Achieving accurate and flexible localization in global navigation satellite system (GNSS)-denied underground spaces is critical for Internet of Things (IoT)-enabled logistics and personnel operations. To this end, this paper proposes a scalable multimodal localization framework requiring only a single ultra-wideband (UWB) transmitter connected to a distributed antenna system, avoiding the deployment of multiple additional anchors. An adjacency-masked, change-point-aware hidden Markov model (AC-HMM) is developed for region identification using only two-path UWB measurements, which avoids full channel impulse response (CIR) processing and reduces computational complexity. A multi-scale factor-graph maximum a posteriori inference method (MS-FGM) is then proposed for dynamic localization by fusing UWB and magnetic-field residuals with region and motion constraint factors. Multi-scale temporal aggregation is further introduced to mitigate motion-induced fluctuations and improve localization accuracy and stability. Experiments conducted along the roadways of an underground parking lot demonstrate an average region classification accuracy of 96.81% and a mean positioning error of 0.88 m, outperforming existing methods by up to 42.19%.

To achieve seamless and continuous positioning for smartphones in tunnels, where the Global Navigation Satellite System (GNSS) signals are unavailable, this paper proposes a new method that leverages the existing leaky coaxial cable (LCX) infrastructure from public land mobile networks to introduce the GNSS signals directly from outside the tunnels. This approach introduces three key innovations. First, a continuous hybrid GNSS-LCX channel is proposed which uses a waveguide-to-wireless model to provide continuous GNSS signal coverage via existing 5G LCX without requiring dedicated hardware. Based on this model, a bidirectional clock bias cancellation mechanism is designed for GNSS signals inside the tunnel. This method establishes a quantitative mapping between the position in the tunnel and the pseudorange observations incorporating clock bias, enabling the residual latency from smartphones and wired transmission in the tunnel to be modeled as a function of signal propagation distance and user tracks within the tunnel environment. Furthermore, a 5G-enhanced factor graph optimization (FGO) method is proposed, which integrates 5G measurements and tunnel topology to suppress positioning fluctuations caused by multipath effects in tunnels, while mitigating GNSS positioning latency and ambiguity induced by 5G cell handovers. Field tests in a 150-m tunnel show 1.21 m median accuracy, achieving 46% higher accuracy than fingerprinting and 3.2×faster convergence than conventional methods. This solution reuses 5G-LCX infrastructure for cost-effective and consistent tunnel positioning that bridges the gap between open-sky and underground tunnel environments.

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.

Deep reinforcement learning (DRL)-based traffic signal control (TSC) algorithms often suffer from overfitting to static training environments and perform poorly in unseen traffic scenarios. Two major challenges remain in previous methods: the scarcity and homogeneity of training data, and the limited adaptability to harsh or challenging traffic environments. To address these issues, we propose RAW-MetaRL, a Robust Adversarial Weighted Meta-Reinforcement Learning framework. RAW-MetaRL introduces an adversarial environment agent that adaptively generates increasingly challenging and diverse traffic environments based on the current policy’s performance, where synthetic environments are optimized to expose weaknesses in the current control policy. Furthermore, we design a weighted meta-learning framework that alternates between local-level adaptation on individual tasks and global-level adaptation across a sampled set of tasks, aiming to effectively train the meta-agent. A meta weight generator is incorporated to prioritize rare and critical environments, enabling the meta agent to generalize effectively across diverse and previously unseen environments. Extensive experiments on real-world and synthetic datasets demonstrate that RAW-MetaRL significantly outperforms existing methods in adaptability and performance across diverse traffic environments.

Multi-satellite cooperative transmission (MSCT) is a promising paradigm to enhance spectral efficiency in Low Earth Orbit constellations. However, heterogeneous Doppler shifts and coupled multi-satellite interference hinder the capacity improvement of conventional precoding in multiple-input multiple-output (MIMO). Although orthogonal time-frequency space (OTFS) modulation exhibits robustness against doubly-selective channels, its direct application in massive MIMO faces prohibitive computational complexity. To address these challenges, this paper proposes a joint precoding and link scheduling (JPL) design for OTFS-based MSCT systems. Specifically, we formulate a sum-rate maximization problem that couples continuous precoding matrices with discrete link indicators, and decompose it into two tractable subproblems. For precoder design, based on regularized zero-forcing (RZF)-criterion, we present single-satellite precoding (SSP) and multi-satellite precoding (MSP) in the delay-Doppler domain for different MSCT modes, and optimize per-satellite regularization coefficients to maximize the sum-rate. Leveraging the quasi-banded MIMO-OTFS structure, a low-complexity RZF algorithm is developed to reduce the cubic complexity to quadratic order without performance loss. Based on random matrix theory, we conduct asymptotic analysis and derive the deterministic equivalents of sum-rate for SSP and MSP. For link scheduling, a two-stage heuristic algorithm is designed based on tabu search to iteratively optimize link indicators for maximizing the sum-rate. By alternately optimizing precoding matrices with each scheduling iteration, the JPL algorithm is proposed to achieve near-optimal sum-rate performance with polynomial computational complexity. Numerical results demonstrate the proposed schemes significantly improve the sum-rate performance compared to existing works.

Clock asynchronism and low sensing-signal-to-noise ratio (SSNR) pose significant challenges in Integrated Sensing and Communications (ISAC) bi-static sensing systems. Existing approaches struggle to address both issues simultaneously. This paper proposes a novel transmissive intelligent surfaces (TIS)-assisted ISAC receiver architecture, using the TIS to optimize multipath signal superposition in two paths. One path retains static components and compensates for clock asynchronism, while the other enhances dynamic component and suppresses static ones to improve the SSNR. Simulation results demonstrate that the proposed architecture effectively mitigates sensing performance degradation over long distances and achieves accurate estimation of the complex gain sequence (CGS) and Doppler velocity.

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.

Indoor spaces have already been accounted for a significant proportion of people's lives, and with the advent of the fifth-generation mobile communication technology (5G) era and the rise of the Internet of Things (IoT), the demand for indoor localization is further increasing. Current solutions for indoor positioning, such as Bluetooth, Wi-Fi, ultra-wideband (UWB), and pseudo-satellite technologies, have faced issues in terms of cost, accuracy, and scalability during the promotion and application process. In this regard, distributed antenna systems (DAS) have an absolute advantage in terms of indoor coverage for communication, which shows enormous potential for fusion with current indoor positioning technologies. This paper proposed a universal one-dimensional localization algorithm fusing UWB technology with the indoor DAS system, aimed at narrow indoor environments such as corridors and cable tunnels, which can achieve high-precision localization as evaluated in a real scenario of an indoor corridor in the Beijing University of Posts and Telecommunications (BUPT). Additionally, our algorithm requires only two anchors, effectively reducing positioning costs while improving accuracy. The results indicate that this localization technique effectively meets the requirements defined for indoor scenarios in 3GPP 38.855, with an error of decimeter level.

Low earth orbit (LEO) satellite constellations are the key enablers to achieve the ubiquitous communication in the 6G era, but the performance is hindered by the Doppler effects and the underutilization of spatial diversity. By leveraging the quasi-static properties of the delay-Doppler (DD) domain, orthogonal time-frequency space (OTFS) demonstrates robustness against the Doppler shifts. Moreover, spatial modulation offers the potential to improve spectrum efficiency and energy efficiency. This paper proposes an OTFS-based spatial modulation (OTFS-SM) paradigm for multi-satellite cooperative transmissions, aiming to enhance system reliability. In order to further exploit the multi-satellite diversity, a satellite selection (SS) strategy based on channel capacity maximization criterion is proposed. Additionally, the average bit error rate (ABER) upper bound of the proposed OTFS-SM system in multi-satellite scenarios is derived using minimum mean square error (MMSE) detection. Numerical results verify the theoretical analysis of the ABER and demonstrate that the proposed strategy significantly outperforms both the single-satellite scenario and the OFDM-SM system in terms of ABER performance.

The global navigation satellite system (GNSS) fails in indoor environments, leading to a growing demand for indoor positioning, ranging from above-ground buildings to under-ground parking lots. Existing indoor positioning technologies face challenges balancing low cost, low latency, and high accuracy, particularly in large underground parking lots (LUPLs). This paper proposes a sparsely deployed Bluetooth and magnetic (SBM) fusion positioning system for LUPL scenarios. Specifically, the spatial characteristics of the received signal strength indicator (RSSI) from sparsely deployed Bluetooth and the magnetic field strength are analyzed. Static and dynamic algorithms are designed for the SBM system by utilizing Bluetooth's area division and coarse positioning, combined with the high precision of magnetic field positioning. Experimental results from a real LUPL deployment demonstrate that, under a sparse beacon distribution (1 beacon per 294 m2), the proposed positioning algorithms achieve static accuracy of 1.32 m, dynamic accuracy of 0.75 m, and low latency on consumer-grade devices.

Recently, a new communication paradigm, semantic communication, has garnered significant attention due to its superior information transmission efficiency compared to conventional communication. However, current research on semantic communication primarily focuses on the design of end-to-end communication systems from the perspective of goal orientation or information reconstruction, without considering integration with the evolving technological landscape of the 5th generation mobile communication (5G) networks. To address this gap, in this paper, we propose an evolved servitization network architecture, called Semi-SemNet, which provides goal-level semantic service capability for the proposed converged goal-oriented semantic communication. To enable dynamic semantic-aware load balancing, the contexts of network functions (NFs) are decoupled from themselves and managed by a common NF, rendering each NF stateless. Additionally, we present an overview of enhanced NFs and system procedures for semantic communications. To meet different target nodes, such as application servers and user equipment (UE), we introduce a semantic translation mechanism to eliminate performance degradation when there are multiple independent wireless links between the source node and the target node. Finally, we use fire video remote monitoring as a case study. Experimental results show that Semi-SemNet achieves an average 26.67% PSNR and 27.21% MS-SSIM performance improvement in information reconstruction, and saves 40.51% bandwidth resources in goal execution. Moreover, with the stateless mechanism and semantic-aware load balancing decisions, it can reduce semantic data transmission delay by an average of 60.59% compared to the benchmark methods. The all code and original data of this paper are publicly available at https://github.com/hwniu/sbi_semantic_network.git.

Recently, Semantic Communication (SC) has been recognized as a crucial new paradigm in 6G, significantly improving information transmission efficiency. However, the diverse range of service types in 6G networks, such as high-data-volume services like AR/VR/MR and low-data-volume applications requiring high accuracy, such as industrial control and data collection, presents significant challenges to fully replacing the fundamental technologies with SC. Therefore, we design a Hybrid Semantic Communication Ratio Access Network (HSC-RAN) protocol stack demo for 6G systems to achieve compatibility and smooth transition between SC and non-SC. Specifically, we take the Physical Downlink Shared Channel (PDSCH) as an example, to efficiently integrate SC with Orthogonal Frequency Division Multiplexing (OFDM). Furthermore, we introduce a novel Downlink Control Information (DCI) format that jointly supports SC and non-SC, enabling real-time video transmission via SC and text transmission through non-SC. Experimental results demonstrate that our approach allows simultaneous transmission of semantic and non-semantic information while maintaining high-quality reconstruction at the receiver.

Passive distributed antenna systems (DAS) are widely used for indoor mobile communication quality enhancement and the market space continues to rise. However, in the beamforming process, the passive nature of DAS causes repeated beam scanning and beam correlation issues. This paper proposes an environment-sensing hybrid beamforming method for passive DAS, by which multi-distributed antennas use sensed environment information while considering beam correlation for hybrid beamforming design to improve communication performance. As the initial study, a DAS-oriented method based on compressed sensing for extracting environmental information from superimposed signals is proposed to achieve short-time and high-precision sensing. The flexible design of the hybrid beam transmit path based on the reflected environmental information considering antenna correlation improves the communication rate and bypasses the obstacles. In addition, a dynamic adjustment mechanism for power trade-off between sensing and communication is analyzed to achieve reasonable power allocation, which optimizes the performance of the whole system. Finally, simulation results validate the effectiveness of our proposed method to improve the communication performance while maintaining the sensing accuracy significantly.

