Machine unlearning, as a post-hoc processing technique, has gained widespread adoption in addressing challenges like bias mitigation and robustness enhancement. However, existing non-privacy unlearning-based solutions persist in using a binary data removal framework designed for privacy-driven motivation, even when repurposed for fairness or robustness improvements. This leads to significant utility loss, a phenomenon known as “over-unlearning”. While over-unlearning has been largely described in many studies as primarily causing utility degradation, we investigate deeper insights in this work through counterfactual leave-one-out analysis. Based on insights, we introduce a soft weighting strategy that assigns tailored weights to each sample by solving a convex quadratic programming problem analytically, which enables fine-grained model adjustments to address the over-unlearning. We demonstrate that the proposed soft-weighted scheme can be seamlessly integrated into most existing unlearning algorithms. Extensive experiments show that in fairness- and robustness-driven tasks, the soft-weighted scheme significantly outperforms hard-weighted schemes in fairness/robustness metrics and alleviates the decline in utility metric, thereby enhancing unlearning algorithm as an effective correction solution.

To protect clients' right to be forgotten in federated learning, federated unlearning aims to remove the data contribution of leaving clients from the global learned model. While current studies mainly focused on enhancing unlearning efficiency and effectiveness, the crucial aspects of efficiency fairness and performance fairness among decentralized clients during unlearning have remained largely unexplored. In this study, we introduce FedShard, the first federated unlearning algorithm designed to concurrently guarantee both efficiency fairness and performance fairness. FedShard adaptively addresses the challenges introduced by dilemmas among convergence, unlearning efficiency, and unlearning fairness. Furthermore, we propose two novel metrics to quantitatively assess the fairness of unlearning algorithms, which we prove to satisfy well-known properties in other existing fairness measurements. Our theoretical analysis and numerical evaluation validate FedShard's fairness in terms of both unlearning performance and efficiency. We demonstrate that FedShard mitigates unfairness risks such as cascaded leaving and poisoning attacks and realizes more balanced unlearning costs among clients. Experimental results indicate that FedShard accelerates the data unlearning process 1.3-6.2 times faster than retraining from scratch and 4.9 times faster than the state-of-the-art exact unlearning methods.

The proliferation of mobile devices and real-time status updating applications has motivated the optimization of data freshness in the context of age of information (AoI). Meanwhile, increasing computational demands have inspired research on CPU scheduling. Since prior CPU scheduling strategies have ignored data freshness and prior age-minimization strategies have considered only constant CPU speed, we formulate the first CPU scheduling problem as a constrained semi-Markov decision process (SMDP) problem with uncountable space, which aims to minimize the long-term average age of information, subject to an average CPU power constraint. We optimize strategies that specify when the CPU sleeps and adapt the CPU speed (clock frequency) during the execution of update-processing tasks. We consider the age-minimal CPU scheduling problem for both predictable task size (PTS) and unpredictable task size (UTS) cases, where the task size is realized at the start (PTS) or at the completion (UTS) of the task, respectively. To address the non-convex objective, we employ Dinkelbach's fractional programming method to transform our problem into an average cost SMDP. We develop a value-iteration-based algorithm and prove its convergence to obtain optimal policies and structural results for both the PTS and UTS systems. Compared to constant CPU speed, numerical results show that our proposed scheme can reduce the AoI by 50\% or more, with increasing benefits under tighter power constraints. Further, for a given AoI target, the age-minimal CPU scheduling policy can reduce the energy consumption by 50\% or more, with greater AoI reductions when the task size distribution exhibits higher variance.

Mobile Edge Computing (MEC) leverages computational heterogeneity between mobile devices and edge nodes to enable real-time applications requiring high information fresh ness. The Age-of-Information (AoI) metric serves as a crucial evaluator of information timeliness in such systems. Addressing AoI minimization in multi-user MEC environments presents significant challenges due to stochastic computing times. In this paper, we consider multiple users offloading tasks to heterogeneous edge servers in an MEC system, focusing on preemptive and non-preemptive task scheduling mechanisms. The problem is first reformulated as a Restless Multi-Arm Bandit (RMAB) problem, with a multi-layer Markov Decision Process (MDP) framework established to characterize AoI dynamics in the MEC system. Based on the multi-layer MDP, we propose a nested index framework and design a nested index policy with provably asymptotic optimality. This establishes a theoretical framework adaptable to various scheduling mechanisms, achieving efficient optimization through state stratification and index design in both preemptive and non-preemptive modes. Finally, the closed-form of the nested index is derived, facilitating performance trade-offs between computational complexity and accuracy while ensuring the universal applicability of the nested index policy across both scheduling modes. The experimental results show that in non preemptive scheduling, compared with the benchmark method, the optimality gap is reduced by 25.43%, while in preemptive scheduling, the gap has reduced by 21.21%. As the system scale increases, it asymptotically converges in two scheduling modes and especially provides near-optimal performance in a non preemptive structure.

Large language models (LLMs) have demonstrated remarkable capabilities across a wide range of natural language processing tasks. Exploiting the heterogeneous capabilities of edge LLMs is crucial for diverse emerging applications, as it enables greater cost-effectiveness and reduced latency. In this work, we introduceMixture-of-Edge-Experts (MoE2), a novel collaborative inference framework for edge LLMs. We formulate a joint gating and expert selection problem to optimize inference performance under energy and latency constraints. Unlike conventional MoE problems, LLM expert selection becomes significantly more challenging due to the combinatorial nature and the heterogeneity of edge LLMs across various attributes. To this end, we propose a two-level expert selection mechanism through which we uncover an optimality-preserving property of gating parameters across expert selections. This property enables the decomposition of the training and selection processes, significantly reducing complexity. Furthermore, we leverage the objective’s monotonicity and design a discrete monotonic optimization algorithm for optimal expert selection. We implement edge servers with NVIDIA Jetson AGX Orins and NVIDIA RTX 4090 GPUs, and perform extensive experiments. Our results validate the performance improvements for various LLM models and show that our MoE2 method can achieve optimal trade-offs among different delay and energy budgets, and outperforms baselines under various system resource constraints. We further demonstrate its strong robustness in dynamic, non-stationary environments and its effectiveness in achieving load balancing.

Data streams collected from multiple sources are rarely independent. Values evolve over time and influence one another across sequences. These correlations improve prediction in healthcare, finance, and smart-city control yet violate the record-independence assumption built into most Differential Privacy (DP) mechanisms. To restore rigorous privacy guarantees without sacrificing utility, we introduce Correlated-Sequence Differential Privacy (CSDP), a framework specifically designed for preserving privacy in correlated sequential data. CSDP addresses two linked challenges: quantifying the extra information an attacker gains from joint temporal and cross-sequence links, and adding just enough noise to hide that information while keeping the data useful. We model multivariate streams as a Coupling Markov Chain, yielding the derived loose leakage bound expressed with a few spectral terms and revealing a counterintuitive result: stronger coupling can actually decrease worst-case leakage by dispersing perturbations across sequences. Guided by these bounds, we build the Freshness-Regulated Adaptive Noise (FRAN) mechanism—combining data aging, correlation-aware sensitivity scaling, and Laplace noise—that runs in linear time. Tests on two-sequence datasets show that CSDP improves the privacy-utility trade-off by approximately 50% over existing correlated-DP methods and by two orders of magnitude compared to the standard DP approach.

