Federated split learning (FSL) has rapidly emerged as a promising paradigm for enabling ubiquitous intelligence in next-generation networks. However, current FSL approaches incur significant communication overhead and diminished training efficiency due to the frequent transmission of high-dimensional smashed data and gradients between devices and the base station. To address these issues, we propose a low-rank approximation (LoRA)-based FSL scheme, referred to as low-rank FSL. We analyze the convergence performance of low-rank FSL by considering the influence of LoRA rank on non-convex loss functions. To minimize a weighted sum of overall training latency and energy consumption in resource-constrained wireless networks, we formulate a long-term optimization problem by jointly optimizing computing frequency, power allocation, decoding order, LoRA rank, and split layer selection. An iterative optimization algorithm is then developed to solve this problem with low computational complexity. Numerical results demonstrate that our low-rank FSL reduces communication overhead by at least 300% while maintaining high learning performance. Moreover, our optimization algorithm achieves a low weighted cost in terms of training latency and energy consumption.

This paper studies the problem of long-term, high-quality data collection in dynamic industrial environments using autonomous mobile robots (AMRs). We formulate a max–min data-rate optimization problem that jointly designs AMR trajectories, receive beamforming, sensor association, and decoding policy under mobility, communication, and battery constraints. The problem is challenging because motion decisions directly influence link quality, which affects decoding and beamforming efficiency, while all of these factors are tightly coupled with battery dynamics, resulting in a mixed-integer, time-varying, and non-convex design. To handle this strong coupling and the need for scalable long-horizon decision-making, we propose a semi-distributed reinforcement learning framework that combines cloud-level global coordination with fog-level local adaptability. In this framework, a cloud-layer deep Q-network determines AMR-sensor associations, and a fog-layer federated actor-critic algorithm jointly designs continuous trajectories and beamforming. A Fubini–Study distance-assisted k-means clustering method enhances multi-antenna directional gains, and next-generation multiple access (NGMA) improves spectral efficiency and interference suppression. Theoretical analysis confirms the convergence and computational efficiency of the proposed algorithm. Simulations show that the proposed approach achieves faster and more stable convergence, sustains large-scale coverage through periodic recharging, and significantly improves the minimum data rate compared with orthogonal multiple access-based baselines.

In this paper, we propose a hybrid learning framework that combines federated and split learning, termed semi-federated learning (SemiFL), in which over-the-air computation is utilized for gradient aggregation. A key idea is to strategically adjust the learning rate by manipulating over-the-air distortion for improving SemiFL’s convergence. Specifically, we intentionally amplify amplitude distortion to increase the learning rate in the non-stable region, thereby accelerating convergence and reducing communication energy consumption. In the stable region, we suppress noise perturbation to maintain a small learning rate for improving SemiFL’s final convergence. Theoretical results demonstrate the antagonistic effects of over-the-air distortion in different regions, under both independent and identically distributed (IID) and non-IID data settings. Then, we formulate two energy consumption minimization problems, one for each region, which implements a two-region mean square error threshold configuration scheme. Accordingly, we propose two resource allocation algorithms with closed-form solutions. Simulation results show that under different network and data distribution conditions, strategically manipulating over-the-air distortion can efficiently adjust the learning rate to improve SemiFL’s convergence. Moreover, energy consumption can be reduced by using the proposed algorithms.

To meet the high-precision positioning demands of Internet of Things (IoT) applications, carrier phase positioning in cellular networks is promising. However, dynamic clock offsets among base stations (BSs) and user equipments (UEs) challenge the accuracy of range-based positioning systems. To achieve centimeter-level positioning in time-varying asynchronous cellular networks, we propose a collaborative framework leveraging the coupled ranging information between UEs and BSs obtained from double-differenced (DD) Time of Arrival (ToA) and Carrier Phase of Arrival (CPoA) measurements. Coarse UE position estimates are first obtained iteratively from DD ToA, then refined via DD CPoA-based integer ambiguity resolution for joint high-accuracy estimation across time. By leveraging coupled measurements and time-invariant integer ambiguities, the method can estimate the UE positions without a dedicated reference node to eliminate clock offsets. Simulations show centimeter-level accuracy in dynamic asynchronous scenarios.

To facilitate fast data aggregation in Internet of Things, over-the-air computation (AirComp) is a communication-efficient enabler by virtue of its high spectrum efficiency and low transmission latency. However, traditional AirComp faces challenges such as signal misalignment, imperfect channel state information (CSI), and noisy fading channels. In this paper, we employ a multi-functional reconfigurable intelligent surface (MF-RIS) to alleviate mean square error (MSE) of AirComp through a joint design of transceiver beamforming and MF-RIS coefficients, but it necessitates solving a mixed-integer nonlinear programming problem. To tackle this issue, we propose an alternating optimization algorithm based on the semidefinite relaxation approach and difference-of-convex programming. Numerical results underscore the performance gains achieved by the proposed algorithm, as well as the remarkable proficiency of the MF-RIS in suppressing MSE under imperfect CSI.

Space-air-ground integrated networks have emerged as a promising paradigm to meet the increasing demands of next-generation wireless communication systems. However, the direct ground-to-low Earth orbit (LEO) satellite communication may be inefficient for latency-sensitive or bandwidth-intensive applications, particularly in remote or complex-terrain regions. Motivated by this challenge, we introduce the unmanned aerial vehicle (UAV) as dual-function relay nodes within the aerial intermediate layer. The UAV receives communication data from ground users and sensing data from targets, relaying it to LEO satellites. To handle fluctuating link capacities caused by satellite mobility, the UAV caches data during low LEO-UAV connectivity periods for later offloading. We formulate a completion time minimization problem, jointly optimizing UAV trajectory, bandwidth allocation, and scheduling under cache, bandwidth, and mobility constraints. Due to the dynamic environmental conditions and satellite link variability, we develop a deep reinforcement learning (DRL)-based frame-work to efficiently control the dual-function UAV. Simulations show our approach achieves higher rewards than DRL baselines and reduces completion time compared to predefined trajectory baselines.

Autonomous mobile robot (AMR) systems in indoor environments face challenges in jointly optimizing visual navigation and communication efficiency. Existing methods often decouple path planning from communication allocation or rely on simplified 2D models, limiting adaptability to 3D environments.. To address these limitations, we introduce the non-orthogonal multiple access technology to enhance spectral efficiency and ensure real-time control during AMR movement. We formulate a joint optimization problem to maximize the long-term average data rate via coordinated planning of AMR trajectories, transmit power, and precoding matrix. To handle wireless fading and localization inaccuracy, we propose a vision-enhanced federated deep reinforcement learning framework, where AMRs independently learn policies while engaging in privacy-preserving model aggregation. Leveraging RGB-D visual features, object detection, and egocentric pose estimation, our approach enables robust decision-making in 3D environments under imprecise localization. Extensive experiments on the RoboThor simulator demonstrate that our proposed framework accelerates training convergence, improves navigation and communication efficiency, and outperforms baselines in reward accumulation.

The non-independent and identically distributed (non-IID) data distribution in federated learning (FL) poses significant challenges to the training performance of FL models. To tackle this problem, we propose an novel framework called Federated Learning with Manifold Mixup of Prototype (FedMP) which takes advantage of both prototype learning and manifold mixup. Prototype learning ensures the effectiveness of the training results but also, in conjunction with manifold mixup, further enhances the generalization ability of the FL model. We also design a mixup matrix to regulate the training process and propose a strategy to redistribute the matrix weights. Additionally, to enhance the reliability of prototypes in the latent space, we introduce a conditional invertible generative network (cINN) to reconstruct the obtained prototypes. These reconstructed prototypes, in turn, participates in the training process to improve the model performance. We conduct experiments on two real-word datasets for the validation of the proposed FedMP framework. Experimental results show that FedMP outperforms other baselines on both training performance and communication costs.

By utilizing the large amount of unlabeled data among distributed clients, federated semi-supervised learning (FSSL) has become a new research topic. However, the knowledge discrepancies among diverse clients and non-independent and identically distributed (Non-IID) data pose significant challenges to the model performance and generalization in FSSL. To tackle these challenges, we propose a FSSL framework with the dynamic aggregation and prototype retraining method (FedDAPR). FedDAPR improves the performance of the global model by developing a dynamic aggregation scheme. Moreover, to enhance the degraded training performance caused by the Non-IID data, we propose a prototype-based retraining mechanism for the classifier of the global model. Experimental results on two benchmark datasets demonstrate the effectiveness of the proposed FedDAPR.

In a Flying Ad-hoc Network (FANET), the resilience of a network is defined as the ability to rebound from a threat event. The bi-connectivity of topology which can keep the network connected after a single unmanned aerial vehicle (UAV) fails, is an important feature of resilience. Due to the high dynamic and the limited node energy of FANET, it is difficult to generate and maintain the bi-connectivity. In this paper, in order to enhance network resilience, we proposed a distributed energy-efficient bi-connectivity generation and restoration mechanism based on double reference nodes (DEBGR-DRN) through adjusting UAV's transmission power. Our algorithm can save the transmission power and communication cost by selecting the shortest edge to be added and limiting the path length. NS3 network simulations demonstrate the validity of the proposed algorithm.

In scenarios where high-precision localization is required, even tiny calibration errors of the base station (BS) can directly and significantly impact the positioning accuracy of the device. Inspired by the excellent precision of carrier phase positioning, this paper proposes a high-precision positioning algorithm based on carrier phase and unscented Kalman filter (UKF) to address BS calibration errors. The state vector of the positioning system is initialized with the initial coordinates of the terminal, which is estimated via Chan's algorithm. The coordinates of the BSs and the terminal are then iteratively refined by combining the double-differential carrier phase and time difference of arrival (TDoA) measurements of multiple moments. Numerical simulations demonstrate that the proposed algorithm achieves centimeter-level positioning accuracy even in the presence of significant BS calibration errors, which effectively overcomes the impact of BS calibration errors on positioning performance.

In existing deep learning-based semantic communication systems, centralized training of semantic models brings a risk of privacy leakage, whereas distributed training imposes a huge computational burden on user equipments (UEs). To address these challenges, we propose a hybrid federated learning (Hybrid-FL) framework to alleviate the computational burden on UEs while protecting the user privacy. Specifically, each UE uploads local gradients and semantic symbols to the base station for the collaborative training of global and local semantic models. Furthermore, we propose a joint communication and computation scheme for supporting the model aggregation and semantics transmission. To gain deep insights, we expose the joint impact of communication and computation on the convergence behavior of Hybrid-FL by deriving an upper bound. Then, we formulate a mixed-integer nonlinear programming problem to improve the convergence performance of Hybrid-FL, which is then effectively solved by using our proposed algorithm that developed based on alternating and matching theory. Experimental results demonstrate that Hybrid-FL outperforms conventional FL by achieving a 20% accuracy gain and a 80% latency reduction.

Over-the-air computation has emerged as a high-spectrum efficient and low-latency solution for model aggregation in federated learning (FL) by leveraging the superposition property of wireless channels. However, traditional over-the-air FL (AirFL) faces challenges such as signal misalignment, imperfect channel state information (CSI), and noisy fading channels. To enable more efficient and reliable AirFL in Internet of Things (IoT), we employ a multifunctional reconfigurable intelligent surface (MF-RIS) to alleviate mean square error (MSE) of AirFL model aggregation. By deriving the convergence analysis of AirFL in both convex and nonconvex settings, we unveil the impact of MSE on MF-RIS-aided AirFL under varying conditions. Based on the theoretical insights, we aim to minimize the MSE through a joint design of transceiver beamforming and MF-RIS coefficients, but it necessitates solving a mixed-integer nonlinear programming (MINLP) problem. To solve it efficiently, we propose an alternating optimization (AO) algorithm based on the semidefinite relaxation (SDR) approach and difference-of-convex (DC) programming. The efficacy of our approach is corroborated by numerical results, which underscore the performance gains achieved by the proposed algorithm. Additionally, the MF-RIS demonstrates remarkable proficiency in suppressing MSE and bolstering AirFL performance, even under conditions of imperfect CSI.

Distributed over-the-air computation (AirComp) is a promising technology for fast data aggregation in wireless networks by leveraging multiple access channel to achieve communication and computation simultaneously. However, device-to-device (D2D) links applied are vulnerable to obstacles, and the performance of distributed AirComp is restricted by the device with the worst channel condition. To tackle these issues, we introduce a multi-functional reconfigurable intelligent surface (MF-RIS) to reconstruct the wireless propagation environment, where the MF-RIS can achieve signal reflection, refraction, and amplification simultaneously. Specifically, we propose an MF-RIS-aided distributed AirComp framework, where MF-RIS receives the aggregated data from all devices and then transmits it to each device for post-processing. We formulate a mean-squared error (MSE) minimization problem by jointly optimizing transmit scalar, receive scalar, and MF-RIS coefficients. To address this non-convex problem, we employ an alternating optimization (AO) technique to decompose it into three subproblems, where semi-closed form or closed form solutions are obtained. Then, we extend the single-input single-output (SISO) system into multiple-input multiple-output (MIMO) one. Next, we derive the asymptotic MSE performance for SISO and MIMO cases when the number of RIS elements and that of transmit/receive antennas are very large. Numerical results demonstrate the superiority of MF-RIS in improving MSE performance compared to the baseline without RIS. Additionally, the MF-RIS outperforms its passive counterparts, which reveals the advantages of deploying MF-RIS in distributed AirComp systems to reduce data aggregation error.

The robust beamforming design in multi-functional reconfigurable intelligent surface (MF-RIS) assisted wireless networks is investigated in this work, where the MF-RIS supports signal reflection, refraction, and amplification to address the double-fading attenuation and half-space coverage issues faced by traditional RISs. Specifically, we aim to maximize the system energy efficiency by jointly optimizing the transmit beamforming vector and MF-RIS coefficients in the case of imperfect channel state information (CSI). We first leverage the S-procedure and Bernstein-Type Inequality approaches to transform the formulated problem into tractable forms in the bounded and statistical CSI error cases, respectively. Then, we optimize the MF-RIS coefficients and the transmit beamforming vector alternately by adopting an alternating optimization framework, under the quality of service constraint for the bounded CSI error model and the rate outage probability constraint for the statistical CSI error model. Simulation results demonstrate the significant performance improvement of MF-RIS compared to benchmark schemes. In addition, it is revealed that the cumulative CSI error caused by increasing the number of RIS elements is larger than that caused by increasing the number of transmit antennas.

The rapid evolution of edge computing and artificial intelligence (AI) paves the way for pervasive intelligence in the next-generation network. As a hybrid training paradigm, federated split learning (FSL) leverages data and model parallelism to enhance training efficiency. However, existing FSL encounters unacceptable waiting latency due to device heterogeneity and synchronous model aggregation. To address this issue, we propose a semi-asynchronous FSL (SAFSL) framework that enables personalized model splitting and aperiodic model aggregation. We derive the convergence upper bound by considering factors such as the number of devices, training iterations, and data heterogeneity. To minimize the long-term average training latency while maintaining high energy efficiency in resource-constrained wireless networks, we formulate a stochastic mixed-integer nonlinear programming problem. By decomposing it into multiple sub-problems in each round, we propose a Lyapunov-based alternating optimization algorithm to solve it in an online manner. Numerical results demonstrate that our SAFSL achieves faster convergence with reduced communication overhead while maintaining high prediction performance under non-independent and identically distributed data, outperforming state-of-the-art benchmarks. Moreover, our algorithm achieves a low training latency, highlighting its superior performance and effectiveness.

This paper presents a novel user-centric sensing framework, where a user equipment (UE) acts as the receiver of a multi-static radar sensing system and utilizes the communication signals emitted by base stations (BSs) and scattered by the targets for joint UE and target tracking. Specifically, we propose to locate the UE using the least squares (LS) estimator with a one-dimensional (1D) search and then develop a two-dimensional (2D) target identification approach using the estimated target location and motion of each path based on mean-shift clustering. After the motion parameters of the UE and targets are estimated, a joint UE and target tracking algorithm is designed based on the analysis of the localization and motion estimation errors. Extensive simulations corroborate the ability of our approach to estimate target parameters and cluster and identify targets. Specifically, the average estimation error of the target number is only 0.18. The speed and heading accuracy of the UE and targets is [0.121 m/s, 3.879°] and [0.199 m/s, 4.669°], respectively. In joint UE and target tracking, our scheme outperforms the benchmarks of the extended Kalman filter (EKF) and belief propagation (BP) by at least 33.16% and 10.29%, respectively, even though the EKF and BP require a-priori knowledge of the motion parameters and target identification.

