With the growing demand for device-free and privacy-preserving sensing solutions, Wi-Fi sensing has emerged as a promising approach for human pose estimation (HPE). However, existing methods often process vast amounts of channel state information (CSI) data directly, ultimately straining networking resources. This paper introduces TinySense, an efficient compression framework that enhances the scalability of Wi-Fi-based human sensing. Our approach is based on a new vector quantization-based generative adversarial network (VQ-GAN). Specifically, by leveraging a VQGAN-learned codebook, TinySense significantly reduces CSI data while maintaining the accuracy required for reliable HPE. To optimize compression, we employ the K-means algorithm to dynamically adjust compression bitrates to cluster a large-scale pre-trained codebook into smaller subsets. Furthermore, a Transformer model is incorporated to mitigate bitrate loss, enhancing robustness in unreliable networking conditions. We prototype TinySense on an experimental testbed using Jetson Nano and Raspberry Pi to measure latency and network resource use. Extensive results demonstrate that TinySense significantly outperforms state-of-the-art compression schemes, achieving up to 1.5 × higher HPE accuracy score (PCK20) under the same compression rate. It also reduces latency and networking overhead, respectively, by up to 5× and 2.5×. The code repository is available online at https://github.com/icclabo/CloudSense.

Serverless edge computing enables low-latency Internet of Things (IoT) services but faces scalability challenges due to complex concurrency and resource management. While existing approaches address function initialization and edge-cloud offloading, they often overlook the joint optimization of serverless concurrency and physical-layer resources, leading to potential service degradation and increased costs. To tackle this, we propose OPLA, a novel cross-layer framework for joint latency and concurrency optimization, designed to minimize end-to-end latency while optimizing concurrent serverless functions. OPLA models interactions between physical-layer resources (e.g., bandwidth, transmission power, offloading ratios) and application-layer concurrency decisions. The formulated problem is a highly non-convex mixed-integer nonlinear program (MINLP), which we prove to be at leastNP-complete in certain cases. To approximate its optimal solution efficiently, we propose an iterative exploration-exploitation procedure (EEP). The exploration phase, which is embarrassingly parallelizable, balances solution quality and efficiency with single parameter tuning. The exploitation phase is just a simple successive convex approximation to OPLA. Moreover, we also develop a presolve-postsolve heuristic with deterministic rounding to ensure feasibility for OPLA. Numerical results demonstrate that EEP consistently achieves solutions within a 6% optimality gap relative to a global solver across a wide range of network scales and workloads, confirming its effectiveness and scalability for real-world serverless edge deployments.

Hierarchical edge-cloud computing-aided Internet of Things (IoT) networks offer low-latency and cost-efficient services to a growing number of data-intensive IoT devices. However, optimizing service placement, which involves determining the most suitable locations within a network to deploy various services, is critical to balancing workloads dynamically and ensuring efficient resource utilization. In this paper, we jointly optimize service placement, edge/cloud cooperation, task offloading, and bandwidth allocation to enhance processing efficiency and response times. The main objective is to minimize both the overall end-to-end latency and the system cost, including service deployment and operational costs. The formulated problem belongs to the class of non-convex mixed-integer nonlinear programming, where finding a feasible solution is already challenging. Towards a stable system, we first transform the original problem into a more tractable form and then decompose it into sub-problems which are solved at different timescales. Combining tools from relaxation and the successive convex approximation method, we develop iterative algorithms to solve these problems efficiently. With an appropriate penalty parameter, the proposed algorithms guarantee convergence to at least a local optimum. We produce extensive numerical results to demonstrate the superior performance of the proposed algorithms over benchmark schemes as well as emphasize the significance of the joint service placement and resource allocation in enhancing system performance and efficiency.

This paper explores the energy efficiency (EE) of integrated sensing and communication (ISAC) systems employing massive multiple-input multiple-output (mMIMO) techniques to leverage spatial beamforming gains for both communication and sensing. We focus on an mMIMO-ISAC system operating in an orthogonal frequency-division multiplexing setting with a uniform planar array, zero-forcing downlink transmission, and mono-static radar sensing to exploit multi-carrier channel diversity. By deriving closed-form expressions for the achievable communication rate and Cramér-Rao bounds (CRBs), we are able to determine the overall EE in closed-form. A power allocation problem is then formulated to maximize the system’s EE by balancing communication and sensing efficiency while satisfying communication rate requirements and CRB constraints. Through a detailed analysis of CRB properties, we reformulate the problem into a more manageable form and leverage Dinkelbach’s and successive convex approximation (SCA) techniques to develop an efficient iterative algorithm. A novel initialization strategy is also proposed to ensure high-quality feasible starting points for the iterative optimization process. Extensive simulations demonstrate the significant performance improvement of the proposed approach over baseline approaches. Results further reveal that as communication spectral efficiency rises, the influence of sensing EE on the overall system EE becomes more pronounced, even in sensing-dominated scenarios. Specifically, in the high ω regime of 2 × 10−3, we observe a 16.7% reduction in overall EE when spectral efficiency increases from 4 to 8 bps/Hz, despite the system being sensing-dominated.

Integrated sensing and communication (ISAC) systems promise efficient spectrum utilization by jointly supporting radar sensing and wireless communication. This paper presents a deep learning-driven framework for enhancing physical-layer security in multicarrier ISAC systems under imperfect channel state information (CSI) and in the presence of unknown eaves-dropper (Eve) locations. Unlike conventional ISAC-based friendly jamming (FJ) approaches that require Eve’s CSI or precise angle-of-arrival (AoA) estimates, our method exploits radar echo feedback to guide directional jamming without explicit Eve’s information. To enhance robustness to radar sensing uncertainty, we propose a radar-aware neural network that jointly optimizes beamforming and jamming by integrating a novel nonparametric Fisher Information Matrix (FIM) estimator based on f-divergence. The jamming design satisfies the Cramér–Rao lower bound (CRLB) constraints even in the presence of noisy AoA. For efficient implementation, we introduce a quantized tensor train-based encoder that reduces the model size by more than 100 times with negligible performance loss. We also integrate a non-overlapping secure scheme into the proposed framework, in which specific sub-bands can be dedicated solely to communication. Extensive simulations demonstrate that the proposed solution achieves significant improvements in secrecy rate, reduced block error rate (BLER), and strong robustness against CSI uncertainty and angular estimation errors, under-scoring the effectiveness of the proposed deep learning–driven friendly jamming framework under practical ISAC impairments.

This paper investigates the integration of active reconfigurable intelligent surfaces (ARISs) with unmanned aerial vehicles (UAVs) in a mixed free-space optics radio frequency (FSO-RF) downlink communication system, enabling simultaneous lightwave information and power transfer (SLIPT). The proposed architecture addresses key challenges in UAV-based networks, including limited endurance and backhaul constraints, by allowing the UAV to harvest energy from the optical backhaul while transmitting RF signals enhanced via ARIS to ground users. The system design aims to maximize the minimum achievable rate among users by jointly optimizing the UAV’s beamforming strategy, 3D placement, ARIS reflection coefficients, optical ground station (OGS) transmit power and the power splitting (PS) ratio at the UAV. An alternating optimization framework is developed to decompose the resulting non-convex problem into efficiently solvable subproblems using inner approximation techniques. Simulation results confirm that the proposed approach significantly outperforms baseline schemes, such as passive RIS, fixed UAV deployment, and static PS configurations, delivering improved rate fairness and energy efficiency. These results demonstrate the potential of ARIS-assisted SLIPT-enabled UAVs to support robust and sustainable downlink communications in next-generation wireless networks.

Integrated sensing and communications (ISAC) has emerged as a key enabler for 6G and beyond. However, ISAC systems face significant challenges, including the sensing function that introduces interference and degrades communication performance, as well as high sensing power consumption that reduces overall communication efficiency, particularly in complex urban environments. To address these issues, we propose a reconfigurable intelligent surface (RIS)-assisted orthogonal frequency division multiplexing (OFDM) multiple-input multiple-output (MIMO) ISAC system, where a RIS enhances connectivity for users in localized coverage gaps. We formulate and study two optimization problems: i) maximizing system sum spectral efficiency and ii) maximizing global energy efficiency, by jointly optimizing transmit precoding, subcarrier allocation, and RIS phase shifts under power, quality of service, and sensing accuracy constraints. These problems are classified as mixed-integer nonlinear programs, which are generally difficult to solve optimally. To tackle this, we develop efficient iterative algorithms leveraging successive convex approximation, alternating optimization, Riemannian manifolds, and Dinkelbach’s method to obtain at least locally optimal solutions. Simulation results validate the effectiveness of the proposed designs, demonstrating their superiority over benchmark schemes, achieving up to 40% higher spectral efficiency and up to 60% improvement in energy efficiency compared to conventional overlap and random-phase approaches.

