This study proposes a low-complexity genetic algorithm-based (LC-GA) detector to achieve the complexity-accuracy trade-off in non-coherent short-packet communication (SPC) employing modulation on conjugate-reciprocal zeros (MOCZ). This study rigorously proves that the inverse matrix in the maximum likelihood (ML) detector is a Hermitian positive definite matrix. In addition, by exploiting Cholesky decomposition, the computationally intensive full matrix inversion is reduced to the lower triangular matrix inversion, thus significantly decreasing the fitness function’s complexity under the ML criterion. The proposed genetic algorithm (GA) incorporates tailored crossover and mutation operators combined with an elitism strategy and achieves optimal detection accuracy without exhaustive search. Further, single-input multiple-output (SIMO) diversity reception is validated through simulations to combat frequency-selective fading. The results demonstrate that for transmitted data length ofK= 16, the LC-GA detector can achieve identical bit error rate (BER) performance as the ML detector with 88% fewer iterations, yielding the SNR improvements of 0.5 dB and 1.2 dB over the Viterbi and direct zero-testing (DiZeT) detectors at BER = 10−3, respectively. Similarly, forK= 32, the proposed LC-GA detector reduces the iteration number by six orders of magnitude compared to the ML detector, which requires 1010iterations and is computationally prohibitive, while maintaining the SNR improvements of 0.5 dB and 1.3 dB compared to the existing detectors at BER = 10−3.

Pinching antenna system (PASS) configures the positions of pinching antennas (PAs) along dielectric waveguides to change both large-scale fading and small-scale scattering, which is known as pinching beamforming. A novel non-orthogonal multiple access (NOMA) assisted PASS framework is proposed for downlink multi-user multiple-input multiple-output (MIMO) communications. The transmit power minimization problem is formulated to jointly optimize the transmit beamforming, pinching beamforming, and power allocation. To solve this highly nonconvex problem, both gradient-based and swarm-based optimization methods are developed. 1) For gradient-based method, a majorization-minimization and penalty dual decomposition (MM-PDD) algorithm is developed. The Lipschitz gradient surrogate function is constructed based on MM to tackle the nonconvex terms of this problem. Then, the joint optimization problem is decomposed into subproblems that are alternatively optimized based on PDD to obtain stationary closed-form solutions. 2) For swarm-based method, a fast-convergent particle swarm optimization and zero forcing (PSO-ZF) algorithm is proposed. Specifically, the PA position-seeking particles are constructed to explore high-quality pinching beamforming solutions. Moreover, ZF-based transmit beamforming is utilized by each particle for fast fitness function evaluation. Simulation results demonstrate that: i) The proposed NOMA assisted PASS and algorithms outperforms the conventional NOMA assisted massive antenna system. The proposed framework reduces over 95.22% transmit power compared to conventional massive MIMO-NOMA systems. ii) Swarm-based optimization outperforms gradient-based optimization by searching effective solution subspace to avoid stuck in undesirable local optima.

This paper investigates energy-minimal data collection from multiple low-power ground devices (GDs) adopting a rotary-wing unmanned aerial vehicle (UAV). Unlike most prior works that assume flexible trajectories, the UAV in our study adheres to a fixed or predetermined flight path, due to practical requirements from e.g. patrol and inspection missions. To improve communication performance and prolong GDs’ operational lifetime, the UAV employs a directional antenna for wirelessly transferring energy to the GDs before collecting their data. We jointly optimize the UAV’s flight speeds, hovering locations, and radio resource allocation along the predefined flight path to minimize the UAV’s total energy consumption. For acyclic flight paths, we show that the UAV’s propulsion energy consumption is a strictly convex function of flight speed. However, the fixed path imposes a stringent nonconvex constraint, complicating the optimization. To overcome this challenge, we decompose the problem into two layers and solve it by proposing a novel monotonic optimization method in polar coordinates, referred to aspolar polyblock approximation. This method guarantees a globally optimal solution under mild conditions. Additionally, we propose a low-complexity suboptimal algorithm to balance system performance and computational efficiency. Simulation results show that both the proposed optimal and suboptimal algorithms can effectively mitigate the limitation of a fixed flight path, resulting in significant reductions in the UAV’s energy consumption during data collection, with more energy savings achieved as antenna directivity increases.

Although the latest artificial intelligence technologies can greatly improve work efficiency by automatically generating feasible solutions in the digital world (DW), they are incapable of discovering or creating new knowledge, i.e., lack of human intelligence or creativity. To break this limitation, this article describes and elaborates the masterplan of the digital intelligent world (DIW), wherein everyone has an intelligent agent (IA) for searching, exchanging, and processing information and knowledge autonomously. First, a data-information-knowledge-intelligence (DIKI) model is proposed to illustrate the challenges of creating intelligence from raw data, and of realizing the DIW from the DW. Specifically, the DIW adopts knowledge-driven approaches and could achieve huge productivity enhancement through cross-domain innovations and deep intelligentization with broader creativity. Second, at the individual level, a knowledge processing architecture of IA is defined to support knowledge-centric operations and services. Third, at the system level, a framework of knowledge market (KM) is established for fair, effective, and autonomous collaborations among massive IAs. Inspired by basic laws in statistical thermodynamics, information sciences, and economics, three fundamental principles are developed and discussed for guaranteeing a prosperous KM and the sustainable DIW.

The popularity of artificial intelligence generated content (AIGC) is prompting the deployment of large language model (LLM) from cloud to edge networks, leading to mobile edge generation (MEG). Due to high latency and limited computational capabilities of mobile devices, personalized image generation for mobile healthcare and education requires edge-mobile generation paradigm. In this paper, a novel non-orthogonal multiple access (NOMA) assisted multi-user MEG framework is proposed for text-guided mobile image generation. NOMA enables concurrent access from multiple user equipments (UEs) to the edge-deployed large model, facilitating adjustable generation splitting. Specifically, the edge server (ES) partially generates the image and transmits it via downlink NOMA, while UEs complete the remaining parts using lightweight models. Both unlimited and limited energy budget scenarios are considered. 1) For unlimited energy budget, a joint generation splitting ratio and NOMA power allocation optimization problem is formulated, which minimizes the maximum (min-max) latency of UEs to ensure fairness. The closed-form globally optimal solutions based on Karush-Kuhn-Tucker (KKT) and Lambert-W theory are derived. Moreover, the superiority of MEG-NOMA over conventional MEG-orthogonal multiple access (OMA) is mathematically proved. 2) For limited energy budget, a multi-objective programming problem is formulated to minimize the latency of each UE, which leads to a user-centric latency minimization problem. The closed-form solutions of generation splitting ratio and power allocation are derived. Simulation results illustrate that the proposed MEG-NOMA outperforms the MEG-OMA in both two-user and multi-user cases. Compared to conventional MEG-OMA, the MEG-NOMA framework reduces the min-max latency and the user-centric latency by 33.01% and 9.86%, respectively.

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.

This paper studies resource allocation for a reconfigurable intelligent surface (RIS)-assisted mixed near- and far-field communication utilizing a directional antenna. The resource allocation algorithm is designed as a non-convex optimization problem to maximize the data rate in the communication system. To circumvent the problem intractability, the non-convex problem is transformed into a standard semidefinite relaxation (SDR) programming problem by exploiting radiation field electromagnetic theory. This allows us to characterize the solution structure of the joint height and axis of the directional antenna and the RIS phase shift matrix that facilitates the design of an efficient iterative algorithm for obtaining the solution. We reveal that the maximal data rate of RIS-assisted mixed near- and far-field communication is achieved if the axis of the directional antenna aligns the plane that includes both the center of the RIS and the UE's location. Simulation results demonstrate a significant data rate improvement with the proposed resource allocation compared to the two baseline schemes.

Network slicing is one of the key enablers for B5G and 6G to support diversified IoT services and application scenarios. In this paper, the problem of network slicing for supporting time-sensitive edge learning in massive-scale IoT networks is studied. In particular, a novel distributed network slicing framework based on a new control plane entity, called D-orchestrator, is proposed. This framework can jointly optimize the allocation and orchestration of communication and edge computational resources without requiring exchanges of the local data or resource information between base stations (BSs) and edge servers. A distributed joint resource allocation algorithm is developed based on the alternating direction method of multipliers with partial variable splitting (DistADMM-PVS) that minimizes the average service response-time of a set of service instances when the coordination among the D-orchestrator, BSs, and edge servers is perfectly synchronized. Motivated by the observation that the synchronization of coordination may result in high coordination delay that can be intolerable in many practical scenarios, particularly for large IoT networks, a novel asynchronized ADMM (AsyncADMM) algorithm is proposed. In AsyncADMM, the D-orchestrator, BSs, and edge servers can be coordinated asynchronously. AsyncADMM is then shown to converge to the global optimal solution with improved scalability and negligible coordination delay. The performance of the proposed framework is evaluated using two-month of traffic data collected in an in-campus smart transportation system supported by a 5G network. Extensive simulations are conducted for both pedestrian and vehicular-related services during peak and non-peak hours. Simulation results show that the proposed distributed network slicing framework offers a significant reduction in the service response time for both supported services.

Autonomous aerial vehicles (AAV)-enabled IoT networks have shown promising potential in a range of novel applications and service scenarios, such as extending the network coverage, extending the battery lifetime of IoT networks, and also supporting temporary data collecting and processing needs in various emergency situations. This article studies a federated edge intelligence (FEI) network based on the data collected and uploaded by a AAV-enabled IoT network. More specifically, a set of AAVs has periodically collected and uploaded the data generated by an IoT network to a set of edge servers. Edge servers will then collaboratively construct shared models based on the uploaded datasets. The data uploading performance of a AAV-enabled IoT network and the computational capacity of edge servers are entangled with each other in influencing the overall model training process. We propose a new framework called AAV-enabled IoT network for FEI (U-FEI). This framework enables edge servers to assess how many data samples need to be collected based on the energy costs of the AAV-enabled IoT network. It also considers the local data processing capacity of the edge servers. As a result, the edge servers can request just the right amount of data from the AAVs, which is enough to train a satisfactory model. We evaluate the energy cost for data uploading of AAVs when the data can be uploaded from two different types of frequencies: 1) licensed bands (e.g., using 5G) and 2) unlicensed bands (e.g., using Wi-Fi, ZigBee, or 5G NR-U). We prove that the cost minimization problem of the entire AAV-enabled IoT network is separable and can be divided into a set of subproblems, each of which can be solved by an individual edge server. We also introduce a mapping function to quantify the computational load of edge servers under the combinations of three key parameters: 1) size of the dataset; 2) local batch size; and 3) number of local training passes. Finally, we adopt an alternative direction method of multipliers (ADMM)-based approach to jointly optimize the energy cost of the AAV-enabled IoT network and average resource utilization of edge servers. We prove that our proposed algorithm does not cause any data leakage nor disclose any topological information of the AAV-enabled IoT networks. Simulation results show that our proposed framework significantly improves the resource efficiency of both the AAV-enabled IoT network and edge servers.

