) coded OFDM system has been recently proposed for cooperative communications to achieve both full cooperative and multipath diversities without the time synchronization requirement among relay nodes. In this paper, we consider the signal detection problem in an SF coded OFDM system for cooperative communications where multiple CFOs may occur from relay nodes. By exploiting the structure of the SF codes that are rotational based in this paper, we propose a novel inter-carrier interference (ICI) mitigation method called the multiple fast Fourier transform (M-FFT) method. Simulation results illustrate that for the same symbol error rate (SER) level, the M-FFT based detection methods require less computations than other classic approaches.

In user cooperative communications, time offsets among the relay nodes may distort the structure of a distributed space-time code, and therefore impair the diversity gain. In this paper, a distributed linear convolutive space-time coding scheme is proposed for asynchronous scenario. We present a systematic construction method of distributed linear convolutive space-time codes that can achieve the full diversity under any delay profile. Furthermore, we show that with the MMSE decision feedback equalizer (MMSE-DFE) receiver, our proposed codes also achieve the full diversity, even when time offsets occur.

A fast recursive algorithm for the vertical Bell Laboratories layered space-time (V-BLAST) with the optimal ordered successive interference cancellation (SIC) detection has been proposed by Benesty-Huang-Chen and two improved algorithms have been also recently introduced by Szczecinski-Massicotte and Zhu-Lei-Chin. In this paper, we first incorporate the existing improvements into the original fast recursive algorithm to give an algorithm that is the fastest known for the optimal SIC detection of V-BLAST. Then, we present a further improvement from which a new algorithm is proposed. Compared with the fastest known algorithm from the existing improvements, this new proposed algorithm has a speedup of 1.3 times in both the number of multiplications and the number of additions.

In cooperative communications, carrier frequency offsets (CFOs) between pairs of nodes may be different, making CFOs compensation difficult if not impossible. These multiple CFOs may drastically degrade the performance of a space-frequency (SF) coded cooperative system. In this paper, we consider the signal detection problem in SF coded cooperative communication systems with multiple CFOs, where the SF codes are rotational based and can achieve both the full cooperative and the full multipath diversities. By exploiting the structure of the SF codes, we propose two signal detection methods to deal with the multiple CFOs problem in the SF coded OFDM systems. They are the minimum mean-squared error filtering (MMSE-F) method, the two-stage simple frequency shift Q taps (FS-Q-T) method, both of which offer different tradeoffs between performance and computational complexity. Simulation results indicate that our proposed detection methods work well as long as the carrier frequency offsets between nodes are not unreasonably large.

In this paper, we propose a partial interference cancellation (PIC) group decoding for linear dispersive space-time block codes (STBC) and a design criterion for the codes to achieve full diversity when the PIC group decoding is used at the receiver. A PIC group decoding decodes the symbols embedded in an STBC by dividing them into several groups and decoding each group separately after a linear PIC operation is implemented. It can be viewed as an intermediate decoding between the maximum likelihood (ML) receiver that decodes all the embedded symbols together, i.e., all embedded symbols are in a single group, and a zero-forcing (ZF) receiver that decodes all the embedded symbols separately and independently, i.e., each group has and only has one embedded symbol, after the ZF operation is implemented. The PIC group decoding provides a framework to adjust the complexity-performance tradeoff by choosing the sizes of the symbol groups. Our proposed design criterion (group independence) for the PIC group decoding to achieve full diversity is an intermediate condition between the loosest ML full rank criterion of codewords and the strongest ZF linear independence condition of the column vectors in the equivalent channel matrix. A design example is also given.

In cooperative communications, due to distributed nature, cooperative transmission may not be either time or frequency synchronized. In this paper, we propose computationally efficient minimum mean square error (MMSE) and MMSE decision feedback equalizer (MMSE-DFE) equalizers for cooperative communication systems when multiple time offsets and frequency offsets present, where the equalization matrix inverses do not need to be retaken every symbol. Our proposed equalization methods apply to linear convolutively coded cooperative systems, where the linear convolutive space-time coding is used to achieve the full cooperative diversity when there are timing errors from the cooperative users or relay nodes, i.e., asynchronous cooperative communication systems.

In user cooperative communications, time offsets among the relay nodes may distort the structure of a distributed space-time code, and therefore impair the diversity gain. In this paper, we first recall some of the existing schemes for dealing with this problem and then introduce a distributed linear space- time convolutional coding scheme for asynchronous scenario. We present a systematic construction of distributed linear space- time convolutional codes that can achieve the full diversity under any delay profile. Furthermore, we show that with the MMSE decision feedback equalizer (MMSE-DFE) receiver, our proposed codes also achieve the asynchronous full diversity.

In this letter, a synthetic aperture radar (SAR) data reformatting approach named Doppler keystone transform (DKT) is proposed to correct the range migration of a moving target. By using the DKT, the SAR imaging program, i.e., the 2-D matched filtering, can be transformed into separate 1-D operations along range or azimuth direction, and therefore, the DKT is suitable for the parallel implementation of SAR imaging of the moving target. Our simulations show that by combining the DKT and the Doppler phase compensation methods, the moving target can be well imaged in high signal-clutter-ratio case.

A robust phase unwrapping based on the Chinese remainder theorem (CRT) has been proposed lately, and it has applications in synthetic aperture radar (SAR) imaging for moving targets. It also leads to a type of robust CRT. This algorithm requires a two-dimensional searching. In this letter, we propose a new and fast algorithm that only does one-dimensional searching and therefore reduces the complexity significantly.

