Illicit activities such as financial fraud and fake promotions are increasingly prevalent on social applications. The diverse behavioral structures of different types of illicit accounts pose difficulties in designing unified detection strategies. Unlike existing studies that focus on binary detection and often rely on user static profiles, we introduce a novel task: illicit account detection based solely on user behavior sequences. This task presents two key challenges: 1) illicit accounts often mimic benign users by performing normal-looking behavior subsequences, and 2) behaviors with the same action (e.g., add-friend, initiate-transaction) can serve different purposes, such as illicit or benign. To address these challenges, we propose a Pattern-aware Illicit Accounts Detection (PIAD) framework that consists of three components: 1) a dual-perspective pattern mining module that extracts category-specific self- and interaction-behavior patterns from behavior sequences to capture distinct behavioral regularities across different user types; 2) a contextualized action semantic encoding algorithm that aligns action codings with contextual dependencies among behaviors within user sequences to capture variations in purposes when behaviors with the same actions occur under different contexts; and 3) a pattern-aware fusion model that integrates the mined patterns with the context and interaction in behavior sequences to learn discriminative representations for detection. Extensive experiments on real-world datasets demonstrate that PIAD consistently outperforms state-of-the-art baselines with an average 7.51% improvement on F1 score.

Federated Learning (FL) has emerged as a promising paradigm for decentralized machine learning, where a central server coordinates distributed clients to collaboratively train a global model without direct access to raw data. Despite its advantages, heterogeneous and long-tail data distributions across clients remain a major bottleneck, particularly in IoT scenarios with diverse devices and sensing modalities. To address these challenges, we propose FedSM, a novel framework that integrates multimodal semantic knowledge with balanced pseudo features to enhance global model optimization. Unlike conventional approaches that rely on single-modal information, FedSM leverages CLIP’s cross-modal representations and open-vocabulary priors to guide semantic-aware data augmentation. A probabilistic selection mechanism further refines local features by mixing them with global prototypes, ensuring pseudo features are semantically reliable and reducing bias caused by skewed client distributions. Almost all computations are performed locally at the client side, thereby alleviating server overhead and improving scalability in resource-constrained IoT environments. Extensive experiments on long-tail benchmarks including CIFAR-10-LT, CIFAR-100-LT, and ImageNet-LT demonstrate the superiority of FedSM over state-of-the-art baselines, highlighting its potential for robust communication-efficient FL in IoT networks.

Distributed optimization is pivotal for large-scale signal processing and machine learning, yet communication overhead remains a major bottleneck. Low-rank gradient com-pression, in which the transmitted gradients are approximated by low-rank matrices to reduce communication, offers a promising remedy. Existing methods typically adopt either randomized or greedy compression strategies: randomized approaches project gradients onto randomly chosen subspaces, introducing high variance and degrading empirical performance; greedy methods select the most informative subspaces, achieving strong empirical results but lacking convergence guarantees. To address this gap, we propose GreedyLore-the first Greedy Low-Rank gradient compression algorithm for distributedlearning with rigorous convergence guarantees. GreedyLore incorporates error feedback to correct the bias introduced by greedy compression and introduces a semi-lazy subspace update that ensures the com-pression operator remains contractive throughout all iterations. With these techniques, we prove that GreedyLore achieves a convergence rate of$\mathcal{O}(\sigma/\sqrt{NT}^{-}+1/T)$under standard optimizers such as Adam-marking the first linear speedup convergence rate for low-rank gradient compression. Extensive experiments are conducted to validate our theoretical findings.

Communication remains a central bottleneck in large-scale distributed machine learning, and gradient sparsi-fication has emerged as a promising strategy to alleviate this challenge. However, existing gradient compressors face notable limitations: Rand-K discards structural information and per-forms poorly in practice, while Top-$K$preserves informative entries but loses the contraction property and requires costly All-Gather operations. In this paper, we propose ARC-Top-K, an All-Reduce-Compatible Top-K compressor that aligns sparsity patterns across nodes using a lightweight sketch of the gradient, enabling index-free All-Reduce while preserving globally significant information. ARC-Top-$K$is provably con-tractive and, when combined with momentum error feedback (EF21M), achieves linear speedup and sharper convergence rates than the original EF21M under standard assumptions. Empirically, ARC-Top-K matches the accuracy of Top-K while reducing wall-clock training time by up to 60.7%, offering an efficient and scalable solution that combines the robustness of Rand-K with the strong performance of Top-$K$.

