Integrated Communication and Navigation (ICAN) has emerged as a crucial technology for embedding navigation functions into 6G satellite internet waveforms. However, the effective ICAN poses challenges for ICAN-enabled Low Earth orbit (LEO) satellites. Existing methods struggle to balance the requirements of communication and navigation within a single waveform and fail to adequately address the high-dynamic scenarios of LEO satellites. To overcome these challenges, this paper proposes an Orthogonal Frequency Division Multiplexing (OFDM)-based ICAN signal scheme. Leveraging the symmetry of the pilot cross-ambiguity function, it enables precise joint time-frequency channel estimation. Firstly, by directly utilizing the intermediate results of communication, it achieves the delay and Doppler estimation limits while ensuring communication performance. Secondly, an analytical solution for the delay and Doppler estimation error is derived, allowing the direct determination of the minimum pilot allocation under any given accuracy requirements, effectively balancing the trade-off between communication and navigation. Thirdly, field results from an ICAN-enabled LEO satellite (altitude 1100 km) show that the delay and Doppler estimation accuracy can be controlled within 10 cm and 12 Hz under a pilot ratio of 0.089, respectively, with an approximate 58.34% accuracy and 10-15 dB Carrier-to-Noise Ratio improvement over that of traditional Global Navigation Satellite System. These findings offer valuable insights and practical references for the design and implementation of ICAN-enabled LEO satellites.

This paper proposes a unified analytical framework for reconfigurable intelligent surface (RIS)-assisted two-way full-duplex (TW-FD) communication systems in 6G Internet of Things (IoT) scenarios. The proposed framework specifically addresses RISs with N reflective elements, facilitating efficient bidirectional communication. A novel unified moment-based analytical approach is developed, accommodating diverse fading models including Rayleigh, Nakagami-n, Weibull, Nakagami-m, and κ-μ, thereby significantly enhancing the versatility and practicality for complex 6G IoT environments. To comprehensively validate the applicability of our analysis, both independently identically distributed (i.i.d.) and independently non-identically distributed (i.n.i.d.) fading channel scenarios are investigated. By employing the Edgeworth expansion method, we derive analytical expressions for the probability density function (PDF) and cumulative distribution function (CDF) of the end-to-end signal-to-interference-plus-noise ratio (SINR). Additionally, closed-form expressions for probability, average symbol error rate (SER) for various modulation schemes, and end-to-end ergodic rate are provided. Monte Carlo simulations demonstrate the accuracy and robustness of the theoretical models proposed. The results presented in this work not only contribute substantially to the analytical methodologies for RIS-assisted communication but also offer practical guidance for the design and optimization of future 6G IoT systems.

The extremely large reconfigurable intelligent surface (XL-RIS) is an architecture that shows potential in expanding the transmission region to meet high requirements of sixth-generation communication systems. In XL-RIS-assisted scenarios, users transmit signal through spherical-wavefront near-field channel, where the channel estimation presents great challenges. Additionally, XL-RIS elements are prone to failure due to accidental damages or blockages, which further complicates the channel state. To address these issues, we propose a three-stage signal-assisted sparse representation-based channel estimation (SA-SRCE) scheme to robustly recover near-field channel and diagnose RIS failure state with low pilot overhead. The first stage utilizes a double domain filter (DDF) to eliminate noise based on sparse representation in the angle and polar domains. Then, based on the filtered signal, a failure-aware orthogonal matching pursuit (FA-OMP) algorithm is proposed to iteratively reconstruct the channel, as well as eliminate the perturbation invoked by RIS element failures. Moreover, considering the limited availability of pilot, we further exploit the information of compressed signal and propose the signal-assisted failure-aware double-sparsity OMP (FADS-OMP) algorithm as the third stage to improve the estimation performance in FA-OMP. The effectiveness and robustness of our proposed scheme is validated through theoretical analysis and extensive simulations.

We provide new recovery bounds for hierarchical compressed sensing (HCS) based on prior support information (PSI). A detailed PSI-enabled reconstruction model is formulated using various forms of PSI. The hierarchical block orthogonal matching pursuit with PSI (HiBOMP-P) algorithm is designed in a recursive form to reliably recover hierarchically block-sparse signals. We derive exact recovery conditions (ERCs) measured by the mutual incoherence property (MIP), wherein hierarchical MIP concepts are proposed, and further develop reconstructible sparsity levels to reveal sufficient conditions for ERCs. Leveraging these MIP analyses, we present several extended insights, including reliable recovery conditions in noisy scenarios and the optimal hierarchical structure for cases where sparsity is not equal to zero. Our results further confirm that HCS offers improved recovery performance even when the prior information does not overlap with the true support set, whereas existing methods heavily rely on this overlap, thereby compromising performance if it is absent.