An efficient way to reduce the peak-to-average power ratio (PAPR) in OFDM systems is clipping. After the clipping in an MIMO-OFDM system, the additive noise may not be white. In this paper, we develop fast (single-symbol) ML decoding algorithms for orthogonal space-time block codes (OSTBC) and (linearly transformed) quasi orthogonal space- time block codes (QOSTBC) in clipped MIMO-OFDM systems by using a clipping noise model with Gaussian approximation. By using the statistics of the clipping distortions, our newly developed fast ML decoding algorithms improve the performance for clipped MIMO-OFDM systems with OSTBC and QOSTBC while the decoding complexities are not increased. Simulation results are presented to illustrate the improvement.

Velocity synthetic aperture radar (VSAR) is equipped with a linear array to receive the echoes from a radar illuminating area via multiple channels, each of which can reconstruct a reflectivity image for the same stationary scene. Based on analysis of pixel vector sampled among multi-images, VSAR may effectively suppress the strong ground clutter and improve moving target detection and location. In this paper, different Doppler-distributed properties are derived for the moving target and clutter, respectively. Then, we propose a novel parametric statistical model for VSAR by dividing the pixel vector into three components, namely, target, clutter, and noise. Furthermore, a method of adaptive implementation of optimal processing (AIOP-VSAR) is presented for moving target detection. It is shown that the optimum detection performance may be obtained via AIOP-VSAR, particularly for the slowly moving target in an inhomogeneous clutter environment. Also, the Cramer-Rao bounds (CRBs) are derived for the estimation of unknown model parameters, as well as the azimuth locations of moving targets, and the maximum-likelihood methods are proposed to reach these CRBs. Based on the proposed target detection and parameter estimation methods, we present a complete parametric flowchart for VSAR. It is demonstrated that the proposed flowchart may effectively mitigate the "azimuth location ambiguity" of VSAR and has the super-resolution ability to resolve "velocity layover" for multiple targets. Finally, some detailed numerical experiments and scene simulations are provided to show the effectiveness of the proposed methods.

Based on a parametric model and some optimum methods, it has been previously proved that parametric velocity synthetic aperture radar (VSAR) may improve the performances of moving target detection and parameter estimation simultaneously. In this paper, multilook processing is studied for parametric VSAR. At first, statistical signal models are established for azimuth multilook processing (AMLP) and range multilook processing (RMLP), respectively. By combining the multiple AMLP sublook pixel vectors, it is shown that the clutter parameter estimation accuracy can be further improved via the maximum-likelihood estimation methods. Meanwhile, based on the adaptive implementation for the optimum processing of VSAR, RMLP can be used to improve slowly moving target detection performance via the noncoherent integration of multiple range sublooks. Furthermore, it is shown that the Doppler frequencies of moving targets vary linearly with different range sublooks due to the different carrier frequencies of sublooks. Therefore, based on a proposed novel two-step multilook diversity (TS-MLD) estimator for RMLP, the "azimuth location ambiguity" of VSAR can be well resolved via least squares linear regression, without configuration change of the conventional VSAR. Also, the estimation accuracy of target's unambiguous Doppler frequency, as well as target's azimuth location, is derived for the proposed TS-MLD method. Finally, numerical experiments and scene simulations are provided to demonstrate the effectiveness of the proposed multilook-based methods.

In most of the existing space-time code designs, achieving full diversity is based on maximum-likelihood (ML) decoding at the receiver that is usually computationally expensive and may not have soft outputs. Recently, Zhang-Liu-Wong introduced Toeplitz codes and showed that Toeplitz codes achieve full diversity when a linear receiver, zero-forcing (ZF) or minimum mean square error (MMSE) receiver, is used. Motivated from Zhang-Liu-Wong's results on Toeplitz codes, in this paper, we propose a design criterion for space-time block codes (STBC), in which information symbols and their complex conjugates are linearly embedded, to achieve full diversity when ZF or MMSE receiver is used. The (complex) orthogonal STBC (OSTBC) satisfy the criterion as one may expect. We also show that the symbol rates of STBC under this criterion are upper bounded by 1. Subsequently, we propose a novel family of STBC that satisfy the criterion and thus achieve full diversity with ZF or MMSE receiver. Our newly proposed STBC are constructed by overlapping the 2times2 Alamouti code and hence named overlapped Alamouti codes in this paper. The new codes are close to orthogonal and their symbol rates can approach 1 for any number of transmit antennas. Simulation results show that overlapped Alamouti codes significantly outperform Toeplitz codes for all numbers of transmit antennas and also outperform OSTBC when the number of transmit antennas is above 4.

In this correspondence, we prove that the optimal normalized diversity product of lattice-based diagonal space-time block codes with Gaussian integer (or QAM) signal constellations, i.e., , and any generating matrices of complex entries (not necessarily algebraic extensions of as commonly used) is . This result implies that lattice-based diagonal space-time block codes with Gaussian integer signal constellations and generating matrices of entries from quadratic algebraic extensions of have already reached the optimal normalized diversity product.

Jing and Hassibi recently presented a detailed analysis on the problem of distributed space-time coding for a synchronized wireless relay network, where only linear transforms are implemented at relay nodes, and proposed a linear dispersion coding scheme. In this letter, we extend it to asynchronous wireless networks by employing OFDM to combat timing errors from relay nodes. Based on a layered structure, we present a distributed space-time code design achieving full spatial diversity for an asynchronous wireless relay network.