We investigate robust federated learning under ad-versarial settings where a subset of participating devices may behave in a Byzantine manner, submitting arbitrarily corrupted updates to disrupt global model training. To jointly address the critical challenges of device bottlenecks, slow convergence, and Byzantine threats, we propose Byrd-Katyusha-a Byzantine-resilient, memory-efficient federated learning algorithm that integrates Katyusha's momentum-based acceleration, SVRG-style variance reduction, and robust aggregation. Byrd-Katyusha strategically distributes the computational burden between server and workers, achieving fast convergence and robustness while maintaining an 0 (1) memory footprint per device. The algorithm leverages corrected mini-batch gradients and Krum aggregation to mitigate the impact of adversarial updates, while avoiding the per-sample memory overhead and parallelism limitations of prior SAGA-based methods. Extensive experiments on benchmark datasets such as IJCNNI and CIFAR-IO demonstrate that Byrd-Katyusha consistently outperforms existing baselines in terms of convergence speed, accuracy, and robustness to adversarial behavior.

With the extraordinary success of generative artificial intelligence, large pretrained models (LPMs) have been widely used to achieve human-level performance. Despite the one-shot capability, it is always preferred to fine-tune the LPMs for domain-specific downstream tasks. Therefore, the federated learning system is leveraged to fine-tune the large pretrained models enabling concurrrently use multiple distributed clients as well as their local datasets. While the first-order fine-tuning methods suffer from high computational and memory costs due to the backward propagation, we are motivated to propose a federated zeroth-order fine-tuning method with only forward propagation. Moreover, we also leverage differential privacy to further preserve the data privacy of local clients. Experimental results illustrate that our proposed federated zeroth-order method can reduce the memory and retain a similar testing accuracy over the state-of-the-art benchmarks.

Video grounding (VG) task focuses on locating specific moments in a video based on a query, usually in text form. However, traditional VG struggles with some scenarios like streaming video or queries using visual cues. To fill this gap, we present a new task named Online Video Grounding with Hybrid-modal Queries (OVG-HQ), which enables online segment localization using text, images, video segments, and their combinations. This task poses two new challenges: limited context in online settings and modality imbalance during training, where dominant modalities overshadow weaker ones. To address these, we propose OVG-HQ-Unify, a unified framework featuring a Parametric Memory Block (PMB) that retain previously learned knowledge to enhance current decision and a cross-modal distillation strategy that guides the learning of non-dominant modalities. This design enables a single model to effectively handle hybrid-modal queries. Due to the lack of suitable datasets, we construct QVHighlights-Unify, an expanded dataset with multi-modal queries. Besides, since offline metrics overlook prediction timeliness, we adapt them to the online setting, introducing oR@n, IoU=m, and online mean Average Precision (omAP) to evaluate both accuracy and efficiency. Experiments show that our OVG-HQ-Unify outperforms existing models, offering a robust solution for online, hybrid-modal video grounding. Source code and datasets are available at https://github.com/maojiaqi2324/OVG-HQ.

Personalized Federated Learning (PFL) targets client-specific models under heterogeneous and limited data. However, conventional methods often use heuristic or data-size-based averaging and overlook the true contributions of client updates. We propose a contribution-oriented PFL framework that quantifies client contributions via gradient alignment and prediction discrepancy for informed aggregation. We further develop a parameter-wise personalization mechanism for adaptive local updates and a mask-aware momentum optimizer for stable training. Preliminary results on CIFAR10 validate its effectiveness.