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 metaverse services are promising to embrace multi-sensory experiences of human beings, which mainly include audio-visual and tactile senses. From the perspective of wireless transmission, tactile transmission requires ultra-reliable low-latency communications, while audio-visual transmission requires enhanced mobile broadband communications. Besides, the audio-visual segment can be divided into several correlated data packets, any loss of packets would result in failed decoding at users, thus degrading users’ immersive experiences. In multi-user wireless metaverse systems, the heterogeneous transmission characteristics of multi-modal streams and integrity requirements of audio-visual stream transmission pose a great challenge to the limited wireless resource scheduling. To this end, we design a multi-user resource schedule scheme for multi-modal stream transmission by jointly considering the integrity of audio-visual stream transmission and the puncturing-based tactile stream transmission. We model the multi-modal perception utility function based on the multi-attribute utility theory and wireless transmission performance of multi-modal streams. Then, we formulate the average multi-modal perception utility maximization problem, and we adopt the Lyapunov theory to decompose the original maximization problem. Furthermore, we integrate the matching-based two-timescale spectrum resource allocation algorithm and alternating direction method of multipliers-based power allocation algorithm to obtain the optimal spectrum and power allocation strategies. Simulation results show that, compared with the resource allocation scheme without considering the transmission integrity, the average multi-modal perception utility of the proposed scheme is maximumly improved by 25%.

This paper investigates the dynamics of spatio-temporal interference correlations in wireless networks, focusing on the interplay between node spatial distribution and interference patterns. The study employs analytical frameworks such as the Matérn hard-core point process (MHCP) and clustering models, revealing that spatial clustering typically enhances positive interference correlation, while spatial rejection mechanisms inherent to MHCP induce negative correlation. Notably, the analysis extends to various temporal traffic models, highlighting distinct interference correlation behaviors between temporally correlated and independent traffic scenarios. The novel contribution of this work lies in deriving advanced mathematical formulations to quantify the interference correlation coefficient, which uncover the non-monotonic relationship between correlation and network parameters like node density and burst traffic duration. These insights provide a deeper understanding of interference dynamics, offering valuable guidelines for optimizing network performance and reliability, particularly in the design of next-generation wireless systems.

The radio stripe (RS) is an emerging architecture for cell-free massive multiple-input-multiple-output (CFm-MIMO) communication systems, where access points (APs) are sequentially connected via one optical-fiber link. The traditional precoded signal vector quantization approach is designed for star-topology MIMO architecture with a large demand for fronthaul capacity thereby it is not suitable for RS architecture. To address this challenge, a joint precoding matrix optimization and pruning approach is proposed to reduce the fronthaul traffic in the RS architecture. Central to our strategy is optimizing and pruning the precoding coefficient matrix (PCM), which is decomposed from the stationary point of the weighted sum-rate maximization problem. An iterative algorithm with closed-form iteration expression is developed for the joint optimization and pruning of PCMs. Note that the size of PCM only depends on the number of data streams rather than the antenna number. In contrast to transmitting different precoding matrices to different APs, the required PCMs for different APs are proved to be the same, which illustrates that only one PCM needs to be transmitted in the RS. Simulation results demonstrate that the proposed approach can reduce the fronthaul link traffic by 739% compared to the weighted minimum mean square error (WMMSE) precoding algorithm. When each signal symbol is quantized with 10 bits, the proposed approach can improve the sum-rate by 284% and 544% compared to the zero-forcing and maximum ratio transmission algorithms, 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.

The dense deployment of wireless nodes in the next generation of wireless local area networks (WLANs) poses a potential threat to network performance. Enhancing spatial reuse (SR) in WLANs can effectively address this issue. Dynamic clear channel assessment (CCA) threshold and transmit power control are crucial techniques to improve SR. This paper formulates the SR problem as a multi-armed bandit problem. A Bayesian optimization online learning algorithm with Gaussian process is proposed to optimize CCA thresholds and transmit power jointly. Finally, the proposed algorithm is compared with the default configuration and Thompson sampling algorithm across four performance metrics with the NS-3 simulator. The results demonstrate that our algorithm can significantly diminish cumulative regret, amplify total throughput, reduce the number of nodes in starvation, and improve overall network fairness.

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%.

This paper investigates the spatio-temporal interference correlation in wireless networks, emphasizing the impact of spatial node rejection modeled by two types of Matern Hard-Core Point Processes (MHCP). We explore how the spatial rejection inherent to MHCP types I and II shapes interference patterns, with a particular focus on the negative correlation effects on network performance. Our study further examine the temporally correlated traffic model, which reveals significant insights into how temporal correlation affects interference. By deriving expressions for the interference correlation coefficient, we illustrate the critical role of path loss and spatial rejection parameters. The work contributes to a refined understanding of interference dynamics in networks modeled by MHCP and guides the strategic development of efficient wireless systems in the presence of correlated traffic.

Unmanned aerial vehicle (UAV)-assisted millimeter-wave (mmWave) communication presents a promising solution for high data-rate wireless applications in urban environments. However, due to limited energy supply and communication capability, UAVs can only provide temporary communication services. This challenge motivates the exploration of three-dimensional (3D) integrated aerial and ground mmWave communications utilizing the multi-connectivity (MC) technique. By leveraging both ground and aerial mmWave links over licensed and unlicensed mmWave spectrums, respectively, the MC technique can effectively exploit spatial and frequency diversity to enhance the connectivity and reliability of UAV-assisted mmWave communications. We develop a unified framework based on stochastic geometry and Markov chain to analyze the coverage and outage probabilities of the 3D integrated aerial/ground mmWave networks. Furthermore, we show that an optimal UAV flight altitude for maximizing the coverage probability of UAV communication exists and derive it in a closed-form expression. Simulation results demonstrate that UAVs can maintain reliable mmWave connections even when connections from terrestrial mmWave base stations (BSs) are obstructed by buildings, underscoring the benefits of MC in enhancing the robustness of 3D integrated aerial and ground mmWave networks.

Federated learning (FL) over wireless networks offers a promising approach to enable decentralized machine learning among massive mobile edge nodes while ensuring privacy in training data. However, the convergence speed of FL is limited by the straggler effect, which arises from heterogeneous nodes, wireless fading channels, and non-independently and identically distributed (non-IID) training data. In this paper, we consider an adaptive semi-asynchronous FL to mitigate the straggler effect, by dynamically selecting subsets of nodes over time to synchronize the global model. We jointly optimize the node scheduling and computing/communication resource allocation to minimize the completion time required for convergence of the adaptive semi-asynchronous FL. Leveraging the convergence condition of semi-asynchronous FL, we further propose a greedy heuristic policy for node scheduling while tackling the remaining computing/communication resource allocation problem by exploiting a hidden convexity. Simulation results on open datasets demonstrate that, compared with existing FL algorithms, our proposed adaptive semi-asynchronous algorithm can significantly lower the latency of FL convergence.

To meet the imperative for sustainable low-carbon wireless communications, integrating distributed renewable energy sources with base stations is essential. However, there is often a mismatch between the energy generated by renewable sources and the energy required by base stations. To address this issue, optimization strategies such as traffic offloading and energy sharing are commonly employed. This paper introduces a spatial model based on stochastic geometry, mapping the spatial distribution of base stations and users, and quantifying the probability of coverage in cellular networks powered by renewable energy with energy-sharing and traffic offloading capabilities. Complementing this spatial analysis, we apply queuing theory to represent the base station energy status as a Markov chain, leading to a nuanced carbon emissions model post energy-sharing. Furthermore, a new metric called carbon efficiency is defined for accurately capturing the trade off between carbon emissions and performance of cellular networks. Simulations show that there is an optimal traffic offloading probability that can minimize carbon emissions in cellular networks while sacrificing the expected ergodic rate of users. These insights offer a foundation for developing optimization strategies that elevate the carbon efficiency of renewable-energy-driven cellular networks.

Efficient channel feedback is crucial for optimizing Wireless Local Area Networks (WLANs), where traditional channel sounding often incurs substantial overhead, primarily from beamforming feedback. This work presents an innovative Adaptive Quantization Model (AQM) leveraging Artificial Intel - ligence and Machine Learning (AIML) to optimize the channel sounding feedback mechanism. The AQM intelligently adjusts feedback quantization levels, achieving a balanced quantization accuracy while minimizing resource consumption. It dynamically selects the optimal number of quantization bits for feedback angle vectors, tailored to current channel states and device operational parameters. Notably, our method achieves a signif - icant reduction in overhead, safeguarding Packet Error Rate (PER) and thus ensuring the integrity of communication qual - ity. Through rigorous simulations, the AQM has demonstrated marked improvements in throughput and a notable decrease in channel sounding overhead. The findings also suggest potential avenues for incorporating such AIML - based approaches into future WLAN protocol standards, charting a course for more sophisticated and resource - efficient wireless communications.

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.

With the rapidly increasing number of connected and automated vehicles (CAVs) in intelligent transportation systems, identifying and predicting the fine-grained spatial distribution (SD) of CAV clusters is important to improve the positioning accuracy of CAVs. Although some practical schemes based on computer vision technologies and wireless sensor networks are developed to count the number of CAVs in the global navigation satellite system denied areas, the inference of fine-grained SDs of CAVs from coarse SD observations has not been studied. Based on the collective graphical model, a novel information geometry inference approach is proposed to apply the individual-CAV mobility model for refining the SD observations of CAVs. Within the proposed approach, the identification and prediction of fine-grained SDs of CAVs are formulated as a non-convex constrained Kullback-Leibler divergence minimization problem. From the information geometry perspective, the non-convex problem is reformulated as the I-projection searching problem on a dually flat manifold and then is solved based on the general Pythagorean theorem. Furthermore, an iterative algorithm with closed-form iteration expression is designed to converge to the optimal solution for the I-projection searching problem. Simulation results demonstrate that the designed algorithm outperforms the IMM-MC and UR schemes for identifying the SD of CAVs. Compared with the IMM-MC scheme, the designed algorithm improves the accuracy of predicting the SD of CAVs by up to 71.6%.

To reduce the energy consumption of digital communication systems, chips based on the complementary metal-oxide-semiconductor (CMOS) technology are facing the challenge of low energy consumption for signal processing in communication systems. Some typical technologies, such as shortening the transistor size, reducing the number of electrons and lowering the supply voltage are widely used by present chips to achieve low energy consumption. However, as the gate size of transistors is getting closer to the mesoscopic scale, how to model and analyze the non-equilibrium information processing of transistors is an essential challenge for digital integrated circuits. In this paper, based on the stochastic thermodynamics theory, an energy consumption model of a single electron transistor XOR gate considering the input state transition is proposed. Moreover, the Landauer limit and mismatch theory are combined to derive the lower bound of the energy consumption of XOR gate for one operation. Simulation results show that the average energy consumption of XOR gate is the lowest when the supply voltage is five times the thermal noise voltage. Based on the proposed energy consumption model of XOR gate, an energy consumption model of parity check circuits is proposed. Then an optimization algorithm is designed to reduce the energy consumption of parity check circuits. Compared with the energy consumption of parity check circuits without optimization, simulation results show that the energy consumption of parity check circuits using the energy consumption optimization algorithm is maximumly reduced by 41.71%.

One of the most prominent physical aspects of mobile communication systems is that they are inherently non-equilibrium systems. Traditional researches on the energetic costs of communication systems pay little attention to the relationship between the energy and the information transmitted and processed by the system. On the other hand, recent breakthroughs in nonequilibrium thermodynamics have led to a deeper understanding of the thermodynamics of information. To investigate the energetic costs of a mobile communication system at a fundamental level, in this paper, an entropy-based energy dissipation model based on nonequilibrium thermodynamics is first proposed for mobile communication systems. The energy dissipation model relates the energy and information through the common concept “entropy” from thermodynamics and information theory. Moreover, the theoretical minimal energy dissipation limits are derived for typical modulations in mobile communication systems. Simulation results show that the practical energy dissipation of information processing and information transmission is three and seven orders of magnitude away from the theoretical minimal energy dissipation limits in mobile communication systems, respectively. The energy dissipation model and results derived in the paper provide guidelines on how to design future mobile communication systems to minimize the thermodynamic costs.