Machine unlearning strives to uphold the data owners' right to be forgotten by enabling models to selectively forget specific data. Recent advances suggest pre-computing and storing statistics extracted from second-order information and implementing unlearning through Newton-style updates. However, the Hessian matrix operations are extremely costly and previous works conduct unlearning for empirical risk minimizer with the convexity assumption, precluding their applicability to high-dimensional over-parameterized models and the nonconvergence condition. In this paper, we propose an efficient Hessian-free unlearning approach. The key idea is to maintain a statistical vector for each training data, computed through affine stochastic recursion of the difference between the retrained and learned models. We prove that our proposed method outperforms the state-of-the-art methods in terms of the unlearning and generalization guarantees, the deletion capacity, and the time/storage complexity, under the same regularity conditions. Through the strategy of recollecting statistics for removing data, we develop an online unlearning algorithm that achieves near-instantaneous data removal, as it requires only vector addition. Experiments demonstrate that our proposed scheme surpasses existing results by orders of magnitude in terms of time/storage costs with millisecond-level unlearning execution, while also enhancing test accuracy.

Random Forests are widely recognized for establishing efficacy in classification and regression tasks, standing out in various domains such as medical diagnosis, finance, and personalized recommendations. These domains, however, are inherently sensitive to privacy concerns, as personal and confidential data are involved. With increasing demand for the right to be forgotten, particularly under regulations such as GDPR and CCPA, the ability to perform machine unlearning has become crucial for Random Forests. However, insufficient attention was paid to this topic, and existing approaches face difficulties in being applied to real-world scenarios. Addressing this gap, we propose the DynFrs framework designed to enable efficient machine unlearning in Random Forests while preserving predictive accuracy. Dynfrs leverages subsampling method Occ(q) and a lazy tag strategy Lzy, and is still adaptable to any Random Forest variant. In essence, Occ(q) ensures that each sample in the training set occurs only in a proportion of trees so that the impact of deleting samples is limited, and Lzy delays the reconstruction of a tree node until necessary, thereby avoiding unnecessary modifications on tree structures. In experiments, applying Dynfrs on Extremely Randomized Trees yields substantial improvements, achieving orders of magnitude faster unlearning performance and better predictive accuracy than existing machine unlearning methods for Random Forests.

Large language models (LLMs) have demonstrated remarkable capabilities across various natural language processing tasks. Exploiting the heterogeneous capabilities of edge LLMs is crucial for emerging applications, enabling greater cost-effectiveness and reduced latency. In this work, we introduce Mixture-of-Edge-Experts (MoE2), a collaborative inference framework for edge LLMs. We formulate the joint gating and expert selection problem to optimize inference under energy and latency constraints. Unlike conventional MoE problems, expert selection here is more challenging due to the combinatorial nature and heterogeneity of edge LLMs. To address this, we propose a two-level expert selection mechanism and uncover an optimality-preserving property of gating parameters that decouples training and selection, reducing complexity. We further leverage the objective's monotonicity and design a discrete monotonic optimization algorithm. Implemented on Jetson Orin and RTX 4090 platforms, MoE2 achieves optimal trade-offs across delay and energy budgets, outperforming baselines under various resource constraints.

The increasing reliance on fresh data in real-time applications underscores the significance of commoditized fresh data. However, current research often neglects the crucial data correlation, essential in applications like intelligent transportation. This paper examines the trading of correlated fresh data, where a platform monitors the time-varying numerical status of multiple correlated data sources. Data users arrive stochastically, each seeking to obtain data from the platform to estimate the real-time status of a specific source of interest. To facilitate data trading, we propose a dynamic pricing policy that allows the platform to adjust prices in real time. We demonstrate that dynamic pricing is an NP-hard mixed integer programming problem and propose an approximate algorithm. Our approach begins with threshold-based data allocation and uses linear programming to optimize pricing, achieving a logarithmic approximation ratio. For binary data sources, we derive an optimal closed-form solution, revealing that data correlation can benefit both the platform and users by offsetting data aging with spatially correlated fresher data. Interestingly, despite users placing a higher valuation on fresher data, the presence of correlation results in fresher data being priced lower. This counterintuitive pricing strategy is designed to encourage users to engage in crosssource data purchasing. Numerical results show that the proposed approximate dynamic pricing policy can achieve at least 90 % of the maximum dynamic pricing revenue. Additionally, data correlation can amplify the platform's revenue by up to 100 % compared to scenarios without data correlation.

While federated learning enables intelligent services and personalized user experiences, it raises privacy concerns due to regulatory requirements and user demands for data protection. Federated unlearning offers a potential solution to these issues. However, despite increasing demand for its practical implementation driven by right-to-be-forgotten regulations, the economic implications of federated unlearning on user behavior and platform profitability remain underexplored, potentially hindering its adoption. In this paper, we formulate a set of contract design problems for both unlearning-disabled and unlearning-enabled scenarios. Challenges arise when the unlearning-enabled platform jointly designs compensation for both learning and unlearning to incentivize users' sequential decisions to balance the expected revenue and unlearning cost. We first conduct a questionnaire survey that reveals that federated unlearning increases users' willingness to participate in federated learning. We then provide a necessary condition for maximizing the surplus of an unlearning-enabled platform, enabling the point-wise decomposition for the optimal contract design problem, based on which we minimize the incentive cost and maximize the surplus for the platform. Our further analysis reveals that i) the incentive effects of unlearning grow quadratically with users' privacy sensitivity, and ii) enabling unlearning may even profit more than disabling it when the training cost increases at a faster rate than the probability of privacy leakage as effort levels rise. Our numerical results show that the platform's profitability is primarily influenced by users' privacy sensitivity. When users have a relatively high privacy sensitivity, enabling unlearning can significantly improve profitability.

We study the age of information (AoI) in a random access network consisting of multiple source-destination pairs, where each source node is empowered by energy harvesting capability. Every source node transmits a sequence of data packets to its destination using only the harvested energy. Each data packet is encoded with finite-length codewords, characterizing the nature of short codeword transmissions in random access networks. By combining tools from bulk-service Markov chains with stochastic geometry, we derive an analytical expression for the network average AoI and obtain closed-form results in two special cases, i.e., the small and large energy buffer size scenarios. Our analysis reveals the trade-off between energy accumulation time and transmission success probability. We then optimize the network average AoI by jointly adjusting the update rate and the blocklength of the data packet. Our findings indicate that the optimal update rate should be set to one in the energy-constrained regime where the energy consumption rate exceeds the energy arrival rate. This also means if the optimal blocklength of the data packet is pre-configured, an energy buffer size supporting only one transmission is sufficient.

