We introduce a new analytical framework, developed based on the spatial network calculus, for performance assessment of Low Earth Orbit (LEO) satellite networks. Specifically, we model the satellites' spatial positions as a strong ball-regulated point process on the sphere. Under this model, proximal points in space exhibit a locally repulsive property, reflecting the fact that intersatellite links are protected by a safety distance and would not be arbitrarily close. Subsequently, we derive analytical lower bounds on the conditional coverage probabilities under Nakagami-$m$ and Rayleigh fading, respectively. These expressions have a low computational complexity, enabling efficient numerical evaluations. We validate the effectiveness of our theoretical model by contrasting the coverage probability obtained from our analysis with that estimated from a Starlink constellation. The results show that our analysis provides a tight lower bound on the actual value and, surprisingly, matches the empirical simulations almost perfectly with a 1 dB shift. This demonstrates our framework as an appropriate theoretical model for LEO satellite networks.

As new applications evolve rapidly, wireless networks increasingly require low-delay communication to significantly enhance the quality of user experience. In response, the evolution of the medium access control (MAC) layer has gained more attention, particularly through the application of reinforcement learning to optimize access strategies. In order to meet the low-delay requirements, we propose a reinforcement learning-based MAC protocol, named soft actor-critic multiple access (SAC-MA). To mitigate frequent collisions caused by the exploratory behavior, we propose a multiple waiting actions mechanism that allows stations to wait for multiple time slots. This mechanism enables the agent to develop a more flexible and intelligent access strategy, thereby effectively reducing delay. Additionally, we introduce an innovative formulation in which the head-of-line packet is treated as the agent, enabling more timely feedback and observations. We conduct extensive simulations to demonstrate that SAC-MA: 1) reduces delay by approximately 27.9% and 56.5% compared to the conventional MAC protocol with standard parameters under the collision and capture models, respectively; 2) adapts to environmental changes in dynamic scenarios; 3) coexists harmoniously with legacy stations and reduces the network delay in heterogeneous scenarios. Finally, we perform ablation studies to evaluate the effectiveness of the proposed mechanisms.

The emergence of new intelligent applications has fostered the development of a task-oriented communication paradigm, where a comprehensive, universal, and practical metric is crucial for unleashing the potential of this paradigm. To this end, we introduce an innovative metric, the Task-oriented Age of Information (TAoI), to measure whether the content of information is relevant to the system task, thereby assisting the system in efficiently completing designated tasks. We apply TAoI to a wireless monitoring system tasked with identifying targets and transmitting their images for subsequent analysis. To minimize TAoI and determine the optimal transmission policy, we formulate the dynamic transmission problem as a Semi-Markov Decision Process (SMDP) and transform it into an equivalent Markov Decision Process (MDP). Our analysis demonstrates that the optimal policy is threshold-based with respect to TAoI. Building on this, we propose a low-complexity relative value iteration algorithm tailored to this threshold structure to derive the optimal transmission policy. Additionally, we introduce a simpler single-threshold policy, which, despite a slight performance degradation, offers faster convergence. Comprehensive experiments and simulations validate the superior performance of our optimal transmission policy compared to two established baseline approaches.

This paper focuses on spectrum sharing in heterogeneous wireless networks, where nodes with different Media Access Control (MAC) protocols to transmit data packets to a common access point over a shared wireless channel. While previous studies have proposed Deep Reinforcement Learning (DRL)-based multiple access protocols tailored to specific scenarios, these approaches are limited by their inability to generalize across diverse environments, often requiring time-consuming retraining. To address this issue, we introduce Generalizable Multiple Access (GMA), a novel Meta-Reinforcement Learning (meta-RL)-based MAC protocol designed for rapid adaptation across heterogeneous network environments. GMA leverages a context-based meta-RL approach with Mixture of Experts (MoE) to improve representation learning, enhancing latent information extraction. By learning a meta-policy during training, GMA enables fast adaptation to different and previously unknown environments, without prior knowledge of the specific MAC protocols in use. Simulation results demonstrate that, although the GMA protocol experiences a slight performance drop compared to baseline methods in training environments, it achieves faster convergence and higher performance in new, unseen environments.

Federated learning (FL) and split learning (SL) are two effective distributed learning paradigms in wireless networks, enabling collaborative model training across mobile devices without sharing raw data. While FL supports low-latency parallel training, it may converge to less accurate model. In contrast, SL achieves higher accuracy through sequential training but suffers from increased delay. To leverage the advantages of both, hybrid split and federated learning (HSFL) allows some devices to operate in FL mode and others in SL mode. This paper aims to accelerate HSFL by addressing three key questions: 1) How does learning mode selection affect overall learning performance? 2) How does it interact with batch size? 3) How can these hyperparameters be jointly optimized alongside communication and computational resources to reduce overall learning delay? We first analyze convergence, revealing the interplay between learning mode and batch size. Next, we formulate a delay minimization problem and propose a two-stage solution: a block coordinate descent method for a relaxed problem to obtain a locally optimal solution, followed by a rounding algorithm to recover integer batch sizes with near-optimal performance. Experimental results demonstrate that our approach significantly accelerates convergence to the target accuracy compared to existing methods.

Recent popularization of the Internet of Vehicles (IoVs) and vehicles-to-everything (V2X) enables the emergence of real-time vehicular applications, posing challenges to resourcelimited vehicles. Toward this end, vehicle edge computing (VEC) has been proposed to alleviate the computational burden on vehicles by leveraging resources from roadside units (RSUs) and VEC servers. While existing works mainly focus on the task requirement for either vehicles or RSUs, the joint task offloading for both V2X and RSUs-to-everything (R2X) has not been fully studied. In this paper, we aim at optimizing the task offloading strategies for both vehicles and RSUs, and adopt a multi-hop task offloading manner to fully utilize the VEC network resources. This problem introduces a severe state-action space shift issue with varying dimensions and representation, which poses challenges for conventional DRL approaches. To address it, we propose a Bidirectional Encoder Representations from Transformers (Bert)-based matching Q-network (BMQN) algorithm. First, we design the BMQN model to efficiently capture correlations among all vehicles and RSUs through bidirectional attention. Then, we propose type-embedded grouped attention and available action embedding to mitigate the overfitting sequence length issue, thereby enhancing generalization capacity. Moreover, we propose to address the state-action space shift issue through a matching-based manner, which can significantly enhance the task offloading ability by matching the states among devices. Simulation results demonstrate that: 1) the BMQN can achieve much better performance than other approaches in scenarios comprising various numbers of vehicles and RSUs as well as diverse road lengths; 2) the BMQN has sufficient generalization capacity to adapt to inexperienced scenarios through matching-based architecture and available action embedding.

The rapid scalability of large language models (LLMs) has driven significant advancements across various natural language processing tasks. However, the immense size of LLMs and the growing demand for large-scale datasets present challenges in fine-tuning these models in resource-constrained environments. Federated learning (FL) has emerged as a promising solution, enabling collaborative model fine-tuning on distributed private data without requiring data sharing. Despite its potential, the heavy computational and communication burdens imposed by LLMs hinder the widespread adoption of FL-based fine-tuning. To mitigate these challenges, we propose FedSIT (Federated Split Importance-Based Tuning), a novel federated fine-tuning framework designed to optimize LLM training in environments with limited computational resources. FedSIT splits the pre-trained model into Bottom, Trunk, and Top layers, offloading the computationally intensive Trunk layer to the server while distributing the Bottom and Top layers to client devices. Additionally, FedSIT leverages layer importance scores to selectively fine-tune the most critical layers, reducing the number of parameters to be fine-tuned. Our extensive experiments demonstrate that FedSIT achieves comparable performance to existing methods while significantly reducing resource requirements, offering an efficient and scalable solution for federated fine-tuning of LLMs in real-world settings.

Traditional bus systems with fixed routes and timetables struggle to accommodate dynamic and diverse passenger demands. Demand-Responsive Transit (DRT) offers a flexible solution through dynamic route planning. However, multi-route cooperative scheduling faces challenges such as high combinatorial optimization complexity and insufficient real-time responsiveness. We propose a Multi-Agent Deep Reinforcement Learning framework for cooperative optimization in dynamic multi-route DRT scheduling (MARL-DRT). The problem is modeled as a multi-agent Markov Decision Process (MDP) aimed at minimizing a weighted total cost, including operating costs, passenger waiting costs, trip cancellations, and real-time demand profit. We employ the Multi-Actor-Attention-Critic (MAAC) algorithm to solve the problem, where each agent dynamically generates station sequences through a policy network based on an encoder-decoder structure. A centralized critic network and the policy gradient method are used to improve global cooperation and scheduling stability. Extensive experiments on real-world and benchmark networks demonstrate that our algorithm outperforms baseline methods in total cost, responsiveness, and service quality, providing a more efficient DRT system with lower operational costs and higher passenger satisfaction.

The rapid proliferation of Internet of Things (IoT) is generating an unprecedented volume of distributed data, necessitating efficient decentralized learning paradigms. Federated learning (FL) has emerged as a compelling distributed collaborative intelligence framework, renowned for its privacy protection benefits. However, the communication overhead associated with intermediate model exchanges remains a critical bottleneck in FL. Aiming at reducing the communication cost of FL equipped with promising over-the-air computation (AirComp) technique, this work designs specialized model compression schemes for both uplink and downlink communications. For uplink transmission with AirComp, we analyze its unique constraints and propose a hybrid global sparsification scheme that combines the benefits of conventional Top-k and Rand-k algorithms. We further develop an algorithm to strategically allocate transmission budgets between the two concatenated sparsification operations, accounting for both model temporal correlation and the cost of index synchronization. For downlink transmission, we introduce a group-based mixed-precision quantization (MPQ) scheme and integrates the broadcast of grouping information with uplink sparsification pattern to further mitigate communication burden. Moreover, we conduct theoretical analysis under realistic channel conditions and typical FL settings to validate the advantages and establish convergence guarantees of our approaches. Experimental results demonstrate that, compared to existing schemes, the proposed methods significantly improve communication efficiency and ensure client scalability, and concurrently verify the benefits of the uplink-downlink synergistic design.

Industrial Cyber-Physical Systems (ICPS) have emerged as a critical component in the industrial domain. To facilitate seamless collaboration among massive devices, cloud-edge computing architectures have emerged as a key enabler for ICPS, leveraging distributed intelligence to orchestrate devices and computational tasks. In cloud-edge computing, efficient task offloading and resource management are essential for optimizing task performance and reducing energy costs. However, conventional centralized resource management strategies struggle to satisfy the real-time, adaptability, and performance demands of dynamic ICPS systems. Industrial Cyber-Physical Systems (ICPS) have emerged as a critical component in the industrial domain. To facilitate seamless collaboration among massive devices, cloud-edge computing architectures have emerged as a key enabler for ICPS, leveraging distributed intelligence to orchestrate devices and computational tasks. In cloud-edge computing, efficient task offloading and resource management are essential for optimizing task performance and reducing energy costs. However, conventional centralized resource management strategies struggle to satisfy the real-time, adaptability, and performance demands of dynamic ICPS systems. In this paper, we propose the Distributed Transformer-based Actor-Critic (DTAC) algorithm to jointly determine task offloading and resource management decisions in cloud-edge computing networks, particularly for delay-sensitive applications in ICPS. The DTAC algorithm integrates the powerful transformer model with the popular actor-critic architecture to address the challenge of a hybrid high-dimensional action space. We first train a centralized model to learn coordination among user equipments (UEs) and then introduce a decentralized transfer learning (TL) approach to efficiently adapt the centralized model into the DTAC framework. Using the DTAC model, each UE can independently manage its local resources based solely on local information, avoiding the significant signaling overhead inherent in centralized approaches. Simulation results demonstrate that DTAC not only outperforms other MARL and TL schemes in both small-and large-scale scenarios, but also exhibits strong generalization capabilities in inexperienced settings. Furthermore, DTAC and decentralized TL approaches significantly reduce training costs by 73% compared to other methods, making them more practical for ICPS deployment.

Age of information (AoI), a metric for data freshness in status update systems, has received growing attention in various applications. This paper focuses on dynamic power control to minimize the average AoI of all users in a sum-power constrained downlink network, where each user has multi-level selectable powers in every slot. By relaxing instantaneous sum-power constraint to a time-averaged sum-power constraint, we decompose the Lagrangian minimization problem into multiple independent power allocation subproblems with each corresponding to a particular user. We show that the optimal policy of each subproblem under a given Lagrange multiplier holds a multi-threshold property, based on which the full indexability property can be possessed by every subproblem. By comparing the Whittle indices and the Lagrange multiplier, the full indexability allows us to obtain the Lagrange multiplier which can effectively utilize the power budget. Given this, a low-complexity power allocation approach is proposed to solve the problem. Moreover, we prove the proposed approach is asymptotic optimality when the number of selectable powers are three. Simulation results verify the analysis and show that our proposed approach outperforms benchmark policies.

Joint sensing and communication (JSC) systems aim to leverage the same spectral resources for both communication and sensing tasks within a single system. These systems have the potential to enhance sensing capabilities through advanced communication techniques, while also utilizing precise localization and tracking information from sensing technologies to improve communication. However, the integration of information obtained from sensing and transmitted in communication is not yet fully understood. This paper investigates the challenge of guaranteeing timely delivery of sensing information within JSC systems. We introduce a novel metric, termed as the age of estimation information (AoEI), which integrates radar mutual information (MI) and age of information (AoI). This unified metric effectively captures both the passage of time and the accuracy of estimation information, making it well-suited for the JSC system. Further, we delve into the joint optimization of time and power allocation for a single JSC node with both sensing and communication capabilities. Our objective is to minimize the long-term average AoEI while adhering to a long-term average power constraint. To tackle this problem, we formulate it as an average-reward constrained Markov decision process (CMDP) and propose a model-free constrained deep reinforcement learning (CDRL) algorithm, namely the average policy optimization (APO)-Lagrangian based algorithm. Simulation results demonstrate that our proposed algorithm effectively meets the constraint in dynamic and uncertain environments while achieving a favorable balance between AoEI and power consumption. Additionally, our algorithm outperforms four baseline schemes, showcasing its superior performance.

Autonomous aerial vehicles (AAVs) have the potential to greatly aid Internet of Things (IoT) networks in mission-critical data collection, thanks to their flexibility and cost-effectiveness. However, challenges arise due to the AAV’s limited onboard energy and the unpredictable status updates from sensor nodes (SNs), which impact the freshness of collected data. In this paper, we investigate the energy-efficient and timely data collection in IoT networks through the use of a solar-powered AAV. Each SN generates status updates at stochastic intervals, while the AAV collects and subsequently transmits these status updates to a central data center. Furthermore, the AAV harnesses solar energy from the environment to maintain its energy level above a predetermined threshold. To minimize both the average age of information (AoI) for SNs and the energy consumption of the AAV, we jointly optimize the AAV trajectory, SN scheduling, and offloading strategy. Then, we formulate this problem as a Markov decision process (MDP) and propose a meta-reinforcement learning algorithm to enhance the generalization capability. Specifically, the compound-action deep reinforcement learning (CADRL) algorithm is proposed to handle the discrete decisions related to SN scheduling and the AAV’s offloading policy, as well as the continuous control of AAV flight. Moreover, we incorporate meta-learning into CADRL to improve the adaptability of the learned policy to new tasks. To validate the effectiveness of our proposed algorithms, we conduct extensive simulations and demonstrate their superiority over other baseline algorithms.

