Semantic communication has emerged as a promising paradigm for next-generation wireless networks, offering enhanced efficiency by reducing transmission data. However, implementing semantic communication faces significant challenges, particularly in resource-constrained devices that cannot support the complex artificial intelligence (AI) models required for semantic extraction. This paper addresses this challenge by proposing a novel unmanned aerial vehicle (UAV)-enabled semantic-bit coexisting relay system, where the UAV serves as intermediate nodes to assist transmissions from resource-limited users to the base station. By deploying semantic extraction models at the UAV, the proposed system solves the computational resource limitations for user devices while minimizing transmission latency via data size reduction. In such a system, we formulate a system latency minimization problem that jointly considers semantic compression model selection and bandwidth allocation. To address this complex problem, we develop an effective solution method by decomposing the original problem into a semantic compression model selection based on performance-latency trade-offs and a bandwidth-allocation optimization via convex optimization techniques. Extensive numerical evaluations demonstrate that the proposed framework consistently outperforms conventional schemes across diverse network settings and compression parameters, significantly reducing end-to-end latency while maintaining high-quality semantic communication.

This work explores combining the rate-splitting multiple-access (RSMA) technique with an active reconfigurable intelligent surface (RIS) to improve the quantized multiuser multiple-input single-output network. The active RIS facilitates communication between the base station (BS) and users equipped with low-resolution quantizers, whereas RSMA improves downlink transmission efficiency. By maximizing the spectral efficiency while minimizing the power consumption at the transmitter and active RIS, we formulate an energy efficiency maximization problem by jointly designing the BS precoding matrix and active RIS reflecting matrix. The optimization problem presents nonconvexity, which makes finding the optimal solution challenging. Therefore, we reformulate the problem into a reinforcement learning-based problem that is solvable by applying deep reinforcement learning (DRL) algorithms. To ensure action accuracy, we design a constraint-matching function that integrates with the DRL algorithm, forming a DRL framework securing all problem constraints. To assess the proposed DRL algorithm, we propose an alternating-based solution that decomposes the problem into precoding matrix optimization and active reflecting matrix optimization sub-problems, which are solvable using the successive convex approximation-based method. The performance evaluations demonstrate the convergence and effectiveness of the proposed approaches in various scenarios.

The domain adaptation issues in semantic communications become critical when transmitter and receiver operate across different multiple domains or when input data during inference have different distributional characteristics than the data used to train semantic encoders and decoders. In this paper, we introduce the Multidomain Adaptive Deep Semantic Communication (MA-DeepSC) framework, designed to enhance semantic communications across multiple domains. Our framework consists of two core components: the Multidomain Adaptive Semantic Coding Network (MASCN), inherently designed to adapt semantic encoding and decoding across multiple domains, and the multidomain data adaptation network (MDAN), which transforms actual observable data into the data on which the system was initially trained, thus obviating the need for retraining the existing pre-trained semantic coding network. We validate our approach through experiments on digit datasets and CelebA, observing significant outperformance over existing techniques. In addition, we analyze the strategic benefits and drawbacks of both MASC and MDAN, assessing their applicability under various scenarios. The source code for MA-DeepSC is available at https://github.com/wongdongwook/JSAC_MA-DeepSC.

Recently, there has been a growing interest in exploring new multi-antenna technologies for 6 G wireless networks, including the use of extremely largescale antenna arrays, tremendously high frequencies, and novel antenna technologies. These emerging trends introduce unique characteristics that cannot be adequately addressed by classical far-field communication techniques with planar wavefronts. As a result, there is a need to investigate near-field communication design with spherical wavefronts. In this paper, we present a comprehensive overview of near-field communications, focusing on basic concepts, applications, and future directions.

This research examines quantized uplink multi-user MIMO communication systems with low-resolution quantizers at users and base stations (BS). In such a system, we employ the non-orthogonal multiple access (NOMA) technique for communication between users and the BS to enhance communication performance. To maximize the number of users that satisfy the quality of service (QoS) requirement while minimizing the user’s transmit power, we jointly optimize the transmit power and precoding matrices at the users and the digital beamforming matrix at the BS. Owing to the non-convexity of the objective function, we transform the problem into a reinforcement learning-based problem and propose a deep reinforcement learning (DRL) framework named QNOMA-DRLPA to overcome the challenge. Because the nature of the action decided by the DRL algorithm may not satisfy the problem constraints, we propose a postactor process to redesign the actions to meet all the problem constraints. In the simulation, we assess the proposed framework’s performance in training convergence and demonstrate its superior performance under various environmental parameters compared with other benchmark schemes.

The use of directional antennas in high-frequency bands (e.g., millimeter-wave) is essential to support applications requiring high throughput and low latency. However, communications using directional antennas require intricate scheduling by a central coordinator to avoid collision and deafness problems. Thus, in this study, we propose a directional medium access control (DMAC) protocol based on a deep$Q$-network (DQN) framework wireless ad hoc networks (WANETs) for Internet of Things (IoT). In our model, even though there is no central coordinating unit (e.g., edge/cloud server), each IoT device can intelligently avoid the collision and deafness through its learning agent. In addition, to maximize the throughput, we design a reinforcement learning (RL) architecture and propose a DQN-based DMAC such that each IoT device intelligently selects the time-slot and transmitting beam without any central coordinator. The proposed schemes are evaluated using carrier-sense multiple access (CSMA) and adaptive learning-based DMAC (AL-DMAC) protocols. The evaluation results reveal that the proposed double DQN scheme outperforms the existing schemes by approximately 54.1% and 57.2% in terms of the throughput.

The exponential growth of the Internet of Multimedia Things (IoMT) traffic has posed a threat of service quality degradation due to the limitation of current communication, networking, and computing advances in mobile networks. In this regard, managing the Quality-of-Experience (QoE) for IoMT services is a vital challenge to meet user satisfaction. To cope with this problem, we investigate the joint optimization of video quality variation and latency in multiuser downlink rate-splitting multiple-access (RSMA) networks, especially within imperfect network conditions and state information. To accomplish this, we first formulated the joint optimization problem into a Markov decision process framework, then exploited a deep reinforcement learning approach to adaptively calculate the optimal configuration of the RSMA against environment dynamics. As a result, the proposed deep deterministic policy gradient on RSMA-based video streaming system (DDPG-RMAVS) provides QoE maintenance by minimizing video resolution reduction and latency. Extensive simulation results revealed that the proposed DDPG-RMAVS algorithm surpasses existing algorithms by achieving higher video quality, lower delay, larger buffer capacity, and limited stalling events, representing a significant breakthrough in IoMT streaming optimization.

