Clustered cell-free networking paves a new way for enabling scalable joint transmission among access points (APs) by partitioning the whole network into non-overlapping sub-networks. Previous works adopted clustering algorithms, graph partitioning methods or conventional continuous optimization theories to partition a network based on the channels between all users and all APs, resulting in huge channel measurement and computational costs. This makes these methods difficult to be implemented in practical systems since the optimal network partition could vary frequently due to user mobility. In addition, existing methods were usually designed for specific clustered cell-free networking problems with different optimization algorithms employed. In this paper, we leverage deep reinforcement learning (DRL) for clustered cell-free networking so as to rapidly adapt to user movements in dynamic environments, and propose a deep deterministic policy gradient based clustered cell-free networking (DDPG-C2F) framework that can be adapted in various application scenarios. Moreover, in our framework, only one single channel needs to be estimated at each AP as the input of the neural network, which greatly reduces the channel measurement costs for clustered cell-free networking, and the training and inference costs of our framework. The proposed DDPG-C2F framework is then applied to various clustered cell-free networking problems with different objectives and constraints to demonstrate its performance. Simulation results show that our framework outperforms existing baselines in all scenarios. Moreover, we show that the proposed framework can reduce the handover cost over user mobility, and is robust to dynamic scenarios with random user joining or leaving.

The fully-decoupled radio access network (FD-RAN) is an innovative architecture designed for next-generation mobile communication networks, featuring decoupled control and data planes as well as separated uplink and downlink transmissions. To further enhance energy efficiency, this paper explores a green approach to FD-RAN by incorporating adaptive base station (BS) sleeping and resource allocation. First, we introduce a holistic power consumption model and formulate a energy efficiency maximization problem for FD-RAN, involving joint optimization of user equipment (UE) association, BS sleeping, and power control. Subsequently, the optimization problem is decomposed into two subproblems. The first subproblem, involving UE power control, is solved using a successive lower-bound maximization approach based on Dinkelbach’s algorithm. The second subproblem, addressing UE association and BS sleeping, is tackled via a modified, low-complexity many-to-many swap matching algorithm. Extensive simulation results demonstrate the superior effectiveness of FD-RAN with our proposed algorithms, revealing the sources of energy efficiency gains.

The integration of space, air, and ground networks into a fully decoupled radio access network (FD-RAN) is emerging as a promising approach for 6 G, driven by the need for seamless coverage, flexible spectrum allocation, and efficient collaboration among heterogeneous nodes. However, the inherent differences in wireless environments across terrestrial, aerial, and satellite segments pose challenges for traditional feedbackbased transmission. In response to these challenges, this paper introduces a space-air-ground integrated FD-RAN architecture where users can be served by multiple nodes with adaptive resource block (RB) allocation. For downlink transmissions in base stations (BSs), a feedback-free approach is developed by employing a deep learning-based channel state information (CSI) prediction framework, allowing BSs to perform multipleinput multiple-output (MIMO) transmissions using only user geolocation. To enhance cooperation and RB allocation among heterogeneous nodes, a many-to-one matching model is proposed, achieving stable matching with low complexity and fast convergence. Simulation results validate the effectiveness of the proposed framework, showing a 70 % improvement in spectrum efficiency compared to single-connection networks based on optimal path loss and round-robin resource scheduling.

