U-shaped Split Federated Learning (U-SFL) has been widely applied in image coding, as it can effectively balance parallel training, model privacy, and local computational cost. The performance of U-SFL largely depends on the selection of split points and the aggregation frequency, and thus, model splitting (MS) and model aggregation (MA) strategies are critical. In UAV scenarios, fluctuating communication links and computational resources can significantly impact U-SFL performance. To address this challenge, we propose a resource-adaptive U-SFL (AU-SFL) framework, which adaptively selects optimal MS and MA strategies based on the available computational and communication resources. Specifically, we first conduct a theoretical convergence analysis that systematically quantifies the individual and joint impacts of MS and MA strategies on convergence behavior. Then, we formulate an optimization problem aimed at minimizing training latency, grounded in the theoretical convergence analysis. Subsequently, an alternating MS-MA algorithm is proposed to solve the problem, which is decomposed into two subproblems and solved alternatively. Extensive experiments on multiple benchmark datasets demonstrate that AU-SFL achieves a 1.61 times reduction in convergence time while improving multi-scale structural similarity index (MS-SSIM) by 4.4%, conclusively validating our adaptive optimization strategy’s effectiveness.

Camera-based visible light positioning (VLP) is a promising technique for accurate and low-cost indoor camera pose estimation (CPE). To reduce the number of required light-emitting diodes (LEDs), advanced methods commonly exploit LED shape features for positioning. Although interesting, they are typically restricted to a single LED geometry, leading to failure in heterogeneous LED-shape scenarios. To address this challenge, this paper investigates Lamé curves as a unified representation of common LED shapes and proposes a generic VLP algorithm using Lamé curve-shaped LEDs, termed LC-VLP. In the considered system, multiple ceiling-mounted Lamé curve-shaped LEDs periodically broadcast their curve parameters via visible light communication, which are captured by a camera-equipped receiver. Based on the received LED images and curve parameters, the receiver can estimate the camera pose using LC-VLP. Specifically, an LED database is constructed offline to store the curve parameters, while online positioning is formulated as a nonlinear least-squares problem and solved iteratively. To provide a reliable initialization, a correspondence-free perspective-n-points (FreePnP) algorithm is further developed, enabling approximate CPE without any pre-calibrated reference points. The performance of LC-VLP is verified by both simulations and experiments. Simulations show that LC-VLP outperforms state-of-the-art methods in both circular- and rectangular-LED scenarios. Compared to a perspective arcs algorithm, LC-VLP can achieve reductions of both over 30% in average position and rotation errors. Experiments further show that LC-VLP can achieve an average position accuracy of less than 4 cm.

The utilization of urban underground spaces poses new challenges for underground Internet of Things (IoT), such as energy constraint of sensor nodes (SNs) and channel blockage caused by complex spatial structures. Existing approaches rely on centralized access points for wireless energy transfer (WET), suffering from severe energy loss over long distances. Besides, time duplex energy-data transmission is inefficient. To address these issues, we propose a Reconfigurable Intelligent Surface (RIS)-assisted frequency division duplex (FDD) underground IoT architecture, which facilitates concurrent WET to SNs and uplink heterogeneous data transmission across different frequency bands. To minimize the weighted sum of energy consumption and trans-mission pressure—a metric designed to characterize transmission-task execution situations, we formulate an optimization problem that jointly coordinates the time-frequency-energy resource blocks and RIS phase shifts under heterogeneous Quality of Service (QoS) requirements. Then, we design a parameter-sharing Multi-Agent Proximal Policy Optimization (PS-MAPPO) algorithm which stably converging to the optimal solution of the proposed problem while reducing the complexity of the network. Simulation results demonstrate that the proposed method is superior in energy saving, latency guarantee and improving the task completion rate compared to existing methods.

Large Language Models (LLMs) have the potential to greatly enhance the performance of task-oriented semantic communication (TOSC) through their extensive knowledge. However, hallucinations from LLMs may cause semantic noise, thus degrading task performance. To address this issue, we propose a TOSC scheme with an LLM-enabled knowledge base (TOSCLKB). In the considered system, the transmitter transmits the features of the source over a wireless channel to accomplish downstream tasks at the receiver. Meanwhile, the transmitter utilizes the LLM-enabled Knowledge Base (KB) to provide additional data for downstream tasks. To effectively exploit the extensive knowledge of LLMs while mitigate its hallucination, a cross-domain fusion codec framework with a hallucination filtering phase and a cross-domain fusion phase is proposed. In particular, the first phase filters out data irrelevant to the source generated by the LLM-enabled KB based on semantic similarity. Then, a cross-domain fusion phase is proposed which fuses source data with LLM-generated data based on their semantic importance, thereby enhancing task performance. Experiment results on the text-based person retrieval task demonstrate that the proposed TOSC-LKB can achieve up to 26.7 % and 7.1 % performance gains without introducing additional time overhead compared to DeepSC and TOSC-LKB without LLMs.

Deep joint source-channel coding (JSCC) with hybrid automatic repeat request (HARQ) for image transmission has attracted increasing attention due to its flexibility and high efficiency. Existing researches mainly focus on minimizing the distortion of mutiple retransmissions while ignoring the redundancy in retransmitted signal and such redundancy may lead bandwidth waste and reconstruction quality degradation. In this paper, we propose an information bottleneck (IB) guided deep JSCC with HARQ system (HARQ-IBJSC), which aims at improving the reconstruction quality by compressing the redundancy in retransmitted signal. In particular, we first design a new IB objective for deep JSCC with HARQ system, which simultaneously reduces redundancy in the retransmitted signal and minimizes image transmission distortion. Since the mutual information terms in the designed IB objective is intractable, we then derive a differentiable lower bound on the IB objective and use the bound as the loss function of HARQ-IBJSC. Experimental results show that the proposed HARQ-IBJSC system can increase PSNR by up to 1 dB.