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.

Fiber-to-the-room (FTTR) wireless local area networks (WLANs) are a promising sixth-generation (6G) technology for extreme broadband low-latency indoor wireless communications. With dense deployment of access points (APs), namely optical network units (ONUs), and efficient spatial frequency reuse across the ONUs, FTTR WLANs enable the mobile devices to flexibly access any ONU in its communication range and reduce the collisions during data packet transmissions. However, FTTR WLANs share a passive optical network (PON) for time division multiplexing (TDM) based backhauling, which may incur long delays for scheduling packet transmissions over the PON. In this paper, the full-link age of information (FL-AoI) is proposed as a new performance metric to analyze the timeliness of indoor communications in FTTR WLANs, taking into account both the carrier sense multiple access with collision avoidance (CSMA/CA) based wireless transmission and the TDM based packet scheduling over the PON. The FL-AoI of FTTR WLANs is analyzed using stochastic geometry and stretched exponential path-loss (SEPL) based indoor wireless channel model. We show that rather than accessing the nearest ONUs, the FTTR WLAN also enable the mobile devices to access further ONUs to reduce the average FL-AoI. Meanwhile, there exists an optimal transmission distance to achieve minimal average FL-AoI in the FTTR WLAN, whose value depends on the deployment density of ONUs.

The development of Industrial Internet of Things (IIoT) has completely changed the traditional manufacturing industry. The data exchange between controllers and actuators needs to achieve extremely low delay in IIoT. Due to the limited communication resources, it is necessary to reasonably schedule data flow to reduce delay. Although the studies of data flow scheduling exist in IIoT, they have not considered the impact of time-varying environmental factors and most of them adopted centralized scheduling schemes, which increase computation and communication cost rapidly in large-scale network scenarios. In this article, the consensus-based distributed optimal transport (OT) algorithm is proposed to optimize data flow scheduling for IIoT networks. Specifically, a data flow scheduling optimization mechanism based on time-varying environmental factors is proposed and an online distributed data flow scheduling optimization algorithm is designed. Compared with the random data flow scheduling algorithm, numerical results show that the proposed algorithm can maximally reduce the average delay by 87%, increase the transmission rate and the spectral efficiency by 157% and 98%, respectively.

The seamless and ubiquitous wireless access is crucial to the immersive experiences in the metaverse. Considering the limited communication and computing resources, how to provide metaverse services with high Quality of Experience (QoE) for users is still challenging. In this paper, an innovative QoE model for metaverse services based on the virtual distance and network effect is proposed. Especially, we introduce a novel metric called “meta-distance” to measure virtual distance in the metaverse, which jointly considers the service delay and social distance among metaverse users. To solve the QoE utility maximization problem, we propose a Joint Resource Allocation and Metaverse service Selection (JRAMS) scheme, which is composed of a two-step mechanism. In the first step, referred to as the inner loop of JRAMS, a one-to-many matching game with externalities is used to match base stations and metaverse users with Non-Orthogonal Multiple Access (NOMA) based subchannel allocation. In the second step, referred to as the outer loop of JRAMS, a hedonic coalition formation game is used to solve the metaverse service selection problem. After finite iterations, JRAMS can converge to a stable solution. The simulation results show that compared with baselines, the average QoE utility of JRAMS can be significantly improved.

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.

This paper considers optimal resource allocation for data collection from multiple ground devices (GDs) using a rotary-wing unmanned aerial vehicle (UAV). The UAV’s flight path, i.e., the sequence of moving positions, is given a priori due to requirements of e.g. patrol and inspection missions, whereas the UAV’s trajectory, i.e., the path and time schedule of movement, remains dependent on its hovering positions and flying speeds along the path. To improve the spectral and energy efficiency of the GDs, the UAV employs a directional antenna and performs wireless power transfer (WPT) to the GDs before collecting data from them. We jointly optimize the UAV’s flying speeds, hovering locations, and radio resource allocation (including time, bandwidth and transmit power) for minimization of the total energy consumption of the UAV required for completing data collection along the flying path. We show that given any flight path, the propulsion energy consumption of the UAV is a convex function of the flight speeds. However, due to the highly directive transmission, communication and flight of the UAV become strongly coupled and complicates the problem, e.g. the selection of the UAV’s hovering points will affect both the order of serving the GDs and the antenna gain of the UAV. Moreover, nonconvexity in the flight path constraints further obscures an efficient solution to the resource allocation problem. To tackle these challenges, we propose an iterative algorithm based on the branch-and-bound (BnB) method, which can obtain the globally optimal solution when the flight path coincides with the boundary of a convex set. Simulation results show that compared with several baseline algorithms, the proposed algorithm can significantly lower the energy consumption of the UAV during data collection.

This paper presents a stochastic geometry framework for modeling and evaluating the connectivity of uplink transmission in a large-scale intelligent reflecting surface (IRS) assisted millimeter-wave (mmWave) communication network, where the uplink user equipments (UEs) attempt to communicate with the nearest base stations (BSs) either without or with the help of an IRS. We propose a novel elliptical geometry model, which can effectively capture the impact of IRS location and orientation, as well as incident/reflection angle on mmWave signal propagation, while, at the same time, significantly simplifying the analysis of the system performance. Employing the elliptical geometry model, the approximate reflection probability of IRS as well as its upper and lower bounds are derived in closed form. Based on these results, we further analyze the successful connection probability of uplink UEs for IRS-assisted mmWave cellular networks. Our results show that compared with conventional direct UE-to-BS communication without IRS, indirect communication with the aid of IRS exhibits a slower decaying in the connection probability as the communication distance increases, as the latter can significantly increase the connection probability for cell-edge UEs. Moreover, for mmWave BSs with small receiving power thresholds, the deployment of IRS can effectively mitigate the impact of blockages to improve mmWave signal propagation.

Massive Internet of Things (IoT) is an important application scenario for the next-generation mobile communication systems. The channel estimation is crucial for the performance of massive wireless access in IoT. However, when the number of terminals is large, the huge pilot overhead may seriously increase the burden of the wireless access system. Therefore, reducing the pilot overhead while ensuring the accuracy of channel estimation is an important challenge for massive wireless access systems. In this article, we propose a massive wireless access mechanism, which greatly reduces the pilot overhead and improve the energy efficiency (EE) of terminals. Based on the self-similarity of fractal channels, the channel-state information (CSI) of a subset of terminals is sampled to estimate the CSI of all terminals, thereby reducing the pilot overhead and solving the shortage of pilot resources in massive wireless access systems. Meanwhile, the optimal transmission power of terminals based on CSI can save the energy consumption of terminals in IoT. Compared with the traditional algorithm, simulation results indicate that the proposed massive wireless access mechanism improves the EE of terminal by 340% and reduces 70% of the pilot overhead.

The evolution of the Industrial Internet of Things (IIoTs) greatly increases the volume of data generated by the connected IIoT devices. IIoT data are playing an increasingly important role in various industrial sectors. IIoT data sharing helps enterprises make better production decisions and respond to market changes timely. However, the distrust among IIoT entities and IIoT entities’ distrust of data-sharing platforms may hinder the realization of data sharing. In this article, a decentralized IIoT data-sharing scheme based on blockchain and edge computing is proposed. A Proof of Storage and Transmission (PoST) consensus mechanism is proposed to meet data storage and transmission requirements of data owners in IIoT data-sharing networks. Based on the manufacture ties of data owners, shared data request probabilities are derived. The IIoT data sharing interactions between data owners and edge devices are modeled as a multiple-leader and multiple-follower Stackelberg game. The alternating direction method of multipliers (ADMMs) algorithm is used to obtain the optimal IIoT data sharing solutions in a distributed manner. Simulation results show that compared with the cooperative scheme, the total profit of edge devices is maximally increased by 59%, and the total utility of data owners is maximally increased by 52%.

Cognitive radio networks (CRNs) are expected to be promising techniques for improving the spectrum efficiency of wireless network utility in the squeezed sub-6-GHz frequency bands. Nevertheless, frequency allocation and transmission scheduling for secondary users (SUs) in CRNs suffer from no prior knowledge of other SUs’ network behaviors or the distribution of the amount of data generated at each SU. As a countermeasure, this article develops a protocol for the joint channel selection and transmission scheduling such that SUs with heterogeneous data transmission demands could be served with limited spectrum resources. Then, we formulate the dynamic optimization of the protocol as mutually embedded Markov decision processes (MDPs). To address the intractable MDPs,$Q$-learning-based channel selection and transmission scheduling based on reinforcement learning with basis function approximation are, respectively, proposed. It is shown that compared with various baselines, the proposed channel selection algorithm enables each SU to select the best frequency-domain channel that does not interfere with other SUs. In particular, the proposed transmission scheduling algorithm outperforms algorithms based on off-the-shelf approaches, such as$Q$-learning and Lyapunov optimization, in terms of both energy efficiency and long-term accumulative amount of bits at each SU.

The orbital angular momentum (OAM) wireless communication technique is emerging as one of potential techniques for the Sixth generation (6G) wireless communication system. The most advantage of OAM wireless communication technique is the natural orthogonality among different OAM states. However, one of the most disadvantages is the crosstalk among different OAM states which is widely caused by the atmospheric turbulence and the misalignment between the transmitting and receiving antennas. Considering the OAM-based multiple-input multiple-output (OAM-MIMO) transmission system with unaligned antennas, a new channel model is proposed for performance analysis. Moreover, the purity and crosstalk models of the OAM-MIMO transmission system with unaligned antennas are derived for the non-Kolmogorov turbulence. Furthermore, the error probability, bit error rate (BER) and capacity models are derived for OAM-MIMO transmission systems with unaligned antennas. To overcome the disadvantage caused by the unaligned antennas and non-Kolmogorov turbulence, a new optimization algorithm of OAM state interval is proposed to improve the capacity of the OAM-MIMO transmission system. Numerical results indicate that the capacity of OAM-MIMO transmission system is improved by the proposed optimization algorithm. Specifically, the capacity increment of the OAM-MIMO transmission system adopting the proposed optimization algorithm is up to 28.7% and 320.3% when the angle of deflection between the transmitting and receiving antennas is −24 dB and −5 dB, respectively.

The computation power of baseband units (BBUs) is a major source of power consumption in millimeter wave (mmWave) massive multiple-input multiple-output (MIMO) systems with a large number of users due to complex signal processing. The effective reduction of computation power is critical for improving system energy efficiency. In this paper, the principle of reducing the computation power of BBUs is first investigated in mmWave massive MIMO systems with a hybrid precoding structure. A recursive constraint in decomposing the baseband precoding matrix is derived for reducing the computation power of hybrid precoding systems. Furthermore, the optimal number of sub-matrices minimizing the maximum error in decomposing the baseband precoding matrix is obtained. Based on the proposed principle, consisting of the recursive constraint and the optimal number of sub-matrices, a fast Monte Carlo baseband precoding (FMCBP) algorithm is developed to reduce the computation power of BBUs and improve system energy efficiency. Simulation results show that the total transmission rate and energy efficiency of mmWave systems are coupled with the computation power of BBUs, based on the FMCBP algorithm. Moreover, the FMCBP algorithm maximally improves the energy efficiency of multi-user mmWave massive MIMO communication systems by 124 percent, compared with the conventional equivalent zero-forcing algorithm.

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.