The emergence of large language models (LLMs) with multi-task generalization capabilities is expected to improve the performance of artificial intelligence (AI)-as-a-service provision in 6G networks. By fine-tuning LLMs, AI services can become more precise and tailored to the demands of different downstream tasks. However, centralized fine-tuning paradigms pose a potential risk to user privacy, and existing distributed fine-tuning methods incur significant wireless transmission burdens due to the large-scale parameter transmission of LLMs. To tackle these challenges, by leveraging the low rank feature in LLM fine-tuning, we propose a wireless over-the-air federated learning (AirFL) based low-rank adaptation (LoRA) framework that integrates LoRA and over-the-air computation (AirComp) to achieve efficient fine-tuning and aggregation. Based on multiple-input multiple-output (MIMO) and orthogonal frequency division multiplexing (OFDM), we design a multi-stream AirComp scheme to fulfill the aggregation requirement of AirFL-LoRA. Furthermore, by deriving an optimality gap, we gain theoretical insights into the joint impact of rank selection and gradient aggregation distortion on the fine-tuning performance of AirFL-LoRA. Next, we formulate a non-convex problem to minimize the optimality gap, which is solved by the proposed backtracking-based alternating algorithm and the manifold optimization algorithm iteratively. Through fine-tuning LLMs for different downstream tasks, experimental results reveal that the AirFL-LoRA framework outperforms the state-of-the-art baselines on both training loss and perplexity, closely approximating the performance of FL with ideal aggregation.

As the demand for positioning accuracy reaches the centimeter level, Carrier Phase Positioning (CPP) technology has received widespread attention in 5G New Radio (NR) positioning scenarios due to its high ranging accuracy. In this paper, we presents a new single-shot CPP method suitable for 5G NR cellular networks. By performing a search in the coordinate neighborhood space of the Time Difference of Arrival (TDOA) positioning results, the proposed method bypasses the need for resolving the integer ambiguity required for CPP. It also includes a wrapping effect detection and elimination for the carrier phase measurements. The proposed method finally get the positioning result by calculating cost function which considers both TDOA and CPP results. Numerical simulations demonstrate superior performance compared to other positioning solutions.

To enhance the robustness and convergence of hierarchical federated learning (HFL) in wireless networks with imperfect channel state information (CSI), a quantized HFL (QHFL) framework is proposed. Considering the local training and communication latency, the outage probability of quantized gradient transmission is modeled under imperfect CSI. Then, the convergence of the proposed QHFL with transmission outage and gradient quantization is analyzed. Simulation results demonstrate the correlation between quantization accuracy and transmission outage, along with their joint impact on the HFL convergence, which align with the insight behind the convergence analysis.

To facilitate edge intelligence in Internet of Robotic Things (IoRT), over-the-air federated learning (AirFL) is a communication-efficient enabler by virtue of its high spectrum efficiency and low transmission latency. Supported by multi-functional reconfigurable intelligent surface (MF-RIS), the model aggregation process of AirFL can be facilitated thanks to full-space signal amplification. However, the learning performance of AirFL may be degraded by the uncertainty of wireless channels originated from inaccurate channel estimation and robot mobility. In this paper, we investigate the model aggregation problem of MF-RIS-aided AirFL in a dynamic IoRT system with imperfect channel state information (CSI). We aim to minimize the long-term mean square error (MSE) of AirFL model aggregation by jointly optimizing MF-RIS coefficients and transceiver beamforming. To enable online decision-making in the dynamic system, we propose a novel deep reinforcement learning (DRL)-based double-agent algorithm, where one agent first decides operating modes of MF-RIS elements, and then the other agent devises transceiver beamforming and other MF-RIS coefficients referring to the mode switching strategy. Numerical results unveil the effectiveness and robustness of the proposed DRL-based algorithm in suppressing long-term MSE under hostile CSI.

In the field of few-shot learning (FSL), extensive research has focused on improving network structures and training strategies. However, the role of data processing modules has not been fully explored. Therefore, in this paper, we propose Meta-DM, a generalized data processing module for FSL problems based on diffusion models. Meta-DM is a simple yet effective module that can be easily integrated with existing FSL methods, leading to significant performance improvements in both supervised and unsupervised settings. We provide a theoretical analysis of Meta-DM and evaluate its performance on several algorithms. Our experiments show that combining Meta-DM with certain methods achieves state-of-the-art results.

Mars images are important scientific data in Mars exploration, whose study can contributes to the understanding of Mars’ geological features, environmental conditions, and meteorological phenomena. However, due to sensor equipment and the imaging environment, the observed Mars images are often inevitably corrupted by various noise. The challenge of denoising Mars images lies in the zero-shot paradigm and the lack of prior knowledge. In this paper, we propose a self-supervised model called SAM-MD. We integrate the prior knowledge of large vision model into the proposed model, so as to denoise single Mars image without any training data or knowledge of the noise distribution. Experimental results on Mars images and Tiangong-2 remotely sensed imagery show the reliability and superiority of SAM-MD.

The Low-Earth-Orbit (LEO) constellation has emerged as a promising component for seamless and fast global connectivity. With the increasing number of satellites in LEO constellations, multiple satellites can simultaneously cover the same geographical area. The selection of a user's handover (HO) target can significantly impact the quality of service (QoS) for communication and the load status of the satellite network. Motivated by this, the paper introduces a multi-slot load balancing (MSLB) scheme to offer users HO target selection strategies. An optimization problem is formulated to determine the HO sequence for users according to the MSLB scheme. Subsequently, a HO sequence graph mapping (HSGM) algorithm and an iterative shortest path solving (ISPS) algorithm are developed to address this optimization problem by transforming it into a shortest path searching problem. Simulation results demonstrate that, in comparison to traditional single-slot schemes, the MSLB scheme exhibits superior performance in load balancing and improving user communication QoS.

In time-division duplex (TDD) systems, where uplink and downlink channels alternate during transmission process, the continuity of carrier phase measurements becomes challenging to maintain. This leads to periodic lock-loss in the phase-locked loop (PLL), rendering the integer ambiguity non-constant. To address this issue, a carrier phase-based localization method for TDD systems via extended Kalman filtering is proposed in this study. The proposed method modifies the one-step predicted state within each TDD cycle based on extended Kalman filtering (EKF) to overcome the effects of PLL lock-loss in TDD. Simulation results verificate that the proposed method can overcome the problem of discontinuous measurement in TDD system, accurately solve integer ambiguities and achieve high-precision positioning.

Federated learning has been envisioned as a promising technique to enable the intelligence of Industrial Internet of Things (IIoT). An efficient resource management algorithm is critical yet hard to design in IIoT due to limited power devices and the variety of co-existent services. In this paper, we propose a multi-service joint scheduling algorithm for federated learning on IIoT devices to maximize model accuracy by forming a loss function minimization problem under long-term device energy constraints. The problem is then reformulated into a single-round optimization, which can be solved through a binary search and a greedy algorithm. The transmit power of devices, the CPU frequency for model training, and the selection of devices' services are jointly optimized. Simulations demonstrate that our proposed algorithm outperforms the benchmarks in model accuracy, especially when the energy of the device battery is constrained.

Abstract Conventional RGB‐T salient object detection treats RGB and thermal modalities equally to locate the common salient regions. However, the authors observed that the rich colour and texture information of the RGB modality makes the objects more prominent compared to the background; and the thermal modality records the temperature difference of the scene, so the objects usually contain clear and continuous edge information. In this work, a novel mirror‐complementary Transformer network (MCNet) is proposed for RGB‐T SOD, which supervise the two modalities separately with a complementary set of saliency labels under a symmetrical structure. Moreover, the attention‐based feature interaction and serial multiscale dilated convolution (SDC)‐based feature fusion modules are introduced to make the two modalities complement and adjust each other flexibly. When one modality fails, the proposed model can still accurately segment the salient regions. To demonstrate the robustness of the proposed model under challenging scenes in real world, the authors build a novel RGB‐T SOD dataset VT723 based on a large public semantic segmentation RGB‐T dataset used in the autonomous driving domain. Extensive experiments on benchmark and VT723 datasets show that the proposed method outperforms state‐of‐the‐art approaches, including CNN‐based and Transformer‐based methods. The code and dataset can be found at https://github.com/jxr326/SwinMCNet .

In mobile-edge network, adaptive cache placement and resource allocation are vital for meeting the dynamic requirements of users. The current network cache performance has not been fully implemented, due to the lack of comprehensive knowledge of content popularity dynamics and the impact of network resource deployment on the dynamics. In this article, an epidemic model is applied to characterize the content propagation among users. Based on this model, a joint optimization problem of cache placement and base station resource allocation is formulated, maximizing the satisfaction rate of users. The joint optimization problem is decomposed into two subproblems: 1) caching placement and 2) resource allocation. First, the cache placement strategy is determined based on the dynamic propagation model. Second, considering the impact of network decisions on content propagation, a belief propagation-based iterative algorithm is proposed for dynamic resource allocation. Finally, simulation results reveal that the proposed joint optimization algorithm outperforms the benchmark algorithms.

In addressing the challenges of heterogeneous data and limited communication resources in wireless networks, which often hinder the performance of federated learning, this article introduces a personalized learning approach. This approach not only addresses data heterogeneity but also optimizes resource management in wireless networks. We construct an optimization model aimed at maximizing the decay of the global loss function in a single iteration. The problem is divided into two subproblems: 1) allocation of device-local fine-tuning learning rates and 2) communication resources, tackled through an iterative method. The solutions involve determining near-optimal fine-tuning learning rates and optimizing device resource block and transmission power allocation. Our simulation results demonstrate that, under the constraints of wireless network resources and data heterogeneity, our algorithm outperforms baseline methods in terms of convergence speed and accuracy in personalized federated learning.

The development of industry 4.0 relies on emerging technologies of digital twin, machine learning, blockchain and Internet of Things (IoT) to build autonomous self-configuring systems that maximize manufactory efficiency, precision and accuracy. In this paper, we propose a new distributed and secure digital twin driven IIoT framework that integrates federated learning and Directed Acyclic Graph (DAG) blockchain with sharding. The proposed framework includes three planes: the data plane, the blockchain plane and the digital twin plane. Specifically, the data plane performs federated learning through a set of cluster heads to train models at network edges for twin model construction. The blockchain plane, which supports sharding, utilizes a hierarchical consensus scheme based on DAG blockchain to verify both local model updates and global model updates. The digital twin plane is responsible for constructing and maintaining twin model. Then, an efficient resource scheduling scheme is designed by considering performance of both federated learning and DAG blockchain with sharding. Accordingly, an optimization problem is formulated to maximize long-term utility of the digital twin driven IIoT. To cope with mapping error in the digital twin plane, a multi-agent Proximal Policy Optimization (MAPPO) approach is developed to solve the optimization problem. Numerical results illustrate that comparing with traditional approach, the proposed MAPPO improves utility by about 37 %, and reduces time latency by about 14%. Moreover, it also can well adapt to the mapping error.

Many wearable Internet of Medical Things (IoMT) devices have limited computing power and small storage space. Additionally, the healthcare data sensed by a single IoMT device is not enough to train a sophisticated deep learning model. To address these challenges, we propose a federated split learning (FedSL) framework that allows for collaborative healthcare analytics on multiple IoMT devices with limited resources. Compared to centralized learning, FedSL can protect user privacy by not sending raw data over wireless networks. Furthermore, FedSL offers more flexibility than other federated learning methods. It enables even low-end IoMT devices to participate in model training and result inference. Experimental results show that our FedSL performs well on medical imaging tasks with different data distributions.

To facilitate distributed machine learning in wireless networks, over-the-air federated learning (AirFL) is proposed to provide data privacy protection and high communication efficiency by leveraging the superposition property of wireless channels. However, as a typical parameter attack method, Byzantine attack brings challenges to the stable operation of AirFL systems. In this letter, we integrate orthogonal frequency division multiplexing and SignSGD with majority vote to enhance the resilience of AirFL against Byzantine attacks by performing one-bit gradient quantization. Theoretical analysis and numerical simulations are provided to validate the effectiveness of the proposed AirFL scheme under different channel states and Byzantine attacker percentages.

Radio simultaneous localization and mapping (SLAM) is challenging due to multipath propagation. While line-of-sight (LoS) and first-order non-LoS (NLoS) paths, referred to as NLoS-1 paths, play a critical role in SLAM, no existing techniques can effectively separate them from high-order NLoS paths, i.e., NLoS-npaths (n≥ 2). This paper presents a new framework to accurately identify the LoS/NLoS-1 paths and conduct SLAM. The key idea is to define the virtual user equipment (UE) of a NLoS-npath as then-th order reflection of the UE. We discover that the centers of the circles encompassing the UE, a virtual UE associated with a LoS/NLoS-1 path, and each of some other virtual UEs are aligned in a line, if and only if those virtual UEs are all associated with NLoS-1 paths. Accordingly, we propose to identify the LoS/NLoS-1 paths using Hough transform-based line detection, and estimate the UE’s location and the environments with the identified LoS/NLoS-1 paths using maximum likelihood estimation and mean-shift clustering. We analytically confirm that the localization error asymptotically approaches the Cramér-Rao Lower Bound. Simulations show that our approach outperforms the state of the art in localization accuracy by up to 91.93%, even when the latter assumed all NLoS-1 paths are perfectly identifieda-priori.

Due to the distributed deployment and inter-network dependence, distributed cloud network (DCN) is vulnerable to large-scale damage, making emergent system recovery of vital importance. Given limited resources at an early stage of network recovery, we propose a computation-aware link repair (CALR) algorithm to meet the computation demands of data centers in heavily damaged DCNs. Taking into account both network structure and traffic dynamics, we formulate a total system cost minimization problem to guarantee network repair performance. To tackle this challenging mixed-integer programming problem, we leverage the Benders decomposition (BD) to transfer it into an iteration problem with the mutually independent master problem and subproblem, which are solved by the cutting plane and the minimum cost flow algorithms, respectively. To accelerate the convergence speed of the proposed BD-based approach, we apply a small perturbation on the subproblem for facilitating the recovery of large-scale networks. Moreover, the computational complexity is reduced significantly by generating maximal non-dominated Benders cuts. Numerical simulations demonstrate that the proposed approach outperforms benchmarks under different settings such as network scale, data significance, available resources, and topology.

Internet of Robotic Things (IoRT) emphasizes the integrated robotic, artificial intelligence computing, and communication technologies, enabling more sophisticated operations and decision-making. As a crucial element of IoRT, mission-critical applications, such as industrial manufacturing and emergency services, impose stringent requirements on ultra-reliable and low-latency communication (URLLC). The paper focuses on addressing URLLC challenges in the context of IoRT, particularly when autonomous mobile robots (AMRs) coexist with static sensors. We prioritize safe and efficient AMRs’ travel through trajectory design and communication resource allocation in IoRT systems without the need of any prior knowledge. To enhance network connectivity and exploit diversity gains, we introduce the flexible decoding and free clustering as the next-generation multiple access technologies in spectrum-limited downlink IoRT system. Then, aiming at minimizing the decoding error probability and travel time, we formulate a long-term multi-objective optimization problem by jointly designing AMRs’ trajectory and communication resource. To accommodate the inherent dynamics and unpredictability in the IoRT system, we introduce a multi-agent actor-critic deep reinforcement learning (DRL) framework, offering four distinct implementations, each accompanied by comprehensive complexity analyses. Simulation results reveal the following insights: 1) in terms of DRL implementations, off-policy algorithms with deterministic policies outperform their on-policy counterparts, achieving approximately a 67% increase in rewards; 2) In terms of communication schemes, our proposed flexible decoding and free clustering strategies under designed trajectories can effectively reduce decoding errors; and 3) In terms of algorithm optimality, our DRL framework shows superior flexibility and adaptability in communication environments compared to traditional A* search and heuristic methods.

In this paper, we propose and study a multi-functional reconfigurable intelligent surface (MF-RIS) architecture. In contrast to conventional single-functional RIS (SF-RIS) that only reflects signals, the proposed MF-RIS simultaneously supports multiple functions with one surface, including reflection, refraction, amplification, and energy harvesting of wireless signals. As such, the proposed MF-RIS is capable of significantly enhancing RIS signal coverage by amplifying the signal reflected/refracted by the RIS with the energy harvested. We present the signal model of the proposed MF-RIS, and formulate an optimization problem to maximize the sum-rate of multiple users in an MF-RIS-aided non-orthogonal multiple access network. We jointly optimize the transmit beamforming, power allocations as well as the operating modes and parameters for different elements of the MF-RIS and its deployment location, via an efficient iterative algorithm. Simulation results are provided which show significant performance gains of the MF-RIS over SF-RISs with only some of its functions available. Moreover, we demonstrate that there exists a fundamental trade-off between sum-rate maximization and harvested energy maximization. In contrast to SF-RISs which can be deployed near either the transmitter or receiver, the proposed MF-RIS should be deployed closer to the transmitter for maximizing its communication throughput with more energy harvested.