Quick response (QR) code is one of the most popular beacons for indoor positioning. Existing QR code-based algorithms typically require several QR codes for precise positioning, which may need intense QR code deployment or have limited coverage. This paper proposes a visual positioning algorithm based on feature points and triangular structures on a QR code (V-FTQ), which can achieve precise position and orientation estimation using only a monocular camera and a single QR code beacon. In the considered system, a few QR codes are placed on the ceiling, and a user holds a camera in hand to capture the QR code for positioning. In particular, we first propose an efficient matching mechanism to match the feature points of the captured QR beacon to their projections in the image plane of the camera. Then, based on the geometry relations between the feature points and their projections, V-FTQ can calculate the coordinates of the feature points in the camera coordinate. Finally, the orientation and position of the user are estimated based on linear algebra and spatial geometry. Moreover, since V-FTQ may have a few ambiguous solutions, a singular solution elimination scheme is further proposed based on plane geometry and linear programming, which can effectively eliminate the incorrect solutions. Both simulation and experimental results demonstrate that V-FTQ can achieve a positioning accuracy within 10 cm with only one captured beacon, outperforming baseline algorithms in terms of precision and robustness to noise. Note to Practitioners—Existing positioning algorithms typically require multiple beacons for accurate positioning, which may limit its coverage or require dense beacon deployment. The paper proposes a novel positioning algorithm that achieves 3D orientation and position estimation with only a single beacon and a single camera. We applied the proposed method to the mobile phone and developed an APP for actual positioning tests. Experimental results verify that the positioning scheme reduces both the number of beacons required and the cost of the positioning system, making it suitable for the automatic navigation of robots and other applications in industrial automation.

In this paper, a multi-modal data based semi-supervised learning (SSL) framework that jointly use channel state information (CSI) data and RGB images for vehicle positioning is designed. In particular, an outdoor positioning system where the vehicle locations are determined by a base station (BS) is considered. The BS equipped with several cameras can collect a large amount of unlabeled CSI data and a small number of labeled CSI data of vehicles, and the images taken by cameras. Although the collected images contain partial information of vehicles (i.e. azimuth angles of vehicles), the relationship between the unlabeled CSI data and its azimuth angle, and the distances between the BS and the vehicles captured by images are both unknown. Therefore, the images cannot be directly used as the labels of unlabeled CSI data to train a positioning model. To exploit unlabeled CSI data and images, a SSL framework that consists of a pretraining stage and a downstream training stage is proposed. In the pretraining stage, the azimuth angles obtained from the images are considered as the labels of unlabeled CSI data to pretrain the positioning model. In the downstream training stage, a small sized labeled dataset in which the accurate vehicle positions are considered as labels is used to retrain the model. Simulation results show that the proposed method can reduce the positioning error by up to 30% compared to a baseline where the model is not pretrained.

Information leakage through wireless channels poses a significant security concern within contemporary cellular networks, such as 5G new radio (NR). Among the myriad of potential attack vectors, passive traffic analysis (PTA) stands out as a pervasive and surreptitious threat, which allows attackers to discern the specific services utilized by unsuspecting victims without their noticing. In this work, we present a pioneering approach to achieve fine-grained service identification by adopting an unexplored perspective: mapping traffic transmission patterns to physical layer time-frequency occupancy patterns, which we refer to as Passive Time-Frequency Traffic (PTTF). Additionally, it selects the uplink control channel that carries the acknowledgment/negative acknowledgment (ACK/NACK) feedback within the Hybrid Automatic Repeat reQuest (HARQ) process as the data source. Statistical features of ACK/NACK time-frequency resources are extracted for traffic classification, and activities are recognized from a three-tier classification algorithm. For validation, we conduct field experiments targeting commercialized smartphones within the real-world operator’s network. This setup effectively mirrors practical scenarios, as the resources within the target frequency band can also be allocated to other equipment in the operator’s network. Furthermore, cross-validation experiments involving different smartphone brands and various network formats are conducted in order to ascertain the generalizability of the proposed PTTF.

Large scale artificial intelligence (AI) models possess excellent capabilities in semantic representation and understanding, making them particularly well-suited for semantic encoding and decoding. However, the substantial scale of these AI models imposes unacceptable computational resources and communication delays. To address this issue, we propose a semantic communication scheme based on robust knowledge distillation (RKD-SC) for large scale model enabled semantic communications. In the considered system, a transmitter extracts the features of the source image for robust transmission and accurate image classification at the receiver. To effectively utilize the superior capability of large scale model while make the cost affordable, we first transfer knowledge from a large scale model to a smaller scale model to serve as the semantic encoder. Then, to enhance the robustness of the system against channel noise, we propose a channel-aware autoencoder (CAA) based on the Transformer architecture. Experimental results show that the encoder of proposed RKD-SC system can achieve over 93.3% of the performance of a large scale model while compressing 96.67% number of parameters. Code: https://github.com/echojayne/RKD-SC.

Task-oriented communication exploits the task to improve communication efficiency. Most existing works on task-oriented communication transmit analog signals without quantization, which limits its application in digital communication systems. This paper studies digital task-oriented communication systems with hardware-limited scalar quantization (TOC-SQ) for computation-limited scenarios such as the Internet of Things, where the source data is encoded by a source encoder and then quantized by hardware-limited quantizers for digital transmission. Accordingly, the receiver contains a source decoder to decode the received signal. Our goal is to minimize the mean squared error (MSE) between the transmitted and received task-relevant signals to achieve optimal task performance under a certain bit budget. In particular, we first establish a theoretical analysis framework for TOC-SQ. Then, the closed-form expressions of the source encoder and decoder are derived for linear tasks. Finally, the lower bound of the MSE between the transmitted and received task-relevant information is analyzed to evaluate the task performance. Simulation results verify that the proposed TOC-SQ achieves 6.9 dB MSE gains in frequency-selective channels compared with analog TOC systems.

