The evolution of communication network architectures is steering towards more sustainable, flexible, and lightweight designs, particularly with the advent of sixth-generation (6G) mobile communications. Spectrum-rich optical integrated systems are anticipated to play a crucial role in this transformation, offering significant advantages such as high data rates, reduced interference, and improved energy efficiency. This paper introduces and experimentally demonstrates a novel optical integrated communication, sensing, and power transfer (O-ICSPT) system. The proposed system integrates optical wireless communication, sensing, and wireless power transfer into a multifunctional framework, addressing the limitations of existing systems in terms of flexibility and resource utilization. The experimental setup investigates the effects of bias current, peak-to-peak voltage, and light source wavelength on the performance of each functional module. Experimental results indicate that the O-ICSPT system achieves a maximum data rate of approximately 632.58 Mbps, a best ranging root mean square error approaching 0 m, and a peak energy harvesting capability of about 10.02 mW. These findings underscore the potential of the O-ICSPT system in future 6G integrated communication networks, marking the first experimental validation of such a system.

Integrated satellite–terrestrial networks (ISTNs) are envisioned as a key component of 6G to support global connectivity for massive Internet of Things (IoT) devices. However, while terminals operate under strict energy and computational constraints and face additional challenges from satellite dynamics, current designs rarely integrate orbital information with device energy states in a unified system design, leaving significant room for improvement. Motivated by this, we propose a joint trajectory- and energy-aware framework for ISTNs operating in dynamic environments, supported by a low-complexity algorithm tailored for energy-constrained IoT devices. Specifically, we first develop a system model centered on a day–night separated hybrid energy-harvesting (DNEH) mechanism, where each device harvests light energy during the daytime and radio frequency (RF) energy from the gateway (GW) at night. Meanwhile, the GW broadcasts simplified orbital information to assist device operations, thereby forming an integrated communication–energy transfer framework that enhances system sustainability. Analytical characterizations are subsequently derived for the joint coverage probability, and simulations capture orbital dynamics such as the real-time distance variations. Building on the semi-analytical model, the proposed drift-regularized per-frame (DRPF) algorithm determines transmission opportunities that optimize the average Age of Information (AoI) by capturing the coupling between trajectory dynamics and energy availability. Numerical evaluations confirm the theoretical advantages of the proposed framework, showing substantial performance gains over the Immediate and Minimum Distance transmission baselines. Moreover, the optimized TD-SWIPT configuration and transmission scheduling translate these findings into actionable design guidelines for energy-constrained ISTNs.

Visible light communication (VLC), as one of the key technologies for new spectrum communication in 6G, has drawn much attention from both academia and industry. The femtocell-like deployment of VLC in indoor environments gives rise to the concept of optical attocells, where each light-emitting diode (LED) serves as an optical access point (AP), enabling illumination and communication simultaneously. However, the majority of existing optical attocell networks rely on wired backbone links (e.g., Ethernet or power-line connections) for inter-attocell connectivity, and this dependency renders the overall network vulnerable to backbone link failures. To this end, we introduce a novel network architecture that exploits the inherent non-line-of-sight (NLOS) optical channels between adjacent attocells to enable inter-attocell communication, enhancing network resilience and flexibility. In particular, we design a chirp signal based on chirp spread spectrum (CSS) modulation tailored for intensity-modulated VLC to improve the noise resilience in NLOS channels for reliable communication under low signalto-noise ratio (SNR) conditions. Moreover, we propose a twostage window alignment approach that integrates coarse-grained with fine-grained alignment to achieve precise synchronization while reducing real-time decoding latency. Finally, we build two prototypes of NLOS optical attocells based on different hardware platforms and conduct extensive field experiments using three modulation schemes, i.e., on-off keying (OOK), frequency-shift keying (FSK), and CSS, to evaluate their performance under variable parameters. Experimental results indicate that the CSS modulation scheme demonstrates superior robustness than the other two schemes, achieving bit error rates (BER) of 3.1×10-5 and 8.3×10-5 and packet reception rates (PRR) of 98.53% and 99.71% on two platforms, respectively, at a horizontal distance of 8 m between adjacent optical devices.

