In this paper, a cloud-edge-end integrated resource allocation and task scheduling architecture for industrial meta-verse is proposed. By leveraging the digital twins (DTs) technology, the proposed architecture enables factory operators to monitor, review historical data, and adjust equipment settings via a metaverse interface. In view of the dynamic generation, offloading and processing of compute-intensive tasks, an optimization problem on minimizing the system energy consumption is formulated by adhering to the resource constraints and soft task execution deadlines. In order to solve this non-convex nonlinear programming problem that is NP-hard in general, the process of task generation and execution is first modeled as a Markov Decision Process (MDP). Then a Soft Actor-Critic (SAC)-based ‘two-scale’ resource allocation and task scheduling algorithm is proposed to adapt to the varying network conditions while reducing the overhead of frequent decision makings. To be specific, the resource allocation is performed on a slot basis, whereas the task scheduling is performed as per the instantaneous requirements of tasks. Extensive simulations are conducted by comparing with Twin Delayed Deep Deterministic Policy Gradient (TD3), Deep Deterministic Policy Gradient (DDPG) and Proximal Policy Optimization (PPO) algorithms, where the average system energy consumption is reduced by up to 39.3%, 30.1%, and 17.9% respectively by the proposed SAC-based two-scale algorithm.

In order to address latency issues and enhance interactivity in live streaming, a communication-computing-caching (3C) enabled Artificial Intelligence Fog Radio Access Network (AI-FRAN) architecture is proposed in this paper. For a practical scenario featuring dynamic channel conditions and random user requests, a coordinated resource optimization problem is formulated to minimize the average download delay under 3C resource constraints. In order to solve this mixed integer nonlinear programming problem that is NP-hard in general, a hybrid 3C scheduling algorithm based on Twin Delayed Deep Deterministic Policy Gradient and Deep Q-Network (TD3-DQN) is then proposed. Specifically, TD3 is employed to jointly schedule communication and computing resources, while DQN is utilized to make intelligent caching decisions. Extensive simulation results demonstrate a substantial reduction in user download delays when compared to DDPG-DQN, highest-version caching (HVC), HVC without transcoding (HVCwt), and no caching (NC), where the average download delay is reduced by up to 12.0%, 36.4%, 53.3% and 63.0% respectively by the proposed TD3-DQN algorithm.

In this paper, a cache-enabled device-to-device (D2D) network is investigated, for which a three-tier hierarchical architecture is first established, depending on whether the requested content is fetched from the local cache, from a proximal device, or from the cloud. In order to improve the quality of service (QoS), an optimization problem on minimizing the average content provision delay is formulated, which relies on a collaborative learning among devices to make efficient caching decisions continuously. However, traditional federated learning (FL) is incapable of handling the non-IID data distributions among devices, and faces the limitations of a centralized server architecture that is vulnerable to the single point of failure. To address these challenges, a novel decentralized and personalized federated learning (DPFL)-based collaborative D2D caching algorithm is proposed. By exchanging parameters via D2D links, a personalized model is constructed and maintained at each device, which corresponds to a weighted mixture of its local model and the aggregated model from its neighboring devices. By selecting an appropriate mixture factor, simulation results indicate that a balance is reached between the common preferences in the neighborhood and the unique preferences locally. To be specific, the average content provision delay of the proposed DPFL-based collaborative D2D caching algorithm is reduced by 4.68%, 4.31%, 3.27% and 1.53%, as compared to Random Caching (RC), Least Recently Used (LRU), Decentralized Deep Q Learning (DecDQL) and Federated Deep Q Learning (FedDQL), respectively.

The dynamic spectrum sharing technology in cognitive radio can effectively improve the utilization rate of spectrum and relieve the current spectrum pressure. To realize spectrum sharing without trust between SUs and PUs belonging to various operators, a decentralized spectrum sharing scheme based on blockchain is proposed in this paper. A latency model and a decentralization degree model of blockchain-enabled spectrum sharing network are formulated. Network scale law in this paper is defined as the change law of structure and scale in a network. Based on the proposed decentralization degree and latency model, the scale law of blockchain networks under the constraints of latency and decentralization is studied. The simulation analyzes the change of the proportion of consensus nodes in the blockchain network with different requirements for latency and decentralization. The results show that when blockchain networks have both low latency and decentralization characteristics, the upper limit of the total number of nodes is 390, and the value range of the proportion of consensus nodes is between 0.13 and 0.56.

Mobile computing devices are supporting higher information transmission and processing rates. However, the massive amount of information transmitted and processed also means that a vast amount of heat is produced. Due to the constraints of safety and portability, the problem of overheating is becoming a main challenge for mobile computing devices to achieve their ideal performance. Compared to studies on improving the quality of wireless communications and mobile computing, little attention has been paid to the temperature and its direct impact on the computing performance of the device. In this paper, we propose the heat transfer model of the processor of a mobile computing device based on thermodynamics. Considering Landauer’s principle, the maximum computing rate of the processor is derived. Moreover, the performance of devices adopting multi-processor is evaluated, where each processor works periodically to complete the computing task. The conditions for stable computing and the thermodynamic advantage of multi-processor are given. Based on the evaluation, an optimization method is proposed to lower the average temperature of the processor, and simulation results show that the maximum computing rate can be improved up to 20.9%.