Current privacy-aware joint source-channel coding (JSCC) works aim at avoiding private information transmission by adversarially training the JSCC encoder and decoder under specific signal-to-noise ratios (SNRs) of eavesdroppers. However, these approaches incur additional computational and storage requirements as multiple neural networks must be trained for various eavesdroppers’ SNRs to determine the transmitted information. To overcome this challenge, we propose a novel privacy-aware JSCC for image transmission based on disentangled information bottleneck (DIB-PAJSCC). In particular, we derive a novel disentangled information bottleneck objective to disentangle private and public information. Given the separate information, the transmitter can transmit only public information to the receiver while minimizing reconstruction distortion. Since DIB-PAJSCC transmits only public information regardless of the eavesdroppers’ SNRs, it can eliminate additional training adapted to eavesdroppers’ SNRs. Experimental results show that DIB-PAJSCC can reduce the eavesdropping accuracy on private information by up to 20% compared to existing methods.

Task-oriented communication is a new paradigm that aims at providing efficient connectivity for accomplishing intelligent tasks rather than the reception of every transmitted bit. This paper proposes a deep learning-based task-oriented communication architecture for end-to-end (E2E) semantics transmission, where extracted semantics is compressed by the proposed adaptable semantic compression (ASC) method. However, accommodating multiple users in a delay-intolerant system poses a challenge. Higher compression ratios conserve channel re-sources but cause semantic distortion, while lower ratios demand more resources and may lead to transmission failure due to delay constraints. To address this, we optimize both compression ratio and resource allocation to maximize task success probability. Specifically, due to the nonconvexity of the problem, we propose a compression ratio and resource allocation (CRRA) algorithm that separates the problem into two subproblems and solving them iteratively. Simulation results show that the proposed algorithm can obtain at least 14.3% success gains over baseline algorithms.

Task-oriented semantic communication has gained increasing attention due to its ability to reduce the amount of transmitted data without sacrificing task performance. Although some prior efforts have been dedicated to developing semantic communications, the semantics in these works remains to be unexplainable. Challenges related to explainable semantic representation and knowledge-based semantic compression have yet to be explored. In this article, we propose a triplet-based explainable semantic communication (TESC) scheme for representing text semantics efficiently. Specifically, we develop a semantic extraction method to convert text into triplets while using syntactic dependency analysis to enhance semantic completeness. Then, we design a semantic filtering method to further compress the duplicate and task-irrelevant triplets based on prior knowledge. The filtered triplets are encoded and transmitted to the receiver for completing intelligent tasks. Furthermore, we apply the proposed TESC scheme to two emblematic text tasks: 1) sentiment analysis and 2) question answering, in which the semantic codec is meticulously customized for each task. Experimental results demonstrate that 1) the TESC scheme outperforms benchmarks in terms of Top-1 accuracy and transmission efficiency and 2) the TESC scheme enjoys about 150% performance gain compared to the traditional communication method.

Semantic communication, as a new paradigm for next-generation communication, aims to transmit semantic symbols for artificial intelligence (AI) tasks. Existing research typically requires extracting and transmitting specialized semantics for each AI task when multiple target AI tasks exist. Considering that each AI task may share common semantics, this article proposes a joint source–channel coding scheme for a multitask semantic communication (MTSC) system, which can extract the common semantics required by multiple AI tasks, and thus reduce the overall semantic transmission. To this end, we first formulate the MTSC problem as a rate–distortion problem that simultaneously considers the rate of extracted semantics and the distortion of multiple AI tasks. Then, we derive a new form of rate–distortion, called extended rate–distortion, which can guide the compact semantics extraction of multiple AI tasks simultaneously. Additionally, we derive a self-consistent equation for this extended rate–distortion form, theoretically proving the effectiveness of this approach. To ensure proper tradeoff between the rates and distortions of multiple AI tasks, we further propose a rate adjustment module that can dynamically adjust the rate according to channel conditions. We validate our experimental results on multiple data sets, which show that the proposed method can reduce transmission overhead by 40%–50% and achieve a 7.6% improvement in multitask performance.

Positioning and sensing have long been an important area of research. Recently, this field has attracted more attention due to the rapid deployment of emerging applications and next-generation communication networks. On the one hand, emerging applications like extended reality (XR) and autonomous vehicle systems need to precisely “see” the physical world, thus greatly increasing the demands on positioning and sensing technologies. Moreover, these applications also require data rate communication links, and thus technologies like cellular networks and WiFi are excellent for supporting these applications. On the other hand, with the evolution of wireless networks, positioning, and sensing have also been considered important functions of future wireless networks that can further enhance communication performance. Although existing wireless communication has achieved significant success in the past several decades, achieving satisfying positioning and sensing performance for these emerging applications remains a challenge due to the complexity of the wireless environment and the stringent performance requirements.

Positioning has recently received considerable attention as a key enabler in emerging applications such as extended reality, unmanned aerial vehicles, and smart environments. These applications require both data communication and high-precision positioning, and thus they are particularly well-suited to be offered in wireless networks (WNs). The purpose of this paper is to provide a comprehensive overview of existing works and new trends in the field of positioning techniques from both academic and standard perspectives. The paper provides a comprehensive overview of indoor positioning in WNs, covering the background, applications, measurements, state-of-the-art technologies, and future challenges. The paper outlines the applications of positioning from the perspectives of public facilities, enterprises, and individual users. We investigate the key performance indicators and measurements of positioning systems, followed by the review of the key enabler techniques such as artificial intelligence/large models and adaptive systems. Next, we discuss a number of typical wireless positioning technologies. We extend our overview beyond the academic progress, to include the standardization efforts, and finally, we provide insight into the challenges that remain. The comprehensive overview of existing efforts and new trends in the field of indoor positioning from both academic and standardization perspectives would be a useful reference to researchers in the field.

This is Part II of the double-part Special Issue (SI) on Positioning and Sensing Over Wireless Networks. The two-part SI aims to bring cutting-edge and novel contributions on positioning and sensing over wireless networks for future and emerging applications. The accepted 51 papers are arranged into eight groups: 1) fundamental performance analysis and optimization; 2) positioning and sensing with cellular networks; 3) positioning and sensing with WiFi networks; 4) positioning and sensing with emerging communication technologies; 5) positioning and sensing applications; 6) cooperative positioning and sensing; 7) reconfigurable intelligent surfaces (RIS)-assisted positioning and sensing; and 8) privacy and security. The contributions made by the papers in Part II are summarized as follows, which correspond to the last four paper groups.

