Exploiting near-field spherical wavefronts can improve the performance of integrated sensing and communication (ISAC) systems. Existing studies on near-field ISAC mainly consider either fully-digital or static hybrid beamforming, which may not be able to efficiently exploit the distance-dependent degrees of freedom (DoFs) of near-field channels. In this paper, we propose a dynamic hybrid beamforming architecture that adaptively adjusts the number of active radio frequency (RF) chains and further utilize a reconfigurable intelligent surface (RIS) to enhance the performance in ISAC systems. We formulate an energy efficiency maximization problem which aims to jointly optimize the hybrid precoding matrices at the base station, phase-shifts at the RIS, and hybrid combining matrices at the user equipment, subject to the constraint on guaranteeing the minimum beam pattern gain toward sensing targets. To tackle this intractable problem, we propose an alternating optimization algorithm by leveraging fractional programming, matrix lifting, and semidefinite relaxation techniques. Simulation results demonstrate that our proposed algorithm outperforms three baseline schemes in both optimizing the number of active RF chains and maximizing the energy efficiency.

Large artificial intelligence (AI) models have demonstrated impressive performance in a wide range of fields. Despite their versatility, adapting large AI models to specific downstream applications often requires fine-tuning on decentralized and privacy-sensitive data, posing significant challenges in resource-constrained wireless networks. In this paper, we propose a novel zeroth-order federated fine-tuning framework for efficient fine-tuning large AI models to alleviate the computation, communication, and memory bottleneck issues. Specifically, to address the computation limitation on edge devices, we adopt the split learning architecture, hosting the most computation-intensive component of the large AI model on the edge server. Besides, we employ a memory-efficient zeroth-order fine-tuning algorithm to further reduce the GPU memory consumption. Furthermore, we conduct a rigorous convergence analysis to illustrate how device scheduling influences the learning performance. Based on the analysis, we formulate a global loss minimization problem that jointly optimizes device scheduling, transmit power, and receive beamforming under the average transmission latency constraint. To tackle this complex mixed-integer nonlinear programming problem, we apply Lyapunov theory to break down the long-term optimization problem into multiple subproblems, followed by designing an effective online algorithm. Simulation results show that the proposed framework can lower the GPU memory consumption by up to 84% and GPU hours by 39% compared to the baselines, while achieving comparable accuracy.

This paper presents mmDFRBC, a symbol-level millimeter-wave (mmWave) backscatter communication system that reuses commercial mmWave FMCW radar infrastructure as the dual-function access point (AP) without hardware modification. We propose M-ary Frequency Shift Modulation (MFSM), a lightweight encoding scheme that enables the tag to modulate information using orthogonal frequency blocks over adjacent chirps, achieving symbol-level modulation with data rates exceeding kbps. To robustly extract the modulation signals from strong radar sensing clutter, we introduce a Coherent Cancellation Demodulation (CCD) method that exploits the coherence difference between sensing signals and modulated reflections. We develop a soft synchronization strategy for operating asynchronously and requiring no synchronization or downconversion circuits at the tag, supporting kbps-level data rates with low power and cost. We implement mmDFRBC using a commercial mmWave radar and verify its performance across static and mobile scenarios, achieving BERs below$10^{-3}$over 4 meters, with strong resilience to radar clutter interference. mmDFRBC supports data rates up to 50 kbps, highlighting DFRBC’s potential to enable high-performance DFRC systems using existing infrastructure.

To support ambient intelligence with federated edge learning (FEEL) over resource-constrained wireless networks, it is essential to jointly design and optimize the sensing, computation, and communication processes. In this paper, we propose an integrated sensing, computation, and communication (ISCC) enabled FEEL framework, where each edge device performs wireless sensing to enrich local datasets, executes local model training with accumulated local datasets, and transmits updated local gradients for global model aggregation. Via analyzing the convergence of ISCC-enabled FEEL, we explicitly characterize the impact of newly sensed dataset size in each training round on the optimality gap. Due to the coupling of the sensing, computation, and communication processes, we formulate a long-term optimality gap minimization problem involving the joint optimization of newly sensed dataset size, computation frequency, communication bandwidth, and transmit power. By leveraging Lyapunov optimization, we develop an online optimization algorithm, where, at each iteration, the optimization variables are all derived in closed-form. Moreover, we prove that the proposed algorithm achieves its asymptotic optimal performance and conduct simulations to show the superiority of the proposed ISCC-enabled FEEL.

The Information Bottleneck (IB) principle has emerged as a promising approach for enhancing the generalization, robustness, and interpretability of deep neural networks, demonstrating efficacy across image segmentation, document clustering, and semantic communication. Among IB implementations, the IB Lagrangian method, employing Lagrangian multipliers, is widely adopted. While numerous methods for the optimizations of IB Lagrangian based on variational bounds and neural estimators are feasible, their performance is highly dependent on the quality of their design, which is inherently prone to errors. To address this limitation, we introduce Structured IB, a framework for investigating potential structured features. By incorporating auxiliary encoders to extract missing informative features, we generate more informative representations. Our experiments demonstrate superior prediction accuracy and task-relevant information preservation compared to the original IB Lagrangian method, even with reduced network size.

Large language models (LLMs) have demonstrated remarkable success across various domains. However, the substantial computational and memory demands pose significant challenges for deploying LLMs at the network edge. To address this issue, the existing studies mainly focus on either reducing the size of LLMs or distributing LLMs across multiple devices. However, the former approach suffers from performance degradation, while the latter incurs high communication cost due to the transmission of high-dimensional intermediate features. To tackle these issues, we propose a novel collaborative framework to support vision-language model (VLM) inference at the network edge, expanding speculative decoding into a multi-device scenario. In this framework, each edge device first utilizes its small VLM to generate draft tokens in an auto-regressive manner. These tokens are then transmitted to an edge server, where they are corrected in parallel by a large VLM. By benefiting from speculative decoding, the number of calls to the large VLM is reduced without degrading the inference performance. Furthermore, we minimize the average latency of inference tasks by developing a deep reinforcement learning algorithm to optimize the number of draft tokens generated at each iteration. Simulation results confirm that the proposed algorithm achieves a lower average latency compared to other baselines.

Federated learning (FL) with synchronous model aggregation suffers from the straggler issue because of heterogeneous transmission and computation delays among different agents. In mobile wireless networks, this issue is exacerbated by time-varying network topology due to agent mobility. Although asynchronous FL can alleviate straggler issues, it still faces critical challenges in terms of algorithm design and convergence analysis because of dynamic information update delay (IU-Delay) and dynamic network topology. To tackle these challenges, we propose a decentralized FL framework based on gradient descent with momentum, named decentralized momentum federated learning (DMFL). We prove that DMFL is globally convergent on convex loss functions under the bounded time-varying IU-Delay, as long as the network topology is uniformly jointly strongly connected. Moreover, DMFL does not impose any restrictions on the data distribution over agents. Extensive experiments are conducted to verify DMFL’s performance superiority over the benchmarks and to reveal the effects of diverse parameters on the performance of the proposed algorithm.

Artificial intelligence (AI) is envisioned to be natively integrated into the sixth-generation (6G) mobile networks to support a diverse range of intelligent applications. Federated edge learning (FEEL) emerges as a vital enabler of this vision by leveraging the sensing, communication, and computation capabilities of geographically dispersed edge devices to collaboratively train AI models without sharing raw data. This article explores the pivotal role of FEEL in advancing both the “wireless for AI” and “AI for wireless” paradigms, thereby facilitating the realization of scalable, adaptive, and intelligent 6G networks. We begin with a comprehensive overview of learning architectures, models, and algorithms that form the foundations of FEEL. We, then, establish a novel task-oriented communication principle to examine key methodologies for deploying FEEL in dynamic and resource-constrained wireless environments, focusing on device scheduling, model compression, model aggregation, and resource allocation. Furthermore, we investigate the domain-specific optimizations of FEEL to facilitate its promising applications, ranging from wireless air-interface technologies to mobile and the Internet of Things (IoT) services. Finally, we highlight key future research directions for enhancing the design and impact of FEEL in 6G.

Space-ground integrated networks hold great promise for providing global connectivity, particularly in remote areas where large amounts of valuable data are generated by Internet of Things (IoT) devices, but lacking terrestrial communication infrastructure. The massive data is conventionally transferred to the cloud server for centralized artificial intelligence (AI) models training, raising huge communication overhead and privacy concerns. To address this, we propose a hierarchical learning and computing framework, which leverages the low-latency characteristic of low-earth-orbit (LEO) satellites and the global coverage of geostationary-earth-orbit (GEO) satellites, to provide global aggregation services for locally trained models on ground IoT devices. Due to the time-varying nature of satellite network topology and the energy constraints of LEO satellites, efficiently aggregating the received local models from ground devices on LEO satellites is highly challenging. By leveraging the predictability of inter-satellite connectivity, modeling the space network as a directed graph, we formulate a network energy minimization problem for model aggregation, which turns out to be aDirected Steiner Tree (DST)problem. We propose a topology-aware energy-efficient routing (TAEER) algorithm to solve theDSTproblem by finding a minimum spanning arborescence on a substitute directed graph. Extensive simulations under real-world space-ground integrated network settings demonstrate that the proposed TAEER algorithm significantly reduces energy consumption and outperforms benchmarks.

Deploying intelligent service and executing inference tasks in the proximity of the edge enable models to access enormous real-time data generated by the edge devices. However, the dilemma of fulfilling service demands with limited resources at edge devices impairs the efficacy of conventional data-oriented communication systems. To achieve a better trade-off between inference accuracy and communication overhead, in this paper, we propose a dynamic unmanned aerial vehicle (UAV)-assisted cooperative edge inference system, where a UAV acts as an edge server to aggregate the wide-view features from mobile sensors through Over-the-Air computation (AirComp) to complete the inference task cooperatively. Discriminant gain, an effective indicator for the inference accuracy, is adopted to realize task-oriented design. To exploit channel diversity and data diversity in the multi-device cooperative edge inference system, we maximize the discriminant gain of the AirComp feature aggregation by jointly optimizing the UAV trajectory and the power allocation policy with respect to the different important levels of feature dimensions. An alternating algorithm and a successive convex approximation (SCA)-based method are then proposed to solve the optimization problem. Numerical simulations further validate the efficacy of the proposed design compared to the baselines.

Pre-trained foundation models (FMs), with extensive number of neurons, are key to advancing next-generation intelligence services, where personalizing these models requires massive amount of task-specific data and computational resources. The prevalent solution involves centralized processing at the edge server, which, however, raises privacy concerns due to the transmission of raw data. Instead, federated fine-tuning (FedFT) is an emerging privacy-preserving fine-tuning (FT) paradigm for personalized pre-trained foundation models. In particular, by integrating low-rank adaptation (LoRA) with federated learning (FL), federated LoRA enables the collaborative FT of a global model with edge devices, achieving comparable learning performance to full FT while training fewer parameters over distributed data and preserving raw data privacy. However, the limited radio resources and computation capabilities of edge devices pose significant challenges for deploying 3 LoRA over wireless networks. To this paper, we propose a split federated LoRA framework, which deploys the computationally-intensive encoder of a pre-trained model at the edge server, while keeping the embedding and task modules at the edge devices. The information exchanges between these modules occur over wireless networks. Building on this split framework, the paper provides a rigorous analysis of the upper bound of the convergence gap for the wireless federated LoRA system. This analysis reveals the weighted impact of the number of edge devices participating in FedFT over all rounds, motivating the formulation of a long-term upper bound minimization problem. To address the long-term constraint, we decompose the formulated long-term mixed-integer programming (MIP) problem into sequential sub-problems using the Lyapunov technique. We then develop an online algorithm for effective device scheduling and bandwidth allocation. Simulation results demonstrate the effectiveness of the proposed online algorithm in enhancing learning performance.

A continuous-aperture array (CAPA)-based integrated sensing and communication (ISAC) framework is proposed in this paper, where CAPA transceivers are optimized to enhance both target sensing and user communication performance. Novel expressions for achievable communication and sensing rates are derived and CAPA-oriented beamforming is designed to balance the dual-functional Pareto-optimal tradeoff in two scenarios: i) For the single-user single-target case, closed-form continuous beamformers are derived based on communication-, sensing-, and Pareto-optimal criteria to reveal the interrelation of the ISAC rate region with the antenna aperture and channel gains; ii) For the multi-user multi-target case, a general CAPA-ISAC beamforming design algorithm is developed to achieve the Pareto optimality. Beamformer design in the continuous spatial domain is transformed into weight design in the discrete wavenumber domain using Fourier series expansions. Furthermore, alternating optimization, successive convex approximation, and difference of convex techniques are employed to tackle the coupling and non-convexity issues. Numerical results demonstrate that: i) The proposed CAPA-ISAC framework significantly improves both sensing and communication performance and expands the ISAC Pareto rate region; ii) CAPAs exhibit superior beamforming capabilities and reach the ultimate performance limits of spatially discrete arrays (SPDAs).

Integrated sensing and communication (ISAC) enables the next-generation network to possess networked sensing capability, propelling the proliferation of various intelligent applications but introducing complex sensing and communication interference. To this end, this paper studies the cooperative transceiver beamforming design for a multi-cell anti-unmanned aerial vehicle (UAV) ISAC system, where multiple base stations (BSs) collaboratively perform joint UAV sensing. Specifically, to ensure reliable detection, we jointly optimize the ISAC transmit and receive beamformers at BSs and downlink users via maximizing the signal-to-clutter-plus-noise ratio of sensing, taking into account the communication requirements and power constraints. To handle the nonconvex fractional problem, we first propose a centralized beamforming algorithm resorting to alternating optimization, successive convex approximation, and Dinkelbach methods. Then, to alleviate heavy backhaul overhead, a distributed algorithm is put forward, adopting the primal decomposition technique to decouple the inter-cell interference. Numerical results verify that: i) Compared with the standalone sensing by a single BS, the proposed cooperative beamforming design achieves notable enhancement in sensing performance; ii) The designed transceiver beamforming is constructive for interference and clutter suppression in multi-cell ISAC systems.

