Recent advancements in pretrained foundation models have shown considerable promise across various machine learning tasks. However, their application in the broader IoT industry remains limited, particularly in IoT physical-layer authentication. In this domain, the presence of domain shift between training and deployment environments, combined with privacy and data security concerns, renders traditional domain adaptation methods that rely on source domain data impractical. Motivated by these challenges, source-free unsupervised domain adaptation (SFUDA) presents a more feasible solution. In this paper, we propose a novel SFUDA framework that leverages a pretrained generative foundation model to augment target domain data without requiring access to source domain information. Additionally, we integrate an uncertainty-aware pseudo-labeling strategy along with consistency regularization to further enhance the adaptation process. Experimental results validate that our approach significantly outperforms conventional techniques, providing an effective and robust solution for IoT physical-layer authentication under realistic constraints.

Abstract— Radio Frequency Fingerprinting Identification (RFFI) serves as a pivotal technology in the Industrial Internet of Things (IIoT), witnessing significant strides over the last decade primarily due to advances in deep learning. However, most existing studies assume that training and test data are independent and identically distributed (i.i.d.), an assumption that often breaks down in real-world IIoT applications, leading to substantial performance degradation in cross-domain scenarios. To address this, we propose FATransformer, a novel unsupervised domain adaptation technique. The method is built on a robust theoretical foundation, ensuring stability and reliability across different domains. Specifically, FATransformer employs a Transformer-based architecture to efficiently process and align intermediate feature maps from both domains. Incorporating an attention-based module, it dynamically adjusts the weights of alignment at various layers, thereby improving the model’s flexibility to handle diverse real-world data. Extensive evaluations across multiple datasets underscore FATransformer’s superiority over existing methods.

Integrated sensing and communication (ISAC) systems have emerged as a promising solution to improve spectrum efficiency and enable functional convergence. However, ensuring secure information transmission while maintaining high-quality sensing performance remains a significant challenge. In this paper, we investigate an integrated sensing and covert communication (ISCC) system, in which a base station (BS) simultaneously serves multiple downlink users and senses malicious targets that may act as both potential eavesdroppers (Eves) and wardens. We propose a novel symbol-level precoding (SLP)-based waveform design for ISCC that achieves covert communication intrinsically, without requiring additional transmission resources such as artificial noise. The proposed design integrates symbol shaping to enhance reliability for legitimate users and noise shaping to obscure transmission activities from the targets. For imperfect channel state information (CSI), the framework incorporates bounded uncertainty models for user channels and target angles, yielding a more robust design. The resulting ISCC waveform optimization problem is non-convex; to address this, we develop a low-complexity proximal distance algorithm (PDA) with closed-form updates under both PSK and QAM modulations. Simulation results demonstrate that the proposed method achieves superior covertness and sensing-communication performance with negligible degradation compared to traditional beamforming and conventional SLP approaches without noise-shaping mechanisms.

Integrating massive multiple-input multiple-output (mMIMO) systems with intelligent reflecting surfaces (IRS) presents a promising paradigm for enhancing physical-layer security (PLS) in wireless communications. However, deploying high-resolution quantizers in large-scale mMIMO arrays, along with numerous IRS elements, leads to substantial hardware complexity. To address these challenges, this paper proposes a cost-effective PLS design for IRS-assisted mMIMO systems by employing one-bit digital-to-analog converters (DACs). The focus is on jointly optimizing one-bit quantized precoding at the transmitter and constant-modulus phase shifts at the IRS to maximize the secrecy rate. This leads to a highly non-convex fractional secrecy rate maximization (SRM) problem. To efficiently solve this problem, two algorithms are proposed: (1) the WMMSE-PDD algorithm, which reformulates the SRM problem into a sequence of non-fractional programs with auxiliary variables using the weighted minimum mean-square error (WMMSE) method and solves them via the penalty dual decomposition (PDD) approach, achieving superior secrecy performance; and (2) the exact penalty product Riemannian gradient descent (EP-PRGD) algorithm, which transforms the SRM problem into an unconstrained optimization over a product Riemannian manifold, eliminating auxiliary variables and enabling faster convergence with a slight trade-off in secrecy performance. Both algorithms provide analytical solutions at each iteration and are proven to converge to Karush–Kuhn–Tucker (KKT) points. Simulation results confirm the effectiveness of the proposed methods and highlight their respective advantages.

Radio-frequency (RF) signal processing has seen significant advancements with the advent of deep learning, providing more accurate and efficient solutions for tasks such as signal classification and generation. However, most existing methods are heavily dependent on large labeled datasets, which are often scarce and costly to obtain in real-world RF environments. Furthermore, these approaches tend to be task-specific, limiting their ability to generalize across various RF applications. To address these challenges, this paper proposes RF-MAE, a self-supervised adaptive frequency masked autoencoder. RF-MAE leverages self-supervised learning (SSL) to capture intrinsic patterns from large-scale unlabeled RF data. Central to RF-MAE is a novel Adaptive Frequency Masked (AFM) strategy, which dynamically masks frequency components based on their energy distribution. Supported by a robust theoretical foundation, AFM ensures the model focuses on the most informative signal components, thereby enhancing generalization across RF tasks. By pretraining on unlabeled data and fine-tuning on specific tasks, RF-MAE significantly reduces the reliance on labeled datasets while improving adaptability across diverse RF signal processing tasks. Experimental results demonstrate that RF-MAE consistently outperforms traditional models, underscoring its potential to generalize across tasks and deliver superior performance in a wide range of RF signal applications.

With the rapid proliferation of edge computing, Radio Frequency Fingerprint Identification (RFFI) has become increasingly important for secure device authentication. However, practical deployment of deep learning-based RFFI models is hindered by a critical challenge: their performance often degrades significantly when applied across receivers with different hardware characteristics due to distribution shifts introduced by receiver variation. To address this, we investigate the source-data-free cross-receiver RFFI (SCRFFI) problem, where a model pretrained on labeled signals from a source receiver must adapt to unlabeled signals from a target receiver, without access to any source-domain data during adaptation. We first formulate a novel constrained pseudo-labeling–based SCRFFI adaptation framework, and provide a theoretical analysis of its generalization performance. Our analysis highlights a key insight: the target-domain performance is highly sensitive to the quality of the pseudo-labels generated during adaptation. Motivated by this, we propose Momentum Soft pseudo-label Source Hypothesis Transfer (MS-SHOT), a new method for SCRFFI that incorporates momentum-center-guided soft pseudo-labeling and enforces global structural constraints to encourage confident and diverse predictions. Notably, MS-SHOT effectively addresses scenarios involving label shift or unknown, non-uniform class distributions in the target domain—a significant limitation of prior methods. Extensive experiments on real-world datasets demonstrate that MS-SHOT consistently outperforms existing approaches in both accuracy and robustness, offering a practical and scalable solution for source-data-free cross-receiver adaptation in RFFI.

Source-free unsupervised domain adaptation (SFUDA) aims to improve performance in unlabeled target domain data without accessing source domain data. This is crucial in scenarios with data-sharing restrictions due to privacy or compliance constraints. Existing SFUDA approaches often rely on pseudo-labeling techniques based on entropy or confidence metrics. These often overlook fine-grained data features, resulting in noisy pseudo-labels that degrade model performance. To overcome this limitation, we develop a new method called fine-grained pseudo-labeling and feature alignment (FGPLFA) to enhance SFUDA's performance. FGPLFA starts with a gradient-based metric that integrates insights from both model knowledge and data features, creating a more reliable sample metric. To enhance fine granularity, the fine-grained pseudo-labeling (FGPL) module was introduced. This module clusters data based on the magnitude and direction of gradients, allowing for dataset partitioning into subsets at the sample level. The subsets are pseudo-labeled with category-specificity and domain specificity, establishing a multilevel granularity structure that reduces noisy pseudo-labels. Subsequently, the mean-covariance adjustment feature alignment (MCAFA) method was introduced. Features from the subsets are aligned in a specified sequence, enhancing model adaptability in the target domain. Extensive experiments conducted across multiple datasets validate the superiority of FGPLFA.

Movable antennas (MAs) represent a novel approach that enables flexible adjustments to antenna positions, effectively altering the channel environment and thereby enhancing the performance of wireless communication systems. However, conventional MA implementations often adopt fully digital beamforming (FDB), which requires a dedicated RF chain for each antenna. This requirement significantly increase hardware costs, making such systems impractical for multi-antenna deployments. To address this, hardware-efficient analog beamforming (AB) offers a cost-effective alternative. This paper investigates the physical layer security (PLS) in an MA-enabled multiple-input single-output (MISO) communication system with an emphasis on AB. In this scenario, an MA-enabled transmitter with AB broadcasts common confidential information to a group of legitimate receivers, while a number of eavesdroppers overhear the transmission and attempt to intercept the information. Our objective is to maximize the multicast secrecy rate (MSR) by jointly optimizing the phase shifts of the AB and the positions of the MAs, subject to constraints on the movement area of the MAs and the constant modulus (CM) property of the analog phase shifters. This MSR maximization problem is highly challenging, as we have formally proven it to be NP-hard. To solve it efficiently, we propose a penalty constrained product manifold (PCPM) framework. Specifically, we first reformulate the position constraints as a penalty function, enabling unconstrained optimization on a product manifold space (PMS), and then propose a parallel conjugate gradient descent algorithm to efficiently update the variables. Simulation results demonstrate that MA-enabled systems with AB can achieve a well-balanced performance in terms of MSR and hardware costs.