In the traditional cloud–edge collaborative Internet of Things (IoT), the high-communication cost and slow convergence of the models often result in high-delay and energy consumption. In this article, a model and data dual-driven resource optimization mechanism is proposed for cloud–edge collaborative IoT applications. The model and data dual-driven mechanism is a joint delay and energy consumption optimization mechanism based on optimal transport and federated actor–critic (OTFAC) is proposed, which combines the offline and online learning. To be specific, in the model-driven offline learning phase, an optimization problem on the bandwidth and computation resource allocation is first formulated. The optimal transport (OT)-based offline optimization model is constructed. And then the OT-based algorithm is proposed to solve the optimization problem. In the data-driven online learning phase, federated actor–critic-based online optimization model are constructed. And then, the federated learning (FL) and actor–critic (AC) learning-based online resource optimization algorithm is designed to further reduce the delay and energy consumption with edge servers serving as local aggregators. Simulation results illustrate that the proposed OTFAC in this article reduces the average delay by 55% and the average energy consumption by 51% as compared with the benchmark hierarchical aggregation method HierFAVG. Compared with benchmark FL-based deep deterministic policy gradient method DDPG, the average delay is reduced by 47% and the average energy consumption is reduced by 43% by the proposed method.

Users’ behaviors in wireless metaverse networks are usually affected by the surrounding people and limited network resources. How to allocate network resources in a human-centric way remains an open problem in wireless metaverse scenarios. To capture the psychological influence of bandwagon effects on users’ behaviors, we first propose bandwagon effect-based metaverse behavior metrics, including the metaverse bandwagon threshold and metaverse bandwagon probability, based on the multi-dimensional contract theory. The bandwagon effect-based metaverse behavior metrics are used to quantify the number of service adopters, which consider both users’ behaviors and resource allocation strategies. Moreover, the metaverse behavior utility is derived for wireless metaverse networks based on the multi-attribute utility theory. To solve the metaverse behavior utility maximization problem, a bandwagon effect optimal transport-based (BEOT) algorithm is proposed to optimize the resource allocation strategies considering users’ behavior characteristics. Compared with the maximum metaverse behavior utility of virtual reality tracking-based resource allocation (VRT), soft actor-critic with graph convolutional networks (SAC-GCN) and the deep Q-learning-based (DQL) algorithms, simulation results show that the maximum metaverse behavior utility of proposed BEOT algorithm is improved by 22.55%, 12.36% and 18.21%, respectively.

In traditional non-orthogonal multiple access (NOMA) systems, the secondary user often encounters decoding challenges caused by a lower signal-to-noise ratio (SNR) as it is assigned less power to prevent the primary user from decoding errors. One way to address this issue is by employing semantic communication, which is known for its robustness in low SNR conditions. In this paper, multi-user semantic communication is investigated in a traditional-semantic hybrid NOMA (TSH-NOMA) system, enabling simultaneous transmissions from both the bit user and the semantic user at the same frequency. However, the compatibility between continuous semantic signals and discrete bit signals remains an open problem. Quantization of semantic features has been attempted to address this problem, but it often results in noticeable performance degradation. To tackle this issue, a novel digital semantic constellation design that allows the encoder to generate a semantic constellation similar to typical digital modulation is proposed. This enables the semantic user’s signal to be readily transmitted over the digital channel without affecting the traditional bit user. Simulation results demonstrate that the proposed method allows for the transmission of the semantic user’s signal on the digital channel with negligible performance degradation. Furthermore, the secondary user in the proposed TSH-NOMA system exhibits considerable performance improvement over its counterpart in traditional NOMA, particularly at low-to-medium SNR, without compromising the performance of the primary user.

Security and energy efficiency are two critical design metrics in the future wireless communication networks. In this paper, a Reconfigurable Intelligent Surface (RIS) assisted multiple-input single-output (MISO) downlink network is investigated. In order to counteract the multiple randomly distributed eavesdroppers, an artificial noise (AN) jamming scheme is proposed. For achieving a desirable performance tradeoff between security and energy efficiency, a new metric of secrecy energy efficiency (SEE) is proposed. In order to maximize the SEE, an optimization problem is then formulated, subject to the maximum transmit power limit and minimum required data rate. For tackling the challenging non-convex fractional order problem with multiple mutually coupled variables, an efficient alternating optimization algorithm based on Dinkelbach and semi-definite programming (SDP) relaxation is proposed. This corresponds to a joint design of the transmit pre-coding matrix, the covariance matrix of AN, and the phase shifts of RIS. Simulation results demonstrate that an inherent trade-off exists between the secrecy rate and SEE, and significant performance gains in terms of SEE are achieved by the proposed scheme as compared to existing schemes. Furthermore, the introduction of AN into the transmit beamforming plays a crucial role in enhancing the SEE, especially as the number of eavesdroppers increases.

In recent years, the emergence of targeted cleanlabel poisoning attacks, which maliciously influence the training data without controlling over the labeling process to manipulate the behavior of the predictive model, is shown to be crucial threats to compromise deep learning systems. Prior targeted clean-label poisoning attacks have been demonstrated to target only one sample at a time, which is not always applicable in restricted real-world situations. In this paper, we explore targeted clean-label poisoning attacks on a per-class basis, which refers to misclassify samples from a victim class to the desired class while maintaining the classification accuracy of samples on other classes in multi-class classification tasks. To achieve this, we present the first class-targeted clean-label poisoning attack, called CTCL, which firsts craft clean label poisons along with multiple directions in the target feature space and enhance the attacking capability of poisons by reducing their the feature information of the target class. We illustrate the effectiveness of the proposed CTCL on various deep neural network models. The experiment results demonstrate that our attack is effective, with the attacking success rate over 80% compared to the other two baseline attacks on average, while the detection accuracy of state-of-the-art defenses is lower than 65% illustrating that CTCL can escape the detection of existing defenses readily.