Semantic communication (SemCom) has received considerable attention for its ability to reduce data transmission size while maintaining task performance. However, existing works mainly focus on analog SemCom with simple channel models, which may limit its practical application. To reduce this gap, we propose an orthogonal frequency division multiplexing (OFDM)-based SemCom system that is compatible with existing digital communication infrastructures. In the considered system, the extracted semantics is quantized by scalar quantizers, transformed into OFDM signal, and then transmitted over the frequency-selective channel. Moreover, we propose a semantic importance measurement method to build the relationship between target task and semantic features. Based on semantic importance, we formulate a sub-carrier and bit allocation problem to maximize communication performance. However, the optimization objective function cannot be accurately characterized using a mathematical expression due to the neural network-based semantic codec. Given the complex nature of the problem, we first propose a low-complexity sub-carrier allocation method that assigns sub-carriers with better channel conditions to more critical semantics. Then, we propose a deep reinforcement learning-based bit allocation algorithm with dynamic action space. Simulation results demonstrate that the proposed system achieves 9.7% and 28.7% performance gains compared to analog SemCom and conventional bit-based communication systems, respectively.

Joint source and channel coding (JSCC) has attracted increasing attention in semantic communications. However, JSCC is vulnerable to privacy issues due to the high relevance between the source image and channel input. In this paper, we propose a disentangled information bottleneck guided privacy-protective JSCC (DPJSCC) for image transmission, which aims at protecting private information and achieving superior image transmission performance. In particular, we propose a disentangled information bottleneck objective to compress the private information in public subcodewords and improve the reconstruction quality simultaneously. To optimize JSCC neural networks using the proposed objective, we derive a differentiable estimation based on variational approximation and the density-ratio trick. Additionally, we design a password-based privacy-protective algorithm that encrypts the private subcodewords, achieving joint optimization with JSCC neural networks. The proposed algorithm involves an encryptor for encrypting private information and a decryptor for recovering it at the legitimate receiver. A loss function is derived based on the maximum entropy principle for jointly training the encryptor, decryptor, and JSCC decoder to maximize eavesdropping uncertainty and improve reconstruction quality. Experimental results show that DPJSCC reduces eavesdropping accuracy on private information by up to 18% and decreases inference time by 10%.

Visible light positioning (VLP) is an accurate and low-cost positioning technique. However, existing VLP algorithms require multiple luminaires or multiple sensors to achieve the desired positioning accuracy, which may not be satisfied in practice. To circumvent this challenge, a novel visual odometry (VO) assisted VLP algorithm (VO-VLP) is proposed, which can achieve accurate positioning using only a single luminaire at the transmitter and a single camera at the receiver. In the considered model, the luminaires are equipped on the ceiling and consistently broadcast coordinate information of the luminaires by visible light communication (VLC). A user equipped with a camera captures photos of the ceiling so as to locate its position via VO-VLP. In particular, VO-VLP first uses the single luminaire’s circle feature and VLC information to obtain the pose and location of the user. However, there are dual solutions due to the limited received information in the single-luminaire scenario and the symmetry of the circular luminaire. Then, we propose a duality elimination method to eliminate the wrong one by introducing VO to exploit the visual features on the ceiling in two consecutive images, which are captured when the user moves. To verify the feasibility of our designed VO-VLP, a prototype is implemented. A cooperative multi-information image processing method is proposed for the prototype to ensure that the VLC information and the visual information of the luminaire and the ceiling can be simultaneously received for real-time positioning. Simulations and experiments are conducted to prove that VO-VLP can achieve accurate positioning with only a single luminaire and a camera without any extra sensors, such as an inertial measurement unit. In particular, simulation results show that the proposed indoor positioning algorithm can achieve a 97% positioning accuracy of around 10 cm, and experimental results show that the average positioning accuracy is less than 10 cm.

Visible light positioning (VLP) is a promising positioning technique, which, however, typically requires multiple luminaires to achieve accurate positioning. This paper proposes a novel visual odometry (VO) assisted visible light positioning algorithm (VO-VLP) in achieving positioning with only a single luminaire. In the considered model, a user equipped with a camera jointly uses geometric features in the captured images and coordinates information obtained via visible light communication (VLC) for positioning. The proposed VLP algorithm does not rely on any extra inertial measurement unit and relaxes the tilted angle limitation at the user. In particular, VO-VLP first uses the circle feature of a luminaire to obtain dual normal vectors of the luminaire. Then, the basic principle of VO is used to eliminate the wrong normal vector by exploiting the geometric features in two consecutive images captured when the user moves. Finally, the pose and location of the user are obtained by using an artificially marked point on the luminaire's contour. VO-VLP can achieve accurate positioning with only a single luminaire and a camera. Simulation results show that the proposed indoor positioning algorithm can achieve a 97th-percentile positioning accuracy of around 10 cm.

Current image coding methods for semantic communication typically concentrate on intelligent tasks or image reconstruction separately, and seldom consider both aspects simultaneously. To balance these two aspects during wireless image transmission, we propose a joint source-channel coding method based on the semantics of pixels (SP), which can retain both pixel information for reconstruction and semantic information for intelligent tasks. Specifically, we first design a gradient-based mechanism to quantify the semantic importance of downstream intelligent tasks on pixels. Then, we design the SP-based loss function to train the deep joint source-channel coding network. Experiment results demonstrate that the proposed method maintains reconstruction performance and improves the task performance by 1.61% and 4.06%, respectively, compared to the state-of-the-art deep joint source-channel coding method and traditional separate source-channel coding method at the same transmission rate and signal-to-noise ratio.

