The emerging concept of extremely-large holographic multiple-input multiple-output (HMIMO), beneficial from compactly and densely packed cost-efficient radiating metaatoms, has been demonstrated for enhanced degrees of freedom even in pure line-of-sight conditions, enabling tremendous multiplexing gain for the next-generation communication systems. Most of the reported works focus on energy and spectrum efficiency, path loss analyses, and channel modeling. The extension to secure communications remains unexplored. In this paper, we theoretically characterize the secrecy capacity of the HMIMO network with multiple legitimate users and one eavesdropper while taking into consideration artificial noise and max-min fairness. We formulate the power allocation (PA) problem and address it by following successive convex approximation and Taylor expansion. We further study the effect of fixed PA coefficients, imperfect channel state information, inter-element spacing, and the number of Eve's antennas on the sum secrecy rate. Simulation results show that significant performance gain with more than 100% increment in the high signal-to-noise ratio (SNR) regime for the two-user case is obtained by exploiting adaptive/flexible PA compared to the case with fixed PA coefficients.

This paper investigates the generalized linear system (GLS), widely employed to evaluate the impact of nonlinear preprocessing on wireless transceivers. Two state-of-the-art signal recovery algorithms, namely generalized approximate message passing (GAMP) and generalized orthogonal/vector AMP (GOAMP/GVAMP), are comparatively studied. They have demonstrated Bayesian optimality for independently and identically distributed (IID) Gaussian matrices and unitary matrices, respectively. However, Bayesian optimality does not inherently guarantee error-free signal recovery. For coded GLS, the information-theoretic (i.e., achievable rate) limit of GAMP remains unknown, and there are still no analytical comparisons between GAMP and GOAMP/GVAMP in terms of the mean-square error and information-theoretic limit. To address these issues, we present the achievable rate analysis and optimal coding principle for GAMP with IID Gaussian matrices, as well as provide comprehensive comparisons with GOAMP/GVAMP with unitary matrices. Specifically, based on the celebrated I-MMSE lemma and the preconditions for state evolution (SE) to hold, the simplified variational SEs of GAMP and GOAMP/GVAMP are derived, leveraging the IID and unitary matrix properties to analyze the achievable rate and optimal coding principle, respectively. On this basis, it is proven that GOAMP/GVAMP outperforms GAMP in terms of asymptotic MSE and maximum achievable rate while requiring less complexity. Furthermore, two common nonlinear functions, clipping and quantization, are used as examples to demonstrate the theoretical comparisons and practical low-density parity-check (LDPC) code design for GAMP and GOAMP/GVAMP. Numerical results show that GAMP and GOAMP/GVAMP with optimized LDPC codes can approach the theoretical limits within 0:3 dB and overcome the decoding deterioration and even divergence of the existing state-of-the-art methods, particularly under low-resolution quantization.

Deep Image Manipulation Localization (IML) models suffer from training data insufficiency and thus heavily rely on pre-training. We argue that contrastive learning is more suitable to tackle the data insufficiency problem for IML. Crafting mutually exclusive positives and negatives is the prerequisite for contrastive learning. However, when adopting contrastive learning in IML, we encounter three categories of image patches: tampered, authentic, and contour patches. Tampered and authentic patches are naturally mutually exclusive, but contour patches containing both tampered and authentic pixels are non-mutually exclusive to them. Simply abnegating these contour patches results in a drastic performance loss since contour patches are decisive to the learning outcomes. Hence, we propose the Nonmutually exclusive Contrastive Learning (NCL) framework to rescue conventional contrastive learning from the above dilemma. In NCL, to cope with the non-mutually exclusivity, we first establish a pivot structure with dual branches to constantly switch the role of contour patches between positives and negatives while training. Then, we devise a pivot-consistent loss to avoid spatial corruption caused by the role-switching process. In this manner, NCL both inherits the self-supervised merits to address the data insufficiency and retains a high manipulation localization accuracy. Extensive experiments verify that our NCL achieves state-of-the-art performance on all five benchmarks without any pre-training and is more robust on unseen real-life samples. https://github.com/Knightzjz/NCL-IML.

The present work investigates the performance of non-orthogonal multiple access (NOMA) in a hybrid visible light communication (VLC) / radio frequency (RF) wireless network. In particular, we investigate the energy efficiency of the proposed architecture assuming imperfect channel state information (CSI), which is a realistic assumption that is encountered in practical indoor and outdoor wireless communication scenarios. We demonstrate that the performance of the proposed scheme in terms of energy efficiency outperforms by four-fold the corresponding performance of its orthogonal frequency division multiple access (OFDMA) counterpart. In addition, it is shown that the energy efficiency of the proposed scheme is more robust to CSI errors and line of sight (LOS) variations than the OFDMA-based scheme, which appears to be more susceptible to the CSI error and the LOS availability probability. Finally, our findings reveal that the performance gain of NOMA over OFDMA in the considered hybrid VLC/RF set up is directly proportional to the probability of LOS availability. These results are expected to be useful in the efficient design and efficient operation of hybrid VLC/RF wireless systems.

The present paper is devoted to the evaluation of energy detection based spectrum sensing over different multipath fading and shadowing conditions. This is realized by means of a unified and versatile approach that is based on the particularly flexible mixture gamma distribution. To this end, novel analytic expressions are firstly derived for the probability of detection over MG fading channels for the conventional single-channel communication scenario. These expressions are subsequently employed in deriving closed-form expressions for the case of square-law combining and square-law selection diversity methods. The validity of the offered expressions is verified through comparisons with results from respective computer simulations. Furthermore, they are employed in analyzing the performance of energy detection over multipath fading, shadowing and composite fading conditions, which provides useful insighs on the performance and design of future cognitive radio based communication systems.