In autonomous driving systems, detecting pedestrian risky behavior is crucial for ensuring the safety of human-vehicle interactions. The behavior of pedestrians is not only determined by their individual actions but also influenced by their interactions with the surrounding environment. Considering the impact of environmental context on pedestrian behavior, recent studies have proposed various methods for extracting contextual information. However, efficiently modeling the environmental contextual features of pedestrian behavior remains a challenge. In this paper, we propose a target-oriented environmental context modeling method that accounts for the role of target-specific features in constructing context features, achieving target-adaptive context feature extraction. Our solution, called State DEtection TRansformer (SDETR), provides an end-to-end framework for risky pedestrian behavior detection. First, we devise a Dual-level Feature Encoder that effectively decouples high-level target semantic and low-level environmental texture. Specifically, the texture encoding enables label-free environmental feature extraction. Then, we develop a Object-Environment Perception Decoder that flexibly decodes cross-feature domain contextual features using object features. Finally, a Feature Fusion Head is employed to merge object features with environmental state features for the detection output. Experiments demonstrate the outstanding performance of SDETR in two typical risky behavior detection tasks (crossing detection and intrusion detection). We report a new record of 87.3% accuracy on the JAAD dataset and 76.3% accuracy on the Cityintrusion dataset, which significantly outperforms all previously published results. Note to Practitioners—The motivation of this paper is to detect risky pedestrian behavior in traffic scenes, with a particular focus on the construction of pedestrian environmental context. Existing methods for extracting environmental context typically involve complex feature extraction or label descriptions of the background, followed by a mechanistic establishment of the interplay between pedestrians and their surroundings. This paper proposes a flexible and cost-effective method for modeling environmental context. We employ an attention mechanism to autonomously decode cross-feature domain contextual state features, using target features as query features. Moreover, we employ dual-level feature extraction methods for targets and backgrounds (target semantics and background textures), significantly reducing the labeling cost for environmental description. Preliminary experiments suggest that this method is feasible in the detection of two risky pedestrian behaviors: pedestrian crossing and pedestrian intrusion. However, it has not yet been extended to other pedestrian dangerous behavior tasks. In future research, we intend to delve deeper into the recognition of unlabeled pedestrian abnormal and risky behaviors, expanding our research beyond the current scope.

The immense value of IoT data in real-time applications has led to the rise of fresh IoT data trading. Existing research often neglects strategic users who optimally time their data purchases, significantly affecting market demand and revenue. This paper studies a fresh data market with strategic users arriving stochastically and having heterogeneous data valuations. Strategic users decide purchase timing based on data freshness and price, while the platform optimizes its data pricing policy to maximize profit. We first examine a dynamic pricing policy, offering a price menu to each arriving user. This analysis is technically challenging due to the varied integer programming problems faced by heterogeneous users, making direct price optimization infeasible. To address this, we adopt a mechanism design approach, analytically deriving the optimal dynamic pricing policy. To reduce implementation complexity, we also study two simpler pricing policies: single and two-price pricing. In a two-period refreshing model, we derive the optimal single and two-price pricing policies analytically. Our findings reveal that the optimal two-price policy significantly outperforms the single pricing policy, guaranteeing at least$96\%$of the revenue achieved by the optimal dynamic pricing policy in a two-period refreshing model. Surprisingly, despite having more purchasing options, strategic users may be worse off than if they were myopic due to higher prices. The platform actually benefits from strategic users, generating up to five times more profit with strategic users than with myopic users, even while reducing data refresh frequency.

Network utility maximization (NUM) is a fundamental framework for optimizing next-generation networks. However, self-interested agents with private information pose challenges due to potential system manipulation. To address these challenges, the literature on economic mechanism design has emerged. Existing mechanisms are not suited for large-scale networks due to their complexity, high implementation costs, and difficulty to adapt to dynamic settings. This paper proposes a large-scale mechanism design framework that mitigates these limitations. As the number of agents$I$approaches infinity, their incentive to misreport decreases rapidly at a rate of$\mathcal {O}(1/I^{2})$. We introduce a superimposable framework applicable to any NUM algorithm without modifications, reducing implementation costs. In the dynamic setting, the large-scale mechanism design framework introduces the decomposability of the problem, enabling agents to align their own interests with the objectives of the dynamic NUM problem. This alignment helps overcome the additional, more stringent incentive constraints encountered in dynamic settings. Extending our results to dynamic settings, we present the design of a Dynamic Large-Scale mechanism with desirable properties and the corresponding Dynamic Superimposable Large-Scale mechanism. Our numerical experiments validate the fact that our proposed schemes are approximately$I$times faster than the seminal VCG mechanism.

Existing works on data trading often overlook the impact of data freshness on its valuation. This paper explores a fresh data market, where a platform offers data with varying freshness levels, such as real-time traffic data, to users who arrive stochastically. We categorize data updates into two types: lightweight (e.g., noise level) and computation-intensive (e.g., traffic images). Initially focusing on lightweight updates, we introduce three pricing policies: uniform, dual, and dynamic. The challenge lies in jointly optimizing the platform’s data sampling and pricing, a complex non-smooth mixed integer programming problem. Nevertheless, we achieve closed-form optimal solutions for all three policies by analyzing a relaxed version of the problem. Our findings reveal the surprising insight that higher data acquisition costs lead the platform to lower uniform data prices due to staler, less valuable data. Our numerical analysis indicates that the optimal dual pricing policy closely matches the dynamic pricing policy in performance and substantially exceeds the uniform pricing, tripling profits in some cases. Extending our work to computation-intensive updates, which require preprocessing, adds extra complexity. We tackle this by applying fractional programming. Numerical results show that profits from optimal uniform and dual pricing closely approach those from dynamic pricing, as the platform can adjust processing time.

Mobile edge computing (MEC) is essential for next-generation mobile network applications that prioritize various performance metrics, including delays and energy efficiency. However, conventional single-objective scheduling solutions cannot be directly applied to practical systems in which the preferences (i.e., the weights of different objectives) are often unknown or challenging to specify in advance. In this study, we formulate a multi-objective offloading problem for MEC with multiple edges to minimize the sum of expected long-term energy consumption and delay while considering unknown preferences. To address the challenge of unknown preferences and the potentially diverse MEC systems, we propose a generalizable multi-objective (deep) reinforcement learning (GMORL)-based tasks offloading framework, which employs the Discrete Soft Actor-Critic (Discrete-SAC) method. Our method uses a single policy model to efficiently schedule tasks based on varying preferences and adapt to heterogeneous MEC systems with different CPU frequencies and server quantities. Under the proposed framework, we introduce a histogram-based state encoding method for constructing features for multiple edges in MEC systems, a sophisticated reward function for accurately computing the utilities of delay and energy consumption, and a novel neural network architecture for improving generalization. Simulation results demonstrate that our proposed GMORL scheme enhances the hypervolume of the Pareto front by up to 121.0% compared to benchmarks.