Knowledge distillation as a practical tool to enhance the performance of small-capacity student networks on downstream tasks comes at the cost of a lengthy distillation process due to the online inference of teacher networks, especially when there is a large capacity gap between them. Therefore, in this paper, we propose a fast distillation framework called TeDri based on region images by offline saving relevant regional information and its teacher guidance. Specifically, first, to alleviate the lack of diversity caused by the fixed augmentation path in region images, we propose Teacher-driven MixUp strategies with mild intensity and advocate binding the mixing factor$\lambda$with teacher guidance confidence, where more confident category representations dominate the MixUp process. Furthermore, recognizing the need to evaluate these randomly cropped regions, and we propose region contrastive learning, encourage the student network to mimic the region partitioning behavior of the teacher, promoting a comprehensive understanding of global semantic content from multiple local perspectives. Finally, we introduce region mutual learning, employing spatial constraints among regions to require the student network towards consistent content interpretation across localized regions. Experiments on CIFAR-100 and ImageNet-1 K validate the effectiveness of the proposed TeDri, achieving competitive performance while significantly reducing training time.

We consider the turbo frequency domain equalization (FDE) approach based on expectation propagation (EP) to the single-carrier with index modulation (SCIM) transmission over frequency-selective channels. The SCIM-FDE system implements vector-by-vector signal transmission in the time domain, in which elements within each transmitted symbol vector display correlation. In this paper, we propose a novel vector-wise EP (VW-EP) equalization, where the discrete prior distribution of each symbol vector is approximated with a multivariate complex Gaussian distribution. Hence the extrinsic information in the EP feedback is extended to be in terms of the mean vector and covariance matrix. The effectiveness of the proposed scheme can be validated through the error-correction performance and extrinsic information transfer (EXIT) analysis. Numerical results show that the SCIM-FDE transmission with the VW-EP turbo equalization demonstrates remarkable performance.

In frequency division duplex (FDD) massive multiple input multiple output (MIMO) systems, reliable downlink channel estimation is essential but requires huge pilot overhead due to hundreds of antennas at base station (BS). In order to reduce pilot overhead without compromising the channel estimation, compressive sensing (CS) has been widely applied for channel estimation by exploiting the inherent sparse structure of massive MIMO channel in angular domain. However, it still suffers from high complexity during the optimization process and the requirement of prior information on the number of spatial paths (PINP). To overcome these challenges, this paper develops a novel hybrid channel estimation scheme by integrating model-driven CS and data-driven deep unrolling techniques. The proposed scheme is composed of a coarse estimation part and a fine correction part, which is implemented in a two-stage manner by exploiting both inter- and intra-frame sparsities of channels in angular domain. Additionally, a threshold function is proposed to eliminate the requirement of the number of spatial paths. Theoretical results are provided to indicate the convergence of both fine correction and coarse estimation. Numerical results demonstrate that our scheme can achieve high accuracy with less pilot overhead and low complexity.

Data-free knowledge distillation (DFKD) enables knowledge transfer from a pre-trained teacher to a student network without accessing the real dataset. However, generator-based DFKD methods struggle to ensure that the synthetic images accurately reflect the real dataset distribution. The update of the generator network relies heavily on teacher category guidance, but varying teacher prediction accuracy across categories leads to inconsistent synthetic image quality. Such variations introduce a distribution shift between synthetic and real datasets, negatively impacting student network performance during knowledge distillation. To address this challenge, we propose the SRIF, comprising two components: Student-Driven Flexible Filtering (SDFF) and Re-weighting for Independent Regularization (RIR). SDFF filters out synthetic images affected by the category distribution shift during data generation, producing a more reliable dataset. RIR, applied during distillation, encourages the student to learn stable causal relationships through sample reweighting. Both components flexibly integrate into existing DFKD frameworks, improving performance while reducing training costs.

Self-supervised learning and knowledge distillation intersect to achieve exceptional performance on downstream tasks across diverse network capacities. This paper introduces MIM-HD, which implements enhancements for masked image modeling (MIM) distillation, in two key aspects. First, a vision transformer head-level relation adaptive distillation approach is proposed, allowing the student to dynamically draw multi-source knowledge from the teacher based on its evolving state, compatible with scenarios where teacher-student transformer block head count differs. Second, to address the overemphasis on the encoder and neglect of the decoder role in maintaining representation consistency in previous MIM distillations, a dual-view decoding strategy for latent visual representations is introduced, reusing the teacher’s decoder to alleviate MIM burdens on smaller networks. MIM-HD effectiveness is demonstrated through evaluations on ADE20K (mIoU) and ImageNet-1K (Acc), achieving +1.4% and +0.5% improved performance, respectively, compared to state-of-the-art methods, with substantial advantages on smaller pre-training datasets. Moreover, MIM-HD achieves superior efficiency, reducing pre-training epochs from 300 to 100.