The proliferation of Internet-of-Things applications has given rise to several challenges, including network congestion and high energy consumption. Among the promising technologies for beyond-5G networks, nonorthogonal multiple access (NOMA) and ambient backscatter communications (BackComs) stand out. These technologies enhance wireless access capacity and enable energy-efficient data sharing. In this study, we propose a novel energy-efficient resource allocation scheme for 6G NOMA-assisted BackCom networks. Our network model comprises a central reader (RD) and distributed backscatter devices (BDs) that harvest energy from incident signals to modulate useful data and reflect it toward the RD. To maximize energy efficiency (EE), we formulated a joint optimization problem of channel resource allocation and BDs’ reflection coefficients. However, solving this problem is challenging because of its nonconvexity and system dynamics. To address this issue, we developed a novel deep-learning-based algorithm that leverages the advantages of deep reinforcement learning. During training, we estimated the state components without relying on exact channel state information (CSI), which is computationally expensive. This estimation reduces the communication overhead raised in collecting CSI data. Extensive simulations were conducted to demonstrate the superiority of the proposed scheme. Simulation results show that the proposed scheme notably enhances EE compared to existing benchmarks. Specifically, improvements of approximately 30.3%, 41.7%, 6.0%, and 4.4% were observed when compared to the greedy approach, random approach, deep Q-Network, and successive convex approximation approach, respectively.

Unmanned aerial vehicles (UAVs) have been widely applied in wireless communications because of its high flexibility and line-of-sight transmission. In this study, we develop low-complexity and robust device-to-device (D2D) link scheduling in UAV-assisted industrial-Internet-of-Things (IIoT) networks. First, we propose a sparse convolutional neural network (SCNN) model that uses the geographical map of transmission links as input. The model consists of three main blocks: 1) generic feature filtering, 2) speed–accuracy balancing, and 3) deep feature processing. Unlike other state-of-the-art methods, the proposed SCNN directly processes the geographical map collected using a connected UAV. Second, we propose a deep deterministic policy gradient-based reinforcement learning model that processes the output feature map from the SCNN to optimize the D2D scheduling decision and maximize the achievable system rate in the long run. Extensive simulations revealed that the proposed scheme significantly improved the achievable rate over other benchmark comparison schemes, such as transmitters and receivers density-based deep learning (DL), ResNet-based DL, VGGNet-based DL, random scheduling, and all-active schemes, respectively. The simulations also demonstrated that the proposed scheme reduces computational complexity. With reduced complexity and nearly optimal performance, the proposed solution can be more efficiently applied to large-scale and dense IIoT networks.

Compared to static time-division duplexing (TDD), dynamic TDD (D-TDD) has significantly increased the spectral efficiency of cellular networks. However, conventional systems operate based on exact channel state information, resulting in high communication overhead and delay. Spatial deep learning refers to using spatial geographical information as the training data. This study investigates a spatial deep learning-based D-TDD scheme for 6G hotspot networks. First, we represent geographical location information in forms of traffic demand density grid matrices. Second, we use spatial convolution filters to extract discriminative features of uplink and downlink service gains and harms, taking the traffic demand density grid matrices as the input. Subsequently, extracted feature matrices are processed with sparse convolution blocks to reduce computation cost for the classification. Finally, we develop novel deep dueling neural networks, leveraging the extracted features to efficiently learn the near-optimal radio slot configurations for all base stations. Numerical results show that the proposed approach improves average rate per user by 2.5%, 6%, and 523.3% over those achieved in state-of-the-art centralized D-TDD, the competitive reinforcement learning, and greedy approaches, respectively. In addition, the proposed approach achieves up to 98.7% of the data rate performance of the optimum scheme with an exhaustive search algorithm.

In order to build realistic metaverse systems, enabling high synchronization between physical-space and virtual meta-space is essentially required. For this purpose, this paper proposes a novel system-wide coordination algorithm for high synchronization under characteristics (i.e., highly realistic meta-space construction under the constraints of physical-space). The proposed algorithm consists of the following three stages. The first stage is quantum multi-agent reinforcement learning (QMARL)-based scheduling for low-delay temporal-synchronization using differentiated age-of-information (AoI) during data gathering in physical-space by observers for meta-space construction. This is beneficial for scalability according to action dimension reduction in reinforcement learning computation. The second stage is for creating virtual contents under delay constraints in meta-space based on the gathered data. When rendering regions that have received more user attention, avatar-popularity is considered for spatio-synchronization. Thus, a stabilized control mechanism is designed for time-average reality quality maximization for each region. The last stage is for caching based on avatar-popularity and AoI which can be helpful in constructing low-delay realistic meta-space. Furthermore, the concept of AoI is divided into two separate sub-concepts of physical AoI and virtual AoI such that the AoI in virtual meta-space can be thoroughly implemented.

One of the essential factors for enabling sixth-generation systems is efficiently ensuring diverse quality-of-service (QoS) performance metrics to support the upcoming massive ultra-reliable low-latency communication (URLLC). This work proposes efficient transmission control in intelligent reflecting surface (IRS)-assisted nonorthogonal multiple access (NOMA) networks in the finite blocklength (FBL) regime that statistically guarantee stringent URLLC QoS requirements. Thus, we formulate a nonconvex problem that maximizes the sum effective capacity (SEC) while ensuring statistical delay QoS constraints. To make the problem more tractable, we propose a tight upper bound for the objective function based on Jensen’s inequality and employ the concept of opportunistically minimizing an expectation. Then, we decompose the problem into two subproblems: active beamforming at the base station and phase-shift optimization at the IRS. Each subproblem is convexified by employing slack variables, penalty functions, and linear approximation, and solved using successive convex approximations. The subproblems are iteratively solved until convergence using alternating optimization. The convergence to a suboptimal stationary solution and the computing complexity of the proposed algorithm are rigorously analyzed. Finally, extensive numerical evaluations confirm that the proposed control in the FBL regime significantly improves the SEC under various QoS parameters compared to existing benchmark schemes. In particular, as the number of antennas and IRS elements increases, the proposed method becomes more efficient than the semi-definite relaxation-based approach in terms of complexity and performance.

