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The battery management system (BMS) is a crucial component of electric vehicles (EVs) owing to its sustainable operation. To ensure optimal performance of the BMS, state of charge (SOC) of the equipped battery is required to be effectively and accurately estimated. In this paper, the authors consider high‐order equivalent circuit model (ECM) to capture the dynamic characteristics of lithium‐ion batteries, which are connected in series with internal resistance by 2‐RC networks. The parameters of the RC network are determined by mathematically solving the working conditions of the two states. Moreover, the parameters of the battery can be derived by hybrid pulse power characterization (HPPC) tests. Then, based on the open‐circuit voltage, the proposed feedback‐based extended Kalman filtering (FEKF) algorithm is established. The parameters from the simulation have shown that the highest error is 0.0306 V, the optimal knowledge of which can improve the SOC estimation approach remarkably and can provide a reference value. Afterwards, the non‐linear predicting and corrective techniques are applied to the experiment in the extended calculation process. The original error is reduced by the FEKF algorithm, where the maximum and average errors are 0.0298 and 0.0240 V, respectively. Consequently, the established high‐order ECM utilizing the FEKF algorithm may provide SOC estimation with an error of 1.5% or less, resulting in superb performance from the lithium‐ion battery pack. [ABSTRACT FROM AUTHOR]

Autonomous underwater vehicles (AUVs)-assisted mobile data collection in underwater wireless sensor networks (UWSNs) has received significant attention because of their mobility and flexibility. To satisfy the increasing demand of diverse application requirements for underwater data collection, such as time-sensitive data freshness, emergency event security as well as energy efficiency, in this paper, we propose a novel multi-modal AUV-assisted data collection scheme which integrates both acoustic and optical technologies and takes advantage of their complementary strengths in terms of communication distance and data rate. In this scheme, we consider the age of information (AoI) of the data packet, node transmission energy as well as energy consumption of the AUV movement, and we make a trade-off between them to retrieve data in a timely and reliable manner. To optimize these, we leverage a deep reinforcement learning (DRL) approach to find the optimal motion trajectory of AUV by selecting the suitable communication options. In addition to that, we also design an optimal angle steering algorithm for AUV navigation under different communication scenarios to reduce energy consumption further. We conduct extensive simulations to verify the effectiveness of the proposed scheme, and the results show that the proposed scheme can significantly reduce the weighted sum of AoI as well as energy consumption. [ABSTRACT FROM AUTHOR]

Due to its inherent ability in reducing hardware cost and power consumption while maintaining high system capacity, hybrid precoding is deemed as one of the key technologies in the upcoming 5G/6G millimeter-wave (mmWave) massive multiple-input multiple-output (MIMO) systems. However, it is challenging to design high performance hybrid precoders/combiners with low computational complexity. In this paper, based on the singular value decomposition (SVD) technique and the concept of equivalent channel, joint hybrid precoding strategies with high spectral-efficiency and low complexity are proposed for both single-user and multi-user massive MIMO systems. Specifically, for single-user massive MIMO scenarios, after transforming the design of hybrid beamforming into the problem of maximizing the square of sum eigenvalues for an equivalent channel, a two-stage successive method is conceived to design the analog precoder and combiner jointly, and the corresponding equivalent channel is constructed. Then, the digital precoding and combining operations are realized directly by applying the SVD technique to the matrix of equivalent channel. Meanwhile, the hybrid precoding strategy is extended to the multi-user scenario for achieving high performance resultant from multi-user diversity. Extensive simulations are conducted to verify the effectiveness of the precoding/combing schemes. The results show that our proposed schemes can achieve superior performance with lower complexity compared to the existing ones. [ABSTRACT FROM AUTHOR]