To address the growing challenge of energy efficiency in next-generation coordinated multipoint (CoMP) communication systems, this article develops a green CoMP optimization framework that integrates renewable energy harvesting, smart grid interactions, and real-time power control. We formulate a stochastic long-term weighted sum-rate maximization problem, incorporating transmit covariance variables and joint channel-aware precoding. To enable online implementation, we convert the time-averaged problem into an equivalent per-slot formulation and design an online dynamic beamforming and energy management (ODBEM) algorithm. The proposed ODBEM integrates three synergistic mechanisms: 1) dual-driven energy pricing; 2) Lyapunov drift-plus-penalty scheduling; and 3) momentum-based energy smoothing. We further conduct rigorous convexity and Karush–Kuhn–Tucker optimality analysis to ensure algorithmic correctness and convergence. Simulation results demonstrate that ODBEM outperforms baseline strategies in both throughput and energy cost, confirming its effectiveness for sustainable and adaptive CoMP transmission.

Scalable video coding (SVC) and power division multiplexing (PDM) are crucial technologies for video streaming in internet-of-vehicle (IoV) networks due to their robustness over time-varying channels. However, the correlation of video tiers and channel fluctuations induce significant complexity in the joint design of SVC and PDM in IoV networks. To reduce such complexity, we propose a novel transmission strategy for SVC-based video streaming in PDM technology by facilitating cross-layer control across the application, data link, and physical layers. Specifically, we propose a concise framework where the modulation order, the power control, and prioritization of video tiers are jointly controlled. Moreover, we leverage the network abstraction layer units within an arbitrary group-of-pictures to improve the overall video data rate. The formulated joint control of the modulation order, the transmission power, and video-tier prioritization is proved to be a quasi-concave problem. Consequently, our proposed cross-layer optimization based SVC video transmission strategy could efficiently utilize the prioritized video tiers in the application layer, power control in the data-link layer, and modulation orders in the physical layer. Numerical results are used to demonstrate performance improvements over the benchmarks.

Recently, scalable video coding (SVC) has gained significant recognition in mobile video streaming because it can adapt bitstreams to time-varying transmission conditions. However, the coding performance of SVC, which is determined by its coding structure, has not been thoroughly studied. To address this issue, we propose analyzing the redundancy, reduction, distortion, and mutuality of video information within the video coding processes. This analysis facilitates the development of a novel information-theoretical framework for quantifying coding performance, which includes an information theory (IT)-based quantification method and a graphical representation system. The representation system accurately delineates the coding reference structure for encoding each video frame, while the proposed method utilizes mutual information to quantify the achievable coding performance of SVC under the delineated structure. To demonstrate the significance of our research, we apply the proposed framework to encode a basic coding unit, showcasing its effectiveness in improving SVC schemes. Consequently, our framework not only provides an efficient approach for quantifying the coding performance of SVC but also serves as an invaluable tool for optimizing SVC in various applications.

With the rapid development of autonomous driving and edge computing, vehicular edge computing (VEC) has become an emerging paradigm that allows vehicles with abundant computational resources to work as edge nodes. By introducing vehicles as infrastructures, VEC has the potential to improve users’ quality of experience and decrease operator’s deployment expenditure, especially for hot spots. In this article, a novel VEC-based resource reservation framework is designed to handle the time-varying computation requests. To articulate realistic scenarios, the online durations of provider vehicles (PVs) are assumed to be different. Besides, the PVs will not always be online to wait for the reservation assignment for the limited revenue, i.e., the PVs are dynamic and the computational resource reservation points (CRRPs) are static. In this way, dynamic matching is leveraged to model the interaction between the PVs and CRRPs. To prevent the CRRPs from manipulating their preferences for better partners, a strategy-proof and stable resource reservation algorithm is proposed to ensure all CRRPs are truthful during the resource reservation procedure. Finally, numerical simulation results are presented to validate the proofness, truthfulness, and performance of our proposed resource reservation algorithm.