In the sixth generation (6G) Internet of Things (IoT), integrated sensing and communication (ISAC) emerges as a key technology, which is expected to significantly enhance the perception capabilities and spectrum efficiency of base stations (BSs). It holds potential for applications in unmanned aerial vehicle (UAV) monitoring, vehicle positioning, and crowd detection. However, developing an integrated waveform that efficiently conserves spectrum resources while maintaining lower complexity remains challenging. This paper designs an ISAC system that utilizes orbital angular momentum (OAM) waves generated by a uniform circular array (UCA), which enhances communication and sensing capabilities by allocating distinct modes. This paper proposes a multimode multiplexing communication scheme based on a single UCA and an OAM-circular reception method (OAM-CRM) for the two-dimensional direction-of-arrival (DOA) estimation of targets, encompassing both azimuth and elevation angles. Additionally, an OAM-different modes (OAMDM) algorithm is designed to optimize communication and sensing performance across various mode sets. Simulation results verify the effectiveness of the OAM ISAC system and demonstrate the superior performance of the proposed algorithms compared to conventional methods.

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.

The increasing demand for spectrum resources has necessitated spectrum-sharing between Low Earth Orbit (LEO) and Geostationary Earth Orbit (GEO) satellite systems, introducing the risk of inter-system interference. This interference degrades communication performance, making effective mitigation strategies critical. Unlike existing works that utilize fixed-position antennas, this paper proposes a novel interference mitigation scheme for LEO-GEO spectrum-sharing system, aided by movable antennas (MAs). Specifically, the LEO satellite is equipped with MAs which fully leverage spatial degrees of freedom to enhance LEO system service and mitigate interference. To achieve these objectives, we formulate a max-min signal-to-interference-plus-noise ratio (SINR) optimization problem, subject to LEO-GEO interference mitigation constraints. Due to the non-convex nature of the proposed problem, we design an alternating optimization algorithm, which can optimize signal-to-leakage-noise ratio precoding, power allocation, and MA positions alternatively and jointly. Simulation results demonstrate that the proposed scheme significantly improves the worst LEO user's SINR while ensuring that interference to GEO terminals remains below a specified threshold.

Deep learning (DL)-enabled semantic communication has been widely regarded as a promising 6G technology due to its efficient wireless transmission capabilities, providing a novel technological foundation for bandwidth-constrained Internet of Things (IoT) scenarios. Combining semantic communication with classical multicell network architectures, which are commonly employed in long-distance IoT communications, is essential for enabling its large-scale deployment in future 6G systems. However, the performance optimization of semantic transmission in this scene still faces significant challenges due to the complex coupling among compression, transmission, and computation under resource-limited conditions, which is difficult to solve by traditional methods due to the closed box nature of the artificial intelligence models used. To address this problem, in this article, we formulate an optimization problem to minimize the weighted sum of end-to-end latency and information reconstruction performance by adjusting the compression control strategy, radio block assignment, and computing resource allocation decision. The case of image transmission is investigated. To address this nonconvex mixed-integer nonlinear programming problem, we adopt a lower bound approximation method to decompose the formulated problem and design an optimization method combining the Lagrange multiplier method and multiagent deep reinforcement learning algorithm. We also introduce an image reconstruction performance prediction model to empower the agent with semantic-aware optimization ability. The results demonstrate that the proposed method can achieve superior performance compared to other approaches.

Developing trustworthy reinforcement learning (RL) agents for safety-critical control tasks, such as end-to-end autonomous driving, has been a longstanding challenge due to low sample efficiency. Prior works have attempted to address this challenge by performing self-supervised auxiliary tasks like self-reconstruction or predicting long-term future states. However, there still remain unexplored sequential features and causality relationships inherent in sequential state, action, and reward signals in the frequency domain. To fully exploit the temporal and frequential features, we propose a contrastive RL framework called BRain-Inspired causalitY-Aware coNTrastive learning (BRYANT) to achieve efficient representation learning and human-like autonomous driving. Different from existing temporal predictive methods, we transform the sequential latent representations, reward, and action signals into the frequency domain, followed by the symmetric temporal prediction pattern for real and imaginary parts of the frequential signals. To capture the temporal causality for the latent representations, we introduce a brain-inspired network structure called Closed-form Continuous-time (CfC) network to parameterize the derivative of the latent representations and establish the neural dynamic model. Experimental results conducted in the CARLA simulator demonstrate the effectiveness of BRYANT in efficient representation learning, enabling agents to concentrate on potential risks and decrease the collision rate compared to several state-of-the-art RL methods. Furthermore, through the visualization of the latent representation prediction process, we reveal the causal relationships between the critic Q values and the latent representation vectors in the frequency domain, and demonstrate the effectiveness of the frequency domain prediction.

Owing to the inherent advantages of high flexibility and strong Line of Sight (LoS) links, as well as the continuous power supply, tethered autonomous aerial vehicle (TUAV) connected to ground charging stations (GCSs) is regarded as a feasible and effective solution to support stable emergency communications. One of the key issues in the TUAV network deployment is to ensure the flight safety considering the potential risk of tether tangling as well as collision along with TUAVs’ movements. In this article, we propose a theoretical method for analyzing mobile TUAV networks with spacial repulsion constrain. Under the nearest association criterion, we apply the Matérn hard-core point process (MHCPP) to derive the steady-state spacial distance distribution between the TUAV and the reference ground user equipment (GUE). In the vertical direction, we assume that all TUAVs dynamically adjust their altitude following a random waypoint (RWP) mobility model. Then, closed-form expressions for the network performance metrics, such as coverage probability and handover probability are presented, jointly considering the path loss model and generalized Nakagami-m fading channels. Our analytical results, validated through Monte Carlo simulations, demonstrate the efficiency and accuracy of the proposed method.

Satellite communication (SatCom) is regarded as a key enabler for bridging connectivity and capacity gaps in sixth-generation (6G) networks. However, the proliferation of Low Earth Orbit (LEO) satellites raises significant intersystem interference risks with Geostationary Earth Orbit (GEO) systems. This paper introduces a cooperative multi-satellite multi-reconfigurable intelligent surface (RIS) transmission framework to mitigate such interference while enhancing LEO SatCom performance. Specifically, cooperative beamforming is designed under a non-coherent cell-free paradigm, considering both adaptive and max ratio (MR) precoding, as well as statistical and two-timescale channel state information (CSI), aiming to synthesize the advantages of cell-free and RIS into SatCom in a practical way. Firstly, an alternating optimization (AO)-based design leveraging statistical CSI with adaptive precoding is proposed. Then, we propose a power allocation algorithm under MR precoding with given RIS phase shifts obtained from the former, along with a direct two-stage design bypassing prior results. Additionally, we extend derived closed-form expressions and proposed algorithms to exploit two-timescale CSI. Numerical results demonstrate the impact of intersystem interference mitigation constraints, compare the performance of proposed algorithms, draw insights into the effects of transmit power, interference threshold, and Rician factors, validate SatCom performance enhancements achieved by RISs, and discuss the advantages of multi-satellite cooperation.

Large-scale FFT operations in NR system are highly resource-intensive and computationally complicated, constituting a significant aspect of signal processing. Using high-radix to realize large-scale FFT can reduce the algorithm complexity and the number of stages, however, it introduces vector-matrix multiplication (VMM) operations. Memristor based circuits can efficiently perform VMM operations and have great development prospects. Therefore, this paper reports the design of a high-radix FFT circuit based on memristor arrays for large-scale FFT in NR system, from the perspective of analog in-memory computing. Firstly, we introduce the algorithm and implementation of high-radix FFT and its application prospect in NR communication systems. Then, we construct the memristor model based on practical devices, propose a bisection pulse strategy for resistance modulation based on the error threshold, and point out the existence of the over-precision paradox for the first time. After that, we propose the single-memristor mapping scheme aimed at accurate matrix operations. Compared with the memristor pair mapping scheme, it can reduce the number of memristor cells used by nearly 50%. Meanwhile, the computing unit circuit is jointly designed with the mapping scheme to enhance computational efficiency. This design achieves a direct one-step FFT radix operation process. Ultimately, the proposed circuit is applied to address FFT/IFFT in OFDM system modulation and demodulation. Hereon, we propose the Monte Carlo pilot to equalize the memristor array calculations. The memristor array calculation with an error threshold of 1% can also approach the ideal performance. It solves the problem of constellation point shrinkage caused by non-ideal mapping and greatly improves the BER performance of the system.

Low Earth orbit (LEO) communication satellites are becoming an essential component of the integrated spaceterrestrial network for sixth-generation (6G) networks nowadays. However, due to the time-varying communication environment and long round-trip time (RTT), the downlink channel state information (CSI), which is always obtained by satellites from user equipments feedback, is severely outdated. Furthermore, as the satellite moves, changes in elevation angle result in received CSI with varying correlation characteristics and levels of outdatedness. Motivated by these issues, this paper proposes a deep learning (DL)-based prediction scheme named ATNet, which is designed to make channel prediction in frequency division duplexing (FDD) satellite Communication systems. Specifically, the predictor integrate convolutional neural network (CNN) and attention mechanism with the foundation of long short-term with memory (LSTM) network. This scheme considers the relationship between elevation angle and channel correlation, as well as the degree of CSI outdatedness. Simulation results demonstrate that our ATNet scheme has achieved a $7 \%-17 \%$ improvement in prediction accuracy compared to the baseline schemes.

With the rapid development of 5th Generation Mobile Communication Technology (5G), the increased bandwidth not only bolsters communication efficiency but also paves the way for high-precision location-based services within these industrial applications. Positioning by using a single base station (BS) possesses potential vital values in scenarios that multiple BSs are not applicable, such as unmanned aerial vehicle assisted emergency communication scenarios, dynamic and temporary industrial 5G scenarios, etc. However, due to a lack of observed measurements from different base stations, the high accurate and stable positioning by using a single BS faces challenges. To address this problem, this paper introduces a novel approach utilizing the open-source 5G platform OpenAirInterface (OAI) to design application programming interfaces (APIs) for accessing low layer data. By extracting data directly from actual protocol stacks, we have developed a multidimensional data fusion localization system based on Vision Transformer (ViT) in a single-BS mode named FLIT. Conducted within an office setting, our experiments spanned a 100MHz bandwidth, culminating in a system capable of achieving an average positioning error of just 6.9cm, with 90% of measurements within a 9cm error margin and 80% within 5.3cm, thus attaining centimeter-level accuracy in positioning.

With the development of integrated sensing, communication and computation (ISCC), wireless sensing technology is turning a WiFi device into a special sensor. Specifically, the current research focuses on extending the inherent communication attributes of ubiquitous WiFi signals to achieve indoor position tracking. However, previous works based on geometric positioning like triangulation are limited by the number of antennas and bandwidth, and can only achieve decimeter-level positioning accuracy. To this end, we present WiFineTrack, enabling fine-grained position tracking based on Channel State Information (CSI). Instead of representing the continuous absolute positions directly, WiFineTrack first characterizes the relative motion traces by mapping the target’s movements in relation to its prior position. Then the model correlates the angle-based triangulation with the derived relative motion traces to reconstruct the absolute motion traces. Thus, the model shifts the tracking task from traditional absolute geometry to relative length variation. WiFineTrack designs an elaborate denoising framework for tracking refinement, including phase correcting based on the bidirectional transmission mechanism and accumulated error suppression based on the Extended Kalman Filter (EKF). We implement WiFineTrack using off-the-shelf WiFi devices, and empirical results show that WiFineTrack can achieve an accuracy of 10.45 cm under diverse environment conditions.