Next-generation multiple access techniques are crucial for providing low-latency and highly efficient data transmission services. Recently, Deep Reinforcement Learning (DRL) has emerged as a prevalent approach in the multiple access domain, aiming to facilitate user coordination and enhance transmission efficiency. However, current DRL approaches face challenges, including limited generalization ability, low sample efficiency, and the complexities associated with Partially Observable Markov Decision Processes (POMDP), which hinder their application in heterogeneous networks with varying numbers of nodes and configurations. In this paper, we propose a foundation model-based multiple access (FMA) algorithm. To address severe POMDP and sample inefficiency issues, we decompose the multiple access problem into two parts: a transmission decision part and a configuration estimation part. We leverage the strong generalization and inference capabilities of the foundation model, utilizing a Deep Learning (DL) approach instead of DRL for training, and adopt the Low-Rank Adaptation (LoRA) technique to fine-tune the foundation model for downstream multiple access tasks. Simulation results demonstrate that: 1) through the decomposition, the FMA approach exhibits sufficient generalization and inference abilities to adapt to various scenarios with various protocols, configurations, and numbers of heterogeneous nodes; 2) by incorporating expert knowledge, the FMA approach can significantly enhance network performance while ensuring certain fairness requirement for heterogeneous nodes.

The emergence of intelligent mobile technologies and the widespread adoption of 5G wireless networks have made Federated Learning (FL) a promising method for protecting privacy during distributed model training. However, traditional FL frameworks rely on static aggregators such as base stations, encountering obstacles such as increased energy demands, frequent disconnections, and poor model performance. To address these issues, this paper investigates an innovative aUtonomous Aerial Vehicle (UAV)-assisted FL framework, aiming to utilize UAVs as mobile model aggregators to collaborate with devices in training models, while minimizing the total energy consumption of devices and ensuring that FL can achieve the target model accuracy. By adopting the Distributed Approximate NEwton (DANE) method for local optimization, we analyze the convergence of FL and derive device scheduling constraints that aid in convergence. Accordingly, we formulate a problem of minimizing the total energy consumption of devices, integrating a constraint on global model accuracy, and jointly optimizing the UAV trajectory, device scheduling, bandwidth allocation, time slot lengths, as well as the uplink transmission power, CPU frequency, and local convergence accuracy. Then, we decompose this non-convex optimization problem into three subproblems and propose an iterative algorithm based on Block Coordinate Descent (BCD) with convergence guarantee. Simulation results indicate that, compared with various benchmark methods, our proposed UAV-assisted FL framework significantly reduces the total energy consumption of devices and achieves an improved trade-off between energy and convergence accuracy.

In this paper, an unsupervised deep learning-based framework based on dual-path model-driven auto-encoders (AE) is proposed for angle-of-arrivals (AoAs) estimation in massive MIMO systems. Specifically designed for AoA estimation, the proposed framework differs from the conventional AE in two aspects. Firstly, unlike conventional auto-encoders, our framework employs a dual-path neural network for the encoder, decoupling the estimated parameters and enabling independent updates of each paths. Secondly, the decoder has fixed weights that implement the signal propagation model, instead of learnable parameters. This knowledge-aware decoder ensures the output of meaningful physical parameters (i.e., AoAs) which is unattainable by conventional AEs. We also conduct a thorough analysis to characterize the multiple global optima and local optima of the estimation problem. This analysis inspires the design of a low-complexity two-phase training scheme and confirms the convergence of our proposed framework. Consequently, our framework addresses two key challenges in unsupervised learning: the lack of interpretability and the convergence to local optima. Extensive simulations validate our theoretical analysis and demonstrate the performance improvements of our proposed framework.

The emergence of intelligent applications has fostered the development of a task-oriented communication paradigm, where a comprehensive, universal, and practical metric is crucial for unleashing the potential of this paradigm. To this end, we introduce an innovative metric, the Task-oriented Age of Information (TAoI), to measure whether the content of information is relevant to the system task, thereby assisting the system in efficiently completing designated tasks. Also, we study the TAoI in a remote monitoring system, whose task is to identify target images and transmit them for subsequent analysis. We formulate the dynamic transmission problem as a Semi-Markov Decision Process (SMDP) and transform it into an equivalent Markov Decision Process (MDP) to minimize TAoI and find the optimal transmission policy. Furthermore, we demonstrate that the optimal strategy is a threshold-based policy regarding TAoI and propose a relative value iteration algorithm based on the threshold structure to obtain the optimal transmission policy. Finally, simulation results show the superior performance of the optimal transmission policy compared to the baseline policies.

Federated learning (FL) and split learning (SL) are two prominent distributed learning modes. FL allows for parallel training but demands significant computational resources on devices to train deep neural network models. Conversely, SL reduces the computational burden on devices and can enhance learning performance, though it often leads to longer training time due to its sequential nature. In this paper, we introduce a novel distributed learning framework, hybrid split and federated learning (HSFL), which combines the advantages of both FL and SL over wireless networks. To achieve a lower training loss within a shorter latency, we start with the convergence analysis of HSFL, followed by a joint optimization problem of the learning mode selection, model splitting, and bandwidth allocation. To solve the problem, we propose a two-stage algorithm. First, we find the optimal bandwidth allocation and model splitting with a fixed learning mode. Then, we select the optimal learning mode based on the above optimal values. Experimental results validate the superior learning efficacy of our proposed algorithm.

The widespread adoption of deep neural networks (DNNs) is a testament to their profound impact on various domains. However, they are vulnerable to backdoor attacks. Previous defense strategies suffer from requiring additional prior knowledge or performance decreases. To tackle these challenges, we propose a new method to mitigate the impact of backdoor triggers. Specifically, we first devise a simple yet effective detection mechanism based on the region growing algorithm, which enables the identification of triggers within training data without necessitating prior knowledge. Then, we leverage the diffusion model to eliminate the inserted triggers while recovering the data information at the triggers’ locations. Finally, the processed data are fed into the current model for label recovery. Extensive experiments on the CIFAR10, Tiny Imagenet, and GTSRB datasets demonstrate that our method can defend against backdoor attacks effectively and surpasses the state-of-the-art defenses in terms of both main task accuracy (ACC) and backdoor task attack success rate (ASR).

We consider the age of information (AoI) evaluation in an Aloha-based random access network powered by energy harvesters. We derive a closed-form expression for the average AoI with general energy buffer capacity. The average AoI is then optimized by adjusting the update rate. The results indicate that when the sum of the energy arrival rate of all nodes is greater than or equal to one, the optimal average AoI in the Aloha network is equivalent to that in a network without energy constraints, by setting the update rate to one divided by the total number of nodes. The optimized average AoI then grows linearly with the number of nodes. Otherwise, a degradation of the optimal average AoI emerges, and the update rate should be tuned to be higher than the energy arrival rate.

In this paper, we propose a highly accurate unsu-pervised deep learning framework based on auto-encoder (AE) for angle-of-arrival (AoA) estimation in massive MIMO systems. Our method builds on an improvement of the vanilla AE by incorporating the knowledge of signal propagation models into the decoder. In our proposed knowledge-guided AE (KG-AE), instead of having learnable parameters, the decoder has fixed weights that implement the signal propagation model. Such modification forces the encoder to output meaningful physical parameters of interest (i.e., AoA), which cannot be achieved by standard AE. Furthermore, we rigorously analyze the multiplicity of local optima in unsupervised channel estimation problems. Our analysis informs the design of the cost function and the training scheme for the proposed KG-AE. Specifically, we design a two-stage training scheme, different loss functions in the two stages to achieve good initial points and boost the performance of the proposed KG-AE, respectively. Finally, extensive simulations are performed, and the results corroborate the analysis and demonstrate the performance improvements of the proposed KG-AE over the subspace-based algorithms and the state-of-the-art unsupervised learning-based algorithm.

Mobile-edge Computing (MEC) has now emerged as a complement to cloud computing, providing computational capacity for the resources-constrained edge devices. Recently, intelligent computation offloading and cache placement stands as effective approaches to enhance the performance of dynamic MEC networks. In this paper, we propose an online centralized joint resource management approach, named Transformer-based Actor-Critic (TAC), to minimize the task execution time subject to resource constraints. We decouple this mixed-integer non-linear programming (MINLP) problem into a non-convex offloading decision part and a convex joint resources allocation part, and propose the TAC approach to address the non-convex task offloading problem with low computational complexity. In the joint resources management problem, the high-dimensional state-action space is addressed by the transformer-based actor-critic architecture. Through the proposed TAC, the joint cache, communication and computation resource management can be obtained without the knowledge of future task arrivals. Simulation results demonstrate that the TAC can save 48.4% average task execution time with only 2.3% additional computation delay compared to Random with lowest computational complexity. In particular, it further demonstrates great generalization ability to enhance the performance in untrained scenarios.

Federated learning (FL) is a privacy-preserving distributed learning framework where model training is performed locally on distributed devices. Unlike traditional FL, which assumes a fixed local dataset, this paper focuses on the more realistic scenario of FL with streaming data. In Streaming Federated Learning (SFL), new data continuously arrives over time, and due to the limited local storage capacity on devices, some data is inevitably discarded. The discarded data may be forgotten by the model, leading to a decline in model accuracy. To this end, we introduce Federated Data Slimming (FedDS), a data selection scheme designed to determine which data should be stored locally. Particularly, FedDS considers both gradient norms and directions when making data selections. We evaluate the performance of FedDS against several previously proposed schemes using various datasets. Our experimental results demonstrate that FedDS surpasses all baseline schemes, achieving the fastest convergence rate and the highest test accuracy.

Utilizing UAVs and satellites can offer an effective means to collect data for the Internet of remote things (IoRT) networks. However, due to the limited energy of UAVs and the high cost of satellite communication, ensuring the reduction of UAV energy consumption and communication costs while collecting fresh data poses a significant challenge. In this paper, we explore the issue of data gathering in IoRT networks with the assistance of UAVs and satellites. The UAV gathers data from sensor nodes (SNs) and decides whether to relay the collected data via satellite or send it directly to the data processing center. We handle this problem by formulating it as a Markov decision process to minimize the combined weighted sum of the average age of information, the energy consumption of the UAV, and communication costs through the implementation of a compound-action proximal policy optimization (CPPO) method. It can handle the compound actions of the UAV. This approach simultaneously optimizes the UAV's path, SN scheduling, and transmission decisions. Simulation results demonstrate that our algorithm can achieve better performance compared to baseline methods.

Unmanned aerial vehicles (UAVs) offer exceptional maneuverability and mobility, making them valuable for data collection in the Internet of Things (IoT). However, to ensure sustainable data services, UAVs with limited battery capacity require energy replenishment during their operational period. In this study, we investigate the joint design of charging station (CS) placement and UAV trajectory to enable continuous and timely data gathering in IoT networks. We formulate a mixed combinatorial optimization problem aimed at minimizing the network’s peak age of information (AoI) by deploying a specific number of CSs from a set of potential sites and designing the UAV trajectory for data gathering and energy recharging. Convex optimization techniques are employed to find the optimal UAV trajectory, given any feasible CS placement solution. Furthermore, we demonstrate that, with the optimized UAV trajectory, the optimal CS placement problem becomes a maximization problem of a non-submodular, non-decreasing set function under a cardinality constraint, known to be NP-hard. To tackle this challenge, we propose a greedy CS deployment algorithm that provides an approximate optimal solution within a constant factor of 1α1-(1-αγK)K, where α ϵ [0,1] represents the generalized curvature, γ ϵ [0,1] denotes the submodularity ratio, and K represents the number of CSs. Additionally, we introduce a low-complexity CS placement algorithm based on path allocation, which is particularly useful in scenarios involving UAVs with very limited battery capacity. Through simulation results, we demonstrate that our proposed approaches, which jointly optimize CS placement and UAV trajectory, achieve significantly smaller AoI values compared to distance-based strategies, both with and without UAV trajectory optimization.

With the proliferation of communication demand, goal-oriented communication goes beyond traditional bit-level approaches by emphasizing the significance of information and its relevance to specific goals. This paper addresses the goal-oriented status updating problem, where detecting status changes is crucial. We employ the Age of Changed Information (AoCI) as a metric, which considers both the timeliness and content of the update. Our goal is to minimize the weighted sum of AoCI and transmission cost without channel delay statistics. The investigated problem is formulated as a semi-Markov decision process (SMDP) and is tackled by converting it into a multi-variable optimization problem. We prove that the optimal updating policy is of threshold type, and derive a nearly closed-form expression for the optimal threshold. When delay statistics are available, the optimal threshold can be obtained by a bisection searching algorithm. In the absence of prior delay statistics, we develop an online learning policy. We demonstrate that the optimality gap decays at a rate of$\mathcal {O}(\log K / K)$, where K is the number of samples. Simulation results are presented to compare the performance of various policies under different statistical conditions, showcasing the superiority of our proposed algorithm.

Many real-time applications of the Internet of Things (IoT) need to deal with correlated information generated by multiple sensors. The design of efficient status update strategies that minimize the Age of Correlated Information (AoCI) is a key factor. In this paper, we consider an IoT network consisting of sensors equipped with the energy harvesting (EH) capability. We optimize the average AoCI at the data fusion center (DFC) by appropriately managing the energy harvested by sensors, whose true battery states are unobservable during the decision-making process. Particularly, we first formulate the dynamic status update procedure as a partially observable Markov decision process (POMDP), where the environmental dynamics are unknown to the DFC. In order to address the challenges arising from the causality of energy usage, unknown environmental dynamics, unobservability of sensors' true battery states, and large-scale discrete action space, we devise a deep reinforcement learning (DRL)-based dynamic status update algorithm. The algorithm leverages the advantages of the soft actor-critic and long short-term memory techniques. Meanwhile, it incorporates our proposed action decomposition and mapping mechanism. Extensive simulations are conducted to validate the effectiveness of our proposed algorithm by comparing it with available DRL algorithms for POMDPs.

In this paper, we explore the integration of coordinated multipoint (CoMP) transmission and non-orthogonal multiple access (NOMA) in downlink cellular-connected UAV networks, which include both aerial users (AUs) and terrestrial users (TUs). AUs are categorized into CoMP-AUs and Non-CoMP AUs based on a comparison of the desired signal strength and the dominant interference strength. CoMP-AUs receive transmissions from two cooperative Base Stations (BSs) and form two exclusive NOMA clusters with two TUs, respectively. A Non-CoMP AU forms a NOMA cluster with a TU served by the same BS. Leveraging the tools of stochastic geometry, we propose an analytical framework to assess the performance of the CoMP-NOMA-based cellular-connected UAV network in terms of coverage probability and average ergodic rate. We demonstrate the superiority of the proposed CoMP-NOMA scheme by comparing it with three benchmark schemes, and further quantify the impacts of key system parameters on network performance. By harnessing the benefits of both CoMP and NOMA, we prove that the proposed scheme can provide a reliable connection for AUs using CoMP and enhance the average ergodic rate through the application of NOMA technique.