The blockchain is a promising technology to enhance the security and resilience of industrial Internet of Things (IIoT) networks. However, generating blockchain for the IIoT devices usually consumes excessive energy which may not be affordable for battery-powered IIoT devices. To address this problem, in this paper, we consider an unmanned aerial vehicle (UAV) assisted private blockchain-based IIoT system. Thereby, a UAV mounted with computing processor is deployed as a multi-access edge computing platform, which is responsible for collecting data from the IIoT devices, generating blocks based on the collected data, and broadcasting the blocks to the IIoT devices. To minimize the energy consumption of the UAV, joint optimization of the central processing unit (CPU) frequencies for data computation and block generation, the amount of offloaded IIoT data, the bandwidth allocation, and the trajectory of the UAV is formulated as a nonconvex optimization problem and solved via a successive convex approximation (SCA) algorithm. Simulation results show that, compared with several baseline schemes, the proposed scheme can significantly lower the energy consumption required for the blockchain generation in IIoT networks.

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.

The vision for smart city imperiously appeals to the implementation of Internet-of-Things (IoT), some features of which, such as massive access and bursty short packet transmissions, require new methods to enable the cellular system to seamlessly support its integration. Rigorous theoretical analysis is indispensable to obtain constructive insight for the networking design of massive access. In this paper, we propose and define the notion of massive and sporadic access (MSA) to quantitatively describe the massive access of IoT devices. We evaluate the temporal correlation of interference and successful transmission events, and verify that such correlation is negligible in the scenario of MSA. In view of this, in order to resolve the difficulty in any precise spatio-temporal analysis where complex interactions persist among the queues, we propose an approximation that all nodes are moving so fast that their locations are independent at different time slots. Furthermore, we compare the original static network and the equivalent network with high mobility to demonstrate the effectiveness of the proposed approximation approach. The proposed approach is promising for providing a convenient and general solution to evaluate and design the IoT network with massive and sporadic access.

One of the biggest problems facing future mobile systems beyond 5G (B5G) is the energy dissipation of mobile devices at high data rates. The heat generated by these devices can impact the performance as a result of a new type of outage called power-consumption outage. In this article, we propose a general definition of the power-consumption outage and describe its three features. Based on the heat transfer model in smartphones, the power-consumption outage probability is analyzed. Specifically, we derive the joint outage probability of channel and power-consumption outages in relation to the signal-to-noise ratio (SNR), communication duration, and initial temperature of the smartphone-back-plate. The joint outage probability is then used to obtain the upper bound of the maximum receiving rate of a typical smartphone. Furthermore, we propose and analyze the impact on the capacity of the power-consumption outage. Simulation results show that the power-consumption outage probability increases with an increase of SNR and with extension of the communication duration. The upper bound of the maximum receiving rate of a smartphone decreases with an extension of communication duration. Considering the joint outage probability, simulation results show that the outage capacities, i.e., channel and power-consumption outages, decrease with an increase of SNR after reaching a given capacity threshold.

In this paper, the real-time deployment of unmanned aerial vehicles (UAVs) as flying base stations (BSs) for optimizing the throughput of mobile users is investigated for UAV networks. This problem is formulated as a time-varying mixed-integer non-convex programming (MINP) problem, which is challenging to find an optimal solution in a short time with conventional optimization techniques. Hence, we propose an actor-critic-based (AC-based) deep reinforcement learning (DRL) method to find near-optimal UAV positions at every moment. In the proposed method, the process searching for the solution iteratively at a particular moment is modeled as a Markov decision process (MDP). To handle infinite state and action spaces and improve the robustness of the decision process, two powerful neural networks (NNs) are configured to evaluate the UAV position adjustments and make decisions, respectively. Compared with heuristic algorithm, sequential least-squares programming and fixed UAVs methods, simulation results have shown that the proposed method outperforms these three benchmarks in terms of the throughput at every moment in UAV networks.

Network slicing has been considered as one of the key enablers for 5G to support diversified services and application scenarios. This paper studies the distributed network slicing utilizing both the spectrum resource offered by communication network and computational resources of a coexisting fog computing network. We propose a novel distributed framework based on a new control plane entity, regional orchestrator, which can be deployed between base stations and fog nodes to coordi- nate and control their bandwidth and computational resources. We propose a distributed resource allocation algorithm based on Alternating Direction Method of Multipliers with Partial Variable Splitting (DistADMM-PVS). We prove that DistADMM-PVS minimizes the average latency of the entire network and at the same time guarantee satisfactory latency performance for every supported type of service. Simulation results show that DistADMM-PVS converges much faster than some other existing algorithms. In addition, the joint network slicing with both bandwidth and computational resources offers around 15% overall latency reduction compared to network slicing with only a single resource.

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.

The traffic in wireless networks has become diverse and fluctuating both spatially and temporally due to the emergence of new wireless applications and the complexity of scenarios. The purpose of this paper is to quantitatively analyze the impact of the wireless traffic, which fluctuates both spatially and temporally, on the performance of the wireless networks. Specially, we propose to combine the tools from stochastic geometry and queueing theory to model the spatial and temporal fluctuation of traffic, which to our best knowledge has seldom been evaluated analytically. We derive the spatial and temporal statistics, the total arrival rate, the stability of queues and the delay of users by considering two different spatial properties of traffic, i.e., the uniformly and non-uniformly distributed cases. The numerical results indicate that although the fluctuation of traffic (reflected by the variance of total arrival rate) when the users are clustered is much fiercer than that when the users are uniformly distributed, the unstable probability is smaller. Our work provides a useful reference for the design of wireless networks when the complex spatio-temporal fluctuation of the traffic is considered.

Emerging wireless applications starve for the realization of the ultra-reliable and low-latency communication (URLLC). The reliability and latency requirements of URLLC for a specific single link have been explored extensively, but a comprehensive evaluation of the URLLC in a large-scale wireless network is still lacking. In this paper, by using the point process theory we evaluate the probability that the delay and reliability requirement of a typical URLLC user can be satisfied in the large-scale wireless network. This probability is also the ratio of users with satisfactory delay and reliability in the wireless network. In order to improve the performance of URLLC, we propose two retransmission policies, corresponding to time diversity and frequency diversity, in which the retransmissions are silenced randomly to reduce the interference correlation in different frames so to reduce the effect of correlations between different retransmissions. Simulation and numerical results reveal that both of the proposed two retransmission policies improve the performance of URLLC, and the retransmission policy of frequency diversity is better than that of the time diversity.

The conventional outage in wireless communication systems is caused by the deterioration of the wireless communication link, i.e., the received signal power is less than the minimum received signal power. Is there a possibility that the outage occurs in wireless communication systems with a good channel state? Based on both communication and heat transfer theories, a power-consumption outage in the wireless communication between millimeter wave (mmWave) massive multiple-input multiple-output (MIMO) base stations (BSs) and smartphones has been modeled and analyzed. Moreover, the total transmission time model with respect to the number of power-consumption outages is derived for mmWave massive MIMO communication systems. Simulation results indicate that the total transmission time is extended by the power-consumption outage, which deteriorates the average transmission rate of mmWave massive MIMO BSs.

As an important component of resource management, admission control is vital to prevent the wireless network from congestion and ensure the quality of service (QoS). Mobile edge computing (MEC), which provides computing resources at the edge of radio access networks (RAN), is able to better support new mobile services. Therefore, enhanced admission control policies should be designed for mobile cellular networks based on MEC. In this paper, a novel MEC-assisted admission control mechanism is proposed from the perspective of convergence of communication and computation. In this mechanism, MEC computing resources are leveraged to pre-process the transmission content. The purpose is to reduce the consumption of wireless bandwidth and increase the number of accepted services. Next, the admission control process is modeled as a Markov decision process (MDP) with objective to maximize the long-term expected average effective throughput. In consideration of the large state space, a simulation-based optimization algorithm of MDP is adopted to obtain the optimal policy. Simulation results show that our proposed admission control mechanism achieves higher effective throughput than that without MEC computing resources. And the probability of accepted services can also be improved significantly. Furthermore, the optimal amount of MEC computing resources can be acquired according to the system traffic statistics.

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.

To satisfy the requirement of the fifth generation (5G) mobile communications that offers an ultra high data rate of 100Mbps to 1Gbps anytime and anywhere, the coordinated multipoint (CoMP) technique is proposed to mitigate intercell interference to improve the coverage of high data rate services, cell-edge throughput, and system capacity. However, the joint transmission (JT) CoMP technique is difficult to be applied in practice due to the critical time synchronization for multiple coordination links and the bottleneck of backhaul capacity and radio resource at each small cell base stations (SBSs). Moreover, since the coordination SBSs in the conditional scheme are entirely separate from each other, different time of arrivals at the user cause the severe time synchronization problem. The anisotropic propagation environment in the urban scenario makes the implementation condition even worse. To tackle these issues, we propose a novel JT-CoMP scheme with the anisotropic path loss model to minimize the network backhaul traffic subject to the constraints on the radio resource and the differences in time of arrivals. Simulation results demonstrate that the proposed distance-resource-limited CoMP scheme can obtain the maximum achievable rate with the minimum network backhaul traffic, compared with existing schemes.

One of focuses in green communication studies is the energy efficiency (EE) of massive multiple-input multiple-output (MIMO) systems. Although the massive MIMO technology can improve the spectral efficiency (SE) of cellular networks by configuring a large number of antennas at base stations (BSs), the energy consumption of radio frequency (RF) chains increases dramatically. The increment of energy consumption is caused by the increase of RF chain number to match the antenna number in massive MIMO communication systems. To overcome this problem, a generalized spatial modulation (GSM) solution is presented to simultaneously reduce the number of RF chains and maintain the SE of massive MIMO communication systems. A EE model is proposed to estimate the transmission and computation power of massive MIMO communication systems with GSM. Simulation results demonstrate that the EE of massive MIMO communication systems with GSM outperforms the massive MIMO communication systems without GSM. Besides, the computation power consumed by massive MIMO communication systems with GSM is effectively reduced.

In decentralised cognitive radio (CR) networks, establishing communication sessions between a communicating pair requires them to meet each other on a common channel via a ‘rendezvous’ process. Devising distributed CR rendezvous protocol is a challenging task as cognitive nodes are not necessarily synchronised, and may have different perceptions of channel availability. In this study, the authors present M‐Rendezvous , an order‐optimal rendezvous protocol exploiting the performance gain brought by having multiple radios at cognitive nodes. As a distinguished feature, M‐Rendezvous is a unified rendezvous protocol that can operate in both homogenous case where both of the rendezvous nodes are equipped with only one radio or multiple radios, and heterogeneous case where one of the rendezvous nodes has single radio and the other has multiple radios. In both cases, by rigorous analysis, the authors demonstrate that M‐Rendezvous can guarantee rendezvous over every channel with bounded and order‐minimal delay even when rendezvous nodes have asynchronous clocks and asymmetrical channel perceptions.

An increasing number of low-power Internet of Things (IoT) devices will be widely deployed in the near future. Considering the short-range communication of low-power devices, multihop transmissions will become an important transmission mechanism in IoT networks. It is a crucial for low-power devices to transmit data over long distances via multihop in a low-delay and reliable way. The small-world characteristics of networks indicate that the network has an advantage of a small average shortest-path length (ASL) and a high average clustering coefficient (ACC). In this article, a new IoT routing mechanism considering small-world characteristics is proposed to reduce the delay and improve the reliability. The ASL and ACC are derived for the performance analysis of small-world characteristics in IoT networks based on Cayley graphs. Besides, the reliability and delay models are proposed for small-world IoT based on Cayley graphs (SWITCH). The simulation results demonstrate that SWITCH has lower delay and better reliability than that of conventional nearest neighboring routing (NNR). Moreover, the maximum delay of SWITCH is reduced by 50.6% compared with that by NNR.