In an autonomous underwater vehicles– (AUVs–) based optical-acoustic hybrid network, it is critical to achieve ultra high-speed reliable communications, in order to reap the benefits of the complementary systems and perform high-bandwidth and low-latency operations. However, as the mobile AUVs operate in harsh oceanic environments, it is essential to design an effective switching algorithm to execute flexible hybrid acoustic-optical communications and increase the network throughput. In this paper, we propose a Q-learning-based adaptive switching scheme to maximize the network throughput by capturing the dynamics of the varying channels as well as the mobility of AUVs. In order to address the challenge associated with partial observations of the optical channel and improve the switching efficiency in extreme conditions, a blind optical channel estimation method is designed and implemented with the Extended Kalman Filter (EKF), in which the relationship between the underwater acoustic and optical channels is utilized to improve the channel prediction accuracy. Based on this environmental status, a reinforcement learning approach is leveraged to build a near-optimal switching strategy for the hybrid network. We conduct numerical simulations to verify the performance of the scheme, and the simulation results demonstrate that the proposed switching scheme is effective and robust. [ABSTRACT FROM AUTHOR]

The next-generation of wireless network 6G is envisioned to achieve seamless global full coverage, and provide ultra-high-speed and extreme low delay network to connect the Internet of Everything (IoE) on the Earth. To this end, as the space-air-ground-underwater integrated network is a solid foundation of 6G, it is critical to develop reliable and robust cross-boundary communication techniques to connect the sky and the ocean. However, due to the harsh environment-induced channel complexity of different transmission media, penetrating through the air/water interface seamlessly faces significant challenges. In this paper, we present a comprehensive review of recent progress of air/water cross-boundary communications, and group the existing works into three categories, such as 1) optical direct communication, 2) relay-based communication, and 3) non-optical direct communication, and then summarize them respectively. The three types of cross-boundary approaches have their own pros and cons which are suitable for different application scenarios. Among them, the optical direct communication has made tremendous progress recently. However, the volatile wavy surface and depth-temperature-dependent parameters make it a great challenge to build robust communication links. Moreover, the misalignment caused by the dynamics of the channel severely degrades the performance and limits their practical applications. To tackle these challenges, we summarize the complex channel characteristics, analyze the causes of the misalignment and provide potential solutions. In the end of this paper, we also discuss future research directions and hope to inspire expanding research on the air/water cross-boundary communications to further propel towards the space-air-ground-ocean integrated network in 6G. [ABSTRACT FROM AUTHOR]

Despite extensive research efforts, underwater sensor networks (UWSNs) still suffer from serious performance issues due to their inefficient and uncoordinated channel access and resource management. For example, due to the lack of holistic knowledge on the network resources, existing decentralized routing protocols fail to provide globally optimal performance. On the other hand, Software Defined Networking (SDN), as a promising paradigm to provide prominent centralized solutions, can be employed to address the aforementioned issues in UWSNs. Indeed, SDN brings unprecedented opportunities to improve the network performance through the development of advanced algorithms at controllers. In this paper, we study the routing problem in such a network with new features including centralized route decision, global network-state awareness, seamless route discovery while considering the optimization of several long-term global performance metrics. We formulate the entire routing problem of a multi-modal UWSN as an optimization problem while considering the interference phenomenon of ad hoc scenarios and some long-term global performance metrics of an ideal routing protocol. Our formulated problem nicely captures all possible flexibilities of a sensor node no matter it has the full-duplex or half-duplex functionality. Upon the formulation, we recognize the NP-hard nature of the problem for all possible scenarios. We adopt a rounding technique based on the convex programming relaxation concept to solve the formulated routing problem that considers full-duplex scenarios, whereas we solve the problem for half-duplex scenarios using a greedy method upon interpreting it as a submodular function maximization problem. Through extensive simulation via our Python-based in-house simulator, we verify that our proposed globally optimal routing scheme always outperforms three existing decentralized routing protocols (each of these protocols are selected from each of three prominent protocol types, i.e., flooding, cross-layer information and adaptive machine learning based, respectively) in terms of reliability, latency, energy efficiency, lifetime and fairness. [ABSTRACT FROM AUTHOR]