The Rydberg atomic receiver (RARE) has garnered increasing attention in quantum communication due to its capability for high-precision signal sensing and detection. Recent advancements have led to the integration of RARE into multiple-input multiple-output (MIMO) systems. Signal detection in RARE MIMO systems presents a distinct biased phase retrieval (PR) challenge compared to conventional MIMO detection problems associated with radio frequency (RF) chains, rendering many traditional high-performance MIMO detectors inapplicable. This paper investigates the multiuser RARE MIMO problem under M -ary phase-shift keying (MPSK) modulations. The central challenge is to jointly address the biased PR formulation and the discrete MPSK constellation—an area not extensively explored in existing literature. We develop a custom approach that employs a smoothing technique to alleviate the nonsmoothness in the biased PR objective and a penalty transformation to tackle the discrete MPSK structure. The resulting algorithm combines a Majorization-Minimization (MM) framework with a modified Wirtinger flow (WF) method. Numerical simulations demonstrate that our proposed approach achieves superior detection accuracy compared to state-of-the-art detectors while maintaining lower computational complexity.

Movable antennas (MA) allow flexible positioning within a specified region, enhancing wireless communication performance. This paper explores leveraging MA to improve physical layer security in analog beamforming (AB) systems. Specifically, we aim to maximize the secrecy rate by jointly optimizing the AB and MA positions under constant modulus (CM) and position constraints. To solve the resulting non-convex problem, we propose a penalty product manifold (PPM) method, which converts MA position constraints into a penalty function, reformulating the problem as unconstrained optimization on the product manifold space (PMS). We then derive a parallel conjugate gradient descent (PCGD) algorithm to efficiently update both AB and MA positions, providing analytical solutions at each step and ensuring convergence to a KKT point. Simulation results confirm that the MA system achieves a higher secrecy rate than systems with fixed antenna positions.

This paper studies the convergence of clipped stochastic gradient descent (SGD) algorithms with decision-dependent data distribution. Our setting is motivated by privacy preserving optimization algorithms that interact with performative data where the prediction models can influence future outcomes. This challenging setting involves the non-smooth clipping operator and non-gradient dynamics due to distribution shifts. We make two contributions in pursuit for a performative stable solution with these algorithms. First, we characterize the stochastic bias with projected clipped SGD (PCSGD) algorithm which is caused by the clipping operator that prevents PCSGD from reaching a stable solution. When the loss function is strongly convex, we quantify the lower and upper bounds for this stochastic bias and demonstrate a bias amplification phenomenon with the sensitivity of data distribution. When the loss function is non-convex, we bound the magnitude of stationarity bias. Second, we propose remedies to mitigate the bias either by utilizing an optimal step size design for PCSGD, or to apply the recent DiceSGD algorithm [Zhang et al., 2024]. Our analysis is also extended to show that the latter algorithm is free from stochastic bias in the performative setting. Numerical experiments verify our findings.

Designing dual-functional waveforms for integrated sensing and communication (ISAC) under low-resolution hardware constraints remains a significant challenge. In this paper, we propose a novel few-bit multiple-input multiple-output (MIMO) dual-functional radar-communication (DFRC) signal design for uplink-downlink ISAC systems, where the transmitter is equipped with low-resolution digital-to-analog converters (DACs). To mitigate the impact of coarse quantization, we adopt a spatial Sigma-Delta (Σ∆) modulation scheme and formulate an optimization problem to maximize the worst-case signal-to-quantization-plus-noise ratio (SQNR) for target sensing, while satisfying symbol error probability (SEP) constraints for all communication users (CUs). A two-stage solution is proposed: a Σ∆ filter is first optimized, followed by a dual accelerated projected gradient (APG) algorithm for symbol-level precoding to generate the DFRC signal. Simulation results demonstrate the effectiveness of the proposed Σ∆ scheme for suppressing the quantization noise, providing promising performance for both sensing and communication tasks.

In this paper, we focus on the secure beamforming design in a beyond-diagonal reconfigurable intelligent surface (BD-RIS) assisted multiple-input multiple-output (MIMO) downlink network. We consider a scenario where a transmitter sends an information signal to a multi-antenna legitimate user while a multi-antenna eavesdropper attempts to intercept it. In this setting, a BD-RIS reconfigures the wireless environment to enhance secrecy. Aiming at the secrecy rate (SR) maximization, the joint optimization of transmit beamforming and BD-RIS reflection coefficients is formulated as a non-convex problem with power constraints on beamforming and symmetric and unitary constraints on BD-RIS reflection coefficients. To efficiently solve this challenging problem, a low-complexity framework that combines the augmented Lagrangian (AL) method and product manifold gradient descent (PMGD) algorithm is proposed to obtain a high-quality suboptimal solution. Numerical results show that BD-RIS-assisted systems achieve higher secrecy rates compared to conventional RIS-assisted systems.

Specific Emitter Identification (SEI) is crucial in the Internet of Things (IoT) to ensure the authentication and security of devices. With advancements in deep learning (DL), SEI for IoT devices has achieved remarkable progress. However, traditional DL-based SEI relies on a blanket assumption that emitter signals are transmitted in a constant channel environment and collected by a fixed receiver before identification. This assumption overlooks the dynamic characteristics of real-world IoT scenarios, where the channel environment and receiver are subject to change. Such variations can significantly impact SEI systems, potentially leading to a substantial decrease in identification accuracy. This challenge is known as the cross-domain SEI problem, where different receivers and channel environments are viewed as distinct domains. To mitigate this issue, we integrate unsupervised domain adaptation (UDA) into SEI. We propose an innovative UDA framework named domain-specific classifier network (DSCN) for cross-domain SEI. In our method, we initially use a weight-shared extractor for feature extraction. Unlike most existing UDA methods, we do not enforce the extractor to generate domain-invariant features for cross-domain identification. Instead, we design domain-specific classifiers to process features from different domains: source signal features are recognized by a source-specific classifier, while target signal features are recognized by a target-specific classifier. Experimental results demonstrate that the DSCN framework effectively mitigates identification accuracy degradation in cross-domain scenarios and outperforms existing UDA methods.

Radio frequency (RF) fingerprint identification (RFFI) technology uniquely identifies emitters by analyzing unique distortions in the transmitted signal caused by nonideal hardware. Recently, RFFI based on deep learning methods has gained popularity and is seen as a promising way to address the device authentication problem for Internet of Things (IoT) systems. However, in cross-receiver scenarios, where the RFFI model is trained over RF signals from some receivers but deployed at a new receiver, the alteration of receiver’s characteristics would lead to data distribution shift and cause significant performance degradation at the new receiver. To address this problem, we first perform a theoretical analysis of the cross-receiver generalization error bound and propose a sufficient condition, named separable condition (SC), to minimize the classification error probability on the new receiver. Guided by the SC, a receiver-independent emitter identification (RIEI) model is devised to decouple the received signals into emitter-related features and receiver-related features and only the emitter-related features are used for identification. Furthermore, by leveraging federated learning, we also develop a FedRIEI model to eliminate the need for centralized collection of raw data from multiple receivers. Experiments on the two real-world datasets demonstrate the superiority of our proposed methods over some baseline methods.

Reconfigurable intelligent surfaces (RISs) hold significant promise for enhancing physical layer security (PLS). However, conventional RISs are typically modeled using diagonal scattering matrices, capturing only independent reflections from each reflecting element, which limits their flexibility in channel manipulation. In contrast, beyond-diagonal RISs (BD-RISs) employ non-diagonal scattering matrices enabled by active and tunable inter-element connections through a shared impedance network. This architecture significantly enhances channel shaping capabilities, creating new opportunities for advanced PLS techniques. This paper investigates PLS in a multiple-input multiple-output (MIMO) system assisted by BD-RISs, where a multi-antenna transmitter sends confidential information to a multi-antenna legitimate user while a multi-antenna eavesdropper attempts interception. To maximize the secrecy rate (SR), we formulate it as a non-convex optimization problem by jointly optimizing the transmit beamforming and BD-RIS REs under power and structural constraints. To solve this problem, we first introduce an auxiliary variable to decouple BD-RIS constraints. We then propose a low-complexity penalty product Riemannian conjugate gradient descent (P-PRCGD) method, which combines the augmented Lagrangian (AL) approach with the product manifold gradient descent (PMGD) method to obtain a Karush-Kuhn-Tucker (KKT) solution. Simulation results confirm that BD-RIS-assisted systems significantly outperform conventional RIS-assisted systems in PLS performance.