One of the most important features of next-generation communication is to incorporate intelligence towards semantic communication, where highly condensed semantic information considering both source and channel features will be extracted and transmitted. The recent popular large models such as GPT4 and the boosting learning techniques are envisioned to accelerate its practical implementation in the near future. Given the characteristics of “training once and widely use” of those multimodal large language models, we argue that a pay-as-you-go service mode will be suitable in this context, referred to as Large Model as a Service (LMaaS). However, the trading and pricing problem is quite complex with heterogeneous and dynamic customer environments, making the pricing optimization problem challenging in seeking on-hand solutions. In this paper, we optimize the profit of both the seller and customers. We formulate the LMaaS market trading as a Stackelberg game with two steps. In the first step, we optimize the seller's pricing decision and propose an Iterative Model Pricing (IMP) algorithm that optimizes the prices of large models iteratively by reasoning customers’ future rental decisions, which achieves a near-optimal pricing solution. In the second step, we optimize customers’ selection decisions by designing a robust selecting and renting (RSR) algorithm, which is guaranteed to be optimal with rigorous theoretical proof. Extensive experiments confirm the effectiveness and robustness of our algorithms, outperforming the state-of-the-art solution by 43.96% in profit at the customer side and achieving the near-optimal profit at the seller side.

Foundation models (FMs) have shown great success in natural language processing, computer vision, and multimodal tasks. FMs have a large number of model parameters, thus requiring a substantial amount of data to help optimize the model during the training. Federated learning has revolutionized machine learning by enabling collaborative learning from decentralized data while still preserving clients’ data privacy. Despite the great benefits foundation models can have empowered by federated learning, their bulky model parameters cause severe communication challenges for modern networks and computation challenges especially for edge devices. Moreover, the data distribution of different clients can be different thus inducing statistical challenges. In this paper, we propose a novel two-stage federated learning algorithm called FedFMSL. A global expert is trained in the first stage and a local expert is trained in the second stage to provide better personalization. We construct a Mixture of Foundation Models (MoFM) with these two experts and design a gate neural network with an inserted gate adapter that joins the aggregation every communication round in the second stage. To further adapt to edge computing scenarios with limited computational resources, we design a novel Sparsely Activated LoRA (SAL) algorithm that freezes the pre-trained foundation model parameters inserts low-rank adaptation matrices into transformer blocks, and activates them progressively during the training. We employ extensive experiments to verify the effectiveness of FedFMSL, results show that FedFMSL outperforms other SOTA baselines by up to 59.19% in default settings while tuning less than 0.3% parameters of the foundation model.

A wireless federated learning system is investigated by allowing a server and multiple workers to exchange uncoded information via orthogonal wireless channels. Since the workers frequently upload local gradients to the server via band-limited channels, the uplink transmission from the workers to the server becomes a communication bottleneck. Therefore, a one-shot distributed principle component analysis (PCA) is leveraged to reduce the dimension of uploaded gradients to relieve the communication bottleneck. A PCA-based wireless federated learning (PCA-WFL) algorithm and its accelerated version (i.e., PCA-AWFL) are proposed based on the low-dimensional gradients and the Nesterov’s momentum. For the non-convex empirical risk, a finite-time analysis is performed to quantify the impacts of system hyper-parameters on the convergence of the PCA-WFL and PCA-AWFL algorithms. The PCA-AWFL algorithm is theoretically certified to converge faster than the PCA-WFL algorithm. Besides, the convergence rates of PCA-WFL and PCA-AWFL algorithms quantitatively reveal the linear speedup with respect to the number of workers over the vanilla gradient descent algorithm. Numerical results are used to demonstrate the improved convergence rates of the proposed PCA-WFL and PCA-AWFL algorithms over the benchmarks.

Tile-based streaming and super resolution are two representative technologies adopted to improve bandwidth efficiency of immersive video steaming. The former allows selective download of contents in the user viewport by splitting the video into multiple independently decodable tiles. The latter leverages client-side computation to reconstruct the received video into higher quality using advanced neural network models. In this work, we propose CASE, a collaborated adaptive streaming and enhancement framework for mobile immersive videos, which integrates super resolution with tile-based streaming to optimize user experience with dynamic bandwidth and limited computing capability. To coordinate the video transmission and reconstruction in CASE, we identify and address several key design issues including unified video quality assessment, computation complexity model for super resolution, and buffer analysis considering the interplay between transmission and reconstruction. We further formulate the quality-of-experience (QoE) maximization problem for mobile immersive video streaming and propose a rate adaptation algorithm to make the best decisions for download and for reconstruction based on the Lyapunov optimization theory. Extensive evaluation results validate the superiority of our proposed approach, which presents stable performance with considerable QoE improvement, while enabling trade-off between playback smoothness and video quality.