Smart wristband has become a dominant device in the wearable ecosystem, providing versatile functions such as fitness tracking, mobile payment, and transport ticketing. However, the small form-factor, low-profile hardware interfaces and computational resources limit their capabilities in security checking. Many wristband devices have recently witnessed alarming vulnerabilities, e.g., personal data leakage and payment fraud, due to the lack of authentication and access control. To fill this gap, we propose a secure text pin input system, namely Taprint, which extends a virtual number pad on the back of a user's hand. Taprint builds on the key observation that the hand “landmarks”, especially finger knuckles, bear unique vibration characteristics when being tapped by the user herself. It thus uses the tapping vibrometry as biometrics to authenticate the user, while distinguishing the tapping locations. Taprint reuses the inertial measurement unit in the wristband, “overclocks” its sampling rate with the cubic spline interpolation to extrapolate fine-grained features, and further refines the features to enhance the uniqueness and reliability. Extensive experiments on 128 users demonstrate that Taprint achieves a high accuracy (96 percent) of keystrokes recognition. It can authenticate users, even through a single-tap, at extremely low error rate (2.2 percent), and under various practical usage disturbances. [ABSTRACT FROM AUTHOR]

Recently, wearable devices have become increasingly popular in our lives because of their neat features and stylish appearance. However, their tiny sizes bring about new challenges to human-device interaction such as texts input. Although some novel methods have been put forward, they possess different defects and are not applicable to deal with the problem. As a result, we propose an acoustic-based texts-entry system, i.e., EchoWrite, by which texts can be entered with a finger writing in the air without wearing any additional device. More importantly, different from many previous works, EchoWrite runs in a training-free style which reduces the training overhead and improves system scalability. We implement EchoWrite with commercial devices and conduct comprehensive experiments to evaluate its texts-entry performance. Experimental results show that EchoWrite enables users to enter texts at a speed of 7.5 WPM without practice, and 16.6 WPM after about 30-minute practice. This speed is better than touch screen-based method on smartwatches, and comparable with previous related works. Moreover, EchoWrite provides favorable user experience of entering texts. [ABSTRACT FROM AUTHOR]

Federated learning (FL), invented by Google in 2016, has become a hot research trend. However, enabling FL in wireless networks has to overcome the limited battery challenge of mobile users. In this regard, we propose to apply unmanned aerial vehicle (UAV)-empowered wireless power transfer to enable sustainable FL-based wireless networks. The objective is to maximize the UAV transmit power efficiency, via a joint optimization of transmission time and bandwidth allocation, power control, and the UAV placement. Directly solving the formulated problem is challenging, due to the coupling of variables. Hence, we leverage the decomposition technique and a successive convex approximation approach to develop an efficient algorithm, namely UAV for sustainable FL (UAV-SFL). Finally, simulations illustrate the potential of our proposed UAV-SFL approach in providing a sustainable solution for FL-based wireless networks, and in reducing the UAV transmit power by 32.95%, 63.18%, and 78.81% compared with the benchmarks. [ABSTRACT FROM AUTHOR]

High-dimensional with limited-sample size (HDLSS) datasets exhibit two critical problems: (1) Due to the insufficiently small-sample size, there is a lack of enough samples to build classification models. Classification models with a limited-sample may lead to overfitting and produce erroneous or meaningless results. (2) The 'curse of dimensionality' phenomena is often an obstacle to the use of many methods for solving the high-dimensional with limited-sample size problem and reduces classification accuracy. This study proposes an unsupervised framework for high-dimensional limited-sample size data classification using dimension reduction based on variational autoencoder (VAE). First, the deep learning method variational autoencoder is applied to project high-dimensional data onto lower-dimensional space. Then, clustering is applied to the obtained latent-space of VAE to find the data groups and classify input data. The method is validated by comparing the clustering results with actual labels using purity, rand index, and normalized mutual information. Moreover, to evaluate the proposed model strength, we analyzed 14 datasets from the Arizona State University Digital Repository. Also, an empirical comparison of dimensionality reduction techniques shown to conclude their applicability in the high-dimensional with limited-sample size data settings. Experimental results demonstrate that variational autoencoder can achieve more accuracy than traditional dimensionality reduction techniques in high-dimensional with limited-sample-size data analysis. [ABSTRACT FROM AUTHOR]

