Hyperspectral images (HSIs) are generally susceptible to various noise, such as Gaussian and stripe noise. Recently, numerous denoising algorithms have been proposed to recover the HSIs. However, those approaches cannot use spectral information efficiently and suffer from the weakness of stripe noise removal. Here, we propose a tensor decomposition method with two different constraints to remove the mixed noise from HSIs. For a HSI cube, we first employ the tensor singular value decomposition (t-SVD) to effectively preserve the low-rank information of HSIs. Considering the continuity property of HSIs spectra, we design a simple smoothness constraint by using Tikhonov regularization for tensor decomposition to enhance the denoising performance. Moreover, we also design a new unidirectional total variation (TV) constraint to filter the stripe noise from HSIs. This strategy will achieve better performance for preserving images details than original TV models. The developed method is evaluated on both synthetic and real noisy HSIs, and shows the favorable results.

Threshold-based ordered successive interference cancellation (OSIC) detection algorithm is proposed for per-antenna-coded (PAC) two-input multiple-output (TIMO) orthogonal frequency division multiplexing (OFDM) systems. Successive interference cancellation (SIC) is performed selectively according to channel conditions. Compared with the conventional OSIC algorithm, the proposed algorithm reduces the complexity significantly with only a slight performance degradation.

In order to improve tracking, interference and multipath mitigation performance from that possible with existing signals, a new Global Navigation Satellite System (GNSS) signal is needed that can offer additional degrees of freedom for shaping its pulse waveform and spectrum. In this paper, a new modulation scheme called Quinary Offset Carrier modulation (QOC) is proposed as a new GNSS signal design. The pulse waveforms of QOC modulation are divided into two types: convex and concave waveforms. QOC modulations can be easily constructed by selecting different modulation parameters. The spectra and autocorrelation characteristics of QOC modulations are investigated and discussed. Simulations and analyses show that QOC modulation can achieve similar performance to traditional BOC modulation in terms of code tracking, anti-multipath, and compatibility. QOC modulation can provide a new option for satellite navigation signal design.

Base Station (BS) cooperation has been considered as a promising technology to mitigate co-channel interference (CCI), yielding great capacity improvement in cellular systems. In this paper, by combining frequency domain cooperation and space domain cooperation together, we design a new CCI mitigation scheme to maximize the total utility for a multi-cell OFDMA network. The scheme formulates the CCI mitigation problem as a mixture integer programming problem, which involves a joint user-set-oriented subcarrier assignment and power allocation. A computationally feasible algorithm based on Lagrange dual decomposition is derived to evaluate the optimal value of the problem. Moreover, a low-complexity suboptimal algorithm is also presented. Simulation results show that our scheme outperforms the counterparts incorporating BS cooperation in a single domain considerably, and the proposed low-complexity algorithm achieves near optimal performance.

In this paper, a deep learning-based secret key generation scheme is proposed for FDD multiple-input and multiple-output (MIMO) systems. We built an encoder-decoder based convolutional neural network to characterize the wireless environment to learn the mapping relationship between the uplink and downlink channel. The designed neural network can accurately predict the downlink channel state information based on the estimated uplink channel state information without any information feedback. Random secret keys can be generated from downlink channel responses predicted by the neural network. Simulation results show that deep learning based SKG scheme can achieve significant performance improvement in terms of the key agreement ratio and achievable secret key rate.

The distributed antenna system (DAS) offers significant power savings but only if the antennas are properly located. In this letter, we convert antenna location optimization to the codebook design problem. For the widely studied circular-layout DAS with uniform user distribution, we derive closed-form expressions for antenna locations that yield near-optimal performance. For more general user distribution and antenna topology, the codebook design algorithms can provide numerical optimization results with acceptable performance and low complexity.

This paper presents a lossless steganography method based on the multiple-base notation approach for JPEG images. Embedding a large amount of secret data in a JPEG-compressed image is a challenge since modifying the quantized DCT coefficients may cause serious image distortion. We propose two main strategies to deal with this problem: (1) we embed the secret values in the middle-frequency of the quantized DCT coefficients, and (2) we limit the number of nonzero values of the quantized DCT coefficients that participate in the embedding process. We also investigated the effect of modifying the standard quantization table. The experimental results show that the proposed method can embed twice as much secret data as the irreversible embedding method of Iwata et al. under the same number of embedded sets. The results also demonstrate how three important factors: (1) the quantization table, (2) the number of selected nonzero quantized DCT coefficients, and (3) the number of selected sets, influence the image quality and embedding capacity.

Efficient resource allocation for delay-sensitive traffic, such as telephony and video streaming, in Orthogonal Frequency Division Multiple Access (OFDMA) networks is needed to increase system performance. In our system, users try to achieve a low queuing delay and buffer space usage by competing for transmission over the subchannels. We formulate this problem as a bargaining game and use the Nash Bargaining Solution (NBS) to realize a fair and efficient subchannel allocation for the users. Simulation results show performance improvements, with regard to packet dropping and delay distribution, over other algorithms.

