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Internet of Things (IoT) is able to provide various physical objects to exchange their information through the 6G wireless communication network. However, with the large increasing number of the IoT devices (IoDs), the deployment of IoDs faces two basic challenges, i.e., spectrum scarcity and energy limitation. Cooperative spectrum sharing and simultaneous wireless information and power transfer (SWIPT) provide effective ways to improve the spectrum and energy efficiency. In this article, two SWIPT cooperative spectrum sharing methods are proposed to improve the energy and spectrum efficiency for 6G-enabled cognitive IoT network, in which IoDs access to the primary spectrum by serving as orthogonal frequency-division multiplexing (OFDM) relay with the energy harvested from the received radio-frequency (RF) signal. Specifically, in phase1, the IoDs transmitter (DT) in the cognitive IoT network performs information decoding and energy harvesting with the received RF signal. In phase2, DT transmits the signals of the primary system and itself to the corresponding receiver by utilizing orthogonal subcarriers with the harvested energy to avoid the interference. Achievable rates of the cognitive IoT system with amplify-and-forward (AF) and decode-and-forward (DF) relaying mode are maximized through joint power and subcarrier optimization, while ensuring the target rate of the primary system. Simulation results are performed to illustrate the improvement of the spectrum and energy efficiency.

Mobile edge computing (MEC) has been envisioned as a promising approach for enabling the computation-intensive yet latency-sensitive mobile Internet services in future wireless networks. In this paper, we investigate the secrecy based energy-efficient MEC via cooperative Non-orthogonal Multiple Access (NOMA) transmission. We consider that an edge-computing device (ED) offloads its computation-workload to the edge-computing server (ECS) subject to the overhearing-attack of a malicious eavesdropper. To enhance the secrecy of the ED’s offloading transmission, a group of conventional wireless devices (WDs) are scheduled to form a NOMA-transmission group with the ED for sending data to the cellular base station (BS) while providing cooperative jamming to the eavesdropper. We formulate a joint optimization of the ED’s offloaded workload, transmit-power, NOMA-transmission duration as well as the selection of the WDs, with the objective of minimizing the total energy consumption of the ED and the selected WDs, while subject to the ED’s latency-requirement and the selected WDs’ required data-volumes to deliver. Despite the nature of mixed binary and non-convex programming of the formulated problem, we exploit the vertical decomposition and propose a three-layered algorithm for solving it efficiently. To further address the fairness among different WDs, we investigate a system-wise utility maximization problem that accounts for the fairness in the WDs’ delivered data and the total energy consumption of the ED and WDs. By exploiting our previously designed layered-algorithm, we further propose a stochastic learning based algorithm for determining each WD’s optimal data-volume delivered. Numerical results are provided to validate the effectiveness of our proposed algorithms as well as the performance advantage of the secrecy based computation offloading via NOMA.

Deep learning-based algorithms provide a promising solution to efficiently generate offloading decisions in mobile edge computing (MEC) networks. However, considering dynamic MEC devices or offloading tasks, most of them require large-scale training data and long training time to retrain the deep neural networks (DNNs). In this letter, we propose a MEta-Learning-based computation Offloading (MELO) algorithm for dynamic computation tasks in MEC networks. Specifically, it learns from historical MEC task scenarios and adapts to a new MEC task scenario with a few training samples. Numerical results show that the proposed algorithm can adapt to a new MEC task scenario and achieve 99% accuracy via 1-step fine-tuning using only 10 training samples.

Powering cellular networks with hybrid energy supplies is not only environment-friendly but can also reduce the on-grid energy consumption, thus being emerging as a promising solution for green networking. Intelligent management of spectrum and power can increase the network utility in cellular networks with hybrid energy supplies, usually at the cost of higher energy consumption. Unlike prior studies on either the network utility maximization or on-grid energy cost minimization, this paper studies the joint spectrum and power allocation problem that maximizes the system revenue in a heterogeneous small-cell network with hybrid energy supplies. Specifically, the system revenue is considered as the difference between the network utility and on-grid energy cost. By developing the convexity of the optimization problem through transformation and reparameterization, we propose a joint spectrum and power allocation algorithm based on the primal-dual arguments to obtain the optimal solution by iteratively solving the primal and dual sub-problems of the convex optimization problem. To solve the primal sub-problem, we further propose the Lagrangian maximization based on the alternating direction method of multipliers (ADMM), and derive the optimal solution in the closed-form expression at each iteration. It is shown that the proposed joint spectrum and power allocation algorithm approaches the global optimality at the rate of $1/n$ 1 / n with $n$ n being the number of iterations. Also, the proposed ADMM-based Lagrangian maximization algorithm approaches the primal optimal solution with the time complexity of $O(1/\epsilon _r)$ O ( 1 / e r ) iterations with $\epsilon _r$ e r being the termination parameter. Simulation results show that in comparison with the power control with equal frequency allocation algorithm and frequency allocation with equal power allocation algorithms the proposed algorithm increases the system revenue by over 20 and 60 percent without consuming more on-grid energy when the proportional fairness utility and the weighted sum rate utility are considered with the approximate system parameter settings, respectively. Meanwhile, in comparison with the full frequency reuse case, the proposed algorithm increases the system revenue by 20 percent at least in terms of the weighted sum rate utility, although it achieves the similar system revenue when considering the proportional fairness utility. Simulation results also show that our proposed algorithm can perform well under the realistic fast fading channel conditions.

A novel grant-free random access scheme is proposed to support massive connectivity with low access delay and overhead for machine-to-machine communication in massive multiple-input–multiple-output systems. This scheme allows all active user equipments (UEs) to transmit their pilots and uplink messages via the same time–frequency resource and performs the joint active UEs detection and uplink message decoding without channel estimation in one shot by utilizing the proposed ensemble independent component analysis (EICA) decoding algorithm. We call the proposed scheme the EICA-based pilot random access (EICA-PA). We analyze the successful access probability, probability of missed detection, and uplink throughput of the EICA-PA scheme. Numerical results show that the EICA-PA scheme significantly improves the successful access probability and uplink throughput, decreases missed detection probability and provides low-frame error rate at the same time.

