This paper investigates a novel spectral-efficient design for a multi-Unmanned Aerial Vehicle (UAV) system assisted by Reconfigurable Intelligent Surfaces (RIS) within the emerging Integrated Sensing and Communication (ISAC) framework. The proposed system leverages uplink Non-Orthogonal Multiple Access (NOMA) and RIS-enhanced multi-UAV collaboration to jointly serve mobile users and perform target sensing. A hierarchical double-timescale solution is introduced, combining an adaptive Affinity Propagation Clustering (APC) approach for long-term UAV deployment and user-target association, with a short-term iterative algorithm for optimizing user transmit power, UAV beamforming, and RIS phase shifts. To tackle the non-convex optimization problem, a solution is proposed, leveraging Lagrangian dual transform, fractional programming, and minorization methods. Simulation results validate the effectiveness of the proposed approach, demonstrating significant improvements in both communication data rates and sensing information rates compared to existing benchmarks.

Decentralized Federated Learning (DFL) is recognized as a key paradigm for training models in resource-constrained, privacy-sensitive Internet of Things (IoT) environments. However, its real-world deployment is hindered by device heterogeneity, limited resources, and unpredictable node trustworthiness. To address these challenges, an innovative framework, namely Reinforcement Learning-driven Utility-based Decentralized Hierarchical Federated Learning (RL-UDHFL), is proposed, in which Reinforcement Learning (RL) is leveraged for adaptive optimization across three tiers: edge, coordination, and global aggregation. At the edge, participants are selected through an RL-Driven Participant Selection mechanism (RL-AUDPS), based on a utility function that accounts for computational resources, energy, data quality, and reputation. At the coordination level, self-tuning adaptive clustering is applied and a trust-aware gossip protocol is employed to enable robust inter-cluster communication. At the global level, reputation-based weighting is utilized and on-the-fly anomaly detection is performed to ensure model integrity. Through extensive simulations, it is demonstrated that RL-UDHFL achieves a model accuracy of 98%, surpassing hierarchical benchmarks such as HAFedRL (93.5%) and T-FedHA (92%). This superior performance is attributed to the framework’s capability to balance high accuracy, efficient resource utilization, and system reliability, thereby providing a scalable and robust blueprint for deploying sustainable and trustworthy learning systems in complex IoT applications.

This paper investigates energy-efficient federated learning (FL) in multiple-input multiple-output (MIMO) edge-enabled Internet of Things (IoT) networks, where user equipments (UEs) are enabled with simultaneous wireless information and power transfer (SWIPT) capabilities. To jointly optimize communication and computation resources, a hierarchical optimization framework is proposed to minimize the total effective energy consumption per global FL round, while satisfying latency, power, frequency, power-splitting, and local accuracy constraints. By exploiting the time-scale separation between wireless resource allocation and learning accuracy adaptation, the resulting non-convex problem is decomposed into a short-term convexified subproblem for communication and computation resource optimization, solved via successive convex approximation (SCA), and a long-term subproblem for local accuracy updates, addressed using coordinate descent (CD). The proposed algorithm ensures convergence to a stationary solution with polynomial complexity, achieving significant computational savings compared to exhaustive search. Simulation results verify that the proposed framework achieves substantial energy consumption reduction and faster convergence compared with the benchmark schemes. Furthermore, results demonstrate that increasing the base station (BS) antenna array or the energy harvesting efficiency enhances the network sustainability and scalability of FL for energy-constrained IoT devices.

Early diagnosis and treatment of polyps during colonoscopy are essential for reducing the incidence and mortality of Colorectal Cancer (CRC). However, the variability in polyp characteristics and the presence of artifacts in colonoscopy images and videos pose significant challenges for accurate and efficient polyp detection and segmentation. This paper presents a novel approach to polyp segmentation by integrating the Segment Anything Model (SAM 2) with the YOLOv8 model. Our method leverages YOLOv8’s bounding box predictions to autonomously generate input prompts for SAM 2, thereby reducing the need for manual annotations. We conducted exhaustive tests on five benchmark colonoscopy image datasets and two colonoscopy video datasets, demonstrating that our method exceeds state-of-the-art models in both image and video segmentation tasks. Notably, our approach achieves high segmentation accuracy using only bounding box annotations, significantly reducing annotation time and effort. This advancement holds promise for enhancing the efficiency and scalability of polyp detection in clinical settings https://github.com/sajjad-sh33/YOLO_SAM2.

Using massive datasets, foundation models are large-scale, pre-trained models that perform a wide range of tasks. These models have shown consistently improved results with the introduction of new methods. It is crucial to analyze how these trends impact the medical field and determine whether these advancements can drive meaningful change. This study investigates the application of recent state-of-the-art foundation models—DINOv2, MAE, VMamba, CoCa, SAM2, and AIMv2—for medical image classification. We explore their effectiveness on datasets including CBIS-DDSM for mammography, ISIC2019 for skin lesions, APTOS2019 for diabetic retinopathy, and CHEXPERT for chest radiographs. By fine-tuning these models and evaluating their configurations, we aim to understand the potential of these advancements in medical image classification. The results indicate that these advanced models significantly enhance classification outcomes, demonstrating robust performance despite limited labeled data. Based on our results, AIMv2, DI-NOv2, and SAM2 models outperformed others, demonstrating that progress in natural domain training has positively impacted the medical domain and improved classification outcomes. Our code is publicly available at https://github.com/sajjad-sh33/Medical-Transfer-Learning.

ABSTRACT The cell‐free massive multiple input multiple output (CF‐mMIMO) approach, due to its high coverage and the ability to attenuate the large‐scale fading impacts in wireless communications, has drawn a lot of attention. Additionally, because of their movement ability, low power, and low‐cost employed infrastructures, unmanned aerial vehicles (UAVs) are considered a promising technology to provide service on demand in various applications, deployed as either base stations (BSs) or user equipment (UEs). This paper considers a UAV‐equipped CF‐mMIMO wireless network, assuming millimetre‐wave (mmWave) connections between UAV‐BSs and ground users. Leveraging the additive quantisation noise model (AQNM), closed‐form expressions for the uplink data rate and energy efficiency (EE) under maximum ratio combining (MRC) detection are obtained. The impacts of effective parameters, including the UAV's altitude, the number of antennas, and the resolution of analogue‐to‐digital converters (ADCs), on system performance are investigated. Simulation results demonstrate that EE can be optimised for these factors to achieve the maximum value. In addition, the optimal number of quantisation bits to maximise EE is based on the number of antennas and the height of the UAVs. Comparing analytical results with accurate ones shows the accuracy of our approximations due to the same trend of variations.

