Integrating reconfigurable intelligent surface (RIS) into cell-free massive multiple-input multiple-output (MIMO) is a promising approach to enhance the coverage quality, spectral efficiency (SE), and energy efficiency. In this paper, an RIS-assisted cell-free massive MIMO downlink system suffering from the transceiver hardware impairments (T-HWIs) is investigated. To improve the accuracy of the direct estimation (DE) scheme, a modified ON/OFF estimation (MOE) with moderate pilot overhead is proposed. Relying on the knowledge of imperfect channel state information, we derive closed-form expressions of the lower-bound achievable SE with T-HWIs under both DE and MOE schemes. The closed-form results facilitate the investigation of how RIS improves the downlink SE under various system settings and allow us to explore the trade-off strategies between using more hardware-impaired APs and low-cost RISs in terms of the downlink SE and power consumption. Numerical results validate the theoretical analysis and show that the proposed MOE scheme outperforms the DE scheme in terms of the downlink SE. Moreover, the benefits of introducing RIS into hardware-impaired cell-free massive MIMO systems are also illustrated.

While 5G is being deployed all around the world, the industry and academia start the investigation of 6G. Network function virtualization (NFV) is seen as the key enabler towards the flexible resource management and sharing of 6G networks. The main idea of NFV is to decouple the physical devices from the specific functions that run on them. In NFV, virtual network service (NS) is modeled as a service function chain (SFC). The challenges in SFC description, composition, embedding, and scheduling are key issues in NFV. In this paper, we focus on the joint SFC embedding and scheduling for NFV in 6G networks. With the assistance of sensing the physical node resource abilities, we propose a novel SFC embedding and scheduling algorithm, which is named as RA-SFC-6G. A new service is first composed as an SFC which is an ordered sequence of virtual network functions (VNFs). Afterwards, our RA-SFC-6G algorithm will select the physical nodes with stronger resource abilities and abundant resources to accommodate the SFC. In addition, the NS is guaranteed to be implemented without violating the NS maximum allowed scheduling time. If violated, our RA-SFC-6G can re-embed and re-schedule the violated VNFs to suitable positions. In order to validate our RA-SFC-6G performance, we do the experiment work. Experiment results show that our RA-SFC-6G achieves at least 10% higher SFC acceptance ratio than the previous counterparts and the typical heuristics.

Cloud computing built on virtualization technologies can provide Internet service providers (SPs) with elastic virtualized node and link resources. SPs can outsource their virtualized resources as customized virtual networks (VNs) to end users. Hence, how to efficiently embed these VNs is the core issue in virtualization research. This technical issue is virtual network embedding (VNE). Since the issue inception, multiple mapping algorithms have been studied, including the reinforcement learning (RL) approach of machine learning. However, prior mapping algorithms are mostly static. Existing dynamic mapping algorithms just focus on accepting as many VNs as possible. No existing dynamic algorithm considers optimizing the quality of service (QoS) performance of each accepted VN. Optimizing the VN QoS performance is beneficial to guaranteeing service quality in cloud computing environment. On these backgrounds, we jointly investigate the dynamic VN embedding and optimize the QoS performance of each accepted VN. A dynamic heuristic algorithm is proposed in order to be evaluated in continuous time. When one VN service is requested, the VN will be mapped by the dynamic heuristic algorithm. If the QoS demand of the VN is not guaranteed, the re-embedding scheme of the heuristic algorithm will be driven. Certain virtual elements of the VN will be adjusted. The dynamic embedding algorithm ensures flexible VN assignment and fulfills customized QoS demands. Finally, simulation results are illustrated in order to validate the strength of our dynamic algorithm. We perform the comparison with multiple existing dynamic algorithms. For instance, VN acceptance ratio of our dynamic heuristic algorithm improves at least 13%.

Compared with 5G networks, 6G networks are guaranteed to provide various tailored end-to-end network services and emerging cloud-edge applications. Network slicing (NS) is regarded as the key enabler of 6G networks. Softwarization and virtualization technologies, such as software-defined networking and network function virtualization, are accelerating the way toward NS of 6G networks. The resource allocation issue in 6G NS is very crucial, worthy more research attention. In this article, we propose one efficient resource allocation algorithm, labeled as TailoredSlice-6G , so as to realize the tailored slices in 6G. When receiving one slice request, our TailoredSlice-6G will identify the slice resource type in the first place. Then, our TailoredSlice-6G will select its most suitable subalgorithm to do the resource allocation and slicing deployment. Each type of slice corresponds to its specific resource allocation subalgorithm, inserted in the TailoredSlice-6G algorithm. In addition, each subalgorithm in TailoredSlice-6G is guaranteed to run within polynomial time. Thus, TailoredSlice-6G having the potential to be promoted to real networking application. To highlight the merits of TailoredSlice-6G , we do the comprehensive simulation. Simulation results vividly reveal that our TailoredSlice-6G outperforms the selected heuristics that are representative in the literature.

This paper investigates the secure transmission in downlink cell-free massive multiple-input multiple-output (MIMO) systems in the presence of an active multi-antenna eavesdropper (Eve) over Rician fading channels, assuming that each access point (AP) possesses multiple antennas which are connected with low-resolution digital-to-analog converters (DACs). Closed-form expressions of the achievable secrecy rate relied on the additive quantization noise model are derived. Based on these analytical results, we quantify the impacts of key system parameters, such as the antenna array number, DAC resolution, Rician $\mathcal K$ -factor, and balance factor between data and artificial noise power on secrecy enhancement. Several interesting insights are attained by assuming that Eve can or cannot perfectly remove inter-mobile-terminal interference. Moreover, we also propose a power control algorithm that maximizes the achievable secrecy rate, which can be represented as a series of second-order-cone programs for which efficient solvers exist. All the theoretical analyses and the effectiveness of the proposed algorithm are corroborated by simulation experiments.

Unmanned aerial vehicles (UAVs) are widely recognized as crucial supplementary component of 6G networks. Owing to the key attributes of UAVs (mobility, flexibility, and adjustable altitude), UAVs can serve as flying base stations (BSs), flying relays and mobile terminals in order to expand the service coverage and derive more applications. Softwarization is regarded as dominant attribute of network architecture of 6G, mainly realized by network function virtualization (NFV) and software defined networking (SDN). In this paper, we concentrate on researching the resource orchestration and allocation of end-to-end (E2E) slice services in softwarized UAVs-assisted 6G terrestrial networks. Problem models of UAVs-assisted 6G terrestrial networks and E2E slice are firstly introduced. Then, the problem formulation of resource orchestration and allocation of E2E slice is presented. Afterwards, one novel framework design, abbreviated as ReOrcAll-UAVs-6G, is detailed. When receiving one E2E slice, our ReOrcAll-UAVs-6G checks the available softwarized resources. If having available softwarized resources, our ReOrcAll-UAVs-6G turns to serving the slice and fulfilling slice’s tailored resource demands. During the orchestration and allocation phase, wireless and wired resource requests of this slice are considered and executed. Evaluation work and gained results of ReOrcAll-UAVs-6G and selected approaches are illustrated and analyzed. Gained results reveal that our ReOrcAll-UAVs-6G achieves apparent performance advantage, comparing with all selected approaches.