Goal-oriented semantic communication (SC) aims to revolutionize communication systems by transmitting only task-essential information. However, current approaches face challenges such as joint training at transceivers, leading to redundant data exchange and reliance on labeled datasets, which limits their task-agnostic utility. To address these challenges, we propose a novel framework called Goal-oriented Invariant Representation-based SC (SC-GIR) for image transmission. Our framework leverages self-supervised learning to extract an invariant representation that encapsulates crucial information from the source data, independent of the specific downstream task. This compressed representation facilitates efficient communication while retaining key features for successful downstream task execution. Focusing on machine-to-machine tasks, we utilize covariance-based contrastive learning techniques to obtain a latent representation that is both meaningful and semantically dense. To evaluate the effectiveness of the proposed scheme on downstream tasks, we apply it to various image datasets for lossy compression. The compressed representations are then used in a goal-oriented AI task. Extensive experiments on several datasets demonstrate that SC-GIR outperforms baseline schemes by nearly 10%,, and achieves over 85% classification accuracy for compressed data under different SNR conditions. These results underscore the effectiveness of the proposed framework in learning compact and informative latent representations.

Network slicing is a critical feature in 5G and beyond communication systems, enabling the creation of multiple virtual networks(i.e., slices)on a shared physical network infrastructure. This involves efficiently mapping each slice component, including virtual network functions (VNFs) and their interconnections (virtual links), onto the physical network. This paper considers the slice embedding problem in which the order of VNFs can be adjusted. This provides increased flexibility for service deployment, but the selection of the best order of VNFs also complicates embedding. We propose an optimization framework to tackle the challenges of jointly optimizing slice admission control and embedding with flexible VNF ordering. Additionally, we introduce a near-optimal branch-and-bound (BnB) algorithm, combined with the A* search algorithm, to generate embedding solutions efficiently. Extensive simulations on both small and large-scale multi-tiered 5G networks demonstrate that flexible VNF ordering increases the number of deployable slices within a network infrastructure, thereby improving resource utilization and better meeting diverse demands across varied network topologies.

The transition to beyond-fifth-generation (B5G) wireless systems has revolutionized cellular networks, driving unprecedented demand for high-bandwidth, ultra low-latency, and massive connectivity services. The open radio access network (Open RAN) and network slicing provide B5G with greater flexibility and efficiency by enabling tailored virtual networks on shared infrastructure. However, managing resource allocation in these frameworks has become increasingly complex. This paper addresses the challenge of optimizing resource allocation across virtual network functions (VNFs) and network slices, aiming to maximize the total reward for admitted slices while minimizing associated costs. By adhering to the Open RAN architecture, we decompose the formulated problem into two subproblems solved at different timescales. Initially, the successive convex approximation (SCA) method is employed to achieve at least a locally optimal solution. To handle the high complexity of binary variables and adapt to time-varying network conditions, traffic patterns, and service demands, we propose a deep reinforcement learning (DRL) approach for real-time and autonomous optimization of resource allocation. Extensive simulations demonstrate that the DRL framework quickly adapts to evolving network environments, significantly improving slicing performance. The results highlight DRL’s potential to enhance resource allocation in future wireless networks, paving the way for smarter, self-optimizing systems capable of meeting the diverse requirements of modern communication services.

We explore mission assignment and task offloading in Open Radio Access Network (Open RAN)-enabled intelligent transportation systems (ITS), where autonomous vehicles utilize mobile edge computing for efficient processing. Existing studies often overlook mission dependencies and offloading costs, leading to suboptimal decisions. To address this, we formulate a novel optimization problem that integrates these factors and enhances performance through vehicle cooperation. We then develop the chaotic Gaussian-based global artificial rabbit optimization (CG-GARO) algorithm, a new metaheuristic approach, which significantly improves mission assignment efficiency and resource utilization. Simulation results show that our approach surpasses baseline metaheuristics in both system benefits and mission completion rates, demonstrating strong potential for real-world deployment in dynamic ITS environments.

WiFi sensing-based human pose estimation (HPE) has gained significant attention in the academic community due to its advantages over vision- and sensor-based methods, including nonintrusiveness, convenience, and enhanced privacy protection. However, most existing WiFi-based pose Estimators suffer from poor performance and lack robustness in the presence of random noise. To address these challenges, this article presents a novel HPE architecture comprising two key modules: 1) a Denoiser and 2) an Estimator. The Denoiser is based on an autoencoder structure, while the Estimator is based on a new convolutional neural network (CNN) called SDy-CNN, which is designed to dynamically focus on high-information subcarriers of orthogonal frequency division multiplexing signals. Additionally, Bayesian optimization is employed to fine-tune the architecture’s parameters for optimal performance flexibly. Experiments conducted on a comprehensive dataset, MM-Fi, demonstrate that the proposed architecture significantly outperforms existing state-of-the-art Estimators, achieving up to an 8.38% improvement in HPE accuracy in clean data scenarios and up to a 14% improvement in noisy data scenarios. It has also been proven to gain computational efficiency when being much faster than other methods.

In this work, we consider the integration of energy harvesting (EH) and semantic communication strategies in resource-constrained Internet of Things (IoT) systems. The system empowers IoT devices to harvest energy from a base station, utilizing this harvested energy for the extraction and transmission of semantic information (e.g., scene graphs). To maximize the total transmission of image data or scene graphs to the central station, we formulate a comprehensive problem that jointly optimizes the EH duration, original image selection, transmit power, and channel allocation to IoT devices. The challenges arising from the dynamic environments and uncertain system parameters are effectively tackled by policy-based deep reinforcement learning algorithms, i.e., advantage actor-critic (A2C) and proximal policy optimization (PPO). Simulation results are implemented on the real data set clearly showing the superior performance achieved by our proposed algorithms compared to the baseline schemes. Notably, our approach enables IoT devices to transmit a greater number of original images and scene graphs with increased triplets to the central station, as highlighted in the simulation outcomes. This phenomenon showcases the potential of our strategy to enhance the capabilities of IoT systems in dynamic environments.

Wireless communications are particularly vulnerable to eavesdropping attacks due to their broadcast nature. To effectively deal with eavesdroppers, existing security techniques usually require accurate channel state information (CSI), e.g., for friendly jamming (FJ), and/or additional computing resources at transceivers, e.g., cryptography-based solutions, which unfortunately may not be feasible in practice. This challenge is even more acute in low-end IoT devices. We thus introduce a novel deep learning-based FJ framework that can effectively defeat eavesdropping attacks with imperfect CSI and even without CSI of legitimate channels. In particular, we first develop an autoencoder-based communication architecture with FJ, namely, AEFJ, to jointly maximize the secrecy rate and minimize the block error rate (BLER) at the receiver without requiring perfect CSI of the legitimate channels. In addition, to deal with the case without CSI, we leverage the mutual information neural estimation (MINE) concept and design a MINE-based FJ scheme that can achieve comparable security performance to the conventional FJ methods that require perfect CSI. Extensive simulations in a multiple-input-multiple-output (MIMO) system demonstrate that our proposed solution can effectively deal with eavesdropping attacks in various settings. Moreover, the proposed framework can seamlessly integrate MIMO security and detection tasks into a unified end-to-end learning process. This integrated approach can significantly maximize the throughput and minimize the BLER, offering a good solution for enhancing communication security in wireless communication systems.

In the ever-evolving landscape ofNextGwireless networks, Open radio access network (RAN) emerges as a transformative paradigm, revolutionizing network architectures and fostering innovation through its open, intelligent and disaggregated approach. By integrating RAN intelligent controllers (RICs), we can seamlessly implement machine learning (ML) algorithms to cater to diverse vertical applications and deployment environments without the need for intricate planning. However, this architecture suffers from two critical challenges: frequent handovers and load balancing amid varying traffic demands of different services in dynamic environments. To address these issues, this study proposes a joint intelligent user association, congestion control, and resource scheduling (IUCR) scheme. Aligning with the 7.2x functional split (FS) option recommended by the O-RAN Alliance, we present a hierarchical optimization framework incorporating heuristic methods, successive convex approximation (SCA), and a distributed deep reinforcement learning (DRL) approach across different Open RAN components, such as RICs and RAN layers. The simulation results convincingly demonstrate the superior performance of the proposed scheme compared to centralized approaches, validating its effectiveness.

Network slicing has appeared a key feature in 5G and beyond communication networks that enables the creation of multiple virtual networks (i.e., slices) over a shared physical network infrastructure. This process involves efficiently embedding (or mapping) each slice element, including virtual network functions (VNFs) and their interconnections, onto the physical network. This paper explores a scenario where the order of VNFs can be adjusted during slice embedding, offering greater flexibility to increase the number of services deployed on the infrastructure. We formulate a novel optimization framework to tackle the challenges of slice admission control and embedding with this flexibility. A heuristic is also introduced to derive embedding solutions in a timely manner. Simulation results demonstrate that allowing flexible VNF ordering significantly increases the number of slices that can be deployed in the network infrastructure.

The demand for stringent interactive Quality of Service has intensified in both mobile-edge computing (MEC) and cloud systems, driven by the imperative to improve user experiences. As a result, the processing of computation-intensive tasks in these systems necessitates adherence to specific deadlines or achieving extremely low latency. To optimize task scheduling performance, existing research has mainly focused on reducing the number of late jobs whose deadlines are not met. However, the primary challenge with these methods lies in the total search time and scheduling efficiency. In this article, we present the optimal job scheduling algorithm designed to determine the optimal task order for a given set of tasks. In addition, users are enabled to make informed decisions for offloading tasks based on the information provided by servers. The details of performance analysis are provided to show its optimality and low complexity with the linearithmic time$\mathcal {O}(n\log n)$, where n is the number of tasks. To tackle the uncertainty of the randomly arriving tasks, we further develop an online approach with fast outage detection that achieves rapid acceptance times with time complexity of$\mathcal {O}(n)$. Extensive numerical results are provided to demonstrate the effectiveness of the proposed algorithm in terms of the service ratio and scheduling cost.