In an era of big data, face images captured in social media and forensic investigations, etc., generally lack labels, while the number of identities (clusters) may range from a few dozen to thousands. Therefore, it is of practical importance to cluster a large number of unlabeled face images into an efficient range of identities or even the exact identities, which can avoid image labeling by hand. Here, we propose adaptive facial imagery clustering that involves face representations, spectral clustering, and reinforcement learning (Q-learning). First, we use a deep convolutional neural network (DCNN) to generate face representations, and we adopt a spectral clustering model to construct a similarity matrix and achieve clustering partition. Then, we use an internal evaluation measure (the Davies–Bouldin index) to evaluate the clustering quality. Finally, we adopt Q-learning as the feedback module to build a dynamic multiparameter debugging process. The experimental results on the ORL Face Database show the effectiveness of our method in terms of an optimal number of clusters of 39, which is almost the actual number of 40 clusters, our method can achieve 99.2% clustering accuracy. Subsequent studies should focus on reducing the computational complexity of dealing with more face images.

Federated learning (FL) has been considered as an efficient yet privacy-preserving approach for enabling the distributed learning. There have been many studies investigating the applications of FL in different scenarios, e.g., Internet of Things, Internet of Vehicles, and UAV systems. However, due to delivering the trained model via wireless links, FL may suffer from a potential issue, i.e., some malicious users may intentionally overhear the trained model delivered through the wireless links. In this paper, we investigate the energy optimization for nonorthogonal multiple access (NOMA) assisted with secrecy provisioning. Specifically, we consider that the wireless devices (WDs) adopt NOMA to deliver their respectively trained local models to a base station (BS) which serves a parameter-server, and there exists a malicious node that overhears the parameter-server when delivering the aggregated global model to all WDs. We adopt the physical layer security to quantify the secrecy throughput under the eavesdropping attack and formulate an optimization problem to minimize the overall energy consumption of all the WDs in FL, by jointly optimizing the uplink time, the downlink time, the local model accuracy, and the uplink decoding order of NOMA. In spite of the non-convexity of this joint optimization problem, we propose an efficient algorithm, which is based on the theory of monotonic optimization, for finding the solution. Numerical results show that our proposed algorithm can achieve the almost same solutions as the LINGO's global-solver while reducing more than 90% computation-time than LINGO. Moreover, the results also show that our proposed NOMA decoding scheme can outperform some heuristic decoding schemes.

The Internet of Things (IoT) is an emerging technology that proffers to connect massive smart devices together and to the Internet. On the basis of IoT, a smart city is endowed with real-time monitoring, ubiquitous sensing, universal connectivity, and intelligent information processing and control. An IoT-based smart city can offer various smart services to citizens and administrators, thus improving the utilization of public resources regarding transportation, healthcare, environment, entertainment, and energy. The integration of transmitting, computing, and caching is having a profound impact on the development of flexible and efficient IoT in smart cities. However, with the introduction of ultra dense networking (UDN) and mobile edge computing (MEC), we have to carefully consider a joint problem across the physical layer and MAC layer to enable the efficient transmission, computation, and caching of big IoT data generated by massive IoT devices distributed in a city. In doing so, efficient multiple access and computation offloading should be addressed in the physical layer and MAC layer, respectively. In this article, we propose a scalable and sustainable IoT framework that integrates UDN-based hierarchical multiple access and computation offloading between MEC and cloud to support the smart city vision. The proposed integrated framework can substantially reduce the end-to-end delay and energy consumption of computing data from massive IoT devices. Numerical comparison results are presented to show the efficiency of the proposed framework. In addition, we discuss a number of open research issues in implementing the proposed framework. Introduction

Human facial age estimation has been widely used in many computer vision applications, including security surveillance, forensics, biometrics, human-computer interaction (HCI), and so on. We propose a facial age estimation method oriented to non-ideal facial imagery. The method consists of image preprocessing, feature extraction, and age predication. First, we preprocess non-ideal input images in RGB stream, luminance modified (LM) stream, and YIQ stream. Then, we leverage the deep convolutional neural networks (DCNNs) to extract the feature of images preprocessed in each stream. To reduce the training data volume and training complexity, we adopt the transfer learning to build the DCNN structure. With the extracted feature, the weak classifier equipped at every stream is designed to obtain a weak classification prediction of the age range. Moreover, in order to generate estimation, we use the ensemble learning to fuse the three weak classifiers. We design an integrated strategy algorithm based on the combination of voting method and weighted average method. The simulation results show that our proposed algorithm can improve the an exact match (AEM) and an error of one age category (AEO) by 4.75% and 6.75% compared with the best AEM and AEO of the three weak classifiers. Furthermore, in comparison with the unweighted average method, our proposed algorithm can improve the AEM and AEO by 8.68% and 12.79%, respectively.

Equipped with machine learning (ML) models, unmanned aerial vehicle (UAV) swarms can execute various applications like surveillance and target detection. However, the connections between UAVs and cloud servers cannot be guaranteed, especially when executing massive data. Thus, traditional cloud-centric approach will not be suitable, since it may cause high latency and significant bandwidth consumption. In this work, we propose a federated learning (FL) framework via non-orthogonal multiple access (NOMA) for a UAV swarm which is composed of a leader-UAV and a group of follower-UAVs. Specifically, each follower-UAV updates its local model by using its collected data, and then all follower-UAVs form a NOMA-group to send their respectively trained FL parameters (i.e., the local FL models) to the leader-UAV simultaneously. We formulate a joint optimization of the uplink NOMA-transmission durations, downlink broadcasting duration, as well as the computation-rates of the leader-UAV and all follower-UAVs, aiming at minimizing the latency in executing the FL iterations until reaching a specified accuracy. Numerical results are presented to verify the effectiveness of our proposed algorithm, and demonstrate that the proposed algorithm can outperform some baseline strategies.

Computation offloading via mobile edge computing (MEC) has been considered as one of the promising paradigms for enabling computation-intensive yet latency-sensitive services on resource-limited mobile terminals in future wireless systems. In this paper, we propose a channel sharing assisted multi-user computation offloading in MEC via non-orthogonal multiple access (NOMA), in which a group of edge-computing users (EUs) form a NOMA-group and further reuse the cellular user’s (CU’s) channel for computation-offloading transmission. Specifically, we formulate a joint optimization of the EUs’ offloaded workloads, NOMA-transmission duration, as well as the processing-rate allocations (for different EUs) at the edge computing server (ECS), with the objective of minimizing the overall latency for all EUs to complete their tasks. In spite of the non-convexity of the joint optimization, we identify the structural feature of the optimal offloading solution and propose a layered-algorithm for finding the solution efficiently. Numerical results are provided to validate the effectiveness of our proposed algorithm as well as the advantage of our multi-user offloading scheme via NOMA.