ABSTRACT With the increasing demand for high‐quality, computing‐intensive mobile services, the next‐generation networks must provide users with instantly available and sufficient computational resources. As a promising solution to this challenge, Unmanned Aerial Vehicle‐assisted Mobile Edge Computing (UAV‐assisted MEC) has gained significant attention in recent years. However, due to the limited energy available to the user equipment and UAVs, minimising the energy consumption remains a significant challenge. This article tackles the problem of energy‐efficient joint resource management and UAV trajectory optimisation in such networks by incorporating the Non‐Orthogonal Multiple Access (NOMA). To solve this non‐convex optimisation problem, it is divided into two sub‐problems, and the optimal solution to the main problem is then obtained by iteratively solving these two sub‐problems. According to the simulation results, incorporating Non‐Orthogonal Multiple Access (NOMA) method achieves a significant reduction of 44.44% in the overall utility function of the optimisation problem.

Federated Learning (FL) is a decentralized approach that allows for collaborative training of Machine Learning (ML) models across multiple local nodes, ensuring data privacy and security while leveraging diverse datasets. Conventional FL, however, is susceptible to gradient inversion attacks, restrictively enforces a uniform architecture on local models, and suffers from model heterogeneity (model drift) due to non-IID local datasets. To mitigate some of these challenges, the new paradigm of Federated Knowledge Distillation (FKD) has emerged. FKD is developed based on the concept of Knowledge Distillation (KD), which involves extraction and transfer of a large and well-trained teacher model’s knowledge to lightweight student models. FKD, however, still faces the model drift issue. Intuitively speaking, not all knowledge is universally beneficial due to the inherent diversity of data among local nodes. This calls for innovative mechanisms to evaluate the relevance and effectiveness of each client’s knowledge for others, to prevent propagation of adverse knowledge. In this context, the paper proposes Effective Knowledge Fusion (KnFu) algorithm that evaluates knowledge of local models to only fuse semantic neighbors’ effective knowledge for each client. The KnFu is a personalized effective knowledge fusion scheme for each client, that analyzes effectiveness of different local models’ knowledge prior to the aggregation phase. In this context, closeness of clients’ knowledge is measured by estimating the class distributions of local datasets based on the transmitted localized knowledge. Comprehensive experiments were performed on MNIST and CIFAR10 datasets. KnFu outperforms its FL-based, FKD-based, and local training baselines in scenarios where the clients have small datasets with intermediate degree of heterogeneity. KnFu’s source code is accessible throught the following link: https://github.com/jamal94sm/KnFu-Effective-Knowledge-Fusion.git.

Coded/uncoded content placement in Mobile Edge Caching (MEC) has evolved as an efficient solution to meet the significant growth of global mobile data traffic by boosting the content diversity in the storage of caching nodes. To meet the dynamic nature of the historical request pattern of multimedia contents, the main focus of recent researches has been shifted to develop data-driven and real-time caching schemes. In this regard and with the assumption that users’ preferences remain unchanged over a short horizon, the Top-K popular contents. These contents refer to the most requested content in the upcoming period. Most existing data-driven popularity prediction models, however, are not suitable for the coded/uncoded content placement frameworks. On the one hand, in coded/uncoded content placement, in addition to classifying contents into two groups, i.e., popular and non-popular, the probability of content request is required to identify which content should be stored partially/completely, where this information is not provided by existing data-driven popularity prediction models. On the other hand, the assumption that users’ preferences remain unchanged over a short horizon only works for content with a smooth request pattern. To tackle these challenges, we develop a Multiple-model (hybrid) Transformer-based Edge Caching (MTEC) framework with higher generalization ability, suitable for various types of content with different time-varying behavior, that can be adapted with coded/uncoded content placement frameworks. In this work, we consider Top-K content as the output of the 1st Stage of the proposed MTEC framework, which includes both popular and mediocre content. Simulation results corroborate the effectiveness of the proposed MTEC caching framework in comparison to its counterparts in terms of the cache-hit ratio, classification accuracy, and the transferred byte volume.

Mobile-edge caching (MEC) integrated with deep neural networks (DNNs) is an innovative technology with significant potential for the future generation of wireless networks, resulting in a considerable reduction in users’ latency. The mobile-edge caching (MEC) network’s effectiveness, however, heavily relies on its capacity to predict and dynamically update the storage of caching nodes with the most popular contents. To be effective, a DNN-based popularity prediction model needs to have the ability to understand the historical request patterns of content, including their temporal and spatial correlations. Existing state-of-the-art time-series DNN models capture the latter by simultaneously inputting the sequential request patterns of multiple contents to the network, considerably increasing the size of the input sample. This motivates us to address this challenge by proposing a DNN-based popularity prediction framework based on the idea of contrasting input samples against each other, designed for the unmanned aerial vehicle (UAV)-aided MEC networks. Referred to as the contrastive learning-based survival analysis (CLSA), the proposed architecture consists of a self-supervised contrastive learning (CL) model, where the temporal information of sequential requests is learned using a long short-term memory (LSTM) network as the encoder of the CL architecture. Followed by a survival analysis (SA) network, the output of the proposed CLSA architecture is probabilities for each content’s future popularity, which are then sorted in descending order to identify the Top-$K$popular contents. Based on the simulation results, the proposed CLSA architecture outperforms its counterparts across the classification accuracy and cache-hit ratio.

Mobile Edge Caching (MEC) is a revolutionary technology for the Sixth Generation (6G) of wireless networks with the promise to significantly reduce users’ latency via offering storage capacities at the edge of the network. The efficiency of the MEC network, however, critically depends on its ability to dynamically predict/update the storage of caching nodes with the top-K popular contents. Conventional statistical caching schemes are not robust to the time-variant nature of the underlying pattern of content requests, resulting in a surge of interest in using Deep Neural Networks (DNNs) for time-series popularity prediction in MEC networks. However, existing DNN models within the context of MEC fail to simultaneously capture both temporal correlations of historical request patterns and the dependencies between multiple contents. This necessitates an urgent quest to develop and design a new and innovative popularity prediction architecture to tackle this critical challenge. The paper addresses this gap by proposing a novel hybrid caching framework based on the attention mechanism. Referred to as the parallel Vision Transformers with Cross Attention (ViT-CAT) Fusion, the proposed architecture consists of two parallel ViT networks, one for collecting temporal correlation, and the other for capturing dependencies between different contents. Followed by a Cross Attention (CA) module as the Fusion Center (FC), the proposed ViT-CAT is capable of learning the mutual information between temporal and spatial correlations, as well, resulting in improving the classification accuracy, and decreasing the model’s complexity about 8 times. Based on the simulation results, the proposed ViT-CAT architecture outperforms its counterparts across the classification accuracy, complexity, and cache-hit ratio.