Nowadays, smart wristbands have become one of the most prevailing wearable devices, as they are small and portable. However, due to the limited size of the touch screens, smart wristbands typically have poor interactive experience. There are a few works appropriating the human body as a surface to type on. Yet, by using multiple sensors at high sampling rates, they are not portable and are energy-consuming in practice. To break this stalemate, we proposed a portable, cost efficient text-entry system, termed ViType, which first leverages a single small form factor sensor to achieve a practical user input with much lower sampling rates. To enhance the input accuracy with less vibration information introduced by lower sampling rates, ViType designs a set of novel mechanisms, including a fine-grained feature extraction to process the vibration signals, and a runtime calibration and adaptation scheme to recover from the error due to temporal instability. Extensive experiments have been conducted on 30 human subjects. The results demonstrate that ViType is robust against various confounding factors. The average recognition accuracy is 95 percent with an initial training sample size of 20 for each key. The accuracy is 1.54 times higher than the state-of-the-art on-body typing system. Furthermore, when turning on the runtime calibration and adaptation system to update and enlarge the training sample size, the accuracy can reach around 98 percent on average during one month. [ABSTRACT FROM AUTHOR]

This paper considers an analytical approach to evaluate the outage behavior of the device-to-device (D2D) communication, which is underlaid with cellular networks, as an enabling technology for Internet-of-Things (IoTs). In such an architecture, a group of IoT devices (IoTDs) communicate with an IoT gateway by reusing the resources of cellular users (CUEs) to enhance the spectral efficiency of the fifth-generation networks. Two interference management schemes are widely used in the literature for the sharing of D2D spectrum, namely the fixed-power margin (FPM) and the cooperative pairing (CooP) schemes. We investigate and compare the performance of the two schemes from the perspective of outage probability (OP). While satisfying the minimal performance of the system, the OP of an arbitrary pair (i.e., one IoTD and one CUE) under both schemes are derived in closed form in terms of hyper-geometric functions via the Mellin transform technique. Moreover, for the CooP scheme, an iterative alternating Dinkelbach algorithm is proposed as an outage-optimal power allocation scheme. Analytical and simulation results reveal that the CooP scheme is the outage optimum for the high SNR regime while the FPM scheme is the optimal one for the low SNR regime. Simulation results also show that the suitable power margin of the FPM scheme lies in between 2 and 3 dB. Under these two interference management schemes, the accuracy of the analytical results is verified through numerical simulation and it turns out that these are well matched. [ABSTRACT FROM AUTHOR]

Currently, one of the main enablers for network evolution is software-defined networking (SDN), where the control plane is decoupled from the data plane. A controller, as a (logically) centralized entity in the control plane, is the Achilles’ heel of SDN resilience since its failure would affect the proper functioning of the entire network. The resilience of the control plane is strongly linked to the controller placement problem, which deals with the positioning and assignment of controllers to the forwarding devices (i.e., switches). A resilient controller placement problem needs to assign more than one controller to a switch while it satisfies certain quality of service requirements. In this paper, we propose a solution for such a problem that, unlike most of the former studies, takes both the switch-controller /inter-controller latency requirements and the capacity of the controllers into account to meet the traffic load of switches. The proposed algorithms, one of which has a polynomial-time complexity, adopt a clique-based approach in graph theory to find high-quality solutions heuristically. It is evaluated with real wide area network (WAN) topologies and the corresponding results are extensively analyzed. The resultant studies equip the service providers with helpful insights into the design of a resilient software-defined WAN. [ABSTRACT FROM AUTHOR]

