In the realm of the Internet of Vehicles (IoV), the concept of grouping autonomous vehicles into platoons stands out as a promising driving scenario. A platoon comprises interconnected vehicles, with the foremost vehicle designated as the Platoon Leader (PL), while each of those trailing behind is a Platoon Member (PM). In such contexts, information freshness quantified using the Age of Information (AoI) critically ensures road traffic safety. This paper explores the joint packet transmission scheduling and power allocation problem with the objective of minimizing AoI in multi-platoon vehicular networks; these latter exhibiting high dynamics incurring notable uncertainty and complexity. To alleviate this optimization problem’s complexity a decentralized partially observable Markov Decision Process (Dec-POMDP) formulation is adopted. Then, an AoI-aware joint scheduling and power control scheme based on Multi-Agent Twin Delayed Deep Deterministic policy gradient (MATD3) algorithm is proposed. In addition, in order to improve the efficiency of the MATD3’s learning phase, the algorithm has been augmented with Priority Experience Replay (PER). Simulation results show that this approach outperforms the baseline MATD3 method by 17.3% in terms of the achieved mean AoI.

Virtual reality (VR) demands extensive computation while imposing strict requirements for ultra-low latency, placing a significant burden on wireless communication systems. In recent years, there has been a growing interest in leveraging unmanned aerial vehicle (UAV)-assisted mobile edge computing (MEC) as a promising technology to provide flexible computing resources at the edge of wireless networks. To meet the computational demands of VR, we propose a collaborative three-layer edge computing framework assisted by multiple UAVs. This framework enables VR rendering tasks to be executed locally on user devices or offloaded to UAVs and base station (BS) for execution. By jointly optimizing the flight trajectories of UAVs and the rendering modes of users, we aim to maximize the average rendering completion rate, defined as the ratio of successfully completed VR rendering tasks within the specified delay constraints, while minimizing the average energy consumption of UAVs. To enhance adaptability, we adopt a multi-agent twin delayed deep deterministic policy gradient (MATD3) approach that provides an efficient strategy for multi-UAV-assisted VR rendering, even in partially observable scenarios. Simulation results validate our proposed approach and demonstrate that the MATD3 algorithm surpasses the classical multi-agent deep deterministic policy gradient (MADDPG) algorithm in terms of convergence speed and the average rendering completion rate.

Federated learning (FL) paradigm faces critical challenges in communication efficiency and fault tolerance. Recently, the federated probabilistic mask training (FedPM) proposes to learn a binary pruning mask instead of model parameters, which alleviates the communication overhead issue thanks to the binary nature of pruning masks. However, its robustness against malicious participants remains unexplored. This work proposes federated weighted probabilistic mask aggregation (FedWPMA), which utilizes the maximum likelihood estimation for binary masks and adapts a weighted aggregation strategy to mitigate the impact of adversarial clients that may share falsified pruning masks. A warm-up strategy is further proposed and incorporated to facilitate the training process. Extensive experimental results validate the effectiveness of the proposed method.

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.

Uncrewed Aerial Vehicle (UAV) is extensively employed as a mobile base station in areas with inadequate cellular infrastructure to enhance the freshness of vehicle sensors. The Age of Information (AoI) is a metric utilized to characterize the freshness of information produced by vehicle sensors. This paper investigates the use of Uncrewed Aerial Vehicles (UAVs) as mobile base stations to enhance the freshness of vehicle sensor information in areas with inadequate cellular infrastructure. We focus on minimizing the Age of Information (AoI) and UAV energy consumption in a Vehicle-to-UAV (V2U) network within the Manhattan scenario. The challenge lies in jointly optimizing UAV trajectories and vehicle data packet scheduling amidst high vehicle mobility and limited communication range. To address this issue, we employ Reinforcement Learning (RL) to formulate the problem as a Markov Decision Process (MDP), proposing a Dueling Double Deep Q-Network (D3QN) method for trajectory and scheduling optimization. We also introduce Priority Experience Replay (PER) to improve reward acquisition for the UAV, addressing the issue of sparse rewards due to the expansive space for vehicle movement. Simulation results provide empirical evidence supporting the efficacy of the proposed algorithm in comparison to baseline policies.