Abstract In this paper, an emergency ad‐hoc network based on cognitive radio is considered. It is assumed that secondary users (SUs) are moving agents in which some fixed agents namely distributed relays (DRs), forward the data gathered by SUs to destination. The main goal is to maximize the capacity of data gathering in destination with respect to the maximum interference tolerable by primary users (PU), and SU's and DR's power constraints. A mixed‐integer nonlinear model (MINLP) for joint relay assignment and power allocation is shown. Given that MINLP problems are NP‐Hard, a two interdependent solutions are proposed to achieve the relay assignment and power control. A social‐aware dynamic relay assignment algorithm (SADRA) based on many‐to‐one matching is proposed. In this matching, the preference list of DRs is the result of mean opinion scores ranking that two roulette wheel selection and random walk selection techniques are applied to eliminate the same priorities. In the second phase, a greedy adaptive algorithm for non‐convex power allocation problem that converges to the component‐wise minimal power is proposed. It is shown that the SADRA algorithm converges to a two‐sided exchange stable matching. The superior performance of the proposed SADRA algorithm is verified by the simulation results.

This article deals with the problem of statistical and system heterogeneity in a cross-silo federated learning (FL) framework where there exist a limited number of Consumer Internet of Things (CIoT) devices in a smart building. We propose a novel graph signal processing (GSP)-inspired aggregation rule based on graph filtering dubbed “G-Fedfilt.” The proposed aggregator enables a structured flow of information based on the graph’s topology. This behavior allows capturing the interconnection of CIoT devices and training domain-specific models. The embedded graph filter is equipped with a tunable parameter which enables a continuous tradeoff between domain-agnostic and domain-specific FL. In the case of domain-agnostic, it forces G-Fedfilt to act similar to the conventional federated averaging (FedAvg) aggregation rule. The proposed G-Fedfilt also enables an intrinsic smooth clustering based on the graph connectivity without explicitly specified which further boosts the personalization of the models in the framework. In addition, the proposed scheme enjoys a communication-efficient time scheduling to alleviate the system heterogeneity. This is accomplished by adaptively adjusting the amount of training data samples and sparsity of the models’ gradients to reduce communication desynchronization and latency. Simulation results show that the proposed G-Fedfilt achieves up to 3.99% better classification accuracy than the conventional FedAvg when concerning model personalization on the statistically heterogeneous local data sets, while it is capable of yielding up to 2.41% higher accuracy than FedAvg in the case of testing the generalization of the models. Furthermore, the proposed communication optimization scheme can boost the framework’s efficiency by reducing the computation, communication desynchronization, and latency up to 70.21%, 99.65%, and 44.61%, respectively, at the cost of 0.36% accuracy and under the system heterogeneity.

This paper investigates an Unmanned Aerial Vehicle (UAV)-enabled network consisting of smart mobile devices and multiple UAVs as aerial base stations in a Multiple-Input Multiple-Output (MIMO) architecture. Mobile devices are partitioned into several clusters and offload their tasks to the UAV servers via the Non-Orthogonal Multiple Access (NOMA) protocol. The main goal of the paper is to jointly maximize the number of served terrestrial users and their scheduling. Moreover, the number of UAV servers and their 3D placement are optimized. To this end, we formulate an optimization problem subject to some Quality of Service (QoS) constraints. The resulting problem is non-convex and intractable to solve. Therefore, we break the problem into two subproblems. We propose an efficient algorithm based on machine learning to solve the first subproblem, i.e., optimizing the number of UAVs and their 3D placements, and the user association. Different from existing literature, our proposed algorithm can achieve low computational complexity and fast convergence. The second subproblem, the user scheduling, is non-convex too. We utilize the$\ell _p$-norm concept to find a convex upper bound for the subproblem and optimize the user scheduling by applying the Successive Convex Approximation (SCA) algorithm. The aforementioned process is performed iteratively until the overall algorithm converges and a near-optimal solution is achieved for the optimization problem. Moreover, the computational complexity of the proposed scheme is analyzed. Finally, we evaluate the performance of our proposed algorithm via the simulation results. Regarding fast convergence and low computational complexity of the proposed algorithm, its superior performance is confirmed through numerical results.

As a consequence of the COVID-19 pandemic, the demand for telecommunication for remote learning/working and telemedicine has significantly increased. Mobile Edge Caching (MEC) in the 6G networks has been evolved as an efficient solution to meet the phenomenal growth of the global mobile data traffic by bringing multimedia content closer to the users. Although massive connectivity enabled by MEC networks will significantly increase the quality of communications, there are several key challenges ahead. The limited storage of edge nodes, the large size of multimedia content, and the time-variant users’ preferences make it critical to efficiently and dynamically predict the popularity of content to store the most upcoming requested ones before being requested. Recent advancements in Deep Neural Networks (DNNs) have drawn much research attention to predict the content popularity in proactive caching schemes. Existing DNN models in this context, however, suffer from long-term dependencies, computational complexity, and unsuitability for parallel computing. To tackle these challenges, we propose an edge caching framework incorporated with the attention-based Vision Transformer (ViT) neural network, referred to as the Transformer-based Edge (TEDGE) caching, which to the best of our knowledge, is being studied for the first time. Moreover, the TEDGE caching framework requires no data pre-processing and additional contextual information. Simulation results corroborate the effectiveness of the proposed TEDGE caching framework in comparison to its counterparts.