Diffuse waste data and associated privacy concerns present significant challenges for effective waste classification in the Internet of Things (IoT) realm. This research introduces a novel approach that leverages differential privacy (DP) and federated transfer learning (FTL) to address the issues, enabling waste classification while preserving privacy within the IoT ecosystem. By integrating Federated Learning (FL), Transfer Learning (TL), and DP, our proposed method facilitates collaborative training while ensuring data privacy. In this methodology, a pre-trained model, initially trained on the ImageNet dataset, is disseminated to IoT devices. Subsequently, these devices perform local training using the TrashNet and Garbage Classification datasets. This process allows devices to capture waste characteristics unique to their individual environments. Through the fusion of general knowledge pertaining to the trained model and local insights, the proposed approach achieves efficient waste classification. The study critically examines the implications for privacy, biases resulting from limited local data, and trade-offs between privacy and model performance. The experimental evaluation demonstrates the effectiveness of the approach and underscores the importance of ensuring privacy-sensitive waste classification. This research contributes to the discourse on FTL and encourages further research into privacy-preserving waste classification within the IoT.

To enable an intelligent, programmable and multi-vendor radio access network (RAN) for 6G networks, considerable efforts have been made in standardization and development of open RAN (O-RAN). So far, however, the applicability of O-RAN in controlling and optimizing RAN functions has not been widely investigated. In this paper, we jointly optimize the flow-split distribution, congestion control and scheduling (JFCS) to enable an intelligent traffic steering application in O-RAN. Combining tools from network utility maximization and stochastic optimization, we introduce a multi-layer optimization framework that provides fast convergence, long-term utility-optimality and significant delay reduction compared to the state-of-the-art and baseline RAN approaches. Our main contributions are three-fold:$i$) we propose the novel JFCS framework to efficiently and adaptively direct traffic to appropriate radio units;$ii$) we develop low-complexity algorithms based on the reinforcement learning, inner approximation and bisection search methods to effectively solve the JFCS problem in different time scales; and$iii$) the rigorous theoretical performance results are analyzed to show that there exists a scaling factor to improve the tradeoff between delay and utility-optimization. Collectively, the insights in this work will open the door towards fully automated networks with enhanced control and flexibility. Numerical results are provided to demonstrate the effectiveness of the proposed algorithms in terms of the convergence rate, long-term utility-optimality and delay reduction.

Reconfigurable intelligent surfaces (RISs) have emerged as a game-changing technology to improve wireless network performance by intelligently manipulating and customizing the physical propagation environment. Such capability is especially important for the application of smart cities as it increases wireless service offers and quality to end-users. In this paper, we aim to maximize the number of served users in a challenging RIS-aided smart city street by jointly optimizing the multislot scheduling, precoding, and passive RIS-based beamforming design under quality of service and power constraints. Multislot scheduling is introduced in order to benefit from additional time diversity and thus better exploit the available degrees of freedom. The formulated problem is a mixed integer nonlinear programming, which is NP-hard. To solve the problem with affordable complexity, we develop an efficient iterative algorithm based on binary variable relaxation, alternating optimization, and successive convex approximation techniques. Simulation results demonstrate the superiority of the proposed design over the design without RIS and the design without scheduling, especially in the presence of a large number of users. In addition, results illustrate that by introducing a quality of service margin, the proposed design can improve its robustness to outdated channel state information in mobility scenarios.

Federated Learning (FL) plays a pivotal role in enabling artificial intelligence (AI)-based mobile applications in mobile edge computing (MEC). However, due to the resource heterogeneity among participating mobile users (MUs), delayed updates from slow MUs may deteriorate the learning speed of the MEC-based FL system, commonly referred to as the straggling problem. To tackle the problem, this work proposes a novel privacy-preserving FL framework that utilizes homomorphic encryption (HE) based solutions to enable MUs, particularly resource-constrained MUs, to securely offload part of their training tasks to the cloud server (CS) and mobile edge nodes (MENs). Our framework first develops an efficient method for packing batches of training data into HE ciphertexts to reduce the complexity of HE-encrypted training at the MENs/CS. On that basis, the mobile service provider (MSP) can incentivize straggling MUs to encrypt part of their local datasets that are uploaded to certain MENs or the CS for caching and remote training. However, caching a large amount of encrypted data at the MENs and CS for FL may not only overburden those nodes but also incur a prohibitive cost of remote training, which ultimately reduces the MSP’s overall profit. To optimize the portion of MUs’ data to be encrypted, cached, and trained at the MENs/CS, we formulate an MSP’s profit maximization problem, considering all MUs’ and MENs’ resource capabilities and data handling costs (including encryption, caching, and training) as well as the MSP’s incentive budget. We then show that the problem is convex and can be efficiently solved using an interior point method. Extensive simulations on a real-world human activity recognition dataset show that our proposed framework can achieve much higher model accuracy (improving up to 24.29%) and faster convergence rate (by 2.86 times) than those of the conventionalFedAvgapproach when the straggling probability varies between 20% and 80%. Moreover, the proposed framework can improve the MSP’s profit up to 2.84 times compared with other baseline FL approaches without MEN-assisted training.

This article proposes a novel framework that utilizes an over-the-air Brain-Computer Interface (BCI) to learn Metaverse users’ expectations. By interpreting users’ brain activities, our framework can optimize physical resources and enhance Quality-of-Experience (QoE) for users. To achieve this, we leverage a Wireless Edge Server (WES) to process electroencephalography (EEG) signals via uplink wireless channels, thus eliminating the computational burden for Metaverse users’ devices. As a result, the WES can learn human behaviors, adapt system configurations, and allocate radio resources to tailor personalized user settings. Despite the potential of BCI, the inherent noisy wireless channels and uncertainty of the EEG signals make the related resource allocation and learning problems especially challenging. We formulate the joint learning and resource allocation problem as a mixed integer programming problem. Our solution involves two algorithms: a hybrid learning algorithm and a meta-learning algorithm. The hybrid learning algorithm can effectively find the solution for the formulated problem. Specifically, the meta-learning algorithm can further exploit the neurodiversity of the EEG signals across multiple users, leading to higher classification accuracy. Extensive simulation results with real-world BCI datasets show the effectiveness of our framework with low latency and high EEG signal classification accuracy.

The sixth-generation (6G) wireless network landscape is evolving toward enhanced programmability, virtualization, and intelligence to support heterogeneous use cases. The O-RAN Alliance is pivotal in this transition, introducing a disaggregated architecture and open interfaces within the 6G network. Our paper explores an intelligent traffic steering (TS) scheme within the Open radio access network (RAN) architecture, aimed at improving overall system performance. Our novel TS algorithm efficiently manages diverse services, improving shared infrastructure performance amid unpredictable demand fluctuations. To address challenges like varying channel conditions, dynamic traffic demands, we propose a multi-layer optimization framework tailored to different timescales. Techniques such as long-short-term memory (LSTM), heuristics, and multi-agent deep reinforcement learning (MADRL) are employed within the non-real-time (non-RT) RAN intelligent controller (RIC). These techniques collaborate to make decisions on a larger timescale, defining custom control applications such as the intelligent TS-xAPP deployed at the near-real-time (near-RT) RIC. Meanwhile, optimization on a smaller timescale occurs at the RAN layer after receiving inferences/policies from RICs to address dynamic environments. The simulation results confirm the system’s effectiveness in intelligently steering traffic through a slice-aware scheme, improving eMBB throughput by an average of 99.42% over slice isolation.

We consider unmanned aerial vehicle (UAV)-enabled wireless systems where downlink communications between a multi-antenna UAV and multiple users are assisted by a hybrid active-passive reconfigurable intelligent surface (RIS). We aim at a fairness design of two typical UAV-enabled networks, namely the static-UAV network where the UAV is deployed at a fixed location to serve all users at the same time, and the mobile-UAV network which employs the time division multiple access protocol. In both networks, our goal is to maximize the minimum rate among users through jointly optimizing the UAV’s location/trajectory, transmit beamformer, and RIS coefficients. The resulting problems are highly nonconvex due to a strong coupling between the involved variables. We develop efficient algorithms based on block coordinate ascend and successive convex approximation to effectively solve these problems in an iterative manner. In particular, in the optimization of the mobile-UAV network, closed-form solutions to the transmit beamformer and RIS passive coefficients are derived. Numerical results show that a hybrid RIS equipped with only 4 active elements and a power budget of 0 dBm offers an improvement of 38% — 63% in minimum rate, while that achieved by a passive RIS is only about 15%, with the same total number of elements.