Integrated sensing and communication (ISAC), as an important usage scenario of 6G, is capable of seamlessly integrating wireless sensing and communication for their mutual benefit. Taking full advantage of ISAC heavily relies on effectively solving resource allocation problems, which, however, are generally high-dimensional and non-convex, resulting in the optimization-based algorithms exhibiting high computation complexity and the traditional learning-based algorithms returning infeasible solutions. In this paper, we consider an ISAC scenario featured by multiple communication users and multiple sensing targets, aiming to develop an efficient and scalable algorithm that optimizes the radar transmit beampattern under the communication performance constraint. To this end, we propose a graph neural network (GNN) with implicit projection framework, where GNN captures the intricate interactions between communication users and sensing targets and meanwhile enables the joint optimization of communication and sensing beamforming matrices, and the projection module is applied to ensure the feasibility of the beamforming matrices design. Via capturing the permutation equivalence for communication matrices and the permutation invariance for the sensing matrix, the scalability of the proposed algorithm is guaranteed. Simulation results show that the proposed algorithm significantly reduces the computation complexity compared to the baselines, and achieves excellent algorithmic scalability and constraint satisfaction.

Low-rank adaptation (LoRA) is an emerging fine-tuning method for personalized large language models (LLMs) due to its capability of achieving comparable learning performance to full fine-tuning by training a much smaller number of parameters. Federated fine-tuning (FedFT) combines LoRA with federated learning (FL) to enable collaborative fine-tuning of a global model with edge devices, leveraging distributed data while ensuring privacy. However, limited radio resources and computation capabilities of edge devices pose critical challenges on deploying FedFT over wireless networks. In this paper, we propose a split FedFT framework to separately deploy the computationally-intensive encoder of a pre-trained model at the edge server while reserving the embedding and the task modules at the edge devices, where the information exchanges between these modules are carried out over wireless networks. By exploiting the low-rank property of LoRA, the proposed FedFT framework reduces communication overhead by aggregating the gradient of the task module with respect to the output of a low-rank matrix. To enhance learning performance under stringent resource constraints, we formulate a joint device scheduling and bandwidth allocation problem while considering average transmission delay. By applying the Lyapunov technique, we decompose the formulated long-term mixed-integer programming (MIP) problem into sequential subproblems, followed by developing an online algorithm for effective device scheduling and bandwidth allocation. Simulation results demonstrate the effectiveness of our proposed online algorithm in enhancing learning performance.

Effective information sharing is essential for multi-agent systems to execute cooperative tasks successfully. Typically, agents within such systems are either stationary or possess unrestricted communication ranges. However, in more complex scenarios where agent mobility is introduced, the communication network’s topology becomes dynamic over time. This dynamism can result in partial communication unreachability among certain agents. Consequently, striking a balance between minimizing overall communication overhead and optimizing task performance becomes a formidable challenge. In this paper, we address the issue of dynamic communication in multi-agent systems. We propose a novel approach that leverages the principle of information bottleneck theory to develop a multi-mean field multi-agent reinforcement learning algorithm called MMIB. Through a series of experiments, we demonstrate the effectiveness of our proposed algorithm in reducing communication overhead while maintaining task performance at a level comparable to other state-of-the-art multi-agent reinforcement learning algorithms.

With the surge in the number of low earth orbit (LEO) satellites, continuous research has emerged on using satellite data to train artificial intelligence models. On one hand, traditional centralized training on the ground is not feasible due to privacy concerns and limited bandwidth for downloading raw satellite data. On the other hand, due to the limited energy and computational capability of satellites, training directly on satellites suffers from prolonged latency, especially for large models. To alleviate these issues, we propose a novel satellite-ground collaborative federated fine-tuning architecture, where ground stations (GSs) and satellites collaboratively train a global model without the need for data downloads. In this proposed architecture, satellites serve as edge devices and the ground server serves as a coordinator. However, the short satellite-ground communication windows caused by the high mobility of satellites and the substantial intra-orbit data transmission bring special challenges to the transmission process of federated edge learning. To tackle these challenges, we carefully design the satellite-ground collaborative fine-tuning architecture and utilize an optimized ring all-reduce algorithm and network flow algorithm to enhance the intra-orbit and ground-satellite transmissions, respectively. Experimental results demonstrate that our proposed architecture significantly reduces the training time by 40% compared to training solely on satellite.

Federated learning (FL) with progressive training is a promising privacy-preserving and communication-efficient framework for edge intelligence applications. Specifically, by partitioning the global model into multiple sub-models and dividing the FL training into multiple stages, FL with progressive training enables the gradual training of a large model, thereby significantly reducing the transmission overhead without compromising learning performance. However, implementing FL with progressive training over wireless networks is hindered by the limited radio and energy resources. To address these issues, we adopt over-the-air computation (AirComp) to support FL with progressive training over wireless networks. By balancing the tradeoff between the AirComp transmission distortion and the transition efficiency of progressive training, we formulate a mixed-integer optimization problem with energy and power constraints, which is further decomposed into several subproblems via Lyapunov optimization. Subsequently, we develop a low computational-complexity algorithm that jointly optimizes transmit power, receive beamforming, and transition indicator in an alternating manner. Simulation results demonstrate the effectiveness of our optimization algorithm in improving the learning performance of the considered FL system.

Artificial intelligence (AI) is critical in evolving 5G and developing 6G networks, running on edge devices, and solving resource management challenges. The burgeoning number of edge devices draws attention to the potential of low-earth orbit (LEO) satellite networks with their onboard computing capabilities for edge inference. This paper explores LEO scenarios where multiple remote sensing edge AI inference tasks concurrently process data from a single source. However, due to there being parts with the same functions between different AI applications, traditional monolithic edge AI architecture must be deployed repeatedly and falls short in efficiently harnessing the heterogeneous resources of LEO satellite networks. To solve this problem, we utilize the microservice architecture to decouple a single AI application into several independent microservices to reuse these same functions. However, due to the high latency caused by multiple microservices’ communication, we need to design a deployment strategy to fully utilize resources to reduce the service latency. We present a microservice deployment model to minimize the total service latency across all AI applications and meet resource constraints with the constraints of hardware, energy, and memory limitations. This latency optimization problem is rewritten as a Markov decision process (MDP) to effectively deal with the challenge posed by the time-varying transmission rate caused by satellite mobility. To increase the training data utilization, we employ a Proximal Policy Optimization (PPO) based reinforcement learning algorithm to meet the dynamic environment challenge. Finally, we obtain a sub-optimal solution with minimal accuracy loss and an acceptable solution time.

Federated learning (FL) enables multiple users to collaboratively train a shared model while protecting user privacy. In this paper, we investigate the transmission delay minimization problem for non-orthogonal multiple access (NOMA)-assisted FL. We analyze the convergence rate of heterogeneous quantized FL to demonstrate that the minimum quantization level among scheduled users is crucial in controlling the trade-off between the number of training rounds and the transmission delay of each round. Based on the convergence analysis, we formulate a delay minimization problem for NOMA-assisted FL and propose a communication-efficient heterogeneous compression NOMA scheme for FL. Subsequently, we develop a block coordinate descent (BCD)-based algorithm that jointly optimizes the sub channel allocation, power allocation, and quan-tization level for each scheduled user. Results reveal that our proposed algorithm significantly reduces the transmission delay while achieving the same learning performance compared with conventional FL algorithms.

In this paper, we propose a multi-device co-inference system based on a task-oriented over-the-air computation (Air-Comp) via reconfigurable intelligent surface (RIS). Specially, local feature vectors extracted from the real-time noisy sensory data on devices are aggregated over-the-air by exploiting the waveform superposition in a multi-user channel. Then the aggregated features received at the server are fed into an inference model for decision making or control of actuators. Based on the proposed multi-device co-inference system, we jointly optimize the receive signal strength of the device, the beamforming vector, and RIS phase shifts to suppress the sensing and channel noise and maximize the inference accuracy. To solve the problem, we first transform the original problem into a convex difference (d.c.) problem, and convert the d.c. problem from the complex domain to the real domain. Then, we propose a successive convex approximation based approach to solve the problem in the real domain. With the supportive data and results from the application of human motion recognition, we show the proposed scheme achieves a higher inference accuracy then the conventional approaches.

Video analytics plays a pivotal role in public safety (e.g., criminal suspect detection, traffic flow count, and illegal parking management), which assists the polices in monitoring all anomalous events in the street. In this paper, we consider the scenario with multiple video analytics applications from a single video stream. However, traditional monolithic architecture based video analytics applications shall seriously increase the response latency due to the resource contention of repetitive components. Therefore, we utilize the microservice architecture based video analytics (MAVA) to share the universal microser-vices in different applications, which shall decrease the response latency by reducing the computation load and increasing the resource utilization. To further achieve fast and accurate video analytics, the video analytics microservices are deployed in the edge closing to the cameras and users, and artificial intelligence (AI) methods are used in the microservices to realize specified functions. Therefore, an edge AI enabled MAVA (EAI-MAVA) architecture is proposed to achieve accurate video analytics in real-time. Furthermore, we formulate a microservice deployment problem to determine the location of each microservice in EAI-MAVA, which minimizes the response latency of all applications by considering the resource demands of microservices and the resource constraints of heterogeneous edge devices. Finally, a greedy-based heuristic algorithm is proposed to solve the non-convex microservice deployment problem, which obtains a sub-optimal solution with small loss of accuracy and reduces the solution time obviously.

The rapid advancement of artificial intelligence technologies has given rise to diversified intelligent services, which place unprecedented demands on massive connectivity and gigantic data aggregation. However, the scarce radio resources and stringent latency requirement make it challenging to meet these demands. To tackle these challenges, over-the-air computation (AirComp) emerges as a potential technology. Specifically, AirComp seamlessly integrates the communication and computation procedures through the superposition property of multiple-access channels, which yields a revolutionary multiple-access paradigm shift from “compute-after-communicate” to “compute-when-communicate”. By this means, AirComp enables spectral-efficient and low-latency wireless data aggregation by allowing multiple devices to occupy the same channel for transmission. In this paper, we aim to present the recent advancement of AirComp in terms of foundations, technologies, and applications. The mathematical form and communication design are introduced as the foundations of AirComp, and the critical issues of AirComp over different network architectures are then discussed along with the review of existing literature. The technologies employed for the analysis and optimization on AirComp are reviewed from the information theory and signal processing perspectives. Moreover, we present the existing studies that tackle the practical implementation issues in AirComp systems, and elaborate the applications of AirComp in Internet of Things and edge intelligent networks. Finally, potential research directions are highlighted to motivate the future development of AirComp.

In this article, we study the latency minimization problem for a wireless federated learning (FL) system with heterogeneous computation capability, where different edge devices perform different numbers of local model updates in each communication round. We formulate a total latency minimization problem with probabilistic device selection, taking into account both the communication and computation latency in the whole FL procedure. However, it is highly challenging to optimally solve this problem due to the coupling issues of model convergence and latency minimization problem caused by the heterogeneity of local model updates. Through convergence analysis, we reveal that decoupling the resource allocation variables from the model convergence is essential to reduce the problem to a single-round latency minimization problem. To solve this simplified problem, we propose an alternating optimization scheme to jointly consider communication and computation resource allocation and mitigate the straggler effect. We prove that the resulting subproblems, i.e., bandwidth and computation capacity allocation, are both convex and can be optimally solved in closed form, respectively. Simulation results show that compared with the baseline scheme that allocates the communication and computation resources equally across edge devices, the proposed scheme can achieve up to 47.04% single-round latency reduction.

Federated learning (FL), as an emerging distributed machine learning paradigm, allows a mass of edge devices to collaboratively train a global model while preserving privacy. In this tutorial, we focus on FL via over-the-air computation (AirComp), which is proposed to reduce the communication overhead for FL over wireless networks at the cost of compromising in the learning performance due to model aggregation error arising from channel fading and noise. We first provide a comprehensive study on the convergence of AirComp-based FEDAVG (AIRFEDAVG) algorithms under both strongly convex and non-convex settings with constant and diminishing learning rates in the presence of data heterogeneity. Through convergence and asymptotic analysis, we characterize the impact of aggregation error on the convergence bound and provide insights for system design with convergence guarantees. Then we derive convergence rates for AIRFEDAVG algorithms for strongly convex and non-convex objectives. For different types of local updates that can be transmitted by edge devices (i.e., local model, gradient, and model difference), we reveal that transmitting local model in AIRFEDAVG may cause divergence in the training procedure. In addition, we consider more practical signal processing schemes to improve the communication efficiency and further extend the convergence analysis to different forms of model aggregation error caused by these signal processing schemes. Extensive simulation results under different settings of objective functions, transmitted local information, and communication schemes verify the theoretical conclusions.

Federated learning (FL) is recognized as a promising privacy-preserving distributed machine learning paradigm, given its potential to enable collaborative model training among distributed devices without sharing their raw data. However, supporting FL over wireless networks confronts the critical challenges of periodically executing power-hungry training tasks on energy-constrained devices and transmitting high-dimensional model updates over spectrum-limited channels. In this paper, we reap the benefits of both energy harvesting (EH) and over-the-air computation (AirComp) to alleviate the battery limitation by harvesting ambient energy to support both the training and transmission of local models, and to achieve low-latency model aggregation by concurrently transmitting local gradients via AirComp. We characterize the convergence of the proposed FL by deriving an upper bound of the expected optimality gap, revealing that the convergence depends on the accumulated errors due to partial device participation and model distortion, both of which further depend on dynamic energy levels. To accelerate the convergence, we formulate a joint AirComp transceiver design and device scheduling problem, which is then tackled by developing an efficient Lyapunov-based online optimization algorithm. Simulations demonstrate that, by appropriately scheduling devices and allocating energy across multiple communication rounds, our proposed algorithm achieves a much better learning performance than benchmarks.