Abstract The demand of mobile networks and quality of service have recently increased to higher Spectral Efficiency (SE) or Energy Efficiency (EE) and massive connectivity for 5G wireless communications. The concept of beamforming Multiple‐Input Multiple‐Output (MIMO) is capable of significantly reducing the amount of required Radio Frequency Chains (RFCs) used by massive MIMO systems without remarkable performance loss. However, in existing beamformed MIMO, the amount of supported devices cannot be higher than the amount of RFCs using the same time‐frequency resources, and it is the basic limit for these systems. To address this issue, non‐orthogonal multiple access (NOMA) has been recently recommended, which can accommodate higher covered using via non‐orthogonal resource allocation. Nevertheless, power allocation is a core factor of the NOMA scheme. However, maximizing the sum rate problem based on power allocation in Massive MIMO‐NOMA scenarios is non‐convex and non‐linear, which creates a very challenging situation to acquire the closed‐form approach. Thus, in this paper, an approach to this difficulty is outlined for Massive MIMO‐NOMA systems to maximize the sum rate as a convex and linear problem. Simulation results of the suggested Beamformed MIMO‐NOMA (BMN) algorithm show that a higher achievable sum rate is achieved compared with the usual beamformed MIMO.

This article considers an ultradense heterogeneous network (UDHN) consisting of cache-enabled unmanned aerial vehicles (UAVs) and Internet of Things mobile devices (IMDs) receiving their requested contents via the power domain nonorthogonal multiple access (PD-NOMA) protocol. Employing the fast global${K}$-means (FGKMs) algorithm, IMDs are partitioned into several clusters connecting to either other IMDs or UAV belonging to the same cluster in the presence of untrusted users, known as nonlegitimate eavesdroppers. It is assumed that users located in the cluster edge area communicate with multiple UAVs to obtain their requested contents. The main goal for such a network is to jointly optimize the number of UAVs, their 3-D placements, and the cache placement probability of contents stored in UAVs and IMDs by maximizing the secure cache throughput. Toward this goal, we prove that the objective function is nonconcave. Therefore, we decompose the optimization problem into multiple subproblems. We first employ the FGKM algorithm to optimally determine the number of employed UAVs and their horizontal placements. Then, the UAVs’ altitudes are optimized by employing the interior-point method (IPM), while the convex approximation for the objective function and its constraints are substituted. Then, we optimize the secure cache throughput of IMDs by proposing a caching placement strategy for contents stored in UAVs and IMDs via Device to Device (D2D) and UAV to Device (U2D) links, given illegal eavesdroppers’ presence. Then, we propose an iterative algorithm to achieve the near-optimal solution for the cache throughput of IMDs. Different from existing works, the closed-form expressions for the achievable secrecy rate and the successful probability of D2D communications and U2D transmissions, as well as the secure cache throughput are derived. Finally, simulation results are presented to validate the proposed caching placement strategy. It is shown that the proposed scheme outperforms the conventional most popular caching (MPC) strategy substantially.

Recently, as a consequence of the COVID-19 pandemic, dependence on telecommunication for remote learning/working and telemedicine has significantly increased. In this context, preserving high Quality of Service (QoS) and maintaining low-latency communication are of paramount importance. In cellular networks, the incorporation of unmanned aerial vehicles (UAVs) can result in enhanced connectivity for outdoor users due to the high probability of establishing Line of Sight (LoS) links. The UAV’s limited battery life and its signal attenuation in indoor areas, however, make it inefficient to manage users’ requests in indoor environments. Referred to as the cluster-centric and coded UAV-aided femtocaching (CCUF) framework, the network’s coverage in both indoor and outdoor environments increases by considering a two-phase clustering framework for Femto access points (FAPs)’ formation and UAVs’ deployment. Our first objective is to increase the content diversity. In this context, we propose a coded content placement in a cluster-centric cellular network, which is integrated with the coordinated multipoint (CoMP) approach to mitigate the intercell interference in edge areas. Then, we compute, experimentally, the number of coded contents to be stored in each caching node to increase the cache-hit-ratio, signal-to-interference-plus-noise ratio (SINR), and cache diversity and decrease the users’ access delay and cache redundancy for different content popularity profiles. Capitalizing on clustering, our second objective is to assign the best caching node to indoor/outdoor users for managing their requests. In this regard, we define the movement speed of ground users as the decision metric of the transmission scheme for serving outdoor users’ requests to avoid frequent handovers between FAPs and increase the battery life of UAVs. Simulation results illustrate that the proposed CCUF implementation increases the cache-hit-ratio, SINR, and cache diversity and decrease the users’ access delay, cache redundancy, and UAVs’ energy consumption.

Vehicular networks are known for their dynamic topology, high mobility, and frequent disconnections. Unmanned aerial vehicles (UAVs) have been recently used as instant communication relays to bridge the communication gaps between terrestrial vehicles to improve connectivity in vehicular networks and overcome the aforementioned problems. Despite the existing work in the literature where each vehicle connects directly to UAVs, this work studies how clustering and different densities of vehicles affect delay and energy efficiency in a UAV-based vehicular network integrated with 5G technology. Consequently, this work addresses the problem of UAV enabling vehicular ad-hoc networks (VANETs) in a highway scenario, where UAVs serve as an effective complement to forward data packets between vehicles in the absence of sufficient fixed infrastructures in an emergency situation. The main objective of this work is to minimize the delay while maximizing the energy efficiency by minimizing the power consumption and maximizing the total data rate under realistic conditions, while nonorthogonal multiple access (NOMA) is also adopted as an alternative answer for the effective utilization of limited bandwidth. The free-flowing traffic follows a Poisson stochastic process where each vehicle is assigned a random speed selected from a truncated Gaussian distribution. To this end, a novel modification of fast global$K$-means is adopted to partition vehicles, allowing communication between clusters by vehicle-to-vehicle links, while data packets between clusters are relayed through UAVs. By computing the convex approximation of the objective function and the constraints, the original problem with the mixed-integer, nonconvex, and nonlinear form is solved by the proposed iterative inner penalty function algorithm. Finally, extensive simulations are conducted to validate the superiority of the proposed method in terms of various metrics. The results indicate that the relaying task in the proposed UAV-assisted VANET based on 5G technology is perfectly suited to enhance the network connectivity.

This article considers a wireless network consisting of unmanned aerial vehicles (UAVs), deployed as aerial base stations, and a large number of terrestrial users randomly distributed in a dense urban area. The main objective of this work is to maximize the downlink rate of users along with clustering of users and 2-D initial placement of UAVs, which effectively minimizes the clustering error. To achieve this goal, we estimate the next users’ locations with deep echo-state network (ESN) to find the movement pattern of users with high accuracy. Then, we propose the single- and multiagent actor–critic (AC) algorithms for UAVs’ initial deployment and trajectory design, where the multiagent scheme employs an efficient bandwidth allocation. Simulation results supported by a real data set of the terrestrial users’ coordinates indicate that, when the deep ESN algorithm is used, the accuracy is 93.75% for longitude and 88.36% for latitude compared to the simple ESN performance. Moreover, the use of single- and multiagent AC algorithms display better performance in terms of downlink rate and convergence speed than value-based algorithms such as deep$Q$-network schemes.