Joint source and channel coding (JSCC) for image transmission has attracted increasing attention due to its robustness and high efficiency. However, the existing deep JSCC research mainly focuses on minimizing the distortion between the transmitted and received information under a fixed number of available channels. Therefore, the transmitted rate may be far more than its required minimum value. In this paper, an adaptive information bottleneck (IB) guided joint source and channel coding (AIB-JSCC) method is proposed for image transmission. The goal of AIB-JSCC is to reduce the transmission rate while improving the image reconstruction quality. In particular, a new IB objective for image transmission is proposed so as to minimize the distortion and the transmission rate. A mathematically tractable lower bound on the proposed objective is derived, and then, adopted as the loss function of AIB-JSCC. To trade off compression and reconstruction quality, an adaptive algorithm is proposed to adjust the hyperparameter of the proposed loss function dynamically according to the distortion during the training. Experimental results show that AIB-JSCC can significantly reduce the required amount of transmitted data and improve the reconstruction quality and downstream task accuracy.

Densely deployed light emitting diodes (LEDs) typically lead to high spatial correlation in multiple-input multiple-output (MIMO) visible light communication (VLC) systems. Although precoding can effectively alleviate the spatial correlation issue, most existing precoding algorithms require a dedicated baseband chain for each LED, leading to high energy consumption and hardware complexity when a large number of LEDs are used. In this paper, a successive interference cancellation (SIC)-based precoding scheme with sub-connected architecture (SIC-SA) is proposed. In the considered model, each baseband chain is connected to an LED sub-array containing multiple LEDs to reduce the complexity. Since, in this case, SIC-based precoding can only determine the signal of each baseband chain for an LED sub-array, while its target is to mitigate the spatial correlation between individual LEDs, the electrical/optical power of each LED must also be jointly optimized to accomplish the target. This joint SIC-based precoding, power allocation, and direct current offset design problem is formulated as an achievable sum rate maximization problem under dimming control and electrical power constraints. A two-step iterative algorithm is proposed to solve this problem. In the first step, the SIC-based precoding is designed to alleviate the multi-user interference. In the second step, the power allocation of LEDs is optimized by matrix decomposition and convex optimization, and a closed-form solution of DC offset is derived. Furthermore, considering the dynamic scenarios, a SIC-based precoding scheme with dynamic sub-connected architecture (SIC-DSA) is proposed, in which a switch network is used to adaptively adjust the LED sub-array structure based on the channel state information. Simulation results show that the proposed SIC-SA and SIC-DSA respectively achieve 0.1340 bps/Hz/W and 0.1305 bps/Hz/W energy efficiency gains over the zero-forcing precoding scheme with SA, when the signal-to-noise ratio is 30 dB.

Visible light positioning (VLP) is an accurate indoor positioning technology that uses luminaires as transmitters. In particular, circular luminaires are a common source type for VLP, which are typically treated only as point sources for positioning, while ignoring their geometry characteristics. In this paper, the arc feature of the circular luminaire and the coordinate information obtained via visible light communication (VLC) are jointly used for positioning, and a novel perspective arcs approach is proposed for VLC-enabled indoor positioning. The proposed approach does not rely on any inertial measurement unit and has no tilted angle limitation at the user. First, a VLC assisted perspective circle and arc algorithm (V-PCA) is proposed for a scenario in which a complete luminaire and an incomplete one can be captured by the user. Based on plane and solid geometry theory, the relationship between the luminaire and the user is exploited to estimate the orientation and the coordinate of the luminaire in the camera coordinate system. Then, the pose and location of the user in the world coordinate system are obtained by single-view geometry theory. Considering the cases in which parts of VLC links are blocked, an anti-occlusion VLC assisted perspective arcs algorithm (OA-V-PA) is proposed. In OA-V-PA, an approximation method is developed to estimate the projection of the luminaire’s center on the image and, then, to calculate the pose and location of the user. Simulation results show that the proposed indoor positioning algorithm can achieve a 90th percentile positioning accuracy of around 10 cm. Moreover, an experimental prototype is implemented to verify the feasibility. In the established prototype, a fused image processing method is proposed to simultaneously obtain the VLC information and the geometric information. Experimental results in the established prototype show that the average positioning accuracy is less than 5 cm for different tilted angles of the user.

High spatial correlation is one of the main factors limiting the communication performance of multiple-input multiple-output (MIMO) visible light communication (VLC), which can be alleviate by precoding. However, most existing precoding algorithms require a dedicated baseband chain for each light emitting diode (LED), which may result in high energy consumption and hardware complexity, especially when LEDs are densely deployed. To solve this issue, a successive interference cancellation-based precoding scheme with sub-connected architecture (SIC-SA) is proposed, where each baseband chain is connected to an LED sub-array. In this case, since the SIC-based precoding can only determine the signal of each baseband chain for an LED sub-array, the electrical/optical power of each LED must be jointly optimized to alleviate the spatial correlation among individual LEDs. This joint SIC-based precoding, power allocation and direct current offset design problem is formulated as an achievable sum rate maximization problem under dimming control and electrical power constraints. To solve this problem, the original problem is separated into two subproblems. In the first subproblem, the SIC-based precoding is calculated by successive null space computation. With the obtained SIC-based precoding, the second subproblem optimizes the power allocation and direct-current offset. Finally, these two subproblems are iteratively solved to obtain a convergent solution. Simulation results demonstrate that the proposed SIC-SA achieves 0.1306 bps/Hz/W and 0.1340 bps/Hz/W improvements in terms of energy efficiency compared to minimum mean square error precoding with fully-connected architecture (FA) and zero-forcing precoding with FA, respectively, when the signal-to-noise ratio is 30 dB.

In multi-cell multi-user multiple-input multiple-output (MC MU-MIMO) visible light communication (VLC) systems, each user is equipped with multiple closely-placed photodiodes (PDs) with similar channel gains, leading to severe inter-cell interference and intra-cell interference. To address this problem, this paper proposes a hybrid dimming (HD) scheme with MIMO VLC transceiver design, which jointly optimizes transmit and receive antenna selection (TRAS), cell clustering and precoding (TRASP-HD) for MC MU-MIMO VLC systems. In this scheme, a sum-rate maximization problem under the dimming level and illumination uniformity is formulated and solved by being divided into two sub-problems. In particular, The first sub-problem is on TRAS and cell formation based on the criterion of sum-rate maximization under the illumination uniformity constraint. With the same goal, the second sub-problem is on optimizing the precoding matrices of each cell. Finally, these two sub-problems are iteratively solved to obtain a convergent solution. Simulation results verify that in a typical indoor scenario, the mean bandwidth efficiency of TRASP-HD scheme is 2.36 bit/s/Hz higher than the conventional MC MU-MIMO system.