Wireless systems are upgraded to use green energy (e.g., solar, wind, and tide energy) such that the greenhouse gas emission can be neutralized. This work incorporates the on-grid energy into a green coordinated multi-point (CoMP) system to handle the volatile arrival of green energy. In the green CoMP, the long-term weighted throughput maximization problem is investigated by expecting a non-positive consumption of the long-term on-grid energy. Motivated by the capacity-achieving property and simple implementation, an online zero-forcing dirty paper precoder is proposed to update the precoding matrices by combining statistical learning with the Lyapunov learning. A tradeoff relation is theoretically established to show that the long-term weighted throughput approaches the $\mathcal{O}(V)$ -neighbor of optimal value while the long-term consumed on-grid energy increases at a rate of $\mathcal{O}\left( {{{\log }^2}(V)/\sqrt V } \right)$, where V is an introduced control parameter. Numerical results are used to verify the performance of the online zero-forcing dirty paper precoder.

Industrial Internet of Things (IIoT) systems connect a plethora of smart devices, such as sensors, actuators, and controllers, to enable efficient industrial productions in manners observable and controllable by human beings. Plain model-based and data-driven diagnosis approaches can be used for fault detection and isolation of specific IIoT components. However, the physical models, signal patterns, and machine learning algorithms need to be carefully designed to describe system faults. Besides, the ever-increasing level of connectivity among devices can induce exponential complexity. Knowledge-based fault diagnosis approaches improve interoperability via ontologies so that high-level reasoning and inquiry response can be provided to nonexpert users. Therefore, knowledge-based fault diagnosis approaches are preferred over plain model-based and data-driven diagnosis approaches in recent IIoT systems. In the context of IIoT systems, this work reviews the recent progress on the construction of knowledge bases via ontologies and deductive/inductive reasoning for knowledge-based fault diagnosis. Besides, general inductive reasoning methods are discussed to shed light on their successful applications in knowledge-based fault diagnosis for IIoT systems. Following the trend of large-system decentralization, future fault diagnosis also requires decentralized implementations. Therefore, we conclude this survey by discussing several interesting open problems for decentralized knowledge-based fault diagnosis for IIoT systems.

Following the roadmap of carbon neutrality, wireless communication systems are upgrading to use green energy that comes from renewable sources, e.g., sun, tide, and wind. Due to the volatile arrival of green energy, the on-grid energy is used as a backup for a green coordinated multiple point system. In this work, a weighted sum-rate maximization problem in thegreencoordinated multiple point system is investigated by expecting non-positive consumption of the on-grid energy in the long term. Motivated by the capacity-achieving property and simple implementation, an online zero-forcing dirty paper precoder is proposed to update the precoding matrices by combining statistical learning with the Lyapunov learning technique. A tradeoff relation is theoretically established to show that the long-term weighted sum rate approaches the${\mathcal{ O}}(V)$-neighbor of optimal value while the long-term on-grid energy increases at a rate of${\mathcal{ O}}({\scriptstyle {}^{\scriptstyle \log ^{2}(V)}}\hspace {-0.224em}/\hspace {-0.112em}{\scriptstyle \sqrt {V}})$, where$V$is an introduced control parameter. Numerical results are used to verify the performance of the proposed online adaptive precoder.

User scheduling, beamforming and energy coordination are investigated in smart-grid powered cellular networks (SGPCNs), where the base stations are powered by a smart grid and natural renewable energy sources. Heterogeneous energy coordination is considered in SGPCNs, namely energy merchandizing with the smart grid and energy exchanging among the base stations. A long-term grid-energy expenditure minimization problem with proportional-rate constraints is formulated for SGPCNs. Since user scheduling is coupled with the beamforming vectors, the formulated problem is challenging to handle via standard convex optimization methods. In practice, the beamforming vectors need to be updated over each slot according to the channel variations. User scheduling needs to be updated over several slots (frame) since the frequent scheduling of user equipment can cause reliability issues. Therefore, the Lyapunov optimization method is used to decouple the problem. A practical two-scale algorithm is proposed to schedule users at each frame, and obtain the beamforming vectors and amount of exchanged natural renewable energy at each slot. We prove that the proposed two-scale algorithm can asymptotically achieve the optimal solutions via tuning a control parameter. Numerical results verify the performance of the proposed two-scale algorithm.