By providing shared and flexible communication, computation, and storage resources along the cloud-to-things continuum, fog computing has become an attractive technology to support delay-sensitive applications in Internet of Things (IoT) and future wireless networks. Consider a typical heterogeneous fog network consisting of different types of fog nodes (FNs), wherein some task nodes (TNs) have computation-intensive and delay-sensitive tasks, while some helper nodes (HNs) have spare computation resources for sharing with their neighboring nodes. In order to minimize the delay of every task, these TNs and HNs should be effectively associated in a distributed manner, which is the fundamental multi-task multi-helper (MTMH) problem. To tackle this challenging problem, a potential game called paired offloading of multiple tasks (POMT) is formulated and studied. Theoretical analysis proves the existence of the Nash equilibrium (NE) for this proposed game. Further, the corresponding POMT algorithm is developed for every TN to achieve the NE of the general game. The analytical and simulation results show that our POMT algorithm can offer the near-optimal performance in system average delay and delay reduction ratio (DRR), and achieve more number of beneficial TNs, at two orders of magnitude lower complexity than a centralized optimal algorithm for computation offloading.

Recently, the security of Internet of Things (IoT) has been an issue of great concern. Physical layer security methods can help IoT networks achieve information-theoretical secrecy. Nevertheless, utilizing physical security methods, such as artificial noise (AN) may cost extra power, which leads to low secure energy efficiency. In this paper, the hybrid precoding technique is employed to improve the secure energy efficiency of the IoT network. A secure energy efficiency optimization problem is formulated for the IoT network. Due to the nonconvexity of the problem and the feasible domain, the problem is first transformed into a tractable suboptimal form. Then, a secure hybrid precoding energy efficient (SEEHP) algorithm is proposed to tackle the problem. The numerical results indicate that the proposed SEEHP algorithm achieves higher secure energy efficiency compared with three existing physical layer security algorithms, especially when the number of transmit antennas is large.

The wireless backhaul network provides an attractive solution for the urban deployment of fifth generation (5G) wireless networks that enables future ultra dense small cell networks to meet the ever-increasing user demands. Optimal deployment and management of 5G wireless backhaul networks is an interesting and challenging issue. In this paper, we propose the optimal gateways deployment and wireless backhaul route schemes to maximize the cost efficiency of 5G wireless backhaul networks. In generally, the changes of gateways deployment and wireless backhaul route are presented in different time scales. Specifically, the number and locations of gateways are optimized in the long time scale of 5G wireless backhaul networks. The wireless backhaul routings are optimized in the short time scale of 5G wireless backhaul networks considering the time-variant over wireless channels. Numerical results show the gateways and wireless backhaul route optimization significantly increases the cost efficiency of 5G wireless backhaul networks. Moreover, the cost efficiency of proposed optimization algorithm is better than that of conventional and most widely used shortest path (SP) and Bellman-Ford (BF) algorithms in 5G wireless backhaul networks.

It is anticipated that a considerably higher network capacity will be achieved by the fifth generation (5G) small cell networks incorporated with the millimeter wave (mm-wave) technology. However, the mm-wave signals are more sensitive to blockages than signals in lower frequency bands, which highlight the effect of anisotropic path loss in network coverage. According to the fractal characteristics of cellular coverage, a multi-directional path loss model is proposed for the 5G small cell networks, where different directions are subject to different path loss exponents. Furthermore, the coverage probability, association probability, and the handoff probability are derived for the 5G fractal small cell networks based on the proposed multi-directional path loss model. The numerical results indicate that the coverage probability with the multi-directional path loss model is less than that with the isotropic path loss model, and the association probability with long link distance, e.g., 150m, increases obviously with the increase of the effect of anisotropic path loss in 5G fractal small cell networks. Moreover, it is observed that the anisotropic propagation environment is having a profound impact on the handoff performance. Meanwhile, we could conclude that the resulting heavy handoff overhead is emerging as a new challenge for 5G fractal small cell networks.

We revisit the opportunistic scheduling problem in which a server opportunistically serves multiple classes of users under time-varying multi-state Markovian channels. The aim of the server is to find an optimal policy minimizing the average waiting cost of those users. Mathematically, the problem can be recast to a restless multiarmed bandit one, and a pivot to solve restless bandit by the Whittle index approach is to establish indexability. Despite the theoretical and practical importance of the Whittle index policy, the indexability is still open for opportunistic scheduling in the heterogeneous multi-state channel case. To fill this gap, we mathematically identify a set of sufficient conditions on a channel state transition matrix under which the indexability is guaranteed and consequently, the Whittle index policy is feasible. Furthermore, we obtain the closed-form Whittle index by exploiting the structural property of the channel state transition matrix. For a generic channel state transition matrix, we propose an eigenvalue-arithmetic-mean scheme to obtain the corresponding approximate matrix which satisfies the sufficient conditions, and consequently can get an approximate Whittle index. This paper constitutes a small step toward solving the opportunistic scheduling problem in its generic form involving multi-state Markovian channels and multi-class users.

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.

The traffic in the wireless networks has become diverse and fluctuating both spatially and temporally due to the emergence of new wireless applications. The purpose of this paper is to explore the impact of the wireless traffic with violent fluctuation both spatially and temporally on the performance of the wireless networks. We analyze the spatial statistics, the stability of queues and the delay of users by considering two different spatial distributions of traffic, i.e., the uniformly and non-uniformly distributed cases. The simulation results indicate that although the fluctuations of traffic when the users are clustered, reflected by the variance of total arrival rate, is much fiercer than that when the users are uniformly distributed, the stability performance is much better. Our work provides a useful reference for the design of the wireless networks when the complex spatio-temporal fluctuation of the traffic is considered.

In this paper, we propose a novel joint caching and non-orthogonal multiple access (NOMA) scheme to facilitate advanced downlink transmission for next generation cellular networks. In addition to reaping the conventional advantages of caching and NOMA transmission, the proposed cache-aided NOMA scheme also exploits cached data for interference cancellation which is not possible with separate caching and NOMA transmission designs. Furthermore, as caching can help to reduce the residual interference power, several decoding orders are feasible at the receivers, and these decoding orders can be flexibly selected for performance optimization. We characterize the achievable rate region of cache-aided NOMA and investigate its benefits for minimizing the time required to complete video file delivery. Our simulation results reveal that, compared to several baseline schemes, the proposed cache-aided NOMA scheme significantly expands the achievable rate region for downlink transmission, which translates into substantially reduced file delivery times.

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.

When small cells are densely deployed in the fifth‐generation cellular networks, switching off a part of base stations (BSs) is a practical approach for saving energy consumption considering the variation of traffic load. The small‐cell network with the massive multi‐input–multi‐output system is analysed in this study due to the dense deployment and low‐power consumption. On the basis ofn the BS‐switch‐off strategy with distance constraints, the energy and coverage efficiency are investigated to illustrate the performance of the BS‐switch‐off strategy. Simulation results indicate that the energy efficiency and coverage efficiency of the proposed strategy are better than the random strategy. The energy efficiency increases with the BS intensity and the minimal distance, and a maximum coverage efficiency can be achieved with the increase of the BS intensity and the minimum distance. In this case, the optimal BS‐switch‐off strategy can be designed under this work in the actual scene.

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.

To meet massive wireless traffic demand in the future fifth generation (5G) cellular networks, small-cell networks are emerging as an attractive solution for 5G network deployments. The cellular coverage characteristic is a key issue for the deployment of the small-cell networks. Considering the anisotropic path loss in wireless channels of real cellular scenarios, in this paper the fractal coverage characteristic is first used to evaluate the performance of the small-cell networks. Moreover, the coverage probability, average achievable rate, and the area spectral efficiency are derived for fractal small-cell networks. Compared with the average achievable rate and area spectral efficiency with isotropic path loss models, the average achievable rate and area spectral efficiency with anisotropic path loss models have been underestimated in the fractal small-cell networks. Considering the impact of the anisotropic path loss on wireless channels, most of performances of wireless cellular networks need to be re-evaluated. This paper provides a tractable method to investigate the performance of the small-cell networks with fractal coverage characteristics.

This paper proposes a novel quadrature space-frequency index modulation (QSF-IM) scheme as a promising energy-efficient radio-access technology for the fifth generation (5G) wireless systems. Motivated by the potential energy saving of spatial modulation (SM) with the part of information being carried through antenna indexes, the proposed scheme further leverages the benefits of SM by applying the idea across the spatial and frequency domains. Moreover, by deploying dual antenna constellation for in-phase and quadrature-phase transmission, the proposed scheme can enhance data rate at no extra cost of energy consumption, leading to further improvement in energy efficiency. Theoretical bit error rate and achievable sum-rate of the proposed scheme over frequency-selective correlated Rician and Rayleigh fading channels are derived and are shown to have good agreement with simulations. Furthermore, the effectiveness of the proposed scheme is analyzed through a comprehensive list of performance metrics, including spectral efficiency (SE), energy efficiency (EE), cost efficiency (CE), and economic efficiency. Performance trade-offs between these metrics are thoroughly investigated. Compared with other existing schemes, the proposed QSF-IM scheme is demonstrated to offer better EE-SE and EE-CE tradeoffs, and can therefore be considered as a potential candidate for energy-spectral efficient 5G systems.

In this paper, new enhanced cognitive radio networks (E-CRNs) based on spectrum sharing (SS) and spectrum aggregation (SA) are proposed for fifth generation (5G) wireless networks. The E-CRNs jointly exploit the licensed spectrum shared with the primary user (PU) networks and the unlicensed spectrum aggregated from the industrial, scientific, and medical bands. The PU networks include TV systems in TV white space and different incumbent systems in the long term evolution time division duplexing bands. The harmful interference from the E-CRNs to the PU networks are delicately controlled. Furthermore, the coexistence between the E-CRNs and other unlicensed systems, such as WiFi, is studied. The E-CRNs framework including dynamic spectrum management (DSM) is designed for the key parameters of licensed SS and unlicensed SA. The essential tradeoff between sharing efficiency and aggregation efficiency for the E-CRNs is discussed. Based on this tradeoff, a spectrum lean-management scheme is proposed to fulfill the DSM. Moreover, a water-filling algorithm is designed to dynamically access the available spectrum. Numerical results demonstrate that the proposed E-CRNs can significantly improve the system performance in terms of data rate, outage probability, and spectrum efficiency. In particular, the E-CRNs framework provides a spectrum usage prototype for 5G wireless communication networks.

In this paper, a new non-stationary regular-shaped geometry-based stochastic model (RS-GBSM) is proposed for vehicular visible light communications (VVLC) channels. The proposed model utilizes a combined two-ring model and a confocal ellipse model, in which the received optical power is constructed as a sum of single-bounced (SB) and double-bounced (DB) components, in addition to the line-of-sight (LoS) component. Using the proposed RS-GBSM, the channel impulse response is generated and utilized to investigate VVLC channel characteristics, such as channel gain and root-mean-square (RMS) delay spread. The received optical power is computed considering the distance between the optical transmitter (Tx) and the optical receiver (Rx). Moreover, the impact of the Rx height on the received power is considered for the LoS scenario. The results show that the LoS power highly depends on the distance, Rx height, and the optical source pattern. For the SB components, it is confirmed that the channel gain in dB and the RMS delay spread follow Gaussian distributions. Finally, the results indicate that the detected optical power from the DB components is low enough to be overlooked.