Mobile edge computing (MEC) is a promising paradigm for real-time applications with intensive computational needs (e.g., autonomous driving), as it can reduce the processing delay. In this work, we focus on the timeliness of computational-intensive updates, measured by Age-of-Information (AoI), and study how to jointly optimize the task updating and offloading policies for AoI with fractional form. Specifically, we consider edge load dynamics and formulate a task scheduling problem to minimize the expected time-average AoI. The uncertain edge load dynamics, the nature of the fractional objective, and hybrid continuous-discrete action space (due to the joint optimization) make this problem challenging and existing approaches not directly applicable. To this end, we propose a fractional reinforcement learning (RL) framework and prove its convergence. We further design a model-free fractional deep RL (DRL) algorithm, where each device makes scheduling decisions with the hybrid action space without knowing the system dynamics and decisions of other devices. Experimental results show that our proposed algorithms reduce the average AoI by up to 57.6% compared with several non-fractional benchmarks.

The proliferation of real-time status updating applications and ubiquitous mobile devices have motivated the analysis and optimization of data freshness in the context of age of information. At the same time, increasing requirements on computer performance have inspired research on CPU scheduling, with a focus on reducing energy consumption. However, since prior CPU scheduling strategies have ignored data freshness, we formulate the first CPU scheduling problem that aims to minimize the long-term average age of information, subject to an average power constraint. In particular, we optimize CPU scheduling strategies that specify when the CPU sleeps and adapt the CPU speed (clock frequency) during the execution of update-processing tasks. We formulate the age-minimal CPU scheduling problem as a constrained semi-Markov decision process (SMDP) problem with uncountable space. We develop a value-iteration-based algorithm and further prove its convergence in infinite space to obtain the optimal policy. Compared with existing benchmarks in terms of long-term average AoI, numerical results show that our proposed scheme can reduce the AoI by up to 53%, and obtains greater benefits when faced with a tighter power constraint. In addition, for a given AoI target, the age-minimal CPU scheduling policy can save more than 50% on energy consumption.

Age of information (AoI), defined for an information source as the time elapsed since the latest received update was generated, is a recently proposed metric that quantifies the timeliness of information delivery in a communication system. This paper studies a fundamental problem of how the achievable AoI scales in mobile networks. Specifically, we consider a network consisting of n/2 source-destination (S-D) pairs and employ the protocol model to characterize interference incurred by concurrent transmissions. We consider a general class of scheduling policies potentially with the multi-hop transmission and the duplication of packets to multiple nodes. The analysis of AoI faces significant challenges due to potential out-of-order packet delivery, the inherent tradeoffs between packet-centric metrics (throughput and delay), and their unexplored relation to AoI. We first show that the average per-node AoI in static settings scales as [EQUATION]. In the case of networks with i.i.d. mobility, where the node locations vary independently over time, we introduce an episodic technique that allows us to establish lower bounds and design and analyze scheduling policies as constructive upper bounds. Our analytical results reveal that the average per-node AoI scales as [EQUATION] under i.i.d. mobility, which highlights that mobility can enhance timeliness. Finally, we show that, in a more general class of wireless network settings, one can design the age-minimal scheduling policy by balancing throughput and delay.

The proliferation of real-time applications has motivated extensive research on analyzing and optimizing data freshness in the context of age of information. However, classical frameworks of privacy (e.g., differential privacy (DP)) have overlooked the impact of data freshness on privacy guarantees, which may provide a new tool for time-varying databases. In this work, we introduce age-dependent DP, taking into account the underlying stochastic nature of a time-varying database. In this new framework, we assume knowledge of the data process’s statistical information and establish a connection between classical DP and age-dependent DP. We use this connection to characterize the impact of data staleness and temporal correlation on privacy guarantees. Our characterization reveals that the total variation distance is the sole essential statistical information. Moreover, we demonstrate that aging, which involves utilizing stale data inputs and/or delaying the release of outputs, can serve as a novel strategy for safeguarding data privacy, in addition to the traditional approach of injecting noise in the DP framework. Furthermore, to generalize our results to a multi-query scenario, we present a sequential composition result for age-dependent DP under any publishing and aging policies. We then characterize the optimal tradeoffs between privacy risk and utility and show how this can be achieved. Finally, case studies show that to achieve an arbitrarily small privacy risk in a single-query case, combing aging and noise injection only leads to a bounded accuracy loss, whereas using noise injection only (as in the benchmark case of DP) will lead to an unbounded accuracy loss.

Federated learning (FL) provides a collaborative paradigm for distributedly training a global model while protecting clients' privacy. In addition to communication bottlenecks and non-i.i.d. data distributions, the FL framework introduces two fundamental economic challenges: first, clients are self-interested and strategic in practice, requiring specific incentives to participate in FL; second, each client can misreport its private information to its advantage. Although existing studies have proposed economic mechanisms, they are often restricted to a “binary” participation scenario, leading to communication overheads or biased models due to client heterogeneity. In this paper, we first analyze the convergence bound under arbitrary client sampling probability with a varying number of clients. Then, we consider an optimal mechanism design problem: the FL convergence bound minimization subject to budget constraint, incentive compatibility, and individual rationality. We derive the optimal sampling probability function in a close form. To overcome the unknown prior distribution challenge, we introduce a prior-independent mechanism design, and show how it gradually learns cost distributions by exploiting the incentive compatibility property. We perform extensive experiments and show that, while outperforming the uniform sampling scheme, two proposed schemes (prior-based and prior-independent ones) perform closely to the ideal complete information upper bound.

To enable AI-empowered Internet-of-things (AIoT) applications, it is crucial to achieve real-time data inference (e.g., prediction, control) at network edge. However, resource-constrained Internet-of-things devices (IoTDs) may be incapable of accomplishing those computation-intensive and latency-sensitive inference tasks. To address this issue, it is promising to incorporate mobile edge computing (MEC) systems and let IoTDs offload their inference tasks to edge servers that have already cached the associated neural network model required for inference. In this work, we take into account the limited storage and computing capacity of edge servers and formulate a neural network model caching problem for an MEC system with edge inference, in order to maximize the inference accuracy and reduce the task delay. To handle the exponential growth of signaling overhead and the learning difficulty under huge number of widely-deployed edge servers, we propose a cooperative mean field multi-agent reinforcement learning framework and a mean field actor-critic algorithm to solve the aforementioned problem. Simulation results show that our proposed algorithm outperforms several benchmarks, especially in large-scale edge networks.

