Movable antennas (MAs) fully exploit spatial degrees of freedom (DoFs) in continuous space through dynamic position adjustment, thereby enhancing the performance of integrated sensing and communication (ISAC) systems. This paper investigates robust beamforming and antenna position optimization in MA assisted ISAC systems under bounded channel uncertainty. A position-dependent channel estimation error model is first derived, jointly accounting for errors induced by antenna movement, angle estimation, and signal strength estimation. Based on this model, a robust optimization problem is formulated to maximize sensing performance while satisfying the signal-to-interference-plus-noise ratio (SINR) requirements of multiple communication users. To address the non-convexity arising from the coupling between antenna positions and beamforming vectors, a low-complexity alternating optimization algorithm is developed, incorporating the S-procedure, semidefinite relaxation (SDR), and successive convex approximation (SCA). Simulation results demonstrate that the proposed algorithm significantly outperforms fixed-position antenna (FPA)-based schemes, achieving up to a 40% improvement in sensing beampattern gain while ensuring the SINR requirements.

This paper investigates a covert and reliable short-packet communication system with movable antenna (MA). Covertness is achieved through the uncertainty introduced by short-packet transmission, but the uncertainty will also introduce unreliability. To this end, we formulate an optimization framework that utilizes MA to effectively balance covertness and reliability. Specifically, the minimum effective throughput is maximized by jointly designing beamforming and antenna positions under covertness requirements. The simulation results demonstrate that the system with MA can achieve higher reliability with less resources utilization and higher transmission rate than the systems with fixed position antennas (FPA). Additionally, the effective throughput is improved by approximately 5.7 dB.

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.

In the developing phase of connected autonomous driving, roads are populated with heterogeneous individuals, i.e., connected and unconnected vehicles, autonomous driving and human driving, resulting in intricate interactions and significant uncertainty in their motion trajectories. At present, the research of modeling under mixed traffic scenarios has the following challenges: 1) Traditional path planning ignores dynamic risks from the third-order vehicle state, i.e., acceleration; 2) Existing microscopic models lack the descriptions for the diverse decision-making patterns among heterogeneous individuals, which increases the uncertainty and complexity of the scene. In this paper, we propose a novel Risk Potential Field car-Following Model (RPFFM) tailored for heterogeneous individuals. It employs differentiated parameter adjustments, integrating forward propulsion with potential field repulsion to calculate vehicle acceleration. RPFFM achieves proactive and safe car-following by perceiving the change of dynamic obstacle acceleration through dynamic factors and converting it into the variation of the dynamic potential field. Experimental results based on the Next Generation Simulation (NGSIM) dataset demonstrate that the RPFFM surpasses both the Intelligent Driver Model (IDM) and Optimal Velocity Model (OVM) in terms of effectiveness, safety, and providing enhanced car-following comfort.

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.

Generation (5G) mobile communication provides high-resolution measurements of delays and angles, which make radio simultaneous localization and mapping (SLAM) a possibility. Current radio SLAM systems focus on static environments. However, moving objects may degrade the performance of SLAM systems. In this paper, we propose an integrated radio SLAM and moving object tracking scheme for dynamic environments. Specifically, we first use a grid-based object detection method to detect and filter out measurements belonging to the multipath components (MPCs) of moving objects for accurate radio SLAM. After obtaining the precise vehicle position via SLAM, a curve fitting method is used to track and predict the trajectories of the moving objects, and the prediction results will help in the detection of the object at the next moment. Finally, simulation results demonstrate the performance of the proposed scheme.

This paper studies an integrated sensing and communication (ISAC) system with movable antennas (MAs), where the position of antennas can be flexibly adjusted to reshape the wireless channel. We aim at jointly optimizing the positions of MAs, beamforming of the communication signal, and the covariance of the sensing signal to maximize the overall transmit beampattern gain while ensuring the quality of communication service requirement. Considering the non-convexity of this optimization problem, we propose an efficient algorithm to obtain a high-quality solution by using alternating optimization, semi-definite relaxation (SDR) and Taylors theorem. The simulation results show that the proposed algorithm significantly improves the beampattern gain.