In user cooperative communications, time offsets among the relay nodes may distort the structure of a distributed space-time code, and therefore impair the diversity gain. In this paper, a distributed linear convolutive space-time coding scheme is proposed for asynchronous cooperative communication networks. We propose systematic construction methods of linear convolutive space-time codes (DLC-STC) that are .insensitive to the timing errors, i.e., they achieve the full spatial diversity under any delay profile. We then study the diversity property of the delay-tolerant DLC-STC with suboptimal receivers that have low decoding complexity. We show that with the ZF, MMSE and MMSE-DFE receivers, our proposed codes also achieve the full spatial diversity in the asynchronous scenario.

Space-time/frequency coding (SFC) can achieve the spatial and multipath diversities for a MIMO-OFDM system by coding across subcarriers, multiple antennas, and/or multiple OFDM sysmbols, where an interesting method to achieve the multipath diversity is repeating across subcarriers proposed by Su et al. While most of the existing space-time/frequency codes do not have the fast ML decoding, a family of space-time-frequency codes with single-symbol ML decoding have been proposed lately by Zhang et al to achieve both full spatial and multipath diversities by using orthogonal space-time block codes (OSTBC) across multiple antennas and OFDM symbols and in the meantime repeating across the subcarriers. In this paper, we first generalize the above OSTBC to linearly transformed quasi OSTBC (QOSTBC) in a straightforward way. The main goal of this paper is to modify the repeating process and adjust their phases so that the peak-to-average power ratio (PAPR) of the OFDM system is reduced. In particular, we propose to use Chu sequences and show that the discrete PAPR can be reduced by Gamma times where Gamma is the times of the repeating across subcarriers for any SFC from the repeating.

In this letter, we propose an Alamouti coded orthogonal frequency-division multiplexing (OFDM) scheme for a cooperative communication system robust to both timing errors and frequency offsets. OFDM with cyclic prefix (CP) is used to combat timing errors. In order to mitigate the intercarrier interference (ICI) caused by multiple frequency offsets in the cooperative system, an ICI-self cancellation scheme is constructed, which can suppress ICI effectively. Moreover, in the proposed scheme, if the channels are real-valued fading channels, the received signals at the destination node have the Alamouti code structure on each subcarrier and thus it has the fast symbol-wise ML decoding and when frequency offsets are not large, the new scheme can achieve diversity order 2.

A family of space-time trellis codes that are associated with shift-full-rank (SFR) generator matrices and thus achieve asynchronous full cooperative diversity order has been recently proposed, where it is assumed that the delays occurring at the relays must be integer multiples of the symbol duration. In this letter, we consider a cooperative communication system in which the delays can be fractional (rational) symbol durations and, correspondingly, multiple sampling per symbol interval is used at the destination. We show that the previously proposed space-time trellis codes can still achieve asynchronous full diversity in fractional symbol delay scenarios if their generator matrices satisfy a relaxed definition of SFR matrices, namely, q-shift-full-rank (q-SFR) matrices, and therefore, more designs for their generator matrices than the SFR ones become available. Furthermore, compared with the applications in the system with integer symbol delays, the decoding complexity of the optimal Viterbi algorithm for the codes does not increase and some performance gains can be observed in the system with fractional symbol delays.

A distributed space-frequency (SF) coded cooperative technique is proposed for broadband wireless ad-hoc networks, where both the channels from the source node to relay nodes and from the relay nodes to the destination node are characterized by frequency-selective fading. Using an SF coding at the source node and a circular shift at each relay node, we propose a scheme that can achieve both cooperative and multi-path diversity. The pairwise error probability is analyzed and the result demonstrates that a diversity gain of min(ML1, ML2) can be achieved, where M is the number of the relay nodes, and L1and L2are the number of taps of the multipath fading channels from the source node to the relay nodes and from the relay nodes to the destination node, respectively. Furthermore, the proposed distributed SF coding can achieve full-rate transmission for any number of relay nodes. In particular, it does not need IFFT/FFT processing or decoding at each relay node, thus providing a low-complexity design of the relay nodes in broadband wireless ad-hoc networks.

In a clipped MIMO-OFDM system, the overall additive noise, including the clipping distortion, may not be white. In this paper, we develop fast ML decoding algorithms for orthogonal space-time-frequency block codes (OSTFBC) and quasi orthogonal space-time-frequency block codes (QOSTFBC) in clipped MIMO-OFDM systems by using a clipping noise model with Gaussian approximation. By using the statistics of the clipping distortions, our newly proposed fast ML decoding algorithms improve the performance for clipped MIMO-OFDM systems with OSTFBC and QOSTFBC without increasing the decoding complexity. Simulation results are presented to illustrate the improvement.

Space-time/frequency coding (SFC) can achieve the spatial and multipath diversities for an MIMO-OFDM system by coding across subcarriers, multiple antennas, and/or multiple OFDM symbols, where an interesting method to achieve the multipath diversity is repeating across subcarriers proposed by Su et al. Lately, a family of space-time-frequency codes with single-symbol ML decoding have been proposed by Zhang et al to achieve both full spatial and multipath diversities by using orthogonal space-time block codes (OSTBC) across multiple antennas and OFDM symbols and in the meantime repeating across the subcarriers. The main goal of this paper is to modify the repeating process and adjust their phases by a Chu sequence so that the peak-to-average power ratio (PAPR) of the OFDM system is reduced for any SFC from the repeating and in the meantime, the full spatial and multipath diversities are maintained. In this paper, we also generalize the space-time- frequency coding by Zhang et al from using OSTBC to using linearly transformed quasi-orthogonal space-time block codes (QOSTBC) that possesses higher rate than the OSTBC for more than two transmit antennas, fast ML decoding and full spatial and multipath diversities.