Federated learning (FL) is a framework for realizing distributed machine learning in an environment where training samples are distributed to each device. Recently, FL has employed over-the-air computation enabling all devices to transmit learning model updates simultaneously. This work proposes a communication-efficient sparse one-bit analog aggregation (SOBAA) method, incorporating new power control, layer-wise scaled one-bit quantization, layer-wise sparsification, and an error-feedback mechanism. We derive a tight upper bound of the expected convergence rate of the proposed SOBAA as a closed-form expression. From this expression, we explicitly identify the relationship between the convergence rate and compression and aggregation errors. Based on the theoretical convergence analysis, we formulate a joint optimization problem of the compression ratio and power control to minimize compression and aggregation errors, leading to the fastest convergence. In each communication round, the optimization problem is decomposed, and solved in a computationally efficient and feasible way. From this solution, we characterize the trade-off between learning performance and communication cost. Through extensive experiments on well-known MNIST and CIFAR-10 datasets, we confirm that the proposed method provides an enhanced trade-off performance between test accuracy and communication costs and a faster convergence rate than the other state-of-the-art methods. In addition, it is proven that the proposed method is more effective for more complex datasets and learning models.

This paper proposes a flexible and efficient access control scheme that combines the orthogonal frequency division multiple access and rate-splitting multiple-access techniques for enhancing the uplink transmission in a digital twin edge network system. We formulate a non-convex mixed integer optimization problem that minimizes the energy consumption of all Internet of Things devices (IoTDs) and maximizes the number of successful IoTD tasks. To this end, we propose a deep reinforcement learning (DRL) framework by normalizing a DRL training algorithm named deep deterministic policy gradient for efficiently designing the variables while ensuring the problem constraints. However, in the inference stage, the proposed DRL method may encounter different devices and services. Therefore, we design an exhaustive-improved DRL method that can improve the proposed DRL effectively using information from a digital-twin module. We also propose a mathematical approximation-based solution employing two convexification approach: Dinkelbach’s method and relaxed Linear Matrix Inequality (LMI). Through extensive simulations over different parameters and scenarios, we identify the polynomial complexity, stable convergence, and operating regime of the proposed solutions. It is also confirmed that the proposed approaches work well even with digital twin defects and provide improved performance in terms of energy consumption and number of successful tasks in comparison with benchmark schemes.

Rate-splitting multiple access (RSMA) and reconfigurable intelligent surface (RIS) techniques show promise in enhancing spectral efficiency in sixth-generation Internet of Things (IoT) networks. However, optimizing the synergy between these two methods is challenging due to the complex and dynamic environment. This study focuses on maximizing the sum-rate metric in RIS-assisted uplink multiantenna RSMA IoT networks to address this problem. We jointly optimized the base station beamforming design, power allocation, and RIS phase shifts to enhance the spectral efficiency with multiple mobile IoT devices present. The controlled parameters are continuous variables and the mathematical problem is nonconcave. Therefore, we formulated the problem as a Markov decision process and used the deep deterministic policy gradient (DDPG) to determine the optimal joint actions. We proposed a safe action shaping process for the decision-making actor network to address constraint violations. Through a rigorous performance evaluation, we demonstrated that the DDPG approach with action shaping outperforms the current DDPG algorithm regarding the maximum achievable sum rate.

Aerial access networks, comprising a hierarchical model of high-altitude platforms (HAPs) and multiple unmanned aerial vehicles (UAVs), are considered a promising technology to enhance the service experience of Internet of Things Devices (IoTD), especially in underserved areas where terrestrial base stations (TBSs) do not exist. In such scenarios, optimally orchestrating the limited computation, communication, and energy resources in both HAPs and UAVs is crucial toward for an efficient aerial networking infrastructure. Thus, in this study, we investigate and formulate the joint IoTDs association, partial offloading, and communication resource allocations (JAPORAs) decisions problem in heterogeneous Aerial Access IoT (AAIoT) networks to maximize service satisfaction for IoTDs, while minimizing their total energy consumption. In particular, the formulated problem is transformed into a multiagent Markov decision process (MAMDP) to deal with its nonconvexity and environmental dynamicity. To solve the problem, we propose a multiagent policy-gradient-based deep actor–critic algorithm, named MADDPG-JAPORA, with centralized training and decentralized execution. Our extensive numerical experiments demonstrated that MADDPG-JAPORA reliably converges and provides superior performance compared with other state-of-the-art schemes.

Recently, network coding has become a promising transmission approach to support high throughput and low latency in distributed multihop networks. In this article, we develop a delay-controlled distributed route establishment scheme that can provide maximal bidirectional transmission to enhance network coding gain while satisfying a time-critical route setup. The scheme is called network coding-aware delayed store and forwarding (NC-DSF). It delays the received route information packets before forwarding them according to the link status and network topology. We propose a tight delay function derived using a strict end-to-end delay bound for delay control. Subsequently, we suggest a relaxed delay function derived using realistic and practical conditions. Finally, we propose a load-weighted delay function considering the tradeoff between bidirectionality and network-load balancing. The simulations confirm that the proposed scheme offers increased throughput and decreased latency in mesh and random multihop networks. The proposed transmission scheme, NC-DSF, can be efficiently employed in the future industrial Internet of Things networks requiring a time-constrained route setup, high throughput, and low latency.

Aerial access infrastructures have been considered a compulsory component of the sixth-generation (6G) networks, where airborne vehicles play the role of mobile access points to service ground users (GUs) from the sky. In this scenario, intelligent reflecting surface (IRS) is one of the promising technologies associated with airborne vehicles for coverage extensions and throughput improvements, a.k.a., flying IRS (F-IRS). This study considers a multiuser multiple-input single-output (MISO) F-IRS system, where the F-IRS reflects downlink signals from ground base stations (BSs) to users located at underserved areas where direct communications are unavailable. To achieve the system sum-rate maximization, we proposed a deep reinforcement learning (DRL) algorithm namedFlyReflectto jointly optimize the flying trajectory and IRS phase shift matrix. First, end-to-end communications from a BS to its GUs via the F-IRS are analyzed to identify environmental and operational factors that impact achievable system sum rate. Subsequently, the system is transformed into a DRL model, which is resolvable by the deep deterministic policy gradient (DDPG) algorithm. To improve the action decision accuracy of the DDPG algorithm, we proposed a mapping function to guarantee that all constraints are satisfied regardless of noise additions in the exploration process. Simulation results showed that our proposed algorithm outperforms state-of-the-art algorithms in multiple scenarios.