Software-defined networking (SDN) has introduced noticeable progresses in the design and development of computer networks. In fact, SDN empowers us to run more advanced algorithms for resource management through the separation of control and data planes using a centralized approach. By applying SDN to different communication platforms, e.g., Wi-Fi mesh networks (WMNs), we gain an unprecedented opportunity to design more efficient and cost-effective solutions. In WMNs, the process of service provisioning incorporates tightly correlated steps, including access point association, gateway selection, and backhaul routing. In most of the prior studies, these steps were investigated as independent NP-hard problems. To the best of our knowledge, there is no thorough formulation that considers all the necessary steps at different tiers of WMNs to construct a unified and fine-grained solution. In this paper, we present a structured scheme to address the demands of end-users over SDN-aware WMNs. In contrast to most of the former works, our proposed formulation takes the key characteristics of wireless networks into account, especially for multichannel multiradio WMNs. In addition, for applying our solution to the large-scale scenarios, we introduce a heuristic algorithm that can find a suboptimal solution in polynomial time. The functionality of the presented work is evaluated through extensive numerical results and real testbed implementation. [ABSTRACT FROM AUTHOR]

Ocean and water basically cover the major parts of our planet. To obtain the best utilization of the underlying resources on these parts of the Earth, people have made some research advancements. Specifically, the research on underwater wireless acoustic sensor networks (UWA-SNs) has made great progress. However, wide deployment of UWA-SNs is far from a reality due to several reasons. One important reason is that offshore deployment and field-level experiments of ocean-centric applications are both expensive and labor intensive. Other alternatives to attain this objective are to conduct simulation or experimentation that can reduce cost and accelerate the research activities and their outcomes. However, designing efficient and reliable simulation and experimentation platforms have proven to be more challenging beyond the expectation. In this article, we explore the main techniques (including their pros and cons) and components to develop simulation and experimentation platforms and provide a comprehensive survey report in this area. We classify simulation and experimentation platforms based on some typical criteria and then provide useful guidelines for researchers on choosing suitable platforms in accordance with their requirements. Finally, we address some open and un-resolved issues in this context and provide some suggestions on future research. [ABSTRACT FROM AUTHOR]

Software-defined networking (SDN) provides an unprecedented opportunity to enhance the traffic engineering in IP networks. This emerges from the fine-grained centralized control over routing, which enables an immediate response to network dynamics. However, due to the high operational and performance costs of the migration, network operators prefer a multi-period planning approach for the deployment of SDN-enabled devices. In this letter, we present and evaluate an efficient algorithm to seek a beneficial upgrade policy for a network considering the budget constraints. Our proposed algorithm outperforms the most recent work, particularly for large-scale topologies and an increased number of time periods. [ABSTRACT FROM PUBLISHER]

Non-orthogonal multiple access (NOMA) is envisioned to be one of the most beneficial technologies for next generation wireless networks due to its enhanced performance compared with other conventional radio access techniques. Although the principle of NOMA allows multiple users to use the same frequency resource, due to decoding complication, the information of users in practical systems cannot be decoded successfully if many of them use the same channel. Consequently, assigned spectrum of a system needs to be split into multiple subchannels in order to multiplex that among many users. Uplink resource allocation for such systems is more complicated compared with the downlink ones due to the individual users’ power constraints and the discrete nature of subchannel assignment. In this paper, we propose an uplink subchannel and power allocation scheme for such systems. Due to the NP-hard and non-convex nature of the problem, the complete solution, that optimizes both subchannel assignment and power allocation jointly, is intractable. Consequently, we solve the problem in two steps. First, based on the assumption that the maximal power level of a user is subdivided equally among its allocated subchannels, we apply many-to-many matching model to solve the subchannel-user mapping problem. Then, in order to enhance the performance of the system further, we apply iterative water-filling and geometric programming two power allocation techniques to allocate power in each allocated subchannel-user slot optimally. Extensive simulation has been conducted to verify the effectiveness of the proposed scheme. The results demonstrate that the proposed scheme always outperforms all existing works in this context under all possible scenarios. [ABSTRACT FROM PUBLISHER]