Shift-full-rank (SFR) matrices are matrices that have full row rank no matter how their rows are shifted. SFR matrices have been used lately as generator matrices for a family of space-time trellis codes to achieve full diversity in asynchronous cooperative communications, where the numbers of columns of the SFR matrices correspond to the memory sizes of the trellis codes and an upper bound is assumed for all the possible delays during each packet (frame) transmission. In this paper, we study a variation of SFR matrices with relaxed conditions: limited-shift-full-rank (LT-SFR) matrices, i.e., the matrices that keep full row rank when the shifts of their rows are within some range called delay tolerance. As the generator matrices for the previously proposed space-time trellis codes, LT- SFR matrices can already guarantee asynchronous full diversity of the corresponding codes.

In most of the existing space-time code designs, achieving full diversity is based on maximum-likelihood (ML) decoding at the receiver that is usually computationally expensive and may not have soft outputs. In this paper, we propose a novel family of space-time block codes (STBC) that achieves full diversity with a linear receiver, specifically, zero-forcing (ZF) or minimum mean square error (MMSE) receiver. Our newly proposed STBC are constructed by overlapping the 2times2 Alamouti code and hence namedoverlappedAlamouticodes. The new codes are close to orthogonal while their symbol rates can approach 1 for any number of transmit antennas. Simulation results show that overlapped Alamouti codes outperform orthogonal STBC (OSTBC) when the number of transmit antennas is above 4.

A distributed space-frequency (SF) coded cooperative technique is proposed for broadband wireless ad-hoc networks. By employing an SF coding at the source node and a circular shift at each relay node, the proposed cooperative diversity technique can exploit the maximum relay diversity and multipath diversity. Pairwise error probability analysis shows that the achievable diversity gain is min (ML1, ML2), where M is the number of the relay nodes, and L1and L2are the number of taps of the multipath fading channels from the source node to the relay nodes and from the relay nodes to the destination node, respectively.

Cyclic division algebra (CDA) has recently become a major technique to construct space-time block codes with nonvanishing determinant (NVD). One of the key steps in this technique is the determination of non-norm elements and a sufficient condition for the determination has been given by Kiran and Rajan lately based on algebraic number theory. In this paper, based on Kiran and Rajan's condition, we present a more elementary condition for non-norm elements when signals are QAM, i.e., in Z[i], or HEX, i.e., Z[j], which is easier to check. With this elementary condition, non-norm elements with smaller absolute values than the existing ones can be found.

An efficient way to reduce the peak-to-average power ratio (PAPR) in OFDM systems is clipping. After the clipping in an MIMO-OFDM system, the additive noise may not be white. In this paper, we develop fast ML decoding algorithms for orthogonal space-time block codes (OSTBC) and quasi orthogonal space-time block codes (QOSTBC) in clipped MIMO-OFDM systems by using a clipping noise model with Gaussian approximation. By using the statistics of the clipping distortions, our newly proposed fast ML decoding algorithms improve the performance for clipped MIMO-OFDM systems with OSTBC and QOSTBC without increasing the decoding complexity. Simulation results are presented to illustrate the improvement.

In most of the existing space-time code designs, achieving full diversity is based on maximum-likelihood (ML) decoding at the receiver that is usually computationally expensive and may not have soft outputs. Recently, Zhang-Liu-Wong introduced Toeplitz codes and showed that Toeplitz codes achieve full diversity when a linear receiver, zero-forcing (ZF) or minimum mean square error (MMSE) receiver, is used. Motivated from Zhang-Liu-Wong's results on Toeplitz codes, in this paper, we propose a design criterion for space-time block codes (STBC), in which information symbols and their complex conjugates are linearly embedded, to achieve full diversity when ZF or MMSE receiver is used. Subsequently, we propose a novel family of STBC that satisfy the criterion and thus achieve full diversity with ZF or MMSE receiver. Our newly proposed STBC are constructed by overlapping the 2times2 Alamouti code and hence named overlapped Alamouti codes in this paper. The new codes are close to orthogonal while their symbol rates can approach 1 for any number of transmit antennas. Simulation results show that overlapped Alamouti codes significantly outperform Toeplitz codes for all numbers of transmit antennas and also outperform orthogonal STBC (OSTBC) when the number of transmit antennas is above 4.

In this paper, we propose an Alamouti coded orthogonal frequency-division multiplexing (OFDM) scheme for a cooperative communication system robust to both timing errors and frequency offsets. OFDM with cyclic prefix (CP) is used to combat timing errors. In order to mitigate the intercarrier interference (ICI) caused by multiple frequency offsets in the cooperative system, an ICI-self cancellation scheme is constructed, which can suppress ICI effectively. Moreover, in the proposed scheme, if the channels are real-valued fading channels, the received signals at the destination node have the Alamouti code structure on each subcarrier and thus it has the fast symbol-wise ML decoding and when frequency offsets are not large, the new scheme can achieve diversity order 2.