With the emergence of ultra-reliable and low latency communication (URLLC) services, link adaptation (LA) plays a pivotal role in improving the robustness and reliability of communication networks via appropriate modulation and coding schemes (MCS). LA-based resource management schemes in both physical and medium access control layers can significantly enhance the system performance in terms of throughput, latency, reliability, and quality of service. Increasing the number of retransmissions will achieve higher reliability and increase transmission latency. In order to balance this trade-off with improved link performance for URLLC services, we study a joint subcarrier and power allocation problem to maximize the achievable sum-rate under an appropriate MCS. The formulated problem is mixed-integer nonconvex programming which is challenging to solve optimally. In addition, a direct application of standard optimization techniques is no longer applicable due to the complication of the effective signal-to-noise ratio (SNR) function. To overcome this challenge, we first relax the binary variables to continuous ones and introduce additional variables to convert the relaxed problem into a more tractable form. By leveraging the successive convex approximation method, we develop a low-complexity iterative algorithm that guarantees to achieve at least a locally optimal solution. Simulation results are provided to show the fast convergence of the proposed iterative algorithm and demonstrate the significant performance improvement in terms of the achievable sum-rate, compared with the conventional LA approach and existing retransmission policy.

In this paper, we investigate an effective energy management in a UAV -assisted Metaverse synchronization system. The UAV s perform the data collection for a virtual service provider (VSP) for the synchronization between the physical objects and digital twins (DTs). The UAVs buy energy resources from an energy service provider (ESP). The key issue is to motivate both the ESP and the UAV s to participate in the energy trading market. For this, we design a deep learning (DL)-based auction scheme that maximizes the revenue of the ESP while guaranteeing individual rationality (IR) and incentive compatibility (IC). We provide numerical results to demonstrate the improvement of the DL-based auction scheme compared to the baseline scheme in terms of revenue, IC, and IR.

In this paper, we aim to enable an intelligent traffic (TS) steering application in the open radio access network (O-RAN) by jointly optimizing the flow-split distribution, congestion control and scheduling (i.e. so-called JFCS). To do so, we develop a multi-layer optimization framework based on network utility maximization and stochastic optimization methods. The proposed algorithm provides fast convergence, long-term utility-optimality and significantly low latency compared to state-of-the-art RAN approaches. In particular, our main contributions are as follows: i) we propose the novel JFCS framework to efficiently and adaptively route traffic to indented users in appropriate radio units, and ii) we develop low-complexity algorithms to effectively solve the JFCS problem in different time scales, enabling a closed-loop control of the TS in the O-RAN context. The insights presented in this work will pave the way for 0- RAN that are completely automated, offering improved control and flexibility.

Satellite edge computing (SEC) has emerged as a promising technology to deliver network services to remote users. Coupled with software-defined networking (SDN) and network function virtualization (NFV), SEC can provide flexibility, agility, and efficiency when allocating computing and storage resources. However, there still remain a number of technical challenges in terms of fairness and efficiency of the allocation of physical resources in service provisioning, especially in a satellite network with limited resources and dynamic traffic demands. In this paper, we investigate a dynamic virtual network function (VNF) mapping and scheduling in an SDN/NFV-enabled SEC environment to maximize the fairness between competing services in terms of the E2E delay safe margin to enhance the service acceptance rates in the network. We mathematically formulate the VNF mapping and scheduling problem as a nonlinear integer optimization problem, which is NP-hard. In order to effectively solve the problem, this paper proposes a two-stage heuristic dynamic VNF mapping and scheduling algorithm: i) the path selection algorithm returns all possible paths for a given service request with multiple VNFs, which are sorted in ascending order based on their E2E service delay and executed offline, and ii) the dynamic VNF mapping and scheduling algorithm performs online dynamic remapping and rescheduling of VNFs. Finally, numerical results are provided to demonstrate that the proposed algorithm offers a higher service acceptance rate, computing resource utilization efficiency, and higher fairness compared to a benchmark scheme.

Recently, the advances of low-latency communication technologies and edge intelligence have enabled a wide range of task-oriented time-sensitive applications. This paper aims at designing adaptive service placement, task offloading, and bandwidth allocation for ultra-reliable and low-latency communication (URLLC)-aided edge networks. The main objective is to minimise both the total end-to-end (e2e) latency and number of installed services at edge servers. The optimal solutions are obtained by jointly optimising service placement decisions, task offloading portions and bandwidth allocation at dynamic timescales subject to network budgets and application requirements under uncertain environment. Selective simulation results are provided to validate the effectiveness of the proposed solution in term of reducing the latency as well as optimising service placement decisions.

In this paper, we study joint communication and computation offloading (JCCO) for hierarchical edge-cloud systems with ultra-reliable and low latency communications (URLLC). We aim to minimize the end-to-end (e2e) latency of computational tasks among multiple industrial Internet of Things (IIoT) devices by jointly optimizing offloading probabilities, processing rates, user association policies and power control subject to their service delay and energy consumption requirements as well as queueing stability conditions. The formulated JCCO problem belongs to a difficult class of mixed-integer non-convex optimization problem, making it computationally intractable. In addition, a strong coupling between binary and continuous variables and the large size of hierarchical edge-cloud systems make the problem even more challenging to solve optimally. To address these challenges, we first decompose the original problem into two subproblems based on the unique structure of the underlying problem and leverage the alternating optimization (AO) approach to solve them in an iterative fashion by developing newly convex approximate functions. To speed up optimal user association searching, we incorporate a penalty function into the objective function to resolve uncertainties of a binary nature. Two sub-optimal designs for given user association policies based on channel conditions and random user associations are also investigated to serve as state-of-the-art benchmarks. Numerical results are provided to demonstrate the effectiveness of the proposed algorithms in terms of the e2e latency and convergence speed.

For future networks, it is highly demanding to satisfy a wide range of time-sensitive and computation-intensive services. This is a very challenging task, since it requires a combination of aspects from information, communication and computation in order to establish a digital representation of the real network environment. This paper introduces a fairness-aware latency minimisation (FALM) framework in the digital twin (DT) aided edge computing with ultra-reliable and low latency communications (URLLC), which jointly optimises various communication and computation parameters, namely, bandwidth allocation, transmission power, task offloading portions, and processing rate of user equipments (UEs) and edge servers (ESs). The formulated problem is highly complicated, due to non-convex constraints and strong coupling among optimisation variables. To deal with this problem, we develop both centralised and distributed optimisation approaches. In particular, we first resort to successive convex approximation (SCA) method to develop a low-complexity iterative algorithm and solve the problem in a centralised manner. Combining tools from SCA and alternating direction method of multipliers (ADMM), we develop an efficient distributed solution with parallel computation processing at ESs under global consensus in each iteration and strong theoretical performance guaranteed. Numerical results are provided to validate the proposed solutions in terms of convergence speed and overall latency as well as improving fairness among all UEs.

In this paper, we address a dynamic network resource selection problem for mobile users in a rate-splitting multiple access (RSMA)-enabled network by leveraging evolutionary games. Particularly, mobile users are able to locally and dynamically make their selection on orthogonal resource blocks (RBs), which are also considered as network resources (NRs), over time to achieve their desired utilities. Then, RSMA is used for each group of users selecting the same NR. With the use of RSMA, the main goal is to optimize the beamformers of the common and private messages for users in the same group to maximize their sum rate. The resulting problem is generally non-convex, and thus we develop a successive convex approximation (SCA)-based algorithm to efficiently solve it in an iterative fashion. To model the NR adaptation of users, we propose to use two evolutionary games, i.e. a traditional evolutionary game (TEG) and fractional evolutionary game (FEG). The FEG approach enables users to incorporate memory effects (i.e. their past experiences) for their decision-making, which is more realistic than the TEG approach. We then theoretically verify the existence of the equilibrium of the proposed game approaches. Simulation results are provided to validate their consistency with the theoretical analysis and merits of the proposed approaches. They also reveal that, compared with TEG, FEG enables users to leverage past information for their decision-making, resulting in less communication overhead, while still guaranteeing convergence.

Open radio access network (ORAN) Alliance offers a disaggregated RAN functionality built using open interface specifications between blocks. To efficiently support various competing services,namelyenhanced mobile broadband (eMBB) and ultra-reliable and low-latency (uRLLC), the ORAN Alliance has introduced a standard approach toward more virtualized, open, and intelligent networks. To realize the benefits of ORAN in optimizing resource utilization, this paper studies an intelligent traffic steering (TS) scheme within the proposed disaggregated ORAN architecture. For this purpose, we propose a joint intelligent traffic prediction, flow-split distribution, dynamic user association, and radio resource management (JIFDR) framework in the presence of unknown dynamic traffic demands. To adapt to dynamic environments on different time scales, we decompose the formulated optimization problem into two long-term and short-term subproblems, where the optimality of the latter is strongly dependent on the optimal dynamic traffic demand. We then apply a long-short-term memory (LSTM) model to effectively solve the long-term subproblem, aiming to predict dynamic traffic demands, RAN slicing, and flow-split decisions. The resulting non-convex short-term subproblem is converted to a more computationally tractable form by exploiting successive convex approximations. Finally, simulation results are provided to demonstrate the effectiveness of the proposed algorithms compared to several well-known benchmark schemes.