This paper focuses on the timely delivery of sensing information in a joint sensing and communication (JSC) system to meet the requirements of emerging applications. Specifically, we investigate the time allocation of a single JSC node equipped with both sensing and communication functions to minimize the long-term average age of estimation information (AoEI) while satisfying the long-term average power constraint. The proposed metric, AoEI, combines radar mutual information (MI) and age of information (AoI) to capture both the passage of time and the accuracy of estimation information, making it more suitable for the JSC system. To solve this problem, we formulate the time allocation problem as a constrained Markov decision process (CMDP) and propose a model-free constrained deep reinforcement learning (CDRL) based algorithm. The simulation results demonstrate that the proposed algorithm can achieve a good trade-off between AoEI and power consumption and converge to a policy that satisfies the constraint in a highly dynamic and uncertain environment.

This paper proposes a client selection (CS) method to tackle the communication bottleneck of federated learning (FL) while concurrently coping with FL’s data heterogeneity issue. Specifically, we first analyze the effect of CS in FL and show that FL training can be accelerated by adequately choosing participants to diversify the training dataset in each round of training. Based on this, we lever-age data profiling and determinantal point process (DPP) sampling techniques to develop an algorithm termed Federated Learning with DPP-based Participant Selection (FL-DP3S). This algorithm effectively diversifies the participants’ datasets in each round of training while preserving their data privacy. We conduct extensive experiments to examine the efficacy of our proposed method. The results show that our scheme attains a faster convergence rate, as well as a smaller communication overhead than several baselines.

As the superior improvement on wireless network coverage, spectrum efficiency and energy efficiency, Intelligent reflecting surface (IRS) has received more and more attention. In this work, we consider a large-scale IRS-assisted heterogeneous cellular network (HCN) consisting of$K\ (K\geq 2)$tiers of base stations (BSs) and one tier of passive IRSs. With tools from stochastic geometry, we analyze the coverage probability and network spatial throughput of the downlink IRS-assisted$K$-tier HCN. Compared with the conventional HCN, we observe the significant gain achieved by IRSs in coverage probability and network spatial throughput. The proposed analytical framework can be used to understand the limit of gain achieved by IRSs in HCN.

In this work, we study the age-optimal status update strategy for a large number of sensors in a wireless system with multiple heterogeneous unreliable channels. Particularly, we first formulate the status update procedure as a Restless Multi-Armed Bandit (RMAB) problem so as to minimize the long-term average Age of Information (AoI) cost. Then, a Deep Whittle index-based Q-Network (DWQN) algorithm is devised to solve it, in which an accurate approximation of the Whittle index can be learned. By applying this algorithm, the challenges from both the unknown of the environmental dynamics and large-scale state space can be addressed. Finally, simulations are conducted to validate the effectiveness of our proposed algorithm by comparing it with baseline strategies.

Reinforcement learning has been widely used to train a sequential power control policy from simulation environment in Internet of Vehicles. However, disturbance is usually inevitably introduced when the agent getting into the practical environment from the simulation environment, which leads to a critical challenge when multiple links share a common spectrum. This paper is dedicated to addressing this issue in a two-pronged way. On one hand, we set the aim of policy learning as minimizing the network transmission outage probability from a risk-sensitive perspective. On the other hand, we propose a meta soft actor-critic based power control scheme, where the key hyperparameter is auto-adjusted to adapt to environment variations and L2 regulation is taken in the loss function of critic network to avoid overfitting to the simulation environment. Simulation results show that, the proposed algorithm achieves a higher successful transmission probability under the same conditions and is more robust under disturbed environment, than the baseline schemes.

Due to the superior flexibility and extensive coverage, multiple unmanned aerial vehicles (UAVs) cooperation is a promising approach for data collection in improving the information freshness. In this paper, we consider a multi-UAV-assisted Internet of Things (IoT) network, where UAVs are deployed to collect data from sensor nodes (SNs) and transmit data back to the BS via wireless links so as to improve the information freshness, measured by the age of information (AoI). It is of great challenge to achieve effective cooperation under distributed decision-making because of the time-varying and stochasticity of the environment and the limited communication range of UAVs. To address this issue, we formulate the problem of joint trajectory plan, SN scheduling, and transmission scheduling as a decentralized partially observable markov decision process (Dec-POMDP), and develop an adaptive communication based multi-agent deep reinforcement learning (AC-MARL) algorithm to solve it. By applying our proposed AC-MARL algorithm, a more effective cooperation can be achieved by exploiting the benefits of semantic-aware communications among UAVs.

The unmanned aerial vehicle (UAV) can act as the edge server in delay-sensitive monitoring for data collection and processing in the Internet of things (IoT) networks due to its flexibility and low operational cost. One of its major disadvantages is the limited battery level. This paper focuses on a problem with the rechargeable UAV-assisted energy-efficient and fresh data collection in the IoT networks. In particular, the UAV takes off from the initial position to collect data packets from sensor nodes (SNs) in the IoT networks and needs to reach the final position at a given time. Some charging stations (CSs) are in the IoT networks, which can recharge the UAV by the wireless power transfer technique to keep the UAV’s energy level from falling below the threshold energy. To minimize the weighted sum of the average age of information (AoI) and the average recharging price, we design a Markov Decision Process (MDP) to determine the UAV’s flight trajectory, the scheduling of SNs, and energy recharging. The MDP is then solved using a rechargeable UAV-assisted data collection algorithm based on dueling double deep Q-networks (D3QN). Numerous simulations show that the proposed D3QN algorithm can reduce the weighted sum of the average AoI and the average recharging price more effectively than the baseline algorithms.

This paper focuses on spectrum sharing in heterogenous wireless networks, where different nodes utilize various Media Access Control (MAC) protocols to transmit data packets to a common access point on a shared wireless channel. Previous studies have developed Deep Reinforcement Learning (DRL) based multiple access protocols for specific scenarios within heterogeneous wireless networks. However, there exists a wide range of coexisting scenarios, characterized by varying numbers of nodes and the use of different MAC protocols. Existing approaches require training new models from scratch when encountering unseen scenarios, resulting in significant training time. To address this issue, we propose a novel MAC protocol called Generalized Multiple Access (GMA), which employs the Meta-Reinforcement Learning (meta-RL) algorithm. By learning a meta-policy during training, GMA enable the fast adaptation of the agent node to different and previously unknown heterogeneous network environments, without prior knowledge of the specific MAC protocols used in those environments. We conduct a performance comparison between the proposed GMA protocol and existing DRL-based protocols. Simulation results demonstrate that while the GMA protocol experiences a slight performance loss compared to baseline methods in training environments, it demonstrates faster convergence and higher performance in new environments compared to baseline methods.

Unmanned aerial vehicle (UAV) has been widely deployed in efficient data collection for Internet of Things (IoT) networks. Information freshness in data collection can be characterized by the Age of Information (AoI). It is highly challenging to schedule multiple energy-constrained UAVs to improve information freshness especially when the generation instants of sensing samples are unpredictable. To deal with this issue, we leverage state-of-art reinforcement learning (RL) methods to design flight trajectories of UAVs without knowing the sampling mode each sensor node (SN) adopts. Each SN can sample the environment at periodical or random intervals. Multiple energy-constrained UAVs are dispatched to collect update packets from the SNs when flying over them. The UAV trajectory planning problem for AoI minimization is formulated as a Markov decision process (MDP). The objective is to minimize the average AoI of the SNs under the constraints of energy capacity and collision avoidance for the UAVs. Then, we propose two learning algorithms based on the Sarsa and value-decomposition network (VDN), respectively, which allow the UAVs to fulfill data collection tasks requested by the SNs. By learning directly from the environment, the Sarsa-based algorithm can approach the optimal policy asymptotically when certain conditions are satisfied. As one of the most popular multiagent deep RL methods, the VDN-based algorithm enables each UAV to make its own decision independently on its flight and data collection based on the partially observed network information. Simulation results validate the effectiveness of the proposed two learning-based algorithms compared with baseline policies.

Thanks to their high-flexibility and low-operational cost, unmanned aerial vehicles (UAVs) can be used to support mission-critical applications in the Internet of Things (IoT). However, due to the limited onboard energy, it is difficult for UAVs to provide continuous data collection. In this article, we study the problem of rechargeable-UAV-aided timely data collection in IoT networks, where the UAV collects status updates from multiple sensors and gets recharged from the charging stations (CSs) to keep its energy level above a threshold. To tradeoff the information freshness and energy consumption, we formulate a Markov decision process (MDP) with the objective of minimizing the weighted sum of the average total Age of Information and average recharging price. Under the dynamics and uncertainty of the environment, we propose a multitask transfer deep reinforcement learning method to jointly optimize the UAV ’ s flight trajectory, transmission scheduling, and battery recharging. To enable the application of the learned policy to new environments with similar settings and avoid starting from scratch, we develop a multitask network made up of common knowledge layers and task-specific knowledge layers. It specifically makes it possible for the transfer of common knowledge between environments with different network scales (e.g., different numbers of sensors/CSs) and/or topologies (e.g., different locations of sensors/CSs). Simulation results demonstrate that the proposed algorithm can adapt to new environments and achieve superior performance compared to the baseline algorithms.

Emerged as a promising solution for future wireless communication systems, intelligent reflecting surface (IRS) is capable of reconfiguring the wireless propagation environment by adjusting the phase-shift of a large number of reflecting elements. To quantify the gain achieved by IRSs in the radio frequency (RF) powered Internet of Things (IoT) networks, in this work, we consider an IRS-assisted cellular-based RF-powered IoT network, where the cellular base stations (BSs) broadcast energy signal to IoT devices for energy harvesting (EH) in the charging stage, which is utilized to support the uplink (UL) transmissions in the subsequent UL stage. With tools from stochastic geometry, we first derive the distributions of the average signal power and interference power which are then used to obtain the energy coverage probability, UL coverage probability, overall coverage probability, spatial throughput and power efficiency, respectively. With the proposed analytical framework, we finally evaluate the effect on network performance of key system parameters, such as IRS density, IRS reflecting element number, charging stage ratio, etc. Compared with the conventional RF-powered IoT network, IRS passive beamforming brings the same level of enhancement in both energy coverage and UL coverage, leading to the unchanged optimal charging stage ratio when maximizing spatial throughput.

Delivering as many data packets as possible and making the life time of the network as long as possible is one fundamental request for battery-driven wireless network design, where sleeping schemes are usually adopted for prolonging the life time, while, at the sacrifice of the throughput performance. For random access networks, fulfilling this fundamental request is rather challenging due to the distributed nature of the access behavior of nodes. This paper considers massive Machine-Type Communication (mMTC) networks where each node adapts the representative random access scheme Aloha and periodical sleeping-awake cycle. We aim to address how to maximize the life-time throughput of each node, i.e., average number of packets each node can successfully deliver during its life time, with a guarantee of targeted life time via optimal selection of the channel access probability and the sleeping ratio of each node. By deriving the explicit expressions of the life time and the life-time throughput of each node and jointly tuning both the channel access probability and the sleeping ratio, we characterize the maximum life-time throughput with targeted life time, and the corresponding optimal settings. The analysis reveals that if only the channel access probability is optimally tuned, then the throughput and life-time throughput cannot be optimized simultaneously when the network becomes saturated with a large packet arrival rate. In contrast, the network would operate at unsaturated conditions via the joint tuning of the access probability and the sleeping ratio. In this case, the maximum life-time throughput always grows with the packet arrival rate. In addition, it is shown that the effect of the life-time constraint becomes significant only when it exceeds a threshold, where maximum life-time throughput will sacrifice for life-time expectation. The analysis sheds important light on the access and sleeping scheme design of practical Aloha-type networks. By taking Narrow Band-IoT with Power Saving Mode (PSM) as an example, extensive simulation results corroborate that with the proposed optimal setting, the life-time throughput could be significantly improved, especially when the life time requirement is demanding, e.g., 10 years without battery replacement.

Maintaining the freshness of information in the Internet of Things (IoT) is a critical yet challenging problem. In this paper, we study cooperative data collection using multiple Unmanned Aerial Vehicles (UAVs) with the objective of minimizing the total average Age of Information (AoI). We consider various constraints of the UAVs, including kinematic, energy, trajectory, and collision avoidance, in order to optimize the data collection process. Specifically, each UAV, which has limited on-board energy, takes off from its initial location and flies over sensor nodes to collect update packets in cooperation with the other UAVs. The UAVs must land at their final destinations with non-negative residual energy after the specified time duration to ensure they have enough energy to complete their missions. It is crucial to design the trajectories of the UAVs and the transmission scheduling of the sensor nodes to enhance information freshness. We model the multi-UAV data collection problem as a Decentralized Partially Observable Markov Decision Process (Dec-POMDP), as each UAV is unaware of the dynamics of the environment and can only observe a part of the sensors. To address the challenges of this problem, we propose a multi-agent Deep Reinforcement Learning (DRL)-based algorithm with centralized learning and decentralized execution. In addition to the reward shaping, we use action masks to filter out invalid actions and ensure that the constraints are met. Simulation results demonstrate that the proposed algorithms can significantly reduce the total average AoI compared to the baseline algorithms, and the use of the action mask method can improve the convergence speed of the proposed algorithm.

We consider a homogeneous Poisson bipolar network in which the bipoles represent source-destination pairs. The source nodes need to update their destinations about the new status perpetually, and the communications are taken place over a shared spectrum. The common goal of the source nodes is to minimize the network-wide age of information (AoI). We develop a policy by which every source node can adapt its frequency of generating status updates in a local and decentralized manner. At the same time, the network average AoI is minimized by reducing interference amongst transmitters located in geographical proximity. Following this policy, we also derive mathematical expressions to characterize the distribution of the optimal updating rate at each source node, the network average AoI, and the AoI violation probability, i.e., the probability that the AoI of a typical source node exceeds an age threshold. The analytical results are combined with discrete event simulations to provide a detailed evaluation of the performance of the proposed scheme. Particularly, it is shown that our policy is able to adaptively adjust the updating rate of each source node according to the variant of the network topology. In this manner, it is instrumental in decreasing both the network average AoI and AoI violation probability. Additionally, the scheme can maintain the AoI at a low level even when the network grows in size.

In large-scale wireless networks, severe interference may incur that leads to the age of information (AoI) degradation. It is therefore important to study how to optimize the AoI performance. This paper focuses on the average AoI minimization in random access Poisson networks. By considering the spatiotemporal interactions amongst the transmitters, an expression of the average AoI is derived, based on which the optimal average AoI and the corresponding optimal packet arrival rate and channel access probability are further characterized. We further compare the average AoI optimization with the peak AoI optimization. The comparison reveals that the optimal channel access probability for the average AoI optimization and the peak AoI optimization are the same. Yet, the optimal packet arrival rate for the average AoI optimization is smaller than that for the peak AoI optimization. The gap enlarges when the node deployment density becomes small.