Channel State Information (CSI), characterizing real-time optical channel conditions, is critical for system optimization in visible light communication (VLC) systems. In VLC networks, CSI acquisition typically relies on uplink feedback mechanisms that require frequent updates due to rapid channel variations. As access point (AP) density increases, this feedback process incurs substantial overhead that scales with network size, becoming a major bottleneck for system scalability. ‌By decoupling static channel characteristics from dynamic user positions‌, this paper proposes a dual-step strategy decoupling offline channel quantization from online uplink positioning. This enables efficient downlink CSI reconstruction via pre-trained lookup tables, bypassing real-time computations. The feedback overhead scales as 1/M of conventional methods in M-AP systems, with experiments showing less than 3% spectrum efficiency loss for precoding-sensitive applications in high speed mobile scenarios. The framework’s adaptability to varying LED densities further supports practical deployment in large-scale networks.‌ This work resolves the CSI acquisition bottleneck, enabling scalable VLC systems with minimal performance degradation.

Visible light communication (VLC) is an emerging wireless technology to support high transmission rate for indoor devices by using existing lighting infrastructure, and VLC-based indoor localization is capable of providing high-accuracy localization. However, current VLC-based localization systems suffer from several key challenges such as sensitivity to random tilting of the receiver, which limits its full potential in real-world applications. In this paper, we design an integrated visible light communication and localization (VLCL) system to simultaneously support accurate real-time localization and communication services for indoor devices. To achieve this, an advanced differential phase difference of arrival (A-DPDOA) localization design is developed to simplify hardware and improve tracking robustness. In addition, a joint adaptive modulation, subcarrier and power allocation scheme is also proposed, which aims to improve the communication data rate and localization accuracy. Extensive experiments are performed to demonstrate that the proposed integrated VLCL system achieves higher localization accuracy and transmission data rate, compared to existing systems and schemes. Experiments also illustrate that the localization algorithm is more robust against the random tilting of the receiver under device movement in two-dimensional and three-dimensional scenarios.

Recently, the coherent optic wireless communication (OWC) has received extensive attention due to its superiority over traditional intensity modulated direct detection (IMDD) systems. Yet, the Doppler effect could be a showstopper for coherent OWC which is sensitive to the frequency offset and spread. Thus we introduce a new modulation scheme, which called orthogonal time frequency space (OTFS) modulation, into the coherent OWC to solve the Doppler problem. OTFS transforms traditional time-varying channel into delay-Doppler (DD) domain, which ensures all transmit symbols experience an almost identical and slowly varying sparse channel. Thus full channel diversity in time and frequency can be obtained when a suitable receiver is used. In addition, most of the research on Doppler effect only consider the random Doppler spread and average frequency shift to the signal, while ignoring the spectral spread caused by the frequency-dependent Doppler shift of a wideband signal such as radar signals and terahertz signals. To accurately model the Doppler effect, it is necessary to calculate the Doppler frequency shift according to the frequency bin of each sub-carrier, thereby the overall frequency offset and spectral spread are included. This manuscript proposes a Doppler channel model based on frequency-domain subband partition (FDSP), and for the first time, this wideband Doppler channel model is applied into wideband OTFS and orthogonal frequency division multiplexing (OFDM) systems. We carried out simulation for OTFS and OFDM under different velocities. Simulation results show that the OTFS can resist high Doppler frequency shift in high mobility scenarios, and in the case of large subcarrier spacing, OTFS is less affected by the Doppler frequency shift of each subcarrier than OFDM.

Optical spatial modulation (OSM) and orthogonal frequency division multiplexing (OFDM) are two promising techniques for bandlimited intensity modulation/direct detection (IM/DD) optical wireless communication (OWC) systems. In this paper, we for the first time propose a novel enhanced OFDM-based OSM scheme for spectral efficiency improvement of ban-dlimited IM/DD OWC systems. The proposed enhanced OFDM-based OSM scheme can be considered as the combination of time-domain OSM (TD-OSM) and non-Hermitian symmetry OFDM (NHS-OFDM). In an OWC system adopting enhanced OFDM-based OSM, a pair of light-emitting diode (LED) transmitters are selected from the LED array, which are used to separately transmit the real and imaginary parts of a complex-valued NHS-OFDM signal. A modified maximum-likelihood (ML) detector is further developed to efficiently estimate the indexes of the LED pair and the real and imaginary parts of the transmitted complex-valued NHS-OFDM signal. We show that the proposed enhanced OFDM-based OSM scheme can achieve substantially improved spectral efficiency with moderate inter-channel interference and low transceiver complexity. Simulation results clearly verify the superiority of the proposed enhanced OFDM-based OSM scheme over the existing OFDM-based OSM schemes.