The recent expansion of mobile IoT devices (MIoTDs) along with the exposure of many compute-intensive and latency-critical applications, have given a step rise to the mobile edge computing (MEC) platform to process computational microservices at the edge. The paramount importance of designing an effective incentive mechanism is a very important topic for such systems to get a fair amount of resources and provide incentives to MIoDs. Hence, we design a MEC platform with heterogeneous MIoTDs participating in a computational microservice offloading scheme. Here, we propose an incentive approach applying a double auction mechanism to incentivize the involvement of MIoTDs. In practice, the incentive mechanism typically interacts with the demand estimation scheme that estimates the demand profile of MIoTDs. As a result, we design a novel mechanism for microservices –microservice Incentive Service Offloading (mISO), which comprises an incentive approach and a demand estimation scheme. The mISO mechanism holds truthfulness, rationality, and low computational complexity while guaranteeing positive social welfare and generating the optimal demand profiles for MIoTDs. Simulation results showed that mISO provides 18–21$\%$and 25–30$\%$improvements in terms of average latency and resource utilization compared to existing works.

In this study, we consider an energy-efficient and low-complexity transmission control in a SWIPT-NOMA-based green cellular network (GCN) that consists of a green base station (GBS) and green users (GUEs). First, we formulate a non-convex problem that minimizes transmit power consumption while supporting minimum downlink user service rate, downlink data queue stability, and user battery availability. Then, we transform the problem into a Lyapunov-drift-penalty minimization problem, which can determine a new resource allocation scheme that balances transmit power consumption and queue stability. Second, the Lyapunov-drift-penalty problem is decomposed into subchannel assignment, power allocation, and power splitting (PS) ratio control problems. The subchannel assignment problem is solved using a matching theory-based low-complexity algorithm. The power allocation and PS ratio control problems are solved using the alternating optimization (AO) approach and bisection method. This decomposed subproblem-based control also enables distributed control between the GBS and GUEs. Third, we prove the convergence, optimality, and polynomial computation complexity of the proposed algorithm. Lastly, we demonstrate that the proposed control outperforms the benchmark controls regarding transmit power consumption and the achievable rate. Owing to the optimality and low complexity, the proposed control can be efficiently applied to large-scale and distributed GCNs in sixth-generation environments.

In recent years, video has become a tremendous growth of media in the content delivery network (CDN) coupled with the enormous increase of users. However, it leads to new challenges including an explosion of data demand on networking resources, backhaul bottleneck, and many congestions between transmissions in the network. To address these network congestions and minimize the content download latency, an LEO Satellite-based overlay system in CDN is proposed. A Low Earth orbit (LEO) satellite network, where the user equipment (UE) is covered by multiple satellites, is an important solution to the wireless communication network in the future. To ensure the quality of the service of the LEO satellite network, the handover problem and caching problem need to be considered. The paper focuses on a wireless network consisting of various caching nodes to serve users' requests supported by an LEO satellite-based overlay system. Our goal is the maximization the number of UEs covered by a Satellite by considering the location of users and the Received Signal Strength Indicator (RSSI) of users to select the best action. The Deep Reinforcement Learning (DRL) with multi-agent Q learning algorithm and Caching update algorithm with the popularity parameters are proposed to solve Problem statements of our System Model.

Recent studies have shown that rate splitting multiple access (RSMA), which depends on multi-antenna rate splitting (RS) at the transmitter and successive interference cancellation (SIC) at the receivers, successfully controls interference in multi-antenna communication networks. This paper examines RSMA's applicability to video streaming applications in cloud radio access networks (C-RAN). We aim to address a practical challenge to maximize the perceived quality of end users while keeping the delay constraints remained satisfied using RSMA. We propose a learning-based framework to select appropriate video quality together with beamforming vectors according to current defined system state. The simulation figure confirms that the learning behavior of proposed learning scheme is stable.

Mobile chargers (MCs) equipped with radio-frequency (RF)-based wireless power transfer (WPT) modules have been suggested as a possible solution to battery constraints in wireless rechargeable sensor networks (WRSNs). In RF-based WPT, charging efficiency decreases significantly as the charging distance increases. Therefore, single charging consumes less energy than multicharging because it can generally charge a sensor node at a closer range. However, when the density of nodes is high, multicharging may achieve higher efficiency. We propose an energy-efficient adaptive directional charging (EEADC) algorithm that considers the density of sensor nodes to adaptively choose single charging or multicharging. The EEADC exploits directional antennas to concentrate the energy and improve energy efficiency and identifies the optimum charging points and beam directions to minimize energy consumption. In the EEADC, clustering is performed by considering the density of the sensor nodes. After clustering, the clusters are classified into single-charging/multicharging clusters according to the number of sensor nodes in each cluster. Next, the charging strategy is determined according to the type of cluster. In the case of a multicharging cluster, the problem is nonconvex. Therefore, a discretized charging strategy decision (DCSD) algorithm is proposed. The performance evaluation indicates that EEADC outperforms two existing methods in terms of power consumption and charging delay by 10% and 9%, respectively.

To meet the stringent demands of emerging Internet-of-Things (IoT) applications, such as smart home, smart city, and virtual reality in 5G/6G IoT networks, edge content caching for mobile/multiaccess edge computing (MEC) has been identified as a promising approach to improve the quality of services in terms of latency and energy consumption. However, the limitations of cache capacity make it difficult to develop an effective common caching framework that satisfies diverse user preferences. In this article, we propose a new content caching strategy that maximizes the cache hit ratio through flexible prediction in dynamically changing network and user environments. It is based on a hierarchical deep learning architecture: long short-term memory (LSTM)-based local learning and ensemble-based meta-learning. First, as a local learning model, we employ an LSTM method with seasonal-trend decomposition using loess (STL)-based preprocessing. It identifies the attributes for demand prediction on the contents in various demographic user groups. Second, as a metalearning model, we employ a regression-based ensemble learning method, which uses an online convex optimization framework and exhibits sublinear “regret” performance. It orchestrates the obtained multiple demographic user preferences into a unified caching strategy in real time. Extensive experiments were conducted on the popular MovieLens data sets. It was shown that the proposed control provides up to a 30% higher cache hit ratio than conventional representative algorithms and a near-optimal cache hit ratio within approximately 9% of the optimal caching scheme with perfect prior knowledge of content popularity. The proposed learning and caching control can be implemented as a core function of the 5G/6G standard’s network data analytic function (NWDAF) module.