With the growing interest in the deployment of massive multiple-input-multiple-output (MIMO) systems and millimeter wave technology for fifth generation (5G) wireless systems, the computation power to the total power consumption ratio is expected to increase rapidly due to high data traffic processing at the baseband unit. Therefore in this paper, a joint optimization problem of computation and communication power is formulated for multi-user massive MIMO systems with partially-connected structures of radio frequency (RF) transmission systems. When the computation power is considered for massiv MIMO systems, the results of this paper reveal that the energy efficiency of massive MIMO systems decreases with increasing the number of antennas and RF chains, which is contrary with the conventional energy efficiency analysis results of massive MIMO systems, i.e., only communication power is considered. To optimize the energy efficiency of multi-user massive MIMO systems, an upper bound on energy efficiency is derived. Considering the constraints on partially-connected structures, a suboptimal solution consisting of baseband and RF precoding matrices is proposed to approach the upper bound on energy efficiency of multi-user massive MIMO systems. Furthermore, an oPtimized Hybrid precOding with computation and commuNication powEr (PHONE) algorithm is developed to realize the joint optimization of computation and communication power. Simulation results indicate that the proposed algorithm improves energy and cost efficiencies and the maximum power saving is achieved by 76.59% for multi-user massive MIMO systems with partially-connected structures.

Coordinated multipoint (CoMP) communications are considered for the fifth-generation (5G) small-cell networks as a tool to improve the high data rates and the cell-edge throughput. The average achievable rates of the small-cell base stations (SBS) cooperation strategies with distance and received signal power constraints are respectively derived for the fractal small-cell networks based on the anisotropic path loss model. Simulation results are presented to show that the average achievable rate with the received signal power constraint is larger than the rate with a distance constraint considering the same number of cooperative SBSs. The average achievable rate with distance constraint decreases with the increase of the intensity of SBSs when the anisotropic path loss model is considered. What's more, the network energy efficiency of fractal small-cell networks adopting the SBS cooperation strategy with the received signal power constraint is analyzed. The network energy efficiency decreases with the increase of the intensity of SBSs which indicates a challenge on the deployment design for fractal small-cell networks.

Channel modeling is of vital importance to the development and performance evaluation of wireless communication systems. Though many millimeter-wave (mmWave) channel models have been proposed, few of them concern about staircase environments. This paper analyzes the statistical characteristics of 60 GHz channels in a staircase environment with the transmitter (Tx) side fixed and the receiver (Rx) side moving, especially the variation of characteristics arising from the motion of receiver Rx. Fuzzy c-means (FCM) algorithm is applied in clustering procedure and the Kim-Park (K-P) index combined with the multipath component distance (MCD) are utilized to obtain the optimal cluster number. Simulation results show that almost all of the channel characteristics are related to the Euclidean distance between the Tx and Rx. Also, they are affected by building structures, which will provide guidance on the layout of communication devices.

Most channel models in the literature are based on the assumption that the mobile station (MS) moves along a straight line with a constant speed. In a realistic environment, the MS may experience changes in their speeds and trajectories. In this paper, a three-dimensional (3-D) non-stationary wideband multiple-input multiple-output (MIMO) channel model allowing for velocity variations of the MS is proposed. The parameters are obtained from the WINNER+ channel model to make the simulations more realistic. Statistical properties including spatial cross-correlation function (CCF), temporal autocorrelation function (ACF), and Doppler power spectral density (PSD) are derived and analyzed. Our findings show that a variation of the velocity of the MS has a significant impact on the statistical properties of the channel model. Furthermore, the proposed channel model can be used as a basic framework for future non-stationary channel modeling.

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.

In this paper, we propose a unified framework to analyze the performance of dense small cell networks (SCNs) in terms of the coverage probability and the area spectral efficiency (ASE). In our analysis, we consider a practical path loss model that accounts for both non-line-of-sight (NLOS) and line-of-sight (LOS) transmissions. Furthermore, we adopt a generalized fading model, in which Rayleigh fading, Rician fading and Nakagami-m fading can be treated in a unified framework. The analytical results of the coverage probability and the ASE are derived, using a generalized stochastic geometry analysis. Different from existing work that does not differentiate NLOS and LOS transmissions, our results show that NLOS and LOS transmissions have a significant impact on the coverage probability and the ASE performance, particularly when the SCNs grow dense. Furthermore, our results establish for the first time that the performance of the SCNs can be divided into four regimes, according to the intensity (aka density) of BSs, where in each regime the performance is dominated by different factors.

This paper investigates the blockage loss caused by human body at 11, 16, 28, and 32 GHz by measurements and modeling. The measurements are carried out in an office environment by using a vector network analyzer (VNA) and two horn antennas, with one or two persons walking along or across the line connecting the transmitter (Tx) and receiver (Rx). The METIS knife-edge diffraction (KED) model, Kirchhoff KED model, and geometrical theory of diffraction (GTD) model are used to simulate the human blockage effects. The Gaussian model is also used to fit the measurement data. The human blockage effects are compared for the four millimeter wave (mmWave) bands. The results have shown that as the frequency increases, there is no obvious increasing trend of the losses. The METIS KED model, Kirchhoff KED model, and G TD model can simulate the human blockage effects well.

Recently, the use of unmanned aerial vehicle (UAV) is going to receive great interest in various areas. As a newly emerging area, UAV-aided communications encounter unique communication scenarios and thus need corresponding UAV channel models for better design of such UAV communication systems. In this paper, we propose a three-dimensional (3D) sphere UAV air-to-ground (A2G) multiple-input-multiple-output (MIMO) channel model. Based on the proposed channel model, the space-time (ST) correlation function of this model is derived and analytically studied in terms of various parameters. Some interesting and useful observations are obtained. Finally, we verify the usefulness of the proposed channel model by comparing it with measured data. This model provides a new and practical approach to investigate A2G MIMO channels and gives guidelines for UAV communication system design.

In this paper, we investigate the impact of different parameters on channel characteristics in a high-speed train (HST) ray tracing tunnel channel model. Signal propagation in HST tunnel scenarios differs much from that of other HST scenarios due to the unique construction of tunnels. Ray-tracing method is applied to analyze the received power and power delay profile (PDP) of tunnel channel models. Different parameters, i.e., carrier frequency, tunnel shape, tunnel dimension, the distance between the transmitter (Tx) and receiver (Rx), and antenna polarization, are studied via simulation results.

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.

Emergence of new types of services has led to various traffic and diverse delay requirements in fifth-generation (5G) wireless networks. Meeting diverse delay requirements is one of the most critical goals for the design of 5G wireless networks. Though the delay of point-to-point communications has been well investigated, the delay of multi-point to multi-point communications has not been thoroughly studied, since it is a complicated function of all links in the network. In this paper, we propose a novel tractable approach to analyze the delay in the heterogeneous cellular networks with spatio-temporal random arrival of traffic. Specifically, we propose the notion of delay outage and evaluate the effect of different scheduling policies on the delay performance. Our numerical analysis reveals that offloading policy based on the cell range expansion greatly reduces the macrocell traffic, while bringing a small amount of growth for the picocell traffic. Our results also show that the delay performance of round-robin scheduling outperforms first-in first-out scheduling for heavy traffic, and it is reversed for light traffic. In summary, this analytical framework provides an understanding and a rule-of-thumb for the practical deployment of 5G systems, where delay requirement is increasingly becoming a key concern.

In modern mobile communication networks, such as 3G and 4G networks, sectorized antennas have been widely used to divide each cell into multiple sectors in order to improve coverage, spectrum efficiency, and quality of service. Large-scale path loss from a transmitting antenna to a receiving antenna should include: 1) propagation attenuation that depends on transmission distance; 2) shadowing that depends on surrounding environment; and 3) antenna loss that depends on a sectorized antenna pattern and transmission angle. An in-depth analysis of statistical characteristics of large-scale path losses involving with these three factors is crucial for the design, operation, evaluation, and optimization of modern sectorized wireless networks. In this paper, a sectorized antenna pattern is, for the first time, considered in the derivation of a closed-form expression of a probability density function (pdf) of large-scale path losses. Specifically, we first discover that the normalized pdf of propagation attenuation plus shadowing, which can be approximated by the Gaussian mixture model (GMM) with all system parameters, is fully determined by our newly defined metric 10/ln 10β/σs, namely, the attenuation exponent β to standard deviation of shadowing σsratio (ASR). The convolution of GMM and antenna loss statistics is elaborately transformed to a series of differential equations. A closed-form pdf of large-scale path losses with sectorized antenna pattern can be obtained by solving these differential equations. To reduce the computational complexity, we further prove that the exciting sources of these differential equations can be tightly approximated by weighted Gaussian functions, and thus, the final solutions (i.e., pdf of path losses) can be derived in the form of Gaussian and Dawson functions. Our analytical results are verified by extensive numerical computation and Monte Carlo simulation results, e.g., the impact of ASR on the shape of pdf of propagation attenuation plus shadowing. Compared with traditional Gaussian-fitting approach, our newly derived pdf of large-scale path losses with sectorized antenna patterns is at least two orders of magnitude more accurate in terms of Kullback-Leibler divergence under typical propagation attenuation and shadowing conditions.

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.

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 article, a network model incorporating both line-of-sight (LOS) and non-line-of-sight (NLOS) transmissions is proposed to investigate impacts of blockages in urban areas on heterogeneous network coverage performance. Results show that co-existence of NLOS and LOS transmissions has a significant impact on network performance. We find in urban areas, that deploying more BSs in different tiers is better than merely deploying all BSs in the same tier in terms of coverage probability.

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.

With the growth of wireless transmission rate on user terminals, the backhaul network capacity becomes a bottleneck for improving the performance of future 5G ultra-dense cellular networks. Based on the wireless multi-hop relay technology, the backhaul network capacity of 5G ultra-dense cellular networks with multi-gateways is analyzed in this paper. Moreover, a minimum average hop number (MAN) algorithm is developed to improve the backhaul network capacity and energy efficiency of wireless backhaul networks for 5G ultra-dense cellular networks. Simulation results indicate there exist a stationary backhaul network capacity and a maximum energy efficiency of wireless backhaul networks when the density of small cell BSs is larger than the specified threshold.

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.

With the massive multi-input multi-output (MIMO) antennas technology adopted for the fifth generation (5G) wireless communication systems, a large number of radio frequency (RF) chains have to be employed for RF circuits. However, a large number of RF chains not only increase the cost of RF circuits but also consume additional energy in 5G wireless communication systems. In this paper, we investigate energy and cost efficiency optimization solutions for 5G wireless communication systems with a large number of antennas and RF chains. An energy efficiency optimization problem is formulated for 5G wireless communication systems using massive MIMO antennas and millimeter wave technology. Considering the nonconcave feature of the objective function, a suboptimal iterative algorithm, i.e., the energy efficient hybrid precoding (EEHP) algorithm is developed for maximizing the energy efficiency of 5G wireless communication systems. To reduce the cost of RF circuits, the energy efficient hybrid precoding with the minimum number of RF chains (EEHP-MRFC) algorithm is also proposed. Moreover, the critical number of antennas searching (CNAS) and user equipment number optimization (UENO) algorithms are further developed to optimize the energy efficiency of 5G wireless communication systems by the number of transmit antennas and UEs. Compared with the maximum energy efficiency of conventional zero-forcing (ZF) precoding algorithm, numerical results indicate that the maximum energy efficiency of the proposed EEHP and EEHP-MRFC algorithms are improved by 220% and 171%, respectively.