Semantic communication systems have been proposed for efficient text transmissions in recent years. However, most existing methods mainly focus on word-level recovery, which fails to reflect the semantic fidelity of the model. By leveraging the sentence-level semantic information, a semantic communication system architecture is proposed within the framework of deep learning based joint source-channel coding. Then, in order to evaluate the semantic fidelity of the system, a new metric of Semantic Similarity is proposed, which is more sensitive to semantic differences as compared to existing evaluation metrics, e.g., bilingual evaluation understudy. For guaranteeing the consistency between model training and performance evaluation, the proposed new metric is then incorporated into the objective function, based on which end-to-end performance optimization is performed. Extensive simulation results on European Parliament dataset demonstrate the effectiveness and necessity of the proposed method. Compared with the state-of-the-art, e.g., DeepSC, an improvement of up to 10% is achieved in terms of Semantic Similarity. Furthermore, significant performance gains are achieved by the proposed method in both regimes of low and medium signal-to-noise ratio, as compared to traditional separate source-channel coding methods.

With the emergence of new services and applications, it becomes indispensable to jointly allocate heterogeneous network resources in face of various quality-of-experience (QoE) requirements. In order to quantitatively characterize the tradeoff between communication, computation and caching (3C), which is still unclear, a typical application of adaptive bitrate (ABR) content streaming is considered in fog radio access networks (F-RAN). For improving the QoE, an optimization problem of minimizing the average delay is first formulated subject to constrained 3C resources. To solve the resulting problem that is NP-hard, it is first relaxed as a linear programming problem, then an alternating direction method of multipliers (ADMM)-based algorithm is proposed, which has the advantages of relatively low complexity and fast convergence. Simulation results show that with a joint allocation of 3C, significant performance gains are achieved by the proposed ADMM-based algorithm. Furthermore, while the scarcity of one resource can be compensated by other resources to a certain extent, it requires a matched allocation of 3C resources to effectively improve QoE and at the same time avoid resource waste.

In this paper, edge caching is investigated subject to time-varying content popularity where no systematic distri-bution of content popularity can be known in advance. For achieving efficient caching updates sequentially, two optimization problems of maximizing the long-term accumulated-cache-hit-ratio (ACHR) and minimizing the long-term average-content-provision-cost (ACPC) are formulated. In order to solve these two problems, a deep deterministic policy gradient (DDPG)-based caching algorithm is proposed, which is capable of pro-cessing large-scale and continuous action space and adjusting the caching strategies based on the historical observations of users' requests. To evaluate the performance of the proposed DDPG-based caching algorithm, a real-world data set from MovieLens is adopted. Simulation results demonstrate that sig-nificant performance gains in terms of both ACHR and ACPC are achieved by the proposed algorithm over existing caching strategies. Furthermore, an inherent performance tradeoff exists between the ACHR and the ACPC, and the balance between these performance metrics requires careful system parameter selection.

In order to enhance the service provisioning to users in the indoor environment, a hybrid visible light communication (VLC)/radio-frequency (RF) Internet of Things (IoT) system is proposed based on simultaneous lightwave information and power transfer (SLIPT). A mobile user equipment, which serves as an off-the-grid relay, is able to extract information from the light-emitting diode source and then forward the processed information to the destination far away, thus dividing the signal transmission into two hops. Specifically, the optical signal received at the relay is separated into alternating current and direct current components for information decoding and energy harvesting, respectively, in the first hop. Then, the energy harvested is used to forward the processed source information to the destination by using RF in the second hop. Subject to the constraints imposed on both the average and the peak powers of the source, the end-to-end outage probability of the system is analytically derived in a closed form. On this basis, the minimization of the end-to-end outage probability is formulated as an optimization problem. This problem is then solved with a joint design of the peak amplitude and the direct current bias of the source transmitter, by trading-off between the performance of two successive hops. Simulation results demonstrate that by using the proposed optimal solution, the information flow and energy flow can be dynamically balanced, resulting in significant performance gains in terms of outage probability and throughput.

In recent years, the conventional degrees of freedom in frequency and time have been fully used. It is difficult to further improve the performance of communication systems with such degrees of freedom. Orbital angular momentum (OAM), which provides a new degree of freedom for millimeter-wave (mmWave) wireless communication systems, has been recognized as a key enabling technique for future mobile communication networks. By combining OAM beams that have theoretically infinite and mutually orthogonal states with the generalized spatial modulation (GSM) strategy, a new OAM-GSM mmWave wireless communication system is designed in this paper. A bit error rate (BER) model of the OAM-GSM system is established based on channel flip precoding. The channel capacity, energy efficiency, and BER of the proposed OAM-GSM mmWave wireless communication system are simulated. Numerical results show that, compared with traditional GSM systems, the OAM-GSM system has more complex transmission and reception mechanisms but the channel capacity and maximum achievable energy efficiency are increased by 80% and 54%, respectively, and the BER drops by 91.5%.

Connected vehicular network is one of the key enablers for next generation cloud/fog-supported autonomous driving vehicles. Most connected vehicular applications require frequent status updates and Age of Information (AoI) is a more relevant metric to evaluate the performance of wireless links between vehicles and cloud/fog servers. This paper introduces a novel proactive and data-driven approach to optimize the driving route with a main objective of guaranteeing the confidence of AoI. In particular, we report a study on three month measurements of a multi-vehicle campus shuttle system connected to cloud/fog servers via a commercial LTE network. We establish empirical models for AoI in connected vehicles and investigate the impact of major factors on the performance of AoI. We also propose a Deep Q-Learning Network (DQN)-based algorithm to decide the optimal driving route for each connected vehicle with maximized confidence level. Numerical results show that the proposed approach can lead to a significant improvement on the AoI confidence for various types of services supported.

This paper studies the potential performance improvement that can be achieved by enabling multi-operator wireless connectivity for cloud/fog computing-connected vehicular systems. Mobile network operator (MNO) selection and switching problem is formulated by jointly considering switching cost, quality-of-service (QoS) variations between MNOs, and the different prices that can be charged by different MNOs as well as cloud and fog servers. A double deep Q network (DQN) based switching policy is proposed and proved to be able to minimize the long-term average cost of each vehicle with guaranteed latency and reliability performance. The performance of the proposed approach is evaluated using the dataset collected in a commercially available city-wide LTE network. Simulation results show that our proposed policy can significantly reduce the cost paid by each fog/cloud-connected vehicle with guaranteed latency services.