We propose a frame slotted ALOHA (FSA)-based protocol for source nodes to update status information toward their intended destinations in a random access network. We evaluate the effect of such a protocol on the network’s timeliness performance using the Age of Information (AoI) metric. Specifically, we leverage tools from stochastic geometry to model the geographical positions of the source-destination pairs and capture the entanglement amongst the nodes’ spatial-temporal attributes through the interference they caused to each other. We derive closed-form expressions for the average AoI over a typical transmission link. Our analysis shows that in densely deployed networks, the FSA-based status updating protocol can significantly decrease the average AoI. Furthermore, under the same updating frequency, converting a slotted ALOHA protocol into an FSA-based one always leads to a reduction in the average AoI.

Exploiting the computational heterogeneity of mobile devices and edge nodes, mobile edge computation (MEC) provides an efficient approach to achieving real-time applications that are sensitive to information freshness, by offloading tasks from mobile devices to edge nodes. We use the metric Age-of-Information (AoI) to evaluate information freshness. An efficient solution to minimize the AoI for the MEC system with multiple users is non-trivial to obtain due to the random computing time. In this paper, we consider multiple users offloading tasks to heterogeneous edge servers in a MEC system. We first reformulate the problem as a Restless Multi-Arm-Bandit (RMAB) problem and establish a hierarchical Markov Decision Process (MDP) to characterize the updating of AoI for the MEC system. Based on the hierarchical MDP, we propose a nested index framework and design a nested index policy with provably asymptotic optimality. Finally, the closed form of the nested index is obtained, which enables the performance tradeoffs between computation complexity and accuracy. Our algorithm leads to an optimality gap reduction of up to 40%, compared to benchmarks. Our algorithm asymptotically approximates the lower bound as the system scalar gets large enough.

The interests in obtaining fresh data in real-time applications have facilitated fresh data markets. However, existing works on designing fresh data markets have ignored strategic users. Being strategic means that users can optimally time their data purchases, which affects markets' profit. In this paper, we study a fresh data market, where strategic users with heterogeneous data valuations stochastically arrive over time. The strategic users decide the time of data purchase, considering the evolution of data freshness and prices, while the platform decides the data pricing policy over time to maximize its profit. We first consider a dynamic pricing policy, where the platform offers a price menu to each arrival user. The analysis is technically challenging, as heterogeneous users face different integer programming problems in optimizing their data purchase time, making direct optimization of data prices infeasible. To tackle the challenge, we adopt a mechanism design approach. We show that the direct mechanism design problem relax the original problem and obtain the optimal pricing policy analytically. Next, to reduce the implementation complexity, we study a single pricing policy, where the price is fixed over time. We derive the optimal single price analytically in a two-period refreshing model. Perhaps surprisingly, although strategic users have more purchasing options than non-strategic users, users who behave strategically may be worse off. Simulation results show that, although a platform refreshes the data less frequently in the presence of strategic users than facing myopic users, it can earn up to 5 times higher profit.

Mobile edge computing (MEC) is essential for next-generation mobile network applications that prioritize various performance metrics, including delays and energy consumption. However, conventional single-objective scheduling solutions cannot be directly applied to practical systems in which the preferences of these applications (i.e., the weights of different objectives) are often unknown or challenging to specify in advance. In this study, we address this issue by formulating a multi-objective offloading problem for MEC with multiple edges to minimize expected long-term energy consumption and transmission delay while considering unknown preferences as parameters. To address the challenge of unknown preferences, we design a multi-objective (deep) reinforcement learning (MORL)-based resource scheduling scheme with proximal policy optimization (PPO). In addition, we introduce a well-designed state encoding method for constructing features for multiple edges in MEC systems, a sophisticated reward function for accurately computing the utilities of delay and energy consumption. Simulation results demonstrate that our proposed MORL scheme enhances the hypervolume of the Pareto front by up to 233.1% compared to benchmarks.

We consider a random access network consisting of source-destination pairs. Each source node generates status updates and transmits this information to its intended destination over a shared spectrum. The goal is to minimize the network-wide Age of Information (AoI). We develop a frame slotted ALOHA (FSA)-based policy for generating and transmitting status updates, where the frame size of each source node is adjusted according to its local environment. The proposed policy is of low complexity and can be implemented in a distributed manner. Additionally, it significantly improves the network AoI performance by (a) equalizing the update generation intervals at each source and (b) reducing interference across the network. Furthermore, we derive an analytical expression for the average network AoI attained for that policy. We evaluate the performance of the proposed scheme through simulations, which demonstrate that the locally adaptive FSA policy achieves a remarkable gain in terms of AoI compared to the slotted ALOHA counterpart, confirming the effectiveness of the proposed method.

We propose a frame slotted ALOHA (FSA)-based protocol for a random access network where sources transmit status updates to their intended destinations. We evaluate the effect of such a protocol on the network’s timeliness performance using the Age of Information (AoI) metric. Specifically, we leverage tools from stochastic geometry to model the spatial positions of the source-destination pairs and capture the entanglement amongst the nodes’ spatial-temporal attributes through the interference they caused to each other. We derive analytical expressions for the average and variance of AoI over a typical transmission link in Poisson bipolar and cellular networks, respectively. Our analysis shows that in densely deployed networks, the FSA-based status updating protocol can significantly decrease the average AoI and in addition, stabilizes the age performance by substantially reducing the variance of AoI. Furthermore, under the same updating frequency, converting a slotted ALOHA protocol into an FSA-based one always leads to a reduction in the average AoI. Moreover, implementing FSA in conjunction with power control can further benefit the AoI performance, although the particular values of framesize and power control factor must be adequately tuned to achieve the optimal gain.

In this paper, we study a fresh data acquisition problem to acquire fresh data and optimize the age-related performance when strategic data sources have private market information. We consider an information update system in which a destination acquires, and pays for, fresh data updates from a source. The destination incurs an age-related cost, modeled as a general increasing function of the age-of-information (AoI). The source is strategic and incurs a sampling cost, which is its private information and may not be truthfully reported to the destination. To this end, we design an optimal (economic) mechanism for timely information acquisition by generalizing Myerson's seminal work. The goal is to minimize the sum of the destination's age-related cost and its payment to the source, while ensuring that the source truthfully reports its private information and will voluntarily participate in the mechanism. Our results show that, under some distributions of the source's cost, our proposed optimal mechanism can lead to an unbounded benefit, compared against a benchmark that naively trusts the source's report and thus incentivizes its maximal over-reporting.