Coordinated multi-base station (BS) transmission has emerged as a fundamental access technology to augment network capability and improve spectrum efficiency. However, the computation-intensive feedback of channel state information (CSI) poses significant challenges in determining physical-layer parameters for coordinated BSs. In this paper, we investigate a feedback-free mechanism that leverages fixed precoding matrix indicator (PMI), rank indicator (RI), and channel quality indicator (CQI) for coordinated BS transmission over a fully-decoupled radio access network (FD-RAN). Aiming to maximize user equipment (UE) throughput without CSI feedback, we calculate an optimal feedback-free parameter across spatial, frequency, and time domains only through UE geolocations. First, to determine MIMO transmission layer and precoding strategy in the spatial domain, we introduce a hierarchical reinforcement learning (HRL) framework to jointly select PMI and RI for coordinated BSs. Subsequently, for designing a more fine-grained subband transmission, transformer module is employed to capture the subcarrier correlations within OFDM symbols. Finally, given the unpredictable channel variations, we leverage a diffusion model to generate representative channel for fixed PMI, RI, and CQI over time-varied networks. Simulations demonstrate that 2 BSs feedback-free transmission can enhance 13% throughput compared with 1 BS CLSM transmission, which provides a design principle for next-generation transceiver technologies.

With the development of the Internet of Everything (IoE), future 6G networks will face the challenge of massive terminal access. However, deploying substantial high-cost, full-function base stations will undoubtedly further increase the cost of mobile network deployment, making it difficult for mobile operators to afford it. In this paper, we tackle the problem of low-cost network deployment for fully-decoupled radio access network (FD-RAN) with personalized service for large-scale terminals. We first propose a techno-economic cost model (TECM) for FD-RAN deployment based on the techno-economic approach. Then, we further formulate a cost-minimization problem for decoupled network deployment. Based on the independence brought by uplink and downlink decoupling in FD-RANs, we decompose the original problem into separate subproblems for uplink and downlink network deployment. In the following, we propose a branch and cut based network deployment (BCND) algorithm to solve two decoupled deployment subproblems, respectively. Finally, simulation results show that FD-RANs have significant cost advantages when facing differentiated service demands, and the main factors affecting network cost are power consumption and rental costs.

With the rapid development of low earth orbit (LEO) satellite technologies and constellation projects (like Starlink and OneWeb), the ongoing ultradense LEO satellite networks can provide an efficient solution to ubiquitous connection of aircraft. It is urgent to design the ultradense LEO-network-enabled satellite-to-aircraft communication for in-cabin users with high-speed Internet connectivity requirements. In this article, we investigate the resource scheduling of ultradense LEO-network-enabled satellite-to-aircraft communication and the in-cabin communication inside aircraft in civil aviation. First, we adopt the Lyapunov optimization framework to tackle the high dynamic and fast mobility of satellites and aircraft. For the satellite-to-aircraft access and subchannel assignment, we propose a dynamic access and subchannel allocation algorithm based on the matching theory, which can converge to a stable matching within a finite iterations. To guarantee customized needs of in-cabin users, we propose a power control algorithm based on the successive convex approximation technology to optimize the power allocation of satellites on each subchannel and the transmit power of aircraft for each in-cabin user, which is proven to obtain a saddle point or a local minimum with low complexity. Extensive simulations have been carried out to validate the effectiveness of the proposed resource allocation method.

With the emergence of numerous new applications and the explosive growth of Internet of Things (IoT) devices in beyond 5G (B5G) networks, the massive yet delay-sensitive personalized content delivery has imposed a crucial challenge to mobile network operators (MNOs). Cooperation among MNOs for sharing the communication, caching, and computing (3C) 3-D resources in an economic yet real-time manner has provided a promising solution to address this challenge. In this article, we investigate the 3C resource sharing among multiple MNOs to realize efficiently and economically personalized content delivery, where a third-party 3C resource provider (CRP) is introduced to manage the sharing 3C resource pool. By leveraging the multidimensional contract theory, we propose an optimal 3C resource contract scheme for the CRP in a realistic asymmetric information scenario, and the appointed 3C resources in one contract will be allocated to the MNO who signs it. For each MNO, we establish a partial transcoding model to achieve the optimal orchestration on the 3C resources, where the closed-form solution of caching placement and transcoding strategy is obtained. Then, MNOs can choose the most suitable contracts to sign based on the service requirements from end users. In particular, we analyze the global incentive compatibility and feasibility of the proposed multidimensional contract approach, which is theoretically proven to achieve the optimal solution. Extensive simulation results demonstrate the efficiency of the proposed 3C resource sharing mechanism compared with other benchmark schemes. Specifically, the proposed 3C resource sharing scheme can reduce 35% delivery delay compared with the nonsharing scheme.