Federated learning (FL) has emerged as a promising alternative to centralized machine learning for exploiting large amounts of data generated by networks while ensuring data privacy. Unlike previous FL works that rely on terrestrial base stations, this paper studies an unmanned aerial vehicle (UAV)-assisted FL system where a UAV collects local models from distributed ground devices. By leveraging the UAV’s high altitude and mobility, it can proactively establish short-distance line-of-sight links with devices to mitigate the communication straggler effect and improve communication efficiency in FL. Specifically, we present the convergence analysis of FL without convexity assumptions, demonstrating the effect of device scheduling on the global gradients. Based on the derived convergence bound, we aim to minimize the completion time of FL training by jointly optimizing device scheduling, UAV trajectory, and time allocation. This problem explicitly incorporates the devices’ energy budgets, dynamic channel conditions, and convergence accuracy of FL constraints. Despite the non-convexity of the formulated problem, we exploit its structure to decompose it into two sub-problems and further derive the closed-form solutions via the Lagrange dual ascent method. Simulation results show that the proposed design significantly improves the tradeoff between completion time and test accuracy compared to existing benchmarks.

Vertical federated learning (FL) is a collaborative machine learning framework that enables devices to learn a global model from the feature-partition datasets without sharing local raw data. However, as the number of the local intermediate outputs is proportional to the training samples, it is critical to develop communication-efficient techniques for wireless vertical FL to support high-dimensional model aggregation with full device participation. In this paper, we propose a novel cloud radio access network (Cloud-RAN) based vertical FL system to enable fast and accurate model aggregation by leveraging over-the-air computation (AirComp) and alleviating communication straggler issue with cooperative model aggregation among geographically distributed edge servers. However, the model aggregation error caused by AirComp and quantization errors caused by the limited fronthaul capacity degrade the learning performance for vertical FL. To address these issues, we characterize the convergence behavior of the vertical FL algorithm considering both uplink and downlink transmissions. To improve the learning performance, we establish a system optimization framework by joint transceiver and fronthaul quantization design, for which successive convex approximation and alternate convex search based system optimization algorithms are developed. We conduct extensive simulations to demonstrate the effectiveness of the proposed system architecture and optimization framework for vertical FL.

The proliferation of low-earth-orbit (LEO) satellite networks leads to the generation of vast volumes of remote sensing data which is traditionally transferred to the ground server for centralized processing, raising privacy and bandwidth concerns. Federated edge learning (FEEL), as a distributed machine learning approach, has the potential to address these challenges by sharing only model parameters instead of raw data. Although promising, the dynamics of LEO networks, characterized by the high mobility of satellites and short ground-to-satellite link (GSL) duration, pose unique challenges for FEEL. Notably, frequent model transmission between the satellites and ground incurs prolonged waiting time and large transmission latency. This paper introduces a novel FEEL algorithm, named FEDMEGA, tailored to LEO mega-constellation networks. By integrating inter-satellite links (ISL) for intra-orbit model aggregation, the proposed algorithm significantly reduces the usage of low datarate and intermittent GSL. Our proposed method includes a ring all-reduce based intra-orbit aggregation mechanism, coupled with a network flow-based transmission scheme for global model aggregation, which enhances transmission efficiency. Theoretical convergence analysis is provided to characterize the algorithm performance. Extensive simulations show that our FEDMEGA algorithm outperforms existing satellite FEEL algorithms, exhibiting an approximate 30% improvement in convergence rate.

Hybrid beamforming is a promising technology for enhancing the energy- and spectral-efficiency of wireless networks with large-scale antenna arrays, yet the current designs fall short of concurrently achieving low computational complexity and high communication scalability. In this paper, we propose a scalable and effective hybrid beamforming framework for multi-user systems, where the bipartite graph neural network (BGNN) is leveraged to exploit the graph topological structure for sum-rate maximization. To capture permutation properties of the sum-rate maximization problem, we model the wireless network as a bipartite graph, where two disjoint sets of vertices respectively model users and radio frequency (RF) chains, and the edges connecting adjacent vertices characterize interactions between users and RF chains. Based on the bipartite graph, we partition the hybrid beamforming optimization into the updates of feature vectors at user and RF chain vertices, which are realized by alternately activating four kinds of vertex operators that constitute the proposed BGNN. The inputs and outputs of each vertex operator are specifically designed to be independent of the user number and RF chain number in terms of dimension. Numerical results validate the superiority of the proposed BGNN framework from the perspectives of achievable sum rate, computation complexity, and scalability.

Wireless networked control systems (WNCS) offer great potential for revolutionizing the industrial automation by enabling wireless coordination between sensors, decision centers, and actuators. However, inefficient access control and resource allocation in WNCS are two critical factors that limit closed-loop performance and control stability, especially when the spectral and energy resources are limited. In this paper, we first analyze the optimal scheduling condition for maintaining control stability of a WNCS and then formulate a long-term optimization problem that jointly optimizes the access policy of edge devices, and grant policy and resource allocation at the edge server. We employ Lyapunov optimization to decompose the long-term optimization problem into a sequence of independent sub-problems, and propose a heterogeneous attention graph based multi-agent deep reinforcement learning algorithm that jointly optimizes the access and resource allocation policy. By leveraging the attention mechanism to project the graph representations from heterogeneous agents into a unified space, our proposed algorithm facilitates coordination among heterogeneous agents, thereby enhancing the overall system performance. Simulation results demonstrate that our proposed framework outperforms several benchmarks, validating its effectiveness.

Message-passing graph neural network (MPGNN) shows tremendous promise in modeling complex networks by capturing the interaction among vertices via the messaging-passing mechanism. However, the dimension of MPGNN is tied to the size of vertices in the graph, which varies from graph to graph, resulting in dimension mismatch that hinders the utilization of graph data distributed at the network edge. To address this issue, we in this paper leverage the attention mechanism to project the graph representation of MPGNNs into a unified space and apply over-the-air computation (AirComp) to support federated graph learning (FGL) over wireless networks. By explicitly deriving the upper bound on the convergence of over-the-air FGL, we formulate a long-term transmission distortion minimization problem, which is further decomposed into a series of online optimization problems by using Lyapunov optimization. We further propose a deep reinforcement learning based algorithm to optimize the AirComp transceiver, where the analytical expression of transmit power is exploited in the action design to reduce the searching space and also enhance the training performance. Simulations demonstrate that, compared to the benchmarks, the proposed algorithm attains two orders of magnitude acceleration in the inference stage, while exhibiting enhanced robustness and improving learning performance.

Decentralized federated learning (FL) is capable of enabling efficient and robust collaborative model training with device-to-device (D2D) communications. However, most existing studies on decentralized FL employ half-duplex communication to achieve time-division model aggregation, which is inefficient in scenarios with massive geographically dispersed devices. To address this issue, we in this paper propose decentralized over-the-air FL (DOAFL) with full-duplex (FD) communication, where over-the-air computation (AirComp) and FD communication are fused together to enable parallel model exchange and aggregation, and antenna arrays are leveraged to suppress residual self-interference (SI). Specifically, we first conduct the convergence analysis for DOAFL to characterize the influence of the consensus error introduced by residual SI, channel fading, and receiver noise on the learning performance. Subsequently, we formulate a joint communication and computation (JC2) optimization problem with an objective to increase both the accuracy and time efficiency of the model training, followed by developing a JC2 design algorithm to efficiently optimize transceiver beamforming and computing frequencies. Simulation results verify the superiority of our proposed DOAFL in terms of training latency, residual SI suppression, and learning performance under low energy budgets.

In this paper, we propose an online federated learning framework with massive random access, aiming to learn a sequence of global models using local data that are sequentially collected by massive edge devices. As only a subset of devices is capable of collecting data and performing local model update at any specific moment, the communication pattern between the edge server and devices is random and sporadic, which is referred to assporadic local updates. This motivates us to adopt a two-phase grant-free random access scheme that consists of the activity detection and model transmission phases to facilitate efficient communication between the edge server and devices. We first provide the regret analysis for online federated learning, and derive the optimality gap in terms of successful transmission probabilities. Then, we characterize the achievable transmission rate of each active device using random matrix theory and establish the relationship between the pilot length and the outage probability. Furthermore, we propose an optimal pilot length design by minimizing the optimality gap. To validate our scheme, we provide comprehensive experimental results that demonstrate the superiority of the proposed scheme over traditional schemes in various online tasks.

To tackle the issue of heterogeneous input raw data samples obtained by different devices and enhance the feature extraction capability of edge devices, we propose a vertically split neural network based edge-device collaborative artificial intelligence (AI) inference framework. The local results calculated by various light-size sub-networks at edge devices are transmitted and aggregated at the server for the downstream inference task. Nevertheless, the transmission of such high-dimensional local results involves severe communication overhead. To resolve this issue, the technique of over-the-air computation (AirComp) is adopted to enable low-latency aggregation. The same entry of all devices’ local results is transmitted over a same wireless resource block and aggregated via the waveform superposition property. Furthermore, to simultaneously support the aggregation of all dimensions of the local results, we consider a broadband channel and leverage orthogonal frequency division multiplexing (OFDM) to divide the system bandwidth into multiple subcarriers which are then assigned for different dimensions. Consequently, an extra degree of freedom is introduced to design the aggregation of all dimensions. We then propose a scheme of joint subcarrier allocation, power allocation, and receiver beamforming to minimize the aggregation distortion and enhance inference performance. Extensive experiments are conducted to verify the superiority of the proposed design over benchmarks.

Grant-free random access is an effective technology for enabling low-overhead and low-latency massive access, where joint activity detection and channel estimation (JADCE) is a critical issue. Although existing compressed sensing algorithms can be applied for JADCE, they usually fail to simultaneously harvest the following properties: effective sparsity inducing, fast convergence, robust to different pilot sequences, and adaptive to time-varying networks. To this end, we propose an unfolding framework for JADCE based on the proximal gradient method. Specifically, we formulate the JADCE problem as a group-row-sparse matrix recovery problem and leverage a minimax concave penalty rather than the widely-used$\ell _{1}$-norm to induce sparsity. We then develop a proximal gradient-based unfolding neural network that parameterizes the algorithmic iterations. To improve convergence rate, we incorporate momentum into the unfolding neural network, and prove the accelerated convergence theoretically. Based on the convergence analysis, we further develop an adaptive-tuning algorithm, which adjusts its parameters to different signal-to-noise ratio settings. Simulations show that the proposed unfolding neural network achieves better recovery performance, convergence rate, and adaptivity than current baselines.

For the purpose of training a machine learning model via exploiting data from multiple devices without compromising their privacy, federated learning (FL) has become a popular approach. Meanwhile, over-the-air computation (AirComp) enables concurrent model transmission to accelerate model aggregation in the context of FL. However, the performance of model aggregation is significantly hindered by adverse wireless channels. In this paper, we employ intelligent reflecting surface (IRS) to facilitate accurate model aggregation in AirComp-based FL. To ensure compatibility with existing communication standards, this paper adopts uniform quantization for both downlink model broadcast and uplink AirComp-based gradient aggregation. Furthermore, we quantitatively examine the impact of quantization errors on transmission accuracy and convergence bound. To mitigate signal distortion, we employ an alternating optimization algorithm that optimizes the beamforming vector at the base station, the transmit/receive scalars at the devices, and the phase shifts at the IRS. The simulation results provide compelling evidence for the effectiveness and robustness of our proposed method.

Dual-functional radar-communication (DFRC) attracts extensive attention recently, given its potential to integrate the sensing and communication processes for enhancing the spectrum efficiency and hardware utilization. Due to the co-channel interference, effective resource allocation is a critical issue for DFRC, which typically relies on the accurate channel estimation. However, the conventional estimate-then-optimize algorithms may not work well due to inaccurate channel estimation, high computation complexity, and inconsistent optimization goals. This paper considers a DRFC system with multiuser multiple-input-multiple-output (MIMO) communications and MIMO radar sensing, where an end-to-end learning algorithm is developed to tackle the aforementioned issues. We formulate an optimization problem to maximize the communication performance subject to the radar sensing constraints, via optimizing both the transmit and receive beamforming matrices, while considering channel estimation in the loop. To tackle this challenging problem, we exploit the universal approximation property of the neural network to develop an end-to-end learning algorithm to directly learn the mapping between the pilot signals and the beamforming matrices, and meanwhile appropriately design the loss function to account for the radar sensing constraints. Simulations show that our proposed algorithm achieves a much greater communication performance than the baseline algorithm, while guaranteeing the same sensing performance.

Communication bottleneck and statistical heterogeneity are two critical challenges of federated learning (FL) over wireless networks. To tackle both challenges, in this paper we propose an over-the-air computation (AirComp) assisted hierarchical personalized FL (HPFL) framework, where a device-edge-cloud based three-tier network architecture is adopted to simultaneously learn a global model and multiple personalized local models. We analyze the convergence of the AirComp-assisted HPFL framework and formulate an optimization problem to minimize the transmission distortion, which is an essential component of the convergence upper bound. An efficient algorithm is subsequently developed to optimize the transceiver design by leveraging successive convex approximation and Lagrangian duality. We conduct extensive simulations to demonstrate that our developed algorithm achieves a near-optimal performance and a much greater test accuracy than the baseline algorithms.

Benefiting from the high mobility and the line-of-sight communications, unmanned aerial vehicles (UAVs) and high-altitude platform (HAP) can be, respectively, designated as the edge and cloud servers to aggregate the local and edge models in hierarchical federated learning (HFL). To enable energy-efficient HFL, we manoeuvre the trajectories and control the transmit powers of UAVs over multi-cell wireless networks. Meanwhile, as the channels are reused in different cells, inter-cell interference is inevitable during the aggregation at UAVs, leading to performance degradation of HFL. To tackle these issues, an algorithm based on multi-agent twin delayed deep deterministic policy gradient (MATD3) is proposed to minimize the overall energy consumption of UAVs during the training process. The simulation results show that the proposed MATD3-based algorithm performs much better than the baseline schemes.