Roadside unit (RSU) deployment plays a crucial role in enhancing the quality of service (QoS) in Vehicular Ad-hoc Networks (VANET). To determine the optimal quantities and locations of deployed RSUs for maximum effectiveness, previous research categorized this as a single-objective optimization problem, considering only the performance indicators for vehicle-to-infrastructure (V2I) communication and solving it using heuristic algorithms or learning-based algorithms. Nonetheless, in the event of information interaction among RSUs as a supplementary component to V2I communication, the process of data transmission and dissemination will achieve a heightened level of comprehensiveness and efficiency within the network. In this paper, we construct a multi-objective roadside unit deployment model (MORD) that takes both V2I communication and data interaction between RSUs into account. The optimization problem of MORD is solved by a multi-objective evolutionary algorithm. We perform a series of simulation evaluations in the ideal grid scenario and the real road network scenario. The results prove that compared to other schemes, the proposed algorithm can better balance the relationship between various objectives in MORD. Moreover, the algorithm can provide decision makers with non-dominated solution sets to choose an appropriate deployment scheme that is more suitable for the actual conditions.

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.

We study an integrated sensing and communication (ISAC) system assisted by cooperative double-intelligent reflecting surface (IRS). The IRSs are deployed near the base station (BS) and the users respectively. The BS uses its nearby IRS to sense the potential target in the blocked area and uses the double-reflection and single-reflection links provided by the IRS to realize the communication between the BS and the multi-users. We consider an orthogonal transmission signal, aiming to maximize the weighted sum of the minimum signal-to-interference-plus-noise ratio (SINR) of the sensing and the minimum communication SINR among all users, jointly optimizing the active beamforming of the BS and the passive beamforming of two distributed IRSs. We propose an efficient algorithm for obtaining high quality solutions using alternating optimization and semidefinite relaxation techniques. Simulation results show that, compared with the benchmark algorithm, the proposed joint beamforming design achieves the balance between communication and sensing performance, and shows that the use of cooperative double-IRS is beneficial to improve the performance of ISAC system.

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.

Edge-assisted vehicular crowdsensing (EAVC) system is an emerging data collection paradigm in Internet of Vehicles (IoV), where intelligent vehicles collaboratively perform complex sensing tasks under the guidance of the edge server. One of the main characteristics of EAVC is that large and balanced spatiotemporal coverage is of paramount importance to support various crowdsensing applications. Most existing works have focused on recruiting pervasive nondedicated vehicles to conduct data collection. However, the collected data of nondedicated vehicles cannot satisfy the requirement of spatiotemporal coverage in terms of evenness and coverage rate, as the trajectories are not uniformly distributed in spatial and temporal domain. In this article, we propose a collaborative data collection architecture based on edge intelligence, where nondedicated and dedicated vehicles cooperate to carry out large-scale and fine-grained data collection with the assistance of the edge server. Particularly, we propose an objective function to better evaluate the spatiotemporal evenness of collected data in consideration of different spatiotemporal partitions based on entropy theory. With the objective function, the offline and online scheduling algorithms are designed to guide dedicated vehicles to proactively participate in crowdsensing tasks, using dynamic programming and greedy theories. Through extensive simulations, we have shown the necessity of introducing dedicated vehicles to assist data collection in vehicular crowdsensing system and the effectiveness and superiority of the proposed schemes.

Past few years have witnessed the great potential of exploiting WiFi signals for positioning. Prior work focus on discovering the absolute locations of a radio source, and have achieved promising accuracies of tens of centimeters. However, many applications such as aerial gesture or handwriting tracking are more concerned with the detailed motion shape of the target rather than its exact locations, which require a several fold higher accuracy. To this end, we present MagicInput, a virtual handwriting interface by tracking the motion traces of a WiFi source. Based on channel state information (CSI), MagicInput elaborately devises an incremental motion-based tracking model by correlating the motion traces with the angle and length variations of propagation paths. The model shifts the tracking task from the transceiver view to the antenna array-oriented view, and eliminates the need for prior knowledge of anchor locations. MagicInput proposes an end-to-end pipeline for tracking refinement, by interference suppression, motion segmentation, and an integrated grasp pressure sensor-based motion instance detection. We prototype MagicInput using off-the-shelf WiFi radios, and extensive experiments attest that MagicInput can achieve the accuracy of 8.5 mm confronting diverse users and environment conditions. With ubiquitous WiFi signals, MagicInput can transform any region into an interactive handwriting interface with millimeter accuracy.

