The Integrated Sensing and Communication (ISAC) technique combines communication and environmental sensing in future wireless networks. Since multi-static sensing advantages in spatial diversity, cooperative ISAC networks have garnered extensive attention and investigation, but face challenges such as separation, extraction, and cooperative fusion of multi-target sensing information. To address these challenges, a code division approach is proposed to differentiate targets and signal source base stations. Furthermore, a map-fusion based cooperating localization approach integrating Direction of Arrival (DOA) estimation is introduced. Finally, numerical simulations validate the effectiveness of the proposed approaches. Code division reduces the Root Mean Squared error (RMSE) for angle, range, and velocity estimation by 86.4%, 91.7%, and 52.4% compared to the baseline approach. Additionally, the map-fusion based approach reduces localization RMSE by 83.4% to 95.1% compared to the existing localization approaches.

To meet the multidimensional extreme performance requirements of intelligent services in sixth-generation mobile (6G) networks, it is crucial to implement the joint management of sensing, communication, and computation resources. However, the competition between services and the inherent conflicts among multidimensional resources result in a prominent contradiction between the efficiency of joint resource management and its high complexity. To address the challenges, a multi-service coexistence model is proposed, incorporating sensing, communication, and computing requirements. The optimization problem is decomposed to enable a low-complexity solution. Initially, a service resource management and mode selection algorithm is proposed, leveraging attention-assisted multi-agent reinforcement learning to effectively coordinate service resource competition. Subsequently, a one-to-one matching game is developed for radio resource blocks and users, ensuring stable maximization of joint sensing and communication performance while optimizing radio resource reuse. Finally, a computing resource management algorithm is designed using the Lagrange multiplier method and Karush-Kuhn-Tucker conditions to enhance computing performance. Theoretical analysis and numerical simulations validate the proposed schemes in terms of low complexity and high effectiveness, achieving approximately 20% overall performance improvement over baseline schemes.

With the advantages of high flexibility and rapid deployment, unmanned aerial vehicles (UAVs) are considered as promising wireless access points for efficient emergency communication. However, UAV base stations (UAV-BSs) are characterized by their susceptibility to environmental interference, leading to jitter and prone to beam misalignment. Additionally, the operational time of a UAV-BS is constrained by the power source. To overcome these obstacles, this paper proposes an energy-efficient beam alignment and tracking scheme based on integrated sensing and communication (ISAC) technology. Specifically, beam alignment is performed based on the predicted angles obtained from the extended Kalman filtering (EKF) algorithm. Then, the beamwidth control is realized by changing the number of activated antennas based on the prediction error derived from EKF. To determine the optimal number of activated antennas for maximizing energy efficiency, a block coordinate descent based fractional programming algorithm is designed. Simulation results verify the superiority of the proposed scheme in terms of communication energy efficiency.

Currently, Internet of Things (IoT) services in radio access networks require access to multi-dimensional network resources such as communication, computation, and caching to provide customized services. When resources are limited, there is always competition for resources and multi-dimensional resource requirement conflicts (MRRCs), which will lead to performance degradation of the IoT services. Moreover, the diverse resource requirements of IoT services and the fact that multi-dimensional resources are involved in scheduling make it extremely difficult to solve the MRRCs problem. To depict the above issues, we formulate a hierarchical MRRCs model, which applies the Stackelberg model and the multi-objective optimization model to describe the conflicts among services and users, respectively. Then, to address the aforementioned problem, we propose a deep reinforcement learning scheme with a hierarchically structured action space. Additionally, a case study is designed to simulate the resource conflicts of three different types of services on the spectrum, computation capacity, and caching resources. The numerical simulation results show that the proposed scheme has the best convergence ability and overall performance in terms of the MRRCs' coordination compared with the baseline schemes.

To satisfy the low-latency requirements of emerging computation-intensive vehicular services, offloading these services to edge or cloud servers has been recognized as an effective solution. Due to the limited resources of edge servers and the faraway distance of cloud servers, it is challenging to provide an efficient resource allocation strategy to balance the latency, throughput and the resource utilization. In this paper, an end–edge–cloud collaboration paradigm is presented for computation offloading in fog-based vehicular networks (FVNETs) by incorporating vehicles with idle resources as fog user equipments (F-UEs). To adaptively orchestrate end–edge–cloud resources in different load cases, a two-timescale resource reservation and allocation framework is proposed. Wherein, a Stackelberg-game-based dynamic F-UE incentive problem is first formulated with the cloud server as the leader and multiple F-UEs as the followers, and then an iterative algorithm is proposed to achieve the Stackelberg equilibrium of the computation resource pricing and reservation. On a small timescale, the joint communication and computation resource allocation problem is transferred into a multiagent stochastic game and a lenient multiagent deep-reinforcement-learning-based distributed algorithm is developed to minimize the sum latency. When latency performance deteriorates, F-UE incentive optimization will be triggered to reserve more resources of F-UEs. Simulation results show that the proposed end–edge–cloud orchestrated computation offloading scheme in FVNETs outperforms baselines in terms of average latency.