To combat synchronization errors in cooperative communication, paper (Li, 2006) applied the orthogonal frequency division multiplexing (OFDM) technique to achieve full cooperative diversity and full multipath diversity for asynchronous cooperative communications, where a kind of high rate space-frequency (SF) codes was used at relay nodes. However, OFDM is very sensitive to carrier frequency offset (CFO). For conventional communication system, only one CFO exists and its effect is easy to be compensated. But for cooperative communication, the CFO between each relay and destination may be very different. These multiple CFO make frequency compensation difficult if not impossible. To solve this problem, we propose a detection scheme for SF codes which can work much better than the conventional SISO zero-forcing (ZF) and minimum mean square error (MMSE) methods. Moreover, parallel interference cancellation (PIC) can be used to further improve the system performance. Simulation results show that the proposed scheme combined with PIC is capable to achieve almost the same performance as cases without CFO.

In this paper, a non-unitary differential space-time coding scheme is proposed based on the super orthogonal space-time trellis coding scheme (SOSTTC). First, we extend the SOSTTC scheme to support quadrature amplitude modulation (QAM) constellations. Then we present a differential space-time coding scheme based on these power-efficient SOSTTCs. Several low-complexity suboptimal differential decoders are developed based on the per-survivor processing technique (PSP). The performance analysis shows that the proposed PSP decoding schemes only have a 0.4dB performance loss compared with the coherent scheme under slow fading channels. Simulation results demonstrate that the proposed scheme can achieve much higher coding gain compared with the known unitary space time codes in the high data rate region.

In this letter, we propose a simple orthogonal frequency-division multiplexing (OFDM) scheme for an asynchronous cooperative system, where OFDM is implemented at the source node, and time-reversion and complex conjugation are implemented at the relay nodes. The cyclic prefix (CP) at the source node is used for combating the timing errors from the relay nodes. In this scheme, the received signals at the destination node have the Alamouti code structure on each subcarrier, and thus, it has the fast symbol-wise ML decoding. It should be emphasized that the relay nodes only need to implement the time-reversion, some sign changes from plus to minus, and/or the complex conjugation to the received signals, and no IDFT or DFT operation is needed. It is shown that this simple scheme achieves second-order diversity gain without the synchronization requirement at the relay nodes.

A robust phase-unwrapping algorithm has been recently proposed and applied to accurately locate fast moving targets in multifrequency antenna array synthetic aperture radar imaging. This algorithm is based on two dimensional (2-D) searching and its complexity may be high. In this letter, we propose an efficient implementation of the robust phase-unwrapping algorithm. We show that the range of the 2-D searching, and therefore the complexity of the robust phase-unwrapping algorithm, can be significantly reduced.

In the conventional Chinese remainder theorem (CRT), a small error in a remainder may cause a large error in the solution of an integer, i.e., CRT is not robust. In this letter, we first propose a robust phase unwrapping algorithm with applications in radar signal processing. Motivated from the phase unwrapping algorithm, we then derive a type of robust CRT

In current cooperative communication schemes, to achieve cooperative diversity, synchronization between terminals is usually assumed, which may not be practical since each terminal has its own local oscillator. In this paper, based on the stack construction proposed by Hammons and El Gamal, we first construct a family of space-time trellis codes for BPSK modulation scheme that is characterized to possess the full cooperative diversity order without the synchronization assumption. We then generalize this family of the space-time trellis codes from BPSK to higher order QAM and PSK modulation schemes based on the unified construction proposed by Lu and Kumar. Some diversity product properties of space-time trellis codes are studied and simplified decoding methods are discussed. Simulation results are given to illustrate the performance of the newly proposed codes

In this letter, we propose a new family of space-time trellis codes, which are constructed by combining a super set of quasi-orthogonal space-time block codes with minimum decoding complexity with an outer multiple trellis coded modulation encoder. A systematic set-partitioning method for quadratic amplitude modulation constellations is given. The proposed scheme can be used for systems with four or more than four transmit antennas. Furthermore, its decoding complexity is low because its branch metric calculation can be implemented in a symbolwise way. Simulation results demonstrate that the proposed scheme has a comparable performance as super quasi-orthogonal space-time trellis codes proposed by Jafarkhani and Hassanpour while providing a lower decoding complexity.

In this letter, super-orthogonal space-time trellis codes (SOSTTCs) are extended for quadrature amplitude modulation (QAM) constellations. A systematic set-partitioning method for QAM constellations is given. Furthermore, trellis shaping based on set partitioning is incorporated in SOSTTCs with QAM symbols to achieve extra shaping gain. Peak constraints can be used to limit the constellation expansion ratio and peak-to-average power ratio

A systematic design of high-rate full-diversity space-time-frequency (STF) codes is proposed for multiple-input multiple-output frequency-selective block-fading channels. It is shown that the proposed STF codes can achieve rate Mtand full-diversity MtMrMbL, i.e., the product of the number of transmit antennas Mt, receive antennas Mr, fading blocks Mb, and channel taps L. The proposed STF codes are constructed from a layered algebraic design, where each layer of algebraic coded symbols are parsed into different transmit antennas, orthogonal frequency-division multiplexing tones, and fading blocks without rate loss. Simulation results show that the proposed STF codes achieve higher diversity gain in block-fading channels than some typical space-frequency codes

