Cooperative uplink multi-base station (BS) reception has emerged as a promising technology to enhance received signal strength and improve wireless spectral efficiency. However, realizing the potential performance gains remains challenging due to substantial communication overhead among cooperative BSs and excessive delays in channel state information (CSI) feedback. This paper investigates a CSI feedback-free mechanism that leverages time-invariant physical layer parameters to facilitate cooperative BS reception within a fully-decoupled radio access network (FD-RAN). First, given the dynamically changing characteristics of the wireless environment, we employ conditional variational autoencoder (CVAE), a state-of-the-art generative artificial intelligence (GAI) approach, to generate location-specific representative channels for calculating CSI feedback-free transmission parameters. Subsequently, to maximize the throughput of user equipment (UE), a diffusion model-based deep reinforcement learning (DRL) framework is proposed for jointly selecting cooperative BS reception sets and precoding schemes, utilizing the representative channels generated by CVAE. Extensive simulations conducted on a link-level simulator demonstrate that the proposed CSI feedback-free mechanism for cooperative multi-BS reception can effectively improve spectrum efficiency by 17.3%, which provides a promising design principle for the development of sixth-generation (6G) wireless networks.

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.

Deep learning methods for accurately predicting data center status, which are essential for addressing the exponential growth of energy consumption, have gained significant attention, driven by the vast amounts of data collected through the advancement of Internet of Things (IoT) technologies. However, conventional small models often face data scarcity issues in practical deployment. While large models show promise in addressing this challenge, they encounter obstacles, such as multivariate tasks, computational intensity, and ineffective information capture. Moreover, their applications in data centers remain largely unexplored. In this article, we investigate local semantic capture empowered large model for multivariate time series forecasting in IoT-enabled data centers. We first introduce time series tasks within data centers and propose the Point Lag (Plag)-Llama framework with the Lag-Llama backbone to support zero-shot forecasting and fine-tuning for multivariate point time series forecasting. To address computational intensity and enhance the capabilities of multivariate forecasting, we propose the local semantic capture (LOSEC) for adapter fine-tuning, which captures local semantic information across time and channel dimensions alternately with low-complexity. Specifically, time series are patched into tokens, and channels are clustered together, forming local semantic information that can be captured more effectively. Extensive experiments demonstrate that Plag-Llama exhibits superior zero-shot capability and that the LOSEC empowered adapter fine-tuning achieves state-of-the-art performance on real-world datasets collected from data centers, with ablation studies further validating the effectiveness of each module within the proposed models.

Rate-splitting multiple access (RSMA) has recently gained attention in low earth orbit (LEO) satellite-terrestrial networks (LSTNs), due to its ability to provide high spectral efficiency in the context of constrained energy resources of LEO satellites. However, the impracticality of acquiring perfect real-time channel state information (CSI), due to high satellite mobility and long link delay, poses significant challenges to effective utilization of RSMA in LSTNs. To tackle this challenge, we propose a location-based robust RSMA scheme for downlink data transmission in LSTNs. First, we establish an optimization problem to minimize the power consumption of LEO satellites, while meeting user requirement on the real-time data rate violation probability. Subsequently, we transfer the probability constraints of rate violation probabilities into closed-form inequalities, by utilizing Markov inequality, Jensen’s inequality, and Cauchy-Schwarz inequality. The original problem is then transformed into a Markov decision process (MDP), and a Transformer encoder-based deep reinforcement learning (TDRL) algorithm is proposed to solve the complex problem based on the real-time locations of users and the LEO satellite. Additionally, a multi time-frame location-based training dataset generation method is proposed for the training of TDRL model, considering the mobility of LEO satellite. Simulation results demonstrate that the proposed scheme is effective in guaranteeing the rate violation probability requirement of each user, and RSMA significantly outperforms space division multiple access (SDMA) and non-orthogonal multiple access (NOMA), with TDRL achieving faster convergence than other baselines.

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.