Mission-critical Internet of Things (MC-IoT) will play a vital role in remote healthcare, haptic interaction, and industrial automation. On the one hand, in such application fields, haptic applications have become more critical. Benefitting from the development of the fifth-generation (5G) wireless communication networks and the technological advances of Internet of Things (IoT), the tactile Internet (TI), which provides control communications through the transmission of touch and actuation in real time, has been envisioned as a promising enabler of MC-IoT services. On the other hand, different MC-IoT services could have diverse requirements. This requires a flexible network architecture for enabling different MC-IoT services. Network function virtualization (NFV) is a promising method to tackle this issue. To provide MC-IoT services flexibly, in this article, a 5G network architecture based on the NFV technology is designed to support the implementation of the TI. Moreover, a utility function model is proposed for the performance evaluation of the 5G NFV-based TI. Considering the just-noticeable difference (JND) in the human perception and the corresponding network costs on providing MC-IoT services, a human perception-based TI utility optimization (ACTION) algorithm is developed to optimize the utility for 5G NFV-based TI. The simulation results indicate that the maximum utility achieved under the proposed ACTION algorithm is improved by 35.4% to the network slice requests (NSRs) implementation algorithm.

Different from the existing isotropic path loss model, in this paper, we develop an anisotropic path loss model for the fifth generation (5G) multi-input multi-output (MIMO) fractal cellular networks, in which the coverage boundary has the fractal characteristics including the self-similarity and the detailed structure at arbitrarily small scales of the angle domain. Based on the real-world measurement data collected from the Zhangjiang Road in Shanghai, China, we analytically derive the coverage probability, the area spectral efficiency (ASE), and the sum rate for the fractal small cell networks, with the assumption that the path loss exponent follows the Gamma distribution. Simulation results indicate that compared with the conventional isotropic path loss model, the coverage probability under the anisotropic path loss model has been overestimated in small cell networks. With the anisotropic path loss model, the ASE with MIMO technologies is higher than that with single-input single-output (SISO) technologies in the low signal to interference ratio (SIR) regions and is lower than that with SISO technologies in the high SIR regions. Therefore, the coverage model of small cell network needs to be rethought by taking into account the fractal characteristic in wireless channels.

In this paper, content caching is considered at the edge of the network with an objective of offloading recurrent traffic on the capacity-stringent backhaul links to the vicinity of end users. A radio access network equipped with edge servers is considered for caching contents of various sizes, based on which problems of maximizing the edge cache-hit-ratio and minimizing the average content-provisioning cost are respectively formulated. To solve the underlying 0-1 Knapsack problem, a size-weighted popularity (SWP)-based caching framework is proposed, where both content popularity and content size are taken into account when determining the contents to be cached. Depending on the available knowledge and the manner in which the contents are pre-fetched and cached at the edge servers, two algorithms: proactive and reactive, are proposed for the implementation of SWP-based caching. Simulation results are presented to evaluate the performance of our proposed algorithms. We observe a fundamental tradeoff between the average content-provisioning cost and the cache-hit-ratio, and the proactive algorithm outperforms the reactive algorithm.

Exchange of crucial and confidential information in wireless networks leads to the unprecedented attention on the security problem. Though a number of works have studied the physical layer security, the joint optimization of the end-to-end delay management and the physical layer security, which requires a meticulous cross-layer design, has seldom been evaluated in the literature. In this work, by combining the tools from queueing theory and stochastic geometry, we analyze the tradeoff between delay and physical layer security in large wireless networks. Our numerical results reveal that the security performance is better for larger path loss exponent when the density of legitimate nodes is large, and it is reverse when the density is small. Meanwhile, it is observed that under the condition that a certain confidential rate is guaranteed, the delay performance is better in high signal-to-interference ratio regime than that in low SIR regime. In summary, this work provides an understanding and a rule-of-thumb for the practical design of wireless networks where both the delay and the security are key concerns.

We investigate the design of the optimal routing path in a moving vehicles involved the Internet of Things (IoT). In our model, jammers are present to interfere with the information exchange between wireless nodes, leading to a worsened quality of service (QoS) in communications. In addition, the transmit power of each battery-equipped node is constrained to save energy. We propose a three-step optimal routing path algorithm for reliable and energy-efficient communications. Moreover, results show that with the assistance of moving vehicles, the total energy consumed can be reduced to a large extend. We also study the impact on the optimal routing path design and energy consumption which is caused by the path loss, maximum transmit power constrain, QoS requirement, etc.

Exchange of crucial and confidential information leads to the unprecedented attention on the security problem in wireless networks. Even though the security has been studied in a number of works, the joint optimization of the physical layer security and the end-to-end delay management, which requires a meticulous cross-layer design, has seldom been evaluated. In this paper, by combining the tools from stochastic geometry and queueing theory, we analyze the tradeoff between the delay and the security performance in large wireless networks. We further propose a simple transmission mechanism which splits a message into two packets and evaluate its effect on the mean delay and the secrecy outage probability. Our numerical results reveal that the security performance is better for larger path loss exponent when the density of legitimate nodes is large, and it is reverse when the density is small. Moreover, it is observed that by introducing the simple mechanism of message split, the security performance is greatly improved in the backlogged scenario and slightly improved in the dynamic scenario when the density of legitimate transmitters is large. In summary, this paper provides an understanding and a rule-of-thumb for the practical design of wireless networks where both the delay and the security are key concerns.