Two-tier computing paradigm that takes full advantage of both the end-user and the cloud computation capabilities has emerged as a promising way to deal with computationally-intensive tasks in the next generation wireless networks. For promoting the integration of the two-tier computing, federated learning (FL) provides an effective framework to enable the collaboration between the end-user and the cloud. However, the key performance metric, i.e., FL training latency, will be severely affected by the worst wireless link quality in both uplink and downlink. In this paper, aiming at accelerating the FL enabled end-cloud two-tier computing over the wireless networks, we introduce the uplink and downlink fully-decoupled radio access network (FD-RAN) architecture to enhance the minimum wireless link rate via multiple base stations (BSs) access collaboration and power management solution. First, the Lagrange dual decomposition and the binary variable relaxation methods are leveraged to obtain an optimal multiple BS access scheme for the enhancement of minimum uplink and downlink SINR. Subsequently, we exploit the successive convex approximation (SCA) algorithm to deal with the uplink power control and downlink power allocation with a proved data rate lower bound. Furthermore, considering the dynamic channel realizations, a stochastic optimization technique with a convex surrogate function is utilized to find the best end-cloud two-tier computing scheme for FL applications. Simulation results have demonstrated the effectiveness of our proposed joint multiple access collaboration and power management solution over FD-RAN for achieving a faster FL enabled two-tier computing task.

To enable flexible base stations (BS) association and dynamic resource management for personalized user equipment (UE) download service provision in the next-generation mobile communication network (6G), in this paper, we investigate the downlink (DL) transmission scenario in an origin fully-decoupled radio access network (FD-RAN) architecture. Considering the unique fully-decoupled UL/DL access feature, we propose an efficient two-stage DL channel estimation method in the FD-RAN. We formulate a novel multi-connectivity and dynamic resource cooperation problem with joint multiple-BS and multiple-UE association and coordinated beamforming, aiming at maximizing the weighted sum achievable rate in DL FD-RAN. By leveraging the many-to-many swap-matching theory and fractional relaxation approach, we solve the dynamic UE scheduling problem with multiple-BS and multiple-UE association and the coordinated beamforming problem, respectively. Extensive simulation results based on standard 3GPP 36.873 urban micro channel demonstrate that the proposed framework can improve the average spectral efficiency by 34.9% as compared to the traditional maximum ratio transmission beamforming method.

To cope with the even more urgent spectrum and energy efficiency challenge for trillion-level terminal access and data uploading in the next generation mobile communication network (6G), in this paper, we investigate the uplink transmission in an original fully-decoupled radio access networks (FD-RAN) architecture. Specifically, we propose a resilient uplink base station cooperative reception framework in FD-RAN, which is a large-scale fading based two-tier signal combination approach for the uplink transmission, including the localized signal combination at the base station and centralized signal combination at the edge cloud, respectively. Then, we formulate a weighted sum-rate maximization problem for the uplink transmission optimization, and decompose it into two subproblems. A spectrum-efficiency maximized virtual service cluster selection (SEMVS) algorithm is designed by leveraging the channel statistical information for solving subproblem one, and a fractional programming based power control (FPPC) algorithm is introduced for the power optimization of subproblem two. Compared to the typical RAN architectures with corresponding access and power control methods, simulation results demonstrate the significant performance improvements of uplink FD-RAN with the proposed solution.