In recent years, federated learning has attracted more and more attention as it could collaboratively train a global model without gathering the users' raw data. It has brought many challenges. In this paper, we proposed layer-based federated learning system with privacy preservation. We successfully reduced the communication cost by selecting several layers of the model to upload for global averaging and enhanced the privacy protection by applying local differential privacy. We evaluated our system in non independently and identically distributed scenario on three datasets. Compared with existing works, our solution achieved better performance in both model accuracy and training time.

The user authorization query (UAQ) problem determines whether there exists an optimum set of roles to be activated to provide a set of permissions requested by a user. It has been deemed as a key issue for efficiently handling user's access requests in role-based access control (RBAC). Unfortunately, the weight is a value attached to a permission/role representing its importance, should be introduced to UAQ, has been ignored. In this paper, we propose a comprehensive definition of the weighted UAQ (WUAQ) problem with the role-weighted-cardinality and permission-weighted-cardinality constraints. Moreover, we study the computational complexity of different subcases of WUAQ, and show that many instances in each subcase are intractable. In particular, inspired by the idea of the genetic algorithm, we propose an algorithm to approximate solve an intractable subcase of the WUAQ problem. An important observation is that this algorithm can be efficiently modified to handle the other subcases of the WUAQ problem. The experimental results show the advantage of the proposed algorithm, which is especially fit for the case that the computational overhead is even more important than the accuracy in a large-scale RBAC system.

The objective of critical nodes problem is to minimize pair-wise connectivity as a result of removing a specific number of nodes in the residual graph. From a mathematical modeling perspective, it comes the truth that the more the number of fragmented components and the evenly distributed of disconnected sub-graphs, the better the quality of the solution. Basing on this conclusion, we proposed a new Cluster Expansion Method for Critical Node Problem (CEMCNP), which on the one hand exploits a contraction mechanism to greedy simplify the complexity of sparse graph model, and on the other hand adopts an incremental cluster expansion approach in order to maintain the size of formed component within reasonable limitation. The proposed algorithm also relies heavily on the idea of multi-start iterative local search algorithm, whereas brings in a diversified late acceptance local search strategy to keep the balance between interleaving diversification and intensification in the process of neighborhood search. Extensive evaluations show that CEMCNP running on 35 of total 42 benchmark instances are superior to the outcome of KBV, while holding 3 previous best results out of the challenging instances. In addition, CEMCNP also demonstrates equivalent performance in comparison with the existing MANCNP and VPMS algorithms over 22 of total 42 graph models with fewer number of node exchange operations.

Blockchain is one of the prominent rapidly used technology in the last decade in various applications. In recent years, many researchers explored the capabilities of blockchain in smart IoT to address various security challenges. Integration of IoT and blockchain solves the security problems but scalability still remains a huge challenge. To address this, various AI techniques can be applied in the blockchain IoT framework, thus providing an efficient information system. In this survey, various works pertaining to the domains which integrate AI, IoT and Blockchain has been explored. Also, this article discusses potential industrial use cases on fusion of blockchain, AI and IoT applications and its challenges.

In this paper, the closed-form expressions of signal-to-interference-plus-noise ratio (SINR) and the outage probability are derived for a maximal ratio combining (MRC) two-dimensional (2-D)-RAKE receiver with imperfect power control in a frequency-selective Nakagami fading channel. The impact of power control error (PCE) on the performance of the receiver is analyzed for all kinds of fading environments. The results of numerical derivation and simulation indicate that the performance of 2-D-RAKE receivers degrades due to imperfect power control. But when PCE is not serious, increasing the number of antennae and temporal diversity order can compensate for the performance loss. The exact performance improvement due to space-time processing varies with the PCE and the fading environment.

With the rapid development of multi-view video coding (MVC) and light field rendering (LFR), Free-View Television (FTV) has emerged as new entrainment equipment, which can bring more immersive and realistic feelings for TV viewers. In FTV broadcasting system, the TV-viewer can freely watch a realistic arbitrary view of a scene generated from a number of original views. In such a scenario, the ownership of the multi-view video should be verified not only on the original views, but also on any virtual view. However, capacities of existing watermarking schemes as copyright protection methods for LFR-based FTV are only one bit, i.e., presence or absence of the watermark, which seriously impacts its usage in practical scenarios. In this paper, we propose a robust multi-bit watermarking scheme for LFR-based free-view video. The direct-sequence code division multiple access (DS-CDMA) watermark is constructed according to the multi-bit message and embedded into DCT domain of each view frame. The message can be extracted bit-by-bit from a virtual frame generated at an arbitrary view-point with a correlation detector. Furthermore, we mathematically prove that the watermark can be detected from any virtual view. Experimental results also show that the watermark in FTV can be successfully detected from a virtual view. Moreover, the proposed watermark method is robust against common signal processing attacks, such as Gaussian filtering, salt & peppers noising, JPEG compression, and center cropping.

In this letter, we propose a new modification to the belief propagation (BP) decoding algorithm for Finite-Geometry low-density parity-check (LDPC) codes. The modification is based on introducing feedback into the iterative process, which can break the oscillations of bit log-likelihood ratio (LLR) values. Simulations show that, with a given maximum iteration, the "feedback BP" (FBP) algorithm can achieve better performance than the conventional belief propagation algorithm.