To enhance flexibility and facilitate resource cooperation, a novel fully-decoupled radio access network (FD-RAN) architecture has been proposed. The decoupling of uplink and downlink in FD-RAN renders the current channel feedback mechanism ineffective, particularly when factoring in the feedback overheads and delays inherent in realistic scenarios. To this end, we investigate the feedback-free MIMO spatial multiplexing transmission in FD-RAN. Specifically, we generate a mapping from geolocation to MIMO transmission parameters from the historical channel data. We first obtain optimal precoders from singular value decomposition (SVD) of channel data. Then, a variational autoencoder (VAE) is trained using the optimal precoders, and the representative precoder for each geolocation is selected from the latent Gaussian representations of VAE. Simulations are performed on a link-level simulator using ray-tracing channel data, and the results demonstrate the effectiveness of our scheme, showcasing its feasibility for adoption in FD-RAN.

AI-generated content (AIGC) gained immense popularity across various domains, retrieving valuable training data by using free APIs (Application Programming Interfaces) from various applications and utilizing AI techniques to generate content automatically. However, concerns have been raised regarding unfair payment for the utilization of valuable training data between data owners and AIGC services providers (ASPs). Blockchain oracle can establish trust between them and bridge on-chain and off-chain training data trading. However, the integration of blockchain and AIGC services is challenged by the scalability of oracle consensus. It is essential not only to support a high volume of data requests from ASPs but also to ensure timely and accurate training data responses. To solve the above issues, we first propose an API-based decentralized AIGC data sharing framework and introduce a blockchain oracle to help ASPs retrieve training data from off-chain data owners. We then design flooding-based oracle consensus protocols to achieve scalable and efficient interactions between AIGC and data owners. Theoretical analysis and simulation results demonstrate that the proposed mechanism can significantly reduce communication overheads.

High speed train (HST) communications with multi-user multiple-input multiple-output (MU-MIMO) techniques have shown great potential in system performance improvements. However, the challenges caused by higher pilot overhead and the belated channel state information (CSI) feedback in such high mobility scenarios still need to be further addressed. To this end, fully-decoupled radio access network (FD-RAN) with novel location-based feedback-free transmission scheme and cooperative transmission/reception in separated downlink/uplink networks is regarded as a promising solution. In this paper, we study the uplink precoding design in FD-RAN for HST communications. To capture the inherent relation between location and precoding, the line-of-sight (LoS) channel is derived from location and then fed into a proposed precoding design neural network (PDNN) which learns to jointly optimize the precoding scheme for all the multi-antenna mobile relays (MRs) in uplink MU-MIMO. We adopt a custom loss function to optimize the spectrum efficiency (SE). Moreover, a joint signal reception method is given, also based solely on the LoS channel derived from location, so as to avoid frequent pilot transmission. Simulation results show the advantages of FD-RAN against other architectures and demonstrate that our proposed PDNN achieves better performance than traditional precoding scheme in high mobility scenarios.

With the ever-increasing number of connected vehicles in the fifth-generation mobile communication networks (5G) and beyond 5G (B5G), ensuring the reliability and high-speed demand of cellular vehicle-to-everything (C-V2X) communication in scenarios where vehicles are moving at high speeds poses a significant challenge. Recently, multiconnectivity technology has become a promising network access paradigm for improving network performance and reliability for C-V2X in the 5G and B5G era. To this end, this article proposes an analytical framework for the performance of downlink in multiconnectivity C-V2X networks. Specifically, by modeling the vehicles and base stations (BSs) as 1-D Poisson point processes, we first derive and analyze the joint distance distribution of multiconnectivity. Then through leveraging the tools of stochastic geometry, the coverage probability and spectral efficiency are obtained based on the previous results for general multiconnectivity cases in C-V2X. Additionally, we evaluate the effect of the path-loss exponent and the density of downlink BS on system performance indicators. We demonstrate through extensive Monte Carlo simulations that multiconnectivity technology can effectively enhance network performance in C-V2X. Our findings have important implications for the research and application of multiconnectivity C-V2X in the 5G and B5G era.