Technological advances in fog computing are precipitating an evolution in conventional vehicle networks to a new paradigm called fog-enabled connected vehicle networks (FCVNs). FCVNs provide communication efficiency for ensuring safe transportation through the massive Internet of vehicles. In FCVNs, massive vehicles tend to associate with roadside units and high power nodes, which act as fog nodes (FNs), when they have a good channel quality and/or popular contents. This circumstance may lead to a load imbalance among the FNs. This problem significantly decreases the resource utilization efficiency and service capability of the networks. In this article, we propose a dynamic resource orchestration (DRO) scheme to harmonize resource allocation for connected vehicles by migrating the offloaded services among FNs. A graph-theoretic approach is utilized to transform the FCVN into a directed graph model, where the maximum resource reduction obtained by service migrations is considered the weight of the link between every two FNs. Subsequently, the maximum weight matching solution is used to determine optimal pairs of FNs for migrating services to maximize network resource utilization. Our simulation results reveal that the proposed DRO scheme achieves significant improvements in terms of service capability, throughput, and resource utilization efficiency as compared with existing algorithms.

Mobile edge computing and nonorthogonal multiple access (NOMA) have been considered as promising technologies that can satisfy rigorous requirements of industrial Internet of Things systems. However, system dynamics, including channel states and computation task requests, may continuously change NOMA decoding order and computation uploading time, making it difficult to reduce latency using conventional highly complex optimization methods. In this article, we investigate a novel scheme that effectively reduces the average task delay to improve the quality of service for all users by jointly optimizing subchannel assignment (SA), offloading decision (OD), and computation resource allocation (CRA). To deal with the high complexity, the original multiserver problem is first decomposed into multiple single-server problems. Subsequently, each single-server problem is decoupled into CRA and SA/OD subproblems. Using convex optimization, a closed-form solution is derived for the optimal CRA action. Concurrently, the optimal SA/OD action is obtained using a distributed multiagent deep reinforcement learning algorithm. Simulation results reveal that the proposed scheme significantly outperforms the state-of-the-art schemes. In particular, it reduces the action decision duration by 30 times while achieving a near-optimal performance of up to 97% of the optimum under the exhaustive search scheme.

Intelligent reflecting surface (IRS) is a promising 6G technology that can improve wireless communication capacity in a cost-effective and energy-efficient manner, by adjusting a large number of passive reflectors to appropriately change the signal propagation. In this study, we identified the achievable rate region of a two-hop interference channel with distributed multiple IRS relays. To do so, we formulated a non-convex problem that characterizes the rate-profile, and found its solution using successive convex approximation (SCA). We then proposed an alternating direction method of multipliers (ADMM) and alternating optimization (AO) based distributed and low-complex IRS control that maximizes the achievable sum-rate, and proved its convergence and optimality. We then compared the proposed IRS control with semi-definite relaxation (SDR)-, random phase-, deep reinforcement learning (DRL)- based IRS controls, and optimal amplify-and-forward (AF)-, interference neutralization (IN)-, and decode-and-forward (DF) based relaying schemes. We demonstrated that the proposed control with multiple IRS elements outperforms the benchmark controls in terms of the achievable rate region, achievable sum-rate, and energy efficiency under same power budget. We also confirmed that the discrete phase approximation of the proposed control provides near-optimal performance with fewer bits, and the proposed control is robust under imperfect CSI condition. The proposed controls can be efficiently applied to large-scale multi-pair multihop device-to-device and machine-type device communications in the interference-limited or low-powered dense networks of 5G and 6G environments.

To prolong the battery lifetime of Internet-of-Things (IoT) devices, they can be charged by a mobile charger (MC) equipped with radio frequency (RF)-based wireless power transfer (WPT) capability. By concentrating power toward IoT devices, the energy efficiency of the MC increases when using a directional antenna instead of an omnidirectional counterpart. However, directional antennas have a narrow beamwidth, and thus, several IoT devices cannot be charged simultaneously. In this article, we propose a multiple-directional MC (MDMC) scheme that exploits multiple-directional beams to reduce the charging delay while maintaining the advantages of directional antenna with a higher charging efficiency. In our MDMC, an MC determines its charging points to visit and the directions of its beams at each charging point. The charging points can be determined in consideration of the distribution of the devices and the remaining energy. After selecting charging points, the travel paths are determined by considering the remaining energy of the IoT devices. In addition, to relax the problem complexity, we propose an efficient two-stage multiple-directional beam selection (MDBS) algorithm. In the first stage, the directions of beams are determined. The second stage calculates the charging time of each beam for minimizing delay. The simulation results show that the MDMC outperforms the existing single-directional antenna-based charging schemes in terms of the energy efficiency and charging delay approximately 15% and 25%, respectively.

Mobile-edge computing (MEC) and nonorthogonal multiple access (NOMA) have been regarded as promising technologies for beyond fifth-generation (B5G) and sixth-generation (6G) networks. This study aims to reduce the computational overhead (weighted sum of consumed energy and latency) in a NOMA-assisted MEC network by jointly optimizing the computation offloading policy and channel resource allocation under dynamic network environments with time-varying channels. To this end, we propose a deep reinforcement learning algorithm named ACDQN that utilizes the advantages of both actor-critic and deep Q-network methods and provides low complexity. The proposed algorithm considers partial computation offloading, where users can split computation tasks so that some are performed on the local terminal while some are offloaded to the MEC server. It also considers a hybrid multiple access scheme that combines the advantages of NOMA and orthogonal multiple access to serve diverse user requirements. Through extensive simulations, it is shown that the proposed algorithm stably converges to its optimal value, provides approximately 10%, 27%, and 69% lower computational overhead than the prevalent schemes, such as full offloading with NOMA, random offloading with NOMA, and fully local execution, and achieves near-optimal performance.

Future wireless and mobile network services must accommodate highly dynamic downlink and uplink traffic asymmetry. To fulfill this requirement, the third-generation partnership project (3GPP) introduced the enhanced interference mitigation and traffic adaptation strategy in addition to dynamic time division duplexing (TDD). In this study, we develop a reinforcement learning (RL)-based dynamic TDD framework that effectively controls interference and serves various traffic demands. First, we introduce an interference-penalty model that evaluates interference indirectly based on the duplexing policy. This can significantly reduce overhead for measuring and exchanging channel information in a dense network. Second, we design a new mixed-reward model that consists of the achievable data rate and the implicit interference penalty. Third, we implement deep RL algorithms that base station (BSs) use to train their radio frame configurations (RFCs). The training process at each BS takes into account the traffic demand and the RFCs of the surrounding BSs. The BSs are coordinated in a single-leader multi-follower Stackelberg game, which achieves a global RFC setup that maximizes the data rate and minimizes the interference. Extensive simulations show that the proposed framework stably converges in various environments and provides near-optimal performance equivalent to 95% or more of the full-search-based optimal performance, which is 48.84%, 41.92%, and 62.11% higher than the currently utilized random RFC, fixed RFC, and traffic-matched RFC approaches.