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.

Timely gathering of sensing data is critical in wireless sensor networks (WSNs). However, in delay-sensitive applications, maintaining the freshness of collected data poses a significant challenge. To tackle this issue, an age of information (AoI)-aware data collection method leveraging unmanned aerial vehicle (UAV) and intelligent reflective surface (IRS) is proposed in this work. Particularly, a UAV is employed to traverse over ground sensor nodes (SNs) and reliably collect their sensing data where the received signal strength is enhanced through IRS. The UAV’s flight trajectory and its association with SNs, as well as the IRS phase control strategy are jointly optimized to minimize the weighted sum of the average AoI of the SNs and energy consumption of the UAV. However, this optimization is complicated by potential inaccuracies in IRS channel state estimation. To tackle this challenge, we propose an enhanced deep reinforcement learning (DRL) framework that incorporates a dual-network agent with two nested neural networks (NNs): UAV-NN, which jointly optimizes the UAV trajectory and SN association, and IRS-NN, which dynamically adjusts IRS phase shifts based on sampled channel states, UAV position, and associated SN. By integrating this architecture into proximal policy optimization (PPO) and deep Q-network (DQN), we develop two novel algorithms: PPO-RAC and DQN-RAC, tailored for IRS-assisted UAV data collection. Extensive simulations validate their effectiveness across diverse scenarios, demonstrating significant AoI reduction compared to baseline methods.

Based on the exact source-sensor spatial geometry, a three-dimensional (3-D) spatial-temporal localization algorithm for multiple near-field (NF) sources is proposed without adopting the Fresnel approximation, which simplifies the spatial phase difference by Taylors polynomial. In addition, considering the propagation attenuation which varies from different sensors, the spatial and temporal information can be exploited to construct a third-order parallel factor (PARAFAC) data model and the array manifold matrices can be extracted by trilinear decomposition; then, estimation of the unambiguous range and angle parameters of the NF sources is achieved from the spatial amplitude-phase factors by the least squares method. The obtained 3-D parameters associated with each source require no additional pairing process, as also demonstrated by simulation results.

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.

Network Function Virtualization (NFV) is considered one of the key technologies of 5G/B5G because of its advantages of flexibility, scalability, and manageability. In NFV networks, the flow of network service needs to go through a certain number of Virtual Network Functions (VNFs) which form Service Function Chain (SFC). Compared to link protection in traditional networks, the backup transmission links for different types of VNFs need to be considered to improve the SFCs’ reliability, since any failure of transmission link may interrupt the network service. Due to the uncertainty of VNF placement and routing, the flexible selection of link backup for each VNF to satisfy the reliability requirement of SFC becomes a remarkably challenging problem. In this paper, a Flexible virtual Link Protection (Fle_LP) mechanism is proposed to calculate backup resources accurately, enhancing the reliability of NFV-enabled network service. We mathematically formulate the problem as a Mixed Integer Nonlinear Program (MINLP). An Extended Least Square (ELS) method is introduced to deal with the nonlinear constraints, which transforms MINLP to Mixed Integer Linear Programming (MILP). Owing to the MILP’s remarkable complexity, a Column Generation-based Link Protection (CG_LP) algorithm is proposed, which generates an acceptable sub-optimal solution. Numerical results show that CG_LP reduces the computing time (8-node network: 92.3 %, 16-node network: 99.6 %) while achieving the same bandwidth consumption as MILP.

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.

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.

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.