Non-orthogonal multiple access (NOMA) has been considered as a promising scheme for enabling ultra-high throughput transmission and massive-connectivity in next generation wireless systems. In this paper, we investigate the secrecy-based NOMA transmission for encountering the eavesdropping attack. Exploiting the NOMA-users simultaneous transmission as an artificial jamming, we investigate the joint optimization of NOMA-users’ power allocations and the secrecy-provisioning, with the objective of the effective secure throughput of NOMA-users while ensuring the fairness among them. Despite the non-convexity of the formulated joint optimization problem, we explore its hidden feature and design a search algorithm to compute the optimal solution. Numerical results are provided to validate the performance of our proposed algorithm.1

Mobile edge computing (MEC) has been considered as a promising approach for enabling computation-intensive Internet services in future wireless systems. In this paper, we investigate non-orthogonal multiple access (NOMA) assisted MEC, in which edge-computing users (EUs) adopt NOMA to simultaneously offload part of their computation-workloads to the edge-server (ES). To improve the spectrum-efficiency, we consider a paradigm of underlaying device-to-device (D2D) communications, namely, the EUs reuse a cellular user's (CU's) licensed channel for offloading transmission. We firstly characterize the transmit-powers of EUs and CU in this D2D approach, and then formulate a joint optimization of the EUs' computation- workloads offloading and the ES's computation-resource allocation, with the objective of minimizing the latency in completing the EUs' tasks. In spite of the non-convexity of the formulated problem, we exploit its layered structure and propose an efficient algorithm for computing the optimal solution. Numerical results are provided to validate the effectiveness and efficiency of our proposed NOMA assisted MEC via the D2D sharing 1.

Deep learning has recently been successfully applied in automatic modulation classification by extracting and classifying radio features in an end-to-end way. However, deep learning-based radio modulation classifiers are lacking interpretability, and there is little explanation or visibility into what kinds of radio features are extracted and chosen for classification. In this article, we visualize different deep learning-based radio modulation classifiers by introducing a class activation vector. Specifically, both convolutional neural networks (CNN) based classifier and long short-term memory (LSTM) based classifier are separately studied, and their extracted radio features are visualized. We explore different hyperparameter settings via extensive numerical evaluations and show both the CNN-based classifier and LSTM-based classifiers extract similar radio features relating to modulation reference points. In particular, for the LSTM-based classifier, its obtained radio features are similar to the knowledge of human experts. Our numerical results indicate the radio features extracted by deep learning-based classifiers greatly depend on the contents carried by radio signals, and a short radio sample may lead to misclassification.

This paper studies mobile edge computing (MEC) networks where multiple wireless devices (WDs) choose to offload their computation tasks to an edge server. To conserve energy and maintain quality of service for WDs, the optimization of joint offloading decision and bandwidth allocation is formulated as a mixed integer programming problem. However, the problem is computationally limited by the curse of dimensionality, which cannot be solved by general optimization tools in an effective and efficient way, especially for large-scale WDs. In this paper, we propose a distributed deep learning-based offloading (DDLO) algorithm for MEC networks, where multiple parallel DNNs are used to generate offloading decisions. We adopt a shared replay memory to store newly generated offloading decisions which are further to train and improve all DNNs. Extensive numerical results show that the proposed DDLO algorithm can generate near-optimal offloading decisions in less than one second.

Time-division multiple access (TDMA) based medium access control (MAC) protocol provides a promising solution to well support delay-sensitive safety applications in vehicular ad hoc networks, since a time-slotted access scheme ensures the transmission within the ultra-low delays. However, due to the varying vehicle mobility, existing TDMA-based MAC protocols may result in collisions of slot assignment when multiple sets of vehicles move together. To avoid slot-assignment collisions, in this paper, we propose a mobility-aware TDMA MAC, named as MoMAC, which can assign every vehicle a time slot according to the underlying road topology and lane distribution on roads with the consideration of vehicles’ mobilities. In MoMAC, different lanes on the same road segment and different road segments at intersections are associated with disjoint time slot sets. In addition, each vehicle broadcasts safety messages together with the time slot occupying information of neighboring vehicles; by updating time slot occupying information of two-hop neighbors (obtained indirectly from one-hop neighbors), vehicles can detect time slot collisions and access a vacant time slot in a fully distributed way. We demonstrate the efficiency of MoMAC through theoretical analysis and extensive simulations; compared with state-of-the-art TDMA MACs, the transmission collisions can be reduced by $59.2\%$ , and the rate of safety message transmissions/receptions can be greatly enhanced.

The emerging non-orthogonal multiple access (NOMA), which enables mobile users (MUs) to share same frequency channel simultaneously, has been considered as a spectrum-efficient multiple access scheme to accommodate tremendous traffic growth in future cellular networks. In this paper, we investigate the NOMA downlink relay-transmission, in which the macro base station (BS) first uses NOMA to transmit to a group of relays, and all relays then use NOMA to transmit their respectively received data to an MU. In specific, we propose an optimal power allocation problem for the BS and relays to maximize the overall throughput delivered to the MU. Despite the non-convexity of the problem, we adopt the vertical decomposition and propose a layered-algorithm to efficiently compute the optimal power allocation solution. Numerical results show that the proposed NOMA relay-transmission can increase the throughput up to 30 percent compared with the conventional time division multiple access (TDMA) scheme, and we find that increasing the relays’ power capacity can increase the throughput gain of the NOMA relay against the TDMA relay. Furthermore, to improve the throughput under weak channel power gains, we propose a hybrid NOMA (HB-NOMA) relay that adaptively exploits the benefit of NOMA relay and that of the interference-free TDMA relay. By using the throughput provided by the HB-NOMA relay for each individual MU, we study the multi-MUs scenario and investigate the multi-MUs scheduling problem over a long-term period to maximize the overall utility of all MUs. Numerical results demonstrate the performance advantage of the proposed multi-MUs scheduling that adopts the HB-NOMA relay-transmission.