This article proposes a novel multiagent deep reinforcement learning-based algorithm which can realize federated learning (FL) computation with Internet-of-Underwater-Things (IoUT) devices in the ocean environment. According to the fact that underwater networks are relatively not easy to set up reliable links by huge fading compared to wireless free-space air medium, gathering all training data for conducting centralized deep learning training is not easy. Therefore, FL-based distributed deep learning can be a suitable solution for this application. In this IoUT network (IoUT-Net) scenario, the FL system needs to construct a global learning model by aggregating the local model parameters that are obtained from individual IoUT devices. In order to reliably deliver the parameters from IoUT devices to a centralized FL machine, base station like devices are needed. Therefore, a joint cell association and resource allocation (JCARA) method is required and it is designed inspired by multiagent deep deterministic policy gradient (MADDPG) to deal with distributed situations and unexpected time-varying states. The performance evaluation results show that our proposed MADDPG-based algorithm achieves 80% and 41% performance improvements than the standard actor–critic and DDPG, respectively, in terms of the downlink throughput.

Internet of Things (IoT) systems are characterized by highly automated operating environments, which comprise several IoT end devices (IDs) that generate vast amounts of data with strict real-time communication and high data rate requirements. Edge computing facilities are an alternative to traditional cloud computing and support massive data processing in IoT systems while reducing the burden on data centers. In this paper, we consider an edge-based IoT system that comprises an edge server (ES), edge gateways (EGs), and IDs that communicate wirelessly. The EGs reduce the load on the ES by preprocessing data received from ID. However, it may not be possible for a few EGs to accommodate a sheer number of IDs, given the limited computing power and communication coverage of the EGs. Therefore, it is necessary for a few IDs to directly connect to the ES without the support of EGs. Thus, we propose a resource orchestration scheme between EGs and ES and/or among EGs based on a Lagrangian and the Karush-Kuhn-Tucker condition. The scheme allocates optimal resources by considering the computing capacities of EGs and ES and manages interference among the EGs to maximize the efficiency of IoT systems. The performance evaluation indicates that the proposed scheme outperforms the existing schemes in terms of aggregate throughput, latency, data reception rate, and workload fairness among EGs by 42%, 59%, 37%, and 40%, respectively.

With the rapid evolution of electric systems, there has been a significant demand for energy Internet (EI) systems that allow sustainable and environmentally friendly energy management. Several research efforts regarding EI systems have been aimed at providing reliable, efficient, and cost-effective techniques. In this paper, we propose a novel algorithm and system for real-time electricity pricing and scheduling. Our algorithm consists of a two-stage operation. The first stage performs real-time pricing to determine the maximum electricity consumption while the second stage performs Internet of Things (IoT) device scheduling. In the second stage, the optimization framework for scheduling is modeled as a 0–1 Knapsack problem; therefore, the solutions to the optimization problem are computed using a dynamic programming framework. Through intensive simulations with well-defined parameters, it is verified that the proposed scheme provides several features, especially reductions in electricity bills with the appropriate parameter settings.

A barrier-coverage has attracted much interests of researchers because it can guarantee to detect any movement of mobile objects. Also, thanks to recent advancement of technology, it is possible to recognize human emotion by facial expression and human motion or activity. Then, the emotion recognition can be applied to various services and applications appropriately. Recently, it has been developed to sense emotion by wireless signal. One of issues for emotion recognition is to increase the recognition accuracy. In this paper, we introduce a new type of barrier, virtual emotion barrier, which is able to detect emotion by devices with wireless signal in Internet of Things (IoT) environment. Then, we formally define a problem whose objective is to construct virtual emotion barrier in the given area including IoT devices such that the detection accuracy of emotion by virtual emotion barrier is maximized. To solve the problem, we propose a greedy-emotion-accuracy approach. Moreover, we discuss future issues and possible research directions for virtual emotion barrier.

In industrial Internet of Things (IIoT) applications for smart manufacturing system, efficient allocation of the carrier and computing resources is crucial. However, existing resource assignment schemes in smart manufacturing system cannot provide timely provision of resources to the inherently dynamic and bursty user demands. To reflect real-time supply and demand for smart manufacturing resources, several research results on auction-style resource assignments have been introduced; however, security, privacy, and trust computing related issues are not actively discussed in the results. The resources should be assigned to devices according to the system policy, which depends on the information provided by IIoT devices. If there are any resource demanding devices, they can report manipulated malicious information for their own interest to obtain more resources. That is, the smart manufacturing system may be vulnerable due to selfish smart manufacturing devices’ behaviors. This reduces the efficiency of the entire system and moreover ceases the plant-wide process. While many research contributions related to the trust computing aim at detecting malicious nodes, this paper presents a novel view of trust computing by showing why devices inside the smart manufacturing system have to act honestly. In this paper, a Vickrey–Clarke–Groves auction-based hierarchical trust computing algorithm is proposed for: 1) computing carrier resources required for wireless communication between IIoT devices and gateways and 2) distributing CPU resources for processing data at central processing controller. Last, simulation results demonstrate that the utilities of each participant are maximized when the IIoT devices and gateways are trustful.

Internet of Things (IoT) technology has accelerated various industries through digital transformation. In an edge computing-based smart factory, a significant number of IoT devices generate large volumes of real-time data. This big data requires efficient routing among edge gateways (EGs) and an edge server for real-time data processing. Existing industrial wireless communication systems provide relatively low data rates and network capacity for real-time sensor data and control information over a wireless channel. This calls for the use of the very large bandwidth available at the mmWave spectrum for real-time data transmission. Existing data routing techniques for the mmWave band are based on traditional mobile ad hoc routing techniques and do not reduce the transmission delay for real-time sensory data in smart manufacturing systems. Therefore, to alleviate the real-time data processing requirement, we propose a new directional routing and link scheduling algorithm based on maximum weight independent set (MWIS). The proposed algorithm solves complicated MWIS problems efficiently and computes backhaul link scheduling results in a relatively short time by lowering the deafness problem among EGs. For transmission fairness, we used a Jain's fairness index method with numerical analysis of the transmission fairness constraint. We measured the efficiency of our proposed scheme in terms of throughput, delay, packet loss rate, and transmission fairness. Our simulation results show that the proposed scheme outperforms existing mmWave routing techniques. Moreover, we investigated the performance difference between the proposed algorithm and the optimal solution.