In the small-cell networks with multiple-input-single-output broadcasting (MISO-BC) backhauls, the joint dirty-paper coding and power control are investigated for the {MISO-BC} backhauls and access links in order to minimize the system transmit power. Considering the proportional rates of MISO-BC backhauls and flow-conservation constraints, the formulated optimization problem is {non-convex}. Moreover, the formulated problem couples the precoding vectors with the power-control variables. In order to handle the {non-convex} optimization problem and decouple the backhaul and access links, the structure of the formulated problem is investigated such that the optimal precoding vectors and optimal power-control variables are independently obtained. Moreover, the optimal precoding vectors are obtained in closed-form expressions. Simulation results are used to show the performance improvement over the benchmark scheme.

In the small-cell networks (SCNs) with multiple small-cell base stations (ScBSs), the joint design of beamforming vectors, user scheduling and ScBS sleeping is investigated with the constraints on proportional rate. A long-term grid-energy expenditure minimization problem is formulated for the considered SCNs, which are powered by the smart grid and natural renewable energy. Since the scheduled user indicators are coupled with the beamforming vectors, the formulated problem is challenging to handle. In order to decouple the beamforming vectors from the scheduled user indicators, the Lyapunov optimization technique is used. As a result, a practical two-scale algorithm is proposed to allocate the user scheduling indicators and ScBS sleeping variables at the coarse-grained granularity (frame) as well as obtain the beamforming vectors at the fine-grained granularity (slot). Numerical results are used to verify the performance of the proposed two-scale algorithm.

The joint design of beamforming vector and artificial noise covariance matrix is investigated for the multiple-input-single-output-multiple-eavesdropper simultaneous wireless information and power transferring (MISOME-SWIPT) systems. In the MISOME-SWIPT system, the base station delivers information signals to the legitimate user equipment and broadcasts jamming signals to the eavesdroppers. A secrecy energy efficiency (SEE) maximization problem is formulated for the considered MISOME-SWIPT system with imperfect channel state information, where the SEE is defined as the ratio of sum secrecy rate over total power consumption. Since the formulated SEE maximization problem is non-convex, it is first recast into a series of convex problems in order to obtain the optimal solution with a reasonable computational complexity. Two suboptimal solutions are also proposed based on the heuristic beamforming techniques that trade performance for computational complexity. In addition, the analysis of computational complexity is performed for the optimal and suboptimal solutions. Numerical results are used to verify the performance of proposed algorithms and to reveal practical insights.

The joint design of beamforming vector and artificial noise covariance matrix is investigated for multiple-input-single-output-multiple-eavesdropper simultaneous wireless information and power transferring (MISOME-SWIPT) systems. A secrecy energy efficiency (SEE) minimization problem is formulated in the MISOME-SWIPT system with imperfect channel state information and proportional secrecy rate constraints. Since the formulated SEE minimization problem is non-convex, it is first recast into a series of convex problems in order to obtain the optimal solution with a reasonable computational complexity. Numerical results are used to verify the performance of the proposed algorithm and to reveal practical insights.

Security is a critical issue in full duplex (FD) communication systems due to the broadcast nature of wireless channels. In this paper, joint design of information and artificial noise beamforming vectors is proposed for the FD simultaneous wireless information and power transferring (FD-SWIPT) systems with loopback self-interference cancellation. To guarantee high security and energy harvesting performance of the FD-SWIPT system, the proposed design is formulated as a secrecy rate maximization problem under energy transfer rate constraints. Although the secrecy rate maximization problem is non-convex, we solve it via semidefinite relaxation and a two-dimensional search. We prove the optimality of our proposed algorithm and demonstrate its performance via simulations.