The rapid growth of mobile traffic has heavily overloaded the cellular networks, making it increasingly desirable to offload mobile users’ (MUs’) traffic to small-cell networks. In this paper, we study the MUs’ optimal uplink traffic offloading scheme based on the new paradigm of small-cell dual-connectivity (DC). Through DC, an MU can flexibly schedule its traffic between a macro-cell base station (BS) and a small-cell access point (AP) via two different radio interfaces. To optimize the overall network radio resource usage, we jointly optimize the BS’ bandwidth allocation as well as the MUs’ traffic scheduling and power allocation. Specifically, for reducing the bandwidth usage, the BS prefers to allocate the MUs small amount of bandwidth to encourage the MUs to utilize the small-cell networks. However, excessive traffic offloading can lead to severe interferences among MUs, which increase the MUs’ power consumption. Hence, our joint optimization strikes a proper balance between these two aspects. Despite the non-convexity of the proposed joint optimization problem, we propose an efficient algorithm to compute the optimal offloading solution. The key idea is to exploit the layered-structure of the joint optimization problem, and decompose it into the BS’ bandwidth allocation problem (on the top-level) and the MUs’ traffic scheduling and power allocation problem (as a subproblem). Such a decomposition enables us to exploit the hidden convexity of the MUs’ problem and the monotonic structure of the BS’ problem for an effective algorithm design. Numerical results show that our proposed algorithm can achieve the global optimum solution with significantly reduced computational time. Moreover, the proposed traffic offloading scheme can significantly reduce the overall system cost, in comparison with using the fixed bandwidth allocation or traffic scheduling schemes.

As cellular networks are evolving toward the fifth generation/long-term evolution systems, cellular radio access networks are expected to provide high throughput and reliable connectivity for massive number of smart devices (SDs), which leads to the emerging narrowband Internet of Things (NB-IoT), a cellular-assisted low-power wide area IoT system. Driven by the potential critical missions, such as transportation safety and video surveillance that require high throughput and low-power consumption, we investigate the small-cell assisted traffic offloading for NB-IoT systems. Taking into account the offloading through small cells operating on unlicensed bands, we account for the secrecy-outage issue in which some malicious eavesdroppers might intentionally overhead the offloaded data delivered to small cells. We first formulate a joint traffic scheduling and power allocation problem to minimize the total power consumption of SDs, while satisfying both the traffic throughput requirement and secrecy-requirement. Despite the nonconvexity of the problem, we propose an efficient algorithm to compute the optimal offloading solution. With the per-SD’s optimal offloading solution, we further investigate a multi-SDs multi access-points (APs) scenario, in which different SDs select different APs for providing offloading service to minimize the overall offloading-cost for all SDs. Specifically, we formulate an optimal SD-AP pairing problem to find the optimal pairing between the SDs and APs. Numerical results have been provided to validate our proposed algorithm and show the performance gain of our proposed traffic offloading for the NB-IoT systems.

The growing developments in vehicular networks and vehicular Internet services have yielded a variety of computation-intensive applications, resulting in great pressure on vehicles equipped with limited computation resources. The cloud/ edge-based service, which enables in-motion vehicles to actively offload computation tasks to cloud/ edge servers, has provided a promising approach to address the intensive computation burden. However, due to the possibility of disclosing private data, offloading computation tasks suffers from potential eavesdropping attacks. In this article, we focus on the eavesdropping attack when vehicular users (VUs) deliver computation tasks to cloud/edge servers over radio frequency channels. We take the tool of physical layer security and investigate resource management for secrecy provisioning when the VUs offload computation tasks. We then discuss three promising technologies, including non-orthogonal multiple access, multi-access assisted computation offloading, and mobility- and delay-aware offloading, which facilitate the enhancement of secrecy against the eavesdropping attack. Finally, as a detailed example of the multi-access assisted computation offloading, we present a case study on the optimal dual-connectivity- assisted computation task offloading with secrecy provisioning and show the performance of the proposed computation offloading.

The radio resource management for relay-assisted access where every terminal user communicates with the base station through relay users is studied. In particular, the problem formulation is to maximise the total utility across relay users and terminal users under the bandwidth constraint of device-to-device (D2D) links. To solve such an optimisation problem, the authors first explore the diversity of channels between the relay users and the terminal users based on the maximum weighted bipartite matching theory, and adopt Hungarian algorithm to select the best relay user for each terminal user. Then, to stimulate relay users to participate in the cooperation, they design a two-stage Stackelberg game to jointly maximise the utilities of the selected relay user and the terminal user. In this doing, every terminal user can obtain the optimal data rate with the aid of relay. Finally, the authors' simulations show that the proposed relay user selection and game-theoretic resource allocation scheme can effectively improve the downloading rates of terminal users and achieve a ‘win-win’ strategy between the terminal users and relay users for relay-assisted access using D2D communications.

The recent paradigm of small cell dual-connectivity (DC) provides a promising solution to facilitate mobile users’ (MUs’) traffic offloading in heterogeneous networks. With DC, an MU can flexibly schedule its traffic to macrocell base station (mBS) and offload data to small-cell access point (sAP). However, a malicious node might intentionally eavesdrop the MU's offloaded data, which could lead to the secrecy exposure. In this paper, we investigate the optimal resource allocation for the MUs’ traffic offloading via DC with guaranteed secrecy. First, we study a single-MU single-sAP case and formulate a joint optimization of the MU's traffic scheduling, power allocation, and bandwidth usage for traffic offloading, which aims to minimize the MU's overall resource usage including the power consumption and bandwidth usage. Although the joint optimization problem is nonconvex, we propose an efficient algorithm to obtain the optimal offloading solution. Second, by using the single-MU's optimal offloading solution, we study the multi-MU multi-sAP case and formulate an optimal offloading-selection problem that aims to maximize the overall served MUs’ throughput with guaranteed secrecy, while taking into account the mBS's and sAPs’ limited bandwidths and the sAPs’ limited backhaul capacities. Despite the NP-hardness of the formulated offloading-selection problem, we propose an efficient heuristic algorithm to achieve the offloading-selection solution. Numerical results are provided to validate the performance gain of the proposed traffic offloading schemes with guaranteed secrecy.