The rapid expansion of low-Earth orbit (LEO) constellations has greatly intensified the difficulty of mitigating interference among them. Beam hopping (BH) technology is widely considered as one of the key technologies to achieve on-demand services while avoiding interference among these constellation systems. However, the majority of existing BH schemes neglect to address the interference among separate constellations. This paper proposes a novel BH scheme to tackle interference challenges in multi-LEO constellations coexistence scenarios. In contrast to existing BH schemes that predominantly target intra-system interference mitigation, our proposed BH scheme has the capability to dynamically adjust the beam bandwidth to avoid both intra-system interference and inter-system interference. We formulate an optimization problem that maximizes throughput, guarantees delay fairness, minimizes transmission power for LEO constellations, and avoids inter-system interference. To tackle this formidable non-convex and non-linear problem, the optimization problem is interpreted as a sequential decision-making problem, modeled by a Markov Decision Process (MDP). By utilizing the Advantage Actor-Critic (A2C) algorithm, the BH pattern is dynamically designed to avoid interference between beams from multiple LEO constellations in a time-varying network environment. Simulation results demonstrate that the proposed scheme outperforms other schemes in terms of long-term throughput and delay fairness.

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.

Low Earth Orbit (LEO) satellite communication systems can provide wide-coverage and low-latency communication services, making them suitable for global mobile communications, navigation positioning, military operations, remote sensing, and resource exploration, etc. Recently, LEO satellite constellation are gaining increasing attention, consequently facing with the challenge of scarcity of spectrum and space resources. As satellite frequency bands follow the principle of “first come, first served”, countries that started late in the satellite field are more eager to seize space resources. In this paper, we derive analytical model for the coverage probability under multi-altitude orbits for a multi-layer LEO satellite constellation based on stochastic geometry. The distribution of the satellite constellation along the latitude at different inclinations angles is non-homogeneous, we compensate for this phenomenon by calculating the effective number of satellites in the constellation. We then perform simulations of the VLEO (very low Earth orbit) constellation in the presence of interference from other satellite constellations, and the results provide insights into the selection of parameters such as constellation density, altitude range, inclination angle and minimum elevation angle which can help to launch the VLEO constellation more efficiently.

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.

Orthogonal Time Frequency Space (OTFS) is a novel multicarrier modulation scheme. The study leverages the delay-Doppler domain for the representation and processing of signals. OTFS has been shown to offer superior performance over conventional Orthogonal Frequency Division Multiplexing (OFDM) in high-mobility scenarios, where the channel exhibits severe Doppler spread. In this paper, we investigate the feasibility and challenges of integrating OTFS technology with the 5G NR standard, using the OpenAirInterface (OAI) as an experimental platform for system-level simulation. We present the design and implementation of two OTFS modules on the OAI platform, as well as a Doppler channel simulator that can emulate realistic channel conditions for OTFS transmissions. We also propose an OTFS frame structure that is compatible with the NR physical (PHY) layer specifications and protocols. Through extensive simulations and experiments, we demonstrate that our OTFS system can be compatible with the 5G NR protocol stack and achieve higher spectral efficiency and robustness against Doppler effects than the OFDM system.

As the concept of the sixth-generation mobile communication technology (6G) continues to mature, Low Earth Orbit (LEO) satellite technology, serving as a crucial component of the high-coverage, fully accessible network in 6G, holds promising development prospects. Consequently, the integration of next-generation mobile communication technology with LEO satellite networks has garnered increased attention in the industry. However, despite some research groundwork in the direction of integrating LEO satellite networks with 5G networks, many challenges persist, especially concerning the issue of signaling storms arising from collective handovers and registrations in the face of a large user base. This paper proposes a process-level prioritized architecture design for the Access and Mobility Management Function (AMF), capable of expediting the processing of critical procedures amid a plethora of mixed signals. Preliminary experimental verification is conducted using a newly developed open-source testbed based on UERANSIM. The presented results demonstrate that the proposed AMF mechanism effectively reduces the latency of completing specified procedures in scenarios involving a substantial volume of mixed signals.

In indoor integrated sensing and communication (ISAC) systems, angle of arrival (AoA) estimation for moving targets via reflected signals plays an important role in passive sensing. It is a challenging task in the presence of multiple static paths which cause significant interference to AoA estimation for dynamic paths. In this paper, we propose a subspace algorithm that achieves accurate AoA estimation for multiple targets using static communication transceivers in general indoor scenarios. We first use a complex vector to model static paths as a merged static path. Then we design a conditional pseudo-likelihood probability density function (PDF) based on the projection matrix of the signal subspace to bridge the signal subspace with the merged static path and AoAs of dynamic paths. We propose to utilize the Particle Filter to track merged static path and AoAs of dynamic paths via signal subspace. Thereby, the interference caused by static path signals can be eliminated and the accurate AoA estimation results are obtained. Simulation results are provided and validate the proposed algorithm.

Previous work have verified the feasibility of Wi-Fi - based indoor position tracking. However, these research rely heavily on anchors' position as prior knowledge, which lowers the deployability in practical scenarios. To this end, we propose a novel system named EasyWiTrack to realize a plug-and-play, rel-ative position tracking by establishing a time-domain correlation model to associate the target path, angle information with target motion, and deduce the target's relative position compared to the previous moment for trajectory shape without any anchor position information. During the system implementation, to address the issue of error introduced by asynchronous Wi-Fi transceivers, we adopt bi-directional data acquisition for denoising and achieve millimeter-level target path length estimation. Additionally, to eliminate ambiguity in linear array angle estimation, we employ circle array and apply Multiple Signal Classification algorithm to estimate signal azimuth. Finally through extensive experimentation, we have validated that EasyWiTrack achieves a fine-grained indoor position tracking performance with 1.48cm and 1.73cm average error respectively in LoS and NLoS scenarios with commodity Wi-Fi.

Transit signal priority (TSP) is an effective strategy to enhance the operational efficiency of buses and increase bus ridership. Nonetheless, existing TSP strategies seldom utilize connected information to control buses collaboratively and take safety into consideration. In this paper, we propose a method that combines signal timing optimization with longitudinal control of Connected and Autonomous Vehicles (CAVs) to improve efficiency and comfort for passengers while ensuring safety. This method modifies signal timing at isolated intersections to prioritize buses as well as controls the delayed CAV bus to catch the green light smoothly and cooperatively change lanes based on Vehicle-to-Everything (V2X) communication. Moreover, the Control Barrier Functions are used as safety-critical constraints in Model Predictive Control to avoid collisions. The effectiveness of the proposed method is evaluated in comparative analyses against controllers without TSP or longitudinal control in CARLA. The simulation results reveal that the proposed method reduces waiting time at intersections and velocity fluctuations while maintaining a safe following distance.

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.

Recent years have witnessed the great potential of Wi-Fi-based crowd counting technique due to its popularity and non-invasive. However, the existing schemes with high accuracy require labeled data with different numbers of people for a priori learning, which poses an obstacle to the usability of crowd counting systems. Hence in this paper, we propose EasyCount, an easy learning-free multi-environment crowd counting system that only needs one person's prior data for simple calibration. First, we model the reflection signal of an individual as a Gaussian stochastic process. Then, we propose the ratio of dynamic power to noise power (RDN), which establishes a mapping between the autocorrelation function (ACF) of Channel State Information (CSI) amplitude and the number of people. The proposed RDN metric is proportional to the number of people, thus eliminating the dependence on labeled data with different numbers of people. Finally, we design a calibration scheme to obtain the base RDN, which enables our system to overcome the impact of different environments. Extensive experiments show that EasyCount can be easily deployed in different environments, achieving an average accuracy of 93.9 % for 0–6 people counting tasks in multiple environments with only one pair of transceivers, which is comparable to the existing state-of-the-art works.

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.

Coordinated Multi-Point (CoMP) transmission enhances the reliability and throughput of communication systems. However, its reliance on precise CSI feedback and retransmission introduces undesirable delays, limiting its applicability in Ultra-Reliable Low Latency Communication (URLLC). To address this challenge, we propose an enhanced RaptorQ-based downlink CoMP scheme that optimizes the decoding process. This scheme utilizes a limited number of Belief Propagation (BP) iterations and a self-adaptive Ordered Statistics Decoder (OSD) to reconstruct ordered information sequences based on the accumulated Log-Likelihood Ratio (LLR) transitions of variable nodes. Simulations demonstrate an average 38.2 % improvement in resource utilization compared to traditional CoMP schemes under a Block Error Rate (BLER) of 10–5in low Signal-to-Noise Ratio (SNR) scenarios.

Orbital angular momentum (OAM) multiplexing techniques have great potential in high-speed and high-capacity short-range wireless communication. However, the divergence angle of OAM waves changes with mode and frequency, increasing the receiver complexity. This paper proposes a Bessel constraint method for generating OAM waves with the same divergence angle. Specifically, this paper first analyzes the factors affecting the divergence angle by combining two uniform circular arrays (UCAs) as uniform concentric circular arrays (UCCAs). Then, this paper defines the intensity proportion between the two UCAs to analyze constraint conditions. Additionally, two algorithms are designed to generate multimode OAM waves with equal divergence angles, considering scenarios with and without multiple frequency bands. Simulation results demonstrate the effectiveness of these algorithms in generating OAM waves with matching maximum strength ring radii, enabling the receiver to fully receive OAM waves of multiple modes and frequencies with just one UCA. Furthermore, the proposed approach facilitates the adjustment of OAM waves with different modes and frequencies by flexibly modifying the intensity proportion without necessitating alterations to the antenna radius.

The growing interest in deploying satellite communication (SatCom) systems results in a proliferation of both Geostationary Earth Orbit (GEO) and Low Earth Orbit (LEO) satellites, leading to the risk of inter-system interference between GEO and LEO systems, which can result in degraded communication performance or even complete system failure. Under this context, this paper investigates Reconfigurable Intelligent Surface (RIS)-aided LEO SatCom with LEO-GEO inter-system interference mitigation. Specifically, multiple satellites and multiple RISs are operated in a cooperative manner with properly designed joint beamforming. We formulate a minimum signal-to-interference-plus-noise (SINR) maximization problem exploiting statistical channel state information (CSI), subject to LEO-GEO interference mitigation constraint. We propose an alternating optimization (AO)-based algorithm, combined with quadratic transform and manifold optimization techniques, to design the cooperative multi-satellite multi-RIS beamforming iteratively. Numerical results show that the proposed scheme ensures the mitigation of LEO-GEO interference while effectively improving the performance of LEO SatCom.

In this paper, we investigate a millimeter wave integrated sensing and communication system with aid of the hybrid reconfigurable intelligent surface (HRIS), where the dual-function radar and communication station (DFBS) applies the hybrid precoding structure. On this basis, we consider the sensing and communication performance, respectively, and formulate two optimization problems. One is to maximize the worst-case illumination power while ensuring the communication quality, and another is to maximize the total achievable rate while satisfying the sensing performance. To solve them, we first decouple each nonconvex problem into three subproblems via the alternative optimization technique. For the former one, we transform DFBS and HRIS beamforming optimization subproblems into the convex ones by the quadratic constrained quadratic programming (QCQP) and semidefinite program relaxation (SDR) techniques, and obtain the solutions by standard convex optimization technique. For the later one, fractional programming is applied to decouple the objective function, and then we transform DFBS and HRIS beamforming design subproblems into the convex ones by QCQP and Taylor expansion techniques, and obtain the solutions by the alternating direction method of multipliers (ADMM). For the hybrid precoding design subproblems of DFBS in both problems, a manifold optimization-alternating minimization (MO-AltMin) algorithm based on minimizing the Euclidean distance is used to obtain the solutions. Simulation results show the effectiveness of the proposed schemes.