This article considers the problem of an unmanned aerial vehicle (UAV)-enabled cloud network under partial computation offloading scenario, where multiple UAV-mounted aerial base stations are employed to serve a group of remote Internet-of-Things ground-based smart devices (ISDs). The main objective of this work is to maximize energy efficiency by minimizing the number of needed drones while minimizing the cost associated with serving the ISDs under some realistic quality of service constraints. To that end, we aim to jointly optimize the 3-D UAV placements, transmit power, and cloud resources. This represents a challenging, nonconvex, and NP-hard optimization problem. In this work, we decompose the optimization problem into three separate subproblems, namely, 2-D UAV positioning, UAV altitude optimization, and UAV-cloud resource association. These subproblems are solved using a modified global$K$-means, successive convex approximation, and successive linear programming techniques. A comprehensive simulation study and comparative evaluation against the state-of-the-art (SOTA) algorithms are conducted to demonstrate the utility of the proposed approach and its benefits in applications of interest.

Given the data-hungry nature of Wireless Multimedia Sensor Networks (WMSNs) due to the need for near real-time processing of a large number of multimedia data, it is of significant practical importance to design/develop energy-efficient routing protocols to extend the WMSN’s collective lifetime. In this regard and to jointly utilize potential benefits that can be achieved by coupling clustering and image compression, the paper proposes a novel routing methodology referred to as the Energy Efficient Cluster-based Image Transmission (EECIT) scheme. In the proposed EECIT scheme, the multimedia-based sensor network is divided into different clusters depending on the node’s density, in which one node is adaptively assigned as the Cluster Head (CH). A key novelty of the proposed EECIT lies in the routing stage where ranking sensor nodes is performed via a new metric named Fair Selection (FS) coefficient, which is designed by considering a combination of mean-deviation and the number of times that nodes involve in routing. As a consequence of the fair distribution of energy consumption across the network, the network’s lifetime increases.

Abstract In this paper, two‐hop parallel N ‐relay networks are considered and the diversity‐multiplexing tradeoff (DMT) is derived over Gamma‐Gamma free‐space optical (FSO) channels with identical average received signal to noise rations in all links. In the derivations, both local and global channel state information (CSI) are investigated. In the local CSI case, the node only knows its incoming link conditions, while in the global CSI case, the nodes are aware of all the CSIs in the network. The listening and transmitting times at the relays as variables in the DMT derivation are further considered which are later used to optimize the performance of the network. It is demonstrated that the optimal DMT is obtained with the static quantize map and forward (SQMF) and dynamic quantize map and forward (DQMF) strategies for different ranges of the multiplexing gain. In addition, the optimal schedule of relays in the DQMF strategy is determined as a function of the local channel conditions in the relays.

This article addresses a real-time monitoring mechanism of vital signs of patients in a three-tier Internet of Medical Things-based software-defined-wireless body area network. The challenging issues are energy efficiency, interference, delay, emergency conditions, and reliability. We propose a two-tier scheduling algorithm in which Walsh Hadamard codes are employed to avoid the interference and decrease the delay and energy consumption in tier I. To schedule the transmission of different patients in tier II [i.e., the transmissions between hubs and access points (APs)], we propose a fair nonorthogonal multiple access-based scheduling algorithm, which jointly considers the channel state, energy consumption, and delay. Some processing tasks of the proposed algorithm are executed by local edge servers connected to APs and managed by a central SD-controller in tier III. Consequently, the transmission delay and energy consumption considerably decrease and the effective throughput increases. The algorithm takes the precedence of some information over other sensed data into account by employing the emergency index. The simulations results illustrate the advantages of the proposed algorithm in terms of energy consumption, network delay, and effective throughput, and the superior performance over other benchmark schemes.

Smart grid is susceptance to a variety of cyber attacks, among which False Data Injection Attacks (FDIA) are shown to be of significantly disruptive nature. Complex, distributed, and interconnected aspects of smart grids make detection of stealthy FDIAs with high accuracy significantly challenging. To address this issue, the paper proposes an innovative Adaptive Multi-Level Features Classification for Stealthy FDIA Detection (AMLFC-SFD) based on the Alternating Current (AC) state estimation. More specifically, we focus on maintaining a trade-off between the accuracy rate of detection and its associated computational complexity by utilizing two different Support Vector Machine (SVM)-based classifiers, in which the number of features as the input of the classifier depends on the strength of the underlying attack. In this regard, we divide potential FDI attacks in smart grids into three decision regions, including strong, moderate, and weak attacks and obtain the most accurate Kernel to separate measurements. To evaluate the proposed AMLFC-SFD framework, comprehensive numerical experiments are performed based on the IEEE 30-bus system. Results illustrate that with a lower number of features a reasonably high detection accuracy can be achieved, leading to a considerably less run time, which is of paramount importance for practical implementation.

This article considers a two-tier heterogeneous network consisting of conventional sub-6-GHz macrocells along with millimeter-wave (mmWave) small cells, where mobile devices (MDs) can connect to either macrocell or small cells opportunistically via the nonorthogonal multiple access (NOMA) protocol. We employ the queuing theory in our network model to conduct an assessment on the execution delay, energy consumption and the total cost of offloading tasks in a mobile-edge computation offloading (MECO) system. The main goal is to design an energy-efficient MECO decision algorithm in an ultradense Internet of Thing (UD-IoT) network to analyze the tradeoff between execution delay and energy consumption. The proposed scheme jointly optimizes the communication and computation resource management, subject to the energy and delay constraints. Due to the mixed-integer nonlinear problem (MINLP) for resource allocation and computation offloading, an iterative algorithm along with the successive convex approximation (SCA) is proposed to achieve the optimum local frequency scheduling, power allocation, and computation offloading. The superior performance of the proposed MECO algorithm in our UD-IoT network is verified by the extensive numerical results.