The extremely large reconfigurable intelligent surface (XL-RIS) is an architecture that shows potential in expanding the transmission region and meeting the high transmission requirements. With the deployment of XL-RIS, users transmit signals through the spherical-wavefront near-field channel, which presents challenges in channel estimation with low pilot overhead. Additionally, XL-RIS is a non-stationary system whose elements are prone to failure due to accidental damages or blockages, thereby further complicating the channel state. To address these issues, this paper proposes a two-stage sparse representation-based channel estimation (SRCE) scheme to jointly recover the near-field channel state and diagnose RIS element failures with low pilot overhead. The first stage utilizes a proposed double-domain filter (DDF) method to eliminate part of the received noise based on the sparse representation in the angle and polar domain. Then, a robust failure-aware double sparsity orthogonal matching pursuit (FA-OMP) algorithm is proposed by iteratively reconstructing the channel state, as well as eliminating the perturbation invoked by RIS element failures. The efficiency and robustness of our proposed scheme are validated through simulations.

Specific emitter identification (SEI) technology is significant in device administration scenarios, such as self-organized networking and spectrum management, owing to its high security. For nonlinear and non-stationary electromagnetic signals, SEI often employs variational modal decomposition (VMD) to decompose the signal in order to effectively characterize the distinct device fingerprint. However, it has not been well investigated in noisy SEI scenarios. Specifically, the existing VMD algorithms do not utilize the stability of the intrinsic distortion of emitters within a certain temporal span, nor do they consider the vulnerability of this distortion estimates to channel noise, both of which constrain its practical applicability in SEI. In this paper, we propose a joint variational modal decomposition (JVMD) algorithm, which is an improved version of VMD by simultaneously implementing modal decomposition and channel noise estimation on multi-frame signals. The consistency of multi-frame signals in terms of the central frequencies and the inherent modal functions (IMFs) is exploited, which effectively highlights the distinctive characteristics among emitters. In addition, channel noise is estimated during signal decomposition, which improves the ability to stably extract these distinctive characteristics. Additionally, the complexity of JVMD is analyzed, which is proven to be more computational-friendly than VMD. Simulations of both modal decomposition and SEI that involve real-world datasets are presented to illustrate that when compared with other SEI schemes, the JVMD-based scheme improves the accuracy of device classification at low SNRs.

Compressive Sensing (CS) technology, which handles large amounts of data in its low-dimensional form, has been shown to enjoy excellent transmission and storage efficiency. To further improve the stability and robustness of CS-based wireless communication system, Reconfigurable Intelligent Surface (RIS) technology has been introduced to enhance the transmission link connectivity. Current researches generally assume perfect RIS; however, some of its elements may be damaged and fail to work, which degrades the sparse signal recovery. Therefore, this paper establishes a double-sparsity optimization model to jointly recover the sparse signal and diagnose the RIS element failure. For the scenarios with and without Channel State Information (CSI) at the receiver, Double-Sparsity based algorithm (DS), and Atomic norm and Double-Sparsity based algorithm (ADS) exploiting Alternating Direction Method of Multipliers (ADMM) framework are proposed respectively. Additionally, a novel ℓB,B norm is incorporated to further constrain the block and binary characteristics of RIS failures, and the resulting improved versions of DS and ADS are termed as Binary and Block-Sparsity based algorithm (BBS) and Atomic norm, Binary and Block-Sparsity based algorithm (ABBS). Simulations verify the effectiveness and robustness of the proposed algorithms, and show the improved performance of the BBS and ABBS algorithms compared with the DS and ADS algorithms.

Specific emitter identification (SEI) is important to guarantee the security of device administration. Recently, to increase the effectiveness of the recognition, traditional SEI employing only one sensor has been extended to the scenario with multiple sensors. However, the inherent distortion at different sensors impacts the radio frequency fingerprints (RFFs) of the emitter independently, which inevitably leads to the non-universalization of the features extracted at different sensors. Besides, variational modal decomposition (VMD), which is an effective preprocessing in SEI, has not been well investigated in noisy scenarios. To combat the environment noise, this paper proposes two joint VMD (JVMD) algorithms, i.e., JVMD for ignoring the distortions at sensors (I-JVMD) and JVMD for considering the distortions at sensors (C-JVMD). Specifically, I-JVMD exploits the consistency of the central frequencies and intrinsic modal functions (IMFs) of multiple sensors, and C-JVMD further estimates and filters out the phase noise at each sensor that may distort the RFFs of the emitter. Simulations of the proposed JVMD algorithms and their corresponding applications in SEI are provided on two real-world datasets. When compared with the traditional VMD, the proposed ones improve the accuracy of device classification and the robustness towards noise.