Vehicular crowdsensing has emerged as a prominent sensing paradigm in the Internet of Things (IoT), and its powerful sensing and computing capabilities can provide sufficient data for various applications. To reduce the cost while ensuring the sensing quality, sparse mobile crowdsensing has been proposed, which only requires data from some sensing areas and utilizes spatiotemporal correlation to infer data for other unsensed areas. In real vehicular crowdsensing scenarios, not only the current period sensing data is required to be inferred but also the prediction of the future whole sensing map is of great significance. In this article, we propose multitask pretraining graph transformer networks (MT-PTGTN) that incorporate graph neural networks (GNNs) and transformer to support both data inference and prediction for vehicular crowdsensing. Comprehensively considering the surface and underlying patterns among the sensing grids, MT-PTGTN utilizes pre-training topological mining GNNs and graph attention networks to model two patterns, respectively, which contributes to improving inference accuracy. The transformer with the multilayer attention mechanism is incorporated to capture the temporal correlation of the sensing data and predict the future complete sensing map. Furthermore, to prevent the error propagation from the inference task to the subsequent prediction task, we propose a dynamic multi-task learning framework that dynamically adjusts the weights of tasks during training. The experimental evaluation on the real-world dataset demonstrates the superiority of MT-PTGTN in data inference and prediction.

Integrating low-Earth orbit (LEO) satellite constellations with terrestrial mobile networks can achieve global coverage and complement terrestrial networks. The inherent mobility of satellites induces frequent handovers of user equipment (UE), generating massive signaling. Coupled with limited satellite resources, the network functions (NFs) deployed on satellites cannot process these signaling promptly, leading to increased queuing time. Additionally, the movement of onboard NFs increases the distance to UE, extending propagation delay. Extended procedure completion time (PCT) of control plane procedures degrades user plane Quality of Service (QoS). To address the above challenges, we propose a satellite-terrestrial integrated core network architecture to enhance signaling processing performance. First, we redesign the control plane NFs and introduce a satellite-ground synergy method (SGSM), categorizing signaling into time-sensitive and time-tolerant types. The former is processed onboard, while the latter is handled terrestrially, utilizing a designed UE context synchronization mechanism. Furthermore, migration is employed to counteract the movement. We devise a migration procedure to reduce transferred data during migration. Moreover, we model instance migration as a Markov decision process and proposed an online NFs migration algorithm based on deep reinforcement learning to determine migration timing and target satellites. Extensive experiments demonstrate that the proposed methods significantly reduce queuing time and the volume of transferred data, while also exhibiting superior performance in terms of propagation delay and the migration frequency.

Detours are inevitable in on-demand customized bus (CB) systems. Previous studies alleviate the impact of detours by predefining high-spatial–temporal similarity of travel orders for CB. However, this assumption is clearly inconsistent with orders’ distribution at the urban level and leads to low-bus occupancy rate in practical use. In this article, we propose a novel service policy to achieve cost-effective CB, which consists of dynamically deployed bus stops and a spatial–temporal heterogeneous-order service. Then, to address the challenge of computational complexity, we provide an order-oriented graphic model named order correlation network (OCN) to formulate the CB design problem. By introducing OCN, we propose a near-optimal computationally efficient solution to the problem, which is scalable and suitable for real-time implementation. Finally, comparative experiments based on the real-world taxi trajectory data set in San Francisco are implemented to verify the performance of our proposed CB in terms of service coverage and travel efficiency.

Wi-Fi-based continuous activity sensing is of great importance to personal healthcare, security monitoring, and healthy lifestyle assessment. However, it remains challenging to understand multiperson continuous activities as the Wi-Fi signals reflected by each person are mixed up in the Wi-Fi channel state information (CSI). To this end, we present MuSense, the first Wi-Fi-based system that enables multiperson continuous activity segmentation and recognition using commodity devices. In MuSense, we design a Wi-Fi network interface card (NIC) combination and calibration (NICCC) algorithm to construct a high-resolution receiving array and calibrate the CSI measurement noises of this array. On this basis, we propose a multiperson reflection signal separation (MPRSS) algorithm to completely and practically separate each person’s Wi-Fi reflection signals, obtaining the amplitude attenuation and phase shift corresponding to each person’s activities on the subcarrier dimension. Finally, we design an unsupervised adversarial continuous activity sensing network (UACAS-Net), with a two-stage adversarial training method to achieve generalized multiperson continuous activity sensing. Through two-stage adversarial training, UACAS-Net can capture distinguishing features of continuous activities from separated reflection signal streams in the source and target domains while minimizing the feature domain discrepancies to segment and recognize each person’s continuous activities in different domains. Intensive experiments are conducted under three different scenarios and the results demonstrate the effectiveness and practicality of MuSense for multiperson continuous activity sensing.

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.

The connected and autonomous vehicles combined with deep reinforcement learning (DRL) are capable of handling complex driving scenarios. However, due to the random exploration feature of reinforcement learning (RL), unexpected actions and collisions that would be inevitable in the real world occur during training, resulting in property damage, injury, and loss of life. To address this issue, in this article, we propose a sophisticated safe DRL in autonomous lane changing that benefits from both exploration and optimization capabilities. The key idea is first to integrate the safety constraints into the RL algorithm to limit the actions that the agent can take during training, which is implemented by designing a vehicle convex occupancy approximation to estimate the candidate action set. Then, adaptive exploration strategies are used, in which an imaginary environment based on domain randomization is built to explore areas of the action–state space where it is uncertain about the outcomes. We present a Monte Carlo tree search to replace unsafe with safe action. The twin-delayed deep deterministic policy gradient is used as the RL algorithm to train action space agents. Experimental results show that our proposed framework significantly enhances safety during the lane-change process with faster and more stable learning than the other methods.

The development of vehicular intelligence and networking has led to the emergence of vehicular crowdsensing as a new perception paradigm. By integrating edge computing, vehicular intelligence, and Internet of Vehicles technologies, vehicular crowdsensing is poised to have far-reaching implications in the domains of intelligent transportation, industrial sensing, and smart cities. In urban sensing scenarios, recruiting a large number of users can provide a lot of useful data, yet is costly due to expected financial incentive plans. As a solution, sparse mobile crowdsensing techniques have been proposed to collect data at a subset of sensing grids for data inference. However, the majority of these methods rely solely on explicit connections between sensing grids and do not consider implicit relations, which are crucial for accurate data inference. To achieve both high-quality data inference and cost reduction, we propose a cost-efficient vehicular crowdsensing scheme based on implicit relation-aware graph attention networks (CVC-IRGAT), which combines missing data inference with active grid selection. First, we design the IRGAT model to capture implicit and explicit relations between grids through a dual-channel mechanism of relation-aware and graph attention. Then, we design a method to assess the inferred data based on the Gaussian mixture model. Given the assessment values, a deviation information score function is proposed to measure the importance of the inferred values and model errors. Finally, we introduce active learning iterations to select the grids in accordance with this function. Extensive experiments have been conducted on real-world datasets, which demonstrate the superiority of the proposed CVC-IRGAT.

The intersection network constitutes the basic skeleton of the urban traffic environment, and informative representation of the intersection plays an important role in supporting the wide variety of applications in the intelligent transportation system. In this paper, we propose the Intersection to Vector model, named Intersec2vec, to achieve an accurate, efficient, and low-dimensional representation of each intersection in the large-scale intersection network. It introduces structural and temporal modules with attention mechanisms to specifically represent the evolution of intersection features in the spatiotemporal dimension, ensuring that intersections with stronger correlations have a higher probability of co-occurrence. Furthermore, our proposed Intersec2vec model is integrated into a traffic signal control method based on deep reinforcement learning by supporting more precise sub-area divisions, named Intersec2vec-TSC. For each sub-area, Intersec2vec-TSC adopts a hierarchical structure to design agents, where the upper agent determines the common cycle length based on the overall states of the sub-area, and lower agents jointly train a centralized evaluation network to achieve optimization of green time for each intersection. We conduct the experiment on 108 signalized intersections using real online car-hailing data, and the experimental results show that our proposed method significantly improves the stability of sub-area division and reduces the waiting time of cars during peak hours compared with other comparison methods.

Using satellite communications as backhaul links facilitates extending network coverage to unconnected areas. However, providing high-quality video streaming service via satellite backhaul is not economical. This paper presents SatSR, a mobile edge computing (MEC)-based super-resolution (SR)-enhanced adaptive on-demand video streaming system for mobile networks with satellite backhauls. Particularly, SR-based video quality enhancement is integrated into the video streaming process, so that low-quality videos with small sizes can be transmitted by satellite links and then enhanced to be high-quality. Meanwhile, SatSR offloads computation-intensive SR processing from user equipment (UE) to the MEC server to relieve UEs’ computation burden and speed up the SR processing. Specifically, the framework and the operation process of SatSR are designed first. Then, to mitigate the impact of SR processing delay, a pipelined mechanism is proposed, which can coordinate the video transmission and SR-based enhancement efficiently. Furthermore, an SR scale factor adaptation algorithm based on deep reinforcement learning is proposed to cope with the fluctuation of communication links. Finally, a system prototype and a chunk-level simulator of SatSR are built, respectively. The experiments results validate that SatSR outperforms baselines significantly, including both the UE-based SR-enhancement video streaming scheme and the traditional bitrate adaptation based video streaming scheme.

Integrating terrestrial cellular network with Low Earth Orbit (LEO) satellite constellation has been a popular trend in beyond 5G and 6G eras, called Satellite-Terrestrial Integrated Core Network (STICN). Core Network (CN) is an essential component responsible for authentication, security, mobility, data routing, etc. However, the terrestrial CN is designed for infrastructure-fixed and user-moving scenarios, which would cause the STICN to experience signaling storms and service interruptions when users access by rapid satellites. In this paper, we propose a distributed lightweight stateless satellite CN architecture, which could fit with the dynamic and limited-resource instincts of LEO satellites. And it can cooperate with terrestrial CN to provide seamless services. Firstly, the contexts of Network Functions (NFs) are decoupled from themselves and are managed in a common repository. Moreover, we design a cooperation mechanism between NFs to avoid frequent transmission of context and service interruption. Finally, extensive experiments are carried out on semi-physical simulation environments. The STICN performance could be improved by selecting the optimal number and location of each NF. Our evaluation shows that the proposed scheme could reduce the delay of the handover procedure by 37% and is more resilient compared with terrestrial CN.

Integrating the User Plane Function (UPF), which is responsible for forwarding user data in 5G, with the Low Earth Orbit (LEO) satellite networks can facilitate communication among users and take advantage of satellite edge computing. Satellite UPF (S-UPF) placement strategy is crucial to the integration performance. Static placement, in which the S-UPF drifts away with the satellite, is difficult to adapt to the dynamic satellite networks. The uneven distribution of terrestrial traffic and the resource limitations of satellites cause overload. The fast movement of S-UPF results in an augmented distance between S-UPF and users. This overload and extended distance degrade the Quality of Service (QoS). Dynamic S-UPF placement on satellites is a potential solution, but little attention is paid to it. To fill the gap, we propose a novel approach called Static Assignment Dynamic Placement (SADP), which comprises two key components: static user assignment and dynamic S-UPF placement. Static user assignment is designed to prevent overload, and dynamic S-UPF placement is applied to overcome the QoS degradation due to the extended distance between S-UPF and user. We evaluate the performance of SADP using real satellite constellations, and experimental results demonstrate its effectiveness in reducing latency and energy consumption. Compared to the static deployment, SADP achieves a significant 69.1% latency reduction and lower energy consumption. In contrast to deploying S-UPF on all satellites, SADP significantly reduces energy consumption by 85.2% while maintaining comparable latency performance.