In this paper, we analyze the maximum likelihood decoding performance of Raptor codes with a systematic low-density generator-matrix code as the pre-code. By investigating the rank of the product of two random coefficient matrices, we derive upper and lower bounds on the decoding failure probability. The accuracy of our analysis is validated through simulations. Results of extensive Monte Carlo simulations demonstrate that for Raptor codes with different degree distributions and pre-codes, the bounds obtained in this paper are of high accuracy. The derived bounds can be used to design near-optimum Raptor codes with short and moderate lengths.

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 practical mobile communication engineering applications, surfaces of antenna array deployment regions are usually uneven. Therefore, massive multi-input-multi-output (MIMO) communication systems usually transmit wireless signals by irregular antenna arrays. To evaluate the performance of irregular antenna arrays, the matrix correlation coefficient and the ergodic received gain are defined for massive MIMO communication systems with mutual coupling effects. Furthermore, the lower bound of the ergodic achievable rate, symbol error rate, and average outage probability is first derived for multi-user massive MIMO communication systems using irregular antenna arrays. Asymptotic results are also derived when the number of antennae approaches infinity. Numerical results indicate that there exists a maximum achievable rate when the number of antennae keeps increasing in massive MIMO communication systems using irregular antenna arrays. Moreover, the irregular antenna array outperforms the regular antenna array in the achievable rate of massive MIMO communication systems when the number of antennae is larger than or equal to a given threshold.

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.

Cell association scheme determines which base station (BS) and mobile user (MU) should be associated with and also plays a significant role in determining the average data rate a MU can achieve in heterogeneous networks. However, the explosion of digital devices and the scarcity of spectra collectively force us to carefully re-design cell association scheme which was kind of taken for granted before. To address this, we develop a new cell association scheme in heterogeneous networks based on joint consideration of the signal-to-interference-plus-noise ratio (SINR) which a MU experiences and the traffic load of candidate BSs1. MUs and BSs in each tier are modeled as several independent Poisson point processes (PPPs) and all channels experience independently and identically distributed (i.i.d.) Rayleigh fading. Data rate ratio and traffic load ratio distributions are derived to obtain the tier association probability and the average ergodic MU data rate. Through numerical results, We find that our proposed cell association scheme outperforms cell range expansion (CRE) association scheme. Moreover, results indicate that allocating small sized and high-density BSs will improve spectral efficiency if using our proposed cell association scheme in heterogeneous networks.

To satisfy the massive wireless traffic transmission generated by multimedia applications, the massive multi-input-multi-output (MIMO) wireless system has emerged as a possible solution for future 5G wireless communication systems. However, the mutual coupling effect of massive MIMO systems has a negative effect potential on the wireless capacity. In this paper, the receive diversity gain is first defined and analyzed for massive MIMO wireless systems. Furthermore, we propose an effective capacity with the mutual coupling effect and the quality of service (QoS) statistical exponent constraint for multimedia massive MIMO wireless systems. Based on numerical results, the effective capacity approaches the effective capacity with the antenna spacing of d = 0.5λ when the antenna spacing is increased in the massive MIMO antenna array.

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.

It is a great challenge to evaluate the network performance of cellular mobile communication systems. In this paper, we propose new spatial spectrum and energy efficiency models for Poisson-Voronoi tessellation (PVT) random cellular networks. To evaluate the user access to the network, a Markov chain based wireless channel access model is first proposed for PVT random cellular networks. On that basis, the outage probability and blocking probability of PVT random cellular networks are derived, which can be computed numerically. Furthermore, taking into account the call arrival rate, the path loss exponent and the base station (BS) density in random cellular networks, spatial spectrum and energy efficiency models are proposed and analyzed for PVT random cellular networks. Numerical simulations are conducted to evaluate the network spectrum and energy efficiency in PVT random cellular networks.

Cognitive radio (CR) is considered one of the prominent techniques for improving the utilization of the radio spectrum. A CR network (i.e., secondary network) opportunistically shares the radio resources with a licensed network (i.e., primary network). In this work, the spectral-energy efficiency trade-off for CR networks is analyzed at both link and system levels against varying signal-to-noise ratio (SNR) values. At the link level, we analyze the required energy to achieve a specific spectral efficiency for a CR channel under two different types of power constraint in different fading environments. In this aspect, besides the transmit power constraint, interference constraint at the primary receiver (PR) is also considered to protect the PR from a harmful interference. Whereas at the system level, we study the spectral and energy efficiency for a CR network that shares the spectrum with an indoor network. Adopting the extreme-value theory, we are able to derive the average spectral and energy efficiency of the CR network. It is shown that the spectral efficiency depends upon the number of the PRs, the interference threshold, and how far the secondary receivers (SRs) are located. We characterize the impact of the multi-user diversity gain of both kinds of users on the spectral and energy efficiency of the CR network. Our analysis also proves that the interference channels (i.e., channels between the secondary transmitter and PRs) have no impact on the minimum energy efficiency.

Wireless broadcast has been increasingly used to deliver information of common interest to a large number of users. There are two major challenges in wireless broadcast: the unreliable nature of wireless links and the difficulty of acknowledging the correct reception of every broadcast packet by every user when the number of users becomes large. In this paper, by resorting to stochastic geometry analysis, we develop a network coding based broadcast scheme that allows a base station (BS) to broadcast a given number of packets to a large number of users, without user acknowledgment, while being able to provide a performance guarantee on the probability of successful delivery. Further, the BS only has limited statistical information about the environment including the spatial distribution of users (instead of their exact locations and number) and the wireless propagation model. Performance analysis is conducted. On that basis, an upper and a lower bound on the number of packet transmissions required to meet the performance guarantee are obtained. Simulations are conducted to validate the accuracy of the theoretical analysis. The technique and analysis developed in this paper are useful for designing efficient and reliable wireless broadcast strategies.

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.

A multiuser massive MIMO system with mutual coupling is investigated in finite-dimensional channel scenarios. The uplink ergodic achievable rate is analytically derived for a multiuser massive MIMO system equipped with a rectangular planar uniform antenna array at the base station (BS). Numerical results show that the mutual coupling effect reduces the uplink achievable rate of multiuser massive MIMO systems when the antenna distance decreases. But when the size of the antenna array is fixed, the uplink achievable rate increases with the growing of antenna number.

This paper describes a device triggering optimization technique for controlling system overload when deploying massive Machine Type Communication (MTC) devices in 3GPP-based cellular networks. Triggering a large number of MTC devices can dramatically overload the underlying transport network system and incur congestion in both the Radio Access Network (RAN) and the Evolved Packet Core (EPC). The proposed solution aims at controlling the rate of device trigger requests that MTC servers can generate in order to reduce the system overload. For this purpose, we propose that the Mobility Management Entity (MME), or an alike core network node, computes the device trigger rate that alleviates congestion, and communicates this value to the MTC-Interworking Function (MTC-IWF) element that enforces MTC traffic control, via admission control or data aggregation, on the device trigger request rate received from the different MTC servers. The proposed solution is evaluated through computer simulations and encouraging results are obtained.

There is an increasing demand for broadcasting information of common interest to a large number of users. The unreliable nature of wireless links and the difficulty of acknowledging the correct reception of every broadcast packet by every user when the number of users becomes large are two major challenges for wireless network broadcasting. In this paper we investigate the problem that a base station broadcasts a given number of packets to a given number of users, without user acknowledgment, while being able to provide a guarantee on the probability of successful delivery. Network coding technique is employed to improve both the efficiency and the reliability of the broadcast. Performance analysis is conducted. Based on the analysis, an upper and a lower bound on the number of packet transmissions required to meet the reliability guarantee are obtained. Simulations are conducted to validate the accuracy of the theoretical analysis. The technique and analysis developed in this paper can be useful for designing strategies to deliver information of common interest to a large number of users efficiently and reliably.

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.

The spectrum overcrowding drives investigating solutions to increase the usage of underutilized spectrum bands, minimize redundant interference and maximize expected throughput. Cognitive radio (CR) has presented itself as an appealing technology for solutions in energy constrained devices. Within this context, this paper introduces a system structure which enhances wireless multi-interfaced objects with cognitive radio. We propose a modular architecture to imbue such multi-interfaced devices with cognitive radio features, and based on it we present numerical results which identify performance boundaries and potential energy savings for an adaptive uncoded modulation scheme. Our results show that the tradeoff between throughput and energy consumption can be leveraged satisfactorily to enhance the lifetime significantly in noisy environments.

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.

It is of great importance and interests for network operators to evaluate the impact of inter-cell co-channel interference on the achievable data rate in cellular networks. In this paper, a closed-form maximum achievable rate over Nakagami-m fading uplink channels based on multicell linear Wyner model is derived. Moreover, the maximum achievable rates in some special cases are investigated. Numerical results show that the maximum achievable rate decreases with the increase of the inter-cell signal interference factor and is also affected by Nakagami-m fading severity parameters and number of users in a cell.

Hybrid Automatic Retransmission reQuest (HARQ) is an effective technique to improve the reliability of wireless communication systems by detecting, correcting, and retransmitting the erroneous packets. Packet-level error sequences obtained from physical layer wireless communication systems are important for the design and performance evaluation of high layer protocols, e.g., HARQ. In this paper, we utilize the open source Vienna long-term evolution (LTE) simulator to study the impact of HARQ on the burst error statistics of packet-level error sequences. Moreover, we propose a generative model that can generate packet-level error sequences with predicted burst error statistics similar to those of error sequences obtained from wireless systems with HARQ. Simulation results demonstrate that the proposed generative model is accurate and efficient in predicting the behavior of HARQ in terms of a set of burst error statistics rather than predicting the packet error rate (PER) only.

Cooperative communication technologies can improve the system throughput energy efficiency and reliability in dynamic wireless networks. For practical multi-cell multi-antenna mobile cellular networks, co-channel interference is a critical issue affecting cooperative transmission (Co-Tx) performance. In this paper, we first derive a cooperative outage probability model and a cooperative block error rate (BLER) model incorporating a binary differential phase shift keying modulation for performance analysis in such cooperative cellular networks. Based on them, a cooperative energy efficiency model is proposed and analyzed under different Co-Tx scenarios, interference levels and wireless channel conditions. As demonstrated by numerical results, our analytical models show that Co-Tx is an effective approach to mitigate co-channel interference and improve the energy efficiency, BLER and overall outage probability performance in multi-cell multi-antenna cooperative cellular networks.

Virtual multiple-input multiple-output (V-MIMO) technology promises significant performance enhancements to cellular systems in terms of spectral efficiency (SE) and energy efficiency (EE). How these two conflicting metrics scale up in large cellular V-MIMO networks is unclear. This paper studies the EE-SE trade-off of the uplink of a multi-user cellular V-MIMO system with decode-and-forward type protocols. We first express the trade-off in an implicit function and further derive closed-form formulas of the trade-off in low and high SE regimes. Unlike conventional MIMO systems, the EE-SE trade-off of the V-MIMO system is shown to be susceptible to many factors including protocol design (e.g., resource allocation) and scenario characteristics (e.g., user density). Focusing on the medium and high SE regimes, we propose a heuristic resource allocation algorithm to optimize the EE-SE trade-off. The fundamental performance limits of the optimized V-MIMO system are subsequently investigated and compared with conventional MIMO systems in different scenarios. Numerical results reveal a surprisingly chaotic behavior of V-MIMO systems when the user density scales up. Our analysis indicates that low frequency reuse factor, adaptive resource allocation, and user density control are critical to harness the full benefits of cellular V-MIMO systems.