The Internet of Things (IoT) is expected to play an important role in the construction of next generation mobile communication services, and is currently used in various services. However, the power-hungry battery significantly limits the lifetime of IoT devices. Among the various lifetime extension techniques, this paper discusses mobile charging, which enables wireless power transfer based on radio frequency with mobile chargers (MCs). MCs function as traveling target IoT networks that provide energy to battery-operated IoT devices. However, MCs with an energy-constrained battery result in limitation of travel-time. This paper formulates a problem to minimize energy consumption for charging IoT devices by determining the path of motion of an MC and efficient charging points, and proves that the problem is NP-hard. An efficient algorithm, named best charging efficiency (BCE), is proposed to solve the problem and the upper bound of the BCE algorithm is guaranteed using the duality of linear programming. In addition, an improved BCE algorithm called branching second best efficiency algorithm with additional searching techniques is introduced. Finally, this paper analyzes the difference in performance among the proposed algorithms, optimal solutions, and the existing algorithm and concludes that the performance of the proposed algorithm is near optimal, within 1% of difference ratio in terms of charging efficiency and delay.

The next-generation mobile network anticipates integrated heterogeneous fronthaul and backhaul technologies referred to as a unified crosshaul architecture. The crosshaul enables a flexible and cost-efficient infrastructure for handling mobile data tsunami from dense Internet of things (IoT). However, stabilization, energy efficiency, and latency have not been jointly considered in the optimization of crosshaul performance. To overcome these issues, we propose an orchestration scheme referred to as the stabilized green crosshaul orchestration (SGCO). SGCO utilizes a Lyapunov-theory-based drift-plus-penalty policy to determine the optimal amount of offloaded data that should be processed either at the eastbound or westbound computing platforms to minimize energy consumption. To achieve system stability, the cache buffer is considered as the main constraint in developing the optimization process. Moreover, the amount of offloaded data transmitted via crosshaul links is selected by adopting the binary min-knapsack problem. Accordingly, a lightweight heuristic algorithm is proposed. As the cache buffer is stabilized and the computations are controlled, the SGCO ensures adjustable computing latency threshold for various IoT services. The performance analysis shows that the proposed SGCO scheme exposes effective energy consumption compared to other existing schemes while maintaining system stability considering latency.

Most previous spectrum sensing techniques use omni-directional antennas. Unlike omni-directional antennas, the use of directional antennas for spectrum sensing is a promising technique that can realize fine-grained sensing for the primary user (PU) with a longer sensing range. In this paper, we propose a centralized cooperative directional sensing technique for cognitive radio networks. We assume that one secondary coordinator called the fusion center (FC), gathers sensing results from secondary nodes. Using the reported information, the FC optimizes the sensing period, sensing power, and sensing beams per secondary node. For optimization, we use a modified gradient descent method with numerical methods to solve the nonlinear optimization problem. The simulation results show that our directional spectrum sensing technique is well suited for the existing cognitive radio environment. The optimal scheme shows proposed here better performance in all simulation factors than the non-optimized scheme.

With the current state of development in demand response (DR) programs in smart grid systems, there have been great demands for automated energy scheduling for residential customers. Recently, energy scheduling in smart grids have focused on the minimization of electricity bills, the reduction of the peak demand, and the maximization of user convenience. Thus, a user convenience model is proposed under the consideration of user waiting times, which is a nonconvex problem. Therefore, the nonconvex is reformulated as convex to guarantee optimal solutions. Moreover, mathematical formulations for DR optimization are derived based on the reformulated convex problem. In addition, two types of pricing policies for electricity bills are designed in the mathematical formulations, i.e., real-time pricing policy and progressive policy. With real-time pricing policy, convexity is guaranteed whereas progressive policy cannot. Then, heuristic algorithms are finally designed for obtaining approximated optimal solutions in progressive policy.

In this paper, we consider a downlink heterogeneous cellular network (HCN) where K tiers operate in a common spectrum and differ in terms of transmit power, target data rate, and base station (BS) density. We employ a repulsive cell activation (or planning) in the HCN by ensuring a minimum separation distance between interfering BSs in each tier. We consider a modified Matern hardcore process (MHP) for rendering a minimum separation distance between the BSs, which is realized by outweighing random BS distribution for closed and open access networks. Repulsive cell activation not only improves the coverage probability but also plays a role in balancing per-cell loads effectively according to varying user density. Assuming a finite BS capacity in terms of a limited number of per-cell users, we point out the importance of a relative BS density control between the tiers, and propose a tier-wise density and power control by introducing a load factor for configuring a HCN distributively while satisfying per-tier user throughput constraints given user density.

In this paper, we consider a two-tier heterogeneous cellular network (HCN) where macrocells and distributed low power cells, namely daughtercells, are operated in a common spectrum. Due to the ad-hoc nature of daughtercell BS deployments such as pico and femto cells, the mutual interference varies and obviously the coverage probability behaves differently in terms of transmit powers and densities of macrocells and daughtercells. In this paper, we employ repulsive cell activation in the interfering daughtercell network and see the impact of a minimum separation distance between the daughtercell BSs in terms of coverage under open access and power efficiency. The control of the minimum separation distance plays a role in balancing cell load effectively according to changing user density and is justified for the coexistence of low power daughtercells. The optimal minimum separation distance in terms of user density and target per-tier user throughput requirements is found by a numerical search based on a simple bisection method. Numerical results show the benefit of cell repulsion in terms of increased user density support and less area power consumption.

In this paper, we investigate the impact of relay cooperation for maintaining coverage area against aggregate interference from incumbent underlaid interferers. We employ a guard zone for uplink so that non-urgent interferers are inhibited and urgent ones are admitted with a controlled access probability as long as the primary receiver can tolerate. Numerical results show the outage probability of the primary relay network with the guard zone and that a desired quality-of-service (QoS) determines the access probability of urgent interferers depending on interfering node density.

In relay communications, the relay is in cooperation without doubt at a price of coordination signaling such as synchronization and associated channel state information (CSI) acquisition. Distributed relay selection based on average channel gain information available at the relays is preferred from the perspective of the cost of coordination signaling only if the link reliability can be maintained. In this paper, we propose a simple distributed scheme for relay selection such that a set of relays that satisfies QoS constraints is only activated and one of them is selected randomly from an opportunistic set of QoS relays. The proposed scheme avoids unnecessary power consumption caused by non-beneficial relay cooperation and performs comparable to the centralized best relay selection while it minimizes signaling overheads.