The dilemma between the ever-increasing demands for data processing, and the limited capabilities of mobile devices in a wireless communication system calls for the appearance of federated learning (FL). As a distributed machine learning (ML) method, FL executes in an iterative manner by distributing the global model parameters and aggregating the local model parameters, which avoids the transmission of huge raw data and preserves data privacy during the training process. However, since FL cannot control the local training and transmission process, this gives malicious users the opportunity to deteriorate the global aggregation. We adopt a reputation model based on beta distribution function to measure the credibility of local users, and propose a reputation-based scheduling policy with user fairness constraint. By taking into account the impact of wireless channel conditions and malicious attack features, we derive tractable expressions for the convergence rate of FL in a wireless setting. Moreover, we validate the superiority of the proposed reputation-based scheduling policy via numerical analysis and empirical simulations. The results show that the proposed secure wireless FL framework can not only distinguish malicious users from normal users but also effectively defend against several typical attack types featured in attack intensity and attack frequency. The analysis also reveals that the effect of average attack intensity on the convergence performance of FL is dominated by the percentage of malicious user equipments (UEs), and imposes even greater negative effect on the convergence performance of FL as the percentage of malicious UEs increases.

Timeliness is an emerging requirement for many Internet of Things (IoT) applications. In IoT networks with a large number of nodes, severe interference may incur that leads to Age-of-Information (AoI) degradation. It is, therefore, important to study how to optimize the AoI performance. This article focuses on the AoI minimization in random-access Poisson networks. By considering the spatiotemporal interactions amongst the transmitters, an expression of the peak AoI is derived, based on which the optimal peak AoI and the corresponding optimal packet arrival rate and channel access probability are further characterized. The analysis shows that when the channel access probability (resp., the packet arrival rate) is given, the optimal packet arrival rate (resp., the optimal channel access probability) is equal to one when nodes are sparsely deployed, and decreases as the node deployment density increases. With a joint tuning of these two system parameters, the optimal channel access probability always equals one. Moreover, with the sole tuning of the channel access probability, the optimal peak AoI is improved with a smaller packet arrival rate only when the node deployment density is high. In contrast, a higher channel access probability always improves peak AoI performance when the packet arrival rate is solely tuned. The analysis in this article sheds important light on freshness-aware design for large-scale networks.

Age of Information (AoI), a notion that measures the information freshness, is an essential performance measure for time-critical applications in Internet of Things (IoT). With the surge of computing resources at the IoT devices, it is possible to preprocess the information packets that contain the status update before sending them to the destination so as to alleviate the transmission burden. However, the additional time and energy expenditure induced by computing also make the optimal updating a nontrivial problem. In this article, we consider a time-critical IoT system, where the IoT device is capable of preprocessing the status update before the transmission. Particularly, we aim to jointly design the preprocessing and transmission so that the weighted sum of the average AoI of the destination and the energy consumption of the IoT device is minimized. Due to the heterogeneity in transmission and computation capacities, the durations of distinct actions of the IoT device are nonuniform. Therefore, we formulate the status updating problem as an infinite horizon average cost semi-Markov decision process (SMDP) and then transform it into a discrete-time Markov decision process. We demonstrate that the optimal policy is of threshold type with respect to the AoI. Equipped with this, a structure-aware relative policy iteration algorithm is proposed to obtain the optimal policy of the SMDP. Our analysis shows that preprocessing is more beneficial in regimes of high AoIs, given it can reduce the time required for updates. We further prove the switching structure of the optimal policy in a special scenario, where the status updates are transmitted over a reliable channel and derive the optimal threshold. Finally, simulation results demonstrate the efficacy of preprocessing and show that the proposed policy outperforms two baseline policies.

Statistical priority-based multiple access (SPMA) protocol has attracted much attention in virtue of its support for multi-priority traffic, and the guarantee of low-latency and high-reliability transmissions for high-priority. In this work, we propose an analytical framework to study the performance of SPMA from spatial perspective with tools from the stochastic geometry. We consider two kinds of priority traffic, including high-priority traffic and low-priority traffic. In SPMA, a packet is split into multiple bursts to reduce the collision probability, and the turbo coding, frequency hopping, and time hopping are employed to further decrease the packet loss rate. We first derive the analytical expressions for the medium access probability (MAP) and burst success probability of two priority users in closed form, taking into account the potential transmitters (PTs) density, ratio of different traffic users, amount of orthogonal resources, channel occupancy statistics (COS) threshold, and statistical sliding window (SSW). Based on the derived MAP and burst success probability, we further obtain the packet success probability and spatial throughput. After evaluating the effect of key parameters on the above performance metrics, we provide guidelines on optimal design of several key system parameters, such as the COS threshold and PTs density, to guarantee the high-priority user a 99% packet success probability.

In Internet of Things (IoT), the freshness of status updates is crucial for mission-critical applications. In this regard, it is suggested to quantify the freshness of updates by using Age of Information (AoI) from the receiver’s perspective. Specifically, the AoI measures the freshness over time. However, the freshness in the content is neglected. In this paper, we introduce an age-based utility, named asAge of Changed Information(AoCI), which captures both the passage of time and the change of information content. By modeling the underlying physical process as a discrete time Markov chain, we investigate the AoCI in a time-slotted status update system, where a sensor samples the physical process and transmits the update packets to the destination. With the aim of minimizing the weighted sum of the AoCI and the update cost, we formulate an infinite horizon average cost Markov Decision Process. We show that the optimal updating policy has a special structure with respect to the AoCI and identify the condition under which the special structure exists. By exploiting the special structure, we provide a low complexity relative policy iteration algorithm that finds the optimal updating policy. We further investigate the optimal policy for two special cases. In the first case where the state of the physical process transits with equiprobability, we show that optimal policy is of threshold type and derive the closed-form of the optimal threshold. We then study a more generalized periodic Markov model of the physical process in the second case. Lastly, simulation results are laid out to exhibit the performance of the optimal updating policy and its superiority over the zero-wait baseline policy.

For the physical (PHY) layer in the 5th generation (5G) networks, there may be only time division multiplexing (TDM) and orthogonal frequency division multiplexing (OFDM) as candidates to serve multiple information flows via a single wireless link. Such two multiplexing schemes require the timing/ frequency synchronizations strictly, which is not suitable for power division based medium access control (MAC) protocols, i.e., non-orthogonal multiple access (NOMA). To address this, associating with multiple-input multiple-output (MIMO) techniques, a power division multiplexing (PDM) scheme is proposed as an alternative option to MIMO-TDM/MIMO-OFDM. The proposed MIMO-PDM directly utilizes the division of transmit power to replace the conventional time-slot/sub-band for the different information flows. Thus, it owns high compatibility with NOMA which is a promising multiple access protocol in 5G. With regarding the quality of service (QoS) required by the information flows, this paper derives the optimum power division of MIMO-PDM in conditions of multiple-input single-output (MISO), single-input multiple-output (SIMO), and MIMO, respectively. Additionally, we study the optimum pre- and post-coding of MIMO when serving arbitrary number of information flows. The proposed optimization algorithms of MIMO-PDM own reasonable computational complexity which is not higher than the classic water-filling algorithms. Consequently, the proposed MIMO-PDM could efficiently achieve the optimum performance as well as ensure the QoS of multiple information flows.

Compared with a single loop, a FLL-assisted-PLL (Frequency-locked Loop, FLL; Phase-locked Loop, PLL) tracking loop which integrates both the dynamic robustness of FLL and the accuracy performance of PLL has better carrier tracking performance. However, under highly dynamic conditions, its tracking ability still cannot meet the requirements. To overcome this problem, a novel combined control loop based on FLL-assisted-PLL is proposed in this paper. The combined control loop adjusts the action effects of FLL and PLL automatically according to the current tracking state without the need for decision processing, which makes full use of the characteristics of FLL and PLL. Simulation results demonstrate that the proposed combined loop does achieve a shorter convergence time and higher tracking accuracy than the traditional FLL-assisted-PLL tracking loop under significant dynamics.

Federated learning (FL) enables devices to update a global model while keeping the training data local, so that data privacy is protected. However, the local data of devices is usually non-independent and identically distributed (non-i.i.d.), which leads to performance degradation. This paper aims to address this issue by a client-selection approach. In particular, in consideration of balancing the label distribution of the selected clients, a new client selection method called grouping based scheduling (GS) scheme is proposed, with which clients are divided into several groups based on a new metric called group earth mover’s distance (GEMD). Experiment results show that the GS can improve the performance of FL algorithms, compared to the random scheduling scheme. An encryption method is further proposed to enhance privacy protection, which facilitates the application of the proposed GS scheme.

In this paper, we consider an unmanned aerial vehicles (UAV)-assisted IoT network and study the trajectory planning problem to optimize the information freshness, in terms of age of information (AoI), where the update arrivals at IoT devices are stochastic and are not known to the UAV. To this end, we first formulate the dynamic UAV trajectory planning problem as a Partially Observable Markov Decision Process (POMDP) with non-uniform time steps, where the set of valid actions is coupled with the agent's observations. Then, a deep recurrent reinforcement learning (DRRL) algorithm is devised to find the policy minimizing the expectation of the weighted average AoI, in which a modified discount mechanism is utilized to deal with the challenge from non-uniform time steps and an action elimination mechanism is introduced to address the coupling between the valid actions and observations. Finally, simulations are conducted to validate the effectiveness of our proposed algorithm by comparing it with baseline strategies.

Efficient data collection has been treated as a key challenge especially in the sparsely deployed Internet of Things (IoT) networks. Compared with the conventional data collection methods using static sinks, mobile data collectors (MDCs) are considered as a more efficient approach where MDCs transfer data from sensors to access points (APs) by roaming over different geographical regions. In this work, we propose an analytical framework to study the coverage performance of IoT with MDCs where MDCs follow a simple random waypoint (SRWP) mobility model. To characterize the interference distribution of the whole network, we first derive exact expressions for the average contact time (CT) and inter-contact time (ICT) between a typical sensor and its associated MDC. Then we determine the active probability of the typical sensor by using the derived CT and ICT. The coverage probability is finally derived by taking into account the communication range of sensors, velocity of MDCs, density of sensors and MDCs, and the SINR threshold. Our results reveal the fact that the velocity of MDCs has little effect on coverage probability while a higher velocity can significantly lower the end-to-end delay.

Low latency and high reliability are significant trends in the development of wireless communications. Both the new generation of data link system, Tactical Targeting Network Technology (TTNT), and the fifth-generation mobile networks (5G) have put forward higher requirements for low latency and high reliability. Statistical priority-based multiple access protocol (SPMA) can be applied to these systems by virtue of its excellent performance. It is very important to model and analyze the performance of SPMA. In this paper, we establish the node state model by discrete-time Markov renewal process under a saturated network. We construct a fixed-point equation to solve the medium access probability. Then the average packet success rate and throughput are obtained. We evaluate the performance of the saturated SPMA system through the three variables. We also analyze the impact of system parameters on protocol performance. Extensive simulations demonstrate that our theoretical results closely match the simulation results. Moreover, the throughput limit can be obtained by adjusting the system parameters.

The dual connectivity is emerging as a promising solution to boost capacity in heterogeneous networks. However, it is challenging to obtain an optimal user association in heterogeneous networks with dual connectivity, due to its non-convex and combinatorial nature. In this paper, we propose a user association scheme based on deep reinforcement learning to maximize the overall network utility, which takes both throughput and user fairness into account, in the downlink of a heterogeneous network. Particularly, each user associates with the macro base station (BS) and a micro BS. We apply a deep Q-network (DQN) to obtain the nearly optimal policy to associate the users and micro BSs. Simulation results demonstrate that DQN-based user association performs better compared to the conventional user association schemes in heterogeneous networks with dual connectivity, and it behaves good scalability when the environment changes.

In this work, we study the UAV-enabled data collection problem for high information freshness in wireless sensor networks, where one UAV is dispatched to collect information of ground Sensor Nodes (SNs). The information freshness is measured by the Age of Information (AoI) of each SN, which is defined as the sum of the SN's data uploading time and the UAV's flight time after leaving this SN. Two optimization problems of age-optimal data collection are formulated to minimize the SNs' maximal AoI and average AoI, respectively. An iterative SN association and trajectory planning policy is proposed to seek the age-optimal solutions via an iterative two-step procedure. Firstly, SN association is performed based on the affinity propagation clustering method with an appropriate weight to find a set of data Collection Points (CPs) at which the UAV hovers to collect data and schedules which SNs to upload in what order. Based on this result, trajectory planning is performed to find the max-AoI-optimal and ave-AoI-optimal trajectories of the UAV along the CPs using dynamic programming or genetic algorithm. With the optimized clustering weight, the proposed scheme can always strike a balance between the SNs' uploading time and the UAV's flight time in various scenarios. Simulation results show that the proposed strategy can improve the freshness of information collected from all the SNs.

In the Internet of Things (IoT) networks, caching is a promising technique to alleviate energy consumption of sensors by responding to users’ data requests with the data packets cached in the edge caching node (ECN). However, without an efficient status update strategy, the information obtained by users may be stale, which in return would inevitably deteriorate the accuracy and reliability of derived decisions for real-time applications. In this paper, we focus on striking the balance between the information freshness, in terms of age of information (AoI), experienced by users and energy consumed by sensors, by appropriately activating sensors to update their current status. Particularly, we first depict the evolutions of the AoI with each sensor from different users’ perspective with time steps of non-uniform duration, which are determined by both the users’ data requests and the ECN’s status update decision. Then, we formulate a non-uniform time step based dynamic status update optimization problem to minimize the long-term average cost, jointly considering the average AoI and energy consumption. To this end, a Markov Decision Process is formulated and further, a dueling deep R-network based dynamic status update algorithm is devised by combining dueling deep Q-network and tabular R-learning, with which challenges from the curse of dimensionality and unknown of the environmental dynamics can be addressed. Finally, extensive simulations are conducted to validate the effectiveness of our proposed algorithm by comparing it with five baseline deep reinforcement learning algorithms and policies.

The notion of age-of-information (AoI) is investigated in the context of large-scale wireless networks, in which transmitters need to send a sequence of information packets, which are generated as independent Bernoulli processes, to their intended receivers over a shared spectrum. Due to interference, the rate of packet depletion at any given node is entangled with both the spatial configurations, which determine the path loss, and temporal dynamics, which influence the active states, of the other transmitters, resulting in the queues to interact with each other in both space and time over the entire network. To that end, variants in the packet update frequency affect not just the inter-arrival time but also the departure process, and the impact of such phenomena on the AoI is not well understood. In this paper, we establish a theoretical framework to characterize the AoI performance in the aforementioned setting. Particularly, tractable expressions are derived for both the peak and average AoI under two different transmission protocols, namely the first-come-first-serve (FCFS) and the last-come-first-serve with preemption (LCFS-PR). Additionally, our analysis also accounts for the effects of channel access controls such as ALOHA on the AoI. The accuracy of the analysis is verified via simulations, and based on the theoretical outcomes, we find that: i) networks operating under LCFS-PR are able to attain smaller values of peak and average AoI than that under FCFS, whereas the gain is more pronounced when the infrastructure is densely deployed, ii) in sparsely deployed networks, ALOHA with a universally designed channel access probability is not instrumental in reducing the AoI, thus calling for more advanced channel access approaches, and iii) when the infrastructure is densely rolled out, there exists a non-trivial ALOHA channel access probability that minimizes the peak and average AoI under both FCFS and LCFS-PR.