The 4G/5G era in the past decades has witnessed the vigorous development of Hybrid Analog and Digital Beamforming (HBF) technologies in the field of communications under cellular Multiple Input Multiple Output (MIMO) systems. As an evolution, the B5G/6G has strong visions of high-accurate positioning capabilities other than the communication quality, thus a beam alignment method towards positioning enhancement is also urgently desired in cellular systems. To this end, this paper proposed a HBF method for positioning enhancement in cellular MIMO systems. We first derive the Fisher Information for multiple-path assisted positioning as the performance criterion of positioning under a wideband channel with both precoder and combiner considered. Then a HBF strategy is proposed to optimize such criterion over multiple resources involving the transmitting power, beam and frequency dimensions, which is referred to asSensing Beamforming. Furthermore, a Newton based heuristic method is proposed for the estimation of sensing elements (e.g. angle of arrival) from multiple paths, and the positioning results are obtained by a proposed multiple-path assisted positioning method considering the multiple path clutters in the environment. The results indicate that the proposed method can enhance the positioning performance with the accurate estimation of sensing elements.

Sensing in Integrated Sensing and Communications (ISAC) systems with clock asynchronism between the transmitter and receiver poses significant challenges. Understanding the fundamental limits of sensing performance in such setups, which remain largely unknown, is crucial. This paper investigates the sensing performance bounds in the presence of clock asynchronism. In both single-carrier and multi-carrier models, we derive the Cramér-Rao bounds (CRB) for estimating dynamic channel path parameters including angle of arrival, delay, and complex gain sequence (CGS). Through mathematical analyses and numerical simulations, we conduct a comprehensive study on how these bounds depend on various system parameters and the impact of clock asynchronism. Our findings highlight the degradation of parameter estimation performance due to clock asynchronism and reveal low-accuracy zones for CGS estimation in strong-line-of-sight scenarios. Additionally, we observe asymptotic mitigation in performance degradation with larger bandwidth, providing valuable insights for system design and optimization.

Full frequency reuse combined with precoding is a promising solution for multibeam satellite systems (MSSs) to meet the evergrowing capacity demand. However, line-of-sight-dominant satellite-ground channels will cause severe channel correlation among the geographically clustered hotspot users (HUs), which restricts multiuser capacity over HUs. In this paper, we propose the reconfigurable intelligent surface (RIS)-aided hotspot capacity enhancement scheme for MSSs. We formulate a hotspot sum rate maximization problem with SINR constraints added on a different user set and present an alternating optimization (AO)-based algorithm for its solution. To reduce computational complexity, we propose a two-stage algorithm that sequentially optimizes RIS phase shift with manifold optimization and satellite precoding, no longer resorting to AO. The RIS phase shift design utilizes semi-orthogonal subspace maximization and pairwise channel decorrelation. This design effectively formulates the interplay between RIS phase shifts and transmit beamforming related to the SINR constraint. To circumvent high channel estimation overhead, we extend the algorithms to low-cost designs exploiting statistical channel state information. Simulation results demonstrate that our proposed RIS-aided MSS designs substantially enhance HUs’ sum rate, attributed to the RIS-enabled channel refinement mechanism. Moreover, the two-stage algorithm achieves a comparable performance to the AO-based algorithm.

This paper proposed a novel integrated localization and communication framework, where reconfigurable intelligent surface-assisted non-orthogonal multiple access simultaneously transmits the communication and localisation signals via line-of-sight links. The bit error rate (BER) performance for communication users and the localization performance for localization users in terms of code phase estimation error (CPEE) and tracking error of the delay locked loop (DLL) are analyzed. The approximate closed forms for BER, CPEE and tracking error of the DLL are derived under Rayleigh fading channels. Finally, the Monte-Carlo simulations are used to validate the analysis. The results show that RIS-NOMA can enhance communication and localization performance compared to NOMA-only. Significantly, the corresponding performance values, i.e. BER, CPEE and tracking error of the DLL, decrease with the number of reflecting elements.

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.

Precoding techniques combined with aggressive full frequency reuse (FFR) are a promising solution to meet the evergrowing capacity demand for multibeam satellite systems (MSSs). However, LOS-dominant satellite-ground propagation environments will cause strong channel correlation among the geographically clustered hotspot users (HUs), and the consequent degradation of spatial multiplexing gain severely restricts multiuser multiple-input multiple-output (MU-MIMO) capacity. In this paper, we propose a reconfigurable intelligent surface (RIS)-aided scheme for MSSs to enhance the hotspot capacity of HUs via RIS-improved spatial-multiplexed transmission. Specifically, we propose to employ a RIS in the MSS's hotspot area formed by HUs for channel decorrelation and then jointly optimize the RIS passive beamforming and satellite precoding to reap the benefits of the spatial multiplexing. In this context, we formulate a novel HUs' sum rate maximization problem, subject to the transmit power constraint, the quality-of-service constraints for non-hotspot users, and the unit-modulus constraint for the RIS. The formulated problem is non-convex, and we propose an efficient iterative algorithm based on quadratic transform, semi-definite relaxation, and alternating optimization methods to solve it. Simulation results show that in our proposed RIS-aided scheme with optimized precoding and passive beamforming, significant sum rate enhancement is achieved for HUs in the MSS, owing to channel reconfigurable capability brought by the RIS.

Low Earth Orbit (LEO) satellite communication (SatCom) is considered a promising solution to supplement terrestrial networks. However, line-of-sight (LoS) links between the satellite and terrestrial terminals are possibly blocked by objects on Earth (e.g., mountains, tall buildings, etc.), leading to communication interruptions. This paper introduces Reconfigurable Intelligent Surface (RIS) into the LEO SatCom system to extend satellite coverage in blockage scenarios. Specifically, in contrast to the signal transmission model under the far-field assumption in usual studies, we present a model combining the near-field and far-field of the RIS, which can characterize the operational differences of each reflecting element more accurately. Next, a user-received power maximization problem is formulated and we solve it by jointly optimizing the RIS phase shifts and orientation. Simulation results demonstrate the effectiveness of the proposed optimization algorithm. Remarkably, it is shown that the scheme based on the proposed algorithm can assist the satellite in extending its coverage and providing higher power than that before blockage occurs compared to the baseline schemes.

Previous works have verified the feasibility of WiFi-based human pose estimation (HPE). However, their crucial limitations lie in requiring favorable placement of Wi-Fi devices and only sensing human poses in a small area. To address these issues, we propose WiLink, a Wi-Fi-based 3D HPE system that selectively uses several existing Wi-Fi links to achieve accurate HPE everywhere indoors. We find that the Channel State Information (CSI) fluctuations caused by human pose changes over different Wi-Fi links are various. According to the effectiveness of Wi-Fi links for human pose sensing, we classify the links as Noise-Dominated Links, Most-Effective Links and Redundant Links. Then we propose a Dynamic Link Selection (DLS) mechanism to adaptively select Most-Effective Links for HPE. This process maximizes the importance and minimizes the redundancy of the selected links. Finally, we feed the CSI samples corresponding to Most-Effective Links into a neural network to estimate human poses. Intensive experiments are conducted, and the results show that WiLink achieves a good performance in the scenario with multiple available Wi-Fi links.

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.

Gesture awareness plays a crucial role in promoting human–computer interface. Previous works either depend on customized hardware or need a priori learning of wireless signal patterns, facing downsides in terms of the privacy concern, availability, and reliability. In this article, we propose CentiTrack, the first centimeter-level passive gesture-tracking system that works with only three commodity WiFi devices, without any extra hardware modifications or wearable sensors. To this end, we first identify the channel state information (CSI) measurement error sources in the physical-layer process, and then denoise CSI by the complex ratio between adjacent antennas. Principal component analysis (PCA) is further adopted to separate the reflected signals from noises. Benchmark experiments are conducted to verify that the phase changes of denoised CSI are proportional to the length changes of the dynamic path reflected off the hand. In addition, we adopt the multiple signal classification (MUSIC) algorithm to estimate the Angle-of-Arrivals (AoAs) of dynamic paths, and then locate the initial position of hands with triangulation. We also propose a novel static componnets elimination algorithm for tracking correction by eliminating the components unrelated to motion. A prototype of CentiTrack is fully realized and evaluated in various real scenarios. Extensive experiments show that CentiTrack is superior in terms of tracking accuracy, sensing range, and device cost, compared with the state-of-the-arts.

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.

Daily activity monitoring is essential to healthy lifestyle assessment and personal healthcare, among which Wi-Fi-based solutions have attracted increasing attention due to their no-intrusive and privacy-protected characters. However, related researches are based on the assumption that there is an interval between two activities, during which the target is thought to be static. This assumption falls short of reality as human activities are performed continuously in daily life. Therefore, this article aims to design a nonintrusive and privacy-protected system, namely, Wi-Monitor, to monitor human activities in daily life. In Wi-Monitor, we first fragmentize Wi-Fi channel state information (CSI) streams into CSI bins and design a feature extraction network to extract activity fragmentation features (AFFs) from these CSI bins. From the extracted AFFs, a temporal convolutional network (TCN) is further used to capture activity continuity features (ACFs), which are used as distinguishing characteristics of continuous activities. Finally, Wi-Monitor utilizes these distinguishing characteristics to segment and recognize human activities in daily life simultaneously to achieve daily activity monitoring. In addition, we design an over-segmentation suppression mechanism with two training stages in Wi-Monitor to overcome the over-segmentation issue and enhance the activity monitoring accuracy. Intensive experiments are conducted in three different scenarios and the results demonstrate the effectiveness and practicality of Wi-Monitor for daily activity monitoring.

The continuous development of intelligent traffic control systems has a profound influence on urban traffic planning and traffic management. Indeed, as big data and artificial intelligence continue to evolve, the traffic control strategy based on deep reinforcement learning (RL) has been proven to be a promising method to improve the efficiency of intersections and save people's travel time. However, the existing algorithms ignore the temporal and spatial characteristics of intersections. In this article, we propose a multiagent RL based on the deep spatiotemporal attentive neural network (MARL-DSTAN) to determine the traffic signal timing in a large-scale road network. In this model, the state information captures the spatial dependency of the entire road network by leveraging the graph convolutional network (GCN) and integrates the information based on the importance of intersections via the attention mechanism. Meanwhile, to accumulate more valuable samples and enhance the learning efficiency, the recurrent neural network (RNN) is introduced in the exploration stage to constrain the action search space instead of fully random exploration. MARL-DSTAN decomposes the large-scale area into multiple base environments, and the agents in each base environment use the idea of "centralized training and decentralized execution" to learn to accelerate the algorithm convergence. The simulation results show that our algorithm significantly outperforms the fixed timing scheme and several other state-of-the-art baseline RL algorithms.

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 aims to solve the trajectory tracking control problem for an autonomous vehicle based on reinforcement learning methods. Existing reinforcement learning approaches have found limited successful applications on safety-critical tasks in the real world mainly due to two challenges: 1) sim-to-real transfer; 2) closed-loop stability and safety concern. In this paper, we propose an actor-critic-style framework SRL-TR2, in which the RL-based TRajectory TRackers are trained under the safety constraints and then deployed to a full-size vehicle as the lateral controller. To improve the generalization ability, we adopt a light-weight adapter State and Action Space Alignment (SASA) to establish mapping relations between the simulation and reality. To address the safety concern, we leverage an expert strategy to take over the control when the safety constraints are not satisfied. Hence, we conduct safe explorations during the training process and improve the stability of the policy. The experiments show that our agents can achieve one-shot transfer across simulation scenarios and unseen realistic scenarios, finishing the field tests with average running time less than 10 ms/step and average lateral error less than 0.1 m under the speed ranging from 12 km/h to 18 km/h. A video of the field tests is available athttps://youtu.be/pjWcN_fV24g.