Massive multiple-input multiple-output (MIMO) technology has revolutionized wireless communication by significantly enhancing spectral efficiency, however its high energy consumption has become a key concern. There is increasing research interest in implementing massive MIMO systems using low-resolution digital-to-analog converters (DACs) to reduce the hardware cost and energy consumption. Meanwhile, the broadcast nature of wireless communications systems poses security risks, exposing user information to potential eavesdroppers (Eve), and this issue has been studied less in the context of low-resolution massive MIMO systems. This paper investigates the potential of low-resolution massive MIMO systems to enhance physical layer security (PLS) without relying on artificial noise (AN). We propose a novel spatial Sigma-Delta modulation technique that strategically leverages quantization noise to obscure confidential communications from Eve, even with limited channel state information. Our design shifts quantization noise away from legitimate users while maintaining its presence near Eve, thus improving PLS. We formulate the resulting non-convex, semi-infinite design problem and apply a proximal majorization-minimization (PMM) algorithm, ensuring convergence to a Karush–Kuhn–Tucker (KKT) point. To enhance computational efficiency, we introduce a proximal distance algorithm (PDA) that addresses the constraints independently, yielding closed-form solutions for projections and proximal operators. Extensive numerical experiments validate our approach, demonstrating effective noise shaping for both users and Eve. Our findings illustrate that quantization noise can be a valuable asset in securing communications in low-resolution massive MIMO systems.

Radio Frequency Fingerprinting Identification (RFFI) has become a critical technology in the physical-layer security (PLS) field, with deep learning emerging as the dominant approach over the past decade. However, most deep learning-based models rely on the assumption that training and testing data follow an independent and identical distribution (i.i.d.), which often does not hold in real-world scenarios. This mismatch significantly degrades model performance in cross-domain settings, making cross-domain RFFI a challenging task. Traditional unsupervised domain adaptation (UDA) methods attempt to address this issue by jointly optimizing task loss and domain loss which is able to reduce the distribution gap between training and testing data. However, we observe that during training, the gradients of these two losses often conflict, hindering effective optimization and limiting cross-domain performance improvements. To address these challenges, we propose a novel framework, Gradient Collaborative Optimization with Dynamic Weighted Feature Alignment (GCODWFA). Specifically, GCODWFA introduces a novel Gradient Collaborative Optimization (GCO) loss, which explicitly adjusts the gradient interaction between task and domain losses by optimizing their angular relationship. Additionally, it incorporates a Dynamic Weighted Feature Alignment (DWFA) strategy, which dynamically adjusts the layer-specific weights for feature alignment based on the angular similarity of task and domain gradients. Extensive experiments conducted on multiple datasets demonstrate the superiority of GCODWFA over existing methods.

Physical layer security (PLS) is an important technology in wireless communication systems to safeguard communication privacy and security between transmitters and legitimate users. The integration of large-scale antenna arrays (LSAA) and intelligent reflecting surfaces (IRS) has emerged as a promising approach to enhance PLS. However, LSAA requires a dedicated radio frequency (RF) chain for each antenna element, and IRS comprises hundreds of reflecting micro-antennas, leading to increased hardware costs and power consumption. To address this, cost-effective solutions like constant modulus analog beamforming (CMAB) have gained attention. This paper investigates PLS in IRS-assisted communication systems with a focus on jointly designing the CMAB at the transmitter and phase shifts at the IRS to maximize the secrecy rate. The resulting secrecy rate maximization (SRM) problem is non-convex. To solve the problem efficiently, we propose two algorithms: 1) the time-efficient Dinkelbach-BSUM algorithm, which reformulates the fractional problem into a series of quadratic programs using the Dinkelbach method and solves them via block successive upper-bound minimization (BSUM), and 2) the product manifold conjugate gradient descent (PMCGD) algorithm, which provides a better solution at the cost of slightly higher computational time by transforming the problem into an unconstrained optimization on a Riemannian product manifold and solving it using the conjugate gradient descent (CGD) algorithm. Simulation results validate the effectiveness of the proposed algorithms and highlight their distinct advantages.

Driven by the pursuit of enhanced performance, deep learning has recently seen rapid developments in the scaling of network architectures and parameters. However, this advancement has led to extremely high computational costs, undesirable in real-time and resource-limited scenarios. To address these challenges, we propose SwiftNet, a cost-efficient deep learning framework. Our novelty lies in SwiftNet's innovative multidimensional early-exit strategy that integrates seamlessly with existing neural network architectures. The framework includes additional branch classifiers concatenated to the backbone network, allowing high-confidence samples to exit early, thereby, reducing computational load. Unlike traditional methods, SwiftNet dynamically assesses confidence levels, ensuring only low-confidence samples proceed to subsequent classifiers or the final layer, optimizing resource usage without compromising accuracy. We have validated SwiftNet on multiple neural network models and datasets, demonstrating its ability to significantly reduce the computational cost of models while maintaining neural network performance.

In this paper, we study the joint admission control and beamforming problem within a network where one multi-antenna base station tries to serve multiple single-antenna users. Unlike most existing studies which merely identify the users that should be denied, our work further suggest better positions in the neighbouring area for them where the previously-rejected users are allowed to access the network. To address this, we assume the knowledge of channel vectors within the network coverage area, and then jointly optimize the transmit beamformer and the channel vector associated with each user to minimize the network power cost, with a penalty measuring the mismatch between the optimized channel and the reference channel with current user position. Specifically, a non-zero mismatch means that the corresponding user is inadmissible at present, but may access network if moving to the position with the channel closest to the optimized result. Basically, this is a challenging non-convex problem, and we design a penalty dual decomposition (PDD)-based algorithm to iteratively achieve a stationary solution. The algorithm is highly efficient since a simple analytical solution is derived in each step.

Active reconfigurable intelligent surface (RIS) is a new RIS architecture that can reflect and amplify communication signals. It can provide enhanced performance gain compared to the conventional passive RIS systems that can only reflect the signals. On the other hand, the design problem of active RIS-aided systems is more challenging than the passive RIS-aided systems and its efficient algorithms are less studied. In this paper, we consider the sum rate maximization problem in the multiuser massive multiple-input single-output (MISO) downlink with the aid of a large-scale active RIS. Existing approaches for handling this problem usually resort to general optimization solvers and can be computationally prohibitive. We propose an efficient block successive upper bound minimization (BSUM) method, of which each step has a (semi) closed-form update. Thus, the proposed algorithm has an attractive low per-iteration complexity. By simulation, our proposed algorithm consumes much less computation than the existing approaches. In particular, when the MIMO and/or RIS sizes are large, our proposed algorithm can be orders-of-magnitude faster than existing approaches.

This paper studies the performative prediction problem where a learner aims to minimize the expected loss with a decision-dependent data distribution. Such setting is motivated when outcomes can be affected by the prediction model, e.g., in strategic classification. We consider a state-dependent setting where the data distribution evolves according to an underlying controlled Markov chain. We focus on stochastic derivative free optimization (DFO) where the learner is given access to a loss function evaluation oracle with the above Markovian data. We propose a two-timescale DFO($\lambda$) algorithm that features (i) a sample accumulation mechanism that utilizes every observed sample to estimate the overall gradient of performative risk, and (ii) a two-timescale diminishing step size that balances the rates of DFO updates and bias reduction. Under a general non-convex optimization setting, we show that DFO($\lambda$) requires ${\cal O}( 1 /\epsilon^3)$ samples (up to a log factor) to attain a near-stationary solution with expected squared gradient norm less than $\epsilon > 0$. Numerical experiments verify our analysis.

This paper studies a risk minimization problem with decision dependent data distribution. The problem pertains to the performative prediction setting in which a trained model can affect the outcome estimated by the model. Such dependency creates a feedback loop that influences the stability of optimization algorithms such as stochastic gradient descent (SGD). We present the first study on performative prediction with smooth but possibly non-convex loss. We analyze a greedy deployment scheme with SGD (SGD-GD). Note that in the literature, SGD-GD is often studied with strongly convex loss. We first propose the definition of stationary performative stable (SPS) solutions through relaxing the popular performative stable condition. We then prove that SGD-GD converges to a biased SPS solution in expectation. We consider two conditions of sensitivity on the distribution shifts: (i) the sensitivity is characterized by Wasserstein-1 distance and the loss is Lipschitz w.r.t.~data samples, or (ii) the sensitivity is characterized by total variation (TV) divergence and the loss is bounded. In both conditions, the bias levels are proportional to the stochastic gradient's variance and sensitivity level. Our analysis is extended to a lazy deployment scheme where models are deployed once per several SGD updates, and we show that it converges to an SPS solution with reduced bias. Numerical experiments corroborate our theories.

While the Internet of Things (IoT) technology is booming and offers huge opportunities for information exchange, it also faces unprecedented security challenges. As an important complement to the physical-layer security technologies for IoT, radio frequency fingerprinting (RFF) is of great interest due to its difficulty in counterfeiting. Recently, many machine learning (ML)-based RFF algorithms have emerged. In particular, deep learning (DL) has shown great benefits in automatically extracting complex and subtle features from raw data with high-classification accuracy. However, DL algorithms face the computational cost problem as the difficulty of the RFF task and the size of the deep neural network have increased dramatically. To address the above challenge, this article proposes a novel cost-effective early-exit neural network consisting of a complex-valued neural network (CVNN) backbone with multiple random forest branches, called hybrid CVNN-RF. Unlike conventional studies that use a single fixed DL model to process all radio frequency (RF) samples, our hybrid CVNN-RF considers differences in the recognition difficulty of RF samples and introduces an early-exit mechanism to dynamically process the samples. When processing “easy” samples that can be well classified with high confidence, the hybrid CVNN-RF can end early at the random forest branch to reduce computational cost. Conversely, subsequent network layers will be activated to ensure accuracy. To further improve the early-exit rate, an automated multidimensional early-exit strategy is proposed to achieve scheduling control from multiple dimensions within the network depth and classification category. Finally, our experiments on the public ADS-B data set show that the proposed algorithm can reduce the computational cost by 83% while improving the accuracy by 1.6% under a classification task with 100 categories.