Compressive sensing (CS) technology, which handles large amounts of data in its low-dimensional form, enjoys excellent transmission and storage efficiency. To further improve the stability and robustness of CS-based wireless communication system, reconfigurable intelligent surface (RIS) technology has been introduced to enhance the transmission link connectivity. Current researches generally assume perfect RIS; however, some of its elements may fail to work, which degrades the sparse signal recovery. Therefore, this paper establishes a double-sparsity optimization model to jointly recover the sparse signal and diagnose the RIS element state. A double-sparsity based algorithm (DS) exploiting Alternating Direction Method of Multiplier framework is proposed as the solution. Additionally, a novel$\ell_{B,B}$norm is incorporated to further constrain the block and binary characteristics of RIS failures, and the resulting improved version of DS is termed as the binary and block-sparsity based algorithm (BBS). Simulations verify the effectiveness and robustness of the proposed algorithms, and show the improved performance of the BBS algorithm compared with the DS algorithm.

Massive multiple-input multiple-output (MIMO) enjoys great advantage in 5G wireless communication systems owing to its spectrum and energy efficiency. However, hundreds of antennas require large volumes of pilot overhead to guarantee reliable channel estimation in FDD massive MIMO system. Compressive sensing (CS) has been applied for channel estimation by exploiting the inherent sparse structure of massive MIMO channel but suffer from high complexity. To overcome this challenge, this paper develops a hybrid channel estimation scheme by integrating the model-driven CS and data-driven deep unrolling technique. The proposed scheme consists of a coarse estimation part and a fine correction part to respectively exploit the inter- and intra-frame sparsities of channels to greatly reduce the pilot overhead. Theoretical result is provided to indicate the convergence of the fine correction and coarse estimation net. Simulation results are provided to verify that our scheme can estimate MIMO channels with low pilot overhead while guaranteeing estimation accuracy with relatively low complexity.

Wideband direction-of-arrival (DoA) estimation is desirable with the ever-increasing data rate in wireless communication systems. In this paper, an iterative coherent signal-subspace method for wideband two-dimensional DoA estimation with a uniform circular array is proposed, which includes three steps in each iteration. The first step selects a subset of frequency points for the subsequent focusing process to avoid unnecessary complexity. The second step realizes the focusing process, where the angle intervals for generating focusing matrices with robust-ness are designed, and then the signal-subspaces at the selected frequency points are focused into a reference frequency. The third step estimates DoAs with the multiple signal classification (MUSIC) algorithm where the range of MUSIC spatial spectrum is constrained by the aforementioned angle intervals, thereby further reducing the complexity. The key parameters of the proposed method in the current iteration are adjusted based on the previous estimation results. The simulation results show that the proposed method enjoys better performance and implementation efficiency when compared with benchmark methods.

This paper proposes two novel schemes of wideband compressive spectrum sensing (CSS) via block orthogonal matching pursuit (BOMP) algorithm, for achieving high sensing accuracy in real time. These schemes aim to reliably recover the spectrum by adaptively adjusting the number of required measurements without inducing unnecessary sampling redundancy. To this end, the minimum number of required measurements for successful recovery is first derived in terms of its probabilistic lower bound. Then, a CSS scheme is proposed by tightening the derived lower bound, where the key is the design of a nonlinear exponential indicator through a general-purpose sampling-controlled algorithm (SCA). In particular, a sampling-controlled BOMP (SC-BOMP) is developed through a holistic integration of the existing BOMP and the proposed SCA. For fast implementation, a modified version of SC-BOMP is further developed by exploring the block orthogonality in the form of sub-coherence of measurement matrices, which allows more compressive sampling in terms of smaller lower bound of the number of measurements. Such a fast SC-BOMP scheme achieves a desired tradeoff between the complexity and the performance. Simulations demonstrate that the two SC-BOMP schemes outperform the other benchmark algorithms.

In practical compressive sensing (CS) communication system, Analog to Digital Converter (ADC) is a necessary component to convert analog signals into digital ones. However, nonlinear distortion in ADC is unavoidable and definitely affects the reception accuracy. Though recent works have studied various methods to combat the negative effect of ADC nonlinear distortion, few of them discuss the solution in communication system with CS. This paper studies the CS recovery method when compressive measurements suffer from ADC nonlinear distortion. A double-sparsity model is first formulated, where the original signal and the ADC nonlinear distortion are both sparse. Then, for the type of clipping ADC, we propose a corresponding algorithm based on the Alternating Direction Method of Multipliers (ADMM) strategy to solve the double-sparsity (DS) problem, named as DS-ADMM. For the type of self-reset (SR) ADC, we explore its essence of rounding operation to design an integer constraint based feedback updating (ICFU) strategy, and accordingly propose the DS-ADMM-ICFU recovery algorithm. Experiment results show that the DS-ADMM algorithm for the double-sparsity problem improves the recovery performance compared with the existing counterpart, and DS-ADMM-ICFU for SR ADC exhibits preferable advantage in typical communication systems.