In recent years, as an evolution of a distributed control system, the industrial Internet-of-Things (IIoT) enables high-degree automation in smart factories or workshops, and full location-awareness of all the interconnected instruments and devices are crucial for realizing IIoT. In this paper, a machine-learning-based indoor visible light positioning (VLP) system for IIoT is proposed and implemented in pursuit of significant reduction of time for offline preparation. An Xgboost-based position estimator using received signal strength (RSS) from light-emitting diode (LED) lighting is implemented. Several other popular machine-learning-based regression algorithms, including deep neural network (DNN), support vector machine (SVM), random forest (RF) are adopted in the position estimator for experimental validation and performance comparison. In the experiment, the receiver was mounted on a smart trolley in a smart workshop, and the two-dimensional positioning performance was studied by placing the smart trolley in several positions within the coverage area. According to the experimental results, the performance of using Xgboost significantly outperforms others in terms of training speed while maintaining comparable positioning accuracy.

In this article, we study the application of unmanned aerial vehicle (UAV) for data collection with wireless charging, which is crucial for providing seamless coverage and improving system performance in the next-generation wireless networks. To this end, we propose a reinforcement learning-based approach to plan the route of UAV to collect sensor data from sensor devices scattered in the physical environment. Specifically, the physical environment is divided into multiple grids, where one spot for UAV hovering as well as the wireless charging of UAV is located at the center of each grid. Each grid has a spot for the UAV to hover, and moreover, there is a wireless charger at the center of each grid, which can provide wireless charging to UAV when it is hovering in the grid. When the UAV lacks energy, it can be charged by the wireless charger at the spot. By taking into account the collected data amount as well as the energy consumption, we formulate the problem of data collection with UAV as a Markov decision problem, and exploit Q-learning to find the optimal policy. In particular, we design the reward function considering the energy efficiency of UAV flight and data collection, based on which Q-table is updated for guiding the route of UAV. Through extensive simulation results, we verify that our proposed reward function can achieve a better performance in terms of the average throughput, delay of data collection, as well as the energy efficiency of UAV, in comparison with the conventional capacity-based reward function.

In this paper, we consider a light fidelity (LiFi)-enabled bidirectional Internet of Things (IoT) communication system, where visible light and infrared light are used in the downlink and uplink, respectively. In order to efficiently improve the energy efficiency (EE) of the bidirectional LiFi-IoT system, non-orthogonal multiple access (NOMA) with a quality-of-service (QoS)-guaranteed optimal power allocation (OPA) strategy is applied to maximize the EE of both downlink and uplink channels. We derive closed-form OPA sets based on the identification of the optimal decoding orders in both downlink and uplink channels, which can enable low-complexity power allocation. Moreover, we propose an adaptive channel and QoS-based user pairing approach by jointly considering users' channel gains and QoS requirements. We further analyze the EE and the user outage probability (UOP) performance of both downlink and uplink channels in the bidirectional LiFi-IoT system. Extensive analytical and simulation results demonstrate the superiority of NOMA with OPA in comparison to orthogonal multiple access (OMA) and NOMA with typical channel-based power allocation strategies. It is also shown that the proposed adaptive channel and QoS-based user pairing approach greatly outperforms individual channel/QoS-based approaches, especially when users have diverse QoS requirements.