WiFi-based passive gesture tracking plays a crucial role in promoting human-computer interface, due to its pervasive availability and cost-effectiveness. Prior works focus on tracking gestures on 2D sensing plane by aggregating multiple WiFi links (typically 2), with Uniform Linear Arrays (ULAs). However, gestures actually contain 3D spatial information instead of just 2D, thus interpreting the 3D traces as 2D may lead to enormous tracking errors. This paper aims at exploring the possibility of passively tracking 3D hand traces likewise leveraging two WiFi links with standard 3-element ULAs, and presents a generic 3D centimeter-level passive gesture tracking system, called CentiTrack-3D. To this end, we make two key observations: (1) The ULA-resolved angle contains the integrated information of the azimuth and elevation in 3D space, despite its inability to estimate azimuth and elevation separately. (2) The radial motion deviating from the sensing plane also leads to length variations of paths. Motivated by the observations, a 3D tracking model namedChaosis elaborately designed to deduce the hand 3D coordinates, so as to track the traces. Extensive experiments yield that CentiTrack-3D achieves an overall median tracking granularity of 2.5 cm in 3D space in case of diverse users and environment conditions.

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.

Based on the Richardson and Urbanke (RU) algorithm, the widely used quasi-cyclic low-density parity-check (QC-LDPC) code encodes the parity check matrix (PCM) in blocks, making hardware implementation possible. However, in a QC-LDPC encoding system with multiple expansion factors, storing information bits in excessive register bit widths would reduce the flexibility and throughput of the encoder when the RU algorithm is used. Therefore, this article proposes a novel architecture to reduce the complexity of the encoder by decreasing the number of expansion factors. This architecture optimizes the storage structure of the PCM and the pipeline structure of the encoding core, which obviously improves the flexibility and throughput of the encoder. In addition, this article presents two algorithms to optimize the pipeline structure, decreasing the latency for information bits to enter the encoder and ensuring that the pipeline of the encoding core would not stall. Moreover, the proposed architecture can be applied to encoding systems with multiple expansion factors, such as 5G and IEEE 802.11, and it has been verified on field-programmable gate array (FPGA). Compared with the most advanced solution, the proposed encoder achieves a 77% increase in resource utilization. As a case study, this encoder improves the performance of the soft base station by 2.59 times.

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.

Multi-target detection and tracking based on fusion of millimeter-wave (MMW) radar and camera play an important role in intelligent transportation system (ITS). However, most previous studies rely heavily on the information from one sensor and assisted by another, or require some additional measurement work. To address this issue, in this paper, we propose a radar-camera fusion method based on position deducing, where the camera and radar serve as mutual reference to deduce the position of the object. Since the azimuth accuracy and target detection rate of visual positioning algorithm are higher than those of radar, the proposed method improve the accuracy of the lateral positioning and reduce the missed detection. Experiments illustrate that the proposed method achieves highprecision positioning with an accuracy of 0. 110m. In addition, when there is at least one reference target, the detection rate and the false alarm rate are approximately 99.15% and 0.03%, respectively.

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.

This paper addresses high-resolution vehicle positioning and tracking. In recent work, it was shown that a fleet of independent but neighboring vehicles can cooperate for the task of localization by capitalizing on the existence of common surrounding reflectors, using the concept of Team Channel-SLAM. This approach exploits an initial (e.g. GPS-based) vehicle position information and allows subsequent tracking of vehicles by exploiting the shared nature of virtual transmitters associated to the reflecting surfaces. In this paper, we show that the localization can be greatly enhanced by joint sensing and mapping of reflecting surfaces. To this end, we propose a combined approach coined Team Channel-SLAM Evolution (TCSE) which exploits the intertwined relation between (i) the position of virtual transmitters, (ii) the shape of reflecting surfaces, and (iii) the paths described by the radio propagation rays, in order to achieve high-resolution vehicle localization. Overall, TCSE yields a complete picture of the trajectories followed by dominant paths together with a mapping of reflecting surfaces. While joint localization and mapping is a well researched topic within robotics using inputs such as radar and vision, this paper is first to demonstrate such an approach within mobile networking framework based on radio data.

Recently, commodity Wi-Fi devices have been shown to be able to construct human pose images, i.e., human skeletons, as fine-grained as cameras. Existing papers achieve good results when constructing the images of subjects who are in the prior training samples. However, the performance drops when it comes to new subjects, i.e., the subjects who are not in the training samples. This paper focuses on solving the subject-generalization problem in human pose image construction. To this end, we define the subject as the domain. Then we design a Domain-Independent Neural Network (DINN) to extract subject-independent features and convert them into fine-grained human pose images. We also propose a novel training method to train the DINN and it has no re-training overhead comparing with the domain-adversarial approach. We build a prototype system and experimental results demonstrate that our system can construct fine-grained human pose images of new subjects with commodity Wi-Fi in both the visible and through-wall scenarios, which shows the effectiveness and the subject-generalization ability of our model.

Heterogeneous network (HetNet) and Non-Orthogonal Multiple Access (NOMA) have been seen as promising ways to improve the network capacity. However, the dense deployment of base stations (BSs) in HetNet causes unbalanced loads and large energy consumption. In this paper, we investigate the joint user association and power control problem with the objective of improving the sum rate and the energy efficiency in the Orthogonal Multiple Access (OMA)-enabled HetNet and NOMA-enabled HetNet, respectively. Particularly, the traditional joint user association and power control algorithms can only solve the problem for one network scenario, which are not applicable to the other networks. Specifically, this paper presents a novel deep reinforcement learning (DRL)-based general optimization framework, which is a unified solution for the user association and power control problem and can adapt to OMA-enabled and NOMA-enabled HetNet scenarios with relatively minor modifications. In the framework, a Deep Deterministic Policy Gradient Algorithm (DDPG)-based joint user association and power control algorithm is proposed that can learn to achieve load balance and improve energy efficiency by interacting with the environment. Specifically, different from the DDPG, the proposed algorithm can solve the discrete and continuous optimization problems together. Finally, simulation results demonstrate the proposed algorithm achieves a higher sum rate and energy efficiency than the traditional algorithms in both OMA-enabled HetNet and NOMA-enabled HetNet.

Mobile edge caching has been proven effective in improving the network utility and users’ quality of experience (QoE). Due to the exponential growth of mobile video traffic and the limited cache capacity of the base station (BS), how to pick appropriate video contents to cache is an important challenge. Different from existing users’ cache hit ratio maximization, this paper aims at improving both the users’ QoE and network utility for the cache-enabled network. To achieve this goal, we investigate an optimization problem of QoE-aware joint segment-based video caching and user association (JSVCUA). Then we propose an iterative framework to decouple the original non-convex problem into the cache placement problem and user association problem. For the cache placement problem, we propose utilizing the neural collaborative filtering (NCF) scheme to accurately predict the higher-level and non-linear preferences between users and video contents. Besides, the segment-based caching strategy is presented to further improve the efficiency of caching. Then the caching decision is made by maximizing the QoE weighted content transmission rate. For the user association problem, convex optimization is applied, and the performance gain of caching is squeezed. Simulation results verify that the proposed algorithm can significantly improve both the network utility and QoE on a real dataset. Specifically, the cache hit ratio increase by 10% on average.

With the development of the 5G mobile communication system, the network infrastructure is becoming more widely deployed. Ubiquitous 5G wireless signals bring new opportunities to the positioning of connected autonomous vehicles. Radio-based positioning can utilize the existing 5G network infrastructure to locate connected vehicles without additional sensor equipment installed in the vehicle. Moreover, location-related information of vehicles can be obtained from V2V communication supported by 3GPP, which provides a new way to increase positioning accuracy. In this paper, we develop a V2V communication assisted cooperative localization algorithm that exploits the location-related information in V2V communication and the multipath radio signals received by vehicles to further improve localization accuracy. A Bayesian model is derived for feature-based simultaneous localization and mapping (SLAM) according to the information exchanged between vehicles and the characteristics of the multipath components, and a cooperative belief propagation algorithm is used to locate vehicles on a factor graph. Simulation results show that this algorithm has better positioning performance than non-V2V-cooperative scenarios.

In this work, we address the issue of Wi-Fi-based human activity recognition (HAR) system in densely deployed Wi-Fi environments. With the benefit of sufficient information provided by Wi-Fi channel state information (CSI), HAR based on Wi-Fi has become an active research area in recent years. Traditional Wi-Fi CSI-based HAR applications usually focus on utilizing one Wi-Fi transmitter and one or several Wi-Fi receivers to extract the activity-related features, ignoring the communication among multiple Wi-Fi devices in the real world. In this paper, we present a novel Wi-Fi link selection model on the basis of continuous state decision-making process in which CSI is modeled as a part of the state. The model, referred to as WiAgent, takes an action of selecting one Wi-Fi link according to current state, and then updates the state for the choice of the next action. From extensive experiment results, our method performs better than other solutions in a given environment where multiple Wi-Fi transmitters exist.

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.

Electric vehicles (EVs) are becoming increasingly popular, but the frequent charging and large charging latency remain major obstacles to the EV industry. This article focuses on the charging scheduling of on-the-move EVs in a transportation network to minimize EVs' charging latency, including driving time to charging stations (CSs), wait time and charging time. We formulate this charging scheduling problem as a graphical game to characterize the strong couplings of charging latency among neighboring EV players. Specially, we investigate correlated equilibrium (CE) to describe the joint strategies of EV players, which is expected to further reduce the charging latency of EVs compared with Nash equilibrium (NE). It is shown that CE always exists in a finite game, and can be found by linear programming tools. In addition, we propose a method of wait time prediction, which can improve the prediction accuracy by combining the data of deterministic EV arrivals and the stochastic property of potential EV arrivals. Simulation studies are used to examine the performance of the proposed game-based approach, the efficiency of CE, the preciseness of our proposed wait time prediction method, the impacts of CS deployment on EVs' charging latency, etc. We can draw a conclusion that our method has apparent advantages in situations where the locations of EV players are in dense manners.

A novel framework is proposed for enhancing the driving safety and fuel economy of autonomous vehicles (AVs) with the aid of vehicle-to-infrastructure (V2I) communication networks. To solve this pertinent problem, a double deep Q-network (DDQN) algorithm is proposed for making collision-free decisions. Thus, the trajectory and velocity of the AV are determined by receiving real-time traffic information from the base stations (BSs). Compared to the conventional deep Q-network algorithm, the proposed DDQN algorithm is capable of overcoming the large overestimation of action values by decomposing the max-Q-value operation into action selection and action evaluation. Numerical results are provided for demonstrating that the proposed trajectory design algorithms are capable of enhancing the driving safety and fuel economy of AVs. We demonstrate that the proposed DDQN based algorithm outperforms the DQN based algorithm. Additionally, it is also demonstrated that the proposed fuel-economy (FE) based driving policy derived from the DRL algorithm is capable of achieving in excess of 24% of fuel savings over the benchmarks.

Device-free gesture recognition plays a crucial role in smart home applications, setting human free from wearable devices and causing no privacy concerns. Prior WiFi-based recognition systems have achieved high accuracy in a static environment, but with limitations in adapting changes in environments and locations. In this paper, we propose a fine-grained deep adaptation networks based gesture recognition scheme (DANGR) using the Channel State Information (CSI). DANGR applies wavelet transformation for amplitude denoising, and conjugate calibration to remove CSI time-variant random phase offsets. A Generative Adversarial Networks (GAN) based data augmentation approach is proposed to reduce the large consumptions of data collection and the over-fitting risks caused by incomplete dataset. The distribution of CSI in various environments may be biased. In order to shrink these domains discrepancies in environments, we adopt domain adaptation based on multikernel Maximum Mean Discrepancy scheme, which matches the mean-embeddings of abstract representations across domains in a reproducing kernel Hilbert space. Extensive empirical evidence shows that DANGR yields mean 94.5% accuracy of gesture recognition confronting environmental variations, providing a promising scheme for practical and long-run implementation.