In this paper, we study short-packet communications in multi-hop networks with wireless energy transfer, where relay nodes harvest energy from power beacons to transmit short packets to multiple destinations. It is proposed a novel cooperative beamforming relay selection (CRS) scheme which incorporates partial relay selection and distributed multiuser beamforming to achieve a high-reliable transmission in two consecutive hops. A closed-form expression for the average block error rate (BLER) of the CRS scheme is derived, based on which an asymptotic analysis is also carried out. To achieve optimal channel uses allocation, we formulate a fairness end-to-end throughput maximization problem which is generally NP-hard due to the non-concavity of the objective function and mixed-integer constraints. To solve this challenging problem efficiently, we first relax channel uses to be continuous and transform the relaxed problem into an equivalent non-convex one, but with a more tractable form. We then develop a low-complexity iterative algorithm relying on inner approximation framework to convexify non-convex parts that converges to at least a locally optimal solution. Towards real-time settings, we design an efficient deep convolutional neural network (CNN) with multiscale-accumulation connections to achieve the sub-optimal solution of the relaxed problem via real-time inference processes. Numerical results are presented to verify the analytical derivations and to demonstrate performance improvements of the CRS scheme over the benchmark ones in terms of BLER, reliability, latency, and throughput in various settings. Moreover, the designed CNN provides the lowest root-mean-square error compared to the state-of-the-art deep learning approaches while the CNN-aided optimization framework estimates accurately the optimal channel uses allocation with low execution time.

A cell-free (CF) massive multiple-input-multiple-output (mMIMO) system can provide uniform spectral efficiency (SE) with simple signal processing. On the other hand, a recently introduced technology called hybrid relay-reflecting intelligent surface (HR-RIS) can customize the physical propagation environment by simultaneously reflecting and amplifying radio waves in preferred directions. Thus, it is natural that incorporating HR-RIS into CF mMIMO can be a symbiotic convergence of these two technologies for future wireless communications. This motivates us to consider an HR-RIS-aided CF mMIMO system to utilize their combined benefits. We first model the uplink/downlink channels and derive the minimum-mean-square-error estimate of the effective channels. We then present a comprehensive analysis of SE performance of the considered system. Specifically, we derive closed-form expressions for the uplink and downlink SE. The results reveal important observations on the performance gains achieved by HR-RISs compared to conventional systems. The presented analytical results are also valid for conventional CF mMIMO systems and those aided by passive reconfigurable intelligent surfaces. Such results play an important role in designing new transmission strategies and optimizing HR-RIS-aided CF mMIMO systems. Finally, we provide extensive numerical results to verify the analytical derivations and the effectiveness of the proposed system design under various settings.

We consider a novel hybrid active-passive reconfigurable intelligent surface (RIS)-assisted unmanned aerial vehicle (UAV) air-ground communications system. Unlike the conventional passive RIS, the hybrid RIS is equipped with a few active elements to not only reflect but also amplify the incident signals for significant performance improvement. Towards a fairness design, our goal is to maximize the minimum rate among users through jointly optimizing the location and power allocation of the UAV and the RIS reflecting/amplifying coefficients. The formulated optimization problem is nonconvex and challenging, which is efficiently solved via block coordinate descend and successive convex approximation. Our numerical results show that a hybrid RIS requires only 4 active elements and a power budget of 0 dBm to achieve an improvement of 52.08% in the minimum rate, while that achieved by a conventional passive RIS with the same total number of elements is only 18.06%.

In this paper, we design an efficient deep convolutional neural network (CNN) to improve and predict the performance of energy harvesting (EH) short-packet communications in multi-hop cognitive Internet-of-Things (IoT) networks. Specifically, we propose a Sum-EH scheme that allows IoT nodes to harvest energy from either a power beacon or primary transmitters to improve not only packet transmissions but also energy harvesting capabilities. We then build a novel deep CNN framework with feature enhancement-collection blocks based on the proposed Sum-EH scheme to simultaneously estimate the block error rate (BLER) and throughput with high accuracy and low execution time. Simulation results show that the proposed CNN framework achieves almost exactly the BLER and throughput of Sum-EH one, while it considerably reduces computational complexity, suggesting a real-time setting for IoT systems under complex scenarios. Moreover, the designed CNN model achieves the root-mean-square-error (RMSE) of 1.33 × 10-2on the considered dataset, which exhibits the lowest RMSE compared to the deep neural network and state-of-the-art machine learning approaches.

We consider in this work a cell-free (CF) massive multiple-input-multiple-output (mMIMO) system where multiple hybrid relay-reflecting intelligent surfaces (HR-RIS) are deployed to assist communication between access points and users. We first present the signal model and derive the minimum-mean-square-error estimate of the effective channels. We then present a comprehensive analysis for the considered HR-RIS-aided CF mMIMO system, where the closed-form expression of the downlink throughput is derived. The presented analytical results are also valid for conventional CF mMIMO systems, i.e., CF mMIMO systems with and without passive reconfigurable intelligent surfaces. Finally, the analytical derivations are verified by extensive numerical results.

We address the problem of minimising latency with computation offloading in digital twin wireless edge networks in industrial Internet-of-Things environment via ultra-reliable and low latency communications links. The minimised latency is obtained by jointly optimising both communication and computation variables, namely transmit power, user association of IoT devices, offloading portions, the processing rate of users and edge servers. To deal with this challenging problem, we propose an iterative algorithm based on alternating optimisation approach combined with inner convex approximation framework. Simulation results demonstrate the proposed algorithm’s effectiveness in reducing the latency compared with other benchmark schemes.

We study a joint communication and computation offloading (JCCO) for hierarchical edge-cloud systems with ultra-reliable and low latency communications (URLLC). We aim to minimize the worst-case end-to-end (e2e) latency of computational tasks among multiple industrial Internet of Things (IIoT) devices by jointly optimizing offloading probabilities, processing rates, user association policies and power control subject to their service delay and energy consumption requirements as well as queueing stability conditions. To tackle the problem, we first decompose the original problem into two subproblems and then leverage the alternating optimization (AO) approach to solve them in an iterative fashion by developing newly convex approximate functions. The numerical results are provided to demonstrate the effectiveness of the proposed algorithms in terms of the e2e latency and convergence speed.

Among a plethora of new wireless communication technologies, reconfigurable intelligent surface (RIS) emerges as one of the revolutionary solutions to provide energy- and cost-efficient signal transmissions. RIS is capable of reflecting electromagnetic signals in a controlled manner. In this paper, we jointly design the active beamforming at the base station and passive beamforming at the RIS to maximize the number of served users in a practical Smart City street scenario, subject to quality of service (QoS) and power constraints. The formulated problem belongs to the difficult class of mixed-integer non-convex programming, which is NP-hard. To arrive at a low-complexity solution, we first decompose the original problem into two subproblems and then propose an alternating optimization algorithm based on successive convex approximation (SCA) to solve them in an iterative manner. Simulation results are provided to verify the performance improvement of the proposed algorithm as compared to baseline schemes.

This paper addresses the problem of minimising latency in computation offloading with digital twin (DT) wireless edge networks for industrial Internet-of-Things (IoT) environment via ultra-reliable and low latency communications (URLLC) links. The considered DT-aided edge networks provide a powerful computing framework to enable computation-intensive services, where the DT is used to model the computing capacity of edge servers and optimise the resource allocation of the entire system. The objective function is comprised of local processing latency, URLLC-based transmission latency and edge processing latency, subject to both communication and computation resources budgets. In this regard, the minimum latency is obtained by jointly optimising the transmit power, user association, offloading portions, the processing rate of users and edge servers. The formulated problem is highly complicated due to complex non-convex constraints and strong coupling variables. To deal with this computationally intractable problem, we propose an iterative algorithm which decomposes the original problem into three sub-problems and resolve this problem in the fashion of alternating optimisation approach combined with an inner convex approximation framework. Simulation results demonstrate the effectiveness of the proposed method in reducing the latency compared with other benchmark schemes.

Federated learning (FL) is capable of performing large distributed machine learning tasks across multiple edge users by periodically aggregating trained local parameters. To address key challenges of enabling FL over a wireless fog-cloud system (e.g., non-i.i.d. data, users’ heterogeneity), we first propose an efficient FL algorithm based on Federated Averaging (called$\mathsf {FedFog}$) to perform the local aggregation of gradient parameters at fog servers and global training update at the cloud. Next, we employ$\mathsf {FedFog}$in wireless fog-cloud systems by investigating a novel network-aware FL optimization problem that strikes the balance between the global loss and completion time. An iterative algorithm is then developed to obtain a precise measurement of the system performance, which helps design an efficient stopping criteria to output an appropriate number of global rounds. To mitigate the straggler effect, we propose a flexible user aggregation strategy that trains fast users first to obtain a certain level of accuracy before allowing slow users to join the global training updates. Extensive numerical results using several real-world FL tasks are provided to verify the theoretical convergence of$\mathsf {FedFog}$. We also show that the proposed co-design of FL and communication is essential to substantially improve resource utilization while achieving comparable accuracy of the learning model.