A prevailing assumption in existing deep learning research posits that data across source and target domains adhere to the independent and identically distributed (i.i.d.) assumption. However, this assumption often proves inadequate in real-world scenarios, leading to significant performance degradation when models encounter data with divergent distributions. To address this challenge, a novel unsupervised domain adaptation (UDA) algorithm, decoupling feature alignment (DFA), is introduced. The approach begins with the establishment of a robust theoretical framework, serving as the foundation for the mean-covariance adjustment feature alignment (MCAFA) algorithm. Simultaneously, a data decoupling (DD) module is introduced, effectively segregating target domain data into two subsets: one that mirrors the source domain and another that diverges markedly. Furthermore, a multidimensional alignment module is employed, leveraging the MCAFA algorithm and the DD module to align target data with source data across various layers and categories. Comprehensive evaluations on multiple data sets underscore the superiority of DFA.

Radio Frequency Fingerprint Identification (RFFI), which exploits non-ideal hardware-induced unique distortion resident in the transmit signals to identify an emitter, is emerging as a means to enhance the security of communication systems. Recently, machine learning has achieved great success in developing state-of-the-art RFFI models. However, few works consider cross-receiver RFFI problems, where the RFFI model is trained and deployed on different receivers. Due to altered receiver characteristics, direct deployment of RFFI model on a new receiver leads to significant performance degradation. To address this issue, we formulate the cross-receiver RFFI as a model adaptation problem, which adapts the trained model to unlabeled signals from a new receiver. We first develop a theoretical generalization error bound for the adaptation model. Motivated by the bound, we propose a novel method to solve the cross-receiver RFFI problem, which includes domain alignment and adaptive pseudo-labeling. The former aims at finding a feature space where both domains exhibit similar distributions, effectively reducing the domain discrepancy. Meanwhile, the latter employs a dynamic pseudo-labeling scheme to implicitly transfer the label information from the labeled receiver to the new receiver. Experimental results indicate that the proposed method can effectively mitigate the receiver impact and improve the cross-receiver RFFI performance.

With the development of wireless communication technology and the increasingly complex electromagnetic environment, radio frequency fingerprinting (RFF) plays a vital role in improving the security of communication and information systems. Recently, many RFF algorithms based on machine learning have emerged. However, most of them focus on improving the accuracy of RFF identification but ignore the computational cost. This paper proposes a novel classifier composed of a convolutional neural network (CNN) backbone with two random forest branches, called hybrid CNN-RF. Under the scheduling of the proposed multi-dimensional early exit strategy, hybrid CNN-RF can end early when processing “easy” samples to reduce the computational cost, and activate the inference of the subsequent network layer when processing “hard” samples to ensure accuracy. Finally, our experiments show that the proposed algorithm can reduce the computational cost by a factor of 3.41 while improving the accuracy by 1.5%.

This paper studies the performative prediction problem which optimizes a stochastic loss function with data distribution that depends on the decision variable. We consider a setting where the agent(s) provides samples adapted to both the learner’s and agent’s previous states. The samples are then used by the learner to update his/her state to optimize a loss function. Such closed loop update dynamics is studied as a state dependent stochastic approximation (SA) algorithm, which is shown to find a fixed point known as the performative stable solution. Our setting captures the unforgetful nature and reliance on past experiences of agents. Our contributions are three-fold. First, we present a framework for state dependent performative prediction with biased stochastic gradients driven by a controlled Markov chain whose transition probability depends on the learner’s state. Second, we present a new finite-time performance analysis of the SA algorithm. We show that the expected squared distance to the performative stable solution decreases as O(1/k), where k is the iteration number. Third, numerical experiments verify our findings.

We consider a scenario where multiple agents are learning a common decision vector from data which can be influenced by the agents’ decisions. This leads to the problem of multi-agent performative prediction (Multi-PfD). In this paper, we formulate Multi-PfD as a decentralized optimization problem that minimizes a sum of loss functions, where each loss function is based on a distribution influenced by the local decision vector. We first prove the necessary and sufficient condition for the Multi-PfD problem to admit a unique multi-agent performative stable (Multi-PS) solution. We show that enforcing consensus leads to a laxer condition for existence of Multi-PS solution with respect to the distributions’ sensitivities, compared to the single agent case. Then, we study a decentralized extension to the greedy deployment scheme [Mendler-Dünner et al., 2020], called the DSGD-GD scheme. We show that DSGD-GD converges to the Multi-PS solution and analyze its non asymptotic convergence rate. Numerical results validate our analysis.

Optimal jamming has important applications in both military and civil communications. There have been a brunch of works investigating the optimal jamming signal design when the signal modes of the opponent are known. In this work, we focus on the less studied hidden mode jamming problem. That is, the jammer has partially recorded the signal modes of the opponent, but there are some hidden modes not revealed to the jammer as of the appearance of these modes. As such, when the hidden modes appear, the jammer has to quickly adapt its jamming strategy to achieve effective jamming. However, it is challenging to do so due to incomplete knowledge of the intrinsic relation between the known and the hidden modes. In this work, a learning-based approach is proposed to attack this problem. Specifically, we custom-devise a jamming network (J-Net) to automatically learn the intrinsic relation among different modes and transfer the jamming strategy from the known modes to the hidden ones. Experimental results demonstrate that the J-Net attains much better jamming effect than pulsed Gaussian jamming and random jamming, and is comparable to the reinforcement learning-based approach, which assumes all the (known and hidden) modes available at the jammer.

Benefiting from the merits of low cost and high mobility, Unmanned aerial vehicle (UAV) enabled communications have recently drawn considerable attentions. In this paper, we consider the UAV-enabled physical-layer secure communications. Specifically, we consider a UAV-ground communication systems with multiple eavesdroppers whose locations are not perfectly known and are assumed to follow Gaussian distribution. An outage probability-constrained average secrecy rate maximization (ASRM) problem is formulated by jointly optimizing the trajectory and the transmit power of the UAV over a given flight duration. The ASRM problem is challenging due to the probabilistic constraints. To circumvent the difficulty, we employ a safe approximation approach and the successive convex optimization method to find a tractable solution to the ASRM problem. The effectiveness and robustness of the proposed design are demonstrated through simulations.

Recently, the intelligent reflecting surfaces (IRSs)-aided wireless transmission has drawn considerable attention. This paper investigates the use of IRS in enhancing the physical-layer security of the multiuser multiple-input single-output (MU-MISO) broadcast system, where a base station (BS) transmits a common data stream to multiple legitimate receivers in the presence of multiple eavesdroppers (Eves). The BS is assumed to have only some erroneous channel state information (CSI) of the Eves. The CSI error is modeled by a moment-based random error model, in which the BS only knows the first- and second-order statistics of the error, but not the exact distribution. Under this CSI error model, we investigate the joint robust design of the secure beamforming at the BS and the phase shift at the IRS to maximize the worst legitimate user’s SNR, while keeping the Eves’ SNR below certain threshold with high probability, evaluated with respect to any distribution fulfilling the given first and second-order statistics. This robust secure transmission design problem is a semi-infinite chance-constrained problem, which is in general intractable. We show that the considered problem admits an equivalent conic reformulation, which can be handled by alternately optimizing the beamformer at the BS and the phase shift at the IRS via semidefinite relaxation (SDR) and penalty convex-concave procedure (CCP), respectively. To further improve the secrecy performance, we also study another rank-two beamformed Alamouti transmission scheme at the BS, which can be seen as a generalization of conventional rank-one beamforming. Simulation results demonstrate that the proposed designs are robust against the error distribution, and that the inclusion of IRS is not only helpful for enhancing the security, but also useful for promoting a low-rank SDR solution.

This paper studies nonconvex quadratically constrained quadratic program (QCQP), which is known to be NP-hard in general. In the past decades, various approximate approaches have been developed to tackle the QCQP, including semidefinite relaxation (SDR), successive convex approximation (SCA), the variable splitting approach, to name a few. While these approaches are effective under some circumstances, they have to either lift the variable dimension or require a feasible starting point, thereby not suitable for the large-scale QCQP or lack of a feasible starting point. In light of this, this work aims at developing an efficient approach to the QCQP without the above mentioned drawbacks. The crux of our approach is the proximal distance algorithm (PDA), which merges the idea of the penalty method and majorization minimization (MM) to provide an efficient (closed-form) iterative algorithm. To demonstrate the effectiveness of the PDA, we test it on the multicast beamforming applications in wireless communications. Simulation results show that the PDA outperforms state-of-the-art algorithms in terms of delivering a better solution with much less running time.

This paper studies the security issue of a gossip-based distributed projected gradient (DPG) algorithm, when it is applied for solving a decentralized multi-agent optimization. It is known that the gossip-based DPG algorithm is vulnerable to insider attacks because each agent locally estimates its (sub)gradient without any supervision. This work leverages the convolutional neural network (CNN) to perform the detection and localization of the insider attackers. Compared to the previous work, CNN can learn appropriate decision functions from the original state information without preprocessing through artificially designed rules, thereby alleviating the dependence on complex pre-designed models. Simulation results demonstrate that the proposed CNN-based approach can effectively improve the performance of detecting and localizing malicious agents, as compared with the conventional pre-designed score-based model.