In this paper, we propose a bistatic linear array synthetic aperture radar (BLA-SAR) system for moving target detection, location, and imaging. In the BLA-SAR system, a geostationary satellite is used as a transmitter, and two airborne linear array radars are used as passive receivers, where the transmitted waveforms from the geostationary satellite may have two different carrier frequencies, two linear array antennas on two different airplanes may be equipped with different spacings, or two airplanes may fly with two different velocities. It is shown that, using the BLA-SAR, not only the stationary clutter can be suppressed but also locations of both slow and fast moving targets can be accurately estimated. Furthermore, an effective BLA-SAR algorithm of moving target imaging is also proposed. Lastly, some numerical experiments are given to demonstrate the effectiveness of the BLA-SAR

A generalized Chinese remainder theorem (CRT) for multiple integers from residue sets has been studied recently, where the remainders in a residue set are not ordered. In this correspondence, we first propose a majority method and then based on the proposed majority method we present a sharpened dynamic range of multiple integers that can be uniquely determined from their residue sets

In this correspondence, we first present a transformation technique to improve the normalized diversity product for a full rate algebraic space-time block code (STBC) by balancing the signal mean powers at different transmit antennas. After rewriting a cyclic division algebra structure into a multilayer structure for a full rate code, we show that the normalized diversity product of the transformed code with the multilayer structure is better than the one of the transformed code with the cyclic division algebra structure. We then present a new full rate algebraic STBC with multilayer structure with nonvanishing determinant (NVD) for three transmit antennas when signal constellation is carved from QAM. We show that the new code has larger normalized diversity product than the existing 3 times 3 NVD full rate STBC for quadrature amplitude modulation (QAM) signals, and we also show that it has the largest normalized diversity product in a family of full rate STBC.

Shift-full-rank (SFR) matrices are matrices that have full row rank no matter how their rows are shifted. SFR matrices have been used lately as generator matrices for a family of space–time trellis codes to achieve full diversity in asynchronous cooperative communications, where the numbers of columns of the SFR matrices correspond to the memory sizes of the trellis codes. A systematic construction of SFR matrices, including the shortest (square) SFR (SSFR) matrices, has been also previously proposed. In this paper, we study a variation of SFR matrices with a relaxed condition: limited-shift-full-rank (LT-SFR) matrices, i.e., the matrices that have full row rank no matter how their rows are shifted as long as the shifts are within some range called delay tolerance. As the generator matrices for the previously proposed space–time trellis codes, LT-SFR matrices can guarantee asynchronous full diversity of the corresponding codes when the timing errors are within the delay tolerance. Therefore, due to the relaxed condition imposed on LT-SFR matrices, more eligible generator matrices than SFR matrices become available.

In this paper, we propose a new pilot tone placement scheme and a novel pilot sequence design to estimate the carrier frequency offset (CFO) in OFDM systems. Unlike the conventional approach in which isolated pilot tones are used, we cluster two pilot tones together as a group and these tone groups are equally spaced along the subcarrier axis. The pilot sequence of each group is carefully designed, in which the pilot symbol on the left side in each cluster is made antipodal to the one on the right. The performance in terms of the signal-to-interference ratio (SIR) can be significantly improved by the proposed scheme. Theoretical analysis and simulation results show that the clustered pilot tones can give a substantially lower variance of CFO estimate than that of the isolated pilot tones. For a given CFO, it demonstrates that the variance of the estimate can be reduced by 3 - 14 dB. The bit error rate (BER) is reduced by about 1 dB both in AWGN and multipath fading channels in the simulated cases

In this letter we apply the general orthogonal space-time block codes (OSTBC) to MIMO-OFDM systems over frequency-selective fading channels and aim to exploit the potential multipath diversity. By replacing the scalar entry of an OSTBC matrix with the vector of repeated symbols, we obtain a new OSTBC which can achieve both spatial diversity and multipath diversity. Moreover, a fast maximum likelihood (ML) decoding is admitted. Simulation results show that the proposed OSTBC, for two transmit antennas, can obtain a higher diversity gain than the Alamouti code at the same ML decoding complexity

Using OFDM and space-time coding to achieve asynchronous cooperative diversity has been proposed by Mei et. al. and Shin et al, where orthogonal space-time block codes, Alamouti code in particular, were used at relay nodes. This proposed space-time coding can not achieve the multipath diversity when the channels from nodes to nodes are frequency selective fading. In this paper, we also consider asynchronous cooperative communications where the channels are assumed to be frequency-selective fading. We design high rate distributed space-frequency codes to achieve both full asynchronous cooperative diversity and multipath diversities.

To achieve full cooperative diversity in a relay network, most of the existing space-time coding schemes require the synchronization between terminals. A family of space-time trellis codes that achieve full cooperative diversity order without the assumption of synchronization has been recently proposed, where it is assumed that the delays occurring at the relays must be integer multiples of the symbol duration. In this paper, we consider a cooperative system in which the delays can be fractional symbol duration and, correspondingly, multiple sampling per symbol interval is used at the destination. We show that the previously proposed space-time trellis codes for the system with integer symbol delays can still achieve asynchronous full diversity in the new scenario. Furthermore, compared with the applications in the system with integer symbol delays, the decoding complexity of the optimal Viterbi algorithm for the codes does not increase in the system with fractional symbol delays.