Recently, mobile crowdsensing (MCS) has emerged as a promising solution for traffic state estimation (TSE), which provides real-time and accurate traffic information for supporting diversified intelligent transportation systems (ITS) applications. However, the prohibitive overhead of collecting massive data in vehicular networks limits the available data amount, while the sparsification of MCS data incurs instability and degrades TSE accuracy. To this end, this paper proposes a novel sparse MCS framework to facilitate cost-effective TSE, which utilizes a small number of vehicular MCS participants distributed across all regions as data sources. By utilizing spatial and temporal correlations of traffic flow, an innovative spatiotemporal deep learning model, namely Transformer Graph Attentional Sample and Aggregate neural network (TGASA), is proposed to improve the TSE accuracy with sparse MCS data. Specifically, we design an incorporated graph neural network (GNN) to aggregate the spatial correlation by taking both node features and edge properties into account. And, the transformer neural network architecture is applied to capture the temporal correlation. Extensive simulation results based on real-world datasets demonstrate that the proposed framework can significantly address the instability incurred by the sparsification of MCS data and effectively achieve a more accurate TSE.

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 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.

The uplink (UL)/downlink (DL) decoupled access has been emerging as a novel access architecture to improve the performance gains in cellular networks. In this paper, we investigate the UL/DL decoupled access performance in cellular vehicle-to-everything (C-V2X). We propose a unified analytical framework for the UL/DL decoupled access in C-V2X from the perspective of spectral efficiency (SE). By modeling the UL/DL decoupled access C-V2X as a Cox process and leveraging the stochastic geometry, we obtain the joint association probability, the UL/DL distance distributions to serving base stations and the SE for the UL/DL decoupled access in C-V2X networks with different association cases. We conduct extensive Monte Carlo simulations to verify the accuracy of the proposed unified analytical framework, and the results show a better system average SE of UL/DL decoupled access in C-V2X.

The significant advances in wireless communication together with edge intelligent (EI) technology have facilitated the decentralized edge computing paradigm for data-intensive and delay-sensitive solution on massive Internet of Things (IoT) devices. In this paper, a Cybertwin assisted asynchronous federated learning (AFL) mechanism is proposed for realizing efficient edge computing by taking full advantage of local computation capability under heterogeneous wireless environment. First, Cybertwin is introduced as intermediary communication assistant to coordinate individual model aggregation between the users and the cloud server under AFL training process. Second, for the sake of flexible and effective utilization of communication-computation resources for edge computing, Cybertwin plays the role of intelligent agent to jointly take the local computing and up-link transmission into consideration. A resource optimization problem considering the diversified computing power, varied data size, and available communication bandwidth is formulated and we leverage the block coordinate descent (BCD) method to obtain optimal resource management solution. Extensive simulations are conducted to demonstrate the effectiveness of our proposed Cybertwin assisted AFL mechanism, which can shed further light on the application of data-intensive edge computing paradigm over wireless communication network.

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.

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.

Recent advancement of connected vehicles technologies combined with machine learning (MA) methods has shown great potential for the improvement of efficiency of Intelligent Transportation System. In this work, considering the spatio-temporal correlations under vehicle distribution on urban road network, neural network based deep learning solution is adopted to obtain vehicle driving characteristics and predict future traffic conditions. First, to address the huge challenge brought by complex traffic environment, we present a fine-grained regional-level forecast structure for the prediction of traffic flow at each road. After that, a residual network based deep learning traffic prediction algorithm called DST-RGTP is proposed for the performance enhancement of vehicle regulation in the entire traffic system. Finally, we use the real traffic data of Beijing and open-source road network data on Openstreetmap to test the proposed method. Simulation results verify the accuracy of prediction approach DST-RGTP, which can help to improve the urban traffic management efficiency.