Being able to provide a relatively easy and inexpensive way to collect data, portable readers have gained increasing popularity in wide-ranging RFID applications. In order to maximize the reader’s battery life, efficient tag identification protocols are of paramount importance in large-scale passive radio frequency identification (RFID) systems. This paper proposes a time- and energy-aware protocol based on$M$-ary collision tree (MCT) for efficient RFID tag identification. Thanks to Manchester encoding, the proposed MCT protocol recursively divides colliding tags into$M$subsets with at least two nonempty ones according to the information of$\log _2 M$colliding bits. Through the new MCT recognition process, MCT can effectively identify all the tags within its reading range. Theoretic analysis demonstrates that it takes the proposed MCT protocol fewer numbers of collision slots and message bits to identify all the tags, which reduces not only the identification time but also the energy cost. Simulation results are also presented to show that compared with other benchmark works, the proposed MCT protocol is able to reduce the average identification time and energy cost by at least 16.12% and 15.73%, respectively.

With the anticipated increase in the number of low power base stations (BSs) deployed in small cell networks, blockage effects becoming more sensitive on wireless transmissions over high spectrums, variable propagation fading scenarios make it hard to describe coverage of small cell networks. In this paper, we propose a dual-directional path loss model cooperating with Line-of-Sight (LoS) and Non-Line-of-Sight (NLoS) transmissions for the fifth generation (5G) fractal small cell networks. Based on the proposed path loss model, a LoS transmission probability is derived as a function of the coordinate azimuth of the BS and the distance between the mobile user (MU) and the BS. Moreover, the coverage probability and the average achievable rate are analyzed for 5G fractal small cell networks. Numerical results imply that the minimum intensity of blockages and the maximum intensity of BSs can not guarantee the maximum average achievable rate in 5G fractal small cell networks. Our results explore the relationship between the anisotropic path loss fading and the small cell coverage in 5G fractal small cell networks.

Existing researches on the theoretical analysis for Proportional Fair (PF) scheduling are mostly based on the full buffer assumption while the user always has a bursty traffic in reality. In this paper, an analytical framework for PF scheduling in an Orthogonal Frequency Division Multiple Access (OFDMA) wireless network is proposed, and the user traffic is set to follow a poisson arrival process. Detailed analysis for each user's Resource Block (RB) competition status is carried out, and an M/M/1 queuing model is adopted for the packet transmission process of each user, with which the analytical expressions for user throughput and packet latency are obtained. Simulation in a single-cell OFDMA network is carried out and the comparision between simulation and analysis results confirms the accuracy of our analysis.

Based on the distinguishing features of multi-tier millimeter wave (mmWave) networks such as different transmit powers, different directivity gains from directional beamforming alignment and path loss laws for line-of-sight (LOS) and non-line-of-sight (NLOS) links, we introduce a normalization model to simplify the analysis of multi-tier mmWave cellular networks. The highlight of the model is that we convert a multi-tier mmWave cellular network into a single-tier mmWave network, where all the base stations (BSs) have the same normalized transmit power 1 and the densities of BSs scaled by LOS or NLOS scaling factors respectively follow piecewise constant function which has multiple demarcation points. On this basis, expressions for computing the coverage probability are obtained in general case with beamforming alignment errors and the special case with perfect beamforming alignment in the communication. According to corresponding numerical exploration, we conclude that the normalization model for multi-tier mmWave cellular networks fully meets requirements of network performance analysis, and it is simpler and clearer than the untransformed model. Besides, an unexpected but sensible finding is that there is an optimal beam width that maximizes coverage probability in the case with beamforming alignment errors.

The orbital angular momentum (OAM) technique provides a new degree of freedom for information transmissions in millimeter wave communications. Considering the spatial distribution characteristics of OAM beams, a new OAM spatial modulation (OAM-SM) millimeter wave communication system is first proposed for future mobile networks. Furthermore, the capacity, average bit error probability, and energy efficiency of OAM-SM millimeter wave communication systems are analytically derived for performance analysis. Compared with the OAM-based multi-input multi-output (MIMO) millimeter wave communication systems, the maximum energy efficiency of OAM-SM millimeter wave communication systems is improved by 227.2%. Moreover, numerical results indicate that the proposed OAM-SM millimeter wave communication systems are more robust to path-loss attenuations than the conventional MIMO millimeter wave communication systems, which makes it suitable for long-range transmissions. Therefore, OAM-SM millimeter wave communication systems provide a great growth space for future mobile networks.

In this paper, content caching is considered in a software-defined hyper-cellular network (SD-HCN) with capacity-limited backhaul connections. To achieve efficient content caching and delivery at the network edge, an analytical framework of minimizing the average content provisioning cost of SD-HCN, e.g., latency, bandwidth, and so on, is first formulated subjected to a sum storage capacity constraint. An optimal solution to this problem requires a joint design of storage allocation and content placement at the centralized control base station (CBS) and distributed traffic base stations (TBSs), which is NP-hard in general. To provide insights, a baseline non-cooperative caching strategy is first introduced between the CBS and TBSs. Then, an efficient cooperative edge caching strategy is proposed by leveraging the vertical cooperation between the CBS and TBSs, and horizontal cooperation between the TBSs. Analytical results demonstrate that the content provisioning cost of SD-HCN is significantly reduced by using the analytically obtained optimal storage allocation between the CBS and TBSs, and the proposed cooperative edge caching strategy always outperforms the non-cooperative caching strategy. Furthermore, by switching between the vertical and horizontal cooperative caching modes, extra performance gains can be achieved by the proposed cooperative edge caching strategy.

Augmented and/or virtual reality (AR/VR) are emerging as one of the main applications in future fifth-generation (5G) networks. To meet the requirements of lower latency and massive data transmission in AR/VR applications, a solution with software-defined networking architecture is proposed for 5G small cell networks. On this basis, a multipath cooperative route (MCR) scheme is proposed to facilitate the AR/VR wireless transmissions in 5G small cell networks, in which the delay of the MCR scheme is analytically studied. Furthermore, a service effective energy (SEE) optimization algorithm is developed for AR/VR wireless transmission in 5G small cell networks. Simulation results indicate that both the delay and SEE of the proposed MCR scheme outperform the delay and SEE of the conventional single-path route scheme in 5G small cell networks.