Unmanned aerial vehicles (UAVs) can be utilized as aerial base stations to provide communication service for remote mobile users due to their high mobility and flexible deployment. However, the line-of-sight (LoS) wireless links are vulnerable to be intercepted by the eavesdropper (Eve), which presents a major challenge for UAV-aided communications. In this paper, we propose a latency-minimized transmission scheme for satisfying legitimate users' (LUs') content requests securely against Eve. By leveraging physical-layer security (PLS) techniques, we formulate a transmission latency minimization problem by jointly optimizing the UAV trajectory and user association. The resulting problem is a mixed-integer nonlinear program (MINLP), which is known to be NP hard. Furthermore, the dimension of optimization variables is indeterminate, which again makes our problem very challenging. To efficiently address this, we utilize bisection to search for the minimum transmission delay and introduce a variational penalty method to address the associated subproblem via an inexact block coordinate descent approach. Moreover, we present a characterization for the optimal solution. Simulation results are provided to demonstrate the superior performance of the proposed design.

This work considers robust beamforming for simultaneous wireless information and power transfer (SWIPT) in multiuser MISO interfering broadcast channels. The base stations (BSs) are assumed to have imperfect channel state information (CSI) of receivers, which is modeled by a moment-based random error model. Specifically, the BSs only know the first- and second-order moments of receivers' CSI errors, but not the exact distribution. Under this error model, the robust SWIPT beamforming problem is formulated as an outage probability-constrained optimization problem, in which we minimize the total transmit power at the BSs while keeping the signal-to-interference-plus-noise ratio (SINR) and energy harvesting (EH) outage probabilities, evaluated with respect to (w.r.t.) any distribution with the given first- and second-order statistics, below a given threshold. By applying recent results in distributionally robust optimization, we show that the proposed problem admits an explicit conic reformulation, which can be approximately solved by the semi-definite relaxation (SDR) technique. Simulation results demonstrate the robustness of the proposed design.

This paper considers symbol-level precoding (SLP) for multiuser multi-input single-output (MISO) downlink transmission with the aid of intelligent reflecting surface (IRS). Specifically, by assuming one-bit transmitted signals at the base station (BS), which arises from the use of low-resolution DACs in the regime of massive transmit antennas, a joint design of one-bit SLP at the BS and the phase shifts at the IRS is proposed with a goal of minimizing the worst-case symbol error probability (SEP) of the users under the PSK modulation. This joint design problem is essentially a mixed integer nonlinear program (MINLP). To tackle it, we alternately optimize the one-bit signal and the phase shifts. For the former, a dual of the relaxed one-bit SLP problem is solved by the mirror descent (MD) method with the maximum block improvement (MBI) heuristics. For the latter, the accelerated projected gradient (APG) method is employed to optimize the phases. Numerical results demonstrate that the proposed joint design can attain better SEP performance than the conventional linear precoding and one-bit SLP.

Explosive growth of mobile data demand may impose a heavy traffic burden on fronthaul links of cloud-based small cell networks (C-SCNs), which deteriorates users' quality of service (QoS) and requires substantial power consumption. This paper proposes an efficient maximum distance separable (MDS) coded caching framework for a cache-enabled C-SCNs, aiming at reducing long-term power consumption while satisfying users' QoS requirements in short-term transmissions. To achieve this goal, the cache resource in small-cell base stations (SBSs) needs to be reasonably updated by taking into account users' content preferences, SBS collaboration, and characteristics of wireless links. Specifically, without assuming any prior knowledge of content popularity, we formulate a mixed timescale problem to jointly optimize cache updating, multicast beamformers in fronthaul and edge links, and SBS clustering. Nevertheless, this problem is anti-causal because an optimal cache updating policy depends on future content requests and channel state information. To handle it, by properly leveraging historical observations, we propose a two-stage updating scheme by using Frobenius-Norm penalty and inexact block coordinate descent method. Furthermore, we derive a learning-based design, which can obtain effective trade-off between accuracy and computational complexity. Simulation results demonstrate the effectiveness of the proposed two-stage framework.

Hyperspectral super-resolution (HSR) is a problem that aims to estimate an image of high spectral and spatial resolutions from a pair of coregistered multispectral (MS) and hyperspectral (HS) images, which have coarser spectral and spatial resolutions, respectively. In this article, we pursue a lowrank matrix estimation approach for HSR. We assume that the spectral-spatial matrices associated with the whole image and the local areas of the image have low-rank structures. The local low-rank assumption, in particular, has the aim of providing a more flexible model for accounting for local variation effects due to endmember variability. We formulate the HSR problem as a global-local rank-regularized least-squares problem. By leveraging on the recent advances in nonconvex large-scale optimization, namely the smooth Schatten-p approximation and the accelerated majorization-minimization method, we develop an efficient algorithm for the global-local low-rank problem. Numerical experiments on synthetic, semi-real, and real data show that the proposed algorithm outperforms a number of benchmark algorithms in terms of recovery performance.

Massive MIMO attains high spectral and power efficiency transmission by leveraging a large number of transmit antennas. However, to capture the benefits of massive MIMO, each antenna should be accompanied with a dedicated RF chain, and consequently, the hardware costs would scale up tremendously with the increase of the antennas. Cheap implementations of massive MIMO have recently gained considerable attention, and constant modulus (CM) signaling is seen as a promising solution, owing to its low peak-to-average power ratio (PAPR). This paper investigates the physical-layer (PHY) security in massive MIMO with an emphasis on the CM signaling. In particular, we consider a transmitter with massive antennas broadcast common confidential information to a group of legitimate receivers, and a number of eavesdroppers overhear the transmission and attempt to intercept the information. Our goal is to design the CM beamforming at the transmitter so that the multicast secrecy rate is maximized. This secrecy rate maximization (SRM) problem is generally NP-hard. To tackle it, two tractable approaches are developed. The first one employs the semidefinite relaxation (SDR) technique and the Charnes–Copper transformation to obtain a convex relaxation of the SRM problem. However, due to the dimension lifting of SDR, this approach is feasible only for small to medium antenna sizes. The second approach leverages the Dinkelbach method to work directly over the beamformer domain; a custom-build nonconvex alternating direction method of multipliers (ADMM) algorithm is proposed to efficiently perform each Dinkelbach update. Simulation results demonstrate that the second approach is computationally more efficient and can achieve nearly optimal performance when the number of antennas is large.

This paper considers multiuser massive MIMO downlink transmission, where the base station (BS) employs a massive number of transmit antennas, each equipped with a low-resolution phase shifter, to simultaneously shape desired symbols at user side, after passing through the channels and receive beamforming. This channel-aided shaping technique, known as symbol-level nonlinear precoding, has recently gained considerable attention owing to its high power efficiency and low implementation cost. However, the design of the transmit signal itself is challenging because the restriction of the transmit signals to a discrete constant envelope (DCE) set leads to a discrete optimization problem. In this paper, we adopt a minimum symbol-error probability design criterion for joint optimization of the transmit DCE signal at the BS and the receive beamformers at the users. An alternating minimization method is built for the problem. The design of the transmit DCE signal leverages on a negative square penalty (NSP) method developed in our recent work. The design of receive beamformers can be decoupled among users and updated by non-convex gradient projection independently. Our simulation results show that the bit-error rate performance markedly improves as the number of receive antennas increases.

This paper considers content delivery of the cache-enabled small cell networks (C-SCNs), where users with the same request form a multicast group and are served by a cluster of small-cell base stations (SBSs) under the coordination of the central processor. The performance of such a coordination is severely limited by the fronthaul link, which may be saturated and degrade quality of service (QoS). To improve user QoS, we propose a latency driven scheme by jointly optimizing fronthaul bandwidth allocation, multicast beamforming, and BS clustering. Accordingly, with min-max fairness among multicast groups, a latency minimization problem is formulated under the constraints of fronthaul bandwidth and transmission power. The resultant problem is a mixed-integer nonlinear program, which is NP-hard. To address such a complex problem, a quadratic penalty-based algorithm is proposed by using a reformulation of binary constraint. Meanwhile, we present the necessary condition for an optimal solution, which shows that fronthaul bandwidth allocation is inherently adaptive to cached contents and patterns of BS cooperation. Finally, simulation results demonstrate that the proposed scheme can effectively reduce latency under different caching strategies.

Simplex-structured matrix factorization (SSMF) is a problem of recovering a basis matrix and the corresponding coefficient vectors from data, where the coefficient vectors are constrained to lie in the unit simplex. SSMF has attracted growing attention in recent years, with numerous applications such as hyperspectral unmixing and document clustering. In this work, we develop a maximum-likelihood (ML) approach for SSMF. Specifically, by modeling the coefficient vectors as random variables following a Dirichlet mixture distribution-which allows us to model more complex data distributions in real-life data, a probabilistic model for SSMF is employed. We consider a marginalized likelihood with respect to the coefficient vectors, and use ML estimation to learn the basis matrix and unknown Dirichlet mixture parameters. The marginalized likelihood does not admit a closed form and is non-concave, and this makes the problem challenging to solve. To handle this challenge, an effective algorithm using sample average approximation and block successive upper-bound minimization is proposed. We consider the aforementioned two real-world applications by simulations. Numerical results show that the proposed algorithm delivers appealing performance in both applications.