Mobile edge computing (MEC) has been recognized as an emerging technology that allows users to send the computation-intensive tasks to the MEC server deployed at the macro base station. This process overcomes the limitations of mobile devices (MDs), instead of sending the data to a cloud server which is far away from MDs. In addition, MEC results in decreasing the latency of cloud computing and improves the quality of service. In this article, an MEC scenario in the 5G networks is considered, in which several users request for computation service from the MEC server in the cell. We assume that users can access the radio spectrum by the nonorthogonal multiple access protocol and employ the queuing theory in the user side. The main goal is to minimize the total power consumption for computing by users with the stability condition of the buffer queue to investigate the power-latency tradeoff, which the modeling of the system leads to a conditional stochastic optimization problem. In order to obtain an optimum solution, we employ the Lyapunov optimization method along with successive convex approximation. Extensive simulations are conducted to illustrate the advantages of the proposed algorithm in terms of power-latency tradeoff of the joint optimization of communication and computing resources and the superior performance over other benchmark schemes.

Although multiple‐input multiple‐output (MIMO) technique is used in free space optical (FSO) systems to reduce the effect of atmospheric turbulence, however, employing this technique without sufficient attention to the interference of laser beams not only does not reduce the effect of atmospheric turbulence, but also it can significantly degrade the performance of the FSO system. This study investigates that the average intensity of laser beams at the receiver aperture of MIMO FSO systems is very sensitive to small variations of path lengths between the transmitter and receiver. Therefore, the destructive interference that degrades the performance of MIMO FSO systems compared to equivalent single‐input single‐output (SISO) FSO ones is due to the difference in path lengths of laser beams between transmitter and receiver apertures. For this reason and in order to mitigate the interference effect, the authors compensate small changes of path lengths through adjusting the sending times and the initial parameters of laser beams in the transmitter apertures. The authors' analysis supported by numerical results show that the adjustment of laser parameters could increase the chance of peak intensity of MIMO‐FSO systems to be higher than the peak intensity of equivalent SISO‐FSO ones.

The cache replacement policy is a crucial phase in caching-based systems that deal with the process of selecting applicable cache contents. In this paper, we propose two novel cache replacement algorithms based on the dataset obtained from a typical wireless femto network. In the first algorithm, called Weighted Least Frequently used with an Adaptive Time Window (WLF-ATW), we aim to make a balance between the network's traffic and the recognition of popular contents. The WLF-ATW algorithm takes the frequency and the recency information of files into account to ascertain the popularity of contents. We suggest another new cache replacement policy namely Fairness Scheduling-based with an Adaptive Time Window (FS-ATW) that is based on fairness scheduling in order to minimize the user's access delay. The novelty of our proposed FS-ATW lies in ranking clients according to their last situations that lead to a further user's experience. The effectiveness of these new algorithms is evaluated from the cache hit ratio, transferred byte volume, user's access delay, user's experience, and load balance. A comprehensive numerical evaluation shows that the performance of the proposed WLF-ATW and FS-ATW algorithms is significantly better than some existing cache replacement strategies.

An overly multiple‐input–multiple‐output cognitive radio femtocell network consisting of P macrocell users equipments (MUEs), known as primary users (PUs), and K femtocell access points (FAPs), known as secondary users is considered in which femtocell user equipments (FUEs) operate in the closed access mode. Each FAP is equipped with a CR device to perform the local channel state information. The main objective is to maximise the average sum‐rate of the macrocell base station (MBS), as well as significantly increase in the FUE’s rate with negligible reduction in the PU’s rate for moderate‐to‐high signal‐to‐noise ratio regimes. This goal is achieved by proposing an opportunistic interference alignment (OIA) technique using the threshold‐based beamforming (TBF) algorithm. Assuming that the receiver and the transmitter of the PU have perfect knowledge of their own channel matrices, the authors use the maximum eigenmode beamforming algorithm for the transmission between the MBS and its MUEs, where the PU releases some of its eigenmodes for the FAPs, in order to transmit signals for their corresponding FUEs over them. To decide on the presence or absence of MUEs, they use the generalised likelihood ratio test detector which is more robust to the noise uncertainty than the energy detector. The proposed OIA‐TBF protocol allows the opportunistic FAPs to send data for FUEs and use the same frequency band of a preexisting MUE to guarantee that no interference is imposed on the MUE’s performance for such a network.

This paper introduces a spectrum sensing scenario based on kernel theory which compares favorably against the conventional Energy Detector (ED) in a cognitive radio system. The so-called Kerenlized Energy Detector (KED) can provide superior accuracy in the case of non-Gaussian noise. The incorporation of the nonlinear kernel function in the KED test statistics allows for the development of a nonlinear algorithm capable of considering both higher order and Fractional Lower Order Moments (FLOMs) in the sensing task. Simulation results show that the proposed semi-blind kernelized spectrum sensing algorithm is much robust against impulsive noises and displays a considerably better detection performance than the conventional ED in practical impulsive man-made noises which are generally modeled as the Laplacian and the α-stable distributions. Moreover, for the Gaussian signal and noise model, the performance of the KED scheme is almost identical to that of the conventional ED.

This paper presents a comprehensive study on the Full‐Rate and Linear‐Receiver (FRLR) STBC proposed as a newly coding scheme with the low decoding complexity for a 2×2 MIMO system. It is shown that the FRLR code suffers from the lack of the non‐vanishing determinant (NVD) property that is a key parameter in designing a full‐rate STBC with a good performance in higher data rates, across QAM constellation. To overcome this drawback, we show that the existence of the NVD feature for the FRLR code depends on the type of the modulation. In particular, it is analytically proved that the FRLR code fulfills the NVD property across the PAM constellation but not for the QAM scheme. Simulation results show that, at a BER equal to 10 −4 , utilising the PAM modulation for the FRLR‐STBC, provides about 2 dB gain over a use of the QAM when the bandwidth efficiency is 6b/s/Hz. In addition, for the PAM constellation, the FRLR code significantly outperforms some existing full‐rate STBCs. Finally, we utilise the moment generating function approach to derive an exact closed‐form expression for the average error probability of the FRLR code with the BPSK modulation.

This paper presents a comprehensive study on the problem of decoding complexity in a Multi-Input Multi-Output (MIMO) Hybrid Automatic Repeat reQuest (HARQ) system based on Space-Time Block Codes (STBCs). We show that there exist two classes of fast-decodable MIMO HARQ systems: independent and dependent STBC structures. For the independent class, two types of protocols namely, fixed and adaptive threshold-based are presented and their effectiveness in both computational complexity reduction and spectral efficiency preservation are discussed. For the dependent class, a fast Sphere Decoder (SD) algorithm with a low computational complexity is proposed for decoding process of HARQ rounds. Two new concepts are introduced to leverage the fast-decodable notion in MIMO HARQ systems. Simulation results show that the proposed fast-decodable MIMO HARQ protocols in both classes provide a significant reduction in the decoding complexity as compared with the original MIMO HARQ method.