Visible light communication (VLC) systems relying on commercial-off-the-shelf (COTS) devices have gathered momentum recently, due to the pervasive adoption of LED lighting and mobile devices. However, the achievable throughput by such practical systems is still several orders below those claimed by controlled experiments with specialized devices. In this paper, we engineer CoLight aiming to boost the data rate of the VLC system purely built upon COTS devices. CoLight adopts COTS LEDs as its transmitter, but it innovates in its simple yet delicate driver circuit wiring an array of LED chips in a combinatorial manner. Consequently, modulated signals can directly drive the on-off procedures of individual chip groups, so that the spatially synthesized light emissions exhibit a varying luminance following exactly the modulation symbols. To obtain a readily usable receiver, CoLight interfaces a COTS PD with a smartphone through the audio jack, and it also has an alternative MCU-driven circuit to emulate a future integration into the phone. The evaluations on CoLight are both promising and informative: they demonstrate a throughput up to 80 kbps at a distance of 2 m, while suggesting various potentials to further enhance the performance.

Utilizing the data caching technology to reduce data transmission is a promising technique for improving the performance of mobile edge computing (MEC), because the delay and energy consumption produced by data transmission constitute the dominant cost of task execution in MEC. Besides, computation tasks generally consist of input parameters, executive codes, and computation results. The executive codes are fixed and can output difference computation results under different input parameters. Motivated by this, we consider to proactively cache executive codes of tasks at the MEC server to reduce the weighted sum of task execution delay and users’ energy consumption. Aiming at establishing optimal system design, we formulate the problem as a non-linear programming problem which involves jointly optimizing the executive code caching strategy, computation offloading decision, wireless resource allocation, and computing resource allocation. We propose to find the optimal solution by employing an alternating optimization framework. The optimal wireless resource and computing resource allocation problem are firstly addressed by utilizing convex optimization technology. Then, a dynamic programming-based algorithm has been developed to achieve the optimal executive code caching and computation offloading strategies. Extensive simulation results show that the proposed scheme operates well and can substantially reduce the system cost over other benchmark schemes.

This paper experimentally demonstrates a new integrated visible light communication and positioning (VLCP) system to support both the communication and positioning services. To maximize the system transmission data rate while meeting different quality-of-service (QoS) requirements of devices (minimum data rate and positioning accuracy constraints), aQoS-driven joint the adaptive modulation, subcarrier allocation and pre-equalization is presented to improve the system performance. The experimental results indicate that the presented integrated VLCP system achieve the higher positioning accuracy than the existing integrated VLCP system, and also verify that the proposed QoS-driven optimized design archives higher data rate, positioning accuracy and QoS satisfied probability, compared with other existing designs.

Internet of Things (IoT) has emerged as one of the key features of the next-generation wireless networks, where timely delivery of status update packets is essential for many real-time IoT applications. Age of Information (AoI) is a new metric to measure the freshness of update. Reduction of the violation probability that AoI of status updates exceeds a given age constraint is of great significance for guaranteeing the data freshness in IoT systems. This work focuses on characterizing the violation probability of AoI in IoT systems where a sensor delivers updates to a monitor under M/M/1 queue with first-come-first-served (FCFS) policy. By exploring the correlation between inter-departure time and system time, the closed-form expression of the violation probability for any AoI constraint is derived. The obtained result induces an accurate characterization of the probability distribution function of AoI. The optimal generation rate of the status update that induces the minimal violation probability is also found. Numerical results show that the optimal update rate can significantly reduce the AoI violation probability for a wide range of AoI constraints.

With the rapid development of the Internet of Things (IoT) in the smart city, smart grid, and smart industry, indoor communication and positioning are important for IoT. However, radio-frequency (RF)-based wireless networks may fail to guarantee different quality-of-service (QoS) requirements of devices, due to the limited bandwidth, severe interference, and multipath reflections. Hence, this article presents a new integrated visible light communication (VLC) and VLC positioning (VLCP) network for IoT to provide both high-speed communication and high-accuracy positioning services. As the network consists of multiple VLC access points (APs), we propose jointly optimizing the AP selection, bandwidth allocation, adaptive modulation, and power allocation approach to satisfy different QoS requirements of indoor devices while maximizing the network data rate. A low-complexity iterative algorithm is presented to solve the resource management (RM) optimization problem by decomposing it into two subproblems. Finally, a robust handover mechanism and a pedestrian dead reckoning (PDR)-assisted VLCP scheme are presented to maintain good performance under line-of-sight (LOS) blockages. The simulation results verify that the proposed solutions outperform other existing solutions in terms of effectively enhancing the data rate, improving the positioning accuracy, and guaranteeing devices' QoS requirements. In detail, the mean position error is reduced from 20 to 4.3 cm by using our presented integrated VLCP model. The proposed RM approach achieves a satisfied QoS level improvement of up to 20.3% compared with the non-QoS-driven RM approach, and it achieves the high data rate up to 1.31 Gb/s.