Due to the limited energy of the Internet of Things (IoT) device, unmanned aerial vehicle (UAV) as a mobile fusion center can effectively prolong the lifetime of IoT device via supporting communication with the device directly. Moreover, since UAV's energy constrained, it will be a good measure to take multiple UAVs to collect data from devices in large areas. In this paper, we investigate multi-UAV collaborative data collection system, where multiple UAVs collect data from two-dimensional distributed devices on flying mode or hovering mode. The objective is to minimize UAVs' total flight time while allowing each device to complete data upload successfully with limited energy. To this end, firstly, a cell partition based on Voronoi diagram is used to allocate the collection areas of each UAV. Then, in each associated area, UAV determines the whole trajectory to serve devices. Lastly, given load requirement of ground devices and energy limitation, the optimal data collection mode of each device is decided to minimize flight time of each UAV. Simulation results show that the proposed multi-UAV data collection scheme can shorten collection task completion time significantly.

In this article, we study the co-channel problem in the 2-tier architecture of vehicular networks [i.e., vehicle-to-vehicle (V2V) communication and vehicle-to-infrastructure (V2I) communication]. The communication technology we consider here is dedicated to short-range radio communication (DSRC), cellular vehicle-to-everything (C-V2X), or a hybrid of both. The V2I communication will interfere V2V communication, and vice versa, because the roadside unit (RSU) cannot sense V2V communication during the downlink period when the V2V communication is in the coverage of RSU. We note that V2V communication will have higher priority since it conveys critical messages for road safety in connected and automated vehicle (CAV) systems. We propose a p-opportunity channel access scheme (p-OCAS) for the RSU to solve the problem. Simulation results validate the correctness of the analytical model. The investigation showed that p-OCAS can substantially minimize the interference from RSU to V2V communications according to the V2V session arrival rate to automated vehicles as well as maintain a high throughput of RSU.

A novel framework is proposed for the trajectory design of multiple unmanned aerial vehicles (UAVs) based on the prediction of users' mobility information. The problem of joint trajectory design and power control is formulated for maximizing the instantaneous sum transmit rate while satisfying the rate requirement of users. In an effort to solve this pertinent problem, a three-step approach is proposed which is based on machine learning techniques. Firstly, a multi-agent Q-learning based placement algorithm is proposed for determining the optimal positions of the UAVs based on the initial location of the users. Secondly, in an effort to determine the mobility information of users based on a real dateset, their position data is collected from Twitter to describe the anonymous user- trajectories in the physical world. In the meantime, an echo state network (ESN) based prediction algorithm is proposed for predicting the future positions of users based on the real dataset. Thirdly, a proposed multi-agent Q-learning based algorithm is invoked for predicting the position of UAVs in each time slot based on the movement of users. The algorithm is proved to be able to converge to an optimal state equation. Numerical results are provided to demonstrate that as the size of the reservoir pool increases, the proposed ESN approach improves the prediction accuracy. Finally, we demonstrate that throughput gains of about 17% are achieved.

5G networks need to support various use cases which may require different quality of service (QoS). Network slicing (NS) is an innovative concept to customize different logical networks from a common general infrastructure for these services. A slice is composed of a set of virtual network functions (VNFs) that can be deployed on standard commodity servers as virtual machines (VMs). However, the packet delay of VMs may be affected by other VMs on the same server. In this paper, we investigate network slice deployment policy to improve the network performance by guaranteeing the latency requirements. Moreover, a realistic mathematical model is formulated, considering the effects of virtualization on end-to-end delay. Due to the diversity of network slices' topologies and various performance requirements, we propose a heuristic algorithm to deploy different network slices. Extensive experiments demonstrate that our algorithm can get solutions with reasonable execution time and achieve lower delay and higher acceptance ratio.

Narrowband Internet of Things (NB-IoT) is a new communication technology designed for machine type communications (MTC), which needs to support much more devices compared to Long Term Evolution (LTE). But the random access (RA) process of it is also based on the slotted-ALOHA mechanism like LTE, which is not that suitable for machine to machine (M2M) communications. When a device's access attempt failed, the time of backoff and access class barring (ACB) is random. This would lead to a lot of meaningless failed attempts when the access load is heavy. And the access successful probability and energy efficiency will be very low. In this paper, we propose a novel access protocol based on resource grouping and distributed queuing (RGDQ) mechanism to effectively solve the massive access issue in NB-IoT. Firstly, we apply DQ mechanism into the access process of NB-IoT. Afterwards, we newly propose an arrival-based access resource grouping mechanism (RG) to reduce the access delay caused by the queuing process of DQ. In addition, we develop an analytical model to accurately estimate the access performance of the proposed protocol. Finally, computer simulations are also performed to validate the accuracy of the analytical model and verify the proposed protocol in comparison with the NB-IoT standard and conventional DQ access schemes.

In the automated driving scenario, to cope with the complex road condition and obtain the comprehensive and accurate identified result to make the automated driving decision, how to fuse the heterogeneous perceptual date perceived by vehicular multi-sensors efficiently and make automated driving decision reliably is the main issue. In this paper, a mathematical model is proposed to abstract the heterogeneous perceptual data from different vehicular sensors into a series of vectors (data set), which have a uniform measurement accuracy, range, and output form and map them to a corresponding automated driving decision. then we fuse abstracted perceptual data of vehicular multi-sensors and make the automated driving decision based on a D-S (Dempster-Shafer) evidence theory. However, using the classical D-S evidence theory to fuse may exist large fusion computational resource consumption and weak ability to fuse high-conflict perceptual data, it causes fusion latency to increase and accuracy of decision-making to decrease. So an improved D-S evidence theory is proposed to overcome the problem above for satisfying the requirements for real-time and reliability in the automated driving scenario.

In the era of Internet of Things, human sensing, which detects and interprets human motions including gestures or postures, has emerged as a challenging problem in areas such as assisted living and remote monitoring. Besides conventional sensing methodologies that rely on wearable devices and camera systems, WiFi-based technologies are evolving as a promising solution for indoor monitoring and activity recognition recently. In this paper, we propose WiRoI, a device-free human sensing scheme which is able to precisely detect and interpret human motions within certain spatial region of interest (RoI) using only commodity WiFi devices. To this end, the channel state information (CSI) data in PHY layer is obtained directly by upgrading the firmware. To make the system robust enough, we propose a method to eliminate the noises caused by other humans between TX and RX. And particularly, we are the first to explore spatial region of interest towards accurate and robust human sensing. Experiments were conducted in a common office and the results demonstrated that WiRoI is able to accurately detect and interpret gestures (In order to test the performance of the proposed method, we build a gesture recognition system.) with the accuracy lowered by less than 5% when there are other humans walking or standing between the TX and RX.

Network slicing enables wireless networks to support various use cases by slicing an infrastructure network into multiple dedicated logical networks. From the perspective of infrastructure network providers, improving network resource efficiency and accepting more network slice requests have direct effects on increasing the revenue. However, the infrastructure providers have to reject some slice requests due to the limitation of physical node capacity. Hence, in this paper, we propose an efficient node capacity expansion algorithm to dramatically increase the revenue of infrastructure providers by reinvesting small cost to expand the capacity of some physical nodes. We adopted complex network theory to obtain the topological information of the infrastructure network. With the topological information, we defined a node priority metric to rank the physical nodes aimed at selecting the nodes with the most embedding potential for virtual nodes. Through extensive simulation study, we demonstrated that our proposed algorithm can expand physical node capacity accurately for providing (i) higher resource efficiency and (ii) higher acceptance ratio of slice requests.

The Internet of Things (IoT) is a new heterogeneous system integrated by the various end users (sensors and terminals) with different technologies. However, the limiting factor is bandwidth in the IoT due to the exploding end users and the network bandwidth requirements. A novel IoT model, which integrates the power-line carrier (PLC) and the wireless network (WN), is proposed to solve the bandwidth problem from the architecture, especially in the areas lacking network facilities. In addition, we exploit an effective virtual layer (EVL) which allows the different end users to access the system model seamlessly. Then, the attractor selection algorithm based on Markov chain (MASA) is employed to select an optimal path among the PLC or WN. The simulation results demonstrate that the proposed system model has the smaller average queuing delay than other algorithms and makes the model more stable and robust.

This paper analyzes the dense IEEE 802.11 wireless local area networks (WLANs) with random topologies using stochastic geometry and Markov chain model. Since Markov chain model for WLANs only focuses on 802.11 MAC layer performance and stochastic geometry model mainly focuses on physical layer effects, our mathematical models take both of these two layers into consideration. The locations of access points (APs) and stations (STAs) are distributed according to two independent homogeneous Poisson point process (PPP) models. Then, an algorithm is proposed for systematically constructing the continuous time Markov chain (CTMC) corresponding to dense WLANs with random topologies. We use a Markov chain model to analyze the throughput performance of dense WLANs based on the CTMC model. The accuracy of our mathematical models is validated by simulations.

To meet the dramatically increasing traffic demand, licensed-assisted access (LAA) has been proposed for long-term evolution (LTE) systems to operate on the unlicensed spectrum. However, it is a huge challenge to achieve fair and efficient coexistence between LAA and Wi-Fi on the same band. In this paper, we devise a novel channel access approach for LAA eNBs to improve system throughput and achieve fair coexistence with Wi-Fi nodes. Specifically, Carrier Sense Multiple Access with Enhanced Collision Avoidance (CSMA/ECA), which is able to reach and maintain collision-free operation by deterministic backoff (DB) after successful transmissions, is introduced to the listen-before-talk (LBT) procedure of LAA eNBs to improve the coexistence system throughput. However, note that the fixed DB value in traditional CSMA/ECA cannot ensure fair coexistence all the time corresponding to different network sizes. Therefore, an adjustable DB value scheme is devised to replace the fixed one, in order to achieve fair coexistence whatever the number of Wi-Fi nodes and LAA eNBs is. Aiming to obtain the maximum system throughput improvement, the behavior of a LAA eNB is modeled as a Markov chain, and the throughput of the coexistence network is derived. Then, we formulate a throughput optimization problem and develop an algorithm of DB value adjustment, which can not only achieve the maximum throughput improvement but also maintain the fair coexistence whatever the number of nodes in the coexistence network is. At last, numerical results are presented which validate the accuracy of our analysis model and demonstrate the effectiveness of the proposed algorithm.

Virtualization technology is considered an effective measure to enhance resource utilization and interference management via radio resource abstraction in heterogeneous networks (HetNet). The critical challenge in wireless virtualization is virtual resource allocation on which substantial works have been done. However, most existing researches on virtual resource allocation focus on improving total utility. Different from the existing works, we investigate the dynamic‐aware virtual radio resource allocation in virtualization based HetNet considering utility and fairness. A virtual radio resource management framework is proposed, where the radio resources of different physical networks are virtualized into a virtual resource pool and mobile virtual network operators (MVNOs) compete for virtual resources from the pool to provide service to users. A virtual radio resource allocation algorithm based on biological model is developed, considering system utility, fairness, and dynamics. Simulation results are provided to verify that the proposed virtual resource allocation algorithm not only converges within a few iterations, but also achieves a better trade‐off between total utility and fairness than existing algorithm. Besides, it can also be utilized to analyze the population dynamics of system.