Deploying federated learning (FL) in wireless networks faces the critical challenge of communication bottlenecks. To address this issue, in this paper, we consider an over-the-air computation (AirComp) assisted hierarchical FL (HFL) framework, where a cloud-edge-device-based three-tier network architecture is constructed to train a global model. We first theoretically characterize the convergence of the AirComp-assisted HFL framework and formulate a combinatorial optimization problem that jointly optimizes the edge interval control and local device transceiver design to minimize the convergence upper bound to boost the overall learning performance and reduce communication cost. We show that the formulated optimization problem can be decoupled into an edge interval control problem and a transceiver design problem, which can be tackled by developing a relaxation and rounding algorithm and an alternating Lyapunov drift-based algorithm, respectively. Extensive simulations demonstrate that our proposed algorithm significantly outperforms the baseline schemes.

In this paper, we study the latency minimization problem for a wireless federated learning (FL) system with heterogeneous computation capability, where different edge devices perform different numbers of local updates in each communication round. We formulate a total latency minimization problem, taking into account both the communication and computation latency in the whole FL procedure. We reveal that decoupling the resource allocation variables from the model convergence is essential to reduce the problem to a single-round latency minimization problem. To solve this simplified problem, we propose an alternating optimization scheme to jointly consider communication and computation resource allocation and mitigate the straggler effect. We prove that the resulting sub-problems, i.e., bandwidth and computation capacity allocation, are both convex and can be optimally solved in closed form, respectively. Simulations show that compared with the baseline scheme that allocates the communication and computation resources equally across edge devices, the proposed scheme can achieve single-round latency reduction.

The safety-critical scenarios of artificial intelligence (AI), such as autonomous driving, Internet of Things, smart healthcare, etc., have raised critical requirements of trustworthy AI to guarantee the privacy and security with reliable decisions. As a nascent branch for trustworthy AI, federated learning (FL) has been regarded as a promising privacy preserving framework for training a global AI model over collaborative devices. However, security challenges still exist in the FL framework, e.g., Byzantine attacks from malicious devices, and model tampering attacks from malicious server, which will degrade or destroy the accuracy of trained global AI model. In this article, we shall propose a decentralized blockchain-based FL (B-FL) architecture by using a secure global aggregation algorithm to resist malicious devices, and deploying a practical Byzantine fault tolerance consensus protocol with high effectiveness and low energy consumption among multiple edge servers to prevent model tampering from the malicious server. However, to implement B-FL system at the network edge, multiple rounds of cross-validation in blockchain consensus protocol will induce long training latency. We thus formulate a network optimization problem that jointly considers bandwidth and power allocation for the minimization of long-term average training latency consisting of progressive learning rounds. We further propose to transform the network optimization problem as a Markov decision process and leverage the deep reinforcement learning (DRL)-based algorithm to provide high system performance with low computational complexity. Simulation results demonstrate that B-FL can resist malicious attacks from edge devices and servers, and the training latency of B-FL can be significantly reduced by the DRL-based algorithm compared with the baseline algorithms.

Reconfigurable intelligent surface (RIS) has emerged as a cost-effective and energy-efficient technique for 6G. By adjusting the phase shifts of passive reflecting elements, RIS is capable of suppressing the interference and combining the desired signals constructively at receivers, thereby significantly enhancing the performance of communication system. In this paper, we consider a green multi-user multi-antenna cellular network, where multiple RISs are deployed to provide energy-efficient communication service to end users. We jointly optimize the phase shifts of RISs, beamforming of the base stations, and the active RIS set with the aim of minimizing the power consumption of the base station (BS) and RISs subject to the quality of service (QoS) constraints of users and the transmit power constraint of the BS. However, the problem is mixed combinatorial and non-convex, and there is a potential infeasibility issue when the QoS constraints cannot be guaranteed by all users. To deal with the infeasibility issue, we further investigate a user admission control problem to jointly optimize the transmit beamforming, RIS phase shifts, and the admitted user set. A unified alternating optimization (AO) framework is then proposed to solve both the power minimization and user admission control problems. Specifically, we first decompose the original non-convex problem into several rank-one constrained optimization subproblems via matrix lifting. A difference-of-convex (DC) algorithm is then developed to solve each decomposed subproblem. The proposed AO framework efficiently minimizes the power consumption of wireless networks as well as user admission control when the QoS constraints cannot be guaranteed by all users. To further address the complexity-sensitive issue for practical implementation, we propose an alternative low-complexity beamforming and RISs phase shifts design algorithm based on zero-forcing (ZF) to enable the green cellular networks.

In this paper, we study unmanned aerial vehicles (UAVs) assisted wireless data aggregation (WDA) in multi-cluster networks, where multiple UAVs simultaneously perform different WDA tasks via over-the-air computation (AirComp) without terrestrial base stations. This work focuses on maximizing the minimum amount of WDA tasks performed by each cluster by optimizing the UAV trajectory and transceiver design as well as cluster scheduling and association, while considering the WDA accuracy requirement. Such a joint design is critical for interference management in multi-cluster AirComp networks, via enhancing the signal quality between each UAV and its associated cluster for signal alignment while reducing the inter-cluster interference between each UAV and its non-associated clusters. Although it is generally challenging to optimally solve the formulated non-convex mixed-integer nonlinear programming, an efficient iterative algorithm as a compromise approach is developed by exploiting bisection and block coordinate descent methods, yielding an optimal transceiver solution in each iteration. The optimal binary variables and a suboptimal trajectory are obtained by using the dual method and successive convex approximation, respectively. Simulations show the considerable performance gains of the proposed design over benchmarks and the superiority of deploying multiple UAVs in increasing the number of performed tasks while reducing access delays.

Over-the-air federated learning (FL) is a promising privacy-preserving edge artificial intelligence paradigm, where over-the-air computation enables spectral-efficient model aggregation by achieving simultaneous communication and aggregation. However, due to limited transmit power, the performance of over-the-air FL is limited by the device with the worst channel condition toward the edge server. In this paper, we leverage reconfigurable intelligent surface (RIS) to mitigate the communication bottleneck of over-the-air FL and explicitly characterize the corresponding convergence upper bound. The convergence analysis illustrates the detrimental impact of the accumulated aggregation error over all rounds and inspires us to formulate a time-average transmission distortion minimization problem by jointly optimizing the transceiver and RIS phase-shifts. To reduce the computation complexity and enhance the model aggregation accuracy, we develop a graph neural network (GNN) based learning algorithm to directly map channel coefficients to the optimized network parameters. By exploiting permutation equivalence and invariance properties of graphs, the parameter dimension of the proposed algorithm is independent of the number of edge devices, which reduces the computational complexity and improves the algorithmic scalability. Simulations show that the proposed algorithm speeds up the computation by three orders of magnitude compared to the baselines, while achieving performance superiority and algorithmic robustness.

Federated learning (FL) leverages the private data and computing power of multiple clients to collaboratively train a global model. Many existing FL algorithms over wireless networks adopting synchronous model aggregation suffer from the straggler issue, due to the heterogeneity of local computing power and channel conditions. To address this issue, we in this paper advocate an asynchronous FL framework with adaptive client selection for training latency minimization, taking into account the client availability and long-term fairness. We consider a practical scenario, where the channel conditions and the locally available computing power are not known in prior. This makes the client selection problem challenging, as the training latency consists of the uplink/downlink transmission time and the local training time. To this end, we tackle the asynchronous client selection problem in an online manner by converting the latency minimization problem into a multi-armed bandit problem, and leverage the upper confidence bound policy and virtual queue technique in Lyapunov optimization to solve the problem. We theoretically show that the proposed algorithm achieves sub-linear regret performance, ensures long-term fairness, and guarantees training convergence. Results show that the proposed algorithm can reduce the training time by up to 50% when compared to the baseline algorithms.

In this paper, we consider communication-efficient over-the-air federated learning (FL), where multiple edge devices with non-independent and identically distributed datasets perform multiple local iterations in each communication round and then concurrently transmit their updated gradients to an edge server over the same radio channel for global model aggregation using over-the-air computation (AirComp). We derive the upper bound of the time-average norm of the gradients to characterize the convergence of AirComp-assisted FL, which reveals the impact of the model aggregation errors accumulated over all communication rounds on convergence. Based on the convergence analysis, we formulate an optimization problem to minimize the upper bound to enhance the learning performance, followed by proposing an alternating optimization algorithm to facilitate the transceiver design for AirComp-assisted FL. As the alternating optimization algorithm suffers from high computation complexity, we further develop a knowledge-guided learning algorithm that exploits the structure of the analytic expression of the transmit power to achieve computation-efficient transceiver design. Simulation results demonstrate that the proposed knowledge-guided learning algorithm achieves a comparable performance as the alternating optimization algorithm, but with a much lower computation complexity. Moreover, both proposed algorithms outperform the baseline methods in terms of convergence speed and test accuracy.

With the massive use of GPU, task scheduling under CPU-GPU clusters has become an indispensable research topic. Unlike existing models, we propose an innovative framework that users offload their tasks in CPU-GPU heterogeneous Edge Clusters (ECs) instead of general-purpose CPU clusters. The framework takes full advantage of the GPU's powerful parallel computing capabilities. Specifically, we decompose each user task into sequential segments and parallel segments, which can be offloaded to CPUs and GPUs of the ECs, respectively. By dis-cretizing the GPU's computing capability, we formulate a Mixed Integer Nonlinear Programming (MINLP), which involves jointly optimizing the task offloading decision, the uplink transmission power of users, and computing resource allocation. To tackle this challenging problem, we propose a Joint Simulated Annealing and Convex Optimization (JSAC) based algorithm to minimize the total overhead consisting of delay and energy consumption. Our experimental simulation results demonstrate that the JSAC algorithm can make full use of GPU's powerful parallel computing capability via allocating GPU resources effectively. In particular, the JSAC algorithm achieves optimal performance in terms of system overhead, number of beneficial UEs, and speedup.

In this paper, we study a remote monitoring Internet of Things (IoT) network, in which an access point (AP) is deployed to aggregate some specific time-varying environment parameters from multiple groups of IoT devices using over-the-air computation (AirComp). As each group of IoT devices monitors a different type of parameters, the AP schedules a group of IoT devices to transmit their status updates each time. To evaluate the freshness of the aggregated data, we introduce the concept of the age of aggregated information (AoAI). We analyze the average AoAI by modelling the instantaneous AoAI evolution of each group as a Discrete-Time Markov Chain and characterize the tradeoff between the average AoAI and mean-squared error (MSE). We further develop two scheduling policies to ensure fair scheduling and achieve the minimum AoAI, respectively. Simulations validate our theoretical analysis and demonstrate the effectiveness of the proposed scheduling algorithms.

Joint activity detection and channel estimation (JADCE) for grant-free random access is a critical issue that needs to be addressed to support massive connectivity in IoT networks. However, the existing model-free learning method can only achieve either activity detection or channel estimation, but not both. In this paper, we propose a novel model-free learning method based on generative adversarial network (GAN) to tackle the JADCE problem. We adopt the U-net architecture to build the generator rather than the standard GAN architecture, where a pre-estimated value that contains the activity information is adopted as input to the generator. By leveraging the properties of the pseudoinverse, the generator is refined by using an affine projection and a skip connection to ensure the output of the generator is consistent with the measurement. Moreover, we build a two-layer fully-connected neural network to design pilot matrix for reducing the impact of receiver noise. Simulation results show that the proposed method out-performs the existing methods in high SNR regimes, as both data consistency projection and pilot matrix optimization improve the learning ability.

Over-the-air computation (AirComp) and millimeter wave (mmWave) communications have the feasibility to perform fast wireless data aggregation (WDA) by allowing simultaneous transmissions and providing abundant spectral resources, respectively. However, AirComp is limited by the link with the worst channel condition, while mmWave communications are vulnerable to the blockages. To address these issues, this paper proposes to leverage reconfigurable intelligent surface (RIS) aided AirComp for WDA in mmWave communication networks. To enhance the system performance, we formulate an optimization problem to minimize the mean-squared error (MSE) of WDA by jointly optimizing the receive beamforming vector of the access point, the transmit scalars of devices, and the phase-shift matrix of the RIS. To this end, we derive the closed-form expression of transmit scalars and then propose a Riemannian conjugate gradient algorithm, which can efficiently tackle the unit-modulus constraints with a low computational complexity. Compared to the baseline algorithms, simulation results reveal that the proposed algorithm achieves a faster convergence rate and a smaller MSE.

Federated learning (FL), as a disruptive machine learning (ML) paradigm, enables the collaborative training of a global model over decentralized local data sets without sharing them. It spans a wide scope of applications from the Internet of Things (IoT) to biomedical engineering and drug discovery. To support low-latency and high-privacy FL over wireless networks, in this article, we propose a reconfigurable intelligent surface (RIS)-empowered over-the-air FL system to alleviate the dilemma between learning accuracy and privacy. This is achieved by simultaneously exploiting the channel propagation reconfigurability with RIS for boosting the received signal power, as well as the waveform superposition property with over-the-air computation (AirComp) for fast model aggregation. By considering a practical scenario, where high-dimensional local model updates are transmitted across multiple communication blocks, we characterize the convergence behaviors of the differentially private federated optimization algorithm. We further formulate a system optimization problem to optimize the learning accuracy while satisfying privacy and power constraints via the joint design of transmit power, artificial noise, and phase shifts at RIS, for which a two-step alternating minimization framework is developed. Simulation results validate our systematic, theoretical, and algorithmic achievements and demonstrate that RIS can achieve a better tradeoff between privacy and accuracy for over-the-air FL systems.

Federated learning (FL) over resource-constrained wireless networks has recently attracted much attention. However, most existing studies consider one FL task in single-cell wireless networks and ignore the impact of downlink/uplink inter-cell interference on the learning performance. In this paper, we investigate FL over a multi-cell wireless network, where each cell performs a different FL task and over-the-air computation (AirComp) is adopted to enable fast uplink gradient aggregation. We conduct convergence analysis of AirComp-assisted FL systems, taking into account the inter-cell interference in both the downlink and uplink model/gradient transmissions, which reveals that the distorted model/gradient exchanges induce a gap to hinder the convergence of FL. We characterize the Pareto boundary of the error-induced gap region to quantify the learning performance trade-off among different FL tasks, based on which we formulate an optimization problem to minimize the sum of error-induced gaps in all cells. To tackle the coupling between the downlink and uplink transmissions as well as the coupling among multiple cells, we propose a cooperative multi-cell FL optimization framework to achieve efficient interference management for downlink and uplink transmission design. Results demonstrate that our proposed algorithm achieves much better average learning performance over multiple cells than non-cooperative baseline schemes.