Recent years have witnessed an exponential growth of mobile data traffic, which may lead to a serious traffic burn on the wireless networks and considerable power consumption. Network densification and edge caching are effective approaches to addressing these challenges. In this study, we investigate joint long-term cache allocation and short-term content delivery in cloud small cell networks (C-SCNs), where multiple small-cell BSs (SBSs) are connected to the central processor via fronthaul and can store popular contents so as to reduce the duplicated transmissions in networks. Accordingly, a long-term power minimization problem is formulated by jointly optimizing multicast beamforming, BS clustering, and cache allocation under quality of service (QoS) and storage constraints. The resultant mixed timescale design problem is an anticausal problem because the optimal cache allocation depends on the future file requests. To handle it, a two-stage optimization scheme is proposed by utilizing historical knowledge of users' requests and channel state information. Specifically, the online content delivery design is tackled with a penalty-based approach, and the periodic cache updating is optimized with a distributed alternating method. Simulation results indicate that the proposed scheme significantly outperforms conventional schemes and performs extremely close to a genie-aided lower bound in the low caching region.

The max–min fairness (MMF) strategy has been widely employed to manage wireless networks since it guarantees fairness among users. However, with a large number of users awaiting service, the network tends to be congested and the quality‐of‐service (QoS) will degrade substantially. This motivates us to study the MMF problem jointly with the consideration of admission control. Specifically, the authors consider a downlink network consisting of a multi‐antenna base station (BS) and multiple single‐antenna users. By jointly optimising the admissible users and the BS transmit beamformers, they aim to maximise the minimum signal‐to‐interference‐plus‐noise‐ratio of the admissible users, such that high QoS and fairness can be guaranteed simultaneously for them. This problem is essentially NP‐hard, and hence they pursue an efficient approximate solution to it. To this end, they first reformulate this problem from the perspective of sparse optimisation, and then develop a low‐complexity algorithm to iteratively solve the approximate problem. Moreover, to facilitate the algorithm's implementation, they further recast the subproblem in each iteration, such that it fits into the framework of the alternating direction methods of multipliers. Finally, an efficient distributed algorithm is designed, with each step being simply computed in a closed form.

In most existing robust array beam pattern synthesis studies, the bounded-sphere model is used to describe the steering vector (SV) uncertainties. In this letter, instead of bounding the norm of SV perturbations as a whole, we explore the amplitude and phase perturbations of each SV element separately, thereby obtaining a tighter SV uncertainty model. On the basis of this model, we formulate the robust pattern synthesis problem from the perspective of the min-max optimization, which aims to minimize the maximum side lobe response, while preserving the main lobe response. However, this problem is difficult due to the infinitely many nonconvex constraints. As a compromise, we employ the worst-case criterion and recast the problem as a convex second-order cone program (SOCP). To solve the SOCP, we further design an alternating direction method of multipliers based algorithm, which is computationally efficient by coming up with closed-form solutions in each step.

Consider a network consisting of one multi-antenna base station (BS) and multiple pairs of multi-antenna user equipment's (UEs). For each UE pair, the communication between transmitter and receiver is established either through BS or via device-to-device (D2D) link. We assume that the D2D transmission and cellular transmission are equally prioritized and share the same resources. To improve the network throughput, we maximize the sum rate by jointly optimizing the transmission mode of each UE pair and the associated transceivers. Due to the NP-hardness of this problem, we first perform some efficient approximation to it and then design an iterative algorithm, which is guaranteed to converge to a stationary solution by solving a series of weighted minimum mean square error (WMMSE) problems. The proposed algorithm has two distinguishing features. First, it only solves the WMMSE problem inexactly in each iteration, which thereby has a simplified algorithm structure and accelerated convergence behavior than the classical one. Second, we further fit the WMMSE problem into the alternating direction method of multipliers (ADMM) framework, making it amenable to parallel and distributed computation. Finally, the approximated problem is solved efficiently and distributively, with simple closed-form solutions in each step.

The performance of cloud-based small cell networks (C-SCNs) relies highly on a capacity-limited fronthaul, which degrade quality of service when it is saturated. Coded caching is a promising approach to addressing these challenges, as it provides abundant opportunities for fronthaul multicast and cooperative transmissions. This paper investigates cache-enabled C-SCNs, in which small-cell base stations (SBSs) are connected to the central processor via fronthaul, and can prefetch popular contents by applying maximum distance separable (MDS) codes. To fully capture the benefits of fronthaul multicast and cooperative transmissions, an MDS codes-aided transmission scheme is first proposed. We formulate the problem to minimize the content delivery latency by jointly optimizing fronthaul bandwidth allocation, SBS clustering, and beamforming. To efficiently solve the resulting nonlinear integer programming problem, we propose a penalty-based design by leveraging variational reformulations of binary constraints. To improve the solution of the penalty-based design, a greedy SBS clustering design is also developed. Furthermore, closed-form characterization of the optimal solution is obtained, through which the benefits of MDS codes can be quantified. The simulation results are given to demonstrate the significant benefits of the proposed MDS codes-aided transmission scheme.

This paper considers mobile computation offloading in fog-radio access networks (F-RAN), where multiple mobile users offload their computation tasks to the F-RAN through a number of fog nodes [a.k.a. enhanced remote radio heads (RRHs)]. In addition to communication capability, the fog nodes are also equipped with computational resources to provide computing services for users. Each user chooses one fog node to offload its task, while each fog node may simultaneously serve multiple users. Depending on computational burden at the fog nodes, the tasks may be completed at the fog nodes or further offloaded to the cloud via fronthaul links with limited capacities. To complete all the tasks as fast as possible, a joint optimization of radio and computational resources of F-RAN is proposed to minimize the maximum latency of all users. This problem is formulated as a mixed integer nonlinear program (MINP). We first show that the MINP can be reformulated as a continuous optimization problem with a difference-of-convex (DC) objective. Then, an inexact DC algorithm is proposed to handle the min-max problem with stationary convergence guarantee. Simulation results show that the proposed algorithm outperforms the minimum distance-based and the random-based offloading strategies.

In this paper, we consider how to maintain the communication quality for high-speed users in array transmission. Due to high user speed, the array transmission angle changes quickly. As a consequence, the phase shifters (beamformers) of traditional phase arrays need to be updated frequently to aim at the user, thus yielding high implementation cost. To alleviate this, we propose a novel frequency diverse array (FDA) approach, which intentionally introduces some frequency offsets across the array antennas to activate an angle-range-time dependent beampattern; i.e., the FDA beampattern peak automatically moves in space. This motivates us to carefully design FDA parameters such that the beampattern peak accompanies the quickly-moving users. To this end, we maximize the average beampattern gain along some given user trace by optimizing the frequency offsets. The block successive upper-bound minimization (BSUM) method is applied to obtain a stationary solution to this non-convex problem. Compared with phase array beamforming, the FDA approach maintains service quality for high-speed users by updating frequency offsets less frequently, thus reducing the implementation cost remarkably.

Massive multiple-input multiple-output (MIMO) has the potential to substantially improve the spectral efficiency, robustness and coverage of mobile networks. However, such potential is limited by hardware cost and power consumption associated with a large number of RF chains. Recently, one-bit quantization is proposed to address this issue by replacing high-resolution digital-to-analog converters (DACs) with one-bit DACs, thereby simplifying the RF chains. Despite low system cost, advanced signal processing techniques are needed to compensate for quantization distortions caused by low-resolution DACs. In this paper, a symbol-error-rate (SER)-based one-bit precoding scheme is proposed to minimize the detection error probability of all users under one-bit constraints. The problem is recast as a continuous optimization problem with a biconvex objective. By applying the block coordinate descent (BCD) method and the FISTA method, we develop an efficient iterative algorithm to obtain a one-bit precoding solution. Simulation results demonstrate its superiority over state-of-the-art algorithms in terms of bit error rate performance in high-order modulation cases.

Transmit beamforming and artificial noise-based methods have been widely employed to achieve physical-layer (PHY) security. However, these approaches may fail to provide satisfactory secure performance if the channels of legitimate user (LU) and eavesdropper (Eve) are highly correlated, which usually occurs in the case of close-located LU and Eve. The goal of this paper is to address the PHY security problem for proximal LU and Eve in millimeter-wave transmissions. To this end, we propose a novel frequency diverse array (FDA) beamforming approach, which intentionally introduces some frequency offsets across array antennas to decouple the highly correlated channels of LU and Eve. By exploiting this decoupling capability, the FDA beamforming can degrade Eve's reception and thus enhance PHY security. Leveraging FDA beamforming, we aim to maximize the secrecy rate by jointly optimizing the frequency offsets and the transmit beamformer. This secrecy rate maximization problem is difficult due to the tightly coupled variables. However, we show that it can be reformulated into a form only depending on the frequency offsets. Building upon this reformulation, we further employ the block successive upper-bound minimization method to iteratively obtain a solution with stationary convergence guarantee. Numerical results demonstrate that FDA beamforming can provide higher secrecy rate than conventional beamforming, especially for proximal LU and Eve.