For multisource single amplify-and-forward (AF) relay networks, the optimality of power allocation is revisited from different solution aspect, and a universal optimization framework is proposed. The proposed framework can be used to solve problems that have some common specific structure. As a baseline of the optimization framework, two theorems are proposed to obtain the extreme value of the problem, which is general to realize the optimal resource allocation under some system-specific constraints. Then, based on the proposed theorems, for multiuser single-AF-relay cooperative networks without direct links between sources and destinations, optimal power allocation strategies with low computational complexity are proposed to minimize sum-source-power consumption and sum-system-power consumption. When the number of mobile user is $n$, the overall complexity of both power allocation schemes is \mbox{O}(n) , which is lower than or same as the existing strategies. While comparing with the traditional methods to solve this type of problem, extensive numerical simulation has been conducted for different system setups to justify the effectiveness and efficacy of our proposed power allocation schemes. [ABSTRACT FROM PUBLISHER]

Because of its outstanding performance in extending coverage and reducing transmit power, wireless relay cooperation is deemed as one of the promising techniques to realize green-broadband communication in the future. In such relay cooperative systems, optimal power allocation not only can prolong the lifetime of users, but also is an effective way to reduce radiation in the environment. In this paper, for amplify-and-forward-relay cooperative networks, where multiple communication pairs share a common relay, we propose a novel power allocation scheme, which has considerably lower complexity compared with the existing solution schemes. The objective of this paper is to minimize sum-source-power consumption under the conditions that the transmit power of all nodes in the network is constrained and their predetermined target signal-to-noise ratios are satisfied. By providing sufficient analytical evidence, we study the optimality, convergence, and computational complexity of our proposed scheme. Through numerical simulation, we further justify the effectiveness and efficacy of our scheme compared with the existing works. [ABSTRACT FROM AUTHOR]

Assuring quality-of-service (QoS) and guaranteeing effective utilization of resources in a network with heterogeneous traffic is difficult due to the conflicting requirements associated with the different types of traffic. Nevertheless, emerging high-bandwidth third/fourth-generation (3G/4G) technologies, such as Long-Term Evolution (LTE) and LTE-Advanced, are expected to meet the demands of new applications by providing high data rates while simultaneously satisfying each of the individual QoS criteria. The relevant standards recommend single-carrier frequency-division multiple access (SC-FDMA) as the preferred uplink transmission scheme. In this paper, we have formulated the uplink scheduling problem for SC-FDMA-based heterogeneous traffic systems considering both standard specific constraints and individual user QoS requirements. To capture the QoS requirements of different types of traffic, we adopt a utility function that is already used for the downlink operation of code-division multiple-access (CDMA)-based systems. We also consider an opportunity cost function that is based on granular resource utilization to increase the usefulness of our results in practical situations. We have used the dual decomposition method to solve the resulting problem. Given the high computational complexity of the optimal solution, we have also proposed a suboptimal algorithm that is less complex but which offers acceptable performance. In our evaluation of the performance of our proposed uplink scheduling scheme, we assume that the offered traffic is a combination of best effort traffic, traffic with specified delay bounds, and traffic with specified throughput requirements. An extensive set of simulation results demonstrate the efficacy and effectiveness of our scheme compared with existing solutions employed by LTE, which is a representative SC-FDMA-based system. [ABSTRACT FROM PUBLISHER]

Relaying is an emerging technique for 3G/4G high bandwidth networks in order to improve the capacity of edge nodes. As the deployment cost is high, there might be a few number of relay nodes in the cell which can help the edge nodes to transmit their data. From this perspective, one of the key problems in a relay equipped node is to make decision which edge nodes to be helped and how much power need to be disseminated among them in order to maximize the system capacity. This problem is formulated as an optimization problem given individual node and total available power constraints. The objective function of the formulated problem is non-convex, and we solve this using geometric programming (GP)-based method. Since the solution of this problem is computationally expensive, we propose a low complexity suboptimal solution. Having noticed the selfless nature of the sources in the centralized solution, we also provide a game theoretical solution. Two separate Stackelberg games are required to solve this power allocation problem. Moreover, given the total power constraint, a centralized entity is necessary to connect these two games. For assigning power among the sources, the centralized entity plays the buyer level game, whereas the sources act as power sellers. On the other hand, to disseminate relay power among the sources, roles of the players are just interchanged. Besides, before staring the game, the centralized entity determines, of total power, how much is for the transmit operation of the sources and how much is for their relay operation. We show that there is a unique Stackelberg Equilibrium (SE) for both games under certain convergence condition. Finally, the proposed game theoretical solution can achieve comparable performance in terms of resource allocation with the centralized optimal one. [ABSTRACT FROM PUBLISHER]