Pliable index coding (PICOD) has recently been regarded as a promising solution that exploits the coding advantage to improve communication efficiency of content-type systems (e.g., recommendation system), where clients are pliable and are interested in receiving any new message that they do not have. PICOD aims to find an effective coding strategy that satisfies the demands of all clients with the minimum number of transmissions. However, most of the previous works mainly provided theoretical understanding on PICOD in special instances based on greedy algorithms. In contrast, in this paper, we present a flexible sparse and low-rank matrix modeling approach to minimize the number of transmissions for the general PICOD problems. This is achieved by establishing generalized pliable alignment conditions to guarantee the requirements of all clients. As the resulting non-convex problem is highly intractable, we further develop an alternating pursuit framework to detect the rank of the matrix to be recovered by using the rank-increasing strategy. To address the feasibility-detection issues in the existing methods, we propose an alternating projection algorithm, which admits closed-form expressions and avoids excessive sparsity inducing. Moreover, we establish the global convergence of the alternating projection algorithm with random initial points. Simulation results demonstrate that the proposed alternating pursuit algorithm significantly reduces the number of transmissions compared to the state-of-the-art methods.

Many of the existing optimization-based transmit power control algorithms suffer from high computational complexity and require instantaneous global channel state information (CSI), both of which hinder their practical implementation. In this paper, we consider a wireless network with multiple transmitter-receiver pairs, where each transmitter only has access to its local CSI fed back from its intended receiver and does not require local CSI exchange with its neighboring transmitters. In such a network scenario, we propose a deep reinforcement learning based decentralized multi-agent power control (DEC-MAPC) algorithm for sum-rate maximization, where each transmitter acts as an intelligent agent. By leveraging the value decomposition technique, we establish a nonlinear mapping from the local reward of each agent to the global reward. Such a design allows each agent to independently control its transmit power based on its local CSI while enabling global collaboration among the agents. The proposed algorithm is scalable to large-scale networks as only local CSI is required, and is robust to the channel and interference variations via interacting with the environment. Simulation results show that the proposed DEC-MAPC algorithm with local CSI achieves comparable sum-rate performance with the centralized optimization algorithms with global CSI, while significantly reducing the computational complexity.

Different from the existing works that focus on transceiver design of over-the-air computation (AirComp) over static networks, we in this paper consider an unmanned aerial vehicle (UAV) aided AirComp system, where the UAV as a flying base station aggregates data from mobile sensors. The trajectory design of the UAV provides an additional degree of freedom to improve the performance of AirComp. We aim to minimize the time-averaged mean-squared error (MSE) of AirComp by jointly optimizing the UAV trajectory, receive normalizing factors, and sensors’ transmit power. To this end, we first propose a novel and equivalent problem transformation by introducing intermediate variables. This reformulation leads to a convex subproblem when fixing any other two blocks of variables, thereby enabling efficient algorithm design based on the principle of block coordinate descent and alternating direction method of multipliers (ADMM) techniques. In particular, we derive the optimal closed-form solutions for normalizing factors and intermediate variables optimization subproblems. We also recast the convex trajectory design subproblem into an ADMM form and obtain the closed-form expressions for each variable updating. Simulation results show that the proposed algorithm achieves a smaller time-averaged MSE while reducing the simulation time by orders of magnitude compared to state-of-the-art algorithms.

Antenna selection is capable of reducing the hardware complexity of massive multiple-input multiple-output (MIMO) networks at the cost of certain performance degradation. Reconfigurable intelligent surface (RIS) has emerged as a cost-effective technique that can enhance the spectrum-efficiency of wireless networks by reconfiguring the propagation environment. By employing RIS to compensate for the performance loss due to antenna selection, in this paper we propose a new network architecture, i.e., RIS-assisted massive MIMO system with antenna selection, to enhance the system performance while enjoying a low hardware cost. This is achieved by maximizing the channel capacity via joint antenna selection and passive beamforming while taking into account the cardinality constraint of active antennas and the unit-modulus constraints of all RIS elements. However, the formulated problem turns out to be highly intractable due to the non-convex constraints and coupled optimization variables, for which an alternating optimization framework is provided, yielding antenna selection and passive beamforming subproblems. The computationally efficient submodular optimization algorithms are developed to solve the antenna selection subproblem under different channel state information assumptions. The iterative algorithms based on block coordinate descent are further proposed for the passive beamforming design by exploiting the unique problem structures. Moreover, the proposed algorithms are feasible to any finite number of antennas, and thus can be applicable in both ordinary MIMO and massive MIMO settings. Experimental results will demonstrate the algorithmic advantages and desirable performance of the proposed algorithms for RIS-assisted massive MIMO systems with antenna selection.

Massive access is a critical design challenge of Internet of Things (IoT) networks. In this paper, we consider the grant-free uplink transmission of an IoT network with a multiple-antenna base station (BS) and a large number of single-antenna IoT devices. Taking into account the sporadic nature of IoT devices, we formulate the joint activity detection and channel estimation (JADCE) problem as a group-sparse matrix estimation problem. This problem can be solved by applying the existing compressed sensing techniques, which however either suffer from high computational complexities or lack of algorithm robustness. To this end, we propose a novel algorithm unrolling framework based on the deep neural network to simultaneously achieve low computational complexity and high robustness for solving the JADCE problem. Specifically, we map the original iterative shrinkage thresholding algorithm (ISTA) into an unrolled recurrent neural network (RNN), thereby improving the convergence rate and computational efficiency through end-to-end training. Moreover, the proposed algorithm unrolling approach inherits the structure and domain knowledge of the ISTA, thereby maintaining the algorithm robustness, which can handle non-Gaussian preamble sequence matrix in massive access. With rigorous theoretical analysis, we further simplify the unrolled network structure by reducing the redundant training parameters. Furthermore, we prove that the simplified unrolled deep neural network structures enjoy a linear convergence rate. Extensive simulations based on various preamble signatures show that the proposed unrolled networks outperform the existing methods in terms of the convergence rate, robustness and estimation accuracy.

Over-the-air computation (AirComp) based federated learning (FL) is capable of achieving fast model aggregation by exploiting the waveform superposition property of multiple-access channels. However, the model aggregation performance is severely limited by the unfavorable wireless propagation channels. In this paper, we propose to leverage intelligent reflecting surface (IRS) to achieve fast yet reliable model aggregation for AirComp-based FL. To optimize the learning performance, we present the convergence analysis of our proposed IRS-assisted AirComp-based FL system, based on which we propose to maximize the number of scheduled devices of each communication round under certain mean-squared error (MSE) requirements. To tackle the formulated highly-intractable problem, we propose a two-step optimization framework. Specifically, we induce the sparsity of device selection in the first step, followed by solving a series of MSE minimization problems to find the maximum feasible device set in the second step. We then propose an alternating optimization framework, supported by the difference-of-convex programming for low-rank optimization, to efficiently design the aggregation beamformers at the BS and phase shifts at the IRS. Simulation results demonstrate that our proposed algorithm and the deployment of an IRS can achieve a higher FL prediction accuracy than the baseline schemes.

This paper investigates the task offloading problem in a hybrid intelligent reflecting surface (IRS) and massive multiple-input multiple-output (MIMO) relay assisted fog computing system, where multiple task nodes (TNs) offload their computational tasks to computing nodes (CNs) nearby massive MIMO relay node (MRN) and fog access node (FAN) via the IRS for execution. By considering the practical imperfect channel state information (CSI) model, we formulate a joint task offloading, IRS phase shift optimization, and power allocation problem to minimize the total energy consumption. We solve the resultant non-convex optimization problem in three steps. First, we solve the IRS phase shift optimization problem with the sequential rank-one constraint relaxation (SROCR) algorithm and semidefinite relaxation (SDR) algorithm for a given power- and computational resource allocation. Then, we exploit a differential convex (DC) optimization framework to determine the power allocation decision that minimizes the total energy consumption. Given the IRS phase shifts, the computational resources, and the power allocation, we propose an alternating optimization algorithm for finding the jointly optimized results. The simulation results demonstrate the effectiveness of the proposed scheme as compared with other benchmark schemes, and the energy efficient offloading strategy for the proposed fog computing system can be chosen according to the asymptotic form of the effective signal-to-interference-plus-noise ratio (SINR).

Federated learning (FL) is a promising learning paradigm that can tackle the increasingly prominent isolated data islands problem while keeping users’ data locally with privacy and security guarantees. However, FL could result in task-oriented data traffic flows over wireless networks with limited radio resources. To design communication-efficient FL, most of the existing studies employ the first-order federated optimization approach that has a slow convergence rate. This however results in excessive communication rounds for local model updates between the edge devices and edge server. To address this issue, in this paper, we instead propose a novel over-the-air second-order federated optimization algorithm to simultaneously reduce the communication rounds and enable low-latency global model aggregation. This is achieved by exploiting the waveform superposition property of a multi-access channel to implement the distributed second-order optimization algorithm over wireless networks. The convergence behavior of the proposed algorithm is further characterized, which reveals a linear-quadratic convergence rate with an accumulative error term in each iteration. We thus propose a system optimization approach to minimize the accumulated error gap by joint device selection and beamforming design. Numerical results demonstrate the system and communication efficiency compared with the state-of-the-art approaches.

Intelligent Reflecting Surface (IRS) is a promising technology that is able to manipulate the wireless propagation channels via smartly adjusting the signal reflection. With continuous phase shifts, IRS has been shown to be effective in enlarging the achievable rate region. In this paper, we investigate the achievable rate region of a IRS-aided multi-user interference channel, where the phase shifts at the IRS can only take a finite number of discrete values. We formulate a multi-objective optimization problem (MOOP) to characterize the achievable rate region. The commonly adopted approaches such as the rate profile method fail to solve MOOP with optimization variables. Although the exhaustive search method can obtain the Pareto-optimal solutions, it suffers from high computational complexity. To this end, we propose a computationally efficient active learning algorithm via Gaussian process (GP). By modeling the objectives of MOOP as a draw from a GP distribution with only a few randomly computed rate-tuples, the active learning algorithm can quickly dominate the non-optimal points and find Pareto-optimal points without calculating rate-tuples. Numerical simulations demonstrate that the achievable rate region of IRS-aided interference channel is much larger than that without IRS and the proposed active learning framework obtains near-optimal Pareto solutions with a much lower computational complexity than the traditional exhaustive search algorithm.

This paper investigates the task offloading problem in a hybrid intelligent reflecting surface (IRS) and massive multiple-input multiple-output (MIMO) relay assisted fog computing system, where multiple task nodes (TNs) offload their computational tasks to computing nodes (CNs) nearby massive MIMO relay node (MRN) and fog access node (FAN) via the IRS for execution. By considering the practical imperfect channel state information (CSI) model, we formulate a joint task offloading, IRS phase shift optimization, and power allocation problem to minimize the total energy consumption. We solve the resultant non-convex optimization problem in three steps. First, we solve the IRS phase shift optimization problem with the semidefinite relaxation (SDR) algorithm. Then, we exploit a differential convex (DC) optimization framework to determine the power allocation decision. Given the IRS phase shifts, the computational resources, and the power allocation, we propose an alternating optimization algorithm for finding the jointly optimized results. The simulation results demonstrate the effectiveness of the proposed scheme as compared with other benchmark schemes.

Grant-free random access has the potential to sup-port massive connectivity in Internet of Things (IoT) networks, where joint activity detection and channel estimation (JADCE) is a key issue that needs to be tackled. The existing methods for JADCE usually suffer from one of the following limitations: high computational complexity, ineffective in inducing sparsity, and incapable of handling complex matrix estimation. To mitigate all the aforementioned limitations, we in this paper develop an effective unfolding neural network framework built upon the proximal operator method to tackle the JADCE problem in IoT networks, where the base station is equipped with multiple antennas. Specifically, the JADCE problem is formulated as a group-sparse-matrix estimation problem, which is regularized by non-convex minimax concave penalty (MCP). This problem can be iteratively solved by using the proximal operator method, based on which we develop a unfolding neural network structure by parameterizing the algorithmic iterations. By further exploiting the coupling structure among the training parameters as well as the analytical computation, we develop two additional unfolding structures to reduce the training complexity. We prove that the proposed algorithm achieves a linear convergence rate. Results show that our proposed three unfolding structures not only achieve a faster convergence rate but also obtain a higher estimation accuracy than the baseline methods.

Over-the-air computation (AirComp) provides a promising way to support ultrafast aggregation of distributed data. However, its performance cannot be guaranteed in long-distance transmission due to the distortion induced by the channel fading and noise. To unleash the full potential of AirComp, this paper proposes to use a low-cost unmanned aerial vehicle (UAV) acting as a mobile base station to assist AirComp systems. Specifically, due to its controllable high-mobility and high-altitude, the UAV can move sufficiently close to the sensors to enable line-of-sight transmission and adaptively adjust all the links' distances, thereby enhancing the signal magnitude alignment and noise suppression. Our goal is to minimize the time-averaging mean-square error for AirComp by jointly optimizing the UAV trajectory, the scaling factor at the UAV, and the transmit power at the sensors, under constraints on the UAV’s predetermined locations and flying speed, sensors’ average and peak power limits. However, due to the highly coupled optimization variables and time-dependent constraints, the resulting problem is non-convex and challenging. We thus propose an efficient iterative algorithm by applying the block coordinate descent and successive convex optimization techniques. Simulation results verify the convergence of the proposed algorithm and demonstrate the performance gains and robustness of the proposed design compared with benchmarks.