Information freshness, which is characterized by a new performance metric called age of information (AoI), significantly influences decision making in numerous applications. In wireless sensor networks, unmanned aerial vehicle (UAV) has been widely adopted for fresh data collection. The key to applying UAV lies in UAV trajectory planning. Considering several fixed waypoints in UAV trajectory, the trajectory planning is an NP-hard combinatorial optimization problem, and is difficult to solve in practice. To well balance between the accuracy and efficiency, we propose an end-to-end AI-based framework in this paper to deal with the UAV trajectory planning within two stages. First, the hover positions of UAV and data transmission time are decided using a clustering module. Then, the AoI-minimal flight path is obtained through a neural trajectory solver. Compared with classic heuristic algorithms, the proposed AI-based framework achieves a smaller AoI with two orders of magnitude lower computational time. Besides, the proposed AI-based framework can be easily generalized to larger-scale scenarios (e.g., up to 2,000 sensor nodes) which cannot be solved by exact algorithms (e.g., dynamic programming) in a limited time. Moreover, the AI-based framework is comparable in accuracy with the commercial open-source solver Google OR-tools, but the efficiency is increased by 200%.

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.

This paper is motivated by the observation that the average queueing delay can be decreased by sacrificing power efficiency in wireless communications. In this sense, we naturally wonder what the minimum queueing delay is when the available power is limited and how to achieve the minimum queueing delay. To answer these two questions in the scenario where randomly arriving packets are transmitted over multi-state wireless fading channel, a probabilistic cross-layer scheduling policy is proposed in this paper, and characterized by a constrained Markov decision process. Using the steady-state probability of the underlying Markov chain, we are able to derive the mathematical expressions of the concerned metrics, namely, the average queueing delay and the average power consumption. To describe the delay-power tradeoff, we formulate a non-linear programming problem, which, however, is very challenging to solve. By analyzing its structure, this optimization problem can be converted into an equivalent linear programming problem via variable substitution, which allows us to derive the optimal delay-power tradeoff as well as the optimal scheduling policy. The optimal scheduling policy turns out to be dual-threshold-based, which means transmission decisions should be made based on the optimal thresholds imposed on the queue length and the channel state.

To exploit the potential of content caching and device-to-device (D2D) communication, we propose a user-centric joint device caching and channel assignment (DCA) policy to facilitate content exchanges between user equipments (UEs). The objective is to minimize the average content delivery delay by effectively leveraging D2D communications using as few channels as possible, subject to the UEs' cache capacities and availability of D2D links. This joint design problem is formulated as a nonlinear combinatorial optimization problem which is NP-hard. We first analyze the optimal DCA policy in two special cases. Then, a low-complexity heuristic algorithm is proposed for general cases which alternatively performs greedy device caching and graphcoloring based channel allocating. Simulation results show that the proposed DCA policy can reduce the average content delivery delay by more than half, in contrast to baseline schemes with locally popular caching.

Recently, mobile-edge computing (MEC) emerges as a promising paradigm to enable computation intensive and delay-sensitive applications at resource limited mobile devices by allowing them to offload their heavy computation tasks to nearby MEC servers through wireless communications. A substantial body of literature is devoted to developing efficient scheduling algorithms that can adapt to the dynamics of both the system and the ambient wireless environments. However, the influence of these task offloading schemes to the mobile users' privacy is largely ignored. In this work, two potential privacy issues induced by the wireless task offloading feature of MEC, location privacy and usage pattern privacy, are identified. To address these two privacy issues, a constrained Markov decision process (CMDP) based privacy-aware task offloading scheduling algorithm is proposed, which allows the mobile device to achieve the best possible delay and energy consumption performance while maintain a pre-specified level of privacy. Numerical results are presented to corroborate the effectiveness of the proposed algorithm.

In this paper, we study delay optimal scheduling of bursty data traffics over multi-state time-varying wireless channels, where bursty packet arrival in the network layer, queueing behavior in the data link layer, and rate adaptive transmission with flexible modulation in the physical layer are jointly considered from a cross-layer perspective. To achieve a minimum queueing delay under a power constraint, a probabilistic queue-aware and channel- aware cross-layer scheduling policy is proposed, and characterized by a Markov chain model, where the transmission rate, i.e., the number of packets delivered in each slot, is selected with probabilities based on the buffer and channel states in this slot. To reveal the optimal delay-power tradeoff, we formulate a non-linear optimization problem, which, however, is very challenging to solve. To make it tractable, we convert the optimization problem equivalently into a Linear Programming (LP) problem, which helps us achieve the optimal three-dimensional threshold-based scheduling policy analytically. It is found that the source should select one transmission rate jointly based on the channel state and the backlog in the queue.