As average user load in wireless local area network (WLAN) becomes heavy, the fundamental CSMA/CA mechanism based on binary exponential backoff (BEB) in the 802.11 protocol is under stress. When a large number of users associate with WLAN, the network suffers from severe throughput deterioration and poor short-term fairness due to the inappropriate BEB algorithm. In this paper, we provide an enhanced backoff (EBO) algorithm to improve the performance of WLANs. Our main motivation is based on the observation that disjointing backoff intervals in different backoff stage can greatly reduce the collision probability. In EBO, the size of backoff interval increases by the initial value of contention window after an unsuccessful transmission, and backoff intervals in different backoff stage are disjoint. Meanwhile, to improve the short-term fairness, we slow down the decrement of contention window by resetting the contention window to initial value after consecutive successful transmissions. Simulation results show that EBO improves the throughput and short-term fairness effectively comparing with BEB in heavy user loaded WLANs.

Energy harvesting from ambient radio frequency (RF) radiation is an efficient approach to provide wireless energy replenishment for mobile devices. At the same time, introducing active/sleep modes in base stations (BSs) is an effective method to save the energy consumption of cellular networks. However, the performance of the two techniques would be dependent and interact with each other if both of them were employed in cellular networks. In this paper, we analyze the performance and investigate the sleeping strategy design problem in the cellular network, in which the mobile devices have the capability of energy harvesting from the ambient RF signals. Specifically, using tools from stochastic geometry theory, we derive analytical expressions of the coverage probability and the harvested energy and show that they are both affected by the sleeping strategy. Then, we formulate a BSs power consumption minimization problem under the coverage probability and harvesting performance constraints. Finally, the optimal operating regime of the sleeping strategy is derived. Numerical results confirm the correctness of the analysis and show the effect of switching off BSs on both performance metrics.

In this paper, the power allocation problem, which aims to optimize the energy efficiency (EE) of downlink OFDMA heterogeneous networks, is researched. Specifically, the terminals are sensor nodes in the Internet of Things (IoT) with RF energy harvesting (EH) technology. This technology enables the sensor nodes to capture energy from the wireless signals in air, constitutes a permanent energy source, and provides self-stainability to them. Traditional interference analysis and EE modeling are unsuitable for our EH introduced problem, so we model the energy-efficient power allocation problem as an EH based non-cooperative (EHNC) game. Then we prove the existence and reveal the uniqueness conditions of the EHNC game equilibrium. We propose a distributed energy-efficient power allocation algorithm to solve the problem, and reveal that the game can converge to Nash equilibrium after certain times of iterations. Simulation results show that the proposed algorithm is effective in increasing energy-efficient performance, harvesting energy and guaranteeing QoS of the IoT sensor nodes simultaneously.

This paper investigates proportional-fair energy-efficient radio resource allocation problem for the uplink transmission of OFDMA smallcell networks. Instead of the fairness measured by users' achievable data rates, this paper concentrates on the fairness in terms of energy efficiency (EE) and aims to provide EE-based proportional fairness guarantee among all users in smallcell networks. Specifically, EE-based global proportional fairness utility optimization problem is formulated, taking into account both the minimum data rates requirements and the cross-tier interference constraints. In order to make the problem more tractable, it is transformed into a weighted sum maximization problem of each user's instantaneous EE utility. Then, a two-step scheme is adopted, which solves subchannel allocation and power allocation separately, and the corresponding algorithms are devised. The proposed subchannel allocation algorithm is heuristic and low-complexity. The power allocation scheme is optimal, and is devised based on a novel method which can solve the sum of ratios problems efficiently. Numerical results verify the effectiveness of the proposed algorithms, especially the good capability of ensuring high level EE fairness among all users in the smallcell network.

WiFi offloading is a cost-effective and practical solution to alleviate the problem of highly congested cellular networks. Recent theoretical and experimental studies show that delayed WiFi offloading where traffic can be delayed to increase the chance of meeting WiFi can significantly improve offloading efficiency. Nevertheless, there is no exact analytic model to analyze the offloading benefits with different types of traffic. In this paper, we propose a preemptive priority queuing analytic model for delayed offloading with multi-level priority traffic and derive expressions for the average delay and offloading efficiency of traffic with different priorities as a function of the WiFi availability, deadlines, and other key parameters. At last, we validate the accuracy of our queuing model by simulation in different scenarios and clarify how to choose a suitable deadline for traffic of different priorities.

The unforeseen mobile data explosion poses a major challenge to the performance of today's cellular networks, and cellular network is in urgent need of original solutions to handle such voluminous mobile data. Obviously, data offloading through third-party WiFi access points (APs) can effectively alleviate the issue of overload in the cellular networks with a low operational and capital expenditure. In this paper, we study the network selection problem in operator-initiate offloading in ultradense wireless networks. To enhance the mobile data offloading, a dynamic and self-adaptive method for network selection is proposed, using the attractor selection mechanism described in biological system. In our proposed algorithm, the operator enables users to dynamically select an appropriate APs according to the dynamic conditions of various available networks. Simulation results show that the proposed algorithm decreases the service delay and achieve a high offloading efficiency in delay offloading.

The explosive growth of mobile multimedia services has caused tremendous network traffic in wireless networks and a great part of the multimedia services are delay-sensitive. Therefore, it is important to design efficient radio resource allocation algorithms to increase network capacity and guarantee the delay QoS. In this paper, we study the power control problem in the downlink of two-tier femtocell networks with the consideration of the delay QoS provisioning. Specifically, we introduce the effective capacity (EC) as the network performance measure instead of the Shannon capacity to provide the statistical delay QoS provisioning. Then, the optimization problem is modeled as a non- cooperative game and the existence of Nash Equilibriums (NE) is investigated. However, in order to enhance the selforganization capacity of femtocells, based on non-cooperative game, we employ a Q-learning framework in which all of the femtocell base stations (FBSs) are considered as agents to achieve power allocation. Then a distributed Q- learning-based power control algorithm is proposed to make femtocell users (FUs) gain maximum EC. Numerical results show that the proposed algorithm can not only maintain the delay requirements of the delay-sensitive services, but also has a good convergence performance.

In this paper, we propose a coordinated scheduling scheme in downlink multi-cell Orthogonal Frequency Division Multiplexing Access (OFDMA) networks. Firstly, a novel decision tree algorithm is introduced to categorize users according to certain attribute parameters to improve system performance. To suppress inter-cluster interference, a filter is used at the receive. To mitigate intra-cluster interference and enhance system performance, we jointly form precoding matrix (using Block Diagonalization precoding) and distribute power for coordinative users. Considering the fact that there is a constraint on number of coordinative users simultaneously served on one resource slot and there may be a large number of users needing coordinative transmission, it is necessary to allocate proper resource slots to them, seeking a tradeoff between coordinative user sum rate and system total capacity. Thus, a performance-based slot distribution is utilized. What needs to be pointed out is that our power control is a per BS power constraint. Simulation results show that our proposed scheme can significantly improve the performance of multi-cell networks.

In this paper, we consider the problem of interference mitigation in the downlink of multicell networks via base station coordination. In this paper, a simple and efficient scheme for interference management based on potential game is proposed. The main emphasis of this paper is placed on the problem of users' quality of service (QoS) in order to maximize the efficient throughput of system. Meanwhile, a pricing factor is introduced which is proportion to the co-channel interference to other base stations. Furthermore, an improved gradient projection rule with variable step size and Jacobi iterative algorithm are utilized to solve the optimization problem. Pareto optimal is verified by using "price of anarchy" as an optimize performance indicators in potential game. Simulation results show that our proposed scheme can significantly improve the performance of multicell networks.

This paper addresses the energy-efficient power allocation problem of downlink transmission with delay quality of service (QoS) constraint in the femtocell networks. Particularly, in order to provide statistical delay guarantee, the effective capacity (EC) is employed as the network performance measure instead of the conventional Shannon capacity. As a result, the energy efficiency (EE) metric of the femtocell is defined to be the total-EC-to-the-overall-power-consumption ratio of the femtocell base station (FBS). The optimization problem is firstly modeled as a supermodular game. Then the existence and characteristics of the Nash Equilibrium (NE) are investigated. A distributed energy-efficient power allocation algorithm is also designed to implement the game. Simulation results demonstrate that, our proposed algorithm delivers substantial energy efficiency improvement while satisfying a wide range of delay requirements.

Nowadays, inspired by the behavior and neuronal architecture of human visual system (HVS), region of interest (ROI) detection methods are investigated by integrating several cognitive features. However, most of them are complex and time-consuming. To solve this problem, a video ROI extraction algorithm based on information theory and cognitive features is proposed in this paper. Based on the information theory, the spatial and temporal information are computed to measure the spatial and temporal content of video sequences respectively. Utilizing the visual features fusion strategy (VFFS), visual information is obtained from the combination of spatial and temporal information. Then, perceptual stimulus-response model (PSRM) is established to map the visual information to match the visual saliency. Regions with saliency scores, which are higher than a self-adaptive threshold, are regarded as ROIs. Experimental results show that the proposed scheme reduces 50% computation complexity, and can extract ROI more effective compared to Itti's method. Furthermore, the proposed ROI extraction scheme can be easily applied in the practical multimedia processing and pattern recognition system, such as video summarization.

Continuous evolution of wireless protocol and the increasing unmanaged deployment of small cells (e.g. Pico-cell, femto-cell or Wi-Fi APs) have raised an urgent need to update mobile network architecture. To support such evolvability, we design the Open Wireless Network (OWN), a novel evolved architecture for radio access networks, including UMTS, 802.11 WLAN, etc. The main contribution of this paper is that it separates radio access networks into control plane and data plane, and introduces a unified controller for different radio access networks. The data plane consists of soft base stations and access points (BS/AP). The LTE base station and Wi-Fi access point have been realized on our GPP-based platform. A cloud-based controller works as the control plane. In this paper, the OWN operating system (OWN OS) is designed for the controller to perform the corresponding control functions, and it provides open application interfaces (APIs) for developing customed network management applications. Our current system has already supported remote system configuration, QoE-oriented wireless resource management, mobility management and load balancing in heterogeneous networks, etc. We believe that OWN is a promising architecture to make the wireless network central controlled, QoE-oriented, feasible to evolve and more open to operators and vendors.

We proposed a novel cross-layer optimization approach for video streaming applications in orthogonal frequency division multiplexing (OFDM) based networks. Parameters in media access control (MAC) layer and physical layer are optimized jointly in a cross-layer framework. The main objective is to maximize video quality by reducing the end-to-end distortion in application layer for each user. Potential games theory is adopted to solve this cross-layer problem in distributed way. Convergence of game is guaranteed by gradient projection, which reduces the time complexity of optimization considerably. Simulation results have confirmed that the gradient projection based cross-layer optimization can significantly enhance the video quality performance with low time complexity and high scalability.

This paper proposes a no-reference video quality estimation model over burst loss wireless networks. The estimation model is an end-to-end, cross-layer framework which considers two parts as feature extraction and quality prediction. The first part considers content-aware parameters obtaining from rebuilt video sequences, transmission-aware parameters getting from network layer and encoding parameters which are set at application layer. Firstly, for the content-dependent parameters, the temporal and spatial features are extracted representing the videos' nature. Then, for transmission-aware features, Principal Component Analysis (PCA) is used to reduce the number of parameters to give high prediction accuracy in test with low training costs. Frame Rate (FR) and Sent Bit Rate (SBR) are selected as the encoding features which bring the effects from quantization. In the second part, Support Vector Machine (SVM) is provided by using all cross-layer parameters to make a tradeoff between accuracy and learning ability. Results show that the prediction values are well correlated with subject scores with Pearson coefficient of 0.86 at least.