To reserve or not for bursty video traffic over wireless access networks has been a long-debated issue. For uplink transmissions in infrastructure-based wireless networks and peer-to-peer transmissions in mesh or ad-hoc networks, reservation can ensure the Quality-of-Service (QoS) provisioning at the cost of a lower degree of resource utilization. Contention-based Medium Access Control (MAC) protocols are more flexible and efficient in sharing resources by bursty traffic to achieve a higher multiplexing gain, but the performance may degrade severely when the network is congested and collisions occur frequently. More and more wireless standards adopt a hybrid approach, which allows the coexistence of resource reservation and contention-based MAC protocols. However, how to cost-effectively support video traffic using hybrid MAC protocols is still an open issue. In this paper, we first propose how to use hybrid MAC protocols to support video streaming over wireless networks. Then, we quantify the performance of video traffic over wireless networks with contention-only, reservation-only, and hybrid MAC protocols, respectively. Admission regions for video streams with these three approaches are obtained. Using the standard WiMedia MAC protocols as an example, extensive simulations with a commonly-used network simulator (NS-2) and real video traces are conducted to verify the analysis. The analytical and simulation results reveal the tradeoff between reservation and contention-based medium access strategies, and demonstrate the effectiveness of the hybrid approach. [ABSTRACT FROM AUTHOR]

Recently, in order to satisfy the heavy demands of network capacity brought about by the proliferation of wireless devices, service providers are increasingly deploying heterogeneous cellular networks (HetNets) for boosting the network coverage and capacity. In this paper, we present an iterative energy-efficient scheduling scheme for downlink OFDM-based HetNets with QoS consideration. We formulate the problem as a nonlinear fractional programming problem aiming to maximize the QoS-aware energy efficiency (QEE) in HetNets. In order to solve this problem, we first transform it into a parametric programming problem, which takes QEE as an evolved parameter in the iterative procedure of iterative energy-efficient scheduling scheme. In each iteration, for the given value of QEE, subchannel and power assignment subproblem is a nonlinear nondeterministic polynomial time-hard problem. And hence, we adopt dual decomposition method for obtaining the optimal assignment of subchannels and power of the subproblem for the given value of QEE. Simulation results depict that both outer QEE parameter search and inner subgradient search can converge in a few iterations, and the resultant solutions outperform the equal power allocation scheme [1] and capacity maximization scheme [2] in terms of QEE. Copyright © 2015 John Wiley & Sons, Ltd. [ABSTRACT FROM AUTHOR]

Federated learning (FL) is a new dawn of artificial intelligence (AI), in which machine learning models are constructed in a distributed manner while communicating only model parameters between a centralized aggregator and client internet-of-medical-things (IoMT) nodes. The performance of such a learning technique can be seriously hampered by the activities of a malicious jammer robot. In this paper, we study client selection and channel allocation along with the power control problem of the uplink FL process in IoMT domain under the presence of a jammer from the perspective of long-term learning duration. We map the interaction between the FL network and the jammer in each learning iteration as a Stackelberg game, in which the jammer acts as the leader and the FL network serves as the follower. We consider the client and channel selection as well as the power control jointly as the strategy of this game. Upon formulating the game, we find the joint best response strategy for both types of players by leveraging the difference of convex (DC) programming approach and the dual decomposition technique. Beside the availability of the complete information to both the players, we also study the problem from the perspective that the FL network knows the partial information of the other player. Extensive simulations have been conducted to verify the effectiveness of the proposed algorithms in the jamming game.