Internet of Things (IoT) has attracted much interest due to its wide applications, such as smart city, manufacturing, transportation, and healthcare. Social and cognitive IoT is capable of exploiting social networking characteristics to optimize network performance. Considering the fact that the IoT devices have different Quality-of-Service (QoS) requirements [ranging from ultrareliable and low-latency communications (URLLCs) to minimum data rate], this article presents a QoS-driven social-aware-enhanced device-to-device (D2D) communication network model for social and cognitive IoT by utilizing social orientation information. We model the optimization problem as a multiagent reinforcement learning formulation, and a novel coordinated multiagent deep-reinforcement-learning-based resource management approach is proposed to optimize the joint radio block assignment and the transmission power control strategy. Meanwhile, the prioritized experience replay (PER) and the coordinated learning mechanisms are employed to enable communication links to work cooperatively in a distributed manner, which enhances the network performance and access success probability. The simulation results corroborate the superiority in the performance of the presented resource management approach, and it outperforms other existing approaches in terms of meeting the energy efficiency and the QoS requirements.

Smart factory under Industry 4.0 and industrial Internet of Things (IoT) has attracted much attention from both academia and industry. In wireless industrial networks, industrial IoT and IoT devices have different quality-of-service (QoS) requirements, ranging from ultra-reliable low-latency communications (URLLC) to high transmission data rates. These industrial networks will be highly complex and heterogeneous, as well as the spectrum and energy resources are severely limited. Hence, this article presents a heterogeneous radio frequency (RF)/visible light communication (VLC) industrial network architecture to guarantee the different QoS requirements, where RF is capable of offering wide-area coverage and VLC has the ability to provide high transmission data rate. A joint uplink and downlink energy-efficient resource management decision-making problem (network selection, subchannel assignment, and power management) is formulated as a Markov decision process. In addition, a new deep post-decision state (PDS)-based experience replay and transfer (PDS-ERT) reinforcement learning algorithm is proposed to learn the optimal policy. Simulation results corroborate the superiority in performance of the presented heterogeneous network, and verify that the proposed PDS-ERT learning algorithm outperforms other existing algorithms in terms of meeting the energy efficiency and the QoS requirements.

With the rapid development of Internet of Things (IoT) in the smart city, smart grid and smart industry, indoor communication and positioning are important fields of applications for indoor IoT. This paper presents an integrated visible light communication and positioning (VLCP) system for indoor IoT, in order to provide the high-speed data rate and high-accuracy positioning for IoT devices. where the filter bank multicarrier-based subcarrier multiplexing (FBMC-SCM) technique is exploited to effectively reduce the out-of-band interference (OOBI) on both adjacent communication and positioning subcarriers. After that, we propose a coordinated resource allocation approach for the system with the purpose of maximizing the sum rate while guaranteeing the minimum data rates and positioning accuracy requirements of devices. To this end, we solve the optimization problem by decomposing it into two subproblems, where a low-complexity suboptimal subcarrier allocation approach is proposed and the sequential quadratic programming (SQP) method is adopted to solve the non-linearly constrained power allocation optimization problem. Numerical results verify the superiority in performance of the presented integrated VLCP system for indoor IoT, and the results also reveal that the proposed coordinated resource allocation approach can effectively improve the sum rate and the positioning accuracy compared with other resource allocation approaches.