In this paper, we consider federated learning (FL) over a noisy fading multiple access channel (MAC), where an edge server aggregates the local models transmitted by multiple end devices through over-the-air computation (AirComp). To realize efficient analog federated learning over wireless channels, we propose an AirComp-based FedSplit algorithm, where a threshold-based device selection scheme is adopted to achieve reliable local model uploading. In particular, we analyze the performance of the proposed algorithm and prove that the proposed algorithm linearly converges to the optimal solutions under the assumption that the objective function is strongly convex and smooth. We also characterize the robustness of proposed algorithm to the ill-conditioned problems, thereby achieving fast convergence rates and reducing communication rounds. A finite error bound is further provided to reveal the relationship between the convergence behavior and the channel fading and noise. Our algorithm is theoretically and experimentally verified to be much more robust to the ill-conditioned problems with faster convergence compared with other benchmark FL algorithms.

This paper establishes the convergence rates for fitting a polynomial neural network with quadratic activation function via the mini-batch Stochastic Gradient Descent (SGD) algorithm. Specifically, we focus on the parallel implementation of calculating mini-batch gradients on a distributed computing platform. We first illustrate that the SGD converges at a linear rate to the optimal solution, and the convergence rate can be characterized as a function of mini-batch sizes. Next, we deploy the SGD with a distributed approach across multiple processors, where the partial mini-batch gradient is calculated and quantized to send to a master processor in each iteration, yielding a Quantized Stochastic Gradient Descent (QSGD) algorithm. This scheme can effectively reduce the communication overhead by the quantization strategy. Furthermore, we reveal that QSGD provably maintains a similar convergence rate of SGD to a globally optimal solution while significantly reduces the communication cost. In particular, the number of bits required for quantization and the mini-batch size affect the convergence rate of QSGD.

In this paper, we consider decentralized federated learning (FL) over wireless networks, where over-the-air computation (AirComp) is adopted to facilitate the local model consensus in a device-to-device (D2D) communication manner. However, the AirComp-based consensus phase brings the additive noise in each algorithm iterate and the consensus needs to be robust to wireless network topology changes, which introduce a coupled and novel challenge of establishing the convergence for wireless decentralized FL algorithm. To facilitate consensus phase, we propose an AirComp-based DSGD with gradient tracking and variance reduction (DSGT-VR) algorithm, where both precoding and decoding strategies are developed for D2D communication. Furthermore, we prove that the proposed algorithm converges linearly and establish the optimality gap for strongly convex and smooth loss functions, taking into account the channel fading and noise. The theoretical result shows that the additional error bound in the optimality gap depends on the number of devices. Extensive simulations verify the theoretical results and show that the proposed algorithm outperforms other benchmark decentralized FL algorithms over wireless networks.

Distributed data aggregation is a critical design aspect in future Internet-of-Things (IoT) networks. Over-the-air computation (AirComp) is capable of achieving ultra-fast data aggregation by exploiting the superposition property of wireless channel. However, the performance of AirComp, measured by the mean-squared-error (MSE), is generally restricted by the unfavorable channel conditions and relies on the availability of perfect channel state information (CSI). In this paper, we propose to use reconfigurable intelligent surface (RIS) to assist the wireless data aggregation in IoT networks via AirComp in the presence of imperfect CSI. By taking into account the constraints of the transmit power at the devices and the unit modulus of the RIS, we formulate an optimization problem to jointly optimize the transmit power of IoT devices, the beamforming vector at the access point, and the phase-shift matrix at the RIS under the expectation-based channel uncertainty model. We present an alternating optimization method to solve this nonconvex problem. In each iteration, the transmit power and the receive beamformer are updated according to Karush-Kuhn-Tucker conditions and a closed-form solution, respectively. Moreover, we also develop a difference-of-convex algorithm to tackle the nonconvex rank-one constraint in the problem of optimizing the phase-shift matrix. Simulation results illustrate the robustness of the proposed algorithm in terms of minimizing the AirComp distortion.

In this paper, we propose to exploit the interconnection in the Internet of Vehicles (IoV) to realize efficient traffic network control, which is indispensable in building intelligent transportation systems (ITS). In addition to control the traffic signals as in conventional traffic network control schemes, we propose to control the detouring behavior of the connected vehicles as well, with an objective to further enhance the traffic efficiency. Specifically, we formulate the joint traffic signal and connected vehicle control problem as a reinforcement learning (RL) problem, the action and state spaces of which are specifically designed to take into account the connected vehicles. To characterize the detouring behavior of the connected vehicles while keeping the decision process simple, we introduce a new concept termed as detouring ratio, which is defined as the fraction of connected vehicles that detour. Moreover, we also design an effective rewarding mechanism that takes into account the impact of the detouring on the network traffic efficiency. By utilizing tools from deep RL, we put forward an efficient algorithm to jointly control the traffic signals and the connected vehicles. Numerical results demonstrate the validity of our proposed models and show that the proposed joint control algorithm can significantly enhance the network traffic efficiency in terms of the travel time and the waiting time.

This article considers a cooperative Internet-of-Things (IoT) system with a source aiming to transmit randomly generated status updates to a designated destination as timely as possible under the help of a relay. We adopt a recently proposed concept, the Age of Information (AoI), to characterize the timeliness of the status updates. In the considered system, delivering the status updates via the one-hop direct link will have a shorter transmission time at the cost of incurring a higher error probability, while the delivery of status updates through the two-hop relay link could be more reliable at the cost of suffering longer transmission time. Thus, it is important to design the relaying protocol of the considered system for optimizing the information freshness. Considering the limited capabilities of IoT devices, we propose two low-complexity Age-oriented Relaying (AoR) protocols, i.e., the source-prioritized AoR (SP-AoR) protocol and the relay-prioritized AoR (RP-AoR) protocol, to reduce the AoI of the considered system. Specifically, in the SP-AoR protocol, the relay opportunistically replaces the source to retransmit the successfully received status updates that have not been correctly delivered to the destination, but the retransmission at the relay can be preempted by the arrival of a new status update at the source. Differently, in the RP-AoR protocol, once the relay replaces the source to retransmit the status updates that have not been successfully received by the destination, the retransmission at the relay will not be preempted by new status update arrivals at the source. By carefully analyzing the evolution of the instantaneous AoI, we derive closed-form expressions of the average AoI for both the proposed AoR protocols. We further optimize the generation probability of the status updates at the source in both protocols. Simulation results validate our theoretical analysis and demonstrate that the two proposed protocols outperform each other under various system parameters. Moreover, the protocol with better performance can achieve near-optimal performance compared with the optimal scheduling policy attained by applying the Markov decision process (MDP) tool.

Fast wireless data aggregation and efficient battery recharging are two critical design challenges of Internet-of-Things (IoT) networks. Over-the-air computation (AirComp) and energy beamforming (EB) turn out to be two promising techniques that can address these two challenges, necessitating the design of wireless-powered AirComp. However, due to severe channel propagation, the energy harvested by IoT devices may not be sufficient to support AirComp. In this article, we propose to leverage the intelligent reflecting surface (IRS) that is capable of dynamically reconfiguring the propagation environment to drastically enhance the efficiency of both downlink EB and uplink AirComp in IoT networks. Due to the coupled problems of downlink EB and uplink AirComp, we further propose the joint design of energy and aggregation beamformers at the access point, downlink/uplink phase-shift matrices at the IRS, and transmit power at the IoT devices, to minimize the mean-squared error (MSE), which quantifies the AirComp distortion. However, the formulated problem is a highly intractable nonconvex quadratic programming problem. To solve this problem, we first obtain the closed-form expressions of the energy beamformer and the device transmit power, and then develop an alternating optimization framework based on difference-of-convex programming to design the aggregation beamformers and IRS phase-shift matrices. Simulation results demonstrate the performance gains of the proposed algorithm over the baseline methods and show that deploying an IRS can significantly reduce the MSE of AirComp.

Over-the-air computation (AirComp) is a disruptive technique for fast wireless data aggregation in Internet of Things (IoT) networks via exploiting the waveform superposition property of multiple-access channels. However, the performance of AirComp is bottlenecked by the worst channel condition among all links between the IoT devices and the access point. In this paper, a reconfigurable intelligent surface (RIS) assisted AirComp system is proposed to boost the received signal power and thus mitigate the performance bottleneck by reconfiguring the propagation channels. With an objective to minimize the AirComp distortion, we propose a joint design of AirComp transceivers and RIS phase-shifts, which however turns out to be a highly intractable non-convex programming problem. To this end, we develop a novel alternating minimization framework in conjunction with the successive convex approximation technique, which is proved to converge monotonically. To reduce the computational complexity, we transform the subproblem in each alternation as a smooth convex-concave saddle point problem, which is then tackled by proposing a Mirror-Prox method that only involves a sequence of closed-form updates. Simulations show that the computation time of the proposed algorithm can be two orders of magnitude smaller than that of the state-of-the-art algorithms, while achieving a similar distortion performance.

Power-domain non-orthogonal multiple access (NOMA) has become a promising technology to exploit the new dimension of the power domain to enhance the spectral efficiency of wireless networks. However, most existing NOMA schemes rely on the strong assumption that users’ channel gains are quite different, which may be invalid in practice. To unleash the potential of power-domain NOMA, we propose a reconfigurable intelligent surface (RIS)-empowered NOMA scheme to introduce desirable channel gain differences among the users by adjusting the phase shifts at the RIS. Our goal is to minimize the total transmit power by jointly optimizing the beamforming vectors at the base station, the phase-shift matrix at the RIS, and user ordering. To address challenge due to the highly coupled optimization variables, we present an alternating optimization framework to decompose the non-convex bi-quadratically constrained quadratic problem under a specific user ordering into two rank-one constrained matrices optimization problems via matrix lifting. To accurately detect the feasibility of the non-convex rank-one constraints and improve performance by avoiding early stopping in the alternating optimization procedure, we equivalently represent the rank-one constraint as the difference between nuclear norm and spectral norm. A difference-of-convex (DC) algorithm is further developed to solve the resulting DC programs via successive convex relaxation, followed by establishing the convergence of the proposed DC-based alternating optimization method. We further propose an efficient user ordering scheme with closed-form expressions, considering both the channel conditions and users’ target data rates. Simulation results validate the ability of an RIS in enlarging the channel-gain difference when the users’ original channel conditions are similar and the superiority of the proposed DC-based alternating optimization method in reducing the total transmit power.

The energy-efficient task offloading problem of a massive multiple-input multiple-output (MIMO)-aided fog computing system is solved, where multiple task nodes offload their computational tasks to be solved via a massive MIMO-aided fog access node to multiple processing nodes in the fog for execution. By considering realistic imperfect channel state information (CSI), we formulate a joint task offloading and power allocation problem for minimizing the total energy consumption, including both computation and communication power consumptions. We solve the resultant non-convex optimization problem in two steps. First, we solve the computational task allocation and computational resource allocation for a given power allocation. Then, we conceive a sequential optimization framework for determining the specific power allocation decision that minimizes the total energy consumption of the fog access node. Given the computational tasks, the computational resources, and the power allocation, we propose an iterative algorithm for the system optimization. The simulation results show that the proposed scheme significantly reduces the total energy consumption compared to the benchmark schemes.

Over-the-air computation (AirComp) is a promising technology that is capable of achieving fast data aggregation in Internet of Things (IoT) networks. The mean-squared error (MSE) performance of AirComp is bottlenecked by the unfavorable channel conditions. This limitation can be mitigated by deploying a reconfigurable intelligent surface (RIS), which reconfigures the propagation environment to facilitate the receiving power equalization. The achievable performance of RIS relies on the availability of accurate channel state information (CSI), which however is generally difficult to be obtained. In this paper, we consider an RIS-aided AirComp IoT network, where an access point (AP) aggregates sensing data from distributed devices. Without assuming any prior knowledge on the underlying channel distribution, we formulate a stochastic optimization problem to maximize the probability that the MSE is below a certain threshold. The formulated problem turns out to be non-convex and highly intractable. To this end, we propose a data-driven approach to jointly optimize the receive beamforming vector at the AP and the phase-shift vector at the RIS based on historical channel realizations. After smoothing the objective function by adopting the sigmoid function, we develop an alternating stochastic variance reduced gradient (SVRG) algorithm with a fast convergence rate to solve the problem. Simulation results demonstrate the effectiveness of the proposed algorithm and the importance of deploying an RIS in reducing the MSE outage probability.

This paper considers a cooperative status update system with a source aiming to send randomly generated status updates to a designated destination as timely as possible with the help of a relay. We adopt a recently proposed concept, the age of information (AoI), to characterize the timeliness of the status updates. We propose a new age-oriented opportunistic relaying (AoR) protocol to reduce the AoI of the considered system. Specifically, the relay opportunistically replaces the source to retransmit the successfully received status updates that have not been correctly delivered to the destination, but the retransmission at the relay can be preempted by the arrival of a new status update at the source. By carefully analyzing the evolution of the AoI, we derive a closed-form expression of the average AoI for the proposed AoR protocol. We further minimize the average AoI by optimizing the generation probability of the status updates at the source. Simulation results validate our theoretical analysis and demonstrate that the average AoI performance of the proposed AoR protocol is superior to that of the non-cooperative system.

Fast wireless data aggregation is a critical design challenge in Internet-of-Things (IoT) networks. In this paper, we consider a real-time status update IoT network, where an access point (AP) aims to aggregate data from multiple IoT devices using over-the-air computation (AirComp). To evaluate the freshness of the aggregated data at the AP, we propose the metric of age of aggregated information (AoAI), extended from the age of information (AoI), which is defined as the time elapsed since the generation of the latest valid aggregated data received at the AP. An aggregated status update is considered to be valid if the AirComp distortion, quantified by the mean-squarederror (MSE), is smaller than a pre-determined threshold. We formulate a constrained Markov decision process (MDP) problem for minimizing the average AoAI subject to the average transmit power constraint of each IoT device. The formulated constrained MDP problem is then reformulated as an unconstrained MDP problem by using the Lagrangian approach. By analyzing the structure of the MDP, we propose a state aggregation procedure to reduce the computational complexity. We further propose both offline and online scheduling algorithms to solve the problem. Simulation results show that the proposed algorithms significantly outperform the baseline algorithm with a fixed scheduling threshold in terms of the AoAI, and also strike a good balance between the AoAI and the total power consumption.