The authors propose Markovian‐based spectrum sensing policies in a cognitive radio system that leverages past sensing outcomes of several cooperating secondary users (SUs) to decide which channel (of primary users – PUs) should be sensed by each SU at a given time. These policies are based on a new finite‐state channel model that captures the fading condition as well as the occupancy state for each primary channel. The multiuser extension of this model is useful when multiple spatially distributed SUs share their sensing outcomes. The proposed schemes allow the asynchronous sensing outcomes obtained by the SUs over different slots to be fused together and converted into a posteriori probabilities for the current states of the primary channels. As the detection threshold in a spectrum detector balances the trade‐off between the false‐alarm and miss probabilities for detecting primary signals in a single primary channel, a design parameter is introduced to allow the system designer to devise policies with different levels of aggressiveness. The authors evaluate the optimality and complexity of the proposed sensing policies and show that our schemes significantly increase secondary use of the spectrum and/or reduce interference with PUs compared to a random selection policy or a cooperative sensing policy based on a two‐state channel model.

Recent achievements in wireless technologies have opened up enormous opportunities for the implementation of ubiquitous health care systems in providing rich contextual information and warning mechanisms against abnormal conditions. This helps with the automatic and remote monitoring/tracking of patients in hospitals and facilitates and with the supervision of fragile, elderly people in their own domestic environment through automatic systems to handle the remote drug delivery. This paper presents a new modeling and analysis framework for the multipatient positioning in a wireless body area network (WBAN) which exploits the spatial sparsity of patients and a sparse fast Fourier transform (FFT)-based feature extraction mechanism for monitoring of patients and for reporting the movement tracking to a central database server containing patient vital information. The main goal of this paper is to achieve a high degree of accuracy and resolution in the patient localization with less computational complexity in the implementation using the compressive sensing theory. We represent the patients' positions as a sparse vector obtained by the discrete segmentation of the patient movement space in a circular grid. To estimate this vector, a compressive-sampling-based two-level FFT (CS-2FFT) feature vector is synthesized for each received signal from the biosensors embedded on the patient's body at each grid point. This feature extraction process benefits in the combination of both short-time and long-time properties of the received signals. The robustness of the proposed CS-2FFT-based algorithm in terms of the average positioning error is numerically evaluated using the realistic parameters in the IEEE 802.15.6-WBAN standard in the presence of additive white Gaussian noise. Due to the circular grid pattern and the CS-2FFT feature extraction method, the proposed scheme represents a significant reduction in the computational complexity, while improving the level of the resolution and the localization accuracy when compared to some classical CS-based positioning algorithms.

This work considers a code division multiple access (CDMA)‐based femtocell system where a fixed set of subscribed users communicate simultaneously to a femtocell access point (FAP) in an asynchronous fashion during the uplink. The main goal of this paper is to present an augmentation protocol for the physical layer of the CDMA2000 femtocell standard with focus on the multiple access interference (MAI) suppression. The above‐closed access femtocell uses a unique set of cyclic orthogonal binary codes to eliminate the MAI caused by packet collisions. This property ensures that the time and data rate asynchronicity of active nodes in a femtocell produces a zero MAI situation at the FAP. The work investigates the optimality and the effectiveness of such codes in femtocells from the link bit error rate performance in a practical Rayleigh‐fading environment, where Kalman filtering is used at the FAP for the channel estimation. Theoretical findings are verified by simulation evaluations and it is shown numerically a significant improvement in the performance of the proposed scheme when compared with conventional non‐cyclic orthogonal codes in a Rayleigh‐fading channel in the asynchronous femtocell.

This work considers a CDMA-based Wireless Body Area Network (WBAN) where multiple biosensors communicate simultaneously to a central node in an asynchronous fashion. The main goal of this paper is to present an augmentation protocol for the physical layer of the IEEE 802.15.6 specifications with focus on the Multiple Access Interference (MAI) mitigation in a proactive WBAN. The proposed methodology uses a new set of orthogonal codes from the conventional Walsh-Hadamard matrix which has the special property of “cyclic orthogonality”. This property ensures that the asynchronous nature of the WBAN does not produce MAI amongst the multiple on-body sensors. The work investigates the optimality of such codes in WBANs from the link Bit Error Rate (BER) performance. We show that the proposed spreading codes outperform conventional non-cyclic orthogonal spreading codes in a practical Rayleigh fading environment.

This paper deals with the delay-throughput analysis of a single-hop wireless network with n transmitter/receiver pairs. All channels are assumed to be block Rayleigh fading with shadowing, described by parameters (α, ω̅), where α denotes the probability of shadowing and ω̅ represents the average cross-link gains. The analysis relies on the distributed on-off power allocation strategy (i.e., links with a direct channel gain above a certain threshold transmit at full power and the rest remain silent) for the deterministic and stochastic packet arrival processes. It is also assumed that each transmitter has a buffer size of one packet and dropping occurs once a packet arrives in the buffer while the previous packet has not been served. In the first part of the paper, we define a new notion of performance in the network, called effective throughput, which captures the effect of arrival process in the network throughput, and maximize it for different cases of packet arrival process. It is proved that the effective throughput of the network asymptotically scales as (log n)/α̂, with α̂=△ αω̅, regardless of the packet arrival process. In the second part of the paper, we present the delay characteristics of the underlying network in terms of the packet dropping probability. We derive the sufficient conditions in the asymptotic case of n → ∞ such that the packet dropping probability tend to zero, while achieving the maximum effective throughput of the network. Finally, we study the trade-off between the effective throughput, delay, and packet dropping probability of the network for different packet arrival processes. In particular, we determine how much degradation will be enforced in the throughput by introducing the aforementioned constraints.

The use of wireless body area networks requires correct delivery of the vital signs of the patient while managing precious energy in tiny biosensors. Toward these goals, we present an energy-efficient protocol suitable for the narrow band physical layer of the IEEE 802.15.6 standard in on-body sensor networks. This study considers a realistic channel model inspired by the Gilbert-Elliott channel including the erasure mode and a binary symmetric channel model. This work studies the feasibility of Raptor codes, the most efficient rateless codes, with the Frequency Shift Keying (FSK) modulation to overcome the reliability and power cost concerns in on-body sensor devices. The main advantage of using Raptor codes is to provide an inherent adaptive power management, due to the flexibility of the code rate and coding gain. Numerical results show that the Raptor coded FSK is more energy efficient and robust than that of the uncoded FSK and LDPC codes, in various channel realization, in particular, when patients make sequential position changes.