In existing federated learning (FL), the base station (BS) coordinates devices to collaboratively train a shared model by avoiding the transmission of raw data. To achieve communication-efficient model uploading, over-the-air computation (AirComp) is often employed to aggregate model parameters. However, in conventional AirComp assisted FL, the BS’s abundant computation resources are underutilized due to its non-involvement in model training. Meanwhile, transmission failures resulting from fluctuating wireless channels impair the quality of model aggregation. In this paper, we propose a retransmission-based semi-federated learning (SemiFL) framework, wherein devices upload model parameters and public privacy-free data for enabling a hybrid implementation of FL and centralized learning (CL). In our new framework, the BS leverages its abundant computation resources to aid CL model training, which mitigates the resource wastage while alleviating local computational burden of devices. Moreover, the proposed new retransmission mechanism effectively overcomes detrimental transmission failures resulting from the fluctuating quasi-static channel, aiming to guarantee improved learning performance of SemiFL. Successful transmission probabilities of both retransmission-based AirComp and retransmission-based digital communication are provided in closed forms. To attain deep insights, we derive an optimality gap to capture the convergence behavior of retransmission-based SemiFL. Then, we formulate a non-convex long-term problem to minimize a weighted sum of overall latency and energy consumption by jointly optimizing communication, computation, and learning parameters. Extensive experimental results show that our retransmission-based SemiFL obtains 21.9%, 30.5%, and 44.1% accuracy gains on three datasets, while efficaciously reducing latency and energy consumption compared to benchmarks. Meanwhile, our scheme enhances learning performance on the fluctuating quasi-static channel compared to state-of-the-art schemes.

Carrier phase positioning has been recently proposed as the potential high-accuracy positioning method in currently ongoing 3GPP 5G New Radio (NR) Release-18. The precise resolution of integer ambiguities is the key to accuracy of carrier phase positioning. In this paper, we propose a new triple-frequency ambiguity resolution in the 5G NR systems, which resolves ambiguities by combining triple-frequency observations. In additional, based on the frequency range of 5G NR systems, the optimal carrier phase combinations are analyzed in this article. Furthermore, we propose a search method based on cost function to optimize the resolution of integer ambiguities. Numerical simulations show that proposed method can achieve a centimeter-level accuracy in wireless cellular networks.

In this paper, we propose a semi-federated learning (SemiFL) framework to ameliorate the performance of conventional federated learning. The base station and devices are coordinated to collaboratively train a shared model. However, due to the rapidly fluctuating channels and irrationally assigned local learning workloads, SemiFL encounters excessive latency. To overcome the challenges, we propose a retransmission-based over-the-air computation mechanism to facilitate model aggregation and data mixing over quasi-static channels. The closed-form probability of successful aggregation is derived, while the communication latency is modeled based on the Pascal distribution. Further, we establish an optimality gap to characterize the convergence performance of SemiFL, wherein the minimum number of iterations for attaining a specific local target accuracy is identified. Next, a joint resource allocation and local target accuracy assignment problem is formulated to minimize the latency of each round, subject to the decay rate, central processing unit (CPU) frequency, and transmit power. To address this non-convex problem, we develop an algorithm using the closed-form solutions for the normalizing factors and CPU frequencies. Simulation results on two real-world datasets confirm the superiority of SemiFL over benchmarks in terms of latency and learning performance.

In this paper, we propose a new multi-functional reconfigurable intelligent surface (MF-RIS) architecture. Different from conventional RIS that only reflects signals, MF-RIS supports multiple functionalities on one surface, including reflection, transmission, amplification, and energy harvesting. As such, MF-RIS is capable of overcoming the double-fading attenuation and achieving full-space coverage by harvesting energy from the base station (BS). The physical implementation and the signal model of MF-RIS are introduced from the perspective of wireless communications. Then, we formulate a sum rate (SR) maximization problem in an MF-RIS-aided non-orthogonal multiple access network. By jointly optimizing the transmit strategy of the BS and the coefficient of the MF-RIS, we design an iterative algorithm to solve the formulated non-convex problem efficiently. Simulation results show that: i) MF-RIS provides up to 98.8% higher SR gain than self-sustainable RIS. ii) There exists a non-trivial trade-off between throughput improvement and self-sustainability, due to the limited number of RIS elements.

In mobile edge network, dynamically changing content requests can affect network resource deployment. In recent years, epidemic model has been explored to describe the dynamic content popularity. In this article, epidemic model is applied to characterize the content propagation in resource-limited edge networks. The epidemic parameters are analyzed through the data collected by network entities. In order to maximize satisfaction rate of users, we decompose the joint optimization problem and propose a base station caching placement and resource allocation algorithm based on propagation dynamics. Simulation results verify that the proposed strategy offers higher satisfaction rate compared with other benchmark algorithms.

In this paper, we propose a multi-dimensional resource management scheme for Federated Learning with non-independent and identically distributed (non-IID) data on battery-powered IIoT devices. Firstly, we formulate an optimization problem that aims to maximize the learning efficiency given long-term energy and time constraints to balance training accuracy and learning latency. Secondly, based on the derived lower bound of expected convergence rate with non-IID data, we solve the short-term problems by cyclically manage the resources (i.e., radio, computation and resource block (RB) resources, and device energy). Simulation results validate that the proposed scheme outperforms other baseline schemes, especially in energy shortage scenarios.

Implementing existing federated learning in massive Internet of Things (IoT) networks faces critical challenges, such as imbalanced and statistically heterogeneous data and device diversity. To this end, we propose a semi-federated learning (SemiFL) framework to provide a potential solution for the realization of intelligent IoT. By seamlessly integrating the centralized and federated paradigms, our SemiFL framework shows high scalability in terms of the number of IoT devices even in the presence of computing-limited sensors. Furthermore, compared to traditional learning approaches, the proposed SemiFL can make better use of distributed data and computing resources, due to the collaborative model training between the edge server and local devices. Simulation results show the effectiveness of our SemiFL framework for massive IoT networks. The code can be found athttps://github.com/niwanli/SemiFL_IoT.

In this article, we propose a multi-functional reconfigurable intelligent surface (MF-RIS) to address the half-space coverage and double-fading attenuation issues faced by existing RISs. By simultaneously reflecting, refracting, and amplifying the incident signal, the proposed MF-RIS is capable of realizing full-space coverage with the mitigated signal degradation. The operation principle of the MF-RIS is first provided, and then an efficient beamforming scheme is proposed for MF-RIS-aided wireless communications. Simulation results show that, through combining multiple functions on one surface, the MF-RIS achieves significant throughput improvement over existing RISs.

Rapid and efficient sensor data acquisition plays a critical role in the decision-making process of each robot in a multi-robot smart factory. This paper investigates the trajectory design of autonomous mobile robots (AMRs) and communication resource allocation problems in industrial Internet of Things. Specifically, by exploiting both power and spatial domains, we adopt non-orthogonal multiple access to improve network connectivity in a spectrum-efficient manner, while the multi-antenna technique is employed to enhance diversity gain. The average sum rate is maximized by jointly optimizing the transmit power of sensors and the trajectory of AMRs. To deal with prior knowledge and dynamic channel conditions, we reformulate the long-term maximization problem as a Markov decision process, and further develop a provably efficient multi-agent reinforcement learning algorithm with a near-optimal regret bound. Our theoretical analysis reveals that both the decentralized execution and the experience exchange method are beneficial to accelerate convergence. Simulation results show that our proposed algorithm can reduce at least 80% convergence time compared to the centralized baseline, and can gain better rewards than the conventional$\epsilon $-greedy exploration.

Although reconfigurable intelligent surfaces (RISs) can improve the performance of wireless networks by smartly reconfiguring the radio environment, existing passive RISs face two key challenges, i.e., double-fading attenuation and dependence on grid/battery. To address these challenges, this paper proposes a new RIS architecture, called multi-functional RIS (MF-RIS). Different from conventional reflecting-only RIS, the proposed MF-RIS is capable of supporting multiple functions with one surface, including signal reflection, amplification, and energy harvesting. As such, our MF-RIS is able to overcome the double-fading attenuation by harvesting energy from incident signals. Through theoretical analysis, we derive the achievable capacity of an MF-RIS-aided communication network. Compared to the capacity achieved by the existing self-sustainable RIS, we derive the number of reflective elements required for MF-RIS to outperform self-sustainable RIS. To realize a self-sustainable communication system, we investigate the use of MF-RIS in improving the sum-rate of multi-user wireless networks. Specifically, we solve a non-convex optimization problem by jointly designing the transmit beamforming and MF-RIS coefficients. As an extension, we investigate a resource allocation problem in a practical scenario with imperfect channel state information. By approximating the semi-infinite constraints with the$\mathcal {S}$-procedure and the general sign-definiteness, we propose a robust beamforming scheme to combat the inevitable channel estimation errors. Finally, numerical results show that: 1) compared to the self-sustainable RIS, MF-RIS can strike a better balance between energy self-sustainability and throughput improvement; and 2) unlike reflecting-only RIS which can be deployed near the transmitter or receiver, MF-RIS should be deployed closer to the transmitter for higher spectrum efficiency.

Under the organization of the base station (BS), wireless federated learning (FL) enables collaborative model training among multiple devices. However, the BS is merely responsible for aggregating local updates during the training process, which incurs a waste of the computational resources at the BS. To tackle this issue, we propose a semi-federated learning (SemiFL) paradigm to leverage the computing capabilities of both the BS and devices for a hybrid implementation of centralized learning (CL) and FL. Specifically, each device sends both local gradients and data samples to the BS for training a shared global model. To improve communication efficiency over the same time-frequency resources, we integrate over-the-air computation for aggregation and non-orthogonal multiple access for transmission by designing a novel transceiver structure. To gain deep insights, we conduct convergence analysis by deriving a closed-form optimality gap for SemiFL and extend the result to two extra cases. In the first case, the BS uses all accumulated data samples to calculate the CL gradient, while a decreasing learning rate is adopted in the second case. Our analytical results capture the destructive effect of wireless communication and show that both FL and CL are special cases of SemiFL. Then, we formulate a non-convex problem to reduce the optimality gap by jointly optimizing the transmit power and receive beamformers. Accordingly, we propose a two-stage algorithm to solve this intractable problem, in which we provide closed-form solutions to the beamformers. Extensive simulation results on two real-world datasets corroborate our theoretical analysis, and show that the proposed SemiFL outperforms conventional FL and achieves 3.2% accuracy gain on the MNIST dataset compared to state-of-the-art benchmarks.

This paper proposes a novel concept of multi-functional reconfigurable intelligent surfaces (MF-RISs). Different from conventional single-functional RISs (SF-RISs) that only reflect signals, the proposed MF-RIS simultaneously supports multiple functionalities, namely, reflection, amplification, and energy harvesting. Specifically, by harvesting energy from incident signals, MF-RIS is able to simultaneously reflect and amplify signals without an external power supply, which is beneficial for overcoming the double-fading attenuation in a flexible manner. A new operation protocol of MF-RIS is presented, and then a sum rate (SR) maximization problem is formulated for an MF-RIS aided multi-user network. Next, an efficient iterative algorithm is proposed to solve this non-convex problem. Furthermore, through theoretical analysis, we determine the number of reflection elements required for MF-RISs to outperform self-sustainable RISs. Finally, our numerical results show that: 1) MF-RISs are able to provide up to 81.1% higher SR than the self-sustainable RISs. 2) Unlike the SF-RIS, which prefers to be deployed near the transmitter or receiver, MF-RISs should be deployed closer to the transmitter for better performance.

This paper proposes a novel semi-federated learning (SemiFL) paradigm, which integrates centralized learning (CL) and over-the-air federated learning (AirFL) into a unified framework, with the aid of a simultaneous transmitting and reflecting reconfigurable intelligent surface (STAR-RIS). In particular, this SemiFL framework allows computing-scarce users to participant in the learning process by using non-orthogonal multiple access (NOMA) to transmit their local dataset to the base station for model computation on behalf of them. During the uplink communication, scarce spectrum resources are shared among AirFL users and NOMA-based CL users, using a STAR-RIS for interference management and coverage enhancement. To analyze the learning behavior of SemiFL, closed-form expressions are derived to quantify the impact of learning rates and noisy fading channels. Our analysis shows that SemiFL can achieve a lower error floor than the CL or AirFL schemes with partial users. Simulation results show that SemiFL significantly reduces communication overhead and latency compared to CL, while achieving better learning performance than AirFL.

As a distributed machine learning paradigm, federated learning (FL) has been regarded as a promising candidate to preserve user privacy in Internet of Things (IoT) networks. Leveraging the waveform superposition property of wireless channels, over-the-air FL (AirFL) achieves fast model aggregation by integrating communication and computation via concurrent analog transmissions. To support sustainable AirFL among energy-constrained IoT devices, we consider that the base station (BS) adopts simultaneous wireless information and power transfer (SWIPT) to distribute global model and charge local devices in each communication round. To maximize the long-term energy efficiency (EE) of AirFL, we investigate a resource allocation problem by jointly optimizing the time division, transceiver beamforming, and power splitting in SWIPT-enabled IoT networks. Considering such multiple closely-coupled continuous valuables, we propose a deep reinforcement learning (DRL) algorithm based on twin delayed deep deterministic (TD3) policy to smartly make downlink and uplink communication strategies with the coordination between the BS and devices. Simulation results show that the proposed TD3 algorithm obtains about 41% EE improvement compared to traditional optimization method and other DRL algorithms.

Ultra-reliable low-latency communication (uRLLC) is envisioned to efficiently support mission-critical scenarios, especially for industrial Internet of Things (IIoT). Considering the requirements of high throughput and massive connectivity in machine-type communications, uRLLC traffic is usually coexisted with enhanced mobile broadband (eMBB) services for the data-intensive industrial cases. To strike a balance between the two distinct tasks, this paper investigates a multi-objective optimization problem by taking into account the performance of both uRLLC and eMBB. Specifically, we aim at maximizing eMBB data rate and uRLLC reliability, while minimizing the communication overhead of control channels caused by uRLLC puncturing. To solve this challenging problem, an analytic hierarchy process method is adopted to estimate the importance of each objective with expert knowledge. Then, a coalitional game is invoked to evaluate the preference degree of resource blocks allocated to uRLLC devices. Following this, an improved Gale-Shapley algorithm is proposed for efficient traffic scheduling. Simulation results demonstrate that the proposed algorithm can achieve better performance in terms of eMBB throughput and uRLLC reliability with the reduced signal overhead.

The mobile feature and the moving positions are important for mobile communications and Internet of Things. Inspired by the potentially excellent precision of carrier phase positioning, this article proposes a mobile feature enhanced high-accuracy positioning algorithm based on the carrier phase and Bayesian estimation. The mobile feature is first estimated using time-differential carrier phase measurements. By combining the estimated position changes with instant ranging and carrier phase measurements, the factorized posterior probability of positioning is established based on the Bayes theorem. In turn, a factor graph-based positioning method is proposed to obtain precise positioning results. The numerical simulation results show that the proposed algorithm can achieve centimeter-level accuracy, and the positioning error can approach the Cramer–Rao lower bound.

By exploiting the superiority of nonorthogonal multiple access (NOMA), NOMA-aided mobile-edge computing (MEC) can provide scalable and low-latency computing services for the Internet of Things. However, given the prevalent stochasticity of wireless networks and sophisticated signal processing of NOMA, it is critical but challenging to design an efficient task offloading algorithm for NOMA-aided MEC, especially under a large number of devices. This article presents an online algorithm that jointly optimizes offloading decisions and resource allocation to maximize the long-term system utility (i.e., a measure of throughput and fairness). Since the optimization variables are temporary coupled, we first apply Lyapunov technique to decouple the long-term stochastic optimization into a series of per-slot deterministic subproblems, which does not require any prior knowledge of network dynamics. Second, we propose to transform the nonconvex per-slot subproblem of optimizing NOMA power allocation equivalently to a convex form by introducing a set of auxiliary variables, whereby the time-complexity is reduced from the exponential complexity to$\mathcal {O} (M^{3/2})$. The proposed algorithm is proved to be asymptotically optimal, even under partial knowledge of the device states at the base station. Simulation results validate the superiority of the proposed algorithm in terms of system utility, stability improvement, and the overhead reduction.