This paper introduces a new IEEE standard, IEEE 802.15.5,which provides mesh capability for wireless personal area network (WPAN) devices. The standard provides an architectural framework enabling WPAN devices to promote interoperable, stable, and scalable wireless mesh topologies. It is composed of two parts: low-rate WPAN mesh and high-rate WPAN mesh. In this paper, we present only low-rate WPAN mesh because it is designed to support wireless sensor networks. IEEE 802.15.5 low-rate part is a light-weight scalable mesh routing protocol that caters well to the requirements of resource-constrained wireless sensor networks. By binding logical addresses to the network topology, IEEE 802.15.5 obviates the need for route discovery. This eliminates the initial route discovery latency, saves storage space and reduces the communication overhead and energy consumption. A distributed link state scheme is further built atop the block addressing scheme to improve the quality of routes, robustness, and load balancing. The routing scheme scales well with regard to various performance metrics. The standard also provides enhanced functions such as multicast, reliable broadcast, power saving, time synchronization, route tracing and portability. We also present the performance evaluation of major functions performed with a 50-nodes tested deployed over a whole floor (100 × 140 ft2) at CUNY Engineering building. The results testify that the IEEE 802.15.5 will serve well for wireless personal area networks and wireless sensor networks.

Monitoring power consumption of a running system is a first step to determine where to take an effort on power optimization to minimize unnecessary idle time in available operation modes. Coordinating hardware and software architecture in a certain block or module to extend its lifetime requires system-wide power breakdown profiling, which then validates applicable power saving techniques because of a trade-off between cost and benefit. Experimental results show that with a commercial 3G WCDMA handset, a multi-channel power monitoring system gathers real-time traces of power consumption of the WCMDA MSM6250 chipset platform under several different conditions, and generates comprehensive power breakdown analysis to verify power saving techniques.

In order to improve the network management ability, an important issue is to design a network survivability management system as an independent function. This paper proposes new structure to preplan and download different response procedures for every network element at different layers, according to overall information of the network. Cooperation among different restoration techniques in different layers is performed by a hybrid escalation mechanism, which is based on the sequential activation of different restoration techniques. General rules and timing issues are also discussed in detail. The design is based on multi-agent framework and describes the cooperation methods for multi-agent system considering the characteristics of network management function

Normal TCP performs well in wired networks. However, when employed in ad hoc wireless networks, its loss-based congestion window progression causes high network buffer utilization due to large bursts of data, which degrades the network bandwidth utilization. This paper argues that the reactive congestion window progression (based on duplicate ACK and timeouts) should be not used in ad hoc networks and proposes a receiver-oriented rate controller (rater), with a congestion window delimiter for the IEEE 802.11 MAC protocol, instead. In addition, the transient nature of medium availability due to medium contention is addressed by a freezing timer (freezer) at the receiver, which freezes the sender whenever heavy contention is perceived. Finally, ad hoc sender enhancements are proposed for optimizing the performance of the receiver-end, as an optional deployment. The new ad hoc TCP demonstrates outstanding results in terms of goodput, as well as throughput.

As in terrestrial reliable multicasting, feedback implosion is a major problem in satellite multicast. The feedback implosion occurs when a large number of receivers sends their feedback to the satellite. A poker-game-based feedback suppression (PFS) algorithm is proposed for scalable satellite reliable multicast protocols. An analytical model is provided for the feedback suppression performance of the PFS scheme. This model is validated by simulation results. Numerical examples show that feedback can be effectively suppressed by introducing the PFS algorithm.

This paper presents an adaptive forward error correction (AFEC) protocol that provides a reliable communication service for real-time traffic over low-earth orbit (LEO) satellite networks. In time-varying wireless links, such as LEO satellite networks. A reliable channel estimation scheme with an appropriate code selection technique is essential for adaptive error-control systems. This paper proposes a new channel estimation scheme that uses receiver-initiated messages (ACKs, NAKs, and INCs). These messages feed back to the transmitter which then selects the code rate for sending packets. In addition, the scheme uses the concept of a dynamic transmittable code set by adjusting the maximum code rate in the set of possible codes from which the transmitter can select. In terms of throughput (from network provider's viewpoint) and packet error rate (from subscriber's viewpoint), performance results show that the proposed scheme guarantees the quality of service (QoS) requirements of real-time applications.

The geographical connection admission control (GCAC) algorithm is introduced for low Earth orbit (LEO) satellite networks. The GCAC scheme estimates the future handover blocking probability of a new call attempt based on the user location database, in order to decrease the handover blocking. By simulation, it is shown that the proposed GCAC scheme guarantees the handover blocking probability to a predefined target level. Since GCAC algorithm utilizes the user location information, performance evaluation shows that this technique also guarantees the target level of handover blocking probability in the nonuniform traffic pattern.

This paper presents a rate-adaptive error control (RAEC) protocol for multimedia multicast services in satellite-terrestrial hybrid networks. The proposed scheme considers user fairness to a multicast group and adapts the code rate according to the channel conditions. In order to provide fairness perceived by user application, a packet is transmitted via satellite, while the retransmission of the packet is carried out over either terrestrial links or satellite link based on the number of negative acknowledgments of the packet. For code rate adaptation, a code is chosen adaptively based on the estimated channel condition. In the RAEC protocol, the transmitter uses a combination of forward and backward channel estimation. The throughput performance shows that the proposed RAEC protocol outperforms the static hybrid ARQ protocol under different channel conditions.

This paper presents an adaptive error control (AEC) scheme for multimedia applications in integrated terrestrial-satellite wireless networks. The AEC protocol supports both real-time and non-real-time applications. In the AEC protocol, we propose new adaptive FEC (AFEC) and hybrid ARQ (HARQ) schemes for real-time and non-real-time traffic, respectively. Throughput performance for non-real-time application shows that the proposed AEC protocol outperforms hybrid ARQ (HARQ) protocols with the same code used. Under real-time application, the AEC protocol outperforms the static FEC (SFEC) protocols with respect to packet miss probability.

Hybrid satellite-terrestrial networks are becoming increasingly popular for asymmetric multimedia services such as file transfer and database services. This paper presents an adaptive error control (AEC) protocol for the hybrid network, in which the transmitter uses a combination of automatic repeat request (ARQ) over terrestrial links and forward error control (FEC) scheme with adaptive code rate over satellite links. Throughput performance shows that the proposed AEC protocol adapts to a time-variant satellite channel better than the static hybrid ARQ protocol under different satellite channel conditions.