Non-orthogonal multiple access (NOMA) based fog radio access networks (F-RANs) offer high spectrum efficiency, ultra-low delay, and huge network throughput, and this is made possible by edge computing and communication functions of the fog access points (F-APs). Meanwhile, caching-enabled F-APs are responsible for edge caching and delivery of a large volume of multimedia files during the caching phase, which facilitates further reduction in the transmission energy and burden. The need of the prevailing situation in industry is that in NOMA-based F-RANs, energy-efficient resource allocation, which consists of cache placement (CP) and radio resource allocation (RRA), is crucial for network performance enhancement. To this end, in this paper, we first characterize an NOMA-based F-RAN in which F-APs of caching capabilities underlaid with the radio remote heads serve user equipments via the NOMA protocol. Then, we formulate a resource allocation problem for maximizing the defined performance indicator, namely network profit, which takes caching cost, revenue, and energy efficiency into consideration. The NP-hard problem is decomposed into two sub-problems, namely the CP sub-problem and RRA sub-problem. Finally, we propose an iterative method and a Stackelberg game based method to solve them, and numerical results show that the proposed solution can significantly improve network profit compared to some existing schemes in NOMA-based F-RANs.

The rapid development of the Internet of Things with the requirements of ultrareliability and ultralow latency has imposed huge challenges on the radio access network operation and maintenance. Using artificial intelligence technologies can provide the accurate fault diagnosis rapidly and efficiently, but it is usually hampered by the lack of historical data as well as the certified fault labels. To deal with these challenges, in this article, an unsupervised deep transfer learning-based fault diagnosis method in fog radio access networks is proposed. Specifically, a transfer learning-based density-based spatial clustering of applications with noise method is first utilized to detect and label fault data in each interval by using the core-level information. Then, an unsupervised deep transfer learning method combining a convolutional neural network with a domain adversarial neural network is applied to classify the categories of unlabeled fault data by using cell-level information. The experimental results show that the proposed method can reduce the missed detection rate than the traditional method, and has better fault diagnosis accuracy than the reference methods.

The joint user association and cache placement problem is challenging in fog radio access networks (F-RANs) due to its difficulty to present the optimal solution with low complexity. Motivated by the recent development of artificial intelligence, we divide the original optimization problem into two subproblems. In particular, the user association problem is solved by a reinforcement-learning-based algorithm in which the enhanced fog access point content placement profiles and the fronthaul constraint are considered. On the other hand, since the popularity profile of the contents is hard to acquire in practice, a stacked autoencoder-based scheme is presented to predict the content popularity, which considers both the local and global user request status within a specified time interval. Based on the popularity prediction, the edge content placement problem is solved by a deep-reinforcement-learning-based algorithm, aiming at maximizing the F-RAN network payoff. Moreover, the complicated interactions and the cyclic dependency among the short time-scale user association and the long time-scale content popularity prediction and placement problems are studied by applying the Stackelberg game theory. The simulation validates the accuracy of the analytical results and proves that the proposal can further improve the performance of F-RANs.

Cellular vehicle-to-everything (V2X) communication is crucial to support future diverse vehicular applications. However, for safety-critical applications, unstable vehicle-to-vehicle (V2V) links, and high signaling overhead of centralized resource allocation approaches become bottlenecks. In this article, we investigate a joint optimization problem of transmission mode selection and resource allocation for cellular V2X communications. In particular, the problem is formulated as a Markov decision process, and a deep reinforcement learning (DRL)-based decentralized algorithm is proposed to maximize the sum capacity of vehicle-to-infrastructure users while meeting the latency and reliability requirements of V2V pairs. Moreover, considering training limitation of local DRL models, a two-timescale federated DRL algorithm is developed to help obtain robust models. Wherein, the graph theory-based vehicle clustering algorithm is executed on a large timescale and in turn, the federated learning algorithm is conducted on a small timescale. The simulation results show that the proposed DRL-based algorithm outperforms other decentralized baselines, and validate the superiority of the two-timescale federated DRL algorithm for newly activated V2V pairs.