In this paper, a design of orthogonal space-time block codes (OSTBC) is proposed for MIMO-OFDM systems operating in frequency-selective fading channels. By stacking the repeated symbols as entries of an OSTBC matrix, the proposed OSTBC can achieve full diversity in frequency-selective fading channels. Moreover, the ML decoding can be performed separately on each symbol, i.e., single-symbol decoding. Simulation results show that for two transmit antennas the proposed OSTBC can achieve a higher diversity gain than the Alamouti code yet maintaining a single-symbol decoding simplicity.

In user cooperative communications, relay nodes are usually asynchronous. By realizing that the processing in the frequency domain is insensitive to the errors in the time domain, Mei et. al. [13] recently applied the OFDM technique to achieve the full cooperative diversity for asynchronous cooperative communications, where orthogonal space-time block codes, Alamouti code in particular, were used for relay nodes and the channels from nodes to nodes are assumed flat fading. In this paper, we also consider asynchronous cooperative communications, but the channels from nodes to nodes are assumed to be frequency-selective fading. We use high rate space-frequency codes to encode the correctly detected information symbols across some subcarriers at relay nodes to achieve both full cooperative diversity and full multipath diversity for asynchronous communications, which, we show, holds if the recent high rate space-frequency codes constructed in [15] are used.

To achieve full cooperative diversity in a relay network, most of the existing space-time coding schemes require the synchronization between terminals. A family of space-time trellis codes that achieve full cooperative diversity order without the assumption of synchronization has been recently proposed. The family is based on the stack construction by Hammons and El Gamal and its generalizations by Lu and Kumar. It has been shown that the construction of such a family is equivalent to the construction of binary matrices that have full row rank no matter how their rows are shifted. We call such matrices as shift full rank (SFR) matrices. In this paper, we systematically study and construct SFR matrices of any sizes for any number of terminals. Furthermore, we construct shortest (square) SFR (SSFR) matrices that correspond to space-time trellis codes with the smallest memory sizes and asynchronous full cooperative diversity

This paper addresses the throughput optimization problem in multihop ultra-wideband (UWB) networks by jointly considering network topology formation and routing. Given a spatial distribution of UWB devices and traffic requirement, we want to form piconets and select paths to maximize the network throughput. Although there have been several works considering the problem of selecting paths to achieve the optimal throughput in multihop wireless networks, to the best of our knowledge, none of them takes the topology formation into the consideration. In this paper, we use Boolean matrices to model role assignment in UWB networks and formulate the throughput optimization problem as a nonlinear programming (NLP) problem. Since the throughput optimization problem is NP-hard, we give an upper bound of the optimal throughput by relaxing some constraints and using pseudo-Boolean optimization to linearize the NLP. We prove that the solution of the upper bound is at most three times of the optimal throughput. Based on the topology formed by solving the upper bound, we formulate a lower bound of the optimal throughput as a linear programming problem and use column generation to solve the lower bound. Numerical results show that the lower bound is very close to the upper bound. Simulation results demonstrate the effectiveness of the scheme.

To achieve full cooperative diversity in a relay network, most of the existing space-time coding schemes require the synchronization between terminals. A family of space-time trellis codes that achieve full cooperative diversity order without the assumption of synchronization has been recently proposed. The family is based on the stack construction by Hammons and El Gamal and its generalizations by Lu and Kumar. It has been shown that the construction of such a family is equivalent to the construction of binary matrices that have full row rank no matter how their rows are shifted, where a row corresponds to a terminal (or transmit antenna) and its length corresponds to the memory size of the trellis code on that terminal. We call such matrices as shift-full-rank (SFR) matrices. A family of SFR matrices has been also constructed, but the memory sizes of the corresponding space-time trellis codes (the number of columns of SFR matrices) grow exponentially in terms of the number of terminals (the number of rows of SFR matrices), which may cause a high decoding complexity when the number of terminals is not small. In this paper, we systematically study and construct SFR matrices of any sizes for any number of terminals. Furthermore, we construct shortest (square) SFR (SSFR) matrices that correspond to space-time trellis codes with the smallest memory sizes and asynchronous full cooperative diversity. We also present some simulation results to illustrate the performances of the space-time trellis codes associated with SFR matrices in asynchronous cooperative communications.

In this letter, we study iterative demodulation and decoding methods for lattice based space-time coded systems concatenated with convolutional codes. We first apply the optimal MAP demodulation and the suboptimal linear MMSE methods to lattice based space-time demodulation and decoding. We then propose two other methods based on the idea of soft interference cancellation and in one method vector Gaussian approximation is used and in the other method scalar Gaussian approximation is used. We also present the EXIT chart analyses for the performance analyses for these iterative methods. Both theoretical and simulation results show that the performance of the vector Gaussian approximation method is the same as that of the linear MMSE method but in some cases the vector Gaussian approximation method has lower complexity. The complexity of the scalar Gaussian approximation method is the lowest among these different methods and grows linearly with the transmission rate while it still has an acceptable performance. Full diversity and full rate lattice based space-time coding is compared with the BLAST scheme and simulations show that the former has a better performance than the later even with the proposed suboptimal iterative demodulation and decoding methods.