Data freshness, measured by Age of information (AoI), is becoming an increasingly significant metric for data valuation. However, most existing data trading markets ignore the impact of such a metric. In this paper, we study a fresh data market, where users with heterogeneous valuations for AoI stochastically arrive over time. The platform decides data sampling (which affects the AoI) and pricing policies (to the users), to maximize its profit. We consider three types of pricing policies with increasing flexibility, i.e., a uniform pricing policy, a dual pricing policy, and a dynamic pricing policy. The joint data sampling and pricing optimization is a non-smooth mixed integer programming problem, which is challenging to solve. Despite the difficulty, we derive the closed-form solutions of the optimal data sampling policies and pricing policies for all three cases. Our analysis yields several interesting practical insights. First, the optimal data prices decrease in the unit sampling cost and increase in the users’ arrival rate. Second, for all three pricing policies, the equal-spacing data sampling policy is optimal. Third, numerical results show that the optimal dual pricing policy significantly outperforms the optimal uniform pricing policy. Specifically, the optimal dual pricing policy produces up to 280% of the profit that is achieved by the optimal uniform pricing policy.

We introduce the concept of fresh data trading, in which a destination user requests, and pays for, fresh data updates from a source provider, and data freshness is captured by the age of information (AoI) metric. Keeping data fresh relies on costly frequent data updates by the source, which motivates the source to price fresh data. In this work, the destination incurs an age-related cost, modeled as a general increasing function of the AoI. The source designs a pricing mechanism to maximize its profit, while the destination chooses a data update schedule to trade off its payments to the source and its age-related cost. Depending on different real-time applications and scenarios, we study both a finite-horizon model and an infinite-horizon model with time discounting. The key challenge of designing the optimal pricing scheme lies in the destination's time-interdependent valuations, due to the nature of AoI, and the infinite-dimensional dynamic optimization. To this end, we exploit three different dimensions in designing pricing by studying three pricing schemes: a time-dependent pricing scheme, in which the price for each update depends on when it is requested; a quantity-based pricing scheme, in which the price of each update depends on how many updates have been previously requested; and a simple subscription-based pricing scheme, in which the price per update is constant but the source charges an additional subscription fee. Our analysis reveals that (1) the optimal subscription-based pricing maximizes the source's profit among all possible pricing schemes under both finite-horizon and infinite-horizon models; (2) the optimal quantity-based pricing scheme is only optimal with a finite horizon; and (3) the time-dependent pricing scheme, under the infinite-horizon model with significant time discounting, is asymptotically optimal. Numerical results show that the profit-maximizing pricing schemes can also lead to significant reductions in AoI and social costs, and that a moderate degree of time discounting is enough to achieve a close-to-optimal time-dependent pricing scheme.

The proliferation of real-time applications has spurred much interest in data freshness, captured by the age-of-information (AoI) metric. When strategic data sources have private market information, a fundamental economic challenge is how to incentivize them to acquire fresh data and optimize the age-related performance. In this work, we consider an information update system in which a destination acquires, and pays for, fresh data updates from multiple sources. The destination incurs an age-related cost, modeled as a general increasing function of the AoI. Each source is strategic and incurs a sampling cost, which is its private information and may not be truthfully reported to the destination. The destination decides on the price of updates, when to get them, and who should generate them, based on the sources' reported sampling costs. We show that a benchmark that naively trusts the sources' reports can lead to an arbitrarily bad outcome compared to the case where sources truthfully report. To tackle this issue, we design an optimal (economic) mechanism for timely information acquisition following Myerson's seminal work. To this end, our proposed optimal mechanism minimizes the sum of the destination's age-related cost and its payment to the sources, while ensuring that the sources truthfully report their private information and will voluntarily participate in the mechanism. However, finding the optimal mechanisms may suffer from prohibitively expensive computational overheads as it involves solving a nonlinear infinite-dimensional optimization problem. We further propose a quantized version of the optimal mechanism that achieves asymptotic optimality, maintains the other economic properties, and enables one to tradeoff between optimality and computational overheads. Our analytical and numerical studies show that (i) both the optimal and quantized mechanisms can lead to an unbounded benefit under some distributions of the source costs compared against a benchmark; (ii) the optimal and quantized mechanisms are most beneficial when there are few sources with heterogeneous sampling costs.

Federated learning enables machine learning algorithms to be trained over decentralized edge devices without requiring the exchange of local datasets. Successfully deploying federated learning requires ensuring that agents (e.g., mobile devices) faithfully execute the intended algorithm, which has been largely overlooked in the literature. In this study, we first use risk bounds to analyze how the key feature of federated learning, unbalanced and non-i.i.d. data, affects agents’ incentives to voluntarily participate and obediently follow traditional federated learning algorithms. To be more specific, our analysis reveals that agents with less typical data distributions and relatively more samples are more likely to opt out of or tamper with federated learning algorithms. To this end, we formulate the first faithful implementation problem of federated learning and design two faithful federated learning mechanisms which satisfy economic properties, scalability, and privacy. First, we design aFaithful Federated Learning (FFL) mechanismwhich approximates the Vickrey–Clarke–Groves (VCG) payments via an incremental computation. We show that it achieves (probably approximate) optimality, faithful implementation, voluntary participation, and some other economic properties (such as budget balance). Further, the time complexity in the number of agents$K$is$\mathcal {O}(\log (K))$. Second, by partitioning agents into several clusters, we present a scalable VCG mechanism approximation. We further design a scalable andDifferentially Private FFL (DP-FFL) mechanism, the first differentially private faithful mechanism, that maintains the economic properties. Our DP-FFL mechanism enables one to make three-way performance tradeoffs among privacy, the iterations needed, and payment accuracy loss.

Wireless power transfer (WPT) technology enables a cost-effective and sustainable energy supply in wireless networks. However, the broadcast nature of wireless signals makes them non-excludable public goods, which leads to potential free-riders among energy receivers. In this study, we formulate the wireless power provision problem as a public goods provision problem, aiming to maximize the social welfare of a system of an energy transmitter (ET) and all the energy users (EUs), while considering their heterogeneous valuations, private information, and self-interested behaviors. We propose a two-phase all-or-none scheme involving a low-complexity Power And Taxation (PAT) mechanism, which ensures voluntary participation, truthfulness, budget balance, and social optimality at every Nash equilibrium (NE). We propose a distributed PAT (D-PAT) algorithm to reach an NE, and prove its convergence by connecting the structure of NEs and that of the optimal solution to a related optimization problem. We further extend the analysis to a multi-channel system, which brings a further challenge of non-strictly concave agents' payoffs. We propose a Multi-Channel PAT (M-PAT) mechanism and a distributed M-PAT (D-MPAT) algorithm to address the challenge. Simulation results show that, our design is most beneficial when there are more EUs and more homogeneous channel gains.