Over the past years, tremendous progresses have been achieved for wireless positioning system based on Received Signal Strength (RSS). However, most researches assume ideal RSS data, which ignore the anomalies that generally exist due to the interference in signal and instrument malfunction. To reduce the negative influence of anomalies on system performance, anomaly detection is regarded as an important preprocessing technique. To better distinguish anomalies from the RSS data that is distributed in multiple blobs, this paper proposes a two-step anomaly detection scheme called K-means aided Extended Isolation Forest (KEIF). The first step is to exploit K-means to cluster the received data according to the RSS features. Then, based on the positions of source node, Extended Isolation Forest (EIF) is employed for each cluster to obtain anomaly scores, which represent the isolated degree of data points. The data with scores higher than a threshold is considered as anomalies. We verify our proposed scheme in a RSS-based fingerprinting wireless positioning system, and the experiments demonstrate that the real dataset processed by KEIF can effectively improve the positioning accuracy, compared with the original dataset without anomaly detection and the datasets processed by other existing anomaly detection schemes.

MOTIVATION: The classification of high-throughput protein data based on mass spectrometry (MS) is of great practical significance in medical diagnosis. Generally, MS data are characterized by high dimension, which inevitably leads to prohibitive cost of computation. To solve this problem, one-bit compressed sensing (CS), which is an extreme case of quantized CS, has been employed on MS data to select important features with low dimension. Though enjoying remarkably reduction of computation complexity, the current one-bit CS method does not consider the unavoidable noise contained in MS dataset, and does not exploit the inherent structure of the underlying MS data. RESULTS: We propose two feature selection (FS) methods based on one-bit CS to deal with the noise and the underlying block-sparsity features, respectively. In the first method, the FS problem is modeled as a perturbed one-bit CS problem, where the perturbation represents the noise in MS data. By iterating between perturbation refinement and FS, this method selects the significant features from noisy data. The second method formulates the problem as a perturbed one-bit block CS problem and selects the features block by block. Such block extraction is due to the fact that the significant features in the first method usually cluster in groups. Experiments show that, the two proposed methods have better classification performance for real MS data when compared with the existing method, and the second one outperforms the first one. AVAILABILITY AND IMPLEMENTATION: The source code of our methods is available at: https://github.com/tianyan8023/OBCS. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

Grant-free non-orthogonal multiple access (NOMA) is considered as one of the most important methodologies for the machine-type communications (MTC). In the field of MTC, compressed sensing based multi-user detection (CS-MUD) has been recognized as an excellent candidate for joint user activity and data detection, since many users sporadically transmit short-size data packets at low rates. This article focuses on the CS-MUD problem in the up-link grant-free NOMA scenario, where users are (in)-active randomly in each time slot yet with high temporal correlation. First, we investigate the CS framework to fully extract the underlying side information in the temporal correlation and propose a novel CS-MUD algorithm. Then, to mitigate the performance degradation due to the imperfect channel estimation in practice, the proposed algorithm is further extended by utilizing the perturbed CS, where the impact of channel estimation errors is modeled as certain perturbation in the measurement matrix. Different from most of the state-of-the-art CS-MUD algorithms, both proposed algorithms can apply even in the absence of prior knowledge on the number of active users. Simulation results indicate that the proposed algorithms achieve better performance than the existing CS-MUD methods. Their convergence and complexity issues are also discussed theoretically and numerically.

We study an energy-efficient unmanned aerial vehicle (UAV) multicast system, in which ground terminals (GTs) requiring a common information (CI) are grouped and a UAV flies to each group to deliver the CI using minimum energy consumption. A machine learning (ML) empowered joint multicast grouping and UAV trajectory optimization framework is proposed to tackle the challenging joint optimization problem. In this framework, we first propose the compressed-feature regression and clustering machine learning (C2ML) for multicast grouping. A support vector regression (SVR) is trained with the silhouette coefficient, a one- dimensional compressed feature regarding the distribution of GTs, to efficiently determine the number of groups that guides the K-means clustering to approach the optimal multicast grouping. With the C2ML- enabled multicast grouping, we solve the UAV trajectory optimization problem by formulating an equivalent centroid-adjustable traveling salesman problem (CA- TSP). An efficient CA-TSP inspired iterative optimization algorithm is proposed for UAV trajectory planning. The proposed ML-empowered joint optimization framework, which integrates the offline C2ML-enabled multicast grouping and the online CA-TSP inspired UAV- trajectory optimization, is shown to achieve excellent energy-saving performance.