This paper proposes a visible light communication (VLC) assisted perspective circle and arc algorithm (V-PCA) for indoor positioning. In the considered model, the coordinate information obtained via VLC and the geometric information of circle feature are jointly used for indoor positioning. In particular, V-PCA first designs a space-time coding model based on VLC, which can help determine the center and a mark point of the circular luminaire on the image plane. It also transmits the world coordinate information of the luminaire to the camera through the activated LEDs. Then, based on the plane and solid geometry, the geometric information of circular feature is exploited to estimate the orientation and the coordinate of the luminaire in the camera coordinate system. Finally, the pose and the location of the camera in the world coordinate system are obtained by the single-view geometry. V-PCA can achieve accurate positioning without tilting angle limitation and inertial measurement unit for the receiver. Simulation results show that the proposed indoor positioning algorithm can achieve a 95th percentile positioning accuracy of around 10 cm.

The sixth-generation mobile communication system proposes the vision of smart interconnection of everything, which requires accomplishing communication tasks while ensuring the performance of intelligent tasks. A joint source-channel coding method based on semantic importance is proposed, which aims at preserving semantic information during wireless image transmission and thereby boosting the performance of intelligent tasks for images at the receiver. Specifically, we first propose semantic importance weight calculation method, which is based on the gradient of intelligent task’s perception results with respect to the features. Then, we design the semantic loss function in the way of using semantic weights to weight the features. Finally, we train the deep joint source-channel coding network using the semantic loss function. Experiment results demonstrate that the proposed method achieves up to 57.7% and 9.1% improvement in terms of intelligent task’s performance compared with the source-channel separation coding method and the deep source-channel joint coding method without considering semantics at the same compression rate and signal-to-noise ratio, respectively.

This article proposes a hybrid dimming (HD) scheme based on joint light-emitting diode (LED) selection and precoding design (TASP-HD) for multiple-user (MU) multiple-cell (MC) visible light communications (VLCs) systems. In TASP-HD, both the LED selection and the precoding of each cell can be dynamically adjusted to reduce the intra- and inter-cell interferences while satisfying illumination constraints. First, an MU-MC-VLC system model is established, and then a sum-rate maximization problem under the dimming level and illumination uniformity constraints is formulated. In this studied problem, the indices of activated LEDs and precoding matrices are optimized, which result in a complex nonconvex mixed-integer problem. To solve this problem, the original problem is separated into two subproblems. The first subproblem, which maximizes the sum rate of users via optimizing the LED selection with a given precoding matrix, is a mixed-integer problem solved by the penalty method. With the optimized LED selection matrix, the second subproblem which focuses on the maximization of the sum-rate via optimizing the precoding matrix is solved by the Lagrangian dual method. Finally, these two subproblems are iteratively solved to obtain a convergent solution. Simulation results verify that in a typical indoor scenario under a dimming level of 70%, the mean bandwidth efficiency (MBE) of TASP-HD is 4.8 bit/s/Hz and 7.13 bit/s/Hz greater than analog dimming (AD) and digital dimming (DD), respectively.

Visible light positioning and computer vision-based localization have the potential to be cost-effective technologies for accurate indoor localization. However, the feasibility of existing methods in this domain is limited. In this paper, a novel visible light communication (VLC)-assisted perspective-four-line algorithm (V-P4L) is proposed for practical indoor localization. The basic idea of V-P4L is to jointly use VLC and computer vision techniques to achieve high localization accuracy regardless of LED height differences. In particular, the space-domain information is first exploited to estimate the orientation and coordinate information of a single rectangular LED luminaire in the camera coordinate system based on plane geometry theory and solid geometry theory. Then, by using time-domain information transmitted by VLC and the estimated luminaire information, the proposed V-P4L can estimate the position and pose of the camera using single-view geometry theory and the linear least square (LLS) method. To further mitigate the effect of height differences among LEDs on localization accuracy, a correction algorithm based on the LLS method and a simple optimization method is proposed. Due to the combination of time- and space-domain information, V-P4L can achieve accurate localization using a single luminaire without limitation on the correspondences between the features and their projections in conventional perspective-n-line (PnL) algorithms. Simulation results show that the position error caused by the proposed V-P4L algorithm is always less than 15 cm and the orientation error is always less than 4° using popular indoor luminaires. Experimental results with real hardware show that the average position error is less than 3 cm under both similar and different heights for the LEDs.

Limited radio frequency (RF) resources restrict the number of users that can participate in federated learning (FL) thus affecting FL convergence speed and performance. In this paper, we first introduce visible light communication (VLC) as a supplement to RF in FL and build a hybrid VLC/RF communication system, in which each indoor user can use both VLC and RF to transmit its FL model parameters. Then, the problem of user selection and bandwidth allocation is studied for FL implemented over a hybrid VLC/RF system aiming to optimize the FL performance. The problem is first separated into two subproblems. The first subproblem is a user selection problem with a given bandwidth allocation, which is solved by a traversal algorithm. The second subproblem is a bandwidth allocation problem with a given user selection, which is solved by a numerical method. The final user selection and bandwidth allocation are obtained by iteratively solving these two subproblems. Simulation results show that the proposed FL algorithm that efficiently uses VLC and RF for FL model transmission can improve the prediction accuracy by up to 10% compared with a conventional FL system using only RF.