Small cell networks with dynamic time-division duplex (D-TDD) have emerged as a potential solution to address the asymmetric traffic demands in 5G wireless networks. By allowing the dynamic adjustment of cell-specific UL/DL configuration, D-TDD flexibly allocates percentage of subframes to UL and DL transmissions to accommodate the traffic within each cell. However, the unaligned transmissions bring in extra interference which degrades the potential gain achieved by D-TDD. In this work, we propose an analytical framework to study the performance of multi-antenna small cell networks with clustered D-TDD, where cell clustering is employed to mitigate the interference from opposite transmission direction in neighboring cells. With tools from stochastic geometry, we derive explicit expressions and tractable tight upper bounds for success probability and network throughput. The proposed analytical framework allows to quantify the effect of key system parameters, such as UL/DL configuration, cluster size, antenna number, and SINR threshold. Our results show the superiority of the clustered D-TDD over the traditional D-TDD, and reveal the fact that there exists an optimal cluster size for DL performance, while UL performance always benefits from a larger cluster.

Drone small cells (DSCs) are served as aerial base stations to complement the terrestrial cellular networks, in order to provide seamless wireless coverage and increased network capacity. In this paper, we study a drone-assisted downlink heterogeneous network (HetNet) consisting of a first tier of DSCs overlaid with a second tier of terrestrial small cells (TSCs), both of which operate on the same frequency band. By considering a flexible biased association policy, we develop an analytical framework to evaluate the network performance. After deriving the association probabilities, and the probability distribution functions (PDFs) of typical link length, we derive exact expressions for the per-tier and overall coverage probabilities. The proposed framework allows to quantify the impact on network performance of most important system parameters, such as the height of DSCs, the bias factor, and the base station density. In absence of interference management, our results show that very limited gains can be obtained in the dense network scenario and the improper deployment of DSCs can only degrade the coverage probability achieved by the single-tier TSC network. What's more, the unbiased cell association is shown to be optimal for the overall coverage probability in the interference-limited network regime.

The freshness of status updates is imperative in mission-critical Internet of things (IoT) applications. Recently, Age of Information (AoI) has been proposed to measure the freshness of updates at the receiver. However, AoI only characterizes the freshness over time, but ignores the freshness in the content. In this paper, we introduce a new performance metric, Age of Changed Information (AoCI), which captures both the passage of time and the change of information content. Also, we examine the AoCI in a time-slotted status update system, where a sensor samples the physical process and transmits the update packets with a cost. We formulate a Markov Decision Process (MDP) to find the optimal updating policy that minimizes the weighted sum of the AoCI and the update cost. Particularly, in a special case that the physical process is modeled by a two-state discrete time Markov chain with equal transition probability, we show that the optimal policy is of threshold type with respect to the AoCI and derive the closed-form of the threshold. Finally, simulations are conducted to exhibit the performance of the threshold policy and its superiority over the zero-wait baseline policy.

Internet of Things (IoT) has emerged as one of the key features of the next-generation wireless networks, where timely delivery of status update packets is essential for many real-time IoT applications. To provide users with context-aware services and lighten the transmission burden, the raw data usually need to be preprocessed before being transmitted to the destination. However, the effect of computing on the overall information freshness is not well understood. In this article, we first develop an analytical framework to investigate the information freshness, in terms of peak age of information (PAoI), of a computing-enabled IoT system with multiple sensors. Specifically, we model the procedure of computing and transmission as a tandem queue and derive the analytical expressions of the average PAoI for different sensors. Based on the theoretical results, we formulate a min-max optimization problem to minimize the maximum average PAoI of different sensors. We further design a derivative-free algorithm to find the optimal updating frequency, with which the complexity for checking the convexity of the formulated problem or obtaining the derivatives of the object function can be largely reduced. The accuracy of our analysis and the effectiveness of the proposed algorithm are verified with extensive simulation results.

Mobile-Edge Computing (MEC) could relieve computing pressure and save energy of resource-constrained Smart Mobile Devices (SMDs) via computation offloading. Nevertheless, offloading strategy design for multiuser MEC systems is challenging. Specifically, offloading operations (i.e., terminal execution strategy, access rate, and cloud execution strategy) are not only inner-coupled for each SMD due to parallel local and cloud execution, but also inter-coupled among SMDs due to competition for radio and computation resources. Worse still, the inner- and inter-coupling interplay each other. However, existing works generally weaken this inner-inter-coupling, resulting in an inability to adapt to network differences, terminal capacity differences, and application requirements differences. Hence, only suboptimal performance could be achieved. As motivated, we jointly optimizes terminal execution strategy, radio resource allocation, and MEC computation resource allocation to minimize weighted sum of terminal energy consumption. Additionally, via dynamically matching individual offloading behavior and group's competitive resources allocation, our proposed algorithm could not only reflect mechanism of interaction between inner- and inter-coupling relationship, but also well adopt to diversities of network conditions, terminal capacity, and application requirements to further harvest MEC gain. Finally, simulation results demonstrate that our algorithm significantly outperforms existing schemes, more specifically up to 73.8% less energy consumption.

Internet of Things (IoT) has been emerging as one of the use cases permeating our daily lives in 5th Generation wireless networks, where status update packages are usually required to be timely delivered for many IoT based intelligent applications. Enabling the collected raw data to be preprocessed before transmitted to the destination can provide users with better context-aware services and lighten the transmission burden. However, the effect from data preprocessing on the overall information freshness is an essential yet unrevealed issue. In this work we study the joint effect of data preprocessing and transmission procedures on information freshness measured by peak age of information (PAoI). Particularity, we formulate the considered multi-source preprocessing and transmission enabled IoT system as a tandem queue where a priority M/G/1 queue is followed by a G/G/1 queue. Then, we respectively derive the closed-form and an information theoretic approximation of the expectations of waiting time for the formulated processing queue and transmission queue, and further get the analytical expression of the average PAoI for packages from different sources. Finally, the accuracy of our analysis is verified with simulation results.

Mobile-Edge Computing (MEC) could relieve computing pressure of resource-constrained Smart Mobile Devices (SMDs) by offloading computation-intensive tasks to nearby/MEC server. However, how to achieve energy efficient computation offloading for SMDs under application-dependent latency constraints remains challenging in multiuser MEC systems. Specifically, the optimal system operations are not only inner- coupled for each SMD due to parallel local and cloud execution, but also inter-coupled among SMDs due to competition for limited radio resource. Additionally, the inner- and inter-coupling influence each other, which further complicates multiuser offloading strategy design. In this paper, we address such a challenge by jointly optimizing computational speed of SMDs via Dynamic Voltage Scaling (DVS) technology, subcarrier allocation, transmit power per subcarrier, data size sent per subcarrier, and offloading ratio, to minimize weighted sum of mobile energy consumption, resulting in a mixed-integer optimization problem. To tackle this NP-hard problem, we propose a fast-convergent suboptimal algorithm with the Lagrangian dual decomposition. Additionally, simulation results verify that the algorithm converges fast and significantly outperforms existing schemes in energy consumption reduction. Meanwhile, we discover that given latency mean, total mobile energy consumption remains stable or increases with the variance of latency requirements, which could direct admission control in practice.

In this paper, we consider an energy harvesting cognitive radio network where each secondary transmitter (ST) harvests radio frequency energy from ambient primary transmitters (PTs), and communicates with its secondary receiver (SR) which suffers co-channel interference from PTs. A positive correlation is observed between the harvested energy at the ST and the aggregate interference at the SR, which illustrates that an ST will have a higher probability to harvest enough energy if there is strong interference at the SR. To exploit the positive correlation, we propose an interference threshold-based transmission strategy for STs, so as to protect secondary transmissions as well as increase the amount of harvested energy. We model the battery level of each ST as a finite state discrete-time Markov chain and derive the expression of the secondary spatial throughput as a function of the transmission probability and coverage probability. We investigate the impact of interference threshold and density of PTs on the secondary spatial throughput, and provide guidelines in the optimal design of these two factors to maximize the spatial throughput. To further improve the spatial throughput of a secondary network, we consider the successive interference cancellation strategy at SRs, and reveal its superiority in the high density regime of PTs.

Wireless energy transfer (WET) is emerging as a promising paradigm that provides sustainability for pervasive battery-powered devices to complete various tasks. Due to high attenuation of WET, it is crucial to design new architecture that conserves energy while guaranteeing task completion. In this paper, we propose an energy bank-based crowdsourcing framework and an incentive mechanism for energy conservation in wireless-powered task-oriented networks. An employer device outsources the whole or a part of its task to several worker devices and pays them energy as reward. Through energy-service trading, the employer consumes less energy and workers make energy profits. The virtual energy bank keeps accounts for all devices, authenticates the trading, and settles payments through a lossless bookkeeping-like manner. We analyze the employer's expense-minimized and workers' profit-maximized decisions and prove that the optimal decisions compose a Stackelberg equilibrium. To quantify the potential in energy saving, we further apply the framework to a relay-based sensor network where a source employs relays to forward data with a minimum rate requirement. An algorithm is developed for the NP-hard expense minimization problem. The simulation results reveal that our proposed framework and mechanism improve the energy efficiency by providing a win-win situation for both sides.

Caching content in devices and sharing content via device-to- device (D2D) communications can help reduce cellular data traffic. However, the content caching in devices will reshape the way the conventional D2D communications work, which has not been fully understood. Therefore, we explore the coupling relationship of content caching and sharing in this paper. Particularly, we first propose a concept of content-topology and give its corresponding graph model, which reveals the relationship among devices, content caching, and D2D links. Subsequently, a heuristic algorithm is proposed to find a content-topology, where the content caching among devices and the link activations for content sharing are well matched. Simulation results show that the scheme based on content- topology outperforms the existing algorithms in terms of the amount of offloaded traffic, the number of link activation, and link efficiency.

The deployment of long term evolution (LTE) in unlicensed spectrum (LTE-U) is a promising solution to overcome the spectrum shortage. However, the interaction between LTE-U and Wi-Fi in unlicensed spectrum has not been well understood. In this paper, we use stochastic geometry to develop a framework for the co-existence between LTE-U and Wi-Fi in unlicensed spectrum. To reduce the intra-and inter-RAT interference, LTE-U employs an ALOHA-like random access scheme and Wi-Fi performs carrier sensing and energy detection before transmission. We derive the retention probability and the coverage probability of Wi-Fi and LTE-U networks. Based on our analysis, we investigate the effect of network parameters on the coverage probability of these networks.

Shortage of available licensed spectrum is a major barrier to the development of 5G networks. The deployment of long term evolution (LTE) in unlicensed spectrum (LTE-U) is a promising solution to overcome such a barrier. However, the interaction between LTE-U and Wi-Fi in unlicensed spectrum has not been well understood. In this paper, we use stochastic geometry to develop a framework for a multi-radio access technologies (multi-RAT) heterogeneous network, which consists of an LTE-U tier and a Wi-Fi tier. To reduce the intra- and inter-RAT interference, LTE-U employs an ALOHA-like random access scheme and Wi-Fi performs carrier sensing and energy detection before transmission. We derive the coverage probability and spatial throughput of Wi-Fi and LTE-U networks, and perform the asymptotic analysis when the density of Wi-Fi and LTE-U nodes approach infinity. Based on our analysis, we investigate the effect of network parameters on the coverage probability and spatial throughput of these networks. Furthermore, in order to achieve weighted max-min fairness, we optimize the retention probability of LTE-U nodes to maximize the minimum weighted spatial throughput of Wi-Fi and LTE-U networks.

Topology control is an important approach to maintain the connectivity of cognitive radio networks (CRNs). Most existing works assumed that secondary users (SUs) must vacate the spectrum reclaimed by primary users (PUs), resulting the inefficient spectrum utilization. In this paper, we consider the simultaneous transmissions of SUs with PUs; meanwhile, SUs are equipped with successive interference cancellation (SIC) to mitigate the interference from PUs, thereby enabling SUs to access the spectrum more aggressively than previous works. Although SIC has been studied in the information theory and signal processing, it is not well investigated in guaranteeing the connectivity of wireless networks, especially the CRNs. On this account, we propose both centralized and distributed SIC-based topology control algorithm to alleviate the impact of the unpredictable activities of PUs and the potential interference between SUs. In particular, we integrate power control with channel assignment to construct a bi-channel-connected and conflict-free CRN with the fewest required channels. Theoretical analysis reveals that the bi-channel-connectivity and conflict-free properties can be ensured by our proposed algorithms. Then, simulation results demonstrate the effectiveness of proposed algorithms in terms of reducing the number of required channels and improving the robustness of topologies, as compared with the prevailing topology control algorithms.

Content caching at base stations (BSs) is a promising technique for future wireless networks by reducing network traffic and alleviating server bottleneck. However, in practice, the content popularity distribution may change with spatio-temporal variation but be unknown for BSs, which is an intractable obstacle for efficient caching strategy design. In this paper, considering unknown popularity distribution, we explore the content caching problem by jointly optimizing the content caching in cooperative BSs, content sharing among BSs, and cost of content retrieving. We tackle the problem from a multi-armed bandit learning perspective, where the learning of the popularity distribution is incorporated with the content caching and sharing process. Specifically, we first propose a centralized algorithm by employing a semidefinite relaxation approach, and we prove that this centralized algorithm learns efficient caching by deriving a sub-linear learning regret bound. To further reduce computational complexity, we propose a distributed algorithm based on alternating direction method of multipliers, where each BS only solves their own problems by exchanging local information with neighbor BSs. Extensive simulation results show the effectiveness of the proposed algorithms in terms of learning content popularity distributions of individual BSs, offloading traffic from the content server, and reducing cost of content retrieving.

Small satellite networks (SSNs) are playing an increasing role in nowadays earth observation due to their less development cost and energy consumption. In SSNs, it is pivotal to transmit a huge amount of data for differentiated missions to ground stations. Nevertheless, due to limited transponders and energy budget, not all contacts, i.e., potential available communication links, are feasible in data delivery. Besides, satellite downlink channel conditions are indeed time-varying due to atmospheric precipitation. Therefore, one daunting challenge is searching for feasible contacts termed as contact plan design with consideration of the differentiation for missions. In this paper, we exploit an extended time-evolving graph to characterize network resources. Based on the graph, we formulate the design of mission-aware contact plan, aiming at maximizing network profit in terms of sum weighted data volume as a mixed-integer linear programming. Due to its NP-hardness, we propose a primal decomposition method to efficiently solve the formulated problem by exploiting its special structure. To further reduce the complexity, we propose a link metric considering the issues of residual energy of satellites, time-varying satellite downlink contact capacity, and the differentiation for missions in the conflict graph. Based on the conflict graph, we devise a heuristic algorithm to design contact plan. Simulation results demonstrate the efficiency of the proposed algorithms and necessitate the consideration of the time-varying downlinks and the differentiation of missions for contact plan design.

