The convergence of computing and networking heralds a promising paradigm for future sixth-generation (6G) systems, enabling low-latency services for end users by deploying modern applications, which typically consist of multiple interdependent functions, at the network edge. However, unstable and short-range Device-to-Device (D2D) links often restrict offloading options, forcing users to either face limited access to nearby devices or rely on distant cloud resources, thereby underutilizing edge resources and diminishing user experience. To address this challenge, we propose utilizing base stations to relay traffic between unconnected edge devices. Additionally, we strategically reuse historical function placements to balance re-deployment costs against dynamic request adaptation. Then, aiming to minimize storage, computation, and transmission resource consumption costs along with function replacement costs, we formulate the Dependent Function Scheduling (DFS) problem as a Mixed Integer Non-Linear Programming (MINLP) problem, which is NP-hard even in single-slot scenarios. We introduce a random rounding-based approach to derive high-quality integer solutions for the single-slot DFS problem. Building on this, we further develop an efficient online algorithm for the general multi-slot DFS problem, which is proved to achieve near-optimal performance with high probability. Extensive real-trace simulations demonstrate that our proposed method significantly outperforms state-of-the-art baselines, achieving up to a 68.22% reduction in total cost.

Cell-free massive multi-input-multi-output (CF-mMIMO) provides significant improvement owing to the distributed architecture. However, it suffers from the constraints including information constraints, and computation resources constraints. In this paper, we propose 4 ranks of available information in CF-mMIMO and aim to find a distributed precoding exploiting randomly accessible side information, which is one-step without iterations and robust against the unreliable fronthaul between distributed central processing units. Quadratic team learning (QTL) is devised which is derived from team theory to handle the distributed underdetermined quadratic programming. The extensive 1440 experiments validate the superiority of QTL and we believe QTL is a definitely excellent choice for CF-mMIMO distributed precoding. To the best of our knowledge, this is the first work utilizing team theory to help the design of artificial intelligence architecture for wireless communications. To prompt the development of QTL, we have open-sourced the implementation code on https://github.com/hzy238221seu/QTL4CF-Precoding.git.

Cell-free massive MIMO (CF-mMIMO) represents the pinnacle of distributed antenna systems, offering superior service to all users. However, the distributed nature of access points leads to fragmented information processing, limiting performance in practical deployments. Additionally, existing studies often overlook the impact of limited fronthaul capacity, which introduces noise and degrades the reliability of shared information. In this work, we implement a practical CF-mMIMO prototype under fifth generation new radio standards and propose a diffusion-based uplink scheme that outperforms conventional distributed cell-free systems without cooperation. Our approach adapts to arbitrary fronthaul topologies by leveraging the law of large numbers. We further analyze the linear effects of fronthaul noise and the correlation of uploaded data, demonstrating that the diffusion uplink excels in Rician fading environments while maintaining robust performance in Rayleigh fading. To the best of our knowledge, this is the first work to employ a diffusion model for mitigating fronthaul non-idealities, enabling distributed cooperative uplink in a real-world CF-mMIMO system.

Quality of Service (QoS) has empowered 5G network to serve burgeoning vertical industries with differentiated network performance. Existing QoS mechanism cannot provide timely state feedback when QoS policies are mismatched with services and resources. This coarse-grained QoS mechanism inevitably leads to severe traffic discarding, delay and retransmission, resulting in low data transmission performance and efficiency. Therefore, this paper proposes a two-way QoS mechanism with two types of negotiation procedures, where the radio access network (RAN) and the user equipment (UE) can provide the core network (CN) with negotiation information when QoS mismatch occurs. The negotiation information principles of UE and RAN are discussed under different negotiation information feedback conditions, based on which, the QoS decision approach of CN is designed. It is also analyzed that the number of signaling can be reduced so that the transmission efficiency can be improved especially for the mismatched QoS flows. This mechanism can realize QoS decision adaptation in a simple and time-saving manner, better delivering the B5G/6G services with fine-grained QoS.

The emerging applications in vertical industries generate diverse time-critical traffic flows with bounded delay requirements. Time-Sensitive Networking (TSN) enhances the traditional Ethernet by using Time-Aware Shaper (TAS), providing delay guarantees for traffic flow transmission. However, the fixed scheduling granularity of physical queues in TAS can bring an obstacle for the flow isolation in per-flow scheduling, such as the complex computing and configuration. This paper proposes a method of Virtual Queues-based Time-Aware Traffic Scheduling (VQ-TATS) in Software-Defined Time-Sensitive Networking (SD-TSN). SD-TSN is a networking architecture that integrates the determinism guarantees of TSN and flexible network resource allocation of Software-Defined Networking (SDN). Through the capability of SD-TSN, the physical queues resource is virtualized for VQ-TATS. VQ-TATS includes the VQ clustering and VQ mapping stages. VQ clustering aggerates virtual queues to adapt to the scheduling granularity of TAS. VQ mapping builds the relationship between virtual and physical queues and generates the gate control list of TAS. The clustering and mapping algorithms are also designed to perform VQ-TATS. The evaluation in an industrial control use case shows the effectiveness of the proposed method in schedulability and runtime.

Multipath algorithms except Equal-Cost Multi-Path(ECMP) which has been widely used in networks are difficult to apply, because multipath provisioning is more complex at cross layers and multipath routing need to get all nodes' information. To address the dilemma, this paper proposes a multipath network virtualization implementation scheme with Software Defined Networking (SDN) and Network Function Virtualization (NFV). In this scheme, SDN schedules network resources in a global view for selecting multiple paths and computing weight of each path, and NFV provides computing and storage resources to split flow, add tag, recover flow, to name a few. This paper also proposes a multipath algorithm for elephant flow with network virtualization. Besides, we build an experimental platform based on OPNFV and SDN, and conduct experiments under this experimental platform. The results show that our proposed algorithm applied on multipath network virtualization experimental platform has superior performance than ECMP applied in networks without virtualization.