Mobile crowd sensing (MCS) has been used to enable a wide range of resource-discovery applications by exploiting the "wisdom" of many mobile users. However, in many applications, a user's valuation depends on other users' sensory data, which introduces the problem of interdependent valuations. This feature can encourage sensory data misreport, hence makes economic mechanisms challenging. While some work has been done to address this problem, the issues of private utility information and communication overheads remain unsolved. In this study, we formulate the first interdependent-valuation model for the resource-discovery MCS systems, aiming to elicit truthful sensory reports and utility information and to maximize expected social welfare. We design a Truthful Sense-And-Bid (T-SAB) Mechanism based on surrogate functions, which can reveal marginal utility information by only requiring each user to submit one-dimensional signaling per resource. We show that the surrogate function and a reward function can limit users' willingness to misreport, when users have small informational sizes, a reasonable condition in large-scale MCS systems. Consequently, our T-SAB Mechanism yields a Perfect Bayesian Equilibrium (PBE) with the efficient allocation outcome, approximate truthfulness, individual rationality, and approximate budget balance. To illustrate the effectiveness of the T-SAB Mechanism, we perform a case study of a cognitive radio network. We demonstrate that the social welfare gain of the T-SAB Mechanism can achieve up to 20% social welfare gain comparing with a benchmark.

Network utility maximization (NUM) is a general framework for optimally allocating constrained resources in many networked applications. When agents have asymmetric and private information, a fundamental economic challenge is how to solve the NUM Problem considering the self-interests of strategic agents. Many previous related works have proposed economic mechanisms that can cope with agents' private utilities. However, the related literature largely neglected the issue of information asymmetries regarding constraints, and limited closely related studies provided solutions only applicable to specific application scenarios. To tackle this issue, we propose the DeNUM Mechanism, the first mechanism for solving a general class of decomposable NUM Problems considering both private utility and constraint information. The key idea is to decentralize the decision process to agents, who will make resource allocation decisions without the need of revealing private information to others. We further show that the DeNUM mechanism yields the network-utility maximizing solution at an equilibrium, and achieves other desirable economic properties (such as individual rationality and budget balance). However, the corresponding equilibrium solution concept, the generalized Nash equilibrium (GNE), makes it difficult to achieve through a distributed algorithm. To address this issue, we further establish the connection between the structure of GNE and that of the primal-dual solution to a reformulated NUM problem, based on which we present the convergent DeNUM Algorithm that is provably convergent. Finally, as a case study, we apply the DeNUM Mechanism to solving the NUM problem for a user-provided network, and show that the DeNUM algorithm improves the network utility by 17% compared to a non-cooperation benchmark.

We introduce the concept of a fresh data market, in which a destination user requests, and pays for, fresh data updates from a source provider. Data freshness is captured by the age of information (AoI) metric, defined as the time elapsed since the latest update has reached the destination. The source incurs an operational cost, modeled as an increasing convex function of the number of updates. The destination incurs an age-related cost, modeled as an increasing convex function of the AoI. The source charges the destination for each update and designs a pricing mechanism to maximize its profit; the destination on the other hand chooses a data update schedule to minimize the summation of its payments to the source and its age-related cost. The interaction among the source and destination is hence game-theoretic. Motivated by the existing pricing literature, we first study a time-dependent pricing scheme, in which the price for each update depends on when it is requested. We show in this case that the game equilibrium leads to only one data update, which does not yield the maximum profit to the source. This motivates us to consider a quantity-based pricing scheme, in which the price of each update depends on how many updates have been previously requested. We show that among all pricing schemes in which the price of an update may vary according to both time and quantity, the quantity-based pricing scheme performs best: it maximizes the source's profit and minimizes the social cost of the system, defined as the aggregate source's operational cost and the destination's age-related cost. Numerical results show that the optimal quantity-based pricing can be 27% more profitable for the source and incurs 54% less social cost, compared with the optimal time-dependent pricing.

Network sharing has become a key feature of various enablers of the next-generation network, such as network function virtualization and fog computing architectures. Network utility maximization (NUM) is a general framework for achieving fair, efficient, and cost-effective sharing of constrained network resources. When agents have asymmetric and private information, however, a fundamental economic challenge is how to solve the NUM problem considering the self-interests of strategic agents. Many previous related works have proposed economic mechanisms that can cope with agents' private utilities. However, the network sharing paradigm introduces the issue of information asymmetries regarding constraints. The related literature largely neglected such an issue; limited closely related studies provided solutions only applicable to specific application scenarios. To tackle these issues, we propose the Decomposable NUM (DeNUM) mechanism and the Dynamic DeNUM (DyDeNUM) mechanism, the first mechanisms in the literature for solving NUM problems considering private utility and constraint information. The key idea of both mechanisms is to decentralize the decision process to agents, who will make resource allocation decisions without the need of revealing private information to others. Under a monitorable influence assumption, the DeNUM mechanism yields the network-utility maximizing solution at an equilibrium and achieves other desirable economic properties (such as individual rationality and budget balance). We further establish the connection between the equilibrium structure and the primal-dual solution to a related optimization problem, based on which we prove the convergence of the DeNUM algorithm to an equilibrium. When the agents' influences are not monitorable, we propose the DyDeNUM mechanism that yields the network-utility maximizing solution at the cost of the balanced budget. Finally, as a case study, we apply the proposed mechanisms to solving the NUM problem for a fog-based user-provided network and show that both mechanisms improve the network utility by 34% compared to a non-cooperation benchmark.

Mobile collaborative Internet access (MCA) enables mobile users to share their Internet through flexible tethering arrangements. This can potentially make better use of network resources. However, from a mobile network operator's (MNO's) viewpoint, it can either reduce revenue or increase congestion, and thus has been blocked by some MNOs in practice. We propose a hybrid pricing framework for MNOs who charge users separately for access and tethering. This scheme serves to coordinate the tethering decisions of mobile users with MNO network management objectives. We analyze the MNOs' equilibrium pricing strategies in both cooperative and competitive scenarios. In the cooperative scenario, at the equilibrium, each user's cost is independent of any chosen tethering links. We then characterize the optimal hybrid pricing strategies of MNOs in this scenario. For the competitive scenario, we formulate the MNOs' competitive interactions as a pricing game, and we show that MNO competition leads to equalized prices for users if an equilibrium exists but does not guarantee its existence. Both insights motivate a quantity competition game, which is shown to guarantee equilibrium. Simulation results show that in scenarios of interest the proposed hybrid pricing schemes can double both MNOs' profit and users' payoff and such improvements increase with the degree of network heterogeneity.