Unmanned aerial vehicle (UAV) communication has emerged as a prominent technology for emergency communications (e.g., natural disaster) in the Internet of Things (IoT) networks to enhance the ability of disaster prediction, damage assessment, and rescue operations promptly. A UAV can be deployed as a flying base station (BS) to collect data from time-constrained IoT devices and then transfer it to a ground gateway (GW). In general, the latency constraint at IoT devices and UAV’s limited storage capacity highly hinder practical applications of UAV-assisted IoT networks. In this paper, full-duplex (FD) radio is adopted at the UAV to overcome these challenges. In addition, half-duplex (HD) scheme for UAV-based relaying is also considered to provide a comparative study between two modes (viz., FD and HD). Herein, a device is considered to be successfully served if its data is collected by the UAV and conveyed to GW timely during flight time. In this context, we aim to maximize the number of served IoT devices by jointly optimizing bandwidth, power allocation, and the UAV trajectory while satisfying each device’s requirement and the UAV’s limited storage capacity. The formulated optimization problem is troublesome to solve due to its non-convexity and combinatorial nature. Towards appealing applications, we first relax binary variables into continuous ones and transform the original problem into a more computationally tractable form. By leveraging inner approximation framework, we derive newly approximated functions for non-convex parts and then develop a simple yet efficient iterative algorithm for its solutions. Next, we attempt to maximize the total throughput subject to the number of served IoT devices. Finally, numerical results show that the proposed algorithms significantly outperform benchmark approaches in terms of the number of served IoT devices and system throughput.

In this paper, we study short-packet communications (SPCs) in multi-hop wireless-powered Internet-of-Things networks (WPINs), where IoT devices transmit short packets to multiple destination nodes by harvesting energy from multiple power beacons. To improve system block error rate (BLER) and throughput, we propose a best relay-best user (bR-bU) selection scheme with an accumulated energy harvesting mechanism. Closed-form expressions for the BLER and throughput of the proposed scheme over Rayleigh fading channels are derived and the respective asymptotic analysis is also carried out. To support real-time settings, we design a deep neural network (DNN) framework to predict the system throughput under different channel settings. Numerical results demonstrate that the proposed bR-bU selection scheme outperforms several baseline ones in terms of the BLER and throughput, showing to be an efficient strategy for multi-hop SPCs. The resulting DNN can estimate accurately the throughput with low execution time. The effects of message size on reliability and latency are also evaluated and discussed.

Federated learning (FL) allows multiple edge computing nodes to jointly build a shared learning model without having to transfer their raw data to a centralized server, thus reducing communication overhead. However, FL still faces a number of challenges such as nonindependent and identically distributed data and heterogeneity of user equipments (UEs). Enabling a large number of UEs to join the training process in every round raises a potential issue of the heavy global communication burden. To address these issues, we generalize the current state-of-the-art federated averaging (FedAvg) by adding a weight-based proximal term to the local loss function. The proposed FL algorithm runs stochastic gradient descent in parallel on a sampled subset of the total UEs with replacement during each global round. We provide a convergence upper bound characterizing the tradeoff between convergence rate and global rounds, showing that a small number of active UEs per round still guarantees convergence. Next, we employ the proposed FL algorithm in wireless Internet-of-Things (IoT) networks to minimize either total energy consumption or completion time of FL, where a simple yet efficient path-following algorithm is developed for its solutions. Finally, numerical results on unbalanced data sets are provided to demonstrate the performance improvement and robustness on the convergence rate of the proposed FL algorithm over FedAvg. They also reveal that the proposed algorithm requires much less training time and energy consumption than the FL algorithm with full user participation. These observations advocate the proposed FL algorithm for a paradigm shift in bandwidth-constrained learning wireless IoT networks.

We investigate the performance of multi-user multiple-antenna downlink systems in which a base station (BS) serves multiple users via a shared wireless medium. In order to fully exploit the spatial diversity while minimizing the passive energy consumed by radio frequency (RF) components, the BS is equipped with$M$RF chains and$N$antennas, where$M < N$. Upon receiving pilot sequences to obtain the channel state information (CSI), the BS determines the best subset of$M$antennas for serving the users. We propose a joint antenna selection and precoding design (JASPD) algorithm to maximize the system sum rate subject to a transmit power constraint and quality of service (QoS) requirements. The JASPD algorithm overcomes the non-convexity of the formulated problem via a doubly iterative algorithm, in which an inner loop successively optimizes the precoding vectors, followed by an outer loop that tests all valid antenna subsets. Although approaching (near) global optimality, the JASPD suffers from a combinatorial complexity, which may limit its application in real-time network operations. To overcome this limitation, we propose a learning-based antenna selection and precoding design algorithm (L-ASPA), which employs a deep neural network (DNN) to establish underlaying relations between key system parameters and the selected antennas. The proposed L-ASPD algorithm is robust against the number of users and their locations, the transmit power of the BS, as well as the small-scale channel fading. With a well-trained learning model, it is shown that the L-ASPD algorithm significantly outperforms baseline schemes based on the block diagonalization and a learning-assisted solution for broadcasting systems and achieves a better effective sum rate than that of the JASPA under limited processing time. In addition, we observed that the proposed L-ASPD algorithm can reduce the computation complexity by 95% while retaining more than 95% of the optimal performance.

This paper investigates the combined benefits of full-duplex (FD) and cell-free massive multiple-input multiple-output (CF-mMIMO), where a large number of distributed access points (APs) having FD capability simultaneously serve numerous uplink and downlink user equipments (UEs) on the same time-frequency resources. To enable the incorporation of FD technology in CF-mMIMO systems, we propose a novel heap-based pilot assignment algorithm, which not only can mitigate the effects of pilot contamination but also reduce the involved computational complexity. Then, we formulate a robust design problem for spectral efficiency (SE) maximization in which the power control and AP-UE association are jointly optimized, resulting in a difficult mixed-integer nonconvex programming. To solve this problem, we derive a more tractable problem before developing a very simple iterative algorithm based on inner approximation method with polynomial computational complexity. Numerical results show that our proposed methods with realistic parameters significantly outperform the existing approaches in terms of the quality of channel estimate and SE.

In-band full-duplex (FD) operation is practically more suited for short-range communications such as WiFi and small-cell networks, due to its current practical limitations on the self-interference cancellation. In addition, cell-free massive multiple-input multiple-output (CF-mMIMO) is a new and scalable version of MIMO networks, which is designed to bring service antennas closer to end user equipments (UEs). To achieve higher spectral and energy efficiencies (SE-EE) of a wireless network, it is of practical interest to incorporate FD capability into CF-mMIMO systems to utilize their combined benefits. We formulate a novel and comprehensive optimization problem for the maximization of SE and EE in which power control, access point-UE (AP-UE) association and AP selection are jointly optimized under a realistic power consumption model, resulting in a difficult class of mixed-integer nonconvex programming. To tackle the binary nature of the formulated problem, we propose an efficient approach by exploiting a strong coupling between binary and continuous variables, leading to a more tractable problem. In this regard, two low-complexity transmission designs based on zero-forcing (ZF) are proposed. Combining tools from inner approximation framework and Dinkelbach method, we develop simple iterative algorithms with polynomial computational complexity in each iteration and strong theoretical performance guaranteed. Furthermore, towards a robust design for FD CF-mMIMO, a novel heap-based pilot assignment algorithm is proposed to mitigate effects of pilot contamination. Numerical results show that our proposed designs with realistic parameters significantly outperform the well-known approaches (i.e., small-cell and collocated mMIMO) in terms of the SE and EE. Notably, the proposed ZF designs require much less execution time than the simple maximum ratio transmission/combining.

In this paper, we propose a novel hybrid user pairing (HUP) scheme in multiuser multiple-input single-output non-orthogonal multiple access networks with simultaneous wireless information and power transfer. In this system, two information users with distinct channel conditions are optimally paired while energy users perform energy harvesting (EH) under non-linearity of the EH circuits. We consider the problem of jointly optimizing user pairing and power allocation to maximize the overall spectral efficiency (SE) and energy efficiency (EE) subject to user-specific quality-of-service and harvested power requirements. A new paradigm for the EE-EH trade-off is then proposed to achieve a good balance of network power consumption. Such design problems are formulated as the maximization of non-concave functions subject to the class of mixed-integer non-convex constraints, which are very challenging to solve optimally. To address these challenges, we first relax binary pairing variables to be continuous and transform the design problems into equivalent non-convex ones, but with more tractable forms. We then develop low-complexity iterative algorithms to improve the objectives and converge to a local optimum by means of the inner approximation framework. Simulation results show the convergence of proposed algorithms and the SE and EE improvements of the proposed HUP scheme over state-of-the-art designs. In addition, the effects of key parameters such as the number of antennas and dynamic power at the BS, target data rates, and energy threshold, on the system performance are evaluated to show the effectiveness of the proposed schemes in balancing resource utilization.

In this paper, physical layer security is considered for cognitive radio networks using an unmanned aerial vehicle (UAV)-enabled jamming noise. In the studied model, a secondary transmitter sends confidential messages to a secondary receiver in the presence of an external eavesdropper (Eve), and the UAV acts as a friendly jammer that degrades the decoding capability of Eve. Therefore, resource allocation in such a network must jointly optimize the transmission power and UAV's trajectory to maximize the secrecy rate, while satisfying a given interference threshold at the primary receiver. The design problem is non-convex, and thus, global optimality is difficult to obtain. Aiming to solve this problem, we first transform it into a more tractable form, and then propose a successive convex approximation-based algorithm for its solutions. The proposed algorithm has a low computational complexity and is guaranteed to obtain at least a locally optimal solution of the original problem. Numerical results are provided to demonstrate the effectiveness of the proposed design, compared to the existing ones.