A novel dimming control scheme, termed as generalized dimming control (GDC), is proposed for visible light communication (VLC) systems. The basic idea of GDC is to exploit adaptively time, spatial and signal domain resources for enhancing communication performance under practical illumination constraints. In particular, to satisfy the illumination constraints, an incremental algorithm for index mapping is first proposed to achieve target optical power and uniform illumination. Next, GDC having the optimal activation pattern is investigated to improve the bit-error rate (BER) performance of VLC. In particular, the BER performance of GDC is analyzed using the union bound technique. Based on the analytical BER bound, the optimal activation pattern of GDC scheme having the minimum BER criterion (GDC-MBER) is obtained by exhaustively searching all conditional pairwise error probabilities. However, since GDC-MBER requires high search complexity, two low-complexity GDC schemes having the maximum free distance criterion (GDC-MFD) are proposed. The first low-complexity GDC-MFD scheme, termed as GDC-MFD1, is derived by a lower bound of the free distance using the Rayleigh-Ritz theorem. The second low-complexity GDC-MFD scheme, termed as GDC-MFD2, is proposed to reduce further the computation by exploiting the time-invariance characteristics of the VLC channel. Simulation and numerical results show that GDC-MBER, GDC-MFD1 and GDC-MFD2 have similar BER performance, and they can achieve more than 3 dB SNR gains compared with conventional dimming control schemes for a BER of 10-4and a dimming level of 50%.

A high-coverage algorithm termed enhanced camera assisted received signal strength ratio (eCA-RSSR) positioning algorithm is proposed for visible light positioning (VLP) systems. The basic idea of eCA-RSSR is to utilize visual information captured by the camera to estimate first the incidence angles of visible lights. Based on the incidence angles, eCA-RSSR utilizes the received signal strength ratio (RSSR) calculated by the photodiode (PD) to estimate the ratios of the distances between the LEDs and the receiver. Based on an Euclidean plane geometry theorem, eCA-RSSR transforms the ratios of the distances into the absolute values. In this way, eCA-RSSR only requires three LEDs for both orientation-free 2D and 3D positioning, implying that eCA-RSSR can achieve high coverage. Based on the absolute values of the distances, the linear least square method is employed to estimate the position of the receiver. Therefore, for the receiver having a small distance between the PD and the camera, the accuracy of eCA-RSSR does not depend on the starting values of the non-linear least square method and the complexity of eCA-RSSR is low. Furthermore, since the distance between the PD and camera can significantly affect the performance of eCA-RSSR, we further propose a compensation algorithm for eCA-RSSR based on the single-view geometry. Experiment results show that positioning errors of less than five centimeters is achievable for eCA-RSSR. Simulation results show that eCA-RSSR can achieve 80th percentile accuracy of about four centimeters and can improve the coverage ratio at low cost.

In this paper, a novel visible light communication (VLC) assisted Perspective-four-Line algorithm (V-P4L) is proposed for indoor localization. The basic idea of V-P4L is to joint the coordinate information obtained by VLC and the geometric information in computer vision for practical indoor localization. In particular, V-P4L first exploits the geometric information to estimate the orientation and coordinate of a single rectangular LED luminaire in the camera coordinate system based on the plane and solid geometry. Then, VLC is used to transmit the world coordinate information of the luminaire to the camera. Next, the position and pose of the camera in the world coordinate system are obtained by the single-view geometry and the linear least square method. Due to the combination of VLC and computer vision, V-P4L only requires a single luminaire for localization and does not rely on the channel model. Therefore, V-P4L is more robust to dust and other factors that interfere visible light signals. Besides, unlike conventional Perspective-n-Line (PnL) algorithms, V-P4L does not require the 3 dimensional (3D)-2 dimensional (2D) correspondences. Simulation results show that for V-P4L the location error is always less than 18 cm and the orientation error is always less than 4.5°.

This paper proposes a hybrid dimming scheme based on joint design of transmit antenna selection and precoding (TASP-HD) for dimmable multi-cell multi-user multiple-input single-output visible light communications systems. In TASPHD, both the direct current bias and the number of activated light-emitting-diodes are dynamically adjusted to achieve efficient communications under the constraints of dimming level and illumination uniformity. With the goal of maximizing the sum-rate of users, a joint transmit antenna selection and precoding design problem is formulated. This problem is a non-convex, mixed integer problem, and thus it is non-deterministic polynomial time hard. To solve the problem, the original problem is separated into two subproblems. The first subproblem is a mixed integer problem in terms of transmit antenna selection, which is solved by the branch-and-bound algorithm. With the obtained transmit antenna selection matrix, the second subproblem in terms of the precoding matrix is solved by the Lagrangian dual method. Finally, these two subproblems are iteratively solved to obtain a convergent solution. The simulation results verify that the proposed TASP-HD can achieve 6 dB signal-to-noise ratio gains over the analog dimming scheme when the dimming level is 70% for a target bit error rate of 10-3.

In this paper, a power efficient deployment for visible light communication (VLC)-enabled unmanned aerial vehicles (UAVs) is studied. In the studied model, each UAV can provide communication service for ground users and illumination builds the VLC links between UAVs and users. Hence, each UAV’s signal transmission and illumination will affect other UAVs’ signal transmission and illumination. Therefore, to deploy VLC-enabled UAVs so as to efficiently service the ground users, the interference caused by the signal transmission and illumination of UAVs must be considered. This problem is formulated as an optimization problem whose goal is to optimize the deployment of UAVs so as to minimize the power consumption for signal transmission and illumination. An iterative algorithm is first proposed to transform the optimization problem into a series of interdependent subproblems, and the transformed problems are then solved by the Lagrangian dual method. In addition, convergence and complexity of the algorithm are also analyzed. Numerical results show that the proposed scheme can reduce at least 53.7% power consumption when compared to the baselines with UAVs at the center of each sub-area.

