Reconfigurable intelligent surface (RIS) is a promising technique for positioning systems. A large number of reference signals is needed for traditional base station (BS) with RIS localization system which occupy communication resources. To solve this problem, we directly use the DMRS (Demodulation Reference Signal), which does not consume additional communication resources to estimate the user equipment (UE) position. In this paper, we consider a RIS-assisted downlink localization system that takes account for the channel time variability caused by UE mobility. To estimate the UE position, DPSCN (Depthwise Separable Convolution) network consisting of three stages is adopted to capture the relationship between the received signal and the UE position.Based on the estimated position, the RIS phase is dynamically adjusted, to achieve accurate and continuous tracking of the UE. Extensive simulation results are also presented to demonstrate that the proposed DPSCN learning algorithm can estimate the UE position accurately and outperforms the traditional algorithms.

orthogonal time frequency space (OTFS) systems are considered as the reliable solution for addressing the challenges of high mobility in sixth generation (6G) scenarios. By modulating data across both the delay and Doppler dimensions, OTFS efficiently handles the double spread problems caused by high mobility. Accurate and efficient channel estimation is essential for ensuring reliable data reception in OTFS systems. In this paper, we propose a convolutional neural network (CNN) based channel estimator within the standard OTFS transceiver framework to estimate the transmitted channel from the received OTFS signal. Simulation results demonstrate that our neural channel estimator achieves superior channel reconstruction and outperforms existing neural-based methods. Moreover, we analyze its performance with higher-order modulation and show that it effectively supports such modulation under good channel conditions, improving spectral efficiency and throughput.

Space-Air-Ground Integrated Network (SAGIN) is emerged as a promising paradigm to realize the vision of global coverage of sixth generation (6G) communication network. As an efficient communication pattern, multicast communication can be widespread among the ever increasing communication requests in the seamless access SAG IN. However, given the extensive network scale, time varying characteristic and heterogeneity of SAG IN, the coordination of heterogeneous networks brings challenges to resource allocation. With virtualization technologies, the physical resources within SAGIN can be virtualized as Virtual Network Functions (VNFs) in virtual resource pool. To ensure that multicast services can be provided, the network deploys Multicast Service Function Chains (MSFCs) where the service flows is processed by VNFs in order during the transmission. In this paper, we study the problem of jointly optimizing VNF placement and multicast routing within SAG IN while considering that different network segments are equipped with different physical network resources and cost coefficients. We formulate it as an Integer Linear Programming (ILP) problem with the objective of maximizing network revenue while minimizing resource cost of computation and bandwidth. Since it is NP-hard, a low complexity heuristic algorithm is developed to find an efficient solution within polynomial time. Simulation results demonstrate the effectiveness of our proposed model and algorithm, and the deployment cost and blocking rate can be significantly reduced in SAGIN compared with independent terrestrial network.

The denser deployment of base stations (BSs) in the radio access network (RAN) results in substantial energy consumption and increases operating overheads. Although the centralized RAN (C-RAN) architecture potentially resolves this problem by centralizing BS functions, the strict front-haul requirements of C-RAN brought obstacles to complete centralization. The virtualized RAN (V-RAN) architecture facilitates a flexible functional split (FS) and crosshaul, achieving a balance between centralization and mid-haul requirements. Additionally, adapting BS sleeping based on traffic variations can further reduce energy consumption. However, managing BS sleeping in V-RAN introduces additional challenges, as it may change the pattern of user association with BSs, thereby impacting FS and routing. Hence, this article investigates the joint orchestration of FS, CU-DU assignment, BS working mode, user association, and routing selection in crosshaul-based V-RAN. This model is formulated as a mixed-integer nonlinear programming (MINLP) problem aimed at minimize total expenditure, An optimal algorithm is proposed, whose optimality is theoretically proved, additionally, we develop a heuristic algorithm within polynomial time, to implement complexity reduction. Simulation results validate the effectiveness of our orchestration architecture and algorithms.

This paper investigates the Joint Optimization of Base station sleeping, Functional split, and Routing selection (JOBFR) in virtualized radio access network (vRAN) architectures to minimize the operator’s total cost, including operating cost and migration cost while satisfying user requirements. Specifically, we first use mathematical methods to describe the relationships between the base station (BS) sleeping, functional split (FS) and routing selection, and then model the goal as a joint optimization problem. Next, we propose a heuristic algorithm called Flexible Sleeping, Functional split, and Routing selection (FSFR) to decide BS sleeping, and FS option, and routing selection, simultaneously. Finally, we perform extensive simulations to evaluate our algorithm. The results demonstrate that our algorithm can significantly save the total cost of operators compared to the baseline approach and can also achieve near-optimal performance.

The intensive deployment of base stations (BSs) in the radio access network (RAN) incurs huge energy consumption and operating overheads. Although the centralized RAN (C-RAN) architecture can significantly relieve the BS overheads by centralizing BS functions, the strict front-haul requirements of C-RAN make it challenging to realize a fully centralized RAN. Recently, the virtualized RAN (V-RAN) architecture has been proposed, allowing flexible function split (FS) and crosshaul to balance the centralization and mid-haul requirements. On the other hand, considering the tidal effect of traffic, sleep some BSs with low loads and migrating their traffic to other BSs is an effective way to further reduce energy consumption. In this paper, we investigate joint BS sleeping and FS orchestration in crosshaul-based V-RAN that jointly optimizes BS working mode, FS, traffic migration, and routing selection. The joint optimization model is formulated as a mixed-integer nonlinear programming (MINLP) that minimizes total expenditure, including RAN energy consumption and operating overheads. Considering the complexity of the problem, we propose a heuristic algorithm to solve it in polynomial time. Simulation results validate that our algorithm can save significant expenditure compared to baselines, and the results can reach within 1.13 times the optimum in a relatively short time.

Multicast is an effective transmission mode to support ever-growing multimedia applications. The introduction of software defined networking (SDN) and network function virtualization (NFV) makes the multicast service operation more flexible and efficient. Nevertheless, one main challenge of SDN/NFV-enabled multicast is optimally orchestrating the service function chain (SFC) to match service and network resources. Compared with unicast, multicast SFC orchestration (MSO) is more challenging due to the features of multicast service like multicast routing and user fluidity (i.e., frequent user arrival and departure). There are still some gaps in the joint optimization of MSO and multicast routing, and very little attention is paid to user fluidity. In this paper, we study the MSO in SDN/NFV-enabled networks encompassing multicast SFC embedding (MSE), multicast SFC readjustment (MSR), and multicast SFC expanding (MSEP), three types of MSO. For each kind of MSO, we simultaneously consider several key optimization factors when jointly optimizing MSO and multicast routing. Besides, aside from MSE, we investigate two new types of MSO: MSR and MSEP, for efficient orchestration under the fluidity of users. Specifically, we define and formulate MSE, MSR, and MSEP problems and develop three novel algorithms to respectively solve them. Simulation results demonstrate that our algorithms outperform benchmark algorithms and achieve near-optimal performance.