To deal with the rapid growth of high-speed and/or ultra-low latency data traffic for massive mobile users, fog radio access networks (Fog-RANs) have emerged as a promising architecture for next-generation wireless networks. In Fog-RANs, the edge nodes and user terminals possess storage, computation and communication functionalities to various degrees, which provide high flexibility for network operation, i.e., from fully centralized to fully distributed operation. In this paper, we study the cache placement problem in Fog-RANs, by taking into account flexible physical-layer transmission schemes and diverse content preferences of different users. We develop both centralized and distributed transmission aware cache placement strategies to minimize users' average download delay subject to the storage capacity constraints. In the centralized mode, the cache placement problem is transformed into a matroid constrained submodular maximization problem, and an approximation algorithm is proposed to find a solution within a constant factor to the optimum. In the distributed mode, a belief propagation-based distributed algorithm is proposed to provide a suboptimal solution, with iterative updates at each BS based on locally collected information. Simulation results show that by exploiting caching and cooperation gains, the proposed transmission aware caching algorithms can greatly reduce the users' average download delay.

In this paper, we study the delay optimal scheduling policy for a multi-state wireless fading channel, by taking bursty packet arrivals and automatic repeat request-based packet transmission into account. In our system, the average delay each packet experiences includes the time it waits in the queue and the time it may take to retransmit due to packet delivery failure. To reduce the average delay, we propose a joint channel aware and queue-aware stochastic scheduling policy to determine whether and with which probability the source should transmit based on channel and buffer states, subject to an average power constraint at the transmitter. To find the optimal scheduling probabilities, we formulate a non-linear power-constrained delay minimization problem with the aid of controlled Markov decision processes. The optimization problem is then converted into an equivalent linear programming problem by introducing new variables from the steady-state probabilities of the underlying Markov chain and transmission probabilities. By analyzing its property, we derive the structure of the optimal solution, and exploit it to obtain the optimal probabilities analytically. It is found that the optimal scheduling policy has a double threshold structure, and can significantly reduce the average delay.

Caching popular contents at the edge of wireless networks has recently emerged as a promising technology to improve the quality of service for mobile users, while balancing the peak-to-average transmissions over backhaul links. In contrast to existing works, where a central coordinator is required to design the cache placement strategy, we consider a distributed caching problem which is highly relevant in dense network settings. In the considered scenario, each Base Station (BS) has a cache storage of finite capacity, and each user will be served by one or multiple BSs depending on the employed transmission scheme. A belief propagation based distributed algorithm is proposed to solve the cache placement problem, where the parallel computations are performed by individual BSs based on limited local information and very few messages passed between neighboring BSs. Thus, no central coordinator is required to collect the information of the whole network, which significantly saves signaling overhead. Simulation results show that the proposed low-complexity distributed algorithm can greatly reduce the average download delay by collaborative caching and transmissions.

Mobile-edge computing (MEC) emerges as a promising paradigm to improve the quality of computation experience for mobile devices. Nevertheless, the design of computation task scheduling policies for MEC systems inevitably encounters a challenging two-timescale stochastic optimization problem. Specifically, in the larger timescale, whether to execute a task locally at the mobile device or to offload a task to the MEC server for cloud computing should be decided, while in the smaller timescale, the transmission policy for the task input data should adapt to the channel side information. In this paper, we adopt a Markov decision process approach to handle this problem, where the computation tasks are scheduled based on the queueing state of the task buffer, the execution state of the local processing unit, as well as the state of the transmission unit. By analyzing the average delay of each task and the average power consumption at the mobile device, we formulate a power-constrained delay minimization problem, and propose an efficient one-dimensional search algorithm to find the optimal task scheduling policy. Simulation results are provided to demonstrate the capability of the proposed optimal stochastic task scheduling policy in achieving a shorter average execution delay compared to the baseline policies.