With the rapid development of Internet of Things (IoT), the digital twin is emerging as one of the most promising technologies to connect physical components with digital space for better optimization of physical systems. However, the limited wireless resource and security concerns impede the deployment of the digital twin in IoT. In this article, we exploit blockchain to propose a new digital twin edge networks framework for enabling flexible and secure digital twin construction. We first develop cooperative federated learning through an access point (AP) to help resource-limited smart devices in constructing digital twin at the network edges belonging to different mobile network operators (MNOs). Then, we propose a model update chain by leveraging directed acyclic graph (DAG) blockchain to secure both local model updates and global model updates. In order to incentivize the APs to help in local models training for resource-limited smart devices and also encourage the APs to contribute resource in local model update verification, we design an iterative double auction-based joint cooperative federated learning and local model update verification scheme. The optimal unified time for cooperative federated learning and local model update verification is solved to maximize social welfare. Numerical results illustrate that the proposed scheme is efficient in digital twin construction.

This article integrates nonorthogonal multiple access (NOMA) and over-the-air federated learning (AirFL) into a unified framework using one simultaneous transmitting and reflecting reconfigurable intelligent surface (STAR-RIS). The STAR-RIS plays an important role in adjusting the decoding order of hybrid users for efficient interference mitigation and omnidirectional coverage extension. To capture the impact of nonideal wireless channels on AirFL, a closed-form expression for the optimality gap (also known as the convergence upper bound) between the actual loss and the optimal loss is derived. This analysis reveals that the learning performance is significantly affected by the active and passive beamforming schemes, as well as wireless noise. Furthermore, when the learning rate diminishes as the training proceeds, the optimality gap is explicitly shown to converge with a linear rate. To accelerate convergence while satisfying quality-of-service requirements, a mixed-integer nonlinear programming (MINLP) problem is formulated by jointly designing the transmit power at users and the configuration mode of STAR-RIS. Next, a trust-region-based successive convex approximation method and a penalty-based semidefinite relaxation approach are proposed to handle the decoupled nonconvex subproblems iteratively. An alternating optimization algorithm is then developed to find a suboptimal solution for the original MINLP problem. Extensive simulation results show that: 1) the proposed framework can efficiently support NOMA and AirFL users via concurrent uplink communications; 2) our algorithms achieve a faster convergence rate on independent and identically distributed (IID) and non-IID settings compared to the existing baselines; and 3) both the spectrum efficiency and learning performance are significantly improved with the aid of the well-tuned STAR-RIS.

The fundamental communication paradigms in the next-generation mobile networks are shifting from connected things to connected intelligence. The potential result is that current communication-centric wireless systems are greatly stressed when supporting computation-centric intelligent services with distributed big data. This is one reason that makes federated learning come into being, it allows collaborative training over many edge devices while avoiding the transmission of raw data. To tackle the problem of model aggregation in federated learning systems, this article resorts to multiple reconfigurable intelligent surfaces (RISs) to achieve efficient and reliable learning-oriented wireless connectivity. The seamless integration of communication and computation is actualized by over-the-air computation (AirComp), which can be deemed as one of the uplink nonorthogonal multiple access (NOMA) techniques without individual information decoding. Since all local parameters are uploaded via noisy concurrent transmissions, the unfavorable propagation error inevitably deteriorates the accuracy of the aggregated global model. The goals of this work are to 1) alleviate the signal distortion of AirComp over shared wireless channels and 2) speed up the convergence rate of federated learning. More specifically, both the mean-square error (MSE) and the device set in the model uploading process are optimized by jointly designing transceivers, tuning reflection coefficients, and selecting clients. Compared to baselines, extensive simulation results show that 1) the proposed algorithms can aggregate model more accurately and accelerate convergence and 2) the training loss and inference accuracy of federated learning can be improved significantly with the aid of multiple RISs.

Inspired by excellent precision of carrier phase positioning, this paper presents a new carrier phase positioning technique for 5G new radio cellular networks with a focus on clock synchronization and integer ambiguity resolution. A carrier-phase based clock offset estimation method is first proposed to achieve precise clock synchronization among base stations, and proved to achieve the Cramér–Rao Lower Bound (CRLB) asymptotically. A fusion method is developed to fuse the estimated positions of a mobile station (MS) based on time-difference-of-arrival, with the estimated position changes based on the temporal changes of carrier phase measurements. While circumventing the integer ambiguities of the carrier phase measurements, the fusion method provides quality interim estimates of the MS positions, at which the measurements can be linearized to resolve the integer ambiguities. As a result, precise MS positions can be obtained based on the disambiguated carrier phase measurements. Numerical simulations show that the proposed carrier phase positioning can achieve a centimeter-level accuracy in wireless cellular networks.

Traditional Wi-Fi-based floor identification methods mainly have been tested in small experimental scenarios, and generally, their accuracies drop significantly when applied in real large and multistorey environments. The main challenge emerges when the complexity of Wi-Fi signals on the same floor exceeds the complexity between the floors along the vertical direction, leading to a reduced floor distinguishability. A second challenge regards the complexity of Wi-Fi features in environments with atrium, hollow areas, mezzanines, intermediate floors, and crowded signal channels. In this article, we propose an adaptive Wi-Fi-based floor identification algorithm to achieve accurate floor identification also in these environments. Our algorithm, based on the Wi-Fi received signal strength indicator and spatial similarity, first identifies autonomous blocks parcelling the whole environment. Then, local floor identification is performed through the proposed Wi-Fi models to fully harness the Wi-Fi features. Finally, floors are estimated through the joint optimization of the autonomous blocks and the local floor models. We have conducted extensive experiments in three real large and multistorey buildings greater than 140 000 m$^2$using 19 different devices. Finally, we show a comparison between our proposal and other state-of-the-art algorithms. Experimental results confirm that our proposal performs better than other methods, and it exhibits an average accuracy of 97.24%.

With the aim of integrating over-the-air federated learning (AirFL) and non-orthogonal multiple access (NOMA) into an on-demand universal framework, this paper proposes a reconfigurable intelligent surface (RIS)-aided hybrid network by leveraging the RIS to flexibly adjust the decoding order of heterogeneous data. A new metric of computation rate is defined to measure the performance of AirFL users. Upon this, the objective of this work is to maximize the achievable hybrid rate by jointly optimizing the transmit power, controlling the receive scalar, and designing the reflection coefficients. Since the concurrent transmissions of all computation and communication signals are aided by the discrete phase-shifting elements at the RIS, the formulated problem (P0) is a challenging mixed-integer programming problem. To tackle this intractable issue, we decompose the original problem (P0) into a non-convex problem (P1) and a combinatorial problem (P2), which are characterized by the continuous and discrete variables, respectively. For the transceiver design problem (P1), the power allocation subproblem is first solved by difference-of-convex programming, and then the receive control subproblem is addressed by successive convex approximation, where the closed-form expressions of simplified cases are derived to obtain deep insights. For the reflection design problem (P2), a relaxation-then-quantization method is adopted to find a suboptimal solution for striking a trade-off between complexity and performance. Afterwards, an alternating optimization algorithm is developed to solve the non-linear non-convex problem (P0) iteratively. Finally, simulation results reveal that i) the proposed RIS-aided hybrid network can support on-demand communication and computation efficiently, ii) the system performance can be improved by properly selecting the location of the RIS, and iii) the designed algorithms are also applicable to conventional networks with only AirFL or NOMA users.

Over-the-air federated learning (AirFL) allows devices to train a learning model in parallel and synchronize their local models using over-the-air computation. The integrity of AirFL is vulnerable due to the obscurity of the local models aggregated over the air. This paper presents a novel framework to balance the accuracy and integrity of AirFL, where multi-antenna devices and base station (BS) are jointly optimized with a reconfigurable intelligent surface (RIS). The key contributions include a new and non-trivial problem jointly considering the model accuracy and integrity of AirFL, and a new framework that transforms the problem into tractable subproblems. Under perfect channel state information (CSI), the new framework minimizes the aggregated model’s distortion and retains the local models’ recoverability by optimizing the transmit beamformers of the devices, the receive beamformers of the BS, and the RIS configuration in an alternating manner. Under imperfect CSI, the new framework delivers a robust design of the beamformers and RIS configuration to combat non-negligible channel estimation errors. As corroborated experimentally, the novel framework can achieve comparable accuracy to the ideal FL while preserving local model recoverability under perfect CSI, and improve the accuracy when the number of receive antennas is small or moderate under imperfect CSI.

This paper proposes a new, compatible, unified framework which integrates non-orthogonal multiple access (NOMA) and over-the-air federated learning (AirFL) via concurrent communication. In particular, a simultaneous transmitting and reflecting reconfigurable intelligent surface (STAR-RIS) is leveraged to adjust the signal processing order for efficient interference mitigation and omni-directional coverage extension. With the aim of investigating the impact of non-ideal wireless communication on AirFL, we provide a closed-form expression for the optimality gap over a given number of communication rounds. This result reveals that the learning performance is significantly affected by the resource allocation scheme and channel noise. To minimize the derived optimality gap, a mixed-integer non-linear programming (MINLP) problem is formulated by jointly designing the transmit power at users and configuration mode at the STAR-RIS. Through developing an alternating optimization algorithm, a suboptimal solution for the original MINLP problem is obtained. Simulation results show that the learning performance in terms of training loss and test accuracy can be effectively improved with the aid of the STAR-RIS.

Robots have been widely used in the era of Inter-net of Things (IoT) and become an important driver behind 6G systems. However, due to the mobility of robots and the complicated wireless environment, it is challenging to control robots without prior knowledge, especially for the dynamic multi-robot systems. This paper investigates data transmission and path planning problems in an indoor multi-robot navigation system, where non-orthogonal multiple access (NOMA) is adopted to provide massive connectivity and improve spectrum efficiency. The considered system is designed for the long-term throughput maximization by jointly designing the downlink transmit power at the access point (AP) and the motion paths of the robots, while satisfying the power budget and mobility constraints. In this paper, a novel federated deep reinforcement learning approach called federated deep Q-network learning (F-DQN) algorithm is proposed to tackle the formulated problem. Simulation results demonstrate that the proposed algorithm not only speeds up the convergence rate and promotes the system throughput, but also is scalable for the number of robots.

This paper investigates the problem of secure communication in the unmanned aerial vehicle (UAV) enabled net-works aided by a reconfigurable intelligent surface (RIS) from the physical layer security perspective. Specifically, the RIS is deployed to assist the wireless transmission from the UAV to the ground user in the presence of an eavesdropper. The objective of this work is to maximize the secrecy rate by jointly optimizing the phase shifts at the RIS as well as the transmit beamforming vector and location of the UAV. However, the formulated problem is difficult to solve directly due to the non-linear and non-convex objective function and constraints. By invoking the successive convex approximation and fractional programming techniques, the intractable original problem is transformed into convex ones, then an alternating algorithm is proposed to solve the challenging problem effectively. Simulations results demonstrate that the designed algorithm for RIS-aided UAV communications can achieve higher secrecy rate than benchmarks.

This paper investigates the model aggregation process in an over-the-air federated learning (AirFL) system, where an intelligent reflecting surface (IRS) is deployed to assist the transmission from users to the base station (BS). Since the successive interference cancellation (SIC) is adopted as a basis to decode local model parameters and analyze their statistic characteristics for detecting malicious devices, the quality-of-service (QoS) requirement is ensured. The objective of this paper is to minimize the mean-square-error by jointly optimizing the receive beamforming vector at the BS, transmit power allocation at users, and phase shift matrix of the IRS, subject to the transmit power constraint for devices, unit-modulus constraint for reflecting elements, SIC decoding order constraint and QoS constraint. To address this complicated problem, alternating optimization is employed to decompose it into three subproblems, where the optimal receive beamforming vector is obtained by solving the first subproblem with the Lagrange dual method. Then, the convex relaxation method is applied to the transmit power allocation subproblem to find a suboptimal solution. Eventually, the phase shift matrix subproblem is addressed by invoking the semidefinite relaxation. Simulation results validate the availability of IRS and the effectiveness of the proposed scheme in improving federated learning performance.

Wireless powered mobile-edge computing has been envisioned as a promising paradigm to enhance the computation capability of low-power wireless devices in industrial Internet of things. An efficient resource scheduling method is critical yet challenging to design in such a scenario due to stochastic traffic arrival, time-coupling uplink/downlink decision, and incomplete system state knowledge. To tackle these challenges, an online optimization algorithm is proposed in this article to maximize long-term system utility balancing throughput and fairness, subject to data age and stability constraints. A set of virtual queues is designed to transform the scheduling task, which is hard to solve due to time-dependent data age constraints, into a stochastic optimization problem. Leveraging Lyapunov and convex optimization techniques, the proposed approach can achieve asymptotically near-optimal online decisions without any prior statistical knowledge, and maintain the asymptotic optimality in the presence of partial and outdated network state information. Numerical simulations corroborate the theoretical analysis and demonstrate the effectiveness of the proposed approach.

Cooperative caching can unify storage across edge clouds and provide efficient delivery of popular contents under effective content placement. However, the placement and delivery are non-trivial in cooperative caching due to the decentralized property of edge clouds, as well as the temporal and spatial correlation of the placement. We propose a new distributed online learning approach to jointly optimize content placement and delivery without the a-priori knowledge on file popularity and link availability. Content placement and delivery can be asymptotically optimized in real-time by running distributed online learning at individual edge servers by exploiting stochastic gradient descent (SGD). The proposed approach can allow operations at different timescales by integrating mini-batch learning for farsighted content placement. The optimality loss, stemming from the different timescales, can asymptotically reduce, as the SGD stepsize declines. Simulations confirm that the proposed approach outperforms existing techniques in terms of cache hit ratio and cost effectiveness. Insights are shed on the optimal placement of popular contents.

This paper aims to produce an effective online scheduling technique, where a base station (BS) schedules the transmissions of energy harvesting-powered Internet-of-Things (IoT) devices only based on the (differently outdated) in-band reports of the devices on their states. We establish a new primal-dual learning framework, which learns online the optimal proactive schedules to maximize the time-average throughput of all the devices. Batch gradient descent is designed to enable stochastic gradient descent (SGD)-based dual learning to learn the network dynamics from the outdated reports. Replay memory is deployed to allow online convex optimization (OCO)-based primal learning to predict channel conditions and prevent over-fitting. We also decentralize the online learning between the BS and devices, and speed up learning by leveraging the instantaneous knowledge of the devices on their states. We prove that the proposed framework asymptotically converges to the global optimum, and the impact of the outdated knowledge of the BS diminishes. Simulation results confirm that the proposed approach can increasingly outperform state of the art, as the number of devices grows.

This article proposes a novel framework of resource allocation in multi-cell intelligent reflecting surface (IRS) aided non-orthogonal multiple access (NOMA) networks, where an IRS is deployed to enhance the wireless service. The problem of joint user association, subchannel assignment, power allocation, phase shifts design, and decoding order determination is formulated for maximizing the achievable sum rate. The challenging mixed-integer non-linear problem is decomposed into an optimization subproblem (P1) with continuous variables and a matching subproblem (P2) with integer variables. In an effort to tackle the non-convex optimization problem (P1), iterative algorithms are proposed for allocating transmission power, designing reflection matrix, and determining decoding order by invoking relaxation methods such as convex upper bound substitution, successive convex approximation, and semidefinite relaxation. In terms of the combinational problem (P2), swap matching-based algorithms are developed for achieving a two-sided exchange-stable state among users, BSs and subchannels. Numerical results demonstrate that: i) the sum rate of multi-cell NOMA networks is capable of being increased by 35% with the aid of the IRS; ii) the proposed algorithms for multi-cell IRS-aided NOMA networks can enjoy 22% higher energy efficiency than conventional NOMA counterparts; iii) the trade-off between spectrum efficiency and coverage area can be tuned by judiciously selecting the location of the IRS.

This paper contributes improvements on both the defect of dot product and the imbalance of the datasets in matrix factorization. Above all, matrix factorization is still the most widely used technology in the recommendation system. However, its dot product does not follow triangle inequality, which restricts the improvement of its effect. We take inspiration from the distance factor of metric learning, and convert the determinants of user-item relevance from the size of the dot product to the distance of the metric factorization. Furthermore, the number of positive examples is much smaller than the negatives in most datasets. Such an unbalanced scenario will affect the accuracy of recommendations. Inspired by the positive semidefinite matrix of the popular Mahalanobis distance in the field of metric learning, we have fully considered the interaction information between users and items and propose the concept of all-weighted matrix. Finally, the combination of the two improved techniques proposed the All-Weighted Metric Factorization (AWMF) method, which is applied to the personalized ranking task. We have done scientific and adequate experiments on three common datasets, and the results outperform several baselines on different evaluation indicators.

In recent years, data traffic has grown at a drastic speed. Caching popular contents proactively at the edge of networks has become one of the effective methods to offload the huge burden on backhaul links. The reinforcement learning (RL) based edge caching method is capable to fit into the changeable environments and transfer its parameters to other caching cells. In order to converge to the optimal policy quickly and avert the cold-start problem, we propose a transferable edge caching method based on reinforcement learning. The method relies on Asynchronous Advantage Actor-Critic (A3C) algorithm and is applied to dense small cell networks (DSCNs) under content popularity diversity. Compared with Q-Learning based and other caching methods, simulation results verifies that the proposed approach offers faster convergence and efficiently avoids coldstart problem.