Layered-division-multiplexing (LDM) is a spectrum-efficient physical-layer technology that can simultaneously support multiple services with diversified quality of service (QoS) requirements. In this paper, we propose a reconfigurable intelligent surface (RIS) assisted LDM system, where the base station (BS) simultaneously transmits non-orthogonal unicast and broadcast messages to multiple users. Our goal is to minimize the transmit power of the BS, while taking into account the QoS requirements of all messages and the unit modulus constraint of the RIS. To this end, we formulate a joint phase-shift matrix optimization as well as unicast and broadcast beamformer design problem. However, the formulated problem is a non-convex bi-quadratic programming problem. After utilizing alternating optimization and matrix lifting techniques, we transform the problem into an alternating sequence of rank-constrained semidefinite programming (SDP) problems. By introducing a difference-of-convex (DC) representation for the rank-one constraints, we develop an efficient DC algorithm to solve the low-rank optimization problem. Simulation results demonstrate the performance gains of the proposed algorithm over the state-of-art methods in reducing the BS transmit power.

The cognitive radio (CR) network is a promising network architecture that meets the requirement of enhancing scarce radio spectrum utilization. Meanwhile, reconfigurable intelligent surfaces (RIS) is a promising solution to enhance the energy and spectrum efficiency of wireless networks by properly altering the signal propagation via tuning a large number of passive reflecting units. In this paper, we investigate the downlink transmit power minimization problem for the RIS-enhanced single-cell cognitive radio (CR) network coexisting with a single-cell primary radio (PR) network by jointly optimizing the transmit beamformers at the secondary user (SU) transmitter and the phase shift matrix at the RIS. The investigated problem is a highly intractable due to the coupled optimization variables and unit modulus constraint, for which an alternative minimization framework is presented. Furthermore, a novel difference-of-convex (DC) algorithm is developed to solve the resulting non-convex quadratic program by lifting it into a low-rank matrix optimization problem. We then represent non-convex rank-one constraint as a DC function by exploiting the difference between trace norm and spectral norm. The simulation results validate that our proposed algorithm outperforms the existing state-of-the-art methods.

This paper focuses on the noisy demixing problem for robust recovery of a sequence of source signals and impulse responses from a sum of their convolution with additional noise. There are two main prevalent paradigms for this nonconvex estimation problem. One is leveraging the convex relaxation approach to provide good theoretical sample complexity guarantees. Another one is based on the nonconvex optimization method to enjoy good computational complexity. However, both methods are explored separately at this stage. Instead, we shall develop a method bridging convex relaxation with nonconvex optimization in a rigorous theoretical way. In fact, we find that the solution of the convex relaxation approach and the critical point of nonconvex optimization method can be almost the same. Based on our work, the good theoretical sample complexity guarantee of convex relaxation approach can be applied to nonconvex optimization method. And on the other hand, the certain stability guarantees from nonconvex optimization method can be propagated to convex relaxation approach.

Intelligent reflecting surface (IRS) has recently attracted increasing research interests due to its great potential in enhancing the energy and spectrum efficiency for wireless networks. In this paper, we shall investigate the downlink transmit power optimization problem for an IRS-assisted multigroup multicast system, where the beamformers at the base station (BS) and the phase shifts at the IRS are jointly optimized. We take into account the quality-of-service (QoS) requirements of all users in each multicasting group as well as the constant-envelope reflection of all IRS elements. The formulated problem turns out to be a non-convex quadratically constrained bi-quadratic programming problem, due to the intricate coupling between the optimization variables and the non-convex constant-envelope constraints. To this end, we present the alternating optimization method with matrix lifting to decouple the optimization variables, followed by ensuring the feasibility of the rank-one constraints via introducing the difference-of-convex (DC) function representation. We develop an effective alternating algorithm to solve the joint optimization problem. Extensive simulation results show the superiority of the proposed algorithm in reducing the transmit power of multigroup multicast systems.

Fast wireless data aggregation and efficient battery recharging are two critical design challenges of Internet of Things (IoT) networks. Over-the-air computation (AirComp) and energy beamforming (EB) are two promising techniques that can tackle these two challenges. In this paper, we propose to leverage the intelligent reflecting surface (IRS) to drastically enhance the efficiency of both downlink EB and uplink AirComp in IoT networks by exploiting the passive beamforming gains at the IRS. Due to the coupled downlink EB and uplink AirComp, we propose the joint design of energy and aggregation beamformers at the access point, downlink/uplink phase-shift matrices at the IRS, and transmit power at the IoT devices to minimize the mean-squared-error (MSE), which quantifies the AirComp distortion. However, the formulated problem is a highly intractable nonconvex quadratic programming problem. To this end, we first obtain the closed-form expressions of the energy beamformer and the transmit power, and then propose an efficient algorithm that alternatively updates other variables using semidefinite relaxation (SDR) to solve the problem. Simulation results demonstrate the performance gains of the proposed algorithm over the baseline methods and show that deploying an IRS can significantly reduce the MSE of AirComp.

We consider the multicast transmission of a real-time Internet of Things (IoT) system, where a server transmits time-stamped status updates to multiple IoT devices. We apply a recently proposed metric, named age of information (AoI), to capture the timeliness of the information delivery. The AoI is defined as the time elapsed since the generation of the most recently received status update. Different from the existing studies that considered either multicast transmission without hard deadlines or unicast transmission with hard deadlines, we enforce a hard deadline for the service time of multicast transmission. This is important for many emerging multicast IoT applications, where the outdated status updates are useless for IoT devices. Specifically, the transmission of a status update is terminated when either the hard deadline expires or a sufficient number of IoT devices successfully receive the status update. We first calculate the distributions of the service time for all possible reception outcomes at IoT devices, and then derive a closed-form expression of the average AoI. Simulations validate the performance analysis, which reveals that: 1) the multicast transmission with hard deadlines achieves a lower average AoI than that without hard deadlines; and 2) there exists an optimal value of the hard deadline that minimizes the average AoI.

In this paper, we consider downlink non-orthogonal multiple access (NOMA) transmission in millimeter wave (mmWave) networks with spatially random users. To facilitate NOMA transmission in mmWave networks, we propose a novel joint angle and distance based user pairing strategy. In particular, the user located nearest to the base station (BS) is paired with another user that is located within a distance threshold from the BS and has the minimum relative spatial angle difference. In consideration of the directional beamforming and the randomness of user locations, the BS opportunistically chooses to enable NOMA or orthogonal multiple access (OMA) based on the instantaneous spatial angle difference between the paired users. The proposed scheme fully exploits the antenna array gain for the paired NOMA users. By using tools from stochastic geometry, we derive the coverage probability of the proposed scheme. Simulations validate the theoretical analysis. Results reveal that the proposed scheme outperforms the angle-based NOMA, distance-based NOMA, and OMA schemes, confirming the importance of exploiting both the angle and distance information for user pairing in mmWave networks. Results also show that there exists an optimal value of the distance threshold that maximizes the coverage probability.

The adoption of reconfigurable intelligent surface (RIS) in wireless networks can enhance the spectrum- and energy-efficiency by controlling the propagation environment. Although the RIS does not consume any transmit power, the circuit power of the RIS cannot be ignored, especially when the number of reflecting elements is large. In this paper, we propose the joint design of beamforming vectors at the base station, active RIS set, and phase-shift matrices at the active RISs to minimize the network power consumption, including the RIS circuit power consumption, while taking into account each user's target data rate requirement and each reflecting element's constant modulus constraint. However, the formulated problem is a mixed-integer quadratic programming (MIQP) problem, which is NP-hard. To this end, we present an alternating optimization method, which alternately solves second order cone programming (SOCP) and MIQP problems to update the optimization variables. Specifically, the MIQP problem is further transformed into a semidefinite programming problem by applying binary relaxation and semidefinite relaxation. Finally, an efficient algorithm is developed to solve the problem. Simulation results show that the proposed algorithm significantly reduces the network power consumption and reveal the importance of taking into account the RIS circuit power consumption.

In this article, we consider the multicast transmission of a real-time Internet-of-Things (IoT) system, where an access point (AP) transmits timestamped status updates to multiple IoT devices. Different from the existing studies that only considered multicast transmission without deadlines, we enforce a deadline for the service time of each multicast status update, taking into account both the fixed and randomly distributed deadlines. In particular, a status update is dropped when either its deadline expires or it is successfully received by a certain number of IoT devices. Considering deadlines is important for many emerging IoT applications, where the outdated status updates are of no use to IoT devices. We evaluate the timeliness of the status update delivery by applying a recently proposed metric, named the Age of Information (AoI), which is defined as the time elapsed since the generation of the most recently received status update. After deriving the distributions of the service time for all possible reception outcomes at IoT devices, we manage to obtain the closed-form expressions of both the average AoI and the average peak AoI. Simulations validate the performance analysis, which reveals that the multicast transmission with deadlines achieves a lower average AoI than that without deadlines and there exists an optimal value of the deadline that can minimize the average (peak) AoI. Results also show that the fixed and random deadlines have respective advantages in different deadline regimes.

Fog computing (FC) has the potential to process computation-intensive tasks in Industrial Internet of Things (IIoT) systems. In parallel with the development of FC, non-orthogonal multiple access (NOMA) has been recognized as a promising technique to significantly improve the spectrum efficiency. In this paper, a NOMA-based FC framework for IIoT systems is considered, where multiple task nodes offload their tasks via NOMA to multiple nearby helper nodes for execution. We formulate a joint task scheduling and subcarrier allocation problem, with an objective to minimize the total cost in terms of the delay and energy consumption, while taking into account the practical communication and computation constraints. Note that the task scheduling includes task, computation resource, and power allocations. Since the task and subcarrier allocations involve binary variables, it is challenging to obtain an optimal solution for such a combinatorial problem. To this end, we solve the task scheduling and subcarrier allocation problem in an online learning fashion. During the online learning process, we propose an iterative algorithm to jointly optimize the subcarrier allocation and task scheduling in each time episode. Simulation results show that the proposed scheme can significantly reduce the sum cost compared to the baseline schemes.

Rate splitting (RS) has the potential to significantly enhance both energy efficiency (EE) and spectral efficiency (SE) of wireless networks. In this paper, we propose joint design of the beamforming and rate allocation to maximize both EE and SE of a downlink multicell multiple-input single-output (MISO) system with rate splitting and common beamforming coordination (RS-CBC). This design problem is formulated as a non-convex quadratically-constrained multi-objective optimization problem (MOOP). By investigating the quasi-concavity relationship between EE and SE, the formulated MOOP is transformed into a single-objective optimization problem (SOOP) to offer a tradeoff between EE and SE by maximizing EE in any achievable SE region. A series of transformations are then applied to make the SOOP tractable, after which an efficient iterative algorithm based on successive convex approximation (SCA) is proposed to solve the problem. Simulation results demonstrate the effectiveness of the proposed algorithm and unveil interesting tradeoffs between EE and SE under different parameter settings.

Fog computing (FC) has the potential to enable computation-intensive applications for the next generation wireless networks. In parallel with the development of FC, nonorthogonal multiple access (NOMA) has been recognized as a promising solution to improve the spectrum efficiency. In this paper, a NOMA-based FC system is considered, where multiple task nodes perform task scheduling via NOMA to a helper node, the helper node with abundant computation resource is required to compute the computation task from the task nodes. We formulate a joint task scheduling, computational resource allocation, and power allocation problem with an objective to minimize the sum cost (i.e., delay and energy consumptions for all task nodes) realizing energy-delay tradeoff. It is challenging to obtain an optimal policy for such a combinatorial optimization problem. To this end, we propose an online learning-based optimization framework to tackle this problem. Simulation results show that the proposed scheme significantly reduces the sum cost compared to the baselines.

We consider an Internet of Things (IoT) monitoring system, in which an IoT device monitors a physical process and transmits randomly generated status updates to its associated access point (AP) as timely as possible. The timeliness of the status updates is characterized by a recently introduced metric, termed the age of information (AoI), which is defined as the time elapsed since the generation of the last successfully received status update. The channel between the IoT device and the AP is considered to be error-prone and thus the status updates suffer from packet loss. Assuming that the AP provides no feedback to the IoT device, we adopt a practical truncated automatic repeat request (TARQ) scheme: the IoT device keeps transmitting the current status update repeatedly until the maximum allowable transmission times is reached or a new status update is generated. We characterize the inherent age-energy tradeoff for the considered IoT monitoring system. Specifically, a larger value of the maximum allowable transmission times reduces the average AoI, at the cost of incurring higher average energy consumption at the IoT device. Based on the evolution of AoI, we derive the closed-form expressions of the average AoI, the average peak AoI, and the average energy consumption. We then minimize the average AoI by optimizing the transmit power of the IoT device and the maximum allowable transmission times under an average transmit power constraint. Simulations validate the theoretical analysis and reveal that under the same average transmit power constraint, the adopted TARQ scheme achieves a lower average AoI than the classical ARQ scheme that allows an infinite number of retransmission times.

Narrowband Internet of Things (NB-IoT) provides energy-efficient communications with extended coverage for the low data rate IoT devices. In this paper, we propose a power-domain non-orthogonal multiple access (NOMA) scheme for the NB-IoT systems to enhance the connection density by allowing multiple IoT devices to simultaneously access one subcarrier. We consider both single-tone and multi-tone transmission modes of the NB-IoT systems, where each device can access a single subcarrier or a bond of contiguous subcarriers, respectively. We formulate joint subcarrier and power allocation problems for both transmission modes to maximize the connection density while taking the quality of service requirements and the transmit power constraints of IoT devices into account. We solve the single-tone nonconvex mixed integer programming problem by transforming it into a mixed integer linear programming problem to obtain the optimal solution. The multi-tone problem is solved by using the difference of convex programming approach to obtain a close-to-optimal solution. We also propose low-complexity heuristic algorithms to solve both problems in a suboptimal manner. The simulations results show that our proposed scheme increases the connection density of NB-IoT systems by 87% in the single-tone mode and by 24% in the multi-tone mode compared to orthogonal multiple access.