Green communications have been attracting increased research interest recently. Equipped with a rechargeable battery, a source node can harvest energy from ambient environments and rely on this free and regenerative energy supply to transmit packets. Due to the uncertainty of available energy from harvesting, however, intolerably large latency and packet loss could be induced, if the source always waits for harvested energy. To overcome this problem, one Reliable Energy Source (RES) can be resorted to for a prompt delivery of backlogged packets. Naturally, there exists a tradeoff between the packet delivery delay and power consumption from the RES. In this paper, we address the delay optimal scheduling problem for a bursty communication link powered by a capacity-limited battery storing harvested energy together with one RES. The proposed scheduling scheme gives priority to the usage of harvested energy, and resorts to the RES when necessary based on the data and energy queueing processes, with an average power constraint from the RES. Through two-dimensional Markov chain modeling and linear programming formulation, we derive the optimal threshold-based scheduling policy together with the corresponding transmission parameters. Our study includes three exemplary cases that capture some important relations between the data packet arrival process and energy harvesting capability. Our theoretical analysis is corroborated by simulation results.

In this paper, we consider a multiple access channel, where multiple users equipped with energy harvesting batteries communicate to an access point. The users are supposed to share the channel via Time Division Multiple Access (TDMA). In many existing works, it is commonly assumed that the users' energy harvesting processes and storage status are known to all the users before transmissions. In practice, such knowledge may not be readily available. To avoid excessive overhead for realtime information exchange, we consider the scenario where the users schedule their individual transmissions according to the users' statistical energy harvesting profiles. We first show that in the case when each node has an infinite-capacity battery, equal-power TDMA is optimal for throughput maximization. Using Markov chain modeling, we then study the system performance for the finite-capacity battery case under the equal-power TDMA framework. We also consider an equal-time TDMA scheme, which assigns equal-length subslots to each user. It is found that equal-power TDMA always outperforms equal-time TDMA in the infinite-capacity battery case, while equal-time TDMA exhibits compatible or even slightly better performance in some scenarios when the batteries have finite capacities.

Multicast/broadcast is regarded as an efficient technique for wireless cellular networks to transmit a large volume of common data to multiple mobile users simultaneously. To guarantee the quality of service for each mobile user in such single-hop multicasting, the base-station transmitter usually adapts its data rate to the worst channel condition among all users in a multicast group. On one hand, increasing the number of users in a multicast group leads to a more efficient utilization of spectrum bandwidth, as users in the same group can be served together. On the other hand, too many users in a group may lead to unacceptably low data rate at which the base station can transmit. Hence, a natural question that arises is how to efficiently and fairly transmit to a large number of users requiring the same message. This paper endeavors to answer this question by studying the problem of multicasting over multicarriers in wireless orthogonal frequency division multiplexing (OFDM) cellular systems. Using a unified utility maximization framework, we investigate this problem in two typical scenarios: namely, when users experience roughly equal path losses and when they experience different path losses, respectively. Through theoretical analysis, we obtain optimal multicast schemes satisfying various throughput-fairness requirements in these two cases. In particular, we show that the conventional multicast scheme is optimal in the equal-path-loss case regardless of the utility function adopted. When users experience different path losses, the group multicast scheme, which divides the users almost equally into many multicast groups and multicasts to different groups of users over nonoverlapping subcarriers, is optimal .