In this paper, we study the problem of jointly optimizing user pairing and beamforming design in multiuser multiple-input single-output (MU-MISO) non-orthogonal multiple access (NOMA) downlink systems with simultaneous wireless information and power transfer (SWIPT). Aiming at maximizing the achievable sum throughput subject to energy harvesting (EH) constraints, we propose a hybrid user pairing beamforming scheme (HBS), where two users with distinct channel conditions are optimally selected to perform user pairing. Moreover, we adopt a non-linear EH model for energy users to reveal their practical circuit characteristics. The sum throughput problem is formulated as a class of mixed-integer nonconvex optimization programming which is computationally prohibitive. To solve this challenging problem, we propose a low-complexity iterative algorithm, yet efficient, based on sequential convex approximation method to arrive at least the local optima. Numerical results are provided to demonstrate the performance improvement of the proposed HBS scheme over the multiuser beamforming one without user pairing, revealing to be an effective scheme for MU-MISO-NOMA downlink systems.

This paper investigates the coexistence of non- orthogonal multiple access (NOMA) and full-duplex (FD), where the NOMA successive interference cancellation technique is applied simultaneously to both uplink (UL) and downlink (DL) transmissions in the same time-frequency resource block. Specifically, we jointly optimize the user association (UA) and power control to maximize the overall sum rate, subject to user-specific quality-of-service and total transmit power constraints. To be spectrally-efficient, we introduce the tensor model to optimize the UL users' decoding order and the DL users' clustering, which results in a mixed-integer non- convex problem. For solving this problem, we first relax the binary variables to be continuous, and then propose a low-complexity design based on the combination of the inner convex approximation framework and the penalty method. Numerical results show that the proposed algorithm significantly outperforms the conventional FD-based schemes, FD-NOMA and its half-duplex counterpart with random UA.

This paper investigates the coexistence of non-orthogonal multiple access (NOMA) and full-duplex (FD) to improve both spectral efficiency (SE) and user fairness. In such a scenario, NOMA based on the successive interference cancellation technique is simultaneously applied to both uplink (UL) and downlink (DL) transmissions in an FD system. We consider the problem of jointly optimizing user association (UA) and power control to maximize the overall SE, subject to user-specific quality-of-service and total transmit power constraints. To be spectrally-efficient, we introduce the tensor model to optimize UL users’ decoding order and DL users’ clustering, which results in a mixed-integer non-convex problem. For practically appealing applications, we first relax the binary variables and then propose two low-complexity designs. In the first design, the continuous relaxation problem is solved using the inner convex approximation framework. Next, we additionally introduce the penalty method to further accelerate the performance of the former design. For a benchmark, we develop an optimal solution based on brute-force search (BFS) over all possible cases of UAs. It is demonstrated in numerical results that the proposed algorithms outperform the conventional FD-based schemes and its half-duplex counterpart, as well as yield data rates close to those obtained by BFS-based algorithm.

This paper considers a full-duplex (FD) multiuser multiple-input single-output system where a base station simultaneously serves both uplink (UL) and downlink (DL) users on the same time-frequency resource. The crucial barriers in implementing FD systems reside in the residual self-interference and co-channel interference. To accelerate the use of FD radio in future wireless networks, we aim at managing the network interference more effectively by jointly designing the selection of half-array antenna modes (in the transmit or receive mode) at the base station with time phases and user assignments. The first problem of interest is to maximize the overall sum rate subject to quality-of-service requirements, which is formulated as a highly non-concave utility function followed by non-convex constraints. To address the design problem, we propose an iterative low-complexity algorithm by developing new inner approximations, and its convergence to a stationary point is guaranteed. To provide more insights into the solution of the proposed design, a general max-min rate optimization is further considered to maximize the minimum per-user rate while satisfying a given ratio between UL and DL rates. Furthermore, a robust algorithm is devised to verify that the proposed scheme works well under channel uncertainty. The simulation results demonstrate that the proposed algorithms exhibit fast convergence and substantially outperform existing schemes.

Consider a full-duplex (FD) multiuser system where an FD base station (BS) is designed to concurrently serve both downlink and uplink users in the presence of half-duplex eavesdroppers (Eves). The target problem is to maximize the minimum secrecy rate (SR) among all legitimate users. A novel user grouping-based fractional time allocation is proposed as an alternative solution, where information signals at the FD-BS are accompanied by artificial noise to degrade the Eves' channels. The SR problem has a highly non-concave and non-smooth objective, subject to non-convex constraints due to coupling between the optimization variables. Nevertheless, we develop a path-following low- complexity algorithm, which involves only a simple convex program of moderate dimensions at each iteration. Numerical results demonstrate the merit of the proposed approach compared to existing well-known ones, i.e., conventional FD and FD non-orthogonal multiple access.

In this paper, a novel method of WiFi fingerprinting for localizing intelligent vehicles in GPS-denied area, such as car parks, is proposed. Although the method itself is a popular approach for indoor localization application, adapting it to the speed of vehicles requires different treatment. By deploying an ensemble neural network for fingerprinting classification, the method shows a reasonable localization precision at car park speed. Furthermore, a Gaussian Mixture Model (GMM) Particle Filter is applied to increase localization frequency as well as accuracy. Experiments show promising results with average localization error of 0.6m.

The authors investigate pilot contamination in a multicell massive multiple‐input multiple‐output system, in which the estimated channel state information (CSI) is significantly different from the real CSI. Pilot contamination is an obstacle to achievable rates for users. The authors first propose an uplink training strategy to limit the effect of intercell pilot contamination on channel estimation with low complexity. As a result, the downlink data rate through the reciprocal channels is significantly improved. However, as the conventional uplink training, this strategy suffers from intra‐cell pilot contamination when the length of pilots is smaller than the number of users in a cell, while increasing the length of pilots degrades the spectral efficiency. The authors, therefore, propose a pilot optimisation that can be incorporated into the proposed training strategy. Based on the minimum mean square error criterion, they derive a closed‐form equation to determine the optimal pilots for all users in a multicell system. The joint pilot optimisation and proposed uplink training strategy is found to outperform other schemes, especially when the number of users exceeds the length of pilots in a cell. Numerical results verify that the downlink achievable rate with the proposed training strategy and pilot optimisation outperforms that with conventional approaches.

We consider a full-duplex (FD) multiuser system where an FD base station (BS) is designed to simultaneously serve both downlink (DL) and uplink (UL) users in the presence of half-duplex eavesdroppers (Eves). The problem is to maximize the minimum (max-min) secrecy rate (SR) among all legitimate users, where the information signals at the FD-BS are accompanied with artificial noise to debilitate the Eves' channels. To enhance the max-min SR, a major part of the power budget should be allocated to serve the users with poor channel qualities, such as those far from the FD-BS, undermining the SR for other users, and thus compromising the SR per-user. In addition, the main obstacle in designing an FD system is due to the self-interference (SI) and co-channel interference (CCI) among users. We therefore propose an alternative solution, where the FD-BS uses a fraction of the time block to serve near DL users and far UL users, and the remaining fractional time to serve other users. The proposed scheme mitigates the harmful effects of SI, CCI, and multiuser interference, and provides system robustness. The SR optimization problem has a highly nonconcave and nonsmooth objective, subject to nonconvex constraints. For the case of perfect channel state information (CSI), we develop a low-complexity path-following algorithm, which involves only a simple convex program of moderate dimension at each iteration. We show that our path-following algorithm guarantees convergence at least to a local optimum. Then, we extend the path-following algorithm to the cases of partially known Eves' CSI, where only statistics of CSI for the Eves are known, and worst-case scenario in which Eves can employ a more advanced linear decoder. The merit of our proposed approach is further demonstrated by extensive numerical results.

This paper considers a full-duplex system for a base station (BS) serving both uplink and downlink users simultaneously on the same frequency. A new design of the selection of the half-array antenna mode at the BS (transmit or receive mode) over the time phases and user assignments is proposed and its beamforming design and power allocation problem are optimized under the effect of both residual self- interference and co-channel interference. The aim is to maximize the overall sum rate subject to the users' quality of service requirements, which is formulated as a highly nonlinear function subject to non-convex constraints. To solve this non-convex problem, we propose an iterative low- complexity algorithm. Simulation results demonstrate that the proposed algorithm provides a fast convergence and substantially outperforms all existing schemes.

This paper aims to design linear precoders for signal superposition at the base stations of non- orthogonal multiple access multiple-input multiple-output multi-cellular systems to maximize the overall sum throughput subject to the users' quality-of-service requirements, which are imposed independently on the users' channel conditions. This design problem is formulated as the maximization of a highly nonlinear and nonsmooth function subject to nonconvex constraints, which is very computationally challenging. A path- following algorithm for its solution, which invokes only a simple convex problem of moderate dimension at each iteration, is developed. Generating a sequence of improved points, this algorithm converges at least to a local optimum. Numerical results are then provided to demonstrate its merit.

In this paper, we propose a cooperative approach to improve the security of both primary and secondary systems in cognitive radio multicast communications. During their access to the frequency spectrum licensed to the primary users, the secondary unlicensed users assist the primary system in fortifying security by sending a jamming noise to the eavesdroppers, while simultaneously protect themselves from eavesdropping. The main objective of this work is to maximize the secrecy rate of the secondary system, while adhering to all individual primary users' secrecy rate constraints. In the case of passive eavesdroppers and imperfect channel state information knowledge at the transceivers, the utility function of interest is nonconcave and involved constraints are nonconvex, and thus, the optimal solutions are troublesome. To address this problem, we propose an iterative algorithm to arrive at a local optimum of the considered problem. The proposed iterative algorithm is guaranteed to achieve a Karush-Kuhn-Tucker solution.