Non-orthogonal multiple access (NOMA) and millimeter wave (mmWave) are two key enabling technologies for the fifth generation (5G) wireless networks. In this paper, we develop a general performance analysis framework for mmWave-NOMA networks with spatially random users taking into account link blockage and directional beamforming. To facilitate NOMA transmission in mmWave networks, we propose an angle-based user pairing strategy. Specifically, the base station first randomly selects one user and then pairs it with the line-of-sight user that has the minimum relative angle difference. NOMA is enabled when the beamwidth of the main lobe created by directional beamforming is not smaller than the angle difference between the paired NOMA users. Tools from stochastic geometry are utilized to derive the coverage probability and the sum rate of the proposed NOMA scheme, where beam misalignment at both the base station and the users is taken into account. Simulations validate the performance analysis and show that the proposed NOMA scheme achieves a larger coverage probability and a higher sum rate than conventional NOMA with distance-based user pairing and orthogonal multiple access.

Non-orthogonal multiple access (NOMA)-based cognitive wireless networks can improve the spectral efficiency to utilize the vacant spectrum resource and exploit the power domain diversity. In this paper, we formulate a max-min resource allocation problem for video traffic in NOMA-based cognitive wireless networks as a mixed integer non-linear programming (MINLP) problem. The max-min video transmission problem is subject to the constraints of maximum accessed user number at each subchannel, total available energy of each secondary user, video encoding characteristics, and interference power threshold. To solve the formulated MINLP problem, we divide it into two subproblems, i.e., a power allocation and secondary user scheduling subproblem, and a video packet scheduling subproblem. First, we apply a successive convex approximation to transform the joint power allocation and secondary user scheduling subproblem into a bi-convex programming problem. Second, the binary search and dual decomposition methods are combined to obtain the approximated optimal power allocation and secondary user scheduling solutions. Finally, we propose a heuristic packet scheduling algorithm. Simulation numerical results show that the proposed algorithm improves the video quality and guarantees the fairness among different secondary users.

Non-orthogonal multiple access (NOMA) is a promising spectrally-efficient multiple access technique for the fifth generation (5G) wireless networks. In this paper, we propose a dynamic decode-and-forward (DDF) based cooperative NOMA scheme for downlink transmission with spatially random users. In DDF-based cooperative NOMA, the base station transmits the superposition of the signals intended for the paired NOMA users. The user closer to the base station forwards the signal intended for the far user as soon as it can successfully decode its own signal and the signal intended for the far user. We consider two user pairing strategies, namely random and distance-based user pairing, which require one-bit feedback and the users' distance information, respectively. For each user pairing strategy, we derive the outage probability of the proposed NOMA scheme by using tools from stochastic geometry. Furthermore, based on the obtained outage probability, we derive the diversity order and the sum rate of the paired NOMA users. Simulation results validate the analytical results and demonstrate that the proposed DDF-based cooperative NOMA scheme achieves a lower outage probability and a higher sum rate than orthogonal multiple access, conventional NOMA, and cooperative NOMA.

In this paper, we consider downlink non-orthogonal multiple access (NOMA) transmission with dynamic traffic arrival for spatially random users of different priorities. By exploiting limited channel state information, we propose an opportunistic NOMA scheme to enable NOMA for high- and low-priority users when high-priority users experience good channel conditions. Opportunistic NOMA improves the transmission opportunities of low-priority users while reducing the adverse effect of NOMA on high-priority users. Moreover, we propose a cooperative NOMA scheme with full-duplex relaying, where low-priority users act as full-duplex relays to assist the high-priority users. The high-priority user constructively combines the signal and its delayed version transmitted by the base station and a selected relay, respectively. The adopted relay selection scheme takes into account the users' spatial distribution, queue status, and channel conditions. By using tools from queueing theory and stochastic geometry, we derive the stable throughput regions of both proposed schemes. Furthermore, we derive the conditions under which the proposed NOMA schemes achieve larger stable throughput regions than orthogonal multiple access (OMA). At the expense of a higher implementation complexity and with appropriate parameter setting, cooperative NOMA with full-duplex relaying achieves a larger stable throughput region than opportunistic NOMA, which in turn outperforms OMA.

Non-orthogonal multiple access (NOMA) and millimeter wave (mm-wave) are two key enabling technologies for fifth generation (5G) wireless networks. In this paper, we develop a general performance analysis framework for downlink NOMA transmission in mm-wave networks with spatially random users taking into account link blockages and directional beamforming. To facilitate NOMA transmission in mm-wave networks, we propose an angle-based user pairing strategy, where the base station first randomly selects one user and then pairs it with the line-of-sight user that has the minimum relative angle difference. The proposed strategy increases the probability that both NOMA users are covered by the main lobe created by directional beamforming. To account for the randomness of link blockages and user locations, we consider dynamic user ordering among the paired NOMA users. Tools from stochastic geometry are utilized to derive the coverage probability, outage sum rate, and ergodic sum rate of the proposed NOMA scheme, where beam misalignment at both the base station and users is taken into account. Simulations validate the performance analysis and show that the proposed NOMA scheme achieves a larger coverage probability and higher outage and ergodic sum rates than conventional NOMA with distance-based user pairing and orthogonal multiple access.

Non-orthogonal multiple access (NOMA) is a promising multiple access technique, which exploits the power domain to enhance the spectral efficiency of the fifth generation (5G) wireless networks. In this paper, we propose a dynamic decode-and-forward (DDF) based cooperative NOMA scheme for downlink transmission to enhance the reception reliability of spatially random users. In DDF-based cooperative NOMA, the user closer to the base station decodes the superimposed mixture of the users' signals received from the base station based on partial reception, and then forwards the signal intended for the far user. To avoid the need for instantaneous channel state information at the base station, we consider random user pairing, where the users are randomly paired for NOMA transmission. Tools from point process theory are utilized to derive the outage probability of the proposed DDF-based cooperative NOMA scheme. Simulation results validate the performance analysis and demonstrate the performance gains of the proposed DDF-based cooperative NOMA scheme over conventional NOMA and cooperative NOMA.

Narrowband Internet of Things (NB-IoT) is a recently standardized technology to support machine-type communications (MTC) in Long Term Evolution-Advanced (LTE-A) Pro networks. NB-IoT can enable energy-efficient communication with extended coverage on a narrow bandwidth of 180 kHz for low-cost MTC devices (MTCDs). The main challenge of supporting MTC in LTE-A Pro networks is to provide connectivity to a massive number of MTCDs. To overcome this challenge, in this paper, we propose a power-domain uplink non-orthogonal multiple access (NOMA) scheme for NB-IoT systems. By allowing multiple MTCDs to share the same sub-carrier, NOMA can provide connectivity to more MTCDs than orthogonal multiple access (OMA). We formulate a joint sub-carrier and transmission power allocation problem to maximize the number of MTCDs satisfying the quality of service (QoS) and transmission power requirements. We decompose the problem into two sub-problems and propose algorithms to solve them. Simulation results show that our proposed NOMA scheme can significantly increase the number of successfully connected MTCDs in NB-IoT systems compared to OMA.

Non-orthogonal multiple access (NOMA) has recently been proposed as a key enabling technology for the fifth generation (5G) wireless networks. Different from the existing works which focus on the performance analysis of NOMA with backlogged traffic, in this paper, we analyze the stable throughput region of downlink NOMA transmission with dynamic traffic arrival for users with different priorities. By utilizing limited instantaneous channel state information (CSI) at the base station, we propose an opportunistic NOMA scheme to enhance the network performance. Considering both NOMA and dynamic traffic arrival leads to interacting queues, which complicate the performance analysis. By using tools from stochastic geometry and queueing theory, we decouple the interacting queues and characterize the stable throughput region of the proposed opportunistic NOMA scheme in terms of the threshold to trigger NOMA and transmission power allocation coefficients. Numerical results show that, compared to the orthogonal multiple access scheme, the proposed opportunistic NOMA scheme can significantly enhance the stable throughput region when the design parameters are appropriately selected.

Cloud radio access network (C-RAN) as a promising and cost-efficient cellular architecture has been proposed to meet the increasing demand of wireless data traffic. The main concept of C-RAN is to decouple the baseband unit (BBU) and the remote radio head (RRH), and place the BBUs in a data center for centralized control and processing. In this paper, we study the resource sharing problem in a fronthaul constrained C-RAN, where multiple service providers lease radio resources from a network operator to serve their subscribers. To provide isolation among different service providers, we introduce a threshold-based policy to control the interference among RRHs, and define a new metric to provide minimum resource guarantee for service providers. By leveraging a mobility prediction method, the user locations are predicted for traffic demand estimation and interference control. We propose a multi-timescale resource sharing mechanism, which consists of a global resource allocation process and multiple local resource allocation processes that are performed at different time scales. Simulation results show that the proposed mechanism achieves efficient resource sharing and isolation among service providers.

In this paper, we investigate the performance of cooperative communication in decentralized wireless networks under unsaturated traffic conditions with randomly positioned single-hop source-destination pairs and relays, where interference is the main performance-limiting factor. The traffic unsaturation and concurrent cooperative transmissions introduce a correlation between the interferer density and the packet retransmission probability, and a correlation of interference power in both space and time domains, which complicate the interference characterization. Based on queueing theory and stochastic geometry, the stationary interferer density is derived by solving a fixed-point equation, which is proved to have a unique solution. According to the relay selection scheme, we characterize the correlation of interference power in two consecutive time-slots by identifying the densities of source and relay retransmissions. Based on the interferer density and interference correlation, we derive the outage probability and average packet delay of the cooperative scheme, while taking into account the dynamic traffic arrivals, interference correlation, relay selection scheme, and spatial node distribution. The performance analysis is validated by extensive simulations. The analytical results provide useful insights on cooperative communication in large-scale networks.

In this paper, we investigate the network throughput achieved by both spatial diversity and spatial frequency reuse in a wireless ad hoc network with randomly positioned single-hop source-destination pairs and relays. Compared with conventional direct transmissions, cooperative communication can enhance single-link transmission reliability but reduce network-wide spatial frequency reuse due to relay transmissions. To study the tradeoff between these two competing effects, we construct a geographically constrained region for relay selection based on channel state information. The network throughput, defined as the product of the success probability of each link and the expected number of concurrent transmissions, is derived as a function of the total number of links, relay density, size of relay selection region, and distance between the source and destination. The performance analysis is carried out for both selection combining and maximum ratio combining at the destination. Such analytical results can evaluate the effectiveness of cooperative communication and provide useful insights on the design of large-scale networks. Finally, extensive simulations are conducted to validate the performance analysis.

In this paper, we study the differences of applying cooperation to fully-connected and multi-hop wireless networks, and find out that both the enlarged interference area and link density play a pivotal role in making the beneficial cooperation decision in a multi-hop network. Through characterizing effects of the enlarged interference area and link density on the overall network performance, a beneficial cooperation opportunity can be identified. By employing a randomized scheduling scheme and deriving the interference-free probability of any two links, the expected numbers of concurrent direct and cooperative transmissions can be obtained, where the ratio of these two numbers is defined as the beneficial cooperation ratio. Such a ratio translates the reduced spatial reuse to a requirement of the cooperation gain and provides a guideline for enabling beneficial cooperation on a single-link basis. Finally, the analytical and simulation results demonstrate that the beneficial cooperation criterion for a multi-hop network derived in this paper is more accurate than that in [1].

Based on the selection decode-and-forward cooperative protocol, a novel distributed quality of service (QoS) aware routing algorithm is proposed to minimise the total energy consumption of the wireless links from the cross-layer design perspective. For the non-cooperative and cooperative truncated automatic repeat request schemes, performance of packet error rate with M-PSK and M-QAM symbols over Nakagami-m fading channels is first analysed, and then system throughput is derived. Subsequently, transmission power is optimally allocated while satisfying the end-to-end throughput requirement. With polynomial complexity, using the traditional distributed shortest path algorithm, the route that includes a cascade of non-cooperative and cooperative building blocks and has the minimal total link cost (defined as the average total consumed power) is constructed to bear the information flow. In contrast to the previous non-cooperative routing schemes, this cooperative routing algorithm can significantly reduce the total energy consumption.

In this paper, we propose a novel link-utility-based cooperative MAC (LC-MAC) protocol for wireless multi-hop networks. By fully utilizing the broadcast nature of wireless multi-hop networks, the node that has overheard data packets in the previous hop may become a partner of the transmitter in the current hop. As diversity gain can be achieved by virtual antenna array formed by transmitter and partner, one-phase cooperative transmission is introduced to improve the throughput. In LC-MAC, based on the instantaneous channel measurements, each node tries to maximize its own link-utility (indicator of a node's ability to cooperate) by jointly adjusting transmission rate and power. Subsequently, distributed backoff procedure is activated to select the best node that has the maximum link-utility. The optimal transmission type, rate and power are uniquely determined by the best node. Since only local information is required, LC-MAC is a completely distributed protocol. Finally, extensive simulations are performed to investigate the impact of scenario and protocol parameters on the performance of LC-MAC. Numerical results show that LC-MAC significantly outperforms the cooperative relay-based rate adaptation (CRBAR) scheme and the receiver-based rate adaptation (RBAR) scheme in terms of throughput and energy efficiency.

We analyze the symbol error probability (SEP) performance of cooperative diversity networks with opportunistic decode-and-forward (ODF) relaying. Assuming that channels suffer from independent nonidentical Rayleigh fading and symbols are M-PSK modulated, the approximate closed-form SEP expressions are derived for opportunistic relaying with adaptive and fixed DF protocols, respectively. Simulations are carried out to validate the theoretical analysis. Results show that the derived approximate SEP is tight particularly at medium and high SNR.