In wireless cellular systems, it is an important and challenging task to reliably multicast to numerous users that require the same contents at one transmission. In this paper, we propose a network coding based multicast scheme for wireless cellular OFDM systems. The base station encodes source packets with linear network coding and multicasts the coded packets to the target users over multicarriers. Thus, the users can correctly recover the source message as long as they successfully receive a certain number of packets. The reliability of coded wireless multicast is characterized by the user outage probability, which can be greatly reduced by efficiently exploiting frequency diversity gain via network coding. We show that the BS shall adjust the data transmission rate per carrier to strike a good balance between the reliability at each subcarrier and the redundancy among coded packets. It is also found that full diversity gain and no diversity gain should be exploited in the high and low SNR regimes, respectively. Simulation results also reveal that network coding generally provides great advantage for reliable wireless multicasting to a large number of users.

The authors propose a cross-layer approach, which exploits distributed beamforming in the physical layer and token passing in the media access control (MAC) layer, to improve quality of service (QoS) for secondary users (SUs) with bursty traffics in cognitive relay systems. In this scheme, source-to-destination transmissions are relayed by some SU nodes, which can form a distributed beamformer to forward messages in busy timeslots while completely eliminating interference to primary users (PUs). In contrast with previous cognitive relaying protocols, this scheme can utilise more spectrum resources, namely idle timeslots (or temporal spectrum holes) as well as busy timeslots (or spatial spectrum holes). Based on a token passing mechanism, an opportunistic scheduling protocol is then developed to dynamically balance available spectrum holes between the source and the relays, and hence adapts to bursty arrival of secondary traffics and random presence of PUs. By formulating a tandem queueing analytical framework, the performance of the proposed scheme is then analysed using a multi-dimensional Markov chain model. Numerical results demonstrate that the proposed scheme can achieve significant QoS gains over conventional cognitive relaying protocols that utilise only idle timeslots.

Cognitive Radio (CR) can significantly improve the utilization of the precious radio spectrum by allowing Secondary Users (SUs) to borrow the licensed spectrum if they do not cause harmful interference to Primary Users (PUs). As a wireless technology, CR confronts the challenges of wireless channels inevitably and thus wishes to employ node cooperation to achieve spatial diversity gain. However, conventional cooperative diversity technologies require two idle timeslots for each transmission. This implies two temporal spectrum holes are needed for each transmission when the technologies are applied to CR Networks (CRNs). This can cause severe delay, as temporal spectrum holes are only available from time to time in CRNs. In this paper, we present a cross-layer approach, where cooperative beamforming is adopted to forward messages in busy timeslots without causing interference to PUs, so as to achieve cooperative diversity gain and improve Quality of Service (QoS) for SUs without consuming additional idle timeslots or temporal spectrum holes. In the physical layer, the beamforming weight vector and the cooperative diversity gain are obtained using a geometric approach. The MAC layer of the cooperative communication in CRNs can be modeled by a tandem queue, where the source queue is the bottleneck. Therefore, we propose an optimal opportunistic priority scheduling scheme in the MAC layer, the timeout probability of which is obtained using an absorbing Markov chain. A cross-layer optimization of the transmission rate is then carried out to jointly reduce the timeout and outage probabilities. Its significant QoS gain is demonstrated by simulations.

There have been increasing interests in integrating cooperative diversity into Cognitive Radios (CRs). However, conventional cooperative diversity protocols require at least two randomly available idle timeslots or temporal spectrum holes for one transmission, thus leading to limited throughput and/or large latency. In this paper, we propose a novel cross-layer approach for efficient scheduling in CR systems with bursty secondary traffics. Specifically, cooperative beamforming is exploited for Secondary Users (SUs) to access busy timeslots or spatial spectrum holes without causing interference to primary users. We first propose a basic cooperative beaMforming and Automatic repeat request aided oppoRtunistic speCtrum scHeduling (MARCH) scheme to balance available spectrum resources, namely temporal and spatial spectrum holes, between the source and the relays. To analyze the proposed scheme, we develop a tandem queuing framework, which captures bursty traffic arrival, dynamic availability of spectrum holes, and time-varying channel fading. The stable throughput region and the average delay are characterized using a structured matrix-analytical method. We then obtain delay optimal scheduling schemes for various scenarios by jointly optimizing the scheduling parameters. Finally, we propose a modified scheme, MARCH-IR, which combines MARCH with Incremental Relay selection to further improve the system performance. Simulation results reveal that the proposed schemes provide significant Quality of Service (QoS) gains over conventional scheduling schemes that access only temporal spectrum holes.