Recently, the concept of 5G vehicular smallcell networking (V-SCN) has been proposed to meet the growing demand of mobile data services in vehicular communications. However, it is a great challenge to explore spectrum and energy efficiency due to the fast vehicle mobility and varying communication environment. In this article, we focus on critical issues, such as interference management and handover, when employing 5G V-SCNs. In order to solve these issues, the network architecture embedded with NOMA is deigned. Furthermore, we propose a hierarchical power control solution to perform the joint optimization of cell association and power control to improve the spectrum and energy efficiency in NOMA-enabled 5G V-SCNs. Numerical comparison results provide some guidelines for developing NOMA-enabled 5G V-SCNs in an economical and highly energy-efficient manner.

The recent small-cell dual-connectivity (DC) in 3GPP specification enables a mobile user (MU) to communicate with the macrocell and offload traffic through a small cell, which facilitates a flexible traffic offloading. The advanced non-orthogonal multiple access (NOMA) further enables small/macro cells to simultaneously accommodate multiple MUs’ offloading using the same channel, which improves spectrum-efficiency. In this letter, driven by the growing demand for greening cellular systems, we investigate the energy-efficient NOMA-enabled traffic offloading through DC, which aims to minimize the total power consumption of the small and macro cells, while satisfying each MU’s QoS-requirement. The optimal traffic offloading corresponds to a two-sided power allocation problem, which, however, is nonconvex. By exploiting the feature of DC, we equivalently transform this power allocation problem into a convex rate-splitting problem, which can be efficiently solved. Numerical results are provided to validate the advantage in saving power consumption by using the proposed optimal NOMA-enabled traffic offloading scheme.

In this paper, we investigate the cooperative traffic offloading among mobiles devices (MDs) which are interested in receiving a common content from a cellular base station (BS). For offloading traffic, the BS first sends the content to some selected MDs which then broadcast the received data to the other MDs, such that each MD can receive the entire content simultaneously. Due to each MD's limited transmit-power and energy budget, the transmission rate of the content should be properly designed, since it strongly influences whether and how long each MD can perform relaying. Therefore, different from most existing MDs cooperative schemes, we focus on a novel joint optimization of the content transmission rate and each MD's relay-duration, with the objective of minimizing the system cost accounting for the energy consumption and the cellular-link usage. To tackle with the technical challenge due to the coupling effect between the content transmission rate and each MD's relay-duration, we exploit the decomposable property of the joint optimization problem, based on which we characterize different possible cases for achieving the optimal solution. We then derive the optimal solution for each case analytically, and further propose an efficient algorithm for finding the globally optimal solution of the original joint optimization problem. Numerical results are provided to validate the proposed algorithm (including its accuracy and computational efficiency) and demonstrate that the optimal MDs’ cooperative offloading can significantly reduce the system cost compared to some heuristic schemes. Several interesting insights about the cooperative offloading are also obtained.

Powering relays with harvested renewable ambient energy has been emerging as a promising solution to reduce the on-grid energy consumption and greenhouse gas emissions in green relaying communication networks. In this paper, we study the joint time scheduling and power allocation problem for the Decode-and-Forward energy-harvesting relay communication network. Particularly, our goal is to maximize the end-to-end throughput by a deadline subject to the finite data and energy storage. Due to the multi-slot optimization, the traditional deep reinforcement learning (RL) framework cannot be directly applied to obtain the optimal solution of maximizing the end-to-end throughput by a deadline in the online manner. To this end, we explore a novel deep reinforcement learning framework consisting of multiple computation units to obtain the online time scheduling and power allocation based on the current causal knowledge of energy arrivals and channel fading at each time slot. Simulation results show that the proposed deep reinforcement learning based algorithm can achieve more than 90% of maximum end-to-end throughput.

This paper studies mobile edge computing (MEC) networks where multiple wireless devices (WDs) offload their computation tasks to multiple edge servers and one cloud server. Considering different real-time computation tasks at different WDs, every task is decided to be processed locally at its WD or to be offloaded to and processed at one of the edge servers or the cloud server. In this paper, we investigate low-complexity computation offloading policies to guarantee quality of service of the MEC network and to minimize WDs&rsquo, energy consumption. Specifically, both a linear programing relaxation-based (LR-based) algorithm and a distributed deep learning-based offloading (DDLO) algorithm are independently studied for MEC networks. We further propose a heterogeneous DDLO to achieve better convergence performance than DDLO. Extensive numerical results show that the DDLO algorithms guarantee better performance than the LR-based algorithm. Furthermore, the DDLO algorithm generates an offloading decision in less than 1 millisecond, which is several orders faster than the LR-based algorithm.

In this work, we investigate the non-orthogonal multiple access (NOMA) enabled power allocation for cooperative jamming under a two-user downlink scenario. In particular, we consider that there exists a malicious eavesdropper overhearing the data transmission of the mobile user (MU) with a stronger channel power gain. Meanwhile, exploiting the simultaneous transmission in NOMA, we consider that the other MU with a weak channel power gain provides cooperative jamming to the eavesdropper for enhancing the secure throughput of the stronger MU. In particular, we formulate a power allocation problem to maximize the secure throughput of the strong MU while satisfying the throughput requirement of the weak MU. Despite the non-convexity of the formulated problem, we provide an efficient algorithm to compute the optimal solution (i.e., the power allocations for the two users). Numerical results are provided to validate the effectiveness of our proposed algorithm and the performance of our optimal power allocation scheme.