This paper proposes a simple dimming control scheme for multi-LED visible light communications (VLC), termed as generalized dimming control (GDC) scheme. The GDC performs dimming control by simultaneously adjusting the intensity of transmitted symbols and the number of active elements in a space-time matrix. The indices of active elements in each space-time matrix, as well as the modulated constellation symbols, are used to transmit information base on the space-time index modulation. Furthermore, the tradeoff between the intensity of transmitted symbols and the number of active elements in the space-time matrix is analyzed for enhancing the reliability of communication. To improve the communication reliability, a GDC with the minimum bit error rate (BER) criterion is first proposed to select the optimal dimming control pattern based on exhaustive search. Furthermore, to reduce the computational complexity, a low complexity GDC with the maximum free distance criterion is proposed. Simulation and numerical results show that the proposed scheme can outperform conventional dimming control schemes at both low and high dimming levels in terms of the BER performance.

Multiuser visible light communication (MU-VLC) systems utilizing multiple-input multiple-output (MIMO) and orthogonal frequency-division multiplexing (OFDM) are gaining increased attentions. Proper dimming schemes are required to deploy MU-VLC in different illumination conditions. The current dimming schemes are typically based on the signal domain, such as analogue dimming (AD) scheme and digital dimming (DD) scheme, or on the spatial domain, like spatial dimming (SD) scheme. However, these single-domain based dimming methods have their own drawbacks, such as the performance loss in AD and DD and the dimming precision deficiency in SD. To tackle the dimming challenges in MU-MIMO-OFDM VLC system, this paper proposes a block diagonalization (BD) precoding based hybrid dimming (HD-BD) scheme. In addition, transmit antenna selection (TAS) algorithm is devised for the optimal LEDs subset to operate at different dimming levels. Compared with conventional schemes, the proposed HD-BD scheme can outperform its counterparts in terms of reliability and dimming precision, which is verified by simulation results.

Uplink transmission is an issue for visible light communications due to the unpleasant irradiance from the source light when placed close to the users. To overcome this problem, this paper proposes the use of relays to lower the required source optical power. A popular multi-carrier modulation scheme, termed direct-current biased optical orthogonal frequency division multiplexing, is employed to achieve high spectral efficiency. In addition, both amplitude-and-forward (AF) and decode-and-forward (DF) protocols are used. The theoretical models of AF and DF protocols are also obtained and verified by simulations. To minimize the source optical power while satisfying reliable communications, this paper formulates the associated optimization problems for AF and DF protocols. Exhaustive search is first used to obtain the optimal configuration for the system. As exhaustive search requires high computation efforts and can be time-consuming, two low-complexity suboptimal designs for AF and DF protocols are then proposed, and the proposed suboptimal designs can approximate the performance of exhaustive search in high signal-to-noise ratio regions. Numerical and experimental results indicate that when compared with the counterpart without a relay, the proposed relay-assisted system requires much lower source optical power under the constraints of reliable transmissions.

Uplink transmission design for visible light communication (VLC) is challenging due to the high optical power requirement for reliable links, which may result in unpleasant irradiance to humans. Current studies have proposed to use radio frequency (RF) communication for uplink while treating VLC as a downlink transmission technique. However, in typical scenarios such as hospitals or aircraft cabins, non-RF communications are preferred. Thus, it is beneficial to design a system which can utilize VLC for uplink transmission while minimizing its optical power. In this paper, we propose a amplify and forward based VLC uplink system which can significantly mitigate the optical power of the source light. The multi- carrier modulation scheme termed DC biased optical orthogonal frequency division multiplexing is employed, and the theoretical model of the proposed uplink system is studied. Based on this analytical model, the key system parameters are optimized. Numerical and simulation results verify that, when compared to an equal power allocation based relay system and a reference system without a relay, the proposed system can simultaneously achieve higher spectral efficiency and lower the source optical power.

Visible light communication (VLC), which can synergistically provide both illumination and data transmission, is garnering increasing attention. Dimming support is one of the main challenges for VLC systems. Under the requirements of the uniformity illuminance ratio (UIR) according to lighting engineers and the indoor illuminance range standardized by the international organization for standardization (ISO), an antenna selection based dimming (ASD) scheme is proposed for multiple-input multiple-output (MIMO) VLC systems equipped with multiple lamps. By fully taking advantage of the available channel state information (CSI) at both the transmitters and the receivers, the proposed scheme can select the best subset of light-emitting diodes (LEDs) from the lamps. To the best of the author's knowledge, this is the first design of a dimming scheme specially for MIMO-VLC systems with considering the UIR as well as the indoor illuminance range. Simulation results verify that the proposed ASD scheme has significant bit error ratio (BER) performance gains over the conventional dimming scheme for various dimming proportions and receiving positions, which makes it a more promising alternative dimming scheme for MIMO-VLC systems.

In the field of indoor wireless networks, visible light communication (VLC), which can synergistically provide both illumination and data transmission, is garnering increasing attention. Realizing dimming function without corrupting the communication performance is one of the main challenges for VLC systems. By fully taking advantage of the available channel state information (CSI) at both the transmitters and the receivers, a novel dimming scheme based on antenna selection is proposed for multiple- input multiple-output (MIMO) VLC systems. To the best of the author's knowledge, this is the first design of a dimming scheme specially for MIMO- VLC systems. Simulation results for various dimming proportions and receiving positions verify that the proposed scheme has significant bit error ratio (BER) performance gains over the conventional dimming scheme, which makes it a promising alternative dimming scheme for MIMO-VLC systems.

In visible light communication (VLC) systems, optical orthogonal frequency division multiplexing (O-OFDM) is an appealing modulation scheme. Recently, a number of O-OFDM schemes have been proposed. Direct current optical orthogonal frequency-division multiplexing (DCO-OFDM) is one of the known O-OFDM schemes for its high spectral efficiency and low-complexity. Since VLC involves a combination of illumination and communication, different optical power is often required to achieve a certain illumination level. However, the performance of DCO-OFDM will severely degrade when a relatively high or low optical power constraint is imposed. To solve this problem, an enhanced DCO-OFDM (eDCO-OFDM) scheme is proposed in this paper. By introducing a piecewise function with adaptive slopes according to the required optical power, eDCO-OFDM is able to demonstrate significant performance advantages over conventional DCO-OFDM in terms of spectral efficiency and biterror rate. Comprehensive simulations were conducted to verify the efficiency of the proposed scheme.