Owing to the unique characteristics of sensor devices, finding the energy-efficient modulation with a low-complexity implementation (refereed to as green modulation) poses significant challenges in the physical layer design of wireless sensor networks (WSNs). Towards this goal, the authors present an in-depth analysis on the energy efficiency of various modulation schemes using realistic models in the IEEE 802.15.4 standard to find the optimum distance-based scheme in a WSN over Rayleigh and Rician fading channels with path loss. The authors describe a proactive system model according to a flexible duty-cycling mechanism utilised in practical sensor apparatus. The present analysis includes the effect of the channel bandwidth and the active mode duration on the energy consumption of popular modulation designs. Path-loss exponent and DC–DC converter efficiency are also taken into consideration. In considering the energy efficiency and complexity, it is demonstrated that among various sinusoidal carrier-based modulations, the optimised non-coherent M-ary frequency shift keying (NC-MFSK) is the most energy-efficient scheme in sparse WSNs for each value of the path-loss exponent, where the optimisation is performed over the modulation parameters. In addition, the authors show that the on–off keying displays a significant energy saving as compared to the optimised NC-MFSK in dense WSNs with small values of path-loss exponent.

Adaptive Demodulation (ADM) is a new rate-adaptive system that operates without requiring Channel State Information (CSI) at the transmitter, instead using adaptive decision region boundaries at the receiver and encoding the data with a rateless code. This paper addresses the design and performance of an ADM scheme for two common differentially coherent schemes: M-DPSK and M-DAPSK. The optimal method for determining the most reliable bits for a given differential detection scheme is presented. In addition, simple (near-optimal) implementations are provided for recovering the most reliable bits from a received pair of differentially encoded symbols for systems using 16-DPSK and 16-DAPSK. The new receivers offer the advantages of a rate-adaptive system, without requiring CSI at the transmitter or a coherent phase reference at the receiver. Bit error analysis for the ADM system in both cases is presented along with numerical results of the spectral efficiency for the rate-adaptive systems operating over a Rayleigh fading channel.

The use of wireless implant technology requires correct delivery of the vital physiological signs of the patient along with the energy management in power-constrained devices. Toward these goals, we present an augmentation protocol for the physical layer of the medical implant communications service (MICS) with focus on the energy efficiency of deployed devices over the MICS frequency band. The present protocol uses the rateless code with the frequency-shift keying (FSK) modulation scheme to overcome the reliability and power cost concerns in tiny implantable sensors due to the considerable attenuation of propagated signals across the human body. In addition, the protocol allows a fast start-up time for the transceiver circuitry. The main advantage of using rateless codes is to provide an inherent adaptive duty cycling for power management, due to the flexibility of the rateless code rate. Analytical results demonstrate that an 80% energy saving is achievable with the proposed protocol when compared to the IEEE 802.15.4 physical layer standard with the same structure used for wireless sensor networks. Numerical results show that the optimized rateless coded FSK is more energy efficient than that of the uncoded FSK scheme for deep tissue (e.g., digestive endoscopy) applications, where the optimization is performed over modulation and coding parameters.

Due to unique characteristics of sensor nodes, choosing an energy-efficient modulation scheme with low-complexity implementation (refereed to as green modulation) is a critical factor in the physical layer of Wireless Sensor Networks (WSNs). The main goal of this paper is to analyze and compare the energy efficiency of various sinusoidal carrier-based modulation schemes using parameters in the IEEE 802.15.4 standard and state-of-the art technology to find the best scheme in a dense WSN over frequency-flat Rayleigh fading channel with path-loss. Experimental results show that M-ary Frequency Shift Keying (MFSK) with small order of M has significant energy saving compared to OQPSK and MQAM for short range scenarios, and could be considered as a realistic candidate in dense WSNs. In addition, MFSK has the advantage of less complexity and cost in implementation than the other schemes.

A non-regenerative dual-hop wireless system based on distributed Alamouti space-time coding is considered. It is assumed that each relay retransmits an appropriately scaled space-time coded version of its received signal. The main goal of this paper is to find a scaling function for each relay to minimize the outage probability. In the high signal-to-noise ratio (SNR) regime for the relay-destination link, it is shown that a threshold-based scaling function (i.e., the relay remains silent if its channel gain with the source is less than its predetermined threshold) is optimum from the outage probability point of view. Numerical results demonstrate a dramatic performance improvement as compared to the case that the relay stations forward their received signals with full power even for finite SNR scenarios.

A single-hop wireless network with K links is considered, where the links are partitioned into M clusters, each operating in a subchannel with bandwidth W/M. We assume that the links in each cluster perform the on-off power allocation strategy proposed in [1]. The problem is to analyze the average sum-rate of the network in terms of M and under the shadow- fading effect with probability a. It is demonstrated that for M ~ o(K) and 0 < alpha les 1, where alpha is a fixed parameter, the average sum-rate of the network scales as W/alpha log K. For M ~ Theta(K), we present an upper bound for the average sum-rate. It is proved that the maximum average sum-rate of the network for every value of 0 < alpha les 1 is achieved at M = 1. In fact, in the proposed model, partitioning the bandwidth W into M subchannels has no gain in terms of enhancing the throughput.

In this paper, an asymptotic analysis for the delay-throughput of a single-hop wireless network with n pairs of nodes is presented. The analysis relies on the decentralized on-off power allocation strategy, in which the on-off transmission policy for each link is based on comparing its direct channel gain with optimum threshold τn. We first provide a new definition of the transmission delay in a homogenous network. It is proved that the delay threshold level that results the dropping probability for each link tends to zero, while achieving the maximum average sum-rate scales as ω(n / log n). Also, the minimum delay in order to make the dropping probability for the whole network approach zero scales as ω(n / log n) + n. Furthermore, we drive lower and upper bounds for the link activation probability, q, such that the order of the average sum-rate is preserved. Based on the upper bound on q, an asymptotic analysis shows that the delay in each link and in the network improves without any significant impact on the the average sum-rate. Finally, we present a new definition of the throughput for the link in the cases of one and infinite buffer size. It is demonstrated that the maximum average throughput of the network with the decentralized on-off power allocation strategy is independent of the buffer size.