Hybrid automatic repeat request (HARQ) is widely used in heterogeneous cellular networks (HCNs) to improve communication reliability. The temporal interference correlation caused by the common set of interferers makes the performance of HARQ more complex, especially for Type-II HARQ where the unsuccessful packets are combined with the new one to decode the packet. In general, due to the complexity of network performance analysis, the existing research focused on the performance of HARQ in single-tier wireless networks without considering cell association or base station (BS) load or the case that the combined number of transmissions is no larger than 2. In view of this, we study the performance of HARQ in HCNs jointly considering the temporally correlated interference, flexible cell association, and BS load. To this end, we adopt the popular HCN model, where different types of BSs in HCNs are modeled as K independent Poisson point processes with different densities and transmission powers. Leveraging the tool of stochastic geometry, we derive the success probability and delay-limited throughput for HCNs with Type-I HARQ and Type-II HARQ, respectively, for any number of transmissions. We show that the network performance in multiple time slots is decided by the performance in a single time slot and the temporal interference correlation. Finally, we conduct simulations to validate our analysis and show that the analysis without considering temporal interference correlation overestimates the performance of HCNs with HARQ.

In this paper, we investigate the energy-saving resource management problem for a new device-to-device (D2D) and cellular coexisting network, where D2D users employ orthogonal frequency division multiple access (OFDMA) and cellular users employ sparse code multiple access (SCMA). This hybrid network can support massive connectivity by exploiting the degrees of freedom in code and space domains, however, the complicated spectrum sharing pattern also leads to serious interference, which further boosts the power consumption of mobile devices (MDs). To tackle this problem, we propose a unified resource management scheme to minimize the total transmit power of all MDs by jointly optimizing mode selection, resource allocation, and power control. First, we analytically get the optimal resource-sharing mode (dedicated mode or reuse mode) for cellular users and D2D users based on the mapping rule between SCMA codebooks and OFDMA resource blocks. For each resource-sharing mode, we reformulate the resource management problems as classical problems in graph theory, and then devise efficient algorithms leveraging the special structure of the constructed graphs. Finally, simulation studies indicate that the network capacity is upgraded with the hybrid multiple access technologies, and the energy efficiency performance is also enhanced through the unified resource management.

Cloud radio access network (C-RAN), regarded as a promising green network architecture, facilitates cooperative transmission among remote radio heads (RRHs) while enabling flexible computation provisioning in the virtualized baseband unit pool. By jointly optimizing cooperative transmission, i.e., transmit power allocation with zero-forcing precoding adopted, and computation provisioning, i.e., virtual machine assignment, this paper minimizes the system power consumption comprised of transmit power and processing power in downlink C-RAN. Specifically, subject to per-RRH power constraint (PRPC) and per-MU quality of service constraint, the system power consumption minimization problem is formulated as a mixed integer nonlinear programming (MINLP) problem. To solve the challenging MINLP, we reformulate the MINLP as a minimum weight perfect matching problem to get the initial solution without considering the PRPC. On this basis, a power-aware greedy algorithm is further devised to modify the solution such that the PRPC is satisfied. Finally, extensive simulations show the superiority of the proposed scheme on system power saving and the tradeoff between transmit power and processing power.

In this paper, we study the uplink spectrum sharing in a device-to-device (D2D) underlaid LTE-Advanced network. The fractional power control (FPC) policy is proposed for both cellular user equipments (CUEs) and D2D users to reduce the cross-tier interference. We present an analytical framework to evaluate the effect of FPC on the network performance in terms of coverage probability and D2D user transmission capacity. By exploiting the CUE's location information, we further propose a location-aware spectrum sharing (LoSS) policy which can increase the D2D user's transmission opportunities while satisfying the CUE's QoS constraint. The proposed framework allows to determine the optimal FPC parameter for a CUE at any given location in the macrocell, and provides a more accurate analysis on the spectrum sharing between CUEs and D2D users. From the perspective of D2D transmission capacity, we provide guidelines for the optimal design of FPC in the D2D underlaid LTE-Advanced network.

In the research of physical layer security, cooperative jamming has recently drawn considerable attention. Jammer plays a friendly role to transmit jamming signal to create interference at the eavesdroppers. However, few literatures consider the scenario in which jammer plays a hostile role to interfere legitimate users. In this paper, we study an ad hoc network where legitimate users transmit with the ALOHA protocol in the presence of hostile users. Each hostile user acts as a jammer or eavesdropper with probability q or 1 - q, respectively. We assess the network performance from both sides of the legitimate user and the hostile user. Particularly, from the perspective of the legitimate user, we evaluate how the connection outage and secrecy outage affect the secrecy transmission capacity, and then derive the optimal ALOHA transmission probability that maximizes the capacity. In view of the hostile user, we obtain the optimal jamming probability that can lead to the largest connection outage probability of legitimate users subject to a given successful eavesdropping probability constraint.

Radio Frequency (RF) energy harvesting has been shown to be a promising way to power wireless devices. In this paper, we consider an energy harvesting cognitive radio network model where each secondary transmitter (ST) harvests RF energy from ambient primary transmitters (PTs). To protect secondary transmissions and improve energy efficiency, we propose an interference threshold-based transmission strategy for STs. We model the battery level of each ST as a finite state discrete-time Markov chain (DTMC) and observe a correlation between the harvested energy at the secondary transmitter and the aggregate interference at the secondary receiver (SR). Based on copula theory, we derive the joint distribution of the harvested energy and the aggregate interference, and then derive the energy outage probability and transmission probability of each ST by combining with the Markov chain model. With the tools from stochastic geometry, we analyze the ST's coverage probability. By analyzing the effect of interference threshold on energy outage probability, transmission probability and coverage probability, we provide guidelines for the optimal design of interference threshold to maximize the spatial throughput of secondary network.

To improve the spectrum efficiency (SE), sparse code multiple access (SCMA) has been proposed as an candidate for 5G wireless networks. Although SCMA has good SE performance, it suffers from high detection complexity, which may degrade its energy efficiency (EE) performance. To make up for this deficiency, we in this paper jointly consider codebook design (i.e., mapping matrix and constellation graph design) and codebook assignment to investigate the detection complexity minimization problem for uplink SCMA networks. To tackle this hard problem effectively, we first borrow the idea of dual coordinate search to devise a cost-efficient algorithm to determine the mapping matrix and codebook assignment. Based on the mapping matrix, we exploit the multi-dimensional modulation characteristic of SCMA to carefully design the constellations for each codebook to further reduce the detection complexity. Finally, we present some simulations to illustrate the performance gain of our proposed algorithm as compared with other schemes.

In energy-constrained Low Earth Orbit (LEO) satellite constellations, in order to prolong the network lifetime, more traffic should be carried by the satellites with high battery level, which, in turn, may result in congestion in such satellites. To strike a balance, we study the multi-path routing problem which aims at Maximizing network Lifetime while maintaining a Guaranteed network Congestion level (MLGC). Particularly, we formulate such a problem as a linear programming. However, it is time-consuming that solving the proposed MLGC needs to joint multiple time intervals. Therefore, we further design an Energy Aware Multi-path Routing (EAMR) strategy without solving the optimization problem. Simulation results show that the performance of EAMR is comparable with MLGC and moreover, compared with available routing strategies, the network lifetime can be effectively improved while the required congestion level being guaranteed by implementing our proposed schemes.

In cellular networks, since Media Access Control (MAC) layer plays a key role in every access equipment, it fascinates that little progress on multiple access protocol could save considerable energy. Accordingly, this paper studies a novel MAC protocol, i.e., the power division multiple access (PDMA) protocol, with the purpose of green communication. As a fundamental study of PDMA, we first propose a power division multiplexing (PDM) scheme, analogous to the time division multiplexing and frequency division multiplexing. It is proved that the transmit power could be divided into multiple regular power segments (PSs) to simultaneously transmit multiple independent information/data streams in peer to peer communications. Based on our fundamental studies of PDM, an orthogonal PDMA (OPDMA) protocol is proposed to utilize multiplexing and degraded channel gains for energy saving. By adopting the orthogonal PSs proposed in OPDMA, multiple information streams in different channels could be transmitted efficiently and concurrently with quality of service guarantee. This paper shows that the proposed OPDMA not only has low computational complexity as the conventional Time Division Multiple Access (TDMA) and Frequency Division Multiple Access (FDMA) protocols but also gains better energy efficiency, which consists with the energy saving requirement in green communications.

This paper investigates the problem of revealing the tradeoff between energy efficiency (EE) and delay in device-to-device (D2D) communications underlaying cellular networks. Considering both stochastic traffic arrivals and time-varying channel conditions, we formulate it as a stochastic optimization problem, which optimizes EE subject to the average power, interference-control, and network stability constraints. With the help of fractional programming and the Lyapunov optimization technique, we develop an algorithm, referred to as the TRADEOFF, to solve the problem. To deal with the nonconvex and NP-hard power allocation subproblem in the TRADEOFF, we adopt the prismatic branch and bound algorithm to find its globally optimal solution, where only a linear programming needs to be solved in each iteration. Thus, the TRADEOFF serves as an important benchmark to evaluate performance of other heuristic algorithms and is usually cost-efficient. The theoretical analysis and simulation results show that the TRADEOFF achieves an EE-delay tradeoff of [O(1/V),O(V)] with V being a control parameter and can strike a flexible balance between them by simply tuning V.

The advent of simultaneous wireless information and power transfer (SWIPT) offers a promising approach to providing cost-effective and perpetual power supplies for energy-constrained mobile devices in heterogeneous cellular networks (HCNs). As energy efficiency (EE) has been envisioned as a key performance metric in 5G wireless networks, we consider a multiple-input single-output (MISO) femtocell cochannel overlaid with a Macrocell to exploit the advantages of SWIPT while promoting the EE. The femto base station sends information to information decoding (ID) femto users (FUs) and transfers energy to energy harvesting (EH) FUs simultaneously, and also suppresses its interference to Macro users. We maximize the information transmission efficiency (ITE) of ID FUs and energy harvesting efficiency (EHE) of EH FUs, respectively, with the QoS of all users, and investigate their relationship. We formulate these problems as fractional programming, which are nontrivial to solve due to the nonconvexity of ITE and EHE. To tackle these problems, we devise two beamformers namely zero-forcing (ZF) and mixed beamforming (MBF), and then propose an efficient algorithm to obtain the optimal power under both beamformers. Simulation results demonstrate that MBF provides better ITE and EHE than ZF, and there exists a tradeoff between ITE and EHE in general.

The incorporation of dynamic voltage scaling technology into computation offloading offers more flexibilities for mobile edge computing. In this paper, we investigate partial computation offloading by jointly optimizing the computational speed of smart mobile device (SMD), transmit power of SMD, and offloading ratio with two system design objectives: energy consumption of SMD minimization (ECM) and latency of application execution minimization (LM). Considering the case that the SMD is served by a single cloud server, we formulate both the ECM problem and the LM problem as nonconvex problems. To tackle the ECM problem, we recast it as a convex one with the variable substitution technique and obtain its optimal solution. To address the nonconvex and nonsmooth LM problem, we propose a locally optimal algorithm with the univariate search technique. Furthermore, we extend the scenario to a multiple cloud servers system, where the SMD could offload its computation to a set of cloud servers. In this scenario, we obtain the optimal computation distribution among cloud servers in closed form for the ECM and LM problems. Finally, extensive simulations demonstrate that our proposed algorithms can significantly reduce the energy consumption and shorten the latency with respect to the existing offloading schemes.

In this paper, we investigate the throughput performance of the network coding (NC) schemes under the slotted ALOHA protocol. We consider the all-inclusive-interfering unbalanced network in which two client groups with different numbers of nodes communicate with each other through a relay node. We derive the closed-form expressions of the network throughput under the physical-layer network coding (PNC), traditional high-layer network coding (HNC), and non-network-coding (NNC), respectively. We also show the necessary and sufficient condition to make the relay node unsaturated. From the analytical results, we find that although PNC has better transmission efficiency in the two-way relay channel (TWRC); it does not always have better network throughput when the network has multiple client nodes. To further improve the network throughput, we propose the hybrid NC scheme, which allows the relay node to turn to HNC scheme if it fails to explore the PNC transmission. We further obtain the closed-form expression of the network throughput and the necessary and sufficient condition to make the relay node unsaturated in the hybrid NC scheme. Simulation results show that the hybrid NC scheme has better throughput performance than the PNC, HNC, and NNC schemes. Moreover, we optimize the network throughput of the hybrid NC scheme in terms of the transmission probability of the relay node. Last but not least, we evaluate the throughput performance of hybrid NC scheme through simulations.

Abstract In this paper, we consider the distributed opportunistic scheduling problem for the Orthogonal Frequency Division Multiplexing OFDM‐based device‐to‐device (D2D) communications, where D2D links contend for access to the dedicated spectrum with limited assistance from cellular infrastructures. Particularly, a synchronous distributed opportunistic scheduling protocol under fairness constraints (DO‐Fast) is prompted. In DO‐Fast, a round‐robin strategy is integrated with the opportunistic scheduling to tackle the trade‐off between system throughput and access fairness. Moreover, without instantaneous channel state information at receivers, we incorporate a priority allocation scheme, where access priorities are assigned randomly in a local fashion. Consequently, DO‐Fast is robust against imperfect channel estimates and inaccurate channel state information ordering. In addition, the opportunistic strategy in DO‐Fast is distinguished from the existing ones in that efficient spatial reuse is exploited by allowing concurrent transmissions based on the signal‐to‐interference ratio scheduling criterion. Meanwhile, access opportunities are moderately granted for poor quality links by the round‐robin strategy for fairness considerations. We analyze and compare three practical scheduling strategies in terms of the access probability. We also evaluate access fairness through Jain's Index. It is shown via numerical and simulation results that DO‐Fast could achieve efficient spectrum utilization and guarantee the short‐term fairness. Copyright © 2014 John Wiley & Sons, Ltd.

In heterogeneous cellular networks (HCNs), the interference received at a user is correlated over time slots since it comes from the same set of randomly located base stations (BSs). This results in the correlations of link successes, thus affecting network performance. Under the assumptions of a K-tier Poisson network, strongest long-term averaged biased-received- power based BS association, and independent Rayleigh fading, we first quantify the correlation coefficients of interference. We observe that the interference correlation is independent of the number of tiers, BS density, signal-to-interference-ratio (SIR) threshold, and transmit power. Then, we study the correlations of link successes in terms of the joint success probability over multiple time slots.We show that analysis without considering the temporal interference correlation underestimates the joint success probability. Moreover, we explore the effects of BS density, transmit power and user association bias on the joint success probability. In particular, BS density and transmit power affect the joint success probability of the overall network by influencing the association probability of each tier. We also reveal that the unbiased cell association outperforms the biased cell association in terms of the joint success probability. Finally, we conduct simulations to validate our analysis.