Non-orthogonal multiple access (NOMA) is regarded as a promising technology for the next-generation wireless communication system. Introducing NOMA into the fog radio access networks (F-RANs) is able to provide simultaneous transmissions to multiple users and significantly enhance F-RAN performance. However, due to the increasing number of users and the constraint of caching storage capacity, there exists a tradeoff between NOMA transmission performance and fronthaul saving. In this paper, a hierarchical game framework is presented to solve the joint optimization problem of user access mode selection and content popularity prediction in NOMA based F-RANs. More specifically, the access mode selection problem is formulated as an evolutionary game. The proposals' evolutionary payoff expressions are derived by stochastic geometry tool, and the cost functions are related to the fog access point (F-AP) content placement profile as well as the fronthaul constraint. Moreover, the problem of what contents the F-AP should cache is modeled as a content popularity prediction problem, and based on both local and global user request states, a machine learning algorithm is presented to solve it. Simulation results validate the accuracy of analytical results and demonstrate our proposed algorithms can further improve the performance of NOMA based F-RANs.

Non-orthogonal multiple access (NOMA) has been considered as a promising communication technology to enhance the spectral efficiency and support massive connections in fog radio access networks (F-RANs). In this paper, with the aim of maximizing the weighted sum rate while taking co-channel interference into consideration, a joint resource block (RB) and power allocation problem is formulated. To solve this problem, we first propose the optimal resource allocation scheme. Specifically, the monotonic optimization is applied and an outer polyblock approximation algorithm is proposed to get the global optimal solution. In order to reduce the computational complexity, we then propose the suboptimal resource allocation scheme. In particular, the original problem is decomposed into separated RB and power allocation problems. The RB allocation problem is modeled as a many-to-one matching game and a modified swap-enabled matching algorithm is proposed. The power allocation problem is converted into a convex form through some approximations and solved by a successive convex approximation algorithm. Simulation results demonstrate that the suboptimal scheme can achieve almost the same performance as the optimal scheme, while requiring much less computational complexity. In addition, the superiority of NOMA-enabled F-RANs over the conventional OMA-enabled F-RANs is verified.

To meet the wide range of 5G use cases in a cost-efficient way, network slicing has been advocated as a key enabler. Unlike the core network slicing in a virtualized environment, radio access network (RAN) slicing is still in its infancy and the corresponding realization is challenging. In this paper, we investigate the realization approach of fog RAN slicing, where two network slice instances for hotspot and vehicle-to-infrastructure scenarios are concerned and orchestrated. In particular, the framework for RAN slicing is formulated as an optimization problem of jointly tackling content caching and mode selection, in which the time-varying channel and unknown content popularity distribution are characterized. Due to the different users' demands and the limited resources, the complexity of original optimization problem is significant high, which makes traditional optimization approaches hard to be directly applied. To deal with this dilemma, a deep reinforcement learning algorithm is proposed, whose core idea is that the cloud server makes proper decisions on the content caching and mode selection to maximize the reward performance under the dynamical channel state and cache status. The simulation results demonstrate the performance in terms of hit ratio and sum transmit rate can be significantly improved by the proposal.

Wireless content caching has recently been considered as an efficient way in fog radio access networks (F-RANs) to alleviate the heavy burden on capacity-limited fronthaul links and reduce delivery latency. In this paper, an advanced minimal delay association policy is proposed to minimize latency while guaranteeing spectral efficiency in F-RANs. By utilizing stochastic geometry and queueing theory, closed-form expressions of successful delivery probability, average ergodic rate, and average delivery latency are derived, where both the traditional association policy based on accessing the base station with maximal received power and the proposed minimal delay association policy are concerned. Impacts of key operating parameters on the aforementioned performance metrics are exploited. It is shown that the proposed association policy has a better delivery latency than the traditional association policy. Increasing the cache size of fog-computing based access points (F-APs) can more significantly reduce average delivery latency, compared with increasing the density of F-APs. Meanwhile, the latter comes at the expense of decreasing average ergodic rate. This implies the deployment of large cache size at F-APs rather than high density of F-APs can promote performance effectively in F-RANs.

An optimization problem on the joint content caching and mode selection for slice instances in fog radio access networks (F-RANs) is researched in this paper, characterizing the unknown content popularity distribution and time-varying channel assumptions. In particular, hotspot and vehicle-to-infrastructure scenarios are considered and corresponding network slice instances are orchestrated in F-RANs. Considering different users' demands and limited resources, there exists a significant high complexity in solving the original optimization problem with traditional optimization approaches. Motivated by the advantages of deep reinforcement learning in solving sophisticated network optimizations, a deep reinforcement learning based algorithm is proposed, wherein the cloud server takes intelligent actions to maximize the hit ratio and sum transmit rate. The performances of the proposed algorithm are demonstrated to be significantly improved.