In this paper, we propose an opportunistic scheduling scheme to serve bursty traffics in cognitive radios, where cooperative beamforming is exploited to access busy timeslots or spatial spectrum holes to forward messages without causing interference to primary users. Specifically, based on cooperative beamforming in the physical layer and automatic repeat request for error recovery in the link layer, our proposed scheme strives to balance available spectrum resources, namely temporal and spatial spectrum holes, between the source and the relays so as to greatly reduce the average delay. To analyze the proposed scheme, we then develop a tandem queueing analytical framework, which captures bursty traffic arrival, dynamic availability of spectrum holes, and time-varying channel fading. By modelling it with a multi-dimensional Markov chain, the average delay is derived using a structured matrix-analytical method. Finally, we obtain delay optimal scheduling schemes by jointly optimizing the scheduling parameters. Simulation results reveal that the proposed scheme provides significant quality of service gains over conventional scheduling schemes that access only temporal spectrum holes.

We propose a cooperative beamforming aided incremental relaying scheme to improve spectrum efficiency of cognitive radio systems. In this scheme, the source and relays can utilize cooperative beamforming to activate packet retransmission in busy timeslots or spatial spectrum holes, if the destination fails to receive the packets transmitted from the source. Therefore, cooperative diversity gain is obtained without consuming extra idle timeslots. Given a packet loss constraint, our proposed scheme endeavors to further improve the system throughput by dynamically adjusting the maximum number of retransmissions. We derive the average throughput of the proposed scheme and obtain the maximum throughput by optimizing the scheduling parameters. Theoretical and simulation results reveal that the proposed scheme obtains a significant throughput gain compared to direct transmission as well as conventional incremental relaying schemes utilizing idle timeslots only.

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.

Recent work has shown that distributed (or cooperative) beamforming can achieve cooperative gain, such as throughput gain and diversity gain, with no need for extra spectral holes in Cognitive Wireless Networks (CWNs). However, how to efficiently schedule cooperative beamforming to improve the quality of service of unlicensed secondary users has not been well addressed. In this paper, a simple opportunistic scheduling scheme is proposed to serve delay-sensitive traffics in CWNs with cooperative beamforming. After the probabilities of outages due to channel fading and random appearance of primary users are analyzed, respectively, the overall outage probability of our scheme is minimized by optimizing the scheduling parameter. The optimal scheduling scheme is then derived for the high-SNR regime. Simulation results show that compared to conventional schemes without cooperative beamforming, our scheduling scheme can significantly lower down the probability of message transmission failure within a given time period.

Cognitive radio (CR) is a powerful solution that can significantly improve the utilization of the precious limited radio spectrum. It allows secondary users (SUs) to opportunistically access spectral holes of the licensed spectrum without causing harmful interference to primary users (PUs). However, waiting for idle timeslots may induce very poor quality of service (QoS) for SUs. To alleviate this, an opportunistic relaying protocol exploiting distributed beamforming and token passing is proposed in this paper. We consider a cognitive radio network (CRN), where SUs constitute a two-hop relaying network. Specifically, a distributed beamforming method is applied to enable concurrent transmissions of PUs and SUs, thereby improving the opportunistic spectrum access. Our protocol applies a token passing mechanism in the MAC layer to dynamically balance transmission opportunities between two hops, and hence adapts to the random packet arrival and PUs' presence. We shall formulate a Markov chain to analyze the performance of this protocol. Numerical results show that our proposed protocol can significantly improve QoS of SUs in terms of the packet-loss rate and average delay, compared to conventional relaying protocols that utilize only silent timeslots.