This paper considers the physical-layer secrecy design for full-duplex (FD) bidirectional communications in the presence of an eavesdropper (Eve). The goal of this work is to maximize the sum secrecy rate (SSR) of the bidirectional transmissions via appropriately designing the transmit covariance matrices at the legitimate nodes. To this end, we propose an alternating difference-of-concave (ADC) approach to iteratively optimizing the transmit covariance matrices. We show that each ADC iteration can be carried out efficiently with a semi-closed- form solution, and that every limit point of ADC iterations is a stationary solution of the SSR maximization problem. Besides the SSR maximization, this paper also deals with a robust SSR maximization problem to account for imperfect CSI of Eve. Assuming a moment-based random CSI error model (i.e., only mean and covariance of the error are known, but the exact distribution is not known), robust transmit designs based on Markov's inequality and the robust conic reformulation are developed. The efficacy of the proposed designs is demonstrated through simulations.

Consider a full-duplex two-way relay network, where two legitimate nodes simultaneously transmit and receive confidential information through a full-duplex multiantenna relay, in the presence of an eavesdropper. To secure the communications, an artificial-noise (AN)-aided amplify-and-forward (AF) strategy is employed at the relay, with a goal of maximizing the sum secrecy rate of the two-way transmissions. This sum secrecy rate maximization (SSRM) problem is nonconvex by nature, but can be converted into the form of the difference-of-concave (DC) functions after the semidefinite relaxation (SDR). Thus, the classical DC programming naturally applies. We prove that the SDR is tight and give a specific way to recover a stationary solution of the SSRM problem from the relaxed DC problem. Moreover, to reduce the iteration complexity of DC, we proposed an inexact DC framework, which uses an approximate solution to iterate, rather than a globally optimal one. The convergence of the inexact DC to a stationary solution of the SSRM problem is also established.

This paper considers a special subclass of separable semidefinite programs (SDPs), with the goal of identifying certain conditions under which the SDP has a low-rank solution. We prove that when the data matrices of the SDP satisfy certain matrix inequalities, the SDP has a low-rank solution. Moreover, the rank of this solution is related to parts of the data matrices only, irrespective of any other factors such as the number of constraints. This is quite different from the well-known Shapiro-Barvinok-Pataki rank reduction result, where the rank of the SDP solution relies on the number of constraints. The usefulness of our result is demonstrated through advanced beamforming applications in simultaneous wireless information and power transfer (SWIPT) and physical-layer security, for which rank-one optimal solutions can be easily identified by checking our derived matrix inequality conditions.

Consider a network that consists of one multi-antenna base station (BS) and multiple pairs of multi-antenna user equipments (UEs). In each UE pair, the communication between transmitter and receiver is established either through BS or via device-to-device (D2D) link. All the D2D transmission and the uplink transmission of BS relaying share the same resources, while causing interference to each other. To improve the network throughput, we consider a sum-rate maximization problem by jointly optimizing the transmission mode of each UE pair and the corresponding transceivers. Due to the NP-hardness of the problem, we seek for some efficient approximate solutions to it. To this end, we first reformulate the problem by the weighted MMSE (WMMSE) approach, and then fit it into the alternating direction method of multipliers (ADMM) framework. Finally, an efficient distributed algorithm, which converges to a stationary solution, is developed. In particular, each step of the algorithm can be computed in closed form, thus giving it very low complexity.

In this paper, we study the problem of joint multiple-input multiple-output (MIMO) amplify-and-forward (AF) relaying and artificial noise (AN) optimization for secure communication between a source-destination pair in the presence of multiple eavesdroppers (eves). The eves' channel state information (ECSI) is subject to arbitrarily distributed random errors. Assuming that only the first and second moments of the ECSI errors are known, we introduce a probabilistically robust design method, which aims to maximize the received signal-to-interference-plus-noise ratio (SINR) at the destination while satisfying a set of robust intercept probability constraints. Since the resultant optimization problem is non-convex, we propose a solution approach by resorting to a duality-based method along with the semidefinite relaxation (SDR) technique, where a global optimal solution to our design problem can be found. Our simulation results demonstrate the improved secrecy of the proposed robust relaying design against eavesdropping and its robustness against the channel uncertainties.

We study the downlink linear precoder design problem in a multicell dense heterogeneous network (HetNet). The problem is formulated as a general sum-utility maximization (SUM) problem, which includes as special cases many practical precoder design problems such as multicell coordinated linear precoding, full and partial per-cell coordinated multipoint transmission, zero-forcing precoding, and joint BS clustering and beamforming/precoding. The SUM problem is difficult due to its nonconvexity and the tight coupling of the users' precoders. In this paper, we propose a novel convex approximation technique to approximate the original problem by a series of convex subproblems, each of which decomposes across all the cells. The convexity of the subproblems allows for efficient computation, while their decomposability leads to distributed implementation. Our approach hinges upon the identification of certain key convexity properties of the sum-utility objective, which allows us to transform the problem into a form that can be solved using a popular algorithmic framework called block successive upper-bound minimization (BSUM). Simulation experiments show that the proposed framework is effective for solving interference management problems in large HetNet.

In this paper, we consider a two-hop one-way relay network for multigroup multicast transmission between long-distance users, in which the relay is equipped with multiple antennas, while the transmitters and receivers are all with a single antenna. Assuming that the perfect channel state information is available, we study amplify-and-forward (AF) schemes that aim at optimizing the max-min-fair (MMF) rate. We begin by considering the classic beamformed AF (BF-AF) scheme, whose corresponding MMF design problem can be formulated as a rank-constrained fractional semidefinite program (SDP). We show that the gap between the BF-AF rate and the SDR rate associated with an optimal SDP solution is sensitive to the number of users as well as the number of power constraints in the relay system. This reveals that the BF-AF scheme may not be well suited for large-scale systems. We, therefore, propose the stochastic beamformed AF (SBF-AF) schemes, which differ from the BF-AF scheme in that time-varying AF weights are used. We prove that the MMF rates of the proposed SBF-AF schemes are at most 0.8317 bits/s/Hz less than the SDR rate, irrespective of the number of users or power constraints. Thus, SBF-AF can outperform BF-AF especially in large-scale systems. Finally, we present numerical results to demonstrate the viability of our proposed schemes.

Multiple-input multiple-output (MIMO) amplify-and- forward (AF) relaying is designed for secure communication between a source-destination pair in the presence of multiple eavesdroppers. Assuming statistical knowledge of the eavesdroppers' channel state information (ECSI) errors, we introduce a probabilistically robust design method, which aims to optimize the source transmission power and AF relaying matrix by maximizing the received signal-to- interference-plus-noise ratio (SINR) at the destination, while satisfying a set of intercept probability constraints. The resultant optimization problem becomes nonconvex, and hence we propose a conservative two-step solution, where the source transmission power and the relaying matrix are sequentially optimized. Our simulation results demonstrate the improved secrecy of the proposed relaying design against eavesdropping and its robustness against the channel uncertainties.

This letter considers multi-input single-output (MISO) downlink multicasting with finite-alphabet inputs when perfect channel state information is known at the transmitter. Two advanced transmit schemes, namely the beamformed (BF) Alamouti scheme and the stochastic beamforming (SBF) scheme, for maximizing the finite-alphabet-constrained multicast rate are studied. We show that the transmit optimization for these two schemes can be formulated as an SNR-based max-min-fair (MMF) problem with Gaussian inputs, which can be handled via the semidefinite relaxation (SDR) technique. Apart from transmit optimization, we analyzed the rate performance of the two schemes. Our analytical results show that for BF Alamouti, the multicast rate degrades with the number of users M at a rate of √M, which is better than the traditional transmit beamforming scheme. For SBF, the multicast rate degradation is less sensitive to the increase in the number of users and outperforms BF Alamouti for large M. All the results were verified by numerical simulations.

Consider a wireless scenario in which a multi-antenna transmitter wants to send a confidential message to a single-antenna information receiver (IR) while transferring wireless energy to a number of multi-antenna energy receivers (ERs). In order to keep the ERs from retrieving the confidential message, an artificial noise (AN)-aided physical-layer secrecy approach is employed at the transmitter. The AN has dual purpose: First, it can interfere with the ERs' information receptions and thus help improve security. Secondly, it provides wireless energy for the ERs to harvest. Assuming imperfect channel state information at the transmitter, we jointly optimize the co-variances of confidential information and AN such that the secrecy rate at the IR is maximized, while each ER receives a prescribed amount of wireless energy. Although this secrecy-rate maximization problem is non-convex, we show that it can be handled by solving a sequence of convex optimization problems. Numerical results are provided to demonstrate the efficacy of the proposed design.