Extensive research has been done on studying the capacity of wireless multi-hop networks. These efforts have led to many sophisticated and customized analytical studies on the capacity of particular networks. While most of the analyses are intellectually challenging, they lack universal properties that can be extended to study the capacity of a different network. In this paper, we sift through various capacity-impacting parameters and present a simple relationship that can be used to estimate the capacity of both static and mobile networks. Specifically, we show that the network capacity is determined by the average number of simultaneous transmissions, the link capacity and the average number of transmissions required to deliver a packet to its destination. Our result is valid for both finite networks and asymptotically infinite networks. We then use this result to explain and better understand the insights of some existing results on the capacity of static networks, mobile networks and hybrid networks and the multicast capacity. The capacity analysis using the aforementioned relationship often becomes simpler. The relationship can be used as a powerful tool to estimate the capacity of different networks. Our work makes important contributions towards developing a generic methodology for network capacity analysis that is applicable to a variety of different scenarios.

Energy efficiency has gained its significance when service providers' operational costs burden with the rapidly growing data traffic demand in cellular networks. In this paper, we propose an energy efficiency model for Poisson-Voronoi tessellation (PVT) cellular networks considering spatial distributions of traffic load and power consumption. The spatial distributions of traffic load and power consumption are derived for a typical PVT cell, and can be directly extended to the whole PVT cellular network based on the Palm theory. Furthermore, the energy efficiency of PVT cellular networks is evaluated by taking into account traffic load characteristics, wireless channel effects and interference. Both numerical and Monte Carlo simulations are conducted to evaluate the performance of the energy efficiency model in PVT cellular networks. These simulation results demonstrate that there exist maximal limits for energy efficiency in PVT cellular networks for given wireless channel conditions and user intensity in a cell.

This paper proposes a non-stationary wideband geometry-based stochastic model (GBSM) for multiple-input multiple-output (MIMO) high-speed train (HST) channels. The proposed model has the ability to investigate the non-stationarity of HST environment caused by the high speed movement of the receiver. Based on the proposed model, the space-time-frequency (STF) correlation function (CF) and STF local scattering function (LSF) are derived for different taps. Numerical results show the non-stationarity of the proposed channel model.

Energy efficiency is becoming increasingly important for wireless communication systems in order to minimize carbon footprint of wireless networks and to increase the battery life of mobile terminals. The spectral-energy efficiency trade-off is of primary significance to determine how much energy per bit is required in a wireless communication system to achieve a specific spectral efficiency. In this paper, we study energy-efficient resource allocation scheme for Orthogonal Frequency- Division Multiple Access (OFDMA) systems with multiple users. The trade-off between spectral and energy efficiencies is analyzed under the constraint of maintaining the fairness among users. We first formulate the energy-efficient optimization problem as an integer fractional programming. We then apply an iterative fractional method to simplify the problem to integer linear programming (ILP) problem. Simulation results demonstrate that the impact of user's quality of service (QoS) is minor on the energy efficiency when a large spectral efficiency is required.

Motivated by the demand for energy efficiency improvement in mobile communication industry, we explore an idea of optimizing energy efficiency for MIMO-OFDM wireless communication systems while maintaining users' quality of service (QoS) requirement. Based on the binary power control scheme,a power allocation criterion for energy efficiency optimization is derived under the total power constraint. From a bit error rate (BER) point of view, a protection constraint is configured to guarantee the system QoS. With the aim of energy efficiency optimization under QoS guarantee in MIMO-OFDM wireless communication systems, an energy-efficient binary power control with BER constraint (EBPCB) algorithm is proposed based on the power allocation criterion and QoS constraint. Simulations results demonstrate the energy efficiency improvement of EBPCB.

In this paper, we present interference models for cognitive radio (CR) networks employing various interference management mechanisms including power control, contention control or hybrid power/contention control schemes. For the first case, a power control scheme is proposed to govern the transmission power of a CR node. For the second one, a contention control scheme at the media access control (MAC) layer, based on carrier sense multiple access with collision avoidance (CSMA/CA), is proposed to coordinate the operation of CR nodes with transmission requests. The probability density functions (PDFs) of the interference received at a primary receiver from a CR network are first derived numerically for these two cases. For the hybrid case, where power and contention controls are jointly adopted by a CR node to govern its transmission, the interference is analyzed and compared with that of the first two schemes by simulations. Then, the interference PDFs under the first two control schemes are fitted by log-normal PDFs to reduce computation complexity. Moreover, the effect of a hidden primary receiver on the interference experienced at the receiver is investigated. It is demonstrated that both power and contention controls are effective approaches to alleviate the interference caused by CR networks. Some in-depth analysis of the impact of key parameters on the interference of CR networks is given as well.

The sleeping strategy has become popular to reduce power consumption of base stations (BSs) by shutting down underutilized BSs in the management of green cellular networks. In this paper, we propose a novel solution for an energy efficient use of cellular networks, based on traffic load balancing. By modeling the power consumption for BSs connected to uniformly distributed users, the relationship between the optimal number of active (or shut down) BSs and the traffic load is then derived through the power ratio, which is the ratio between dynamic and fixed power part of BS power consumption. Both analytical and simulation results demonstrate that, in order to achieve significant energy savings, less BSs should be turned on at low traffic load while more BSs turned on at high traffic load.

Characterization and modeling of co-channel interference is critical for the design and performance evaluation of realistic multi-cell cellular networks. In this paper, based on alpha stable processes, an analytical co-channel interference model is proposed for multi-cell multiple-input multi-output (MIMO) cellular networks. The impact of different channel parameters on the new interference model is analyzed numerically. Furthermore, the exact normalized downlink average capacity is derived for a multi-cell MIMO cellular network with co-channel interference. Moreover, the closed-form normalized downlink average capacity is derived for cell-edge users in multi-cell multiple-input single-output (MISO) cooperative cellular networks with co-channel interference. From the new co-channel interference model and capacity formulas, the impact of cooperative antennas and base stations on cell-edge user performance in the multi-cell multi-antenna cellular network is investigated by numerical methods. Numerical results show that cooperative transmission can improve the capacity performance of multi-cell multi-antenna cooperative cellular networks, especially in a scenario with a high density of interfering base stations. The capacity performance gain is degraded with the increased number of cooperative antennas or base stations.

How to predict the self-similar network traffic with high burstiness is a great challenge for network management. The covariation orthogonal prediction could effectively capture the burstiness in the network traffic, and the artificial neural network prediction could adapt the network traffic change by self-learning. To improve the prediction accuracy, we propose a new hybrid network traffic prediction method based on the combination of the covariation orthogonal prediction and the artificial neural network prediction. Through empirical study, the accuracy of the new prediction method can be effectively improved seen from the mean and the prediction error.

This paper proposes a novel three-dimensional (3D) two-sphere regular-shaped geometry-based stochastic model (RS-GBSM) with only double-bounced rays for non-isotropic scattering narrowband multiple-input multiple-output (MIMO) mobile-to-mobile (M2M) channels. The proposed 3D model has the ability to investigate the joint impact of both the azimuth angle and elevation angle on channel statistics. Based on the proposed model, the space-time (ST) correlation function (CF) is derived and the impact of some important parameters on the resulting ST CF is investigated. Numerical results show that the 3D model results in lower ST correlations than the corresponding 2D model.

This paper proposes a novel three-dimensional (3D) regular-shaped geometry-based stochastic model (RS-GBSM) for non-isotropic multiple-input multiple-output (MIMO) mobile-to-mobile (M2M) Ricean fading channels. The proposed model, combining a two-sphere model and an elliptic-cylinder model, is the first 3D RS-GBSM that has the ability to investigate the impact of the vehicular traffic density (VTD) on channel statistics and jointly consider the azimuth angle and elevation angle. From the proposed model, the space-time (ST) correlation function (CF) and the corresponding space-Doppler (SD) power spectral density (PSD) are derived. Finally, some numerical results and interesting observations are given.

In this paper, we present an interference model for cognitive radio (CR) networks employing power control or contention control scheme. The probability density functions (PDFs) of the interference received at a primary receiver from a CR network are derived for two cases. For the first case, a power control scheme is proposed to govern the transmission power of a CR node. For the second one, a cognitive media access control (MAC) employs carrier sense multiple access with collision avoidance (CSMA/CA) based contention control to coordinate the operation of CR nodes with transmission requests. These two control schemes are compared in terms of their resulting interference distributions. It is demonstrated that both power and contention controls are effective approaches to alleviate the interference caused by CR networks. Some in-depth analysis for the impact of key parameters on the interference of CR networks is given via numerical studies as well.

In the above titled paper (ibid., vol. 7, no. 2, pp. 329-332, Apr. 10), we omitted the sources of funding for our work. That is provided here. We also add an additional author, Xiaohu Ge, who assumes now the role of corresponding author.

Abstract In this paper, we analyze the impacts of different frame types on the self‐similarity and burstiness characteristics of the aggregated frame traffic in a real 802.11 wireless local area network (WLAN). We find that the impacts of different frame types are related to the mean frame sizes and the proportions of specified frame types in the aggregated frame traffic. Furthermore, we propose an analytical model to capture the relationship of self‐similarity characteristics between the aggregated frame traffic and different frame types. These new results provide an insight of frame traffic characteristics and some practical guidelines for developing new efficient algorithms to improve the common medium utilization and system throughput performance. Copyright © 2009 John Wiley & Sons, Ltd.

In wireless mesh access networks, ad hoc and infrastructure modes are both used to support multi-hop data transmission from mesh clients to a mesh router. Traffic will accumulate along the paths towards the mesh router. So the mesh clients close to the router will have more data to transmit and these packets are more likely to collide with each other. In [3], a random access MAC protocol, RSMA (receiver sense multiple access), is proposed dedicated for the final hop client to router communication to deal with the packet collision problem. In this paper, a rigorous mathematical model is developed for cross-layer performance analysis of RSMA. MAC layer random access model, physical layer radio channel model and the power capture model are combined for comprehensive analysis.

In this paper, we analyze a real 802.11 wireless network frame traffic trace, collected from the ACM SIGCOMM 2004 Conference, by calculating its autocorrelation function and Hurst exponents at different time scales. We find this frame traffic trace has second-order self-similar characteristic. This in-depth knowledge of 802.11 wireless network frame traffic can effectively improve the efficiency and performance of network planning, resource management and MAC-layer algorithms.

In this paper the impact of capture effect on the IEEE 802.11 networks has been investigated. In order to have insight into capture effect in the MAC mechanism, a new Markov chain model with capture effect has been built for describing the backoff scheme in the IEEE 802.11 networks. Based on the new iterative algorithm used for calculating the transmission probability, a new throughput model considering the impact of capture effect on the back-off scheme has been proposed. The numerical simulation results show that the capture effect has more impact on the basic access mechanism than that on the RTS/CTS access mechanism.

Because the self-similar processes have an infinite variance, the prediction method cannot be derived from the covariance. However, in the alpha-stable processes the covariation can be used to substitute the role of the covariance. Based on the theory of alpha-stable processes, a simple unbiased linear prediction method is developed for self-similar network traffic. The prediction method can be derived from the property of the covariation, and the prediction coefficients are solved from the cross-covariation matrix. The covariation orthogonality criterion ensures that the procedure of the prediction method is efficient and simple. The simulation experiments show that the new prediction method is able to predict the changes of the self-similar network traffic, especially in forecasting the bursty changes. As a result, this method can be used for network design so as to avoid network congestion.