This paper investigates the energy saving of base station (BS) deployed in a 1-D multi-hop vehicular network with sleep scheduling strategy. We consider cooperative BS scheduling strategy where BSs can switch between sleep and active modes to reduce the average energy consumption utilizing the information of vehicular speeds and locations. Assuming a Poisson distribution of vehicles, we derive an appropriate probability distribution function of distance between two adjacent cluster heads, where a cluster is a maximal set of vehicles in which every two adjacent vehicles can communicate directly when their Euclidean distance is less than or equal to a threshold, known as the communication range of vehicles. Furthermore, the expected value of the sojourn time in the sleep mode and energy saving are obtained. The numerical results show that the sleep scheduling strategy significantly reduces the energy consumption of the base stations.

There is a significant potential in saving energy of cellular networks by switching off some unloaded base stations (BSs). In this paper, we propose a BS switch-off strategy with distance constraints for reducing the energy consumption in random cellular networks. An ergodic capacity and coverage probability of the hard-core point processes (HCPP) random cellular networks are first analyzed. Furthermore, the energy balance of HCPP random cellular networks is proposed. Numerical results indicate that there exists maximal energy balance points in different network environments.

In this paper, a software-defined radio access network (SD-RAN) is considered where content caching is performed at the small-cell base stations (SBSs) under the coordination of a central macro-cell base station (MBS). Two benchmark algorithms are firstly discussed to maximize the content hit ratio within the associated small cells locally and to maximize the content hit ratio within the macro cell, respectively. In order to minimize the content provisioning cost of the entire SD-RAN, it needs to strike a balance between the local hit ratio and the hit ratio within the macro cell. With this inspiration, a heuristic cooperative caching algorithm is proposed by dividing the cache space at each SBS into two portions for storing the duplicated contents and unique contents respectively. The optimal partition factor at which the average content provisioning cost of SD-RAN is minimized is analytically obtained in a closed form.

Dense heterogeneous cellular networks (HCNs) with energy harvesting nodes are promising solutions to meet both the capacity and energy efficiency needs in the next generation cellular networks. This paper studies the system performance of both energy harvesting and information receiving in a K-tier cellular networks with time-switching simultaneous wireless information and power transfer (SWIPT). Based on stochastic geometry theory, we derive the closed-form formulas for the average harvested energy, the average transmission rate and the total power and information gains. Moreover, we propose a scheme that can optimally adjust the bias factors of tier selection while leveraging the system between energy harvesting and information receiving.

With small cell networks becoming core parts of the fifth generation (5G) cellular networks, it is an important problem to evaluate the impact of user mobility on 5G small cell networks. However, the tendency and clustering habits in human activities have not been considered in traditional user mobility models. In this paper, human tendency and clustering behaviors are first considered to evaluate the user mobility performance for 5G small cell networks based on individual mobility model (IMM). As key contributions, user pause probability, user arrival, and departure probabilities are derived in this paper for evaluating the user mobility performance in a hotspot-type 5G small cell network. Furthermore, coverage probabilities of small cell and macro cell BSs are derived for all users in 5G small cell networks, respectively. Compared with the traditional random waypoint (RWP) model, IMM provides a different viewpoint to investigate the impact of human tendency and clustering behaviors on the performance of 5G small cell networks.

In this paper, the spatial modulation (SM) technique is employed at the source and relay nodes in a full-duplex two-way relay channel (FD-TWRC) to support spectral-efficient bi-directional communications while guaranteeing a low cost implementation. Maximum likelihood detectors are employed at each node that is subject to an intrinsic self-loop interference. We first propose a tight upper bound on the average bit error probability (ABEP). Then, based on the ABEP upper bound, an asymptotic ABEP expression is derived in the high signal-to-noise ratio (SNR) regime. Exploiting the asymptotic ABEP, an exact SNR threshold for the selection between FD-TWRC-SM and half-duplex (HD)-TWRC-SM is derived in a closed form, which sheds light on when it is beneficial to select the FD (or HD) mode. In addition, the power allocation (PA) among sources and relay is investigated, through which an optimal PA factor in terms of ABEP is obtained. All analytical results derived in this paper are verified by Monte Carlo simulations, from which some new insights are obtained on the performance of FD-TWRC-SM.

In this paper, we consider a two-path relay channel (TPRC) with the assistance of two decode-and-forward relays alternatively. Upon successfully decoding a source packet, a relay proceeds to forward the decoded packet to the destination, which brings an interference to the other relay. Owing to this inter-relay interference, the decoding result at one relay in the current time slot depends on the decoding result at the other relay in the previous time slot. Exploiting this single-slot memory, the decoding performance of the relays is analyzed using a Markov chain. Furthermore, since the relay transmission is one slot behind the source transmission, the neighboring source packets received at the destination interfere with one another. Then depending on whether a packet is subject to the residual interference from its previous packet, the decoding performance of the destination can be similarly analyzed using a Markov chain. Thus we can obtain the overall outage probability of TPRC in closed-form expressions. Simulation results are provided to demonstrate the performance of the considered TPRC, where the effects of various parameters are evaluated. By comparisons with existing works, a reasonably good performance is achieved for TPRC with only a single-slot delay.