The integration of Device-to-Device (D2D) communications into cellular networks, albeit improving spectrum efficiency, may inevitably lead to cross-tier interference between cellular users and D2D users. In this paper, we endow D2D users with the capability of power control to address the cross-tier interference and theoretically analyze the benefits of power control in enhancing the transmission capacity region (TCR). In particular, based on transmission capacity, the TCR is defined as the enclosure of all feasible combinations of transmitter intensities in cellular and D2D networks. We first employ the stochastic geometry framework to derive closed-form expressions of the TCR for two prevalent spectrum sharing modes, i.e., reuse mode and dedicated mode. As for the reuse mode, we then study how to enlarge the TCR through initializing the power levels of cellular users and D2D users. Finally, the dedicated mode is compared with the reuse mode through TCR. Specifically, given the same target rate for cellular users and D2D users, the reuse mode is shown to outperform the dedicated mode in terms of the TCR when 2α/2≤ θ + 2, where α and θ are, respectively, the path loss exponent and decoding threshold. The analysis provides useful guidance for spectrum regulation and design of efficient power control techniques in D2D integrated cellular networks.

An important consideration for LEO satellite networks is choosing suitable routes to prolong the network lifetime while stringently guarantee the throughput requirement. However, both the highly dynamic network topology and intrinsically time-varying renewable energy availability pose great constraints and challenges in designing such routing schemes. To solve the problem, we resort to Capacity Region Evolving Graph (CREG) and formulate the throughput constrained maximum lifetime routing problem. Unfortunately, solving the problem without exploiting its special structure is indeed time-consuming, since multiple time intervals must be jointly handled. Two efficient routing algorithms, namely, Maximum Lifetime Routing (MLR) and Shortest Path-based Progressive Routing (SPPR), are thus proposed to reduce the execution time of solving the routing problem. Specifically, MLR decomposes the problem into multiple independent subproblems without trading its optimality, while SPPR exploits the deterministic mobility of satellite networks without solving the optimization problem. Simulation results verify that prolonged network lifetime and balanced traffic distribution can be obtained for both the routing algorithms.

In this paper, we investigate the capacity of two- layered satellite networks. Particularly, we propose a unified mathematical framework to formulate the relationship between network capacity and architectural parameters. Then we study the capacity of three typical scenarios. The analytical solutions show that the capacity of individual layer increases linearly with the link bandwidth of that layer. It also increases when there are more orbits and more satellites in each orbit. Moreover, when each LEO satellite can only connect to the nearest MEO satellite, the network capacity is approximately equal to the total capacity of the two layers, and is independent with the architectural parameters such as altitude of both layers and elevation angle of LEO satellites. When each LEO satellite is allowed to connect to all the MEO satellite in its coverage, the network capacity can be further increased. As the coverage size is impacted by the architectural parameters, the network capacity in this case is non-decreasing with the altitude of the MEO layer, and is non-increasing with the altitude of the LEO layer and the elevation angle of the LEO satellites.

A Multilayered Satellite Network (MLSN) tends to be a promising architecture in facilitating global ubiquitous broadband communication. However, unbalanced traffic distribution among its satellite layers should frequently occur, where the lower layers could get relatively congested while the upper layers remain underutilized. This unfair distribution of network traffic can lead to large end-to-end delay and severe throughput degradation. To cope with the above issue, we propose a Tailored Load-Aware Routing (TLAR) strategy to optimally distribute traffic load among the multiple satellite layers, so that the overall traffic congestion in the MLSN is minimized. In TLAR, an optimal portion of network load, which is decided based upon the newly arrived traffic estimation and theoretical analysis of traffic congestion rate in each layer, is detoured through the upper layer. The performance of the proposed routing method has been validated through extensive simulations, which demonstrate that TLAR can significantly alleviate traffic congestion, achieve low end-to-end delay and sustain improved throughput.

In next-generation wireless networks, energy efficiency optimization needs to take individual link fairness into account. In this paper, we investigate a max-min energy efficiency-optimal problem (MEP) to ensure fairness among links in terms of energy efficiency in OFDMA systems. In particular, we maximize the energy efficiency of the worst-case link subject to the rate requirements, transmit power, and subcarrier assignment constraints. Due to the nonsmooth and mixed combinatorial features of the formulation, we focus on low-complexity suboptimal algorithms design. Using a generalized fractional programming theory and the Lagrangian dual decomposition, we first propose an iterative algorithm to solve the problem. We then devise algorithms to separate the subcarrier assignment and power allocation to further reduce the computational cost. Our simulation results verify the convergence performance and the fairness achieved among links, and particularly reveal a new tradeoff between the network energy efficiency and fairness by comparing the MEP with the existing algorithms.

In this paper, we propose interference alignment (IA) schemes for downlink multiple-input-multiple-output heterogeneous networks (HetNets) with partial connectivity, which is induced by the path loss and the low transmission power of small cells. Specifically, we consider two partially connected scenarios of HetNets. In the first scenario, we focus on the partial connectivity among small cells, whereas in the second scenario, we further consider the partial connectivity between the macrocell and small cells. For the first scenario, we first propose a two-stage IA scheme by exploiting the heterogeneity and partial connectivity of HetNets. Then, the influence of the number of served macro users on system degrees of freedom (DoFs) is investigated. In particular, we derive the condition under which serving one macro user achieves more DoFs than serving multiple macro users and design an algorithm to find the optimal number of served macro users to maximize the system DoFs. Afterward, we study the second scenario and extend the two-stage IA to this scenario. The simulation results show that the proposed IA schemes can significantly improve the system sum rate. Moreover, by considering the partial connectivity between the macro cell and small cells, the system performance can be further improved.

In this paper, we characterize the transmission capacity region (TCR) in D2D integrated cellular networks when two prevalent interference management techniques, power control and Successive Interference Cancellation (SIC) are utilized. The TCR is defined as the enclosure of all feasible sets of active transmitter intensities in cellular and D2D systems. Closed-form approximate expressions of TCR are derived for two spectrum sharing modes, i.e., reuse mode and dedicated mode. The analysis provides insights into the impact of network parameters, interference management methods, as well as bandwidth allocation policy on the TCR. Moreover, we compare the reuse mode and dedicated mode in terms of TCR. Specifically, with power control, given the same target rate for cellular users and D2D users, the TCR of the dedicated mode is shown to be entirely enclosed by that of the reuse mode when 2α/2 ≤ θ+2, where α and θ are, respectively, the path loss exponent and decoding threshold. However, with SIC utilized, numerical results show that when θ > 1, better performance can always be achieved by the reuse mode in terms of TCR. The results can serve as a guideline for the design of efficient interference management techniques and spectrum regulation in D2D integrated cellular networks.

Energy efficiency (EE) is very crucial for future wireless communication systems, especially for cognitive radio networks (CRNs). The EE performance relies on channel state information (CSI) of channels. Besides, the interference from secondary users (SUs) to primary users (PUs) also closely depends on CSI in underlay CRNs. However, available works on EE usually assume that CSI is perfect, which is often inaccurate in practical systems. Thus, in this paper we investigate the robust EE maximization problem in underlay CRNs with multiple SUs and PUs. Assuming CSI error to be bounded, we consider that all channels lie in some bounded uncertainty regions. From the perspective of worst-case optimization, we formulate it as the max-min problem with infinite constraint, which is nontrivial even without this constraint. This is because that the outer-maximization problem is non-convex and the inner-minimization problem is a concave minimization problem known as NP-hard in general. We propose a scheme to handle this problem via the fractional programming and global optimization techniques. Particularly, we efficiently solve this problem in two special cases. Simulation results validate that our proposed scheme can improve the worst-case EE of SUs distinctly and strictly guarantee the quality-of-service (QoS) of PUs under all parameters' uncertainties.

Energy harvesting is a promising technique to reduce conventional grid energy consumption, which caters for 5G visions on the green evolution of current cellular networks. To fully exploit the harvested energy, an inefficient factor caused by the battery leakage must be taken into account to tackle the energy dissipation problem, which triggers a new dimensional optimization related to the transmission time. However, most approaches are studied for perfect battery models and neglect the optimization for the transmission time. In this paper, we formulate the battery leakage process into our model to explore the grid energy conservation problem by jointly considering admission control, power allocation, subcarrier assignment, and transmission time determination in cellular networks powered by grid and renewable energy. To tackle this problem, we exploit the Lyapunov optimization technique to develop an online algorithm, referred to as leakage-aware dynamic resource allocation policy (LADRA). Specifically, the LADRA only needs to track the current system states (e.g., channel and energy conditions) but without requiring their prior-knowledge. Furthermore, we prove that the minimum grid energy consumption value can be achieved by our proposed algorithm asymptotically. Simulation results verify the correctness of the theoretical analysis, as well as exhibit the performance improvement against other algorithms in terms of grid energy consumption and queue backlog.

Existing works have addressed the tradeoffs between any two of the three performance metrics: throughput, energy efficiency (EE), and delay. In this paper, we unveil the intertwined relations among these three metrics under a unifying framework and particularly investigate the problem of EE-guaranteed throughput-delay tradeoff in interference-free wireless networks. We first propose two admission control schemes, referred to as the first-out and first-in schemes. We then formulate it as two stochastic optimization problems, aiming at throughput maximization (in the first-out scheme) or dropping rate minimization (in the first-in scheme) subject to requirement of EE (RoE), stability, admission control, and transmit power. To solve the problems, the EE-Guaranteed algorithm for throUghput-delAy tRaDeoff (eGuard), respectively called eGuard-I and eGuard-II in the first-out and first-in schemes, is devised. Moreover, with guaranteed RoE, we theoretically show that the eGuard (I and II) can not only push the throughput arbitrarily close to the optimal with tradeoffs in delay but also quantitatively control the throughput-delay performance on demand. Simulation results consolidate the theoretical analysis and particularly show the pros and cons of the two schemes.

Green communications are becoming an inevitable trend for future wireless network design, meanwhile, as a promising technique, distributed antenna systems (DAS) cater for this evolution. In this paper, we focus on the problem of devising globally optimal antenna selection and power allocation algorithm in downlink DAS to achieve energy efficiency (EE) maximization. We formulate it as a mixed-integer nonlinear programming (MINLP), which maximizes EE subject to rate requirements, transmit power, and antenna selection constraints. By equivalent transformation, an iterative antenna selection and power allocation algorithm is proposed based on nonlinear fractional programming theory, and branch and bound methods. Our algorithm ensures global optimality and thus, it provides an important benchmark for performance evaluation of other heuristic algorithms targeting the same problem. Simulation results show that the computation complexity can be dramatically reduced comparing with exhaustive search, as well as demonstrate that a significant gain can be obtained in terms of EE against the schemes without antenna selection.

We investigate the local connectivity of cognitive radio ad hoc networks (CRAHNs), i.e., node degree and probability of node isolation. The local connectivity of CRAHNs depends on not only its own network parameters but also the primary networks. To analyze the local connectivity, we use stochastic geometry and probability theory to derive the distribution of node degree, probability of available spectrum and probability of node isolation of the Secondary Users (SUs). The relation between the local connectivity of CRAHNs and the parameters of both primary and secondary networks is given. Theoretical analysis and simulation results indicate that the average node degree of SUs scales linearly for increases in the density of SUs with the slope determined by the density of Primary Users (PUs). It also indicates that the SUs' node isolation probability is largely determined by the density of PUs.

In this paper, by jointly considering antenna selection and power allocation, we address the energy efficiency (EE) maximization problem with delay performance taken into account in downlink distributed antenna systems (DAS). To characterise system EE, we first define a revenue-cost (RC) function as the weighted difference between sum transmit rate and total energy consumption. We then formulate the problem as a stochastic optimization model, which maximizes the long-term average RC value subject to network stability (used to depict delay performance) and average power constraints. An Energy-Efficient Antenna selection and Power allocation Algorithm (EE-APA) is proposed based on Lyapunov optimization technique. The EE-APA adapts to time-varying channel conditions and stochastic traffic arrivals without requiring any corresponding prior-knowledge. Moreover, the theoretical analysis shows that the EE-APA can not only push the EE arbitrarily close to the optimal at the cost of delay performance, but also quantitatively control the EE-delay performance. Numerical results validate the adaptiveness of the EE-APA and the correctness of the theoretical analysis.

Since the seminal work of Grossglauser and Tse [1], the two-hop relay algorithm and its variants have been attractive for mobile ad hoc networks (MANETs) due to their simplicity and efficiency. However, most literature assumed an infinite buffer size for each node, which is obviously not applicable to a realistic MANET. In this paper, we focus on the exact throughput capacity study of two-hop relay MANETs under the practical finite relay buffer scenario. The arrival process and departure process of the relay queue are fully characterized, and an ergodic Markov chain-based framework is also provided. With this framework, we obtain the limiting distribution of the relay queue and derive the throughput capacity under any relay buffer size. Extensive simulation results are provided to validate our theoretical framework and explore the relationship among the throughput capacity, the relay buffer size and the number of nodes.

In cognitive radio networks (CRNs), secondary users (SUs) must vacate the spectrum when it is reclaimed by the primary users (PUs). As such, multiple SUs that operate on the same channel requested by the PUs will be affected, resulting in a possible network partition. Therefore, how to maintain the connectivity of CRNs when PU appears is a critical problem. In this paper, we propose a topology control algorithm to address this problem. Particularly, we combine power control and channel assignment to construct a bi-channel-connected and conflict-free topology using minimum number of channels. Theoretical analysis shows that the CRN can maintain connectivity upon any single channel interruption by PUs. The simulation results demonstrate that the proposed algorithm can reduce the number of required channels efficiently and preserve energy spanner property.

In this paper, we study joint link scheduling and power control with successive interference cancellation (SIC), aiming at maximizing the α-utility. The joint link scheduling and power control with SIC (PCSIC) problem is formulated to be a mixed-integer non-linear programming (MINLP), which is NP-hard. In order to solve the problem, we first decompose the MINLP into three sub-problem and then propose an iterative algorithm. We compare our strategy with the scheme without power control from the perspective of system throughput, fairness index and energy consumption. Numerical results show the noticeable performance improvement of the proposed strategy.

Successive Interference Cancellation (SIC) is a potentially powerful technique for improving the performance of multi-hop wireless networks, owing to its ability to enable concurrent receptions from multiple transmitters as well as interference rejection. In this paper, we address the problem of finding the route with maximal end-to-end spectral efficiency in multi-hop wireless networks, under the constraint of optimal bandwidth sharing. By taking advantage of SIC, more transmission opportunities are exploited by the nodes along the selected path. We formulate a cross-layer optimization framework to quantify the spectral efficiency improvement with SIC and then make use of several structural properties to derive exact solutions. Additionally, three SIC-based routing alternatives with low computational complexity are proposed, on the basis of the conventional shortest path algorithm, to obtain spectrum-efficient routes. Numerous simulation results verify that SIC can bring significant gains in terms of spectral efficiency.