Spectrum sensing is an important topic in cognitive radio networks. The traditional schemes suffer from high time consumption, hardware loss and computational complexity. To overcome the above shortcomings, compressed sensing is integrated into cognitive radio networks. In this paper, we propose two compressed spectrum sensing algorithms called Logit Weighted Block Orthogonal Matching Pursuit (LW-BOMP) and Logit Weighted Block Orthogonal Matching Pursuit with Joint Judgement (LW-BOMP-J), which exploit the block sparsity and prior information. The first algorithm integrates support probabilities into the matching pursuit procedure, and the second algorithm extends the first one to the scenario with inaccurate block support probabilities. The performance of the algorithms is simulated and compared with conventional algorithm. The results show that the proposed algorithms are more promising in reconstructing original spectrum and LW-BOMP-J is better than LW-BOMP under the inaccurate prior information condition.

Massive multiple input multiple output (MIMO) is a promising technology that can enhance the wireless communication capacity due to increased degrees of freedom. To fully utilize the spatial multiplexing gains of massive MIMO, accurate channel state information (CSI) is required for coherent detection. Due to the overwhelming pilot overhead of conventional CSI estimation methods, compressed sensing technology is adopted as an effective method to reduce pilot overhead. In this paper, we consider the channel estimation problem in FDD multi-user massive MIMO systems. By exploiting the block sparsity of channel matrices in virtual angular domain among different users, we propose a joint block orthogonal matching pursuit (JBOMP) algorithm to estimate CSI at the base station. The performance of JBOMP is evaluated by simulation, which shows the advantages over existing algorithms.

Compressed sensing (CS) has attracted a lot of research interests in data transmission, since it can significantly reduce the redundancy of the data while still holding the information completely. Generally, in order to deal with the experienced time-varying channel, the data transmission scheme based on CS needs to estimate the channel frequently. In this paper, we propose a novel CS-based data transmission scheme for slowly time-varying channel, which estimates channel and reconstructs data jointly. Specifically, the scheme consists of a data reconstruction part and a channel state information (CSI) update part. The former reconstructs the data with inaccurate CSI by modeling the problem as a perturbed CS reconstruction problem. The latter updates the CSI with the reconstruction result as a semi-blind channel estimation problem. Comparing with the classical scheme, the proposed one enjoys a reduced cost by avoiding inserting pilots into data frame frequently. Simulation results demonstrate that our proposed scheme works robustly in slowly time-varying channel and the performance is comparable to that of the scheme reconstructing the data with perfect CSI.

Compressive sensing (CS) is an emerging theory in that it is possible to reconstruct sparse signals from far fewer measurements than traditional methods use. Existing studies utilizing CS in the field of cognitive radio network (CRN) mainly focus on spectrum sensing in interweave mode. However, few works concern about the application of CS in overlay CRNs. In this paper, we study the overlay CRN, which applies CS technology as a joint source-channel code. The secondary user (SU) not only sends its own message, but also employs decode-and-forward relaying strategy to help with primary transmission, where the primary message is obtained in a causal manner. Dirty paper coding is used to pre-cancel the interference of the primary message at the secondary receiver. We discuss the coding schemes when one SU and two SUs are in the CRN. To either case, we formulate the corresponding system optimization problem, which maximizes the secondary rate while satisfying the primary rate requirement. The performance of the proposed scheme is evaluated by numerical simulations and compared with the nonoptimal causal scheme and the non-causal scheme.

In this paper, we consider an overlay cognitive radio network that transmits discrete-time analog sources over additive white Gaussian noise channels. Our focus is on the case where the primary and secondary sources are correlated. The secondary user (SU) knows the primary message non-causally, and allocates part of its power for transmitting the primary message. We study a hybrid digital-analog coding scheme for the SU, which is the superposition of two analog parts of the sources and a digital part of the secondary source. This coding scheme not only exploits the correlation between the sources, but also helps with the primary transmission. We derive the signal-to-noise ratio (SNR) of both PU and SU, and formulate the optimization problem as achieving maximum SNR of the SU while protecting the PU's SNR from being affected. The simulation results are shown to be consistent with our derivation.

This paper considers a cognitive network that comprises one multi-input multi-output (MIMO) primary link and one MEMO secondary link for the underlay and overlay modes. In the underlay mode, instead of using water-filling algorithm to maximize the primary rate selfishly, we propose adaptive number of eigenmodes beamforming (ANEB) algorithm for the primary user (PU), which can adjust the number of PU's eigenmodes to meet its rate requirement The secondary user (SU) coexists with the PU using interference alignment (IA) scheme. In the overlay mode, the secondary transmitter has non-causal knowledge of the primary message and helps with the primary transmission. We combine IA algorithm with power allocation scheme to maximize the SU's rate as well as meet the PU's rate requirement Finally, numerical simulation results show that our scheme is win-win for both the PU and SU.