Wireless power transfer (WPT) technology enables a cost-effective and sustainable energy supply in wireless networks, where energy users (EUs) can remotely harvest energy from the wireless signal transmitted by energy transmitters (ETs). However, the broadcast nature of wireless signal makes wireless power a non-excludable public good, which renders the traditional market mechanisms inefficient due to the possibility of the free-riders. In this study, we formulate the transmit power provision problem in a single-channel WPT network as a public good provision problem, aiming to maximize the social welfare of all the ET and EUs considering their private information and selfish behaviors. The considered problem also brings both economic and technical challenges in ensuring voluntary participation and distributed algorithm design. To this end, we propose a two- phase all-or-none procedure involving a low-complexity Power And Taxation (PAT) Nash mechanism, which ensures voluntary participation, incentive compatibility, and budget balance, and yields the socially optimal transmit power at all Nash equilibria. We further propose a distributed D-PAT Algorithm and prove its convergence by exploiting the connection between the structure of Nash equilibria and that of the optimal solutions to a related optimization problem. Finally, our simulation results validate the PAT Mechanism and the practical algorithm. We show that our design can significantly improve the social welfare compared to the benchmark market mechanism, especially when there are many and relatively comparable EUs.

In this paper, we study the secure beamforming design for a cognitive radio network (CRN), where a primary transmitter-receiver pair coexists with an untrusted secondary transmitter-receiver pair. Each pair constitutes a multiple-input single-output link. We consider an underlay scheme and a cooperative scheme. For the underlay scheme, the secondary user (SU) is allowed to transmit simultaneously in the presence of the primary transmission. For the cooperative scheme, the secondary transmitter acts as a relay to forward the secrecy information of the primary transmission in exchange for its own transmission. For both schemes, the SU is untrusted and considered a potential eavesdropper. Our goal is to minimize the total power consumption while satisfying the primary user's required secrecy rate and the SU's required information rate. Using suitable optimization tools, we design the jointly optimal secure beamforming for the underlay scheme and an alternative optimizing algorithm for the cooperative scheme. To further reduce the complexity, we also design suboptimal zero-forcing beamformers for both schemes. The simulation results verify the proposed schemes.

Mobile Crowdsourced Access (MCA) enables mobile users (MUs) to share their Internet connections by serving as tethers to other MUs, hence can improve the quality of service of MUs as well as the overall utilization of network resources. However, MCA can also reduce the revenue-generating mobile traffic and increase the network congestion for mobile network operators (MNOs), and thus has been blocked by some MNOs in practice. In this work, we reconcile the conflicting objectives of MNOs and MUs by introducing a pricing framework for MCA, where the direct traffic and tethering traffic are charged independently according to a data price and a tethering price, respectively. We derive the optimal data and tethering prices systematically for MUs with the α-fair utility in two scenarios with cooperative and competitive MNOs, respectively. We show that the optimal tethering prices are zero and the optimal usage-based data prices are identical for all MUs, in both the cooperative and competitive scenarios. Such optimal pricing schemes will lead to mutually beneficial results for MNOs and MUs. Our simulation results show that the proposed pricing scheme approximately triples both the MNOs' profit and the MUs' payoff when the MNOs cooperate, comparing to the case where MCA is blocked. Moreover, competition among MNOs will decrease MNOs' profit and further increase the MUs' payoff.

In this paper, we study the simultaneous wireless information and power transfer in downlink multiuser orthogonal frequency-division multiple access systems, where each user applies power splitting to coordinate the energy harvesting and secrecy information decoding processes. Assuming equal power allocation across subcarriers, we formulate an optimization problem to maximize the aggregate harvested power of all users while satisfying secrecy rate requirements of individual users by joint subcarrier allocation and power splitting ratio selection. Due to the NP-hardness of the problem, we propose two suboptimal algorithms to solve the problem. The first one is an iterative algorithm that optimizes subcarrier allocation and power splitting ratios by an alternating way in dual domain. The second algorithm is based on a two-step approach that allocates subcarriers and selects power splitting ratios sequentially. The numerical results show that the proposed methods outperform the conventional methods and provide good trade offs between performance and complexity.

In this paper, we study simultaneous wireless information and power transfer (SWIPT) in orthogonal frequency division multiple access (OFDMA) systems with the coexistence of information receivers (IRs) and energy receivers (ERs). The IRs are served with best-effort secrecy data and the ERs harvest energy with minimum required harvested power. To enhance the physical layer security for IRs and yet satisfy energy harvesting requirements for ERs, we propose a new frequency-domain artificial noise (AN) aided transmission strategy. With the new strategy, we study the optimal resource allocation for the weighted sum secrecy rate maximization for IRs by power and subcarrier allocation at the transmitter. The studied problem is shown to be a mixed integer programming problem and thus nonconvex, while we propose an efficient algorithm for solving it based on the Lagrange duality method. To further reduce the computational complexity, we also propose a suboptimal algorithm of lower complexity. The simulation results illustrate the effectiveness of proposed algorithms as compared against other heuristic schemes.

In this paper, we consider a cognitive radio network (CRN) consisting of a primary transmitter-receiver pair and an untrusted secondary transmitter-receiver pair, and each pair is a multiple-input single-output (MISO) link. We consider two transmission schemes, namely underlay scheme and cooperative scheme. For the underlay scheme, the secondary user (SU) is allowed to transmit simultaneously in the presence of primary transmission. For the cooperative scheme, the secondary transmitter acts as a relay node to increase the secrecy rate of primary transmission in exchange for its own transmission. For both schemes, the SU is untrusted and considered as a potential eavesdropper. Our goal is to minimize the total power consumption while satisfying the primary user (PU)'s required secrecy rate and SU's required information rate. By suitable optimization tools, we design the joint secure beamforming for both schemes. The simulation results show that in the considered system model, the underlay scheme performs better than the cooperative scheme, especially with high rate requirements and large number of antennas at secondary transmitter.

In this paper, we consider simultaneous wireless information and power transfer (SWIPT) in orthogonal frequency division multiple access (OFDMA) systems with the coexistence of information receivers (IRs) and energy receivers (ERs). The IRs are served with best- effort secrecy data and the ERs harvest energy with minimum required harvested power. To enhance physical- layer security and yet satisfy energy harvesting requirements, we introduce a new frequency-domain artificial noise based approach. We study the optimal resource allocation for the weighted sum secrecy rate maximization via transmit power and subcarrier allocation. The considered problem is nonconvex, while we propose an efficient algorithm for solving it based on Lagrange duality method. Simulation results illustrate the effectiveness of the proposed algorithm as compared against other heuristic schemes.

In this work, we maximize the secrecy rate of the wireless-powered untrusted amplify-and-forward relay networks by jointly designing power splitting (PS) ratio and relay beamforming with the proposed global optimal algorithm (GOA) and local optimal algorithm (LOA). To guarantee secure communication, the destination-based artificial noise is sent to degrade the reception of the untrusted relay, and it also becomes a new source of energy powering relay to forward the information with power splitting (PS) technique. Simulation result shows that LOA can achieve satisfactory secrecy rate performance compared with that of GOA, but with less computation time. It also manifests that both proposed algorithms outperform the benchmark method.