Cognitive radio is a powerful solution that can significantly improve the utilization of the precious limited radio spectrum. It allows secondary users (SUs) to opportunistically access spectral holes in the licensed spectrum without causing harmful interference to primary users (PUs). However, the secondary communication opportunity becomes extremely poor when primary systems are heavily loaded. In this paper, a distributed beamforming method is proposed to allow concurrent transmissions of PUs and SUs, thereby improving the opportunistic spectrum access. Specifically, a SU source broadcasts a message to a set of cognitive users, which can serve as a set of relays, when PUs are absent. The relays that correctly decode the message will create a distributed beamformer to forward the message to the SU destination without causing any interference irrespective of whether PUs are silent or not. To achieve this, we use the method of orthogonal projection to obtain the beamforming weight vector. In addition, we derive the distribution of the received signal power at the SU destination, based on which the average outage probability of our proposed scheme is analyzed when PUs' occupation changes fast. Theoretical and numerical results reveal that the spatial diversity order of this scheme equals the number of SU relays minus that of primary receivers. Furthermore, numerical results show that the outage probability of this scheme outperforms other schemes that access the spectrum only when PUs are absent.

With the proliferation of wireless multimedia applications, multicast/broadcast has been recognized as an efficient technique to transmit a large volume of data to multiple mobile stations at the same time. In most multicast systems, the transmitter (e.g. base station) adapts its data rate to the furthest located users, so as to guarantee service quality to as many users as possible. Predictably, the more users in a multicast group, the lower data rate the base station can transmit. On the other hand, grouping more users together leads to a more efficient utilization of spectrum bandwidth, as these users are served simultaneously. This bring the interesting problem that presses for solution: how to group users in a cell into multicast groups and how to allocate a fixed amount of bandwidth resource to the groups, to achieve a good balance between throughput and fairness in multicast systems. In this paper, we formulate the united user grouping and bandwidth allocation strategy into a utility-based optimization problem. One method of signomial programming is used to solve the non-convex optimization problem. Numerical results will show that this suboptimal algorithm performs well even compared to the optimal one. Moreover, through theoretical analysis, we prove that the best user grouping and bandwidth allocation scheme of throughput maximization is to allocate the entire bandwidth to the unique group containing the users located within a ring-shaped region with an optimal outer radius r*.

With the proliferation of wireless multimedia applications, multicast/broadcast has been recognized as an efficient technique to transmit a large volume of data to multiple mobile stations at the same time. In most multicast systems, the transmitter (e.g., base station) adapts its data rate to the worst channel among all users in the multicast group, so as to guarantee service quality to each user. Predictably, the more users in a multicast group, the lower data rate the base station can transmit. On the other hand, grouping more users together leads to a more efficient utilization of spectrum bandwidth, as these users are served simultaneously. A natural question that arises is how to group users to maximize the throughput of multicast systems, given a fixed amount of bandwidth resource. In this paper, we attempt to answer this important question that has not been addressed before. Through theoretical analysis, we prove that (1) the average throughput increases with the number of users in a multicast group, when the number of subcarriers allocated to a group is proportional to the number of users therein. Moreover, the throughput approaches infinite-bandwidth Gaussian channel capacity when the number of users gets large; (2) the number of users, and hence the number of subcarriers, that is needed for throughput to be arbitrarily close to its asymptotic value increases almost linearly with the transmit SNR. Our analysis is validated through simulations.

Dynamic resource allocation is a key technique that can significantly improve the performance of next generation wireless systems under guaranteed QoS to users. Most of the current resource allocation algorithms are, however, limited to unicast traffics. In practice, how to efficiently allocate various resources in multicast wireless systems is not known. In this paper, we shall study dynamic resource allocation for OFDMA-based single-cell multicast systems. Specifically, we shall formulate an optimization problem to maximize the system throughput given a set of available resources (power and sub-carriers). The optimal resource allocation solution is proposed along with a low- complexity algorithm. In two extreme cases, namely, low and high SNR regimes, the low-complexity allocation algorithm is further simplified. Numerical results will show that the system throughput is significantly improved by using our proposed algorithms.