The theory of compressed sensing (CS) is very attractive in that it is possible to reconstruct sparse signals with a sub-Nyquist sampling rate. Recently many researches have applied CS as the channel code, and show its great potential in communication systems. This paper studies the compressed cooperation in an amplify-and-forward (CC-AF) relay channel, where CS is used to compress the source data and AF relay is used to forward the received signals. When different deployments of measurement matrices are considered, we design different decoding strategies and analyze the corresponding achievable rates. With the derived rates, numerical calculations show that CC-AF outperforms the direct transmission without relay. In addition, the performance of CC-AF and the existing compressed cooperation with decode-and-forward relay is also compared.

Analog-to-information conversion (AIC) is an efficient way to obtain the compressed samples directly from an analog signal. Modulated wideband converter (MWC) proposed by M. Mishali and Y. C. Elda is a successful hardware architecture of AIC that measures the analog signal by K branches of mixers and integrators (BMI). However, MWC has high implementation complexity owing to the fact that its measurement matrix is dense. To reduce the complexity of MWC, in this paper we propose the diagonal modulated wideband converter (D-MWC), where each BMI only works within a partial time period that is non-overlapping instead of each BMI working all the time in MWC. The measurement matrix of D-MWC is sparse that can significantly simplify the sampling structure. Simulations verify the effectiveness of D-MWC. The proposed D-MWC offers a good tradeoff between complexity and sampling performance.

This paper investigates the achievable rate of cognitive radio network when one primary user (PU) and one secondary user (SU) are present, where there is partial channel state information at the transmitter (CSIT). On one hand, SU learns primary message causally in the first phase and then relays it with decode-and-forward strategy in the second phase. On the other hand, SU employs linear assignment Gel'fand-Pinsker coding (LA-GPC) on its own message to cancel the interference from the primary transmitter. We formulate an optimization problem to maximize SU's achievable rate while keeping PU's rate unaffected. An algorithm is proposed to jointly determine the fraction of channel uses in the first phase, the percentage of SU's transmit power used for relaying, and the filter factors in LA-GPC method. The performance of the proposed algorithm is evaluated through numerical simulations.

In this paper, we propose a dirty paper coding (DPC) scheme for the cognitive radio channel where multiple cognitive users coexist with a primary user. The cognitive transmitter is assumed to know the primary signals non-causally and is equipped with two antennas, of which each transmits to a single-antenna receiver. In our proposal, zero-forcing DPC is employed to cancel the interference not only from the primary user but also from other cognitive users. The transmit power constraint is satisfied by the application of the multiuser trellis shaping. The optimal values of the power allocation parameter and the scaling factor are derived. The performance of this scheme is compared using simulations with that employing conventional trellis shaping.

In this paper, we propose a dirty paper coding (DPC) scheme that uses trellis shaping for the overlay cognitive radio channel, where a cognitive user and a primary user transmit concurrently in the same spectrum. Interference of the primary user is assumed to be known at the cognitive transmitter non-causally. Based on this knowledge, the shaping code selection, as a key feature of the proposal, is introduced which enables the constellation to be self adaptively changed. The performance of our proposed scheme is compared using simulations with that based on the conventional trellis shaping and it achieves excellent tradeoff between performance and complexity.

In this paper, a cognitive radio network with multiple licensed users is considered. A dirty paper coding (DPC) based cooperation scheme for the Gaussian multiple-in-multiple-out (MIMO) cognitive radio network (CRN) with partial transmitter side information is studied. The problem of maximizing the sum-rate of MIMO CRN with multiple primary users over the transmitter covariance matrices, which is formulated as an optimization problem, is dealt with. Such an optimization, which needs to be performed jointly with the inflator factors under the DPC-based strategy, is solved by an iterative suboptimal solutions. Simulation results show that the proposed scheme is able to greatly improve the achievable rate performance in CRN.

Prior information is easily obtained in many applications of compressed sensing. This paper considers the sparse signal recovery using certain types of prior information. Our major contribution is proposing two novel reconstruction algorithms with prior information named as logit weight simultaneous orthogonal matching pursuit (LW-SOMP) and logit weight simultaneous orthogonal matching pursuit with amplitude information (LW-SOMP-A) for joint sparsity model of distributed compressed sensing. Simulation results demonstrate improved performance of the proposed algorithms (with respect to the conventional algorithm).

For multi-node networks, a distributed compressed sensing scheme based on one-bit quantization is proposed, where signals at each node consist of common component and innovation component. To reduce the transmission cost, each node derives the measurements as the sign information of the compressed samples by using one-bit quantization. Based on the received sign information from different nodes, two joint recovery algorithms are designed to recover the signal of each node. Simulation results show that the proposed scheme outperforms both the independent reconstruction scheme and the scheme based on multi-bit quantization.