Due to their high mobility, flexible deployment and stable maneuverability, unmanned aerial vehicles (UAVs) have been deemed as a promising and indispensable role for various emerging applications (e.g., dangerous area detection, dynamic target tracking, and map remote sensing). Compared to the static monitoring equipments, UAV-mounted high-definition camera and signal transceiver can be used cost-effectively as an on-demand aerial platform to detect the unknown region and send the real-time data back at the same time. However, these highlighted limitations of battery capacity and communication resource extremely affect the UAV’s performance such as flight endurance and data transmission. Motivated by the above conflicts, this paper aims to minimize the total energy consumed by the UAV during the region detection mission through jointly optimizing the collected data size, transmission time, and flying trajectory. Toward this end, we derive the optimal data collection and transmission time in closed forms via convex optimization, and propose a model-free reinforcement learning-based algorithm for training the UAV to plan its trajectory without knowing the environment information in advance. Simulation results validate the performance of our designs in terms of convergence, energy consumption, and energy efficiency.

The cache-delivery market is generally composed of Content Provider (CP), users, and Mobile Network Operator (MNO) equipped with Base Stations (BSs). In order to deal with the dishonest problems of different parties, we build a caching-chain network based on blockchain. This network is a pure peer-to-peer system, which allows file acquisition transactions without going through a centralized issuer or controller, but attains a reliable and tamper-proof value transfer. The utilization of smart contracts protects the interests of all parties in the untrusted caching market. By reasonably distributing the reward of generating new blocks, we can motivate the MNO to allocate more resources for offloading the traffic of the CP. In addition, we use the linear regression model to predict user mobility and design the cache placement policy accordingly. Furthermore, we compare the performance of three caching algorithms through simulation. And the simulation results show that an appropriate choice of parameters can raise the CP’s profit.

Lyapunov optimization has shown to be effective for online optimization of fog computing, asymptotically approaching the optimality only achievable offline. However, it is not directly applicable to the Internet of Things, as inexpensive sensors have small buffers and cannot generate sufficient backlogs to activate the optimization. This article proposes an enabling technique for the Lyapunov optimization to operate under finite buffers without loss of asymptotic optimality. This is achieved by optimizing the biases (namely, “virtual placeholders”) of the buffers to create sufficient backlogs. The optimization of the placeholders is proved to be a new three-layer shortest path problem and solved in a distributed manner by extending the Bellman-Ford algorithm. The sizes of the virtual placeholders decline fastest along the shortest paths from the sensors to the data center, thereby preventing unnecessary detours and reducing end-to-end delays. Corroborated by simulations, the proposed approach is able to operate under the conditions the direct application of the Lyapunov optimization fails, and significantly increase the throughput and reduce the delays in other cases.

The IEEE 802.11mc-2016 protocol enables certified devices to obtain precise ranging information using time-of-flight-based techniques. The ranging error increases in indoor environments due to the multipath effect. Traditional methods utilize only the ranging measurements of the current location, thus limiting the abilities to reduce the influence of multipath problems. This article introduces a robust positioning method that leverages the constraints of multiple positioning nodes at different positions. We transfer a sequence of temporal ranging measurements into multiple virtual positioning clients (VPCs) in the spatial domain by considering their spatial constraints. Defining an objective function and the spatial constraints of the VPCs as Karush-Kuhn-Tucker conditions, we solve the positioning estimation with nonconvex optimization. We propose an iterative weight estimation method for the time of flight ranging and the VPC to optimize the positioning model. An extensive experimental campaign demonstrates that our proposal can remarkably improve the positioning accuracy in complex indoor environments.

Edge computing allows an edge server to adaptively place virtual instances to serve different types of data. This article presents a new algorithm which jointly optimizes virtual service placement farsightedly and service data admission instantly to maximize the time-average service throughput of edge computing. The data admission is optimized, adapting to fast-changing data arrivals and wireless channels. The service placement is transformed into a two-dimensional knapsack problem by approximating future arrivals and channels with past observations, and solved over a slow timescale to allow services to be properly installed. Different from existing studies, our algorithm considers practical aspects of edge servers, such as finite memory size and bandwidth. We prove that the algorithm is asymptotically optimal and the optimality loss resulting from the approximation diminishes. Simulations show that our approach can improve the time-average throughput of existing alternatives by 16% for our considered simulation setup. The improvement becomes higher, as the memory size becomes increasingly tight. The number of services to be replaced is reduced without loss of throughput, after being placed farsightedly.

Mobile edge computing (MEC) supporting localized context awareness creates a new technological frontier for 5G and beyond. Due to very asymmetric traffic related to MEC and the time division duplexing (TDD) system, we efficiently exploit the networking and computing functionalities for TDD orthogonal frequency division multiple access (TDD-OFDMA) technology supporting multiple services. The primary technical challenge of TDD-OFDMA systems lies in dynamic configuring based on the unknown characteristics of future traffic, i.e., the information lag. Therefore, a model-free online TDD configuration scheme is proposed based on context analysis and multi-armed bandit (MAB) optimization. The characteristics of future traffic are predicted by the context-aware MEC computing, so that TDD configuration is novelly modeled as a contextual MAB problem. Solving MAB by the contextual upper-confidence-bound, TDD configuration can be dynamically adjusted according to network traffic. To simultaneously reduce the energy consumption and makespan of mobile devices (MDs), a greedy resource allocation (GRA) embedded in the TDD configuration is further developed to select MDs and allocate resources. GRA algorithm decomposes the complex multi-factor coupling non-convex problem into a series of convex sub-problems, thereby asymptotically obtaining the selection and allocation with polynomial time complexity. Simulations justify significant performance gain in mobile networking and MEC.

With tremendous computing power in the radio access network, mobile edge computing (MEC) that can support localized context awareness creates a new technological frontier for 5G and beyond. To efficiently exploit the networking and computing functionalities, Time Division Duplex Orthogonal Frequency Division Multiple Access (TDD-OFDMA) type has been considered in this paper. To take advantage of dynamic features in TDD, a model-free online TDD configuration scheme is proposed based on context analysis and Multi-Armed Bandit (MAB) optimization. The TDD configuration problem is therefore novelly modeled as a contextual MAB problem, and is solved by the contextual Upper-Confidence-Bound (C-UCB), which dynamically adjusts TDD configuration to network traffic since that the system cost can be reduced. To further reduce the energy consumption and makespan of mobile devices (MDs), a greedy resource allocation (GRA) embedded in the TDD configuration is developed to select MDs and allocate resources. The simulations demonstrate that proper TDD configuration successfully reduces the system cost, and C-UCB technique approaches the ideal TDD configuration, with significant performance gain when the GRA effectively select and allocate, to strike simultaneous efficiency for mobile networking and MEC.

In complex scenarios where line-of-sight (LOS) and non-line-of-sight (NLOS) paths both exist, a LOS/NLOS identifi-cation method is necessary. In this paper, we propose a millimeter wave (mmWave) LOS/NLOS identification scheme utilizing mean-shift (MS) clustering algorithm and a 3D angle-of-arrival (AOA) localization algorithm using both LOS and one-bound reflection NLOS paths. In order to separate LOS and one-bound NLOS paths from multiple-bound NLOS paths, we first make all possible reflection condition assumptions for all paths to give all possible user equipment (UE) locations. Each path's assumption corresponds to one possible UE location. Then by applying mean-shift clustering to the calculated locations, we find the cluster with the most points as the set of correct hypothetical points. For the points in this cluster, the corresponding LOS/NLOS assumptions are considered to be correct, which means the LOS/NLOS conditions are successfully identified. Given known reflection conditions and original AOA measurements, the position estimate is then solved by the proposed AOA localization algorithm. Simulation results demonstrate that our scheme is capable of achieving high identification accuracy and localization precision.

Mobile edge computing (MEC), which enables the wireless devices to offload their computation tasks to the edge servers, has been considered as a promising technology to offer low-latency computing services and prolong lifetime for the Internet of Things (IoT) devices. To further improve the system efficiency, non-orthogonal multiple access (NOMA) is exploited to the MEC system. In this paper, we investigate a NOMAbased MEC system in ultra dense network and pursue an energy efficient offloading strategy for resource-limited devices, while meeting the execution latency constraint Due to the non-convexity of the optimization problem, we propose a low complexity energy efficient task offloading algorithm based on alternating direction method of multipliers (ADMM) decomposition technique to transform it into multiple parallel convex subproblems and obtain the optimal solution. Numerical results show that the proposed scheme can significantly decrease energy consumption for user and achieve lower complexity.

Edge caching can improve the efficiency of content delivery by making full use of the limited storage capacity. Exploiting cooperation between base stations (BSs) could promote the diversity of contents in the whole network and alleviate backhaul traffic. However, prior works mostly overlook the effect of cooperation during caching placement period. In this paper, firstly we propose a local content cloud cooperation scheme to utilize the cache capacity of adjacent BSs. Considering the impact of the capacity of cooperative link and backhaul link, then we formulate an optimizing problem to minimize the average consumption, which consists of content downloading delay and transmission cost. To reduce the complexity and the expenditure of information exchange, we solve the NP-hard problem by the belief propagation method. Meanwhile, simulation shows that the proposed cooperation-based caching placement decreases the average consumption and enhance the hit ratio compared with other strategies.

For providing highly demanding services with powerful computational ability and ultra low-latency communication, mobile edge computing (MEC) has been recognized as a bright rising star among key technologies for the next-generation networking. Generally, jointly optimizing offloading decision and resource allocation in one multi-variable problem is complicated. To decrease computational scale and develop practicable strategy by splitting problems, we divide the workflow of MEC-enabled base station into two stages. First, through formulating a task offloading problem, we propose a low-complexity improved simulated annealing-based heuristic offloading decision (SAHOD) algorithm to maximize network revenue from the perspective of mobile network operator. Then, the optimal fine-grained resource allocation solution is obtained in closed forms via Lagrange duality decomposition method. Furthermore, an effective realtime sub-gradient-based resource allocation (SGRA) algorithm is presented to converge to a specific optimal allocation strategy within the adjustable accuracy. For given users, simulation results show that our SAHOD algorithm can earn about 20.5% more revenue than value-based greedy algorithm. Besides, our SGRA algorithm can converge within 4 iterations and obtain approximately 19.3% more sum rates than static scheduling method.

Processing data around the point of capture, fog computing can support computationally demanding Internet-of-Things (IoT) services. Distributed online optimization is important given the size of IoT, but challenging due to time variations of random traffic and nonuniform connectivity (or cardinality) of edge servers and IoT devices. This paper presents a distributed online learning approach to asymptotically minimizing the time-average cost of fog computing in the absence of the a-priori knowledge on traffic randomness, for light-weight, and delay-tolerant application scenarios. Stochastic gradient descent is exploited to decouple the optimizations between time slots. A graph matching problem is then formulated for every time slot by decoupling and unifying the nonuniform cardinalities, and solved in a distributed manner by developing a new linear (1/2)-approximation method. We prove that the optimality loss resulting from the distributed approximate graph matching method can be compensated and diminish by increasing the learning time. Corroborated by simulations, the proposed distributed online learning is asymptotically optimal and superior to the state of the art in terms of throughput and energy efficiency.

A spurt of progress in wireless power transfer (WPT) and mobile edge computing (MEC) provides a promising approach for Industrial Internet of Things (IIoT) to enhance the quality and productivity of manufacturing. Scheduling in such a scenario is challenging due to congested wireless channels, time-dependent energy constraints, complicated device heterogeneity, and prohibitive signaling overheads. In this article, we first propose an online algorithm, called energy-aware resource scheduling (ERS), to maximize the system utility comprising throughput and fairness, with consideration on both system sustainability and stability. Based on Lyapunov optimization and convex optimization techniques, the proposed algorithm achieves asymptotic optimality for heterogeneous IIoT systems without prior knowledge of network state information (NSI). Subsequently, we extend the ERS algorithm to a more realistic scenario where the overhead and delay of NSI feedbacks are non-negligible. The optimal scheduling decisions of the scenario are provided, and the optimality loss on system utility under outdated NSI is analyzed. The simulations verify our theoretical claims and demonstrate the gains of our proposed ERS algorithm over alternative benchmark schemes.

To enable machine learning at the edge of wireless networks (such as edge cloud), close to mobile users, is critical for future wireless networks, but challenging since the lower layers in edge cloud are substantially different from existing machine learning configurations in the cloud. In such geo-distributed computing environment, streaming data need to be evenly and cost-efficiently partitioned for different workers to produce an unbiased learning model with reduced parameter synchronization frequency. This paper presents a new online approach to optimally partitioning streaming data under time-varying network conditions. A new measure is proposed to quantify the evenness of data partitioning and restrain the optimization of data admission, partitioning, and processing. Stochastic gradient descent is applied to learn the optimal decisions online and asymptotically maximize the time-average utility of data partitioning. A new protocol is designed to further reduce the measurements of link costs, while preserving the asymptotic optimality, data evenness, and stability of the platform. Simulation results show that the proposed approach is superior to the state of the art in terms of throughput and cost efficiency, while only 24% of the links need to be measured to achieve the asymptotic optimality.

Cooperative caching can unify network storage to improve efficiency, but the effective placement and search of contents are challenging especially in distributed edge clouds with neither a-priori knowledge on content requests nor instantaneous global view. This paper establishes a new profitable cooperative region for every content request admitted at an edge server, within which the content, if cached, can be retrieved with guaranteed profit against a direct retrieval from the network backbone. This narrows down the search for the content. The caching density of the content can also be significantly reduced, e.g., to a cached copy per region. The regions are based on a novel distributed framework which allows individual servers to spontaneously admit/dispatch requests and deliver/forward contents, while asymptotically maximizing the time-average profit of caching. The cooperative region for content is erected at individual servers by comparing the upper and lower bounds for the backlogs of unsatisfied requests of the content. Simulations show the substantially improved profit of the proposed approach over existing solutions. The regions can help automate the placement of contents with reduced density and improved efficiency.

Cloud has yet to be applied to computationally intensive radio signal processing, due to closely coupled computing tasks resulting from interference. This paper presents a new cloud-assisted joint beamforming architecture, where computations are decoupled for individual wireless users and pipelined for cloud execution, using Difference of Convex (DC), ℓ1-norm approximations, and dual decompositions. User-specific tasks are constructed and aligned with the cloud to leverage computation reuses and minimize overhead. The time-complexity is dramatically improved to support networks with tens to hundreds of base stations and users, without compromising the sum rate and quality-of-service. Further, the superiority of DC to the state-of-the-art Weighted Minimum Mean Square Error (WMMSE) in terms of convex relaxation is observed and discussed. Corroborated by simulations, the reason is revealed as WMMSE aggressively increases the data rate at interim stages, hence adversely interacting with ℓ1-norm approximation and reducing the feasible solution regions at later stages.

Wireless self-backhaul is an efficient solution to release the backhaul bottleneck of 5G Heterogeneous Networks (HetNets). This paper aims at jointly optimizing uplink and downlink scheduling to improve system throughput of time division duplex (TDD) HetNets. The problem that considers the uplink-downlink requirements and access-backhaul link coordination constraints is formulated, which has coupled variables and non-polynomial calculation complexity. Based on the structure property of the constructed problem, a joint uplink and downlink scheduling (JUDS) scheme is proposed to manage the complex interference and fully obtain the flexibility of wireless self-backhaul. In JUDS scheme, the original problem is decomposed into a transmission time slot (TTI) orchestration sub-problem and a sub-band scheduling sub-problem, both of which owns high efficient solutions. Simulation results address the effectiveness of JUDS scheme. On the one hand, the total system downlink throughput is significantly improved by JUDS scheme comparing with other existing scheduling schemes. On the other hand, The data rates of access links and backhaul links are well matched.

Content popularity evolution impacts optimization of cache placement and therefore influences design of caching policies. In this paper, we propose a cache updating scheme with popularity prediction and energy cost consideration. Firstly, we use linear regression method to predict future content popularity. Furthermore, we update the preceding cache placement according to predicted popularity aiming at minimizing energy cost. The problem is formulated as a convex optimization problem. Then we transform cache updating cost function, such that the optimal cache updating solution can be gained by Karush-Kuhn-Tucker (KKT) conditions. At last, the simulation results show that our scheme gets better performance in terms of energy consumption compared to the scheme without cache updating energy cost consideration.