Hidden‐tag problem is one of the most important issues in the implementation of radio‐frequency identification (RFID) systems. Due to effects of imperfect wireless channels, RFID tags can be hidden during the identification process by either another tag or an unsuccessful detection. The former is known as the capture effect (CE) while the latter is the detection error (DE). This study newly proposes two modified tree‐based identification protocols, namely tweaked binary tree (TBT) and tweaked query tree (TQT), which are able to tackle the hidden‐tag problem caused by both the CE and DE. The performance of the proposed TBT and TQT protocols, in terms of the average number of slots required to detect a tag, and the tag‐loss rate, is evaluated in comparison with that of previously proposed ones. Computer simulations and numerical results confirm the effectiveness of the proposed protocols.

Consider an underlay cognitive radio network where an eavesdropper (Eve) targets to intercept the information exchanging between the primary nodes. The secondary system is allowed to access the licensed spectrum as long as it does not violate the target quality‐of‐service (QoS) of the primary network. In return, the secondary network also assists the primary network against the malicious attack of the Eve. The authors aim at designing a resource allocation algorithm maximising the secrecy rate of the primary system while also satisfying the QoS requirement of the secondary system. To be more precise, a jamming noise accompanied by the information signal to degrade the Eve's channel and the information beamforming vector at the secondary transmitter is jointly optimised. The problem of interest is formulated as a non‐convex optimisation problem. For the case in which global channel state information (CSI) is available, the authors propose a path‐following algorithm which aims at locating a Karush‐Kuhn–Tucker solution to the original non‐convex program. By novel transformations and approximations, the authors arrive at only a simple convex problem of moderate dimension. For the case in which only statistics of the Eve's CSI are available, the authors reformulate the considered problem by replacing a non‐convex probabilistic constraint with a set of convex constraints. A worst‐case scenario for a secure communication, where an optimal linear decoder is used at the Eve, is also considered. The superior performance of the proposed design is revealed by numerically comparing it with other known solutions.

The throughput of users with poor channel conditions, such as those at a cell edge, is a bottleneck in wireless systems. A major part of the power budget must be allocated to serve these users in guaranteeing their quality-of-service (QoS) requirements, hampering QoS for other users, and thus compromising the system reliability. In non-orthogonal multiple access (NOMA), the message intended for a user with a poor channel condition is decoded by itself and by another user with a better channel condition. The message intended for the latter is then successively decoded by itself after canceling the interference of the former. The overall information throughput is thus improved by this particular successive decoding and interference cancellation. This paper aims to design linear precoders/beamformers for signal superposition at the base stations of NOMA multiple-input multiple-output multi-cellular systems to maximize the overall sum throughput subject to the users' QoS requirements, which are imposed independently on the users' channel conditions. This design problem is formulated as the maximization of a highly nonlinear and nonsmooth function subject to nonconvex constraints, which is very computationally challenging. Path-following algorithms for its solution, which invoke only a simple convex problem of moderate dimension at each iteration, are developed. Generating a sequence of improved points, these algorithms converge at least to a local optimum. Extensive numerical simulations are then provided to demonstrate their merit.

A communication system is considered consisting of a full-duplex multiple-antenna base station (BS) and multiple single-antenna downlink users (DLUs) and single-antenna uplink users (ULUs), where the latter need to harvest energy for transmitting information to the BS. The communication is thus divided into two phases. In the first phase, the BS uses all available antennas for conveying information to DLUs and wireless energy to ULUs via information and energy beamforming, respectively. In the second phase, ULUs send their independent information to the BS using their harvested energy while the BS transmits the information to the DLUs. In both the phases, the communication is operated at the same time and over the same frequency band. The aim is to maximize the sum rate and energy efficiency under ULU achievable information throughput constraints by jointly optimizing beamforming and time allocation. The utility functions of interest are nonconcave and the involved constraints are nonconvex, so these problems are computationally troublesome. To address them, path-following algorithms are proposed to arrive at least at local optima. The proposed algorithms iteratively improve the objectives with convergence guaranteed. Simulation results demonstrate that they achieve rapid convergence and outperform conventional solutions.

In this paper, we investigate the secure communication of cognitive full-duplex relay networks in the presence of multiple eavesdroppers and multiple primary receivers. In the considered network, multiple full-duplex relays are deployed to transfer information in the secondary network, under the malicious attempts of non- colluding/colluding eavesdroppers. Meanwhile, the transmit powers of secondary transmitters are constrained by the quality-of-service of the primary network. The optimal relay selection scheme is proposed to enhance the secrecy performance of the considered system. We study the secrecy performance by providing the exact closed- form and asymptotic expressions of the proposed system secrecy outage probability. We have demonstrated that increasing the number of full- duplex relays can improve the security performance. At the illegitimate side, using colluding eavesdroppers and increasing the number of eavesdroppers put information confidentiality at a greater risk. Besides, the transmit power and the desired outage probability of the primary network have great influences on the secrecy outage probability of the secondary network.

In this paper, we consider beamforming design for an underlay cognitive radio multiple-input single-output broadcast channel, where a pair of secondary users coexists with multiple primary receivers. There exist multiple malicious eavesdroppers who attempt to overhear the confidential messages from the secondary system. When the channel state information of the eavesdroppers can only be obtained in the statistical sense, we transform the constraint which results from the statistical information of the passive eavesdroppers into a linear matrix inequality and convex constraint. To improve the secrecy rate of the secondary system, we aim to design a jamming noise to degrade the eavesdroppers. The main objective of the design is to maximize the secrecy rate of the secondary system while satisfying all the interference power constraints at the primary users and per-antenna power constraint at the secondary transmitter. The original problem is a nonconvex program, which can be reformulated to a convex program by applying the rank relaxation method. To this end, we prove that the rank relaxation is tight and it can be efficiently solved. Moreover, to develop an efficient resource allocation scheme we transform the relaxed problem into an equivalent problem based on a duality result.

Pilot contamination reduction can improve the achievable uplink and downlink rates in a multicell massive multiple‐input–multiple‐output system. To this end, this study first proposes two schemes to mitigate the effect of pilot contamination by implementing uplink training with time‐aligned and time‐shifted pilot transmissions. Accordingly, these techniques enable the system not only to exploit the advantages of time‐shifted training but also to benefit from the use of conventional systems that improve the achievable rates. The authors introduce channel estimation in matrix form by estimating from a near minimum mean square error method. Furthermore, linear detector and precoding methods are used for a base station with a finite number of antennas in order to derive the closed‐form lower bounds of the achievable rates with two uplink training schemes for both uplink and downlink transmissions. Finally, numerical results are presented to verify the analysis.

In this paper, we consider the secure beamforming design for an underlay cognitive radio multiple-input single-output broadcast channel in the presence of multiple passive eavesdroppers. Our goal is to design a jamming noise (JN) transmit strategy to maximize the secrecy rate of the secondary system. By utilizing the zero-forcing method to eliminate the interference caused by JN to the secondary user, we study the joint optimization of the information and JN beamforming for secrecy rate maximization of the secondary system while satisfying all the interference power constraints at the primary users, as well as the per-antenna power constraint at the secondary transmitter. For an optimal beamforming design, the original problem is a nonconvex program, which can be reformulated as a convex program by applying the rank relaxation method. To this end, we prove that the rank relaxation is tight and propose a barrier interior-point method to solve the resulting saddle point problem based on a duality result. To find the global optimal solution, we transform the considered problem into an unconstrained optimization problem. We then employ Broyden-Fletcher-Goldfarb-Shanno method to solve the resulting unconstrained problem, which helps reduce the complexity significantly, compared with the conventional methods. Simulation results show the fast convergence of the proposed algorithm and substantial performance improvements over the existing approaches.

In this paper, we proposed a symbiotic approach for a secure primary network by allowing the secondary users to send the jamming noise to degrade the wiretap ability of the eavesdropper. In particular, assuming that the global channel state information is perfectly known at tranceivers, we consider the case of the primary transmitter equipped with only one antenna, which implies that the primary transmitter does not have beamforming capability. As the reward of having access to the frequency spectrum which is licensed by the the primary user, the secondary transmitter will assist the primary systems in terms of security by sending the jamming noise to the eavesdropper. We propose an algorithm to find the optimal transmit power for maximizing the secrecy capacity of the primary system. Numerical results are presented to validate our proposed scheme.

In this paper, the performance of opportunistic relay selection (ORS) in a cognitive radio is analyzed over flat Rayleigh fading channels. Data transmission between source and destination is assumed to be entirely performed via the relays. Relay nodes are assumed to have ability to harvest energy from the source signal and use that harvested energy to forward the information to the destination. Specifically, we derive an exact expression for the outage probability of the secondary system considering the maximum transmit power at the secondary transmitter and relays, energy harvesting efficiency at relays, and interference constraint at the primary receiver. Under the assumption of perfect channel state information at the receivers, we evaluate the outage probability of a cognitive radio system with ORS and energy harvesting.