Differential space-time modulation (DSTM) has been recently proposed by Hughes, and Hochwald and Sweldens when the channel information is not known at the receiver. On the other hand, the DSTM also needs a recursive memory of a matrix block at the encoder and therefore provides a trellis structure when the channel information is known at the receiver, which is the interest of this paper. This recursive structure of the DSTM has been adopted lately by Schlegel and Grant as an inner trellis code concatenated with an outer binary code to achieve turbo gain. The number of states of the trellis from the recursive structure depends on both the memory size, which is fixed in this case, and the unitary space-time code (USTC). In this paper, we propose a new USTC design criterion to ensure that the trellis structure improves the diversity product over the USTC as a block code. Based on the new criterion, we propose a new class of USTC design for an arbitrary number of transmit antennas that has an analytical diversity product formula for two transmit antennas. We then follow Schlegel and Grant's approach for joint encoding and iterative decoding of a binary coded DSTM (turbo space-time coding) and numerically show that our new USTC designs for the recursive space-time trellis modulation outperforms the group USTC used by Schlegel and Grant.

We propose a new family of space-time trellis codes, which are constructed by combining a super set of QOSTBC with minimum decoding complexity and an outer MTCM encoder. A systematic set-partitioning method for QAM constellations is given. The proposed scheme can be used for systems with four and more than four transmit antennas. Furthermore, its decoding complexity is low because branch metric calculations can be done in a symbol-wise way. Several design examples are presented. Simulation results demonstrate that the proposed scheme has a comparable performance as super quasi-orthogonal space-time trellis codes presented by Jafarkhani and Hassanpour while providing a lower decoding complexity.

In this paper, we propose a systematic design of rate-M/sub t/ full-diversity space-time-frequency (STF) code for MIMO frequency-selective block-fading channels. The full-diversity can be proved to be the product of number of transmit antennas M/sub t/, receiver antennas M/sub r/, fading blocks M/sub b/ and channel taps L. The performance of the proposed STF codes are simulated and compared with some existing SF codes. The full-diversity of STF is further validated from the simulated case.

In this paper, we consider a coded ultra-wide band (UWB) impulse radio system. At the receiver, to estimate multipath delays and amplitudes, the soft information output from a soft-input soft-output (SISO) decoder is used to assist the estimation. The SISO decoder and the channel estimator work in an iterative way and no training sequence is used in the estimation. Compared with the non data-aided channel estimation without using the soft information output from the SISO decoder, the performance of this iterative channel estimation is better. We also explain this iterative method by using the expectation maximization (EM) algorithm, which can be used to prove the convergence of this iterative method.

In this paper, a differential space-time trellis-coded scheme, based on super orthogonal space-time trellis codes (SOSTTC), is proposed. Based on its trellis structure, a low-complexity suboptimal differential decoder using the per-survival processing technique (PSP) is developed. In slow fading channels, it can approach the performance of SOSTTC with coherent decoding. Furthermore, an adaptive receiver is presented for time-varying channels, in which no training data is needed. Simulation results show that our scheme can achieve a good performance in both slow fading and time-varying fading channels with a reasonable complexity.

In current cooperative communication schemes, to achieve cooperative diversity, synchronization between terminals is usually assumed which may no be practical since each terminal has its own local oscillator. In this paper, based on the stack construction proposed by Hammons and El Gamal, we first construct a family of space-time trellis codes from BPSK to higher order QAM and PSK modulation schemes based on the unified construction proposed by Lu and Kumar Some diversity product properties of space-time trellis codes are studied and simplified decoding methods are discussed. Simulation results are given to illustrate the performance of the newly proposed codes.

In current cooperative communication schemes, to achieve cooperative diversity, synchronization between terminals is usually assumed, which may not be practical since each terminal has its own local oscillator. In this paper, based on the stack construction proposed by Hammons and El Gamal, we first construct a family of space-time trellis codes for BPSK modulation scheme that is characterized to possess the full cooperative diversity order without the synchronization assumption. We then generalize this family of space-time trellis codes from BPSK to higher order QAM and PSK modulation schemes based on the unified construction proposed by Lu and Kumar. And also, simplified decoding methods are discussed. Simulation results are given to illustrate the performance of the newly proposed codes.

Based on the normalized diversity product criterion of space-time code designs, we present two optimal 2/spl times/2 diagonal algebraic space-time block codes based on field extensions. We then present an n/spl times/n diagonal space-time code design method directly from 2n real integers based on extended complex lattices. We also present a non-tight upper bound for normalized diversity products of 2/spl times/2 diagonal space-time block codes with QAM information symbols from general 2/spl times/2 complex-valued matrices. We finally use the optimal 2/spl times/2 algebraic diagonal space-time codes to construct two 2/spl times/2 full rate space-time block codes and find that both of them have better normalized diversity products than the Golden code does.

In this paper, we first present a necessary and sufficient condition on linear transformations for an QOSTBC to possess the minimum ML decoding complexity, i.e., real symbol pair-wise decoding. We then present optimal linear transformations of information symbols for quasi-orthogonal space-time block codes (QOSTBC) with minimum ML decoding complexity. The optimality is in the sense that the diversity product (or product distance) is maximized when the mean transmission power is fixed

In this paper, a differential space-time trellis-coded scheme based on super-orthogonal space-time trellis codes (SOSTTCs) is proposed. It achieves full diversity and provides an improved coding gain. Based on the per-survivor processing (PSP) technique, a low-complexity suboptimal differential decoder is developed. In slow fading channels, it can approach the performance of SOSTTC with coherent decoding. Furthermore, in time-varying channels, a bank of recursive least square (RLS) type channel predictors are incorporated into the Viterbi decoder to track the channel variance and the RLS predictors do not need training data and channel statistics. The performance analysis is given. Simulation results are presented to illustrate our analytical results and they show that our scheme can achieve a good performance in both slow fading and time-varying fading channels with modest complexity.