The emerging fifth generation (5G) wireless system will have different requirements in terms of rate, latency and reliability. Network slicing which addresses the deployment of multiple logical networks as independent business operations on a common physical infrastructure has attracted more attentions. However, problems such as isolation and resource allocation of wireless network slices still exist. The paper focuses on the allocation of spectrum resources for 5G RAN slices. Firstly, The hierarchical auction model can satisfy both inter-slice isolation and intra-slice customization. Then, we propose a Hierarchical Auction and Dynamic Programming based Resource Allocation (HA&DP-RA) algorithm to allocate resources for the slices. The dynamic programming algorithm is adopted to obtain a stable solution. Finally, simulation results show the effectiveness of the proposed scheme.

Ultra reliable and low latency communication (URLLC) is envisioned as a new paradigm for the fifth generation (5G) mobile communications. To meet its stringent requirement, multi-connectivity attracts attention from both the academy and industrial, especially in Ultra-Dense Networks (UDN) where the user is under the coverage of several base stations (BSs). The dense deployment of BSs leads to a frequent unnecessary handover of moving user equipment (UE), which results in higher handover latency and more signal overhead. To overcome this problem, a received signal strength (RSS) prediction based multi-connectivity handover (RPMCH) scheme is proposed. In the scheme, a new handover triggering mechanism is adopted and the target serving BSs are selected based on RSS prediction. The simulation shows that the PRMCH scheme reduces the number of handovers by more than 50% compared with state-of-the-art handover scheme. In addition, our scheme contributes to a higher average throughput for medium and high mobility cases.

In this paper, an asynchronous multi-user deep reinforcement learning scheme is developed to control the handover (HO) processes across multiple user equipments (UEs), in the goal of lowering the HO rate while ensuring certain system throughput. In this scheme, we use a deep neural network (DNN) as an HO controller learned by each UE via reinforcement learning in a collaborative fashion. Moreover, we use supervised learning in initializing the DNN controller before the execution of reinforcement learning to exploit what we already know with traditional HO schemes and to mitigate the negative effects of random exploration at the initial stage. Furthermore, we show that the adopted global-parameter-based framework enables us to train faster with more UEs, which could nicely address the scalability issue to support large systems. Finally, simulation results demonstrate that the proposed framework can achieve better performance than the state-of-art on-line schemes, in terms of HO rates.

In this paper, we propose an indoor positioning model using the principle of Bayesian estimation. By analyzing the mobile features of the target and combining these features with state transition probability, we narrow down the belief region of the location distribution. To make our system model more intuitive, some basic concepts about the factor graph and the corresponding sum-product algorithm are introduced. Then, we map our system model onto the factor graph and make basic rules for message propagation. Based on the probability graphic model, mobile features enhanced sum-product algorithm is implemented to calculate the posterior probability of the target given range measurement observations. Simulation results show that the positioning precision of the proposed algorithm is higher than that of the existing algorithms at different speed levels, and the proposed algorithm also performs more robust in noisy indoor environment.

As location information becomes vitally important, positioning has been a highly desirable feature of 5G system which enables a huge amount of location-based applications and services. Millimeter wave (mmWave) is the promising technology for both offering better spectrum resource and positioning performance. A virtualized indoor office scenario with only one mmWave base station (BS) is considered in this paper. User equipment (UE) motion feature, mmWave line-of-sight (LoS) and first order reflection paths' AoA-ToA are fused for indoor positioning. Firstly, an improved least mean square (LMS) algorithm that combines motion message is proposed to refine the multi-path AoA estimation. Furthermore, a modified multi-path unscented Kalman filter (UKF) is proposed to track UE's position in the scenario. The information exchanges of the two stages not only consist of estimates(position, AoA) but also variance of position. Based on the simulation results, the proposed methods provide 2 times LoS-AoA estimation gains and centimeter 3D positioning accuracy respectively. Besides, this strategy is capable of positioning task with insufficient anchor nodes (AN).

Ultra reliable and low latency communication (URLLC) is one of the three typical service scenarios in the fifth generation mobile communications (5G) system, which supports mission-critical machine-type communication. Grant-free non-orthogonal multiple access (NOMA) system is a promising candidate technology for uplink URLLC scenario but it causes the problem of multi-user detection (MUD). In this paper, we propose a dynamic adaptive compressive sensing (DACS)-based MUD algorithm to realize MUD in URLLC scenario by exploiting user activity sparsity. Different from most of the state-of-the-art compressive sensing (CS)-based MUD algorithms, this algorithm needs no input of user activity sparsity level which may be unknown in practical system. Particularly, this algorithm adopts a stage-wise approach to increase estimated number of active users stage by stage for adaptively acquiring the true user activity sparsity level, introduces a backtracking idea to refine the estimated active user set for more accurate detection, and exploits the temporal correlation between active user sets in adjacent time slots for reducing computational complexity. Simulation results demonstrate that, although the proposed DACS-based MUD algorithm lacks the information of user activity sparsity level, it achieves better bit error rate (BER) performance than the conventional CS-based MUD algorithm.

Due to the severe propagation loss nature of millimeter wave (mmWave), multi-hop relay transmission is a promising mmWave backhaul solution in ultra dense network. In this paper, we propose a multi-path routing scheme taking all small cells' backhaul into account to increase the system throughput. Under multi-hop constraint, we aim to optimize the downlink resource allocation of mmWave band. A two-objective link scheduling problem is formulated, where one objective is to ensure fairness among user equipments (UEs), and the other is to increase the system throughput. Since the two objectives are contradictory, we cannot obtain their optimal solutions simultaneously. To solve this problem, we propose a concurrent transmission based heuristic algorithm, which is called QoS (i.e., quality of service) -aware fairness scheduling (QFS). The proposed QFS scheme aims at transmitting as much data as possible in each time slot and scheduling appropriate links to guarantee the fairness. Simulation results show that QFS scheme can provide better fairness than conventional schemes. The percentage of UEs capable of achieving the minimum data rate requirements by the proposed QFS scheme is about 92.4%, which is greatly higher than those of the conventional schemes. In addition, the throughput of QFS scheme is about 3.18 times higher than Time Division Multiple Access (TDMA) scheme.

Recently, network slicing which addresses the deployment of multiple logical networks as independent business operations on a common physical infrastructure has attracted more and more attentions. Despite the advances of network slicing, there are still many open issues regarding the isolation of the wireless slices and resources allocation. The paper focus on spectrum resource allocation for Cloud-RAN slices. We develop a bankruptcy game based algorithm to allocate resource for the Cloud-RAN slices. Cloud and slices are modeled to the bankrupt company and debtors in the game respectively, where Shapley value is adopted to obtain a stable solution. Simulation results show that the bankruptcy game based algorithm significantly improve resource utilization and guarantee the fairness of allocation.

This paper aims at maximizing the sum rate of a time division duplex two-transceiver system via the joint access and backhaul scheduling operation. The original scheduling problem that simultaneously considers the causal relationship constraint, the access-backhaul link matching constraint and the minimum data rate requirement is formulated. By using the access-backhaul constraints, the original problem is equivalently simplified into a zero-one integer linear programming with non-polynomial calculation complexity. Based on the simplified problem, an upper bound of the optimal scheduling is achieved. To obtain a practical scheduling solution with reduced calculation complexity, a two-phase access-backhaul scheduling (TPABS) scheme is proposed to approach the obtained upper scheduling bound. The causal relationship constraint and the minimum required data rate are guaranteed to be satisfied in TPABS. Simulation results indicate that TPABS approaches the proposed upper bound with less than 10% sum rate loss.

Ultra-Dense Network (UDN) is foreseen as one of the promising technologies in 5G to meet the ever-increasing mobile traffic demands. However, with existing cell discovery mechanisms, such massive deployment of access points (APs) would lead to a considerable increase in energy consumption and a significant reduction in offloading opportunity. To solve those problems, a User Location Prediction based Cell Discovery (ULPCD) scheme for User-centric Ultra-Dense Network (UUDN) is proposed. In the scheme, the network deployment information is sent to the user equipment (UE) the first time user enters in the range of cells. Then, with the aid of positioning information, the probability of a user entering into a new cell is calculated. Using the probability, UE can predict whether to send a connection request to activate an idle AP before entering in the AP's cell range. Simulation results demonstrate that our proposed scheme reduces over 60% energy consumption of UE and saves at least 20% working time of APs compared with state-of-the-art schemes. Meanwhile, our scheme provides higher detection probability, shorter access delay, and more offloading opportunity.

Due to the flexible deployment, in-band wireless backhaul solutions are preferred to the small cells in the ultradense networks (UDN). This paper manages the scheduling issues of in-band wireless backhaul in UDN by jointly considering the effect of access-backhaul link constraint, the interference and the minimum user data rate requirement. The scheduling problem that aims at maximizing the system throughput is formulated, whose solution is NP-hard. To reduce the calculation complexity and obtain a practical scheduling solution, A scheme named as coloring based access-backhaul scheduling (CBABS) is proposed. In CBABS, the minimum data rate requirements are satisfied by a QoS guarantee algorithm, the interference is managed by coloring algorithm (CA), the matching is done by ensuring the same data rate on access and backhaul link. Simulation results show that 98% of users' minimum downlink data rate requirements and 99% of users' minimum uplink data rate requirements are satisfied. The RB utility ratio is improved by 30% in CBABS compared to traditional scheduling scheme.

In this paper, we propose a two-layer framework to learn the optimal handover (HO) controllers in possibly large-scale wireless systems supporting mobile Internet-of-Things users or traditional cellular users, where the user mobility patterns could be heterogeneous. In particular, our proposed framework first partitions the user equipments (UEs) with different mobility patterns into clusters, where the mobility patterns are similar in the same cluster. Then, within each cluster, an asynchronous multiuser deep reinforcement learning (RL) scheme is developed to control the HO processes across the UEs in each cluster, in the goal of lowering the HO rate while ensuring certain system throughput. In this scheme, we use a deep neural network (DNN) as an HO controller learned by each UE via RL in a collaborative fashion. Moreover, we use supervised learning in initializing the DNN controller before the execution of RL to exploit what we already know with traditional HO schemes and to mitigate the negative effects of random exploration at the initial stage. Furthermore, we show that the adopted global-parameter-based asynchronous framework enables us to train faster with more UEs, which could nicely address the scalability issue to support large systems. Finally, simulation results demonstrate that the proposed framework can achieve better performance than the state-of-art online schemes, in terms of HO rates.

Fog computing enables resource-limited network devices to help each other with computationally demanding tasks, but has yet to be implemented in large scales due to sophisticated control and network inhomogeneity. This paper presents a new fully distributed online optimization to asymptotically minimize the time-average cost of fog computing, where tasks are selected to be offloaded and processed independently between different links and devices by measuring their cost effectiveness at each time slot. A key contribution is that we optimize the cost-effectiveness measures which achieve the asymptotic optimality over infinite time. Another contribution is that we optimize placeholders at the devices; which create collaborative computing regions of tasks in the vicinity of the point of capture, prevent tasks being offloaded beyond, preserve the asymptotic optimality and reduce delay. This is achieved in a distributed fashion by discovering the optimal substructure of the placeholders. Simulations show that the average size of collaborative regions is only 3.2 out of total 500 servers, and the system income increases by 43% as compared with existing techniques.

Decentralized orchestration of the control plane is critical to the scalability and reliability of software-defined network (SDN). However, existing orchestrations of SDN are either one-off or centralized, and would be inefficient the presence of temporal and spatial variations in traffic requests. In this paper, a fully distributed orchestration is proposed to minimize the time-average cost of SDN, adapting to the variations. This is achieved by stochastically optimizing the on-demand activation of controllers, adaptive association of controllers and switches, and real-time request processing and dispatching. The proposed approach is able to operate at multiple timescales for activation and association of controllers, and request processing and dispatching, thereby alleviating potential service interruptions caused by orchestration. A new analytic framework is developed to confirm the asymptotic optimality of the proposed approach in the presence of non-negligible signaling delays between controllers. Corroborated from extensive simulations, the proposed approach can save up to 73% the time-average operational cost of SDN, as compared to the existing static orchestration.

Task admission is critical to delay-sensitive applications in mobile edge computing, but is technically challenging due to its combinatorial mixed nature and consequently limited scalability. We propose an asymptotically optimal task admission approach which is able to guarantee task delays and achieve (1-ϵ)-approximation of the computationally prohibitive maximum energy saving at a time-complexity linearly scaling with devices. ϵ is linear to the quantization interval of energy. The key idea is to transform the mixed integer programming of task admission to an integer programming (IP) problem with the optimal substructure by pre-admitting resource-restrained devices. Another important aspect is a new quantized dynamic programming algorithm which we develop to exploit the optimal substructure and solve the IP. The quantization interval of energy is optimized to achieve an [O(ϵ), O(1/ϵ)]-tradeoff between the optimality loss and time complexity of the algorithm. Simulations show that our approach is able to dramatically enhance the scalability of task admission at a marginal cost of extra energy, as compared with the optimal branch and bound method, and can be efficiently implemented for online programming.

By performing fog computing, a device can offload delay-tolerant computationally demanding tasks to its peers for processing, and the results can be returned and aggregated. In distributed wireless networks, the challenges of fog computing include lack of central coordination, selfish behaviors of devices, and multi-hop signaling delays, which can result in outdated network knowledge and prevent effective cooperations beyond one hop. This paper presents a new approach to enable cooperations of N selfish devices over multiple hops, where selfish behaviors are discouraged by a tit-for-tat mechanism. The titfor-tat incentive of a device is designed to be the gap between the helps (in terms of energy) the device has received and offered; and indicates how much help the device can offer at the next time slot. The tit-for-tat incentives can be evaluated at every device by having all devices broadcast how much help they offered in the past time slot, and used by all devices to schedule task offloading and processing. The approach achieves asymptotic optimality in a fully distributed fashion with a timecomplexity of less than O(N2). The optimality loss resulting from multi-hop signaling delays and consequently outdated titfor-tat incentives is proved to asymptotically diminish. Simulation results show that our approach substantially reduces the timeaverage energy consumption of the state of the art by 50% and accommodates more tasks, by engaging devices hops away under multi-hop delays.

In this paper, we investigate the energy efficiency (EE) of large-scale distributed multiple-input multiple-output (D-MIMO) systems. Firstly, we derive an analytical closed-form expression of the lower bound on the ergodic capacity for D-MIMO systems, and the asymptotic performance for the large-scale antenna arrays systems over K fading channels is proposed. Utilizing these results, we elaborate on the EE optimization problem with transmitted power constraint. To tackle this multi-objective optimization problem, we propose an one-dimension iterative search algorithm with low complexity to obtain the optimal number of receive antennas, transmitted power and achievable sum rate with optimum EE. Simulation results verify the performance gain of the proposed scheme along with the increasing number of transmit antennas.

In this paper, we investigate the energy efficiency (EE) of massive multiple-input multiple-output (MIMO) systems with linear maximum ratio combining (MRC) and zero-forcing (ZF) receivers. To be practical, the baseband power consumption with algorithm computational complexity (ACC) is taken into account. Utilizing this model, we maximize the global optimal number of receiver antenna and sum rate with optimal EE by fixed the number of users for different receivers, respectively. In parallel, the low complexity iterative algorithm is proposed to reap the optimal values. Simulation results indicate that the EE improves with the number of users for the two receivers.

Licensed-Assisted Access (LAA) allows the operations on unlicensed spectrum for Long Term Evolution (LTE). In this paper, we propose a Markov-based analytical framework using the listen-before-talk (LBT) mechanism with dynamic contention window adaptation to investigate the performance of LAA-LAA and LAA-WiFi coexistence scenarios. Specifically, we introduce a Maximum Contention Window Timer Mechanism (MCWTM) to reduce the waste of resources and improve LAA coexistence performance. Furthermore, we derive the successful transmission probability and system backoff time under the two coexistence cases. Numerical results validate that the proposed dynamic contention window adaptation with MCWTM is effective in LAA-LAA coexistence scenario and can also improve LAA performance in LAA-WiFi coexistence scenario, compared with the static contention window mechanism.

As a prevailing concept in 5G, virtualisation provides efficient coordination among multiple radio access technologies (RATs) and enables multiple service providers to share different RATs’ physical infrastructures. This study proposes a generic framework for virtualising heterogeneous networks with different RATs. A novel virtual medium access control concept is introduced to realise resource offloading (allocation) and mobility management of the framework. The offloading and handover protocols are designed in detail. A novel resource offloading strategy is devised: First, to model the fact that different RATs possess different adaptability to different services, an ‘adaptability ratio’ concept is introduced and calculated using grey relational analysis. After that, a matching theory based framework is proposed to formulate the resource offloading problem and a ‘deferred acceptance’ algorithm is introduced to solve it. Through simulation, it is proved that the proposed protocols can efficiently reduce the service interruptions and improve the resource usage.