This paper investigates the slice-based service function chain embedding (SBSFCE) problem, which is to embed the service function chains (SFCs) of flows from different slices on a physical network for end-to-end network slice deployment. Compared with regarding slice deployment as complete virtual network embedding (VNE), deploying slices from the perspective of SBSFCE is beneficial for achieving more delicate resource allocation and jointly optimizing virtual network function (VNF) mapping and link mapping without the need for particular virtual topology designs. However, performing effective SBSFCE also faces several key challenges like diversified and differentiated requirements of flows, inter-slice and intra-slice VNF sharing, priority-aware admission control, and VNF placement restrictions, and few existing SBSFCE works have comprehensively considered or solved these challenges. In view of this, we address the SBSFCE problem by jointly considering the above key challenges in this paper. Specifically, we formulate the SBSFCE problem as an integer linear programming (ILP) that aims to maximize flow acceptance ratios and minimize network resource costs. Then, we propose two novel heuristic algorithms, weight-oriented embedding (WOE) and weight-oriented ratio embedding (WORE), to solve the problem. Simulation results demonstrate that our algorithms outperform benchmark algorithms and achieve near-optimal performance.

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.

Terahertz (THz) communication is a promising technique for the next generation cellular networks, owning to its rich spectrum resources. However, THz signal is vulnerable to blockage due to its high path loss and low penetration capability. Multi-connectivity is widely used to combat blockage effect, which will handover user equipment (UE) to surrounding candidate access points (APs) when the current AP-UE link encounters blockage. In this paper, we exploit stochastic geometry to analyze the performance of multi-connectivity in THz communication system considering both static and dynamic blockage. Specifically, we establish a 3D THz communication model firstly according to the propagation properties of THz, and then develop an analysis framework to evaluate the effect of number of multi-connectivity links and AP density on the connection probability, blockage duration and ergodic capacity. Finally, simulation results show that increasing the multi-connectivity links would improve the THz communication performance, but the performance gain turn to be saturated when the link number reach a certain value.

Low-complexity and accurate angle-of-departure (AoD) estimation is critical for positioning and channel estimation in millimeter-wave massive multiple-input-multiple-output (mmWave-massive MIMO) systems. This paper address the problem from a new perspective. Specifically, we first designs a multi-beam codebook, named as Comb-Type beam (CTB) codebook, which is particularly designed such that every lobe of a CTB can be distinguished as a Discrete Fourier Transform (DFT) beam. Then a CTB based AoD estimation algorithm is proposed, which uses the receive powers of the two strongest CTB to compute AoD in a closed-form. This algorithm can greatly shorten the AoD estimation time and is robust to noise. Finally, simulation results verify the superiority of the proposed CTB based AoD estimation algorithm in time consumption comparing with the widely used DFT codebook.

Beam alignment is not only a challenging but also an expensive task for massive multiple-input multiple-output (MIMO) enabled millimeter wave (mmWave) vehicular communications. In this paper, We propose a predictive hierarchical beam training strategy that only uses noisy-ranging measurements. The position and also angular deviation are first predicted based on the noisy ranging measurements. Then the initial searching layer and also the corresponding codewords are derived from the predicted position, the angular deviation of vehicular user equipment (VUE), as well as the ranging error. Simulation results show that even with dynamic scatters and imperfect knowledge of the VUE locations, the proposed strategy can reliably find the optimal beam with greatly reduced training time.

Millimeter wave (mmWave) communication is a promising technique to meet the demands of data-rate hungry applications in vehicular networks. Multiple-input multiple-output (MIMO) and beamforming technique are usually adopted in mmWave communications to overcome the high path and penetration losses. However, the high mobility of vehicles would result in significant beam training overhead in the mmWave vehicular communications. Hence, in this paper, a fast consecutive beam tracking (FC-BET) scheme based on long short-term memory (LSTM) is proposed. By predicting beam angles through the LSTM network consecutively, the overhead caused by frequent beam training in mmWave vehicular communications can be reduced. To evaluate the proposed scheme, a time series channel dataset is built by using sequential vehicle information generated from road traffic simulation software named “Simulation of Urban MObility (SUMO)”. Simulation results show that the FCBET scheme can significantly reduce overhead with an acceptable loss in spectral efficiency compared with conventional beam training schemes.

Driven by the soaring traffic demand and the growing diversity of mobile services, wireless networks are evolving to be increasingly dense and heterogeneous. Accordingly, in such large-scale and complicated wireless networks, optimal controlling is reaching unprecedented levels of complexity while its traditional solutions of handcrafted offline algorithms become inefficient due to high complexity, low robustness, and high overhead. Therefore, reinforcement learning (RL), which enables network entities to learn from their actions and consequences in the interactive network environment, attracts significant attention. In this article, we comprehensively review the applications of RL in wireless networks from a layering perspective. First, we present an overview of the principle, fundamentals, and several advanced models of RL. Then, we review the up-to-date applications of RL in various functionality blocks of different network layers, ranging from the low-level physical layer to the high-level application layer. Finally, we outline a broad spectrum of challenges, open issues, and future research directions of RL-empowered wireless networks.