We consider a cooperative multi-path relay channel (MPRC) where multiple half-duplex relays assist in the packet transmissions from a source to its destination. A virtual full-duplex (FD) relaying scheme is proposed that allows the source to transmit a new packet simultaneously with the selected best relay, with the rest of the relays attempting to decode this new packet. Thus, a new source packet can be served in each time slot, as in FD relay systems. Taking into account the effect of inter-relay interference (IRI) that is caused by simultaneous relay and source transmissions, a Markov chain analytical model is used to characterize the decoding performance at the relays, based on which the overall outage probability of MPRC is obtained in closed-form expressions. The asymptotic performance analysis reveals that in low rate scenarios, a close-to-full diversity order is achieved by the proposed scheme while substantially improving the spectrum efficiency. In high rate scenarios, the decoding performance of relays is limited by IRI and the system outage performance experiences an error floor. Simulation results demonstrate the performance gains of the proposed scheme by comparisons with existing half-duplex and FD relay systems in the literature.

Abstract In the fifth generation (5G) wireless communication systems, a majority of the traffic demands are contributed by various multimedia applications. To support the future 5G multimedia communication systems, the massive multiple‐input multiple‐output (MIMO) technique is recognized as a key enabler because of its high spectral efficiency. The massive antennas and radio frequency chains not only improve the implementation cost of 5G wireless communication systems but also result in an intense mutual coupling effect among antennas because of the limited space for deploying antennas. To reduce the cost, an optimal equivalent precoding matrix with the minimum number of radio frequency chains is proposed for 5G multimedia massive MIMO communication systems considering the mutual coupling effect. Moreover, an upper bound of the effective capacity is derived for 5G multimedia massive MIMO communication systems. Two antennas that receive diversity gain models are built and analyzed. The impacts of the antenna spacing, the number of antennas, the quality‐of‐service (QoS) statistical exponent, and the number of independent incident directions on the effective capacity of 5G multimedia massive MIMO communication systems are analyzed. Comparing with the conventional zero‐forcing precoding matrix, simulation results demonstrate that the proposed optimal equivalent precoding matrix can achieve a higher achievable rate for 5G multimedia massive MIMO communication systems. Copyright © 2016 John Wiley & Sons, Ltd.

We consider a cooperative two-path relay channel (TPRC) where a data source transmits new packets to a corresponding destination, with the assistance of two intermediate relays alternatively. When the transmitted source packet is successfully decoded by a relay, the relay proceeds to forward this packet in the subsequent time slot, otherwise it simply stays silent. Due to the inter-relay channels, the decoding result at a relay in the current time slot depends on the decoding result at the other relay in the previous time slot and not on that preceded it. In view of this property, we employ a Markov framework to analyze the decoding performance at the relays. The decoding of successive packets received at the destination can be similarly analyzed by using a Markov chain. Closed-form expressions of the outage probability are derived for TPRC by exploiting the properties of a Markov chain. Numerical results demonstrate that with the proposed scheme, a reasonably good performance is achieved with only a single-slot delay and relatively low complexity.

A cooperative multi-path relay channel (MPRC) with multiple decode-and-forward relay terminals is considered in this paper. In order to enhance the system reliability, the source is encouraged to transmit at a higher power such that more relays can successfully decode the source symbol. This, however, may lead to unnecessary energy waste. For reliable symbol delivery while improving the system energy efficiency, we propose power adaptation schemes at the source and relays depending on the availability of channel state information (CSI). In order to minimize the energy consumption for successfully delivering a source symbol, both the source and relay adaptively select a suitable transmit power from a finite set of discrete power levels. The outage probability and energy efficiency of the cooperative MPRC are analyzed and simulated. Simulation results demonstrate a tradeoff between the outage performance and energy efficiency. Under the same outage performance, the system energy efficiency can be significantly improved by the proposed power adaptation schemes compared to a benchmark case with blind source transmissions.

In this paper, the uplink outage probability and energy efficiency of user terminals are investigated for heterogeneous networks (HetNets). In order to characterize the performance of users in small cells, the probability distribution function (PDF) of the distance between a user terminal and the access point (AP) in the typical small cell is derived in closed-form expressions. On this basis, the signal-to-interference ratio (SIR) of uplink terminals is analyzed to evaluate the performance with APs turning on in small cells. Simulation results show that user's density in small cells has a great impact on the uplink energy efficiency. These results provide some guidelines for developing new energy saving schemes in practical HetNet deployments.

Compared with the conventional hexagonal cellular network structure, Poisson-Voronoi tessellated (PVT) random cellular network models can better capture the topology of real cellular networks. However, the random cellular network models are often complicated to analyze. To overcome this gap, in this paper we propose to analyze the performance of PVT random cellular networks using Markov chains. Using this technique, the blocking probability and the area spectral efficiency (ASE) models are obtained. Numerical results are demonstrated which show that our proposed techniques are effective approaches to evaluate the performance of random cellular networks.

It is of great importance for service providers to evaluate the impact of inter-cell co-channel interference on the achievable data rate in cellular networks. In this paper, the cumulative distribution function (CDF) of the inter-cell interference factor is derived. On this basis, closed-form expression of the maximum achievable rate subject to the interference from finite planar cells is analyzed over Nakagami-m fading uplink channels. Numerical results show that besides Nakagami-m fading severity parameters and path loss coefficient, the maximum achievable rate is also affected by the number of interference cells. According to the numerical results, with an increase in the number of interference cells, the maximum achievable rate is degraded.

This paper investigates the energy efficiency of base station sleep scheduling strategies in 1-D infrastructure-based wireless communication networks formed by vehicles traveling on a highway. In certain scenarios, vehicular speeds and locations can be measured with a high degree of accuracy, and this information can be exploited to reduce the energy consumption of base stations. This paper considers cooperative base station scheduling strategies where base stations can switch between sleep and active modes to reduce the average energy consumption, while guaranteeing the connectivity of every vehicle. Analytical results on the expected amount of energy saving for a base stations are derived, which reveals for the first time the existence of a threshold parameter, determined by both the vehicular density and mobility, below which base stations can switch off and save energy, but above which no energy saving can be achieved.