Ambient energy harvesting has emerged as a promising technique to improve the energy efficiency and reduce the total greenhouse gas emissions for green wireless communications. Energy management for throughput maximization under random energy arrivals has been studied extensively in energy harvesting relay communication networks with either finite-size data buffer or finite-size energy storage. However, the problem is still open when the energy harvesting relay node is subject to both finite-size data and energy storage. In this paper, we study the transmission policy of joint time scheduling and power allocation under a transmission deadline, which maximizes the end-to-end system throughput in a two-hop relay communication network where the energy harvesting relay node is equipped with finite-size data buffer and battery. In particular, we first formulate the throughput maximization problem as a convex optimization problem under an offline optimization framework, and obtain the optimal offline time scheduling and power allocation by the Karush–Kuhn–Tucker conditions based on the full knowledge of energy arrivals and channel states. Then, we formulate the throughput maximization problem as a stochastic dynamic programming problem under the online optimization framework, and obtain the optimal online time scheduling and power allocation by solving a series of convex optimizations based on the casual knowledge of energy arrivals and channel states. Finally, to reduce the computation complexity, we further propose two suboptimal online transmission policies. Numerical results show the impacts of battery capacity and buffer size on the maximum throughput of the proposed policies, as well as the balance between the spectrum efficiency and the delay sensitivity.

Exploiting cooperation of mobile stations (MSs) for content distribution has been considered as a promising approach to offload the rapidly growing traffic demand in cellular networks. This paper proposes a framework of MSs’ energy-efficient cooperation for distributing realtime content blocks (CBs). Given a group of MSs interested in downloading a set of CBs from the cellular base station (BS), we propose a cooperative scheme in which, the BS first unicasts the CBs to some selected MSs, who then relay their received data to other MSs such that all MSs can receive their required CBs. Regarding this cooperative scheme, we aim at jointly optimizing each CB’s transmission rate and each MS’s relay-strategy (namely, relaying which subset of CBs and for how long), such that the total energy consumption of the BS and all MSs can be minimized. Such an optimization problem is challenging to solve, since the CBs’ transmission rates significantly influence the MSs’ relay-strategies. To tackle this difficulty, we explore the layered structure of the formulated optimization problem and propose a corresponding layered algorithm (including a top-layer algorithm and a bottom-layer algorithm) to compute the optimal solution. Numerical results are provided to validate our proposed algorithm and show the performance advantages of our proposed scheme for the MSs’ cooperative content distribution.

The aim of the current study was to assess the effect of pinacidil activation of ATP‑sensitive potassium (KATP) channels prior to skin/muscle incision and retraction (SMIR) surgery on peripheral and central sensitization, and investigate molecular interferential targets for preventive analgesia. Male Sprague-Dawley rats were randomly assigned to one of the following five groups: Control, incision (sham surgery), incision plus retraction (SMIR) group, SMIR plus pinacidil (pinacidil) group and the SMIR plus pyrrolidine dithiocarbamate (PDTC) group. The rats in the pinacidil and PDTC groups were intraperitoneally injected with pinacidil or PDTC, respectively, prior to the SMIR procedure. The mechanical withdrawal threshold (MWT) was determined. Western blotting was performed to detect the alterations in the subunits of the KATP channels, Kir6.1 and SUR2, levels of nuclear factor‑κB (NF‑κB) in the tissue around the incision and c‑Jun N‑terminal kinase (JNK) in the spinal cord. There was a significant increase observed in the levels of NF‑κB and JNK following SMIR surgery compared with the control group, and a significant reduction in MWT and the levels of Kir6.1 and SUR2. Additionally, intraperitoneal administration of pinacidil inhibited the reduction in MWT, and Kir6.1 and SUR2 levels. SMIR was observed to result in increases in the levels of NF‑κB and JNK. In addition, in the PDTC group, the alterations in MWT, NF‑κB, JNK, Kir6.1 and SUR2 resulting from SMIR were blocked. The results of the current study suggest that the deteriorations in the microenvironment resulting from the SMIR procedure can induce peripheral and central sensitization, and that the activation of peripheral KATP by pinacidil prior to SMIR is able to inhibit peripheral and central sensitization via the NF‑κB/JNK signaling pathway, thus resulting in preventive analgesia.

Narrow-band Internet of Things (NB-IoT), one of the emerging paradigms of low power wide area networks (LPWAN) for Internet of Things (IoT), has been envisioned as a promising solution to enable massive connectivity, cost-efficient, and highly reliable Internet of Thing (IoT) systems in future smart cities. In this work, we build up an indoor environment-temperature monitoring system based on NB-IoT. We present a detailed design of our system and illustrate the key technologies. Based on our system and the collected data (i.e., the temperature data), we further design an abnormality-detection mechanism based on the support vector machine (SVM). We provide experimental results to show the performance of our designed system and the proposed abnormality-detection mechanism.

We consider a mobile edge computing system that every user has multiple tasks being offloaded to edge server via wireless networks. Our goal is to acquire a satisfactory task offloading and resource allocation decision for each user so as to minimize energy consumption and delay. In this paper, we propose a deep reinforcement learning-based approach to solve joint task offloading and resource allocation problems. Simulation results show that the proposed deep Q-learning-based algorithm can achieve near-optimal performance.

The traditional speed prediction generally utilizes GPS and video images, and thus the prediction accuracy is heavily dependent on environmental factors. To this end, through using RFID (Radio Frequency Identification) data, this paper proposes a vehicle speed prediction algorithm based on Extended Kalman Filter (EKF). Specifically, the proposed algorithm works as follows. First, the RFID reader equipped in the vehicle acquires the state information of tags deployed on the road. Second, The data processing module equipped in the vehicle demodulation and decoding these information. At the same time, the RFID reader sends information to the RFID label after the current information is encoded and modulated. Third, the vehicle predicts the vehicle speed based on the EKF through establishing the state space model with acquired state data. The simulation results show that the proposed algorithm can effectively predict the vehicle speed at 0.6 s.

Narrowband Internet of Things (NB-IoT) is one kind of Low Power Wide Area Network (LPWAN) technologies to achieve the aims of deep coverage penetration, low power consumption, low cost and massive connections. NB-IoT aims at supporting small data and low rate applications. In this paper, we first introduce the general background of NB-IoT. Then we overview several performances of NB-IoT and make comparison between NB-IoT with other wireless communication technologies, including WiFi, ZigBee and etc. Finally, we design an environmental monitoring system based on NB-IoT. In this proposed system, NB-IoT module transmits the data of the sensor nodes to cloud platform.