This paper considers robust transmit beamforming for multiuser multi-input single-output (MISO) downlink transmission, where imperfect channel state information (CSI) is assumed at the base station (BS). The imperfect CSI is captured by a moment-based random error model, in which the BS knows only the mean and covariance of each CSI error, but not the exact distribution. Under this error model, we formulate a distributionally robust beamforming (DRB) problem, in which the total transmit power at the BS is to be minimized, while each user's SINR outage probability, evaluated w.r.t. any distribution with the given mean and covariance, is kept below a given threshold. The DRB problem is a semi-infinite chance-constrained problem. By employing recent results in distributionally robust optimization, we show that the DRB problem admits an explicit conic reformulation, which can be conveniently turned into a convex optimization problem after semidefinite relaxation (SDR). We also consider the case where the mean and covariance are not perfectly known. We show that the resulting DRB problem still admits a conic reformulation and can be approximately solved using SDR. The robustness of the proposed designs are demonstrated by numerical simulations.

Recently, simultaneous wireless information and power transfer (SWIPT) has received considerable attention. In this paper, we consider a multicast SWIPT system, where a multi-antenna transmitter broadcasts common information to a group of single-antenna information receivers (IRs) and at the same time provides certain amount of energy transfer to a group of single-antenna energy receivers (ERs). Assuming imperfect channel state information (CSI) at the transmitter, two transmit schemes are proposed to maximize the IRs' outage-constrained multicast rate subject to a minimum provision of average energy transfer to ERs. In the first transmit scheme, we consider transmit beamforming and develop a safe approximation approach to obtain a conservative beamforming solution for maximizing the outage-constrained multicast rate. To further improve the performance of transmit beamforming, in the second transmit scheme, we consider a stochastic beamforming (SBF) approach, which allows the beamformer to randomly change over time according to some prescribed distribution. By doing so, the SBF scheme is able to fully exploit the temporal degree of freedom to achieve more balanced outage-constrained achievable rates among IRs. Simulation results demonstrated that the SBF scheme is generally better than the transmit beamforming scheme.

Consider a multi-input multi-output (MIMO) channel wiretapped by multiple multi-antenna eavesdroppers. Assuming imperfect eavesdroppers' channel state information (CSI) at the transmitter, an outage-constrained secrecy rate maximization (OC-SRM) problem is considered. Specifically, we aim to design the transmit covariance matrix such that the outage secrecy rate is maximized for a given outage probability. The OC-SRM problem is challenging, and as a compromise, we resort to a recently developed Bernstein-type inequality approach to obtain a safe (conservative) approximate solution for OC-SRM. The merit of the proposed safe design lies in its tractability. In particular, a safe solution can be efficiently computed by alternately solving two convex conic optimization problems. The efficacy of the proposed design is demonstrated by simulations.

This paper considers transmit covariance optimization for a multi-input multi-output (MIMO) Gaussian wiretap channel. Specifically, we aim to maximize the MIMO secrecy capacity by judiciously designing the transmit covariance under the sum power and per-antenna power constraints. The MIMO secrecy capacity maximization (SCM) problem is nonconvex, and so far there is no tractable solution available. We propose an alternating optimization (AO) approach to handle the SCM problem. In particular, our development consists of two steps: First, we show that the SCM problem can be reexpressed to a form that can be conveniently processed by AO. Second, we develop a custom-designed fast algorithm for each AO iteration. Interestingly, with this fast implementation, the overall AO algorithm can be viewed as performing iterative reweighting and water-filling. Finally, the convergence of the proposed algorithm to a stationary solution of SCM is shown, and numerical results are provided to demonstrate its efficacy.

This paper considers a discrete sum rate maximization (DSRM) problem for transmit optimization in multiuser MISO downlink. Unlike many existing sum rate maximization designs, DSRM focuses on a scenario where each user's achievable rate can only be chosen from a given discrete rate set. This discrete rate-based design is motivated by the fact that practical communication systems can support only a finite number of combinations of modulation and coding schemes. We tackle the DSRM problem first by deriving a novel reformulation of DSRM, in which the discrete rate variables are absorbed by the objective function. Then, from this reformulation, an approximation algorithm based on convex optimization and iterative solution refinement is developed. Simulations results are provided to demonstrate the performance of the proposed algorithm compared with some state-of-the-art algorithms.

This paper considers transmit optimization in multi-input multi-output (MIMO) wiretap channels, wherein we aim at maximizing the secrecy capacity or rate of an MIMO channel overheard by one or multiple eavesdroppers. Such optimization problems are nonconvex, and appear to be difficult especially in the multi-eavesdropper scenario. In this paper, we propose an alternating optimization (AO) approach to tackle these secrecy optimization problems. We first consider the secrecy capacity maximization (SCM) problem in the single eavesdropper scenario. An AO algorithm is derived through a judicious SCM reformulation. The algorithm conducts some kind of reweighting and water-filling in an alternating fashion, and thus is computationally efficient to implement. We also prove that the AO algorithm is guaranteed to converge to a Karush-Kuhn-Tucker (KKT) point of the SCM problem. Then, we turn our attention to the multiple eavesdropper scenario, where the artificial noise (AN)-aided secrecy rate maximization (SRM) problem is considered. Although the AN-aided SRM problem has a more complex problem structure than the previous SCM, we show that AO can be extended to deal with the former, wherein the problem is handled by solving convex problems in an alternating fashion. Again, the resulting AO method is proven to have KKT point convergence guarantee. For fast implementation, a custom-designed AO algorithm based on smoothing and projected gradient is also derived. The secrecy rate performance and computational efficiency of the proposed algorithms are demonstrated by simulations.

This letter studies cooperative secure beamforming for amplify-and-forward (AF) relay networks in the presence of multiple eavesdroppers. Under both total and individual relay power constraints, we propose two schemes, namely secrecy rate maximization (SRM) beamforming and null-space beamforming. In the first scheme, our design problem is based on SRM. Using a suboptimal, but convex, technique-semidefinite relaxation (SDR), we show that this problem can be handled by performing a one-dimensional search which involves solving a sequence of semidefinite programs (SDPs). To reduce the complexity, in the second scheme, we instead maximize the information rate at the destination while completely eliminating the information leakage to all eavesdroppers. We prove that this problem can be exactly solved by SDR with one SDP only. Simulation results demonstrate the performance gains of the two proposed designs.

Consider a robust downlink beamforming optimization problem for secondary multicast transmission in a multiple-input multiple-output (MIMO) spectrum sharing cognitive radio (CR) network. The minimization problem of transmit power is formulated subject to both the quality-of-service (QoS) constraints on the secondary receivers and the interference temperature constraints on the primary users, under the assumption of imperfect channel state information (CSI). The problem is non-convex quadratically constrained quadratic program (QCQP), and it is hard to achieve the global optimality. As a compromise, we present a randomized approximation algorithm for the problem via convex optimization techniques. In particular, we point out that the robust beamforming problem is efficiently solvable when the number of primary and secondary links in the CR network is not larger than three. Simulation results are presented to demonstrate the performance gains of the proposed algorithm over an existing robust design.

This paper considers an artificial noise (AN) aided secrecy rate maximization (SRM) problem for a multi-input single-output (MISO) channel overheard by multiple single-antenna eavesdroppers. We assume that the transmitter has perfect knowledge about the channel to the desired user but imperfect knowledge about the channels to the eavesdroppers. Therefore, the resultant SRM problem is formulated in the way that we maximize the worst-case secrecy rate by jointly designing the signal covariance W and the AN covariance Σ. However, such a worst-case SRM problem turns out to be hard to optimize, since it is nonconvex in W and Σ jointly. Moreover, it falls into the class of semi-infinite optimization problems. Through a careful reformulation, we show that the worst-case SRM problem can be handled by performing a one-dimensional line search in which a sequence of semidefinite programs (SDPs) are involved. Moreover, we also show that the optimal W admits a rank-one structure, implying that transmit beamforming is secrecy rate optimal under the considered scenario. Simulation results are provided to demonstrate the robustness and effectiveness of the proposed design compared to a non-robust AN design.

Recently, there has been growing interest in secure communication via multi-antenna physical-layer designs. In this paper, we consider a transmit covariance design for a secrecy rate maximization problem under a multicast scenario, where a multi-antenna transmitter delivers a common confidential message to multiple single-antenna receivers in the presence of multiple multi-antenna eavesdroppers. This multicast secrecy rate maximization (SRM) problem is nonconvex by nature. By resorting to a convex approximation, we provide an upper bound and lower bounds of the multicast secrecy rate by solving a semidefinite program. In particular, for the case of either i) no more than three legitimate receivers and one eavesdropper, or ii) one legitimate receiver and arbitrary number of eavesdroppers, these bounds are shown to be tight and transmit beamforming is an optimal transmit strategy. We also demonstrate by simulations that the multicast SRM problem can be accurately approximated by the proposed method.

The advances of multi-antenna techniques has recently led to renewed interest in physical-layer secrecy, a meaningful topic that enables us to prevent eavesdroppers from retrieving information intended for a legitimate user through physical layer designs. This paper address a secrecy-rate maximization problem for the scenario of a multi-input single-output channel listened by multiple multi-antenna eavesdroppers; e.g., in downlink. This problem is nonconvex and has no analytical solution. Through a careful analysis and reformulation, we show that the secrecy-rate maximization problem has a convex equivalent in form of a semidefinite program (SDP). We also prove that the respective optimal transmit covariance generally can yield a rank-one structure, implying that transmit beamforming is secrecy-rate optimal in the considered scenario. Simulation results are also provided to illustrate that the optimal transmit design solved by our SDP approach can yield significantly improved secrecy rates than an existing closed-form design.