Recently, issues of power consumption at base stations (BSs) in wireless cellular networks have attracted great interest in both research communities and the industry. In this paper, we investigate the BS power consumption in multiple-input single-output (MISO) Poisson-Voronoi tessellation (PVT) random cellular networks. Taking into account the inter-cell interference, the impact of the traffic demands of users and the spatial traffic intensity on the BS power consumption are jointly considered for MISO PVT random cellular networks. Furthermore, the power consumption required at the BS in a typical PVT cell is modeled through characteristic functions. Simulation results are employed to evaluate the BS power consumption and the performance of the random cellular networks.

We consider a cognitive spectrum sharing system on an interference channel with a cognitive relay (IFC-CR) assisting both the primary and secondary users in forwarding their messages to the respective destination nodes. The CR uses a successive interference cancelation scheme to decode the primary and secondary messages after a first transmission phase. The CR performs power allocation and in the second transmission phase, forwards a linear weighted combination of the decoded primary and secondary messages. We demonstrate that with a properly designed power allocation, secondary spectrum sharing is achieved and at the same time a better performance can be achieved for the primary user than the case without spectrum sharing. The proposed spectrum sharing scheme further does not require any non-causal knowledge of the primary message at the secondary user or at the CR.

In this paper, we consider a hidden terminal scenario where two transmitters A and B, hidden to each other, wish to communicate to a common access point, AP. When a collision occurs at AP, due to the inherent asynchrony between the colliding packets, the mutual interference between them is effectively decreased. This achieves a higher signal-to-interference-plus-noise (SINR) ratio which improves the probability of successfully decoding both colliding packets through conventional successive interference cancellation (SIC). When neither colliding packet can be decoded first through SIC, we propose an enhanced SIC (ESIC) scheme. The proposed decoding scheme does not require synchronization, coordination or power control between the transmitters or a sophisticated coding design. By exploiting the inherent asynchrony between the two colliding packets, there exists, with high probability, an interference-free chunk together with an interfered chunk in a packet ready for decoding. Thus it is still possible for both colliding packets to be recovered eventually from a single collision. Our results demonstrate that through the proposed ESIC scheme, both colliding packets can be recovered with a higher probability thus improving the system throughput.

We consider spectrum sharing in a cognitive radio network where a secondary system co-exists with an automatic repeat-request (ARQ)-based primary system. A cooperate-and-access spectrum sharing protocol is proposed where the secondary system alternates between cooperation and access modes. In the cooperation mode, the secondary system serves as a relay to assist the primary transmission, and in return accumulates credits. The credits allow the secondary system to gain spectrum access by exploiting the ARQ retransmissions of the primary system. We show analytically that through the proposed credit system, as long as the credits accumulated in cooperation mode compensate for the degradation in primary performance during access mode, an equal or higher average throughput is achieved for the primary system than in the case without spectrum sharing, while providing spectrum access opportunities for the secondary system.

In this paper, we will analyze how different amount of receiver side information (RSI) available at the receivers of a broadcast channel will affect the overall network throughput. Two transmission schemes, namely random linear network coding (RLNC) and round robin scheduling (RRS), are considered. Taking into account the differing amount of RSI available at each receiver, closed-form expressions of network throughput and asymptotic throughput at high SNR are obtained for a broadcast channel. Our results show that the asymptotic throughput of RLNC is always no worse than RRS no matter how much RSI is available at each receiver. With a sum constraint for the overall arrival rate of receiver side information in the broadcast channel, we show that the the maximum throughput is achieved for RLNC if each receiver accumulates the same amount of RSI.

In this paper, we consider a wireless multicast network with multiple sources, relays, and destinations. We adopt a multi-hop decode-and-forward relay protocol such that two canonical subnetworks are relevant, namely broadcast channel with receiver side information (BC-RSI) and orthogonal multiple access channel with correlated sources and receiver side information (MAC-CS-RSI). A joint network and channel coding (JNCC) strategy is proposed by exploiting ARQ, RSI, and correlated sources. The proposed JNCC strategy does not require the knowledge of RSI nor any transmit channel state information, such that each transmitter simply performs retransmissions until the intended receivers have accumulated enough mutual information to successfully decode all desired messages. This successful decoding is then conveyed from the receivers to the respective transmitters via an acknowledgement message. To measure the performance of the proposed JNCC strategy with ARQ, we derive closed-form expressions of network throughput by applying the renewal-reward theorem. Analytical results show that the proposed JNCC strategy outperforms, in terms of network throughput, the conventional separate network and channel coding strategy with random linear network coding.

In a typical network coding scenario, a node has to broadcast a set of messages to a group of receiver nodes who have already received a subset of the messages through other links. In this paper, we will first present the achievable rate region for this broadcasting scenario, and then extend the result to the case with retransmissions. With the proposed nonlinear network coding strategy, the achievable rate region can be achieved without requiring the exact knowledge of a priori information available at the receiver nodes. The broadcasting node simply performs retransmissions with independent codebooks until every receiver node has received the whole set of messages. Through the analysis of network throughput, we will show that each receiver node is able to effectively accumulate mutual information over the multiple transmissions thus achieving higher overall throughput than the conventional random linear network coding strategy.

This paper analyzes the outage performance of cooperative protocols for the two-way relaying model in which a half-duplex relay assists in the bi-directional communication of two terminals. We first define the outage events and derive closed form results for the outage probabilities of conventional nonadaptive Amplify-and-Forward (AF) and Decode-and-Forward (DF) two-way relaying protocols.We then consider a simple adaptive protocol in the two-way relaying scenario, which switches between AF and DF depending on the decodability of the two bi-directional data streams at the relay. We also derive the outage probability for this adaptive scheme in closed form expressions, and our results show that it always outperforms the non-adaptive schemes. All the closed form results derived in this paper are applicable to arbitrary channel gains between terminals.