The growing popularity of Internet of Things (IoT) with the requirements of highly reliable and low latency has imposed huge challenges to current cellular networks. Using small aerial platforms like unmanned aerial vehicles (UAVs) to assist terrestrial base stations (BSs) is attractive, but it often challenged by the lack of UAV access selection and resource allocation algorithm to balance the network performance and service cost. In this paper, we study the UAV access selection and BS bandwidth allocation problems in a UAV assisted IoT communication network, where a hierarchical game framework is presented. The complicated interactions among UAVs and BSs as well as the cyclic dependency is studied by applying the Stackelberg game theory. Wherein, the access competition among groups of UAVs is formulated as a dynamic evolutionary game and solved by an evolutionary equilibrium. On the other hand, the problem of how much bandwidth should BSs allocate to the UAVs is modeled as a noncooperative game, where the existence and the uniqueness of Nash equilibrium is analyzed. Stochastic geometry tool is used to model the position distribution of network nodes and drive the payoff expressions by taking into account different network parameters. The analytical results for the proposed hierarchical game model and the corresponding solutions are evaluated via simulations, which verify both the validity of our analysis and the effectiveness of the proposed algorithms.

Non-orthogonal multiple access (NOMA) is promising to further improve spectral efficiency (SE) and decrease transmit latency in fog radio access networks (F-RANs) through serving multi-users in the same frequency-time resource block simultaneously, while the complexity of user association is challenging to exploit the corresponding performance gains. In this paper, a performance analysis framework for the user association in NOMA based F-RANs is proposed and the closed-form analytical results are developed by using stochastic geometry tool. In particular, we propose two user association algorithms based on evolutionary game and reinforcement learning, respectively. The performance model jointly considering quality of service, delay cost, and power consumption is formulated as a payoff function, and the corresponding performance expressions are derived for these two user association algorithms. Numerical and simulation results demonstrate that the derived expressions are accurate, and the NOMA based F-RAN can provide over 50% performance gains on SE compared to the orthogonal multiple access scheme. Furthermore, these two proposed user association algorithms work well with high convergence, which can effectively enhance the overall performance and the fairness of users.

Fog computing based radio access network is a promising paradigm for the fifth generation wireless communication system to provide high spectral and energy efficiency. With the help of the new designed fog computing based access points (F-APs), the user-centric objectives can be achieved through the adaptive technique and will relieve the load of fronthaul and alleviate the burden of base band unit pool. In this paper, we derive the coverage probability and ergodic rate for both F-AP users and device-to-device users by taking into account the different nodes locations, cache sizes as well as user access modes. Particularly, the stochastic geometry tool is used to derive expressions for above performance metrics. Simulation results validate the accuracy of our analysis and we obtain interesting tradeoffs that depend on the effect of the cache size, user node density, and the quality of service constrains on the different performance metrics.

Characterizing user access methods in heterogeneous cloud radio access networks (H-CRANs)is critical for performance optimization. Different from the user access in cloud radio access networks, the inter-tier interference from macro base station has a great impact on user access in H-CRANs. In this paper, after considering the inter-tier interference, the ergodic rates of downlink H- CRANs for two proposed user access methods, namely distance based and cluster based, are analyzed. The corresponding mathematical expressions of ergodic rates have been derived. In particular, the closed-form expression for the upper bound of ergodic rate is proposed. Simulation results corroborate the accuracy of the derived expressions for these two methods. Furthermore, the cluster based user access method outperforms the distance based user access method when the intensity of remote radio heads is sufficiently high.

The performance analysis under various precoding strategies in heterogeneous cloud radio access networks (H-CRANs) is increasingly highlighted. In this paper, we focus on suppressing the inter-tier interference between macro base stations (MBSs) and remote radio heads (RRHs), which is severely affected by the application of precoding schemes. Two precoding schemes including interference cancelation (IC) and beamforming (BF) are characterized for performance evaluation. The average bit error rate (BER) and system sum capacity under these two schemes are derived with closed-form expressions. Monte Carlo simulations are performed to verify our analytical results and demonstrate the effectiveness of precoding schemes. Furthermore, we explore this problem analytically and demonstrate that BF is preferred when the signal-to-noise ratio (SNR) of MBSs is relatively low and IC outperforms BF at the high SNR region.

As the evolution of cloud radio access networks (CRANs), heterogeneous cloud radio access networks (H- CRANs) are now recognized as promising paradigm to achieve high spectral and energy efficiency through taking advantages of both heterogeneous networks and C- RANs. In H-CRANs, the heterogeneous processing node (HPN) guarantees the basic quality of service (QoS) requirement for the user equipment, while remote radio heads (RRHs) are deployed to provide enhanced QoS performances. Inter-tier interference between HPNs and RRHs should be coordinated for achieving high throughput gains in H-CRANs. In this paper, the transmit power for both RRHs and HPNs are researched to mitigate this inter-tier interference. The throughput maximizing problem with and without interference coordination under the power and interference constraints are developed. Since this kind of optimization problem is not convex, these two non- convex optimization problems are transformed into the form of matrix. Through the Perron-Frobenius theory, the optimal power allocation solution is derived. Simulation results show that the proposed solution is converged, and it can achieve significant performance gains.