This paper investigates the achievable rate of MIMO cognitive radio network when one primary user (PU) and multiple secondary users (SU) are present, where the latter adopt dirty paper coding (DPC) to cancel the interference of PU's transmission at their receivers. Perfect channel state information is assumed at receivers, while only the statistic information of channels are known at the transmitters. We formulate an optimization problem to maximize the achievable rate of the system under the constraints of power limits of each transmitter, where the requirement of not affecting PU's transmission rate is also incorporated. An algorithm is proposed to jointly determine the inflation factors in DPC method and the input covariance matrix of each SU. Simulations show that the proposed problem achieves better achievable rate when compared with the existing results without compromising PU's transmission rate.

In this paper, we consider an extension of the cognitive radio channel model in which the secondary transmitter has to obtain (learn) the primary message rather than having non-causal knowledge of it. We formulate an achievable rate region that combines elements of compress-and-forward relaying with coding for the pure cognitive radio channel model. Moreover, we provide parameters design that maximizes the secondary rate without affecting the primary rate in the Rician channel with partial channel state information at the transmitter (CSIT). Simulation results demonstrate that the proposed compress-and-forward based strategy achieves good performance.

In this paper, we consider the sensor network, where multiple sensors communicate with a single fusion center via their respective direct links and relays. We propose schemes exploiting both quantize-and- forward (QF) and compressive sensing (CS) in relay networks, aiming to take full use of the diversity to recover the original sparse signals through the collection of a small number of samples. Such sub- sampling QF framework not only inherits the advantage of QF, but also enjoys the merits of reduced sampling rate by exploiting CS technique. The proposed two sub-sampling QF schemes place emphasis on: i) diversity gain in CS domain by employing different projection matrices in different sensors; ii) diversity gain in coding domain due to the correlations, respectively. We assess and compare the performance of the proposed schemes under AWGN channel. Simulation results demonstrate that the proposed schemes achieve excellent recovery performance with reduced communication overhead.

Recent work in the area of compressed sensing mainly focuses on the perfect recovery of the entire support for sparse signals. However, partial support recovery, where a part of the signal support is correctly recovered, may be adequate in many practical scenarios. In this study, in the high‐dimensional and noisy setting, the authors develop the probability of partial support recovery of the optimal maximum‐likelihood (ML) algorithm. When a large part of the support is available, the asymptotic mean‐square‐error (MSE) of the reconstructed signal is further developed. The simulation results characterise the asymptotic performance of the ML algorithm for partial support recovery, and show that there exists a signal‐to‐noise ratio (SNR) threshold, beyond which the increase of SNR cannot bring any obvious MSE gain.

Wavelet transform has proved to be an attractive tool in terms of analyzing singularities and irregular structures, which can characterize irregular edges of signals. Thus, it is well motivated to apply the wavelet transform approach to wideband spectrum sensing. But the existing wavelet-based spectrum sensing schemes work under the assumption that the frequency response of the analog signal input at the sensing receiver is real. To make this method work for more types of signals, this paper develops a novel wavelet-based approach to compressive wide-band spectrum sensing. In the proposed scheme, the wide-band time domain signal is fed into a number of filters and the sub-Nyquist sampled outputs are utilized to detect the occupancy of spectrum via a wavelet-based edge detector. The filters' outputs are real and nonnegative, regardless of the style of the analog signal. Furthermore, through simply adding the measurement vectors at the fusion center, this scheme can be applied to the case of multiple cognitive radios (CRs), reducing the complexity compared with traditional joint recovery algorithms.

Distributed video coding (DVC) has recently been proposed to reduce the complexity of the encoder, whereas it suffers from the sampling cost of huge amount of image data. To relax such sampling burden, this paper develops a novel sub-sampling distributed video coding (SuDVC) by utilizing compressive sensing (CS) technique. Due to the inherent sparsity in video sources, the video frames are compressively sampled at the encoder. On the other hand, by exploiting the correlation between CS measurements and side information and by performing sparsity recovery, the video frames are recovered at the decoder. When compared with the traditional fully-sampling equivalence, SuDVC enjoys the reduction of transmission rate, the reduction of implementation complexity and the robustness to channel losses, which are verified in the simulations.

End-to-end performance of multihop wireless communication systems with non-regenerative channel state information (CSI)-assisted relays operating over independent not necessarily identically Weibull fading channels is presented. With the aid of the inequality between harmonic and geometric means, the end-to-end signal-to-noise ratio (SNR) is bounded. By considering the product of rational powers of N Weibull random variables, novel expressions are derived for the moment-generating, probability density, and cumulative distribution functions in closed form. Using these results, convenient closed-form bounds are obtained for the average end-to-end SNR, the channel capacity and the average bit-error probability of multihop wireless communication systems. The tightness of the proposed bounds is verified by performing comparisons between numerical evaluation and computer simulations results.