Nowadays, the ultra-dense low earth orbit (LEO) satellite network has become a promising paradigm in the next generation mobile communication network. With the development of content centric communication, multicast technology also attracts much attention. In this paper, we consider the downlink multicast transmission in the ultra-dense LEO satellite network. Multiple LEO satellites provide multicast service for multiple ground user (GU) groups under their coverage, where each GU group requests the same content. To improve the multicast performance, we propose an optimal subchannel allocation and beamforming scheme to maximize the system max-min fair (MMF) capacity of GUs. By leveraging the many-to-many matching model, we obtain the optimal subchannel allocation solution, and we propose a successive convex approximation (SCA) based algorithm for the downlink beamforming in the matching process. The many-to-many matching algorithm is convergent to a stable solution after finite iterations. Simulation results show the superiority and the effectiveness of the proposed subchannel allocation and beamforming method compared with other baseline schemes.

With the rapid development of Internet of Vehicles (IoV), an increasing number of vehicular users (VUEs) will connect to the Internet via 5G and beyond 5G(B5G) networks, which makes the spectrum resource becoming extremely scarce. However, the traditional spectrum allocation method cannot well adapt to the explosive growth of IoV traffic, and an efficient spectrum management for the IoV in B5G networks is an urgent challenge. In this paper, we propose a Stackelberg game and federated learning assisted spectrum sharing framework for the IoV. First, we develop a power control strategy for the region nodes considering its revenue and energy consumption, while VUEs can dynamically change their spectrum resource request strategy to maximize their revenue. We find the Stackelberg equilibrium using the alternating direction method of multiplier (ADMM) algorithm. To maximize the global revenue of region nodes, we leverage federated learning to realize the interaction between the region nodes and the central node. In specific, each region node utilizes deep learning to fit the relationship between the allocated power and its revenue. Then, they upload the network parameters to the central node. After collecting the network parameters from all region nodes, the central node can make the global decision about the spectrum allocation to the region nodes. Numerous simulation results verify the effectiveness of the proposed framework compared with the benchmark methods.

Recent advancements of Vehicle-to-Everything (V2X) communication combined with artificial intelligence (AI) technologies have shown enormous potentials for improving traffic management efficiency and intelligence. To provide innovative and effective data-driven traffic management solution for the coming automated vehicle era, we present a vehicle-road collaboration-enabled nonsignalized intersection management architecture in this paper. First, by dividing the intersection zone into the central section (CS) and the waiting section (WS), a vehicle regulation scheme involved with communication and computation planes is developed for V2X-enabled nonsignalized intersection management. Specifically, in order to guarantee vehicle safety, the definition of no overlapping occupation time in CS and the fastest crossing time point (FCTP) algorithm are employed for vehicle collision avoidance. Second, considering the relative coordination between adjacent intersections, a multiagent-based deep reinforcement learning scheduling (MA-DRLS) algorithm is proposed to realize cooperative multiple intersection management. Through information exchange with different intersection agents, each agent can obtain an optimal scheduling strategy using independent deep reinforcement learning (DRL) network. The features of fixed Q-targets and experience replay are leveraged to improve the reliability of neural network during the training process. Finally, simulation performances in terms of intersection throughput and vehicle waiting time have been provided to validate the effectiveness and demonstrate the superiority of the proposed nonsignalized intersection management solution.

With the advance of unmanned aerial vehicles (UAVs) and low earth orbit (LEO) satellites, the integration of space, air and ground networks has become a potential solution to the beyond fifth generation (B5G) Internet of remote things (IoRT) networks. However, due to the network heterogeneity and the high mobility of UAVs and LEOs, how to design an efficient UAV-LEO integrated data collection scheme without infrastructure support is very challenging. In this paper, we investigate the resource allocation problem for a two-hop uplink UAV-LEO integrated data collection for the B5G IoRT networks, where numerous UAVs gather data from IoT devices and transmit the IoT data to LEO satellites. In order to maximize the data gathering efficiency in the IoT-UAV data gathering process, we study the bandwidth allocation of IoT devices and the 3-dimensional (3D) trajectory design of UAVs. In the UAV-LEO data transmission process, we jointly optimize the transmit powers of UAVs and the selections of LEO satellites for the total uploaded data amount and the energy consumption of UAVs. Considering the relay role and the cache capacity limitations of UAVs, we merge the optimizations of IoT-UAV data gathering and UAV-LEO data transmission into an integrated optimization problem, which is solved with the aid of the successive convex approximation (SCA) and the block coordinate descent (BCD) techniques. Simulation results demonstrate that the proposed scheme achieves better performance than the benchmark algorithms in terms of both energy consumption and total upload data amount.