The rapid development of mobile Internet services has yielded tremendous traffic pressure on cellular radio access networks. Exploiting nonorthogonal multiple access (NOMA) that enables a group of mobile users (MUs) to simultaneously share a same spectrum channel for radio access provides an efficient approach to achieve the goals of ultra-high throughput and massive connectivity in future 5G network. In this paper, we propose a joint time and power allocations for uplink NOMA. We aim at minimizing the delay for transmission and the total energy consumption of all MUs when the MUs send their data to the BS, while satisfying each MU’s constrains on the transmission delay and energy consumption. Numerical results are provided to validate our proposed algorithm and the performance and advantage of our proposed joint optimization for time and power allocations for uplink NOMA.

Non-orthogonal multiple access (NOMA) with successive interference cancellation (SIC) has recently been considered as a key enabling technique for 5G cellular networks to satisfy future users’ network needs, such as ultra-high transmission rate, ultra-high throughput, ultra-low latency and ultra-high density connections. A group of users is allowed to share the same spectrum and multiplex the power domain to transmit data. In this paper, we investigate the optimization of bandwidth allocation and user grouping under the conditions of transmission power limit, bandwidth allocation limit, and user traffic requirements, so that the total resource consumption is minimized. The key idea to solve the problem is to use the layer structure of the problem and divide the problem into the optimization grouping problem and the bandwidth allocation problem. We propose a simulated annealing algorithm to solve the optimization grouping problem.

Future smart grid (SG) has been considered a complex and advanced power system, where energy consumers are connected not only to the traditional energy retailers (e.g., the utility companies), but also to some local energy networks for bidirectional energy trading opportunities. This paper aims to investigate a hybrid energy trading market that is comprised of an external utility company and a local trading market managed by a local trading center (LTC). The existence of local energy market provides new opportunities for the energy consumers and the distributed energy sellers to perform the local energy trading in a cooperative manner such that they all can benefit. This paper first quantifies the respective benefits of the energy consumers and the sellers from the local trading and then investigates how they can optimize their benefits by controlling their energy scheduling in response to the LTC’s pricing. Two different types of the LTC are considered: 1) the nonprofit-oriented LTC, which solely aims at benefiting the energy consumers and the sellers; and 2) the profit-oriented LTC, which aims at maximizing its own profit while guaranteeing the required benefit for each consumer and seller. For each type of the LTC, the optimal trading problem is formulated and the associated algorithm is further proposed to efficiently find the LTC’s optimal price, as well as the optimal energy scheduling for each consumer and seller. Numerical results are provided to validate the benefits of the hybrid energy trading market and the performance of the proposed algorithms.

In this letter, we propose an energy efficient power control scheme for resource sharing between cellular and device-to-device (D2D) users in cellular network assisted D2D communication. We take into account the circuit power consumption of the device-to-device user (DU) and aim at maximizing the DU's energy efficiency while guaranteeing the required throughputs of both the DU and the cellular user. Specifically, we define three different regions for the circuit power consumption of the DU and derive the optimal power control scheme for each region. Moreover, a distributed algorithm is proposed for implementation of the optimal power control scheme.

Dual-connectivity (DC), an emerging paradigm in the recent 3GPP specification, is envisioned as a promising solution to enhance mobile users' (MUs') traffic offloading by aggregating radio resources at both macro and small cells. In this paper, we investigate the energy-efficient DC-enabled traffic offloading through small cells which are powered by the on-grid power supply and harvesting renewable energy from nature. In spite of reducing the on-grid power consumption, powering traffic offloading by energy harvesting (EH) leads to the offloading outage due to the intermittency in EH power-supply, which degrades the offloading throughput. Therefore, to reap both the advantages of DC and the EH-supply, we propose a joint traffic scheduling and power allocation scheme that aims at minimizing the total on-grid power consumption, while accounting for the offloading outage and guaranteeing the MU's quality of service (QoS) requirement. In spite of the non-convexity nature of the joint optimization of traffic scheduling and power allocation, we propose an algorithm to efficiently compute the optimal offloading solution. Numerical results are provided to validate our proposed algorithm and the performance gain of the proposed DC-enabled traffic offloading scheme.

The advanced non-orthogonal multiple access (NOMA) has been considered as a promising scheme to satisfy the ultimate goals of future 5G cellular networks for providing ultra-high throughput and ultra-dense connections. By enabling a group of mobile users (MUs) to simultaneously share a same frequency channel and adopting successive interference cancellation to mitigate the co-channel interference, the NOMA can significantly improve the spectrum efficiency compared with the conventional orthogonal multiple access (OMA). However, due to cellular operators' limited and crowded spectrum resources, a critical question is how to properly size the channel bandwidth for the NOMA- enabled transmission to satisfy all MUs' traffic demands. In this paper, we propose a joint optimization scheme of bandwidth and power allocations for the NOMA- enabled downlink transmission, with the objective of minimizing the overall resource consumption cost that accounts for both the spectrum consumption cost and power consumption cost. In spite of the non-convexity nature of the joint optimization problem, we propose an efficient algorithm to compute the optimal bandwidth allocation and power allocation. Numerical results validate the proposed algorithm and the performance advantage of the proposed NOMA-enabled transmission in saving the overall resource consumption cost.

Offloading data traffic from the conventional macro- cell base stations to densely deployed small-cell base stations (SBSs) has been emerging as a promising technique to support the explosion of data traffic with reduced energy consumption and improved quality of service provision. In this paper, we study the joint spectrum allocation and power allocation problem for the data offloading in energy-harvesting downlink small-cell networks. First, we formulate the resource allocation problem under the revenue maximization criterion, which is expressed as the difference between the total utility across users and the total power payment. By proving the convexity of the problem, we can compute the solution efficiently. Numerical results show that the energy-efficiency can be improved while alleviating the burden of macro-cell base station through using the resource allocation scheme proposed for data offloading.

Ambient energy harvesting has emerged as a promising technique to improve the energy efficiency and reduce the total greenhouse gas emissions for green relay networks. In this paper, we study the joint relay selection and power control problem for the Decode-and-Forward energy-harvesting wireless relay network. In particular, the problem formulation is to maximize the end-to-end system throughput by a deadline under the limitations of data and energy storage. To solve the problem, we decompose such an optimization problem into two subproblems: the joint time scheduling and power control subproblem and the relay selection subproblem. Due to the convex nature of the joint time scheduling and power control subproblem, we derive the optimal solution via the primal decomposition. Based on the obtained system throughput, we can quickly select the best relay that achieves the maximum throughput. Simulation results show that the proposed algorithm can guarantee the maximum system throughput, in comparison with some existing algorithms.