The joint design of beamforming and artificial noise vectors is proposed for the full-duplex simultaneous wireless information and power transferring (FD-SWIPT) systems when multiple eavesdroppers can wiretap information from FD base station (FD-BST) and FD user equipment (FD-UE). To guarantee high security and energy harvesting performance of the FD-SWIPT system, the proposed design is formulated as a sum-information-transmission-rate-maximization (SITRM) problem under information-leakage and energy constraints. Besides, we consider the fairness issue between uplink and downlink information-transmission rates by formulating a fairness-aware-SITRM (FA-SITRM) problem. Since the eavesdroppers can receive the information of FD-BST and FD-UE, the information-leakage region becomes non-convex and challenging to handle. Hence, we propose efficient algorithms to solve the SITRM and FA-SITRM problems optimally via semidefinite relaxation (SDR) and a 1-D search. In each search iteration, our proposed algorithms can recover the rank-one constraints when the application of SDR cannot obtain the optimal solutions. Moreover, we analyze the computational complexities and propose two suboptimal solutions to the formulated problems. For the SITRM problem, our numerical results show that the performance achieved by one of two suboptimal algorithms is close to the performance of optimal algorithm with increasing the maximum transmission power of FD-BST.

We investigate the precoding, remote radio head (RRH) selection and signal splitting in the simultaneous wireless information and power transferring (SWIPT) cloud radio access networks (C-RANs). The objective is to minimize the power consumption of the SWIPT C-RAN. Different from the existing literature, we consider the nonlinear fronthaul power consumption and the multiple antenna RRHs. By switching off the unnecessary RRHs, the group sparsity of the precoding coefficients is introduced, which indicates that the precoding process and the RRH selection are coupled. In order to overcome these issues, a group sparse precoding and signal splitting algorithm is proposed based on the majorization-minimization framework, and the convergence behavior is established. Numerical results are used to verify our proposed studies.

The application of renewable energy is a promising solution for realizinggreen communications. However, if the cellular systems are solely powered by the renewable energy, the weather dependence of the renewable energy arrival makes the systems unstable. On the other hand, the proliferation of the smart grid facilitates the loads with two-way energy trading capability. Hence, a hybrid powered cellular system, which combines the smart grid with the base stations, can reduce the grid energy expenditure and improve the utilization efficiency of the renewable energy. In this paper, the long-term grid energy expenditure minimization problem is formulated as a stochastic optimization model. By leveraging the stochastic optimization theory, we reformulate the stochastic optimization problem as a per-frame grid energy plus weighted penalized packet rate minimization problem, which is NP-hard. As a result, two suboptimal algorithms, which jointly consider the effects of the channel quality and the packet reception failure, are proposed based on the successive approximation beamforming (SABF) technique and the zero-forcing beamforming (ZFBF) technique. The convergence properties of the proposed suboptimal algorithms are established, and the corresponding computational complexities are analyzed. Simulation results show that the proposed SABF algorithm outperforms the ZFBF algorithm in both grid energy expenditure and packet delay. By tuning a control parameter, the grid energy expenditure can be traded for the packet delay under the proposed stochastic optimization model.

In this paper, we consider an orthogonal frequency division multiple access based full duplex distributed antenna system (FDDAS) with energy recycling capability and develop an energy efficient resource allocation scheme for such system. In particular, in order to optimize the energy efficiency of FDDAS, we formulate the problem of joint subchannel allocation and power control as a mixed integer nonlinear programming problem. Since the formulated optimization problem is a NP-hard problem whose computation complexity will rise exponentially with larger network scale, we develop a low complexity subchannel allocation and power control algorithm. The convergence property of the proposed algorithm is established. Simulation results show that the proposed algorithm for FDDAS results in a higher system energy efficiency than the existing algorithm for distributed antenna systems without energy recycling capability.

In this paper, we investigate the interference mitigation in femtocell networks, where femtocell access points (FAPs) are allowed to cooperate as different cooperative groups to allocate resources. We model the femtocell cooperation characteristics as a coalition formation game in partition form with non-transferable utility. Furthermore, a distributed coalition formation algorithm is proposed to enable each FAP to decide to depart from or join in a coalition independently, moreover, we devise a low complex iterative algorithm to optimize the allocation of each coalition's multi-dimensional resources for maximizing its FAPs' payoffs. By applying our proposed coalition formation scheme, a Nash stable FAP partition is formed and FAPs in each coalition can effectively exploit the cooperative gain to mitigate the interference and maximize the sum rate. Numerical results are provided to corroborate our proposed studies.