With a massive number of Internet-of-Things (IoT) devices connecting with the Internet via 5G or beyond 5G (B5G) wireless networks, how to support massive access for coexisting cellular users and IoT devices with quality-of-service (QoS) guarantees over limited radio spectrum is one of the main challenges. In this paper, we investigate the multi-operator dynamic spectrum sharing problem to support the coexistence of rate guaranteed cellular users and massive IoT devices. For the spectrum sharing among mobile network operators (MNOs), we introduce a wireless spectrum provider (WSP) to make spectrum trading with MNOs through the Stackelberg pricing game. This framework is inspired by the active radio access network (RAN) sharing architecture of 3GPP, which is regarded as a promising solution for MNOs to improve the resource utilization and reduce deployment and operation cost. For the coexistence of cellular users and IoT devices under each MNO, we propose the coexisting access rules to ensure their QoS and the priority of cellular users. In particular, we prove the uniqueness of the Stackelberg equilibrium (SE) solution, which can maximize the payoffs of MNOs and WSP simultaneously. Moreover, we propose an iterative algorithm for the Stackelberg pricing game, which is proved to achieve the unique SE solution. Extensive numerical simulations demonstrate that, the payoffs of WSP and MNOs are maximized and the SE solution can be reached. Meanwhile, the proposed multi-operator dynamic spectrum sharing algorithm can support more than almost 40% IoT devices compared with the existing no-sharing method, and the gap is less than about 10% compared with the exhaustive method.

The significant enhancement of vehicular communications together with artificial intelligence (AI) have opened up new horizons for innovative data-driven traffic management solution within intelligent transportation system (ITS). In this paper, to alleviate progressively worse urban traffic, we propose an efficient vehicle-to-everything (V2X) communications enabled non-signalized intersection management framework for automated vehicles. First, a resource reservation model involved with different functional planes has been developed for vehicle collision avoidance in V2X enabled non-signalized intersections. Second, reinforcement learning (RL) solution is leveraged to enhance management efficiency of non-signalized scheduling. Furthermore, considering the dimensionality disaster problem of vehicle state caused by complicated traffic environment, we propose a function approximation based non-signalized intersection control (FA-NIC) algorithm to obtain optimal scheduling strategy. Simulation results are provided to demonstrate the effectiveness of our proposed non-signalized intersection management solution.

The Uplink (UL) and Downlink (DL) decoupled cellular access through flexible cell association has attracted a lot of attention due to numerous benefits such as higher network throughput, better load balancing, and lower energy consumption, etc. In this paper, we introduce a novel reinforcement learning aided decoupled RAN access framework for Cellular Vehicle-to-Everything (V2X) communications, and propose a two-step RAN slicing approach to dynamically allocate the radio resource to V2X services in different time granularity. We derive an innovative QoS metric of V2V cellular mode by taking consideration of the bidirectional nature of V2V cellular communications. Moreover, we maximize the sum utility considering the proposed QoS metric by leveraging the Deep Deterministic Policy Gradient (DDPG) enabled RAN slicing method. Simulation results are provided to demonstrate the advance of the proposed reinforcement learning aided decoupled RAN slicing framework in achieving load balancing, maximizing total network utility and satisfying the QoS metric of Cellular V2X communications.