In this paper, we propose a novel non-orthogonal multiple access (NOMA) technique from a spatial domain (SD) perspective for indoor multiple-input multiple-output visible light communication (MIMO- VLC) systems. By fully exploiting the spatial distributions of light-emitting diode (LED) transmitters in the ceiling and users over the receiving plane, SD-NOMA is achieved by assigning all the users to different LEDs in the MIMO-VLC system. Hence, each user only receives data from a specific LED and users assigned to the same LED can use the overall modulation bandwidth of the system. Moreover, a signal-to-noise ratio (SNR) based LED selection scheme is further proposed for each user to efficiently select its desired LED. The achievable rates of a general indoor MIMO-VLC system using conventional MIMO orthogonal frequency division multiple access (MIMO-OFDMA) and the proposed SD-NOMA are analytically derived. The superiority of SD-NOMA over conventional MIMO-OFDMA for multi-user MIMO-VLC systems is successfully verified by detailed analytical results.

In this paper, we firstly propose a joint subcarrier and power allocation approach for multi-user integrated visible light communication and positioning (VLCP) systems in the presence of practical unique optical constraints. The purpose of the proposed resource allocation approach is to maximize the sum rate of users and meanwhile guarantee the different minimum data rates and positioning accuracy requirements of users. Then, we solve the optimization problem by decomposing it into two subproblems, where a low-complexity suboptimal subcarrier allocation approach is proposed and the sequential quadratic programming (SQP) method is adopted to solve the non-linearly constrained power allocation optimization problem. Numerical results show that the proposed resource allocation approach can effectively improve the sum rate and the positioning accuracy of users compared with other resource allocation approaches.

Visible Light Communication (VLC) systems relying on commercial-off-the-shelf (COTS) devices have gathered momentum recently, due to the pervasive adoption of LED lighting and mobile devices. However, the achievable throughput by such practical systems is still several orders below those claimed by controlled experiments with specialized devices. In this paper, we engineer SynLight aiming to significantly improve the data rate of a practical VLC system. SynLight adopts COTS LEDs as its transmitter, but it innovates in its simple yet delicate driver circuit wiring an array of LED chips in a combinatorial manner. Consequently, modulated signals can directly drive the on-off procedures of individual chip groups, so that the spatially synthesized light emissions exhibit a varying luminance following exactly the modulation symbols. To obtain a readily usable receiver, SynLight interfaces a COTS Photo-Diode with a smartphone through the audio jack. The evaluations on SynLight are both promising and informative: they demonstrate a throughput up to 60 kbps, more than 50× of that achieved by state-of-the-art systems, while suggesting various potentials to further enhance the performance.

This paper considers a wireless powered communication network (WPCN) with group cooperation, where two communication groups cooperate with each other via wireless power transfer and time sharing to fulfill their expected information delivering and achieve “win-win” collaboration. To explore the system performance limits, we formulate optimization problems to maximize the weighted sum-rate (WSR) and minimize the total consumed power. The time assignment, beamforming vector and power allocation are jointly optimized under available power and quality of service requirement constraints of both the groups. For the WSR-maximization, both fixed and flexible power scenarios are investigated. As all problems are non-convex and have no known solution methods, we solve them by using proper variable substitutions and the semi-definite relaxation. We theoretically prove that our proposed solution method guarantees the global optimum for each problem. Numerical results are presented to show the system performance behaviors, which provide some useful insights for future WPCN design. It shows that in such a group cooperation-aware WPCN, optimal time assignment has the greatest effect on the system performance than other factors.

Network coding is famous for its capability in significantly improving the throughput of network. The successful decoding of the network coded data relies on some side information of the original data. In that framework, independent data flows are usually decoded first and then network coded by relay nodes. If appropriate signal design is adopted, physical layer network coding is a natural way in wireless networks. In this work, a network coding tree algorithm which enhances the efficiency of the multiple access system (MAS) is presented. For MAS, researchers try to avoid the collisions but collisions happen frequently under heavy load. By introducing network coding into MAS, our proposed algorithm achieves a better trade-off between average delay and system throughput. When multiple users transmit signal in a time slot, the sum signals are saved and used to jointly decode the collided frames after some component frames of the network coded frame are received. Splitting tree structure is extended to our proposed algorithm for collision solving. The system throughput and average delay of frames are presented in a recursive way. Besides, extensive simulations show that network coding tree algorithm enhances the system performance in terms of system throughput and average frame delay compared with other algorithms.