Mobile edge computing is of particular interest to Internet of Things (IoT), where inexpensive simple devices can get complex tasks offloaded to and processed at powerful infrastructure. Scheduling is challenging due to stochastic task arrivals and wireless channels, congested air interface, and more prominently, prohibitive feedbacks from thousands of devices. In this paper, we generate asymptotically optimal schedules tolerant to out-of-date network knowledge, thereby relieving stringent requirements on feedbacks. A perturbed Lyapunov function is designed to stochastically maximize a network utility balancing throughput and fairness. A knapsack problem is solved per slot for the optimal schedule, provided up-to-date knowledge on the data and energy backlogs of all devices. The knapsack problem is relaxed to accommodate out-of-date network states. Encapsulating the optimal schedule under up-to-date network knowledge, the solution under partial out-of-date knowledge preserves asymptotic optimality, and allows devices to self-nominate for feedback. Corroborated by simulations, our approach is able to dramatically reduce feedbacks at no cost of optimality. The number of devices that need to feed back is reduced to less than 60 out of a total of 5000 IoT devices.

This paper aims at improving the system energy efficiency of orthogonal frequency division multiple access small cell networks under user data rate requirements. Differently, in this paper, we take into account the effect of both forward link and backhaul link when formulating the system energy efficiency optimization problem. The optimal solution to the system energy efficiency problem needs the joint consideration of the scheduling, the transmission power optimization, and the backhaul link data rate control, which is NP-hard and generates huge signaling overhead. To this end, we first compute and obtain the lower bound and upper bound of the optimal system energy efficiency based on the property analysis of the formulated system energy efficiency problem. Then, to reduce the signaling overhead and calculation complexity, we propose a forward and backhaul link energy efficiency optimization scheme (FBEEOS) to approach the achieved system energy efficiency bounds. The system energy efficiency is proved to be convergent in a non-decreasing manner in FBEEOS. The simulation results confirm the properties of FBEEOS. In addition, our results show that the transmission power optimization is necessary to achieve higher system energy efficiency though its contribution to total energy consumption is negligible. This is a result of the effect of transmission power on interference and throughput, and needs to be taken into consideration when we optimize the system energy efficiency.

Green telecommunication is becoming an important part in designing the wireless communication systems. Small Cell Network (SCN) is considered as one of the most promising techniques in the future cellular networks. In order to reduce the high power consumption caused by dense deployment of small base stations (BSs), we first propose a new decentralized precoding algorithm which aims to maximize the energy efficiency of the SCN and the suboptimal solution can be obtained by two times of iterations. For further consideration, when the quantity of UEs is low in the network, it is a waste of energy to open a BS to serve only a few UEs or provide a low data rate to the UEs. So we introduce a BS closing strategy, which takes advantage of the property of SCN that different BSs cover more overlapping areas. Simulation results show that the proposed decentralized precoding algorithm outperforms the traditional precoding algorithms such as BD precoding, and the sleep strategy does not only take linear time complexity but also improve the energy efficiency of the SCN obviously.

In this paper, we investigate the energy efficient optimization for non-regenerative multiple-input- multiple-output (MIMO) Two-Way Relay (TWR) network. Contrary to existing energy efficiency (EE) optimal precoding designs, we maximize the weighted sum per- source energy efficiencies (WSEE) subject to the maximum transmit power constraints of sources and relay, such that different EE requirements from the sources can be investigated. The resulting optimization problem is a non-convex sum of ratios problem. To address this issue, we first reformulate the objective as a more tractable parametric subtraction form by introducing some auxiliary variables. Then we develop an efficient block coordinate decent method to solve the equivalent problem with respect to the individual optimization variable. It is shown that the proposed iterative algorithm is guaranteed to converge.

With mobile applications sharply developing, the battery technology becomes the bottleneck. Meanwhile, mobile users are increasingly sensitive to the latency of an application. The computation offloading in Small Cell Cloud (SCC) can economize the energy consumption of mobile devices efficiently and guarantee the makespan of an application. In this paper, we model the mobile application as a directed acyclic graph (DAG), and formulate an optimization problem of collaborative task execution to minimize the energy consumption on the mobile device while meeting a prescribed latency constraint. In order to solve this NP-hard problem, we propose a greedy algorithm based on partial critical path (GA-PCP) which can solve the problem approximately. The algorithm partitions the DAG into chains and processes these chains with the ``Add- Compare-Select" strategy to obtain the execution strategy. The algorithm can obtain a polynomial time complexity. Simulation results show that the solution of the GA-PCP is close to the optimal solution of the enumeration algorithm. Besides, the GA-PCP execution strategy can significantly save the energy consumption on the mobile device thereby prolonging its battery life, compared to the local execution.

Virtualization is a promising technique to solve the ossification of current wireless networks and meet the ever-increasing mobile data volume. This paper analyses the requirements in wireless network virtualization and proposes a generalized virtualization framework to overcome these challenges. A novel VMAC (virtual medium access control) concept is devised to perform resource virtualization and management, and the functionalities of VMAC are presented in detail, including user plane as well as data control plane. Through VMAC, heterogeneous RANs (radio access networks) can be aggregated with a unified protocol stack, packaged in the form of a service, reconfigured to provide end-to-end user services. The paper is concluded by identifying important open issues to be studied in future research.

Orbital angular momentum (OAM) provides a new dimension for multiplexing and consequently can improve the link capacity remarkably. In the scope of radio communication, uniform circular array (UCA) is usually used to generate OAM signals, where beam axis detection and alignment is a key factor that influences the detection of OAM signal and directly determines the capacity of OAM links. This study analyses the beam axis detection and alignment issues under the ideal (noiseless) condition. The authors first clarify the fact that for single‐mode OAM transmission, the intensity of electric field in radial direction is characterised by the Bessel function of the first kind. Inspired by this property, they propose three practical methods for beam axis detection and alignment, that is, double parallelogram array method, double UCA method and single UCA method. Iterative translation and rotation strategy are applied to enhance the precision. Finally, the effectiveness of the proposed methods is justified by simulation.

Nowadays, multiple access points and subcarrier transmission are widely used in visible light communication(VLC) to achieve seamless coverage and high spectral efficiency. However, the reuse of subcarriers may cause severe interference for users located in overlapping areas using co-channel. Those existing methods utilize interference managements to solve this issue, but at the cost of bandwidth inefficiency. Hence, a designated resource allocation that includes interference mitigation is needed without degrading the performance of the network. In this paper, a graph theory based resource allocation algorithm in the downlink is proposed, which not only mitigates the interference, but also improves the capacity and fairness of the system. Numeric results demonstrate that the proposed scheme exhibits a 133.6% and a 25.0% average system throughput improvement compared to the Round Robin scheme and proportional fairness scheme. Additionally, a better performance in fairness is obtained by the proposed scheme.

Visible light communication (VLC) is considered as a promising high speed wireless access technology. However, the line-of-sight (LOS) nature of visible light limits VLC coverage and user mobility. On the contrary, radio frequency (RF) provides much more extensive coverage, though its link bit rate is lower by contrast. A cooperation of VLC and RF can allow them to enhance each other in certain aspects. Therefore, following existing studies, a hybrid VLC-LTE system is employed. One big challenge for realizing such a heterogeneous network is mobility management due to dynamic traffic and network conditions. Based on two basic vertical handover (VHO) schemes, this paper proposes a VHO algorithm through prediction (PVHO). Interruption durations, message sizes and access delays are critical metrics for prejudging the system state. A mobile terminal (MT) records these key parameters in real time and then processes them to offer guidance for proper handover decisions. Through simulation, the advantages of PVHO are validated by comparing with existing algorithms. Analysis of the numeric results gives an indication that the performance of PVHO is superior under various circumstances.

This paper aims at improving the energy efficiency of small cell networks via cell sleep mechanism. The traditional power consumption minimization problem with quality of service (QoS) constraints is reestablished from the distributed small cell eNodeB (SeNB) sleep algorithm. The proposed algorithm consists of a Rate- based Access and Scheduling (RAS) algorithm and a distributed Diluted Load based Sleep (DLS) algorithm. In RAS algorithm, a many-to-many matching model is adopted to deal with the UE access and resource allocation problem. In DLS algorithm, a metric called Diluted Load is introduced to determine the sleep SeNB and solve the sleep optimization problem in a distributed way. By alternately conducting RAS and DLS algorithms, the energy efficiency of small cells keeps improving. Simulation results show that our algorithm can achieve lower power consumption as well as lower UE blocking ratio compared with existing sleep algorithms.

Network virtualization is considered a promising solution to the gradual ossification of current wireless networks. This paper proposes a two-stage power allocation scheme in LTE air interface virtualization where radio resources are coordinated by a hypervisor among different virtual operators (VOs). In the first stage, VCG auction game is utilized to generate an initial allocation. In the game, VOs are modeled as bidders bidding for power resources on behalf of their users while hypervisor is modeled as the auctioneer. In the second stage, Shapley value in coalition game is introduced to adjust the initial power allocation. The adjustment is made according to users' rate requirements to guarantee a fair allocation among users of different VOs. Simulation proves our scheme can balance between the energy efficiency and users' rate requirements compared with two conventional schemes.

The key obstacle to exploiting orbital angular momentum in wireless communication is the influence of axis misalignment on detection side. This paper summarizes the effects of axis misalignment quantitatively and presents novel automatic methods to overcome the obstacle under the ideal (noiseless) condition. Numerical results in the far field demonstrate the effectiveness of our approach to make orbital angular momentum in wireless communication feasible and reliable.

Plug-and-play femtocells will be an integrating part of future cellular networks. Resource management and interference mitigation become challenging, suffering from severely delayed network control in large-scale deployments. We propose a new game theoretic framework, where fast interference suppression is decoupled from the relatively slow frequency allocation process to tolerate the delayed control. The key idea is to cast femtocell clustering as an outer-loop evolutionary game coupled with bankruptcy channel allocation, which drives the cells to spontaneously switch to less interfered clusters. Within each cluster, we design an inner-loop non-cooperative power control game, such that the requirement of prompt control is eliminated. The two loops interact recursively with analytically confirmed stability. Simulations show that our framework can improve the throughput by 13.2% in a network of 200 cells, compared to the prior art. The gain grows further with the network size.

Due to the lack of spectrum resources in wireless networks, to efficiently make use of the existing heterogeneous wireless networks is of significant importance. Network selection mechanism plays an important role for mobile users to target a network under the principle “always best connected (ABC)” in heterogeneous wireless environment. In this paper, a novel approach for network selection based on the combination of analytic hierarchy process (AHP) and bankruptcy game which is a special type of an N-person cooperative game is proposed and investigated. The AHP method takes the responsibility of evaluating weights of multiple decision criteria, which depends more on the consideration from the user side. On the other hand, the bankruptcy game is mainly used to assess the potentials of available candidate networks. Finally, the combination of AHP and bankruptcy game evaluates the potential contribution ratio (PCR) of each candidate network and the network with the largest PCR is selected.

In a dense small cell deployment scenario, users are always prone to suffer severe interferences from neighbor base stations (BS) because the BSs are usually located closely. Coordinated Multi-Point (CoMP) can be introduced to alleviate these interferences and improve the system performance. It is necessary to determine coordination areas (CA) before implementation. In this paper, a novel dynamic clustering algorithm in CoMP joint transmission system is proposed based on graph theory. Firstly a feedback procedure is designed for interference reports mainly based on large scale fading. By building a graph according to the interferences, the clustering problem is equivalent to dividing the graph into several subgraphs. Each subgraph represents a CoMP cluster. It can be solved through a greedy strategy that each BS searches its best coordinated BSs. Compared with some other dynamic algorithms, the complexity of the proposed scheme is lower because it can be implemented in a decentralized way. Therefore this method is suitable in dense cell deployment with a large number of BSs. The simulation results show that the novel clustering algorithm performs better in user capacity than other traditional dynamic schemes. The influences of some parameters in this method are also considered and evaluated in the simulation.

This paper presents a new resource allocation scheme for orthogonal frequency division multiple access (OFDMA) Device-to-Device (D2D) networks. A two-phase scheme is proposed to maximize the throughput of D2D networks as well as guaranteeing the reliability of D2D communication. In the first phase, an interference management scheme is proposed based on clustering to guarantee the outage probability of D2D communications is less than a target value. The problem of interference management is mapped to the MAX k-CUT problem in graph theory and is solved by a cluster-based heuristic algorithm. In particular, the maximum cluster size is derived on the basic of the outage probability by using the Poisson Point Process (PPP) to model the distribution of D2D pairs. In the second phase, a fine-scale channel assignment is accomplished to maximize the throughput of D2D networks. Heuristic algorithm is proposed to efficiently solve the above problem. Simulations demonstrate the superior performance of the proposed solution compared with those of the existing schemes.

In macro and Closed Subscriber Group (CSG) femtocells co-channel deployment, femtocells and Macrocell User Equipments (MUEs) suffer strong interference from neighboring femtocells. The interference seriously degrades MUEs' throughput performance or even causes outage. To solve this issue, we provide a centralized component carrier selection scheme. By globally optimizing femtocells' and MUEs' component carrier selection, the proposed scheme can not only avoid the interference among femtocells and the MUEs' interference from femtocells simultaneously, but also maximize the system spectral efficiency and improve MUEs' throughput performance on this basis. Simulation results demonstrate under high data rate demands the proposed scheme can greatly improve MUEs' performance both in total throughput and outage rate than the classical universal frequency reuse scheme. Furthermore, the proposed scheme can achieve better femtocells' total throughput performance than the hard frequency reuse 1/2 scheme.

Device-to-Device (D2D) communication can improve spectrum efficiency and link capacity by allowing nearby devices to communicate directly with each other on the licensed frequency bands. However, the interference between D2D users and cellular users on the same resources may decrease the performance of the whole network, and interference management is an indispensable technology. In this paper, an optimized resource allocation method for intra-cluster D2D users is proposed to improve the network throughput. The scheme includes two steps. First, we construct a bipartite graph (BG) to represent the pairing relationship of concurrent D2D and cellular users that share the same resource pool. The resource allocation issue is transformed into a maximum weighted matching (MWM) problem. Then, Kuhn-Munkres (KM) algorithm is introduced to solve the matching problem which maximizes the transmission capacity. The simulation results show that our algorithm not only improves the communication quality of D2D links, but also increases the transmission rate of the allocated channels.

Device-to-Device (D2D) communication has been considered as a promising technique to provide better wireless services in local area. In this paper, we propose an improved intra-cluster D2D multicast protocol to enable data sharing among users. In our scheme, several D2D users take turns to multicast the corresponding packets. The sequence of packet transmissions is determined according to the data demand. In addition, we introduce relay based transmission into the multicast process so as to overcome transmission rate restriction caused by link heterogeneity. Greedy algorithm is applied to select relay nodes inspired by small world phenomenon. Through simulation, we show that the proposed method not only improves the delay performance and capacity gain, but also expands the D2D transmission range.

This paper studies energy efficient power and subchannel allocation in dense small cell networks. After giving the subchannel allocation algorithm, we transform the fractional energy efficient problem into an equivalent iterative optimization problem which has a subtractive form. With the developed utility function of the equivalent problem, we introduce a non-cooperative game to optimize power allocation. The existence and uniqueness of the Nash equilibrium of the game is proved. Besides, the proposed distributed power update needs minor signalling to get the optimal solution. System-level simulation results show that the proposed scheme can quickly reach the convergence and efficiently improve the energy efficiency in small cell networks.

Collaborative filtering has become one of the most used approaches to provide personalized services for users. The key of this approach is to find similar users or items using user-item rating matrix so that the system can show recommendations for users. However, most approaches related to this approach are based on similarity algorithms, such as cosine, Pearson correlation coefficient, and mean squared difference. These methods are not much effective, especially in the cold user conditions. This paper presents a new user similarity model to improve the recommendation performance when only few ratings are available to calculate the similarities for each user. The model not only considers the local context information of user ratings, but also the global preference of user behavior. Experiments on three real data sets are implemented and compared with many state-of-the-art similarity measures. The results show the superiority of the new similarity model in recommended performance.

In this paper, we study the subchannel power allocation problem to maximize the total throughput of the downlink orthogonal frequency division multiplexing (OFDM) multiple access points (APs) systems with non-coherent cooperative transmission. Although the problem has been claimed as a non-convex optimization problem, we prove that the duality gap between the original problem and its dual optimization problem is nearly zero when the number of subchannel is large, which implies solving the problem in the dual domain. Then, the problem is solved with the standard Lagrange dual decomposition method with an acceptable deviation. In addition, in order to reduce the complexity of the dual decomposition based method, we propose a suboptimal low-complexity algorithm, at a minor cost of the total throughput. Numerical results are also given to verify the proposed schemes.