For the massive access of Internet-of-Things (IoT) devices in 5G or beyond 5G (B5G) wireless networks, how to support the coexisting of cellular users and massive IoT devices with quality-of-service (QoS) guarantees over limited spectrum is challenging. In this paper, inspired by the active radio access network (RAN) sharing standard of 3GPP, we present a heterogeneous multi-operator spectrum sharing architecture by leveraging the spectrum trading to support the coexistence of QoS-guaranteed cellular users and massive IoT devices with non-orthogonal multiple access (NOMA). In the architecture, we formulate the spectrum trading between the wireless spectrum provider (WSP) and mobile network operators (MNOs) as a Stackelberg pricing game. Meanwhile, for each MNO, the cellular users and massive IoT devices can be both serviced in the uplink NOMA networks with QoS guarantees. For the pricing game, we prove the uniqueness of the equilibrium solution, which can maximize the payoffs of MNOs and WSP simultaneously. Moreover, we propose an iterative pricing algorithm to achieve the equilibrium solution with theoretical optimality guarantees. Simulation results demonstrate that our framework can support more IoT devices and reach the optimal spectral bandwidth price.

With the emergence of automated driving technology, wireless demand for secure information exchange among automated vehicles has increased dramatically. To this end, we design a security-aware dynamic device-to-device (D2D) spectrum resource sharing mechanism to enhance the security of vehicular D2D communications with the improved spectrum efficiency. Considering both resource block (RB) sharing and power control, we model this joint D2D spectrum resource sharing process as a weighted bipartite graph matching problem whose weights are obtained through deriving the closed-form solutions of power control in an algebraic method. Then, the global optimal solutions of the matching problem are obtained by using the Hungarian algorithm. Furthermore, a security-aware RB and power allocation (SA-RBPA) mechanism compatible with existing cellular networks is proposed for the small cell base station applications. Extensive simulation results have shown that, compared with existing approaches that consider RB reusing strategy or power control scheme optimization alone, the SA-RBPA scheme is able to realize a better spectrum efficiency and security performance.

Vehicular platooning communication has been widely recognized as a promising traffic management technique for connected vehicles to improve the traffic capacity, on-road safety, and energy efficiency, etc. However, due to the high mobility and dense vehicular communication scenario, how to improve the radio resource efficiency of vehicular platooning communications is a challenge issue. In addition, considering the broadcast nature of vehicular communications, vulnerable vehicular platooning communications could be exposed to potential eavesdroppers, which would be a hidden danger for connected vehicle applications. In this paper, we investigate the secure radio resource sharing problem in device-to-device (D2D) enabled multiplatooning vehicular communications. Based on the physical layer security theory, we propose a security aware joint channel and power allocation (SA-JCPA) scheme, in which the closed-form expression for power control is derived through an algebraic method and the channel sharing strategy is obtained by leveraging a maximum weight bipartite graph matching approach. Through extensive simulations, it is demonstrated that our proposed SA- JCPA scheme can achieve efficient and secure radio resource utilization.

With the significant advance of vehicle-to-everything (V2X) techniques, unsignalized intersection coordination has been widely recognized to facilitate the development of automated vehicles (AVs) for the intelligent transportation system. However, how to guarantee driving safety while improving the unsignalized intersection management efficiency is a challenging issue. In this article, we investigate the collision-free and efficient V2X-enabled AV scheduling problem at unsignalized intersections. First, by dividing the intersection zone into different collision sections (CSs), we formulate the intersection collision-free model into an absolute value programming (AVP) problem, which is proved to be NP-hard. We consider both nonplatoon and platoon traffic scenarios, and unlike previous algorithms, which require to control all the AVs at each scheduling step with computational intractability, our scheduling algorithm can assign a feasible time for each arriving AV with low complexity. Further, we propose an alternately iterative descent method (AIDM) to solve the AVP problem by assigning the optimal entering time for each arriving AV. Through extensive simulations with various traffic data generated by SUMO, we demonstrate that our proposed AIDM algorithm can significantly enhance the scheduling performance in terms of passing delay and scheduling throughput. Even though the AIDM algorithm achieves the same level of transportation performances with the state-of-the-art algorithm, it advances dramatically in computational complexity and communication overhead, which is easier to be implemented in practice.