Traffic offloading through small cells is an efficient approach to address the rapidly growing traffic demand in cellular systems. To facilitate traffic offloading, the recent 3GPP Release 12 has proposed a new paradigm of small-cell dual connectivity (DC) that allows a mobile user (MU) to simultaneously communicate with a macro base station (BS) and a small-cell access point (AP) through two different radio interfaces [1, 2]. With DC, an MU can flexibly schedule its traffic to the BS and offload traffic to small-cell AP simultaneously, hence achieving the benefits, such as reducing mobile data cost and improving radio resource utilization. However, in order to achieve these benefits of traffic offloading, we need to properly design the radio resource allocations due to the MUs’ limited radio resources.

With the explosive growth of mobile applications, people are relying more heavily on mobile devices for sharing contents and watching video streaming. Recent report from Cisco has predicted that mobile video will grow at a Compound Annual Growth Rate (CAGR) of 62% between 2015 and 2020, and even three-fourths of mobile traffic will be video streaming by 2020 [1]. Therefore, it is of practical importance for cellular operators to accommodate the mobile traffic (for content distribution and video streaming) in a cost-efficient manner. Offloading traffic based on mobile users (MUs’) device-to-device (D2D) cooperation provides a promising solution to achieve this goal. Given a group of MUs in close proximity and interested in downloading a common video clip, the cellular Base Station (BS) can send part of data to some selected MUs, who then cooperatively offload the received data to other neighboring MUs via the D2D-links. Thanks to short distance among the MUs, such a D2D-assisted traffic offloading can significantly improve radio resource utilization for content distribution, e.g., lowering the energy consumption and reducing the BS’s bandwidth usage [2, 3, 4, 5, 6].

Media-hungry mobile devices and mobile traffic have been experiencing an exponential growth in the past decade. As reported in Cisco Visual Networking Index (VNI) [1], the global mobile traffic grew from 2.1 exabytes/month at the end of 2014 to 3.7 exabytes/month at the end of 2015, corresponding to 74% growth in 2015, and the growth rate is expected to continue at a compound annual growth rate at 53% until 2020. The huge traffic demand has overloaded cellular radio access networks (RANs), which in comparison experience a much slower capacity increase. It becomes a critical challenge for the cellular operators to accommodate the heavy traffic demand in a timely and cost-efficient manner. Directly upgrading RANs may be undesirable from operators’ perspective for the following two concerns: (i) upgrading RANs requires a huge capital investment, which may not be easily recovered even by accommodating the traffic demand, and (ii) acquiring more licensed spectrum bands for the upgraded RANs is difficult and expensive due to the regulation policy. Fortunately, nowadays cellular networks are structured in a multitier architecture, namely, a large number of heterogeneous small cells (such as picocells, femtocells, and WiFi systems) have densely underlaid conventional macrocells [2]. Hence, traffic offloading through small cells provides an effective and cost-efficient way to accommodate mobile users’ traffic.

This letter studies the relay selection scheme for dual-hop decode-and-forward (DF) relay networks over Rayleigh fading channels. In particular, we first provide the problem formulation that minimizes the total transmit power consumption under the constraints of maximum tolerable outage probability and limited power when relay k cooperates. We then derive a closed-form expression of optimal power allocation that solves this optimization problem. Based on the derived closed-form expression, we can quickly select the best relay that consumes the minimum power during the cooperation of transmission.

Future smart grids will be featured by flexible supply-demand management and great penetration of renewable energy to enable efficient and economical grid operations. While the renewable energy offers a cheaper and cleaner energy supply, it introduces supply uncertainty due to the volatility of renewable source. It is therefore of practical importance to investigate the optimal exploitation of renewable energy based on the supply-demand framework of future smart grids, where the energy-providers (or the energy-users) adaptively adjust their energy-provisioning (or energy-demands) according to some system state information that takes into account the volatility of renewable energy. Specifically, we consider cost-efficient energy scheduling for residential smart grids equipped with a centralized renewable energy source. Our scheduling problem aims at: 1) quantifying the optimal utilization of renewable energy that achieves the tradeoff between the system-wide benefit from exploiting the renewable energy and the associated cost due to its volatility; and 2) evaluating how the volatility of renewable energy influences its optimal exploitation. We also propose computationally efficient and distributed algorithms to determine the optimal exploitation of renewable energy as well as the associated energy scheduling decisions.

We investigate mobile user's (MU's) uplink traffic offloading based on the new paradigm of small-cell dual-connectivity (DC) that allows a MU to simultaneously offload traffic to a small-cell base station (sBS) and send data to a macro base station (mBS). In spite of the benefit of reducing the MU's power consumption, traffic offloading via DC leads to a security issue, i.e., a malicious node close to the sBS might intentionally eavesdrop the MU's offloaded data by receiving the MU's radio signal. Therefore, we formulate an optimization problem that jointly determines the MU's traffic scheduling and transmit-powers to the sBS and mBS, with the objective of minimizing the MU's total power consumption, while guaranteeing both the MU's secrecy requirement and traffic demand requirement. To solve the formulated joint optimization problem, we propose an effective layered-algorithm (which exploits the hidden convexity of the problem) to solve it and compute the MU's optimal offloading solution. Numerical results validate the efficiency of our algorithm and the significant influences of the MU's secrecy requirement.

In this paper, we investigate the energy-aware data- offloading of mobile user (MU) which schedules its traffic demand to a macro Base Station (BS) and a small-cell access point (AP) simultaneously. For saving the usage of licensed spectrum, we consider that the MU uses unlicensed spectrum to offload data. The open access of unlicensed spectrum, however, results in that the MU's data offloading suffer from uncontrollable interference, which comprises the benefit of data offloading. We propose an outage-probability to quantify such an adverse influence and formulate a joint rate-splitting and power allocation problem to minimize a system-wise cost accounting for both the MU's power consumption and the BS's licensed channel usage. Despite the non-convexity of the joint optimization problem, we transform it into three rate- allocation problems under different cases and derive the respective optimal solutions, which yield the globally optimal solution for the original problem. Numerical results are provided to validate the optimal offloading-solution.