Orthogonal Frequency Division Multiplexing Access (OFDMA) will be adopted in next generation wireless communication systems, as they provide multiple access channels for initial channel access, resource request, etc. In this paper, for OFDMA systems, an effective and adaptive backoff algorithm is presented based on the Pseudo Bayesian Broadcast algorithm. It not only provides a dynamic backoff window according to the load, but also supports different Quality of Service (QoS) for various access traffics. Through simulation, the proposed algorithm has the advantage of high successful random access rate and low access delay compared with the traditional backoff algorithm.

Multi-access for multi-mode terminals is possible in heterogeneous networks environment which becomes a critical issue for transmission in parallel with meeting user quality of service (QoS) performance requirements and throughput maximization. The previous optimal resource allocation schemes, which do not consider service type, may allocate scarce radio resources inefficiently. To solve this, we propose an optimal resource allocation algorithm with distinguishing the services traffic into two classes: Delay-Constraint (DC) and Best-Effort (BE). In our work, we formulate a mathematical optimal model to support such heterogeneous service requirements in multi-radio access scenario. Then, we develop a optimal radio resource allocation algorithm that achieves the goal of maximizing the total system throughput in heterogeneous networks, while efficiently satisfying the QoS requirement for DC services traffic and fairness for BE services traffic. Simulation results show that the proposed service traffic differentiation based radio resource allocation algorithm significantly outperforms other existing schemes.

In this paper, we study the subchannel (SC) power allocation for orthogonal frequency division multiplexing (OFDM) multiple access points (APs) systems with non-coherent cooperative transmission. The objective is to maximize the total capacity under per-AP power constraints. It can be proved that the optimal solution can be obtained by the combination of an optimal SC partition search and the power allocation across SCs for each feasible partition. Existing work exhaustively searched the optimal SC partition and used Lagrange dual method to compute the power allocation across SCs. Since the entire complexity increases exponentially with the number of SCs, the existing method is unsuitable for practical implementation. In this paper, we propose a novel optimal power allocation algorithm for non-coherent cooperative transmission with a much lower complexity. Firstly, a concept of “cut-off SC” is proposed for searching the optimal SC partition. Then, an efficient optimal power allocation algorithm across SCs is proposed for any given cut-off SC. Simulation results demonstrate that the proposed algorithm is optimal with a polynomial complexity, and ends within an acceptable number of iterations.

In cognitive radio (CR) networks, cross-layer design is an important issue since the behavior of one protocol could affect the performance of others. However, for the energy-constraint CR networks, the previous works mostly focus on maximizing the throughput in physical or transport layer, rather than the energy efficiency of the end-to-end transmission control protocol (TCP). In this paper, we propose a novel cross-layer scheme which takes the lower layers' parameters into consideration, e.g., signal-to-noise ratio (SNR), modulation and frame size, to improve the energy efficiency of TCP. Specifically, we use a finite state Markov channel (FSMC) model to characterize the fading channel, and solve the optimization problem by a restless bandit approach. Simulation results show that the physical and data link layer parameters affect the energy efficiency of the TCP traffic significantly and the performance can be improved by the adjustment of lower layers' parameters compared with the existing method.

Maximizing system sum-rate or minimizing error rate with constrained power are the main research focuses in the previous system-oriented power allocation schemes. However, link-oriented power allocation schemes that minimize power consumption with constrained rates for every link, attracted researchers in recent energy efficiency system design. In this paper, a link-oriented optimal power allocation scheme is firstly proposed for physical layer network coding with DeNoise-and-Forward protocol in multicast system, that two sources and two destinations communicating with the assistance of arbitrary number of relays. We also consider a suboptimal power allocation scheme with low complexity, where a single relay is chosen to minimize the power consumption. The numerical simulation shows that the suboptimal power allocation scheme also can achieve relatively low power consumption, even approach to the optimal scheme in some special case.

In this paper, the inter-cell interference coordination (ICIC) is investigated for the downlink transmission in OFDMA-based multi-cellular systems. The utility function is introduced to balance efficiency and fairness. Firstly, the system model is built based on the resource allocation rules of soft frequency reuse (SFR) and the utility-based optimization problem is formulated. Secondly, the problem is simplified and analyzed in the 3-cell layout to obtain a local optimizing allocation rule. Thirdly, a distributed heuristic ICIC scheme is developed in which a part of the spectrum is defined to be the spectrum pool. Cells make occupation decisions of the spectrum pool according to locally available information of traffic load. The proposed scheme has little signaling overhead and low complexity. Simulation results validate it can improve the cell edge throughput performance effectively.

For UTRAN Long Term Evolution (LTE) systems, inter-cell interference is the most important interference source. Inter-cell interference coordination (IOC) is considered to improve coverage and increase data rate at cell edge. The current consensus of IOC scheme is towards semi-static soft frequency reuse (SFR). Due to the limitation of resource configuration way for semi-statci SFR, problems will be met when applying the traditional admission control strategy to semi-static SFR deployment. In this paper, we have investigated the joint design of SFR and the corresponding call admission control strategy. A scheme is proposed which affords a new resource configuration strategy for SFR and an admission control algorithm which takes full account of the frequency planning of SFR and makes coordination among different cell zones or neighboring cells. Simulation results show that the proposed scheme can achieve good performance in terms of blocking probability, handover outage and resource utilization.

Cooperative relaying is accepted as a promising solution to achieve high data rates over large areas in the future 4G wireless system. In this paper, a cell-cluster based traffic load balancing strategy is proposed to solve the problem of cell congestion in cooperative cellular networks. In the paper, a dynamic cell-cluster construction method is first studied based on the traffic distribution among the hot spot cell and its neighboring cells, which is followed by the calculation of transferred traffic from the hot spot cell to each of the noncongested cells in the cell-cluster in order to minimize the average blocking probability, then, after the study of spectral efficiency for both direct and cooperative relaying transmissions, a mathematic model is formulated to jointly optimize routing and radio resource allocation in traffic load balancing, and a greedy based CJRR (cell-cluster based joint routing and radio resource allocation) algorithm is proposed to find a suboptimal solution. Simulation results show that our proposed traffic load balancing strategy has a satisfying performance in system spectral efficiency and blocking probability comparing with other transmission schemes.

In the group vertical handover (GVHO) scenario, many mobile terminals (MTs) send handover requests almost at the same time. The traditional vertical handover (VHO) schemes assume that the VHO user is coming one by one, so the current user knows the decision results of previous users, then the optimal result can be obtained. In GVHO scenario, multiple VHO decisions need to be made simultaneously, if the traditional VHO scheme was applied in this scenario, it may lead to system performance degradation or network congestion, because the decision-making MT does not know the results of other concurrent VHO users, so it may selfishly select the best networks just like in common VHO scenario. Therefore, three decision-making models for GVHO are proposed in this paper, and there performance comparisons are analyzed through numerical simulations.

MIMO-relay technology is accepted as a promising solution to improve cell edge performance for the future 4G wireless system. In this paper, an inter-cell interference mitigation scheme based on MIMO-relay technique is proposed to improve the average capacity for cell edge users. In the paper, a new network structure with shared relay station (SRS) is first studied, and a two-tier channel decomposition method is proposed based on the structure; then the BS (base station) precoding and relay filtering scheme is designed based on the proposed method, and the power allocation scheme at SRS is further discussed with two solutions of average power allocation algorithm and genetic based optimization algorithm. Numerical results show that compare with other inter-cell interference mitigation schemes, the proposed scheme can effectively cancel the inter-cell interference and significantly improve the capacity performance for cell edge users.

This paper proposes a novel distributed QoSguaranteed multi-cell radio resource allocation (RRA) scheme for OFDMA systems, which includes two separate steps operated on two time levels: the Hungarian algorithm based intra-cell fast scheduling on the frame level and the X2 interface based inter-cell interference coordination (ICIC) algorithm performed on the super-frame level. The scheme is distributed in the sense that both algorithms are carried out by the enhanced Node B (eNB). Through intense analysis, we proved that the ICIC algorithm not only provides fine robustness against ICI but also achieves good load balancing from a new perspective. Simulation results indicate that the performance of the proposed scheme, when compared to the standard "universal reuse" approach without inter-cell coordination, is significantly better in terms of increasing both the network and cell-edge throughputs.

In this paper, a game theory based load-balancing routing (GBLBR) protocol with cooperation stimulation is presented for delay sensitive traffic in wireless ad hoc networks. By calculating the game theory based delay utility function, GBLBR fully utilizes the link capacity across different paths, and obtains the optimal arrival rate for each path. This minimizes the average delay of each packet. Extensive simulation results have shown that the proposed routing protocol can reduce the average end-to-end delay and the percentage of packet loss. This is shown to be better than the conventional shortest path routing with the minimum hops approach. Moreover, the suggested game theory based cooperation stimulation strategy can enforce cooperation among nodes and improve network fairness.

In this paper, an effective relay based load balancing scheme, employing traffic transferring and channel borrowing, is presented for two-hop cellular relaying networks. In the proposed scheme, each relay station is allocated a set of real-time traffic channels so as to perform call relaying from a congested cell to another non-congested cell. Moreover, owing to the limited capacity of each relay station, dynamic channel borrowing is adopted to further facilitate load balancing between neighboring cells. With two different relay deployment scenarios considered, a multi-dimensional markov chain model is then developed to evaluate the performance of the proposed scheme. Simulation results demonstrate that the proposed scheme achieves better performance compared with that of the conventional cellular network in terms of call blocking rate.

In this paper, we propose a novel channel quality indicator (CQI) feedback reduction scheme. In the proposed scheme, the base station (BS) calculates the urgency information of each user according to the quality of service (QoS) and queue conditions and informs users of their urgency information, then the users will send the CQI feedback information with different feedback schemes which are threshold based scheme and best-m scheme. This hybrid scheme overcomes the disadvantages of both threshold based scheme and best-m scheme. It not only reduces the feedback load but also guarantees the fairness and QoS requirement of users with bad channel condition. The proposed scheme is a good tradeoff between the best-m scheme and threshold based scheme. The numerical results confirm that our CQI feedback scheme achieves a remarkable reduction in the amount of feedback information without significant throughput degradation.

Long-term evolution (LTE) of the UMTS terrestrial radio access and radio access network is considered to ensure the competitiveness of 3GPP radio-access technology in a longer time frame. To minimize/optimize the user equipment (UE) power consumption, and further to support various services and large amount of data transmissions, a discontinuous reception (DRX) mechanism with adjustable DRX cycles has been adopted in LTE RRC_CONNECTED mode. In this paper, we take an overview of the DRX cycle adjustable feature of the LTE power saving mechanism and further modeling the mechanism with bursty packet data traffic using a semi-Markov process. The analytical results, which are validated against simulation experiments, show that LTE DRX achieves power saving gains over UMTS DRX at the price of prolonging wake-up delay. Based on the analytical model, effects of the DRX parameters on the power saving and wake-up delay performance are also investigated, and the results verify a trade-off relationship between the power saving and wake-up delay performance.

Various characteristics of applications determine different requirements of mobility management schemes. Realtime applications especially lay strict limitation on handoff latency and latency stretch. This limitation is exacerbated when system robust, scalability and load balancing are required simultaneously. In this paper, we present a peer-to-peer based mobility support scheme that keeps advantages of P2P on robustness and scalability as well as optimizes handoff latency and latency stretch. Moreover, a proxy handoff mechanism is proposed to ensure the optimal performance in movement. Analysis and experiments based on OpenDHT reveal proxy handoff works effectively and handoff latency about 50 ms with latency stretch value 1.14 can be achieved.

In this paper, we propose a novel cross layer packet scheduling algorithm for real time (RT) traffics in multiuser downlink MIMO-OFDMA wireless systems. The algorithm dynamically allocates the resources in space, time and frequency domain based on the channel state information (CSI), users' quality of service (QoS) requirements and queue state information (QSI). To provide high data rate and high spectrum efficiency, adaptive modulation and coding (AMC) is employed on every eigenmode subchannel. The proposed algorithm can improve the cell throughput, increase the number of users that can be supported and guarantee users' QoS requirements and the fairness among all users. Simulation results indicate that the proposed algorithm is able to get superior performances.

In this paper, dynamic resource allocation problems for multi-user downlink MIMO-OFDMA are investigated. With the goal of minimizing the total transmit power under condition that QoS of different users can be guaranteed, a novel dynamic resource allocation algorithm is proposed. First of all, a new subcarrier allocation policy is given based on the perfect CSI. And then a spatial subchannels grouping mechanism is proposed for bit allocation. In order to utilize the spatial resource efficiently, the proposed algorithm exploits all non-zero spatial subchannels to transmit data. The proposed scheme reduces the computational complexity significantly at little expense of the system performance. Numerical simulations show that the proposed algorithm is a good tradeoff between system performance and computational complexity.

Efficient radio resource allocation is essential to provide quality of service (QoS) for wireless networks. In this paper, a cross-layer resource allocation scheme is presented with the objective of maximizing system throughput, while providing guaranteed-QoS for users. With the assumption of a finite queue for arrival packets, the proposed scheme dynamically allocates radio resources based on user's channel characteristic and QoS metrics derived from a queuing model, which considers a packet arrival process modeled by discrete Markov Modulated Poisson Process (dMMPP), and a multirate transmission scheme achieved through adaptive modulation(AM). The cross-layer resource allocation scheme operates over two steps. Specifically, the amount of bandwidth allocated to each user is first derived from a queuing analytical model, and then the algorithm finds the best subcarrier assignment for users. Simulation results show that the proposed scheme maximizes the system throughput while guaranteeing QoS for users.

In this paper, an efficient resource management scheme applied to heterogeneous services scenario in multiple- input and multiple-output/orthogonal frequency division multiplexing (MIMO/OFDM) system is proposed. The design of the scheme is to maximize the system throughput while guaranteeing QoS requirements for users. At medium access control (MAC) layer, an urgency-factor based scheduler is designed to utilize the properties of packet data system. An efficient resource allocation algorithm at physical (PHY) layer, which jointly adapt subcarrier allocation, power distribution and bit loading according to instantaneous channel conditions, is proposed. The scheduler and the resource allocator are tightly coupled together through the cross-layer approach. The simulation results show that the proposed scheme improves the system throughput and makes use of resource more efficiently, while guaranteeing QoS for heterogeneous services.

A modeling method for wireless packet networks based on network calculus (NC) theory, employing the concatenation of NC components to represent the effects of the modules constituting wireless packet networks model, is proposed in this paper. Three well-known scheduling algorithms are modified to agree with the proposed NC model. A new scheduling algorithm for packet networks is proposed in the context of NC. Simulations then are done to study the end-to-end QoS characteristics of the networks with different scheduling algorithms. The result shows that, in terms of the criterions defined in NC model, the scheduling algorithm proposed in this paper outperforms the existing three algorithms.

In this paper, we propose a new cooperative random access strategy for future cooperative networks. This scheme considers the different requirements of services, and exploits the cooperative relaying nature for multi-channel environments, e.g. orthogonal frequency division multiplexing access (OFDMA). Due to differentiation of services and definition of special channels, those users with real-time (RT) request can reserve the access channels in advance, while others with non-real-time (NRT) services can access to the BS or the nearest distributed relay stations (DRSs), which are introduced for cooperative relaying, through sharing those channels remained. The analyses and numerical results demonstrate that our scheme can achieve high throughput, low collision probability and low access delay compared with conventional slotted Aloha.

A random access protocol, particularly suitable for OFDMA system, is proposed and analyzed in this paper. The protocol adopts a new dynamic RACH assignment algorithm and a new adaptive access probability scheme. Under the condition of light load, Base Station (BS) will adjust the number of RACHs to improve the channel utilization. Under the condition of heavy load, BS will take effective measure to guarantee QoS requirements of high priority traffics. In addition, the article introduces a statistical model to estimate system load, which is one of the features of the protocol. The simulation results fully indicate that the proposed random access protocol is efficient and reliable at conditions of both high and low load, and not only provides excellent transmission quality guarantee for higher priority traffics, but also supports lower priority traffics transmission efficiently in the integration traffic environment.

In this paper, a new QoS-Guarantee resource allocation scheme is proposed for multi-user MIMO- OFDM systems. Based on channel state information (CSI) , the base station (BS) employs singular value decomposition (SVD) to transmit the MIMO channel into parallel eigenmode subchannels for each subcarrier, and then subcarriers, bits and power are dynamically allocated to users according to user's support subcarrier rate, the queue status of traffics and user's quality of service (QoS) requirement. Moreover, subcarrier reallocation is executed in order to heighten user's transmission rate and improve the utilization of system's resources. Simulation results demonstrate the superior performance of our proposed scheme, which can not only guarantee user's QoS requirement, keep user fairness, but also can greatly improve the cell throughput.