In cognitive radio networks (CRNs), secondary users (SUs) must vacate the spectrum when it is reclaimed by the primary users (PUs). As such, multiple SUs transmitting on the same channel will be affected when the channel is requested by the PUs, thereby resulting in a possible network partition of CRNs. Therefore, how to maintain the connectivity of CRNs considering the activity of PUs is a critical problem. In this paper, we propose a centralized and a distributed topology control algorithm respectively to address this problem. Particularly, we combine power control and channel assignment to construct a bi-channel-connected and conflict-free topology using the minimum number of channels. In the power control phase, we tailor the topology for the channel assignment in the second phase. In the channel assignment phase, we utilize the graph coloring algorithm to achieve conflict-free transmission by assigning a channel to each SU. Theoretical analysis and simulation study show that the derived topology can maintain connectivity in the event of any single channel interruption by PUs. Simulation results also demonstrate that the proposed algorithms can efficiently reduce the average number of required channels for achieving bi-channel-connectivity and conflict-free transmission and ensure that the minimum power paths in the original network preserved in the final topology.

We aim to maximize system throughput subject to constraints on both short-term and long-term fairness in terms of Jain's index in single cell downlink OFDMA systems, where the transmission power is fixed. While it is accepted that short-term fairness implies long-term fairness, we find that this is not always true. Noting that long-term performance metric is the average of short-term ones, we point out that it depends on the averaging method and the fairness definition. We prove that short-term throughput Jain's index implies long-term throughput Jain's index. Therefore, we can remove the long-term fairness constraint if it is looser than the short-term constraint. Otherwise, we heuristically replace the long-term fairness constraint by a cumulative fairness constraint. We relax the considered discrete subchannel and slot allocation problem into a continuous convex problem, which can be efficiently solved. Then, the discrete resource allocation is derived by rounding the optimal solution. The analysis indicates that the rounding error is small. Simulation results show that we obtain a good suboptimal solution with small deviations from the optimal relaxed system throughput and the Jain's index constraints. Moreover, comparing with the strategies that take into account only long-term fairness, we guarantee both long-term and short-term fairness.

The architecture of decentralization makes future wireless networks more flexible and scalable. However, due to the lack of the central authority (e.g., BS or AP), the limitation of spectrum resource, and the coupling among different users, designing efficient resource allocation strategies for decentralized networks faces a great challenge. In this paper, we address the distributed channel selection and power control problem for a decentralized network consisting of multiple users, i.e., transmit-receiver pairs. Particularly, we first take the users' interactions into account and formulate the distributed resource allocation problem as a non-cooperative transmission control game (NTCG). Then, a utility-based transmission control algorithm (UTC) is developed based on the formulated game. Our proposed algorithm is completely distributed as there is no information exchange among different users and hence, is especially appropriate for this decentralized network. Furthermore, we prove that the global optimal solution can be asymptotically obtained with the devised algorithm, and more importantly, in contrast to existing utility-based algorithms, our method does not require that the converging point is one Nash equilibrium (NE) of the formulated game. In this light, our algorithm can be adopted to achieve efficient resource allocation in more general use cases.

Multi-hop heterogeneous cellular networks (MHCNs) consist of conventional macro cellular networks overlaid with an irregular deployment of low-power base stations (BSs), where the communication between BSs and mobile users can be established through a single hop or multiple hops. By modeling different kinds of randomly located BSs as K tiers of independent homogeneous Poisson Point Processes, we first explore the capacity of downlink MHCNs and derive the expression of capacity under Rayleigh fading channels. Particularly, the capacity gain achieved by cell splitting and multi-hop relaying is quantified for the first time. We then study the effects of BS density, transmit power, and signal-to-interference-plus-noise-ratio (SINR) threshold on the capacity of MHCNs. More importantly, we obtain the spectral efficiency enhancement condition under which the increase of BS density and transmit power improve the spectral efficiency, thereby enhancing the capacity. One interesting observation is that at a given SINR threshold, the capacity increases with BS density when all the tiers have the same SINR threshold. Moreover, the capacity of some special networks (i.e., heterogeneous cellular networks, multi-hop cellular networks, and conventional cellular networks) are derived directly by specializing some system parameters in our results. Finally, numerical studies and simulations are conducted to validate our analysis.

In wireless resource allocation, improving system throughput and simultaneously enhancing user fairness are two fundamental but contradictory objectives. As for fairness, both short-term and long-term fairness are of significant importance. However, less effort has been dedicated to explore the optimal tradeoff between system throughput and the two mentioned fairness in terms of widely used Jain's index. In this context, we aim to maximize system throughput subject to constraints on both short-term and long-term fairness in single cell downlink OFDMA systems. The difficulty of this issue lies in that the considered subchannel and slot allocation problem is a nonlinear integer programming problem, and furthermore seems to be non-causal. To overcome these challenges, we first relax the integer variables. Second, we prove that short-term fairness ensures long-term fairness so that the long-term fairness constraint is redundant and can be removed. Third, the problem is decomposed into a sequence of short-term convex optimization problems that can be easily solved. Numerical results show that the proposed method achieves a good suboptimal solution with small deviations from the optimal relaxed system throughput and the Jain's index constraint.

Enhancing system throughput and improving user fairness are two basic but contradictory objectives for resource allocation in wireless cellular networks. To obtain an efficient tradeoff between these two goals, Network Utility Maximization (NUM) framework has been adopted with log-utility to obtain proportional fairness among all the users in the network. However, such tradeoff can not control the bias towards throughput or fairness. In this paper, we focus on α-fairness NUM in Soft Frequency Reuse (SFR) based cellular networks, where SFR is an attractive frequency reuse technique to mitigate Inter-Cell-Interference (ICI) and α can be utilized to adjust the tradeoff. The difficulty of the considered issue comes from that it is a Mixed Integer Programming (MIP) problem taking into account both intra-cell user scheduling and inter-cell user association. To overcome this challenge, the α-fairness NUM problem is decomposed into two subproblems, which are dealt with one by one. First, maximize intra-cell utility by user scheduling and second, maximize network utility by distributed user association. Numerical results show that the proposed algorithm approaches the optimal solution of the α-fairness NUM problem. Also, we get a better tradeoff between throughput and fairness, where fairness is measured by Jain's index. Particularly, we improve the maximum Jain's index from about 0.3 to about 1.

In this paper, we consider the distributed scheduling problem for the OFDM based device-to-device (D2D) communications. In order to fully exploit the spatial diversity of the channel variation as well as provide access fairness for all D2D links, we propose a synchronous Distributed Opportunistic scheduling protocol under Fairness constraints (DO-Fast). DO-Fast incorporates the opportunistic scheduling with a round-robin strategy. By exchanging local Channel State Information (CSI) in a distributed way, the opportunistic scheduling strategy enables the links with better channel conditions to take precedence for higher access priorities. It leads to more concurrent transmissions and higher system throughput than the random scheduling strategy, where links are allocated with priorities in a random manner regardless of channel conditions. Meanwhile, we prompt a round-robin strategy so that the D2D links would take high priorities alternately, which guarantees the short-term fairness requirements of the links with poor channel conditions. We show via simulations that DO-Fast achieves throughput improvement over the existing scheduling protocol from the network perspective with acceptable delay performance.

Owing to the capability of accumulating mutual information from the transmission of previous nodes, rateless codes can boost the network performance considerably, and hence have sparked much interest recently. However, how to efficiently schedule multi-hop multi-relay transmissions with the aid of rateless codes remains a challenging work. Particularly, it requires high complexity to find an optimal route due to its inherent combinatorial nature. In this paper, we formulate the optimal transmission scheduling as a dynamic programming (DP) problem by defining a novel state and constructing a tree-structured state transition diagram. It is from a point of view of DP that we further propose two low-complexity algorithms, namely S-DIRAC and Fano-DIRAC, with negligible performance loss based on the idea of sequential decoding of convolutional codes. Simulation results indicate that the low-complexity algorithms almost always find the optimal solution and show the superiority of routing with mutual information accumulation compared to conventional shortest path routing.

Most of previous topology control algorithms that aim to extend the network lifetime focus only on the energy consumption of transmissions, and thus construct a static topology without adaptation to the varying energy consumption rates at different nodes. As a result, the network lifetime has not been prolonged to the most extent as expected. However, other topology control algorithms that consider the residual energy levels of nodes have not addressed the problem of fault tolerance. In this paper, we propose an adaptive topology control algorithm, Residual Energy-aware Shortest Path (RESP), which not only balances the energy consumption of different nodes but also provides fault tolerance. Particularly, RESP is able to ensure k-edge connectivity and preserve the minimum-weight path. Simulation results show that RESP extends the network lifetime and is superior to other existing localized fault-tolerant algorithms.

Automatic Repeat reQuest (ARQ) and Rateless Coding (RC) are two major feedback-based error control schemes. However, due to the lack of common metrics for measuring the performance, ARQ and RC have not been systematically compared yet. In this paper, we establish a unified analytical framework based on two novel metrics, namely forward redundancy and feedback redundancy, and present a comparative study on ARQ and RC from the point of view of redundancy. In particular, we conduct a fair comparison of both schemes over point-to-point fading channels and broadcast fading channels. The comparison indicates that RC is capable of beating ARQ completely at low signal-to-noise ratios in broadcast communications. In other cases, neither of them could dominate the other. Therefore, we propose a selection method to determine which scheme to employ with given system parameters.

Cognitive radio (CR) provides a promising solution to the spectrum scarcity problem by implementing opportunistic spectrum access over the licensed spectrum. However, spectrum holes are discontinuous in frequency and time, resulting in a challenge to CR transmissions. Fortunately, rateless codes can be utilised to exploit these distributed spectrum opportunities in an aggregate way. In such system, how to conduct the sensing and transmission is a key problem that affects the system performance. Therefore in this study, the authors propose a rateless-coded transmission protocol in a multi-channel CR system, addressing the media access control (MAC) layer sensing issues. Specifically, how many channels and which ones should be sensed in each time slot. Owing to the dynamics of channel availability, stochastic control is a necessity. Therefore the authors analyse the average throughput and formulate an optimisation problem to find the optimal sensing policy based on the theory of partially observable Markov decision process (POMDP). The myopic sensing policy is also studied owing to intractable computation complexity of a general POMPD. Moreover, the authors propose a heuristic policy with comparable performance and low complexity. Simulation results will show that the heuristic policy is superior to the static policy and has almost the same performance as the myopic policy.

Due to the broadcast nature of wireless transmissions, overheard signals can be exploited in multi- hop wireless networks so as to improve the network performance. It has been shown that energy accumulation and information accumulation are two main approaches to utilize multiple overheard signals. However, how to jointly accumulate energy and information in multi-hop wireless networks is still unknown. In this paper, we propose a new rateless coded relaying scheme in linear multi-hop wireless networks, where energy and information can be jointly accumulated by superimposing two rateless-coded packets generated from the same information bits using different codebooks. The average end-to-end throughput is analyzed. Furthermore, we formulate an optimization problem to find the optimal power allocation ratio and obtain the least transmission time for relay nodes. Simulation results will show that compared to pure energy accumulation and pure information accumulation, our proposed scheme can achieve much higher throughput.

Cooperative communication with rateless codes has attracted much attention recently because it combines spatial diversity of multiple nodes and high bandwidth efficiency of rateless codes. However, relay selection for asynchronous relaying, which shows great potential for practical applications, has not been carefully studied yet. In this paper, based on rateless codes, we propose a simple probabilistic relay selection protocol for asynchronous multi-relay networks, where no symbol-level inter-node synchronization or multiuser detection is needed. In such asynchronous networks, a decoding relay can help the other relays which have not decoded the message yet. Moreover, the tradeoff between the first hop and the second hop can be controlled by setting the decoding relay threshold. We analyze the average end-to-end throughput and find an optimal decoding relay threshold to maximize the throughput. Simulation results show the superiority of the proposed protocol at low Signal-to-Noise Ratios (SNRs) when the relays are close to the source.

In cognitive relay systems, spectrum holes exist in 4 dimensions (4-D), namely, time, frequency, location, and direction. How to efficiently utilize these different kinds of spectrum holes to provide quality-of-service (QoS) guarantees for secondary users (SU) is a challenging task. In this paper, we first identify the benefits of separately applying cooperative beamforming and rateless coding aided relaying technologies. Specifically, cooperative beamforming has the particular advantage of exploiting spatial or directional spectrum holes without causing interference to PUs. On the other hand, rateless coding is capable of utilizing different kinds of spectrum opportunities in an aggregate way with low complexity. Both cooperative beamforming and rateless coding aided cognitive relaying schemes have been proposed to support either elastic or real-time traffics for SUs. Furthermore, we combine cooperative beamforming and rateless coding together in order to provide an efficient and robust way to utilize 4-D spectrum holes in cognitive relay systems, where spectrum sensing and channel estimation may be imperfect. A substantial performance gain can be obtained by the combination, compared to using these two techniques separately.

Rateless coding ensures reliability for time-varying channels by providing ever-increasing redundancy at the packet level. However, the optimal packet length for rateless codes has not been carefully studied from the application layer and the physical layer. In this paper, we present a practical wireless communication system consisting of an LT coding module, a channel coding module, and error detection modules. By analyzing the system performance, we find the impacts of packet length on reliability and efficiency, and formulate the optimization problem that maximizes the throughput efficiency over time- varying channels. We also compare the performance of the rateless coding system with the conventional one which does not utilize LT codes. Simulation results show that the rateless coding system are superior to the conventional system in a large SNR range for slow channel variations and a relative small SNR range for fast channel variations.

Rateless codes can be used for mutual information accumulation in multi-hop wireless networks. However, the impact of spatial reuse and/or node cooperation on performance of the linear multi-hop network employing rateless codes has not been carefully studied yet. In this paper, we present three rateless coded forwarding schemes in linear multi-hop networks, namely, multi-hop forwarding with no spatial reuse, multi-hop forwarding with spatial reuse, and cooperative forwarding with spatial reuse. By analyzing and comparing their performance, we conclude that mutual information accumulation with spatial reuse improves the average throughput but induces a larger latency, while node cooperation, based on rateless codes and spatial reuse, reduces the average delay but suffers a throughput loss.

Existing spectrum management policies have led to significant over-allocation and under-utilization of the licensed spectrum. To overcome this, cognitive radio is proposed for secondary users to share the licensed spectrum without causing harmful interference to primary users. As such, the transmit power of a secondary user is limited even when it accesses the spectrum hole. Therefore, multihop transmission is a potential method to deliver the data of secondary users over large distance. In such relay systems, the utilization of rateless codes is suitable for the opportunistic spectrum access of cognitive radio. There has been some work in this area. However, the multi-relay cognitive communication with rateless codes has not been carefully investigated. In this paper, we propose a rateless coding based cooperative transmission scheme for cognitive radio networks, where the average end-to-end throughput is analyzed and optimized. We also propose a block search algorithm to find the optimal number of decoding relays with low complexity. Simulation results show that the optimized relay cognitive cooperative transmission can achieve the maximal throughput.