The Channel Knowledge Map (CKM) is an emerging technology for enabling future integrated sensing and communication (ISAC) services that have attracted significant research interests in recent years. Current methods for constructing CKM primarily include interpolation-based, model-based, and machine learning algorithms. However, these methods are often limited by their low estimation accuracy or high computational complexity. To address these challenges, we propose a novel approach to construct CKM based on Environment Partitioning and Interpolation Weight Learning (EPIWL). The proposed method leverages channel state information from partially known locations to interpolate and estimate the channel state in the target region, thus completing the CKM construction. To reduce computational complexity, we proposed a Graph Feature Aggregation and Community Detection based Partitioning (GFA-CD-P) algorithm, which selects representative anchor points through environmental partitioning, thereby decreasing the computational load. Furthermore, we propose an interpolation weight learning scheme based on Kolmogorov–Arnold Network (KAN) and Multi-Head Cross Attention (MHCA), namely KM-IWL algorithm, which automatically learn the weight between anchor points and target points, enhancing both the efficiency and accuracy of CKM construction. The experimental results demonstrate that the proposed EPIWL approach achieves a 10 dB improvement in normalized mean squared error (NMSE) and reduces computational complexity by over 60% compared with existing schemes, showcasing robust performance and excellent generalization capability.

To address the challenges of low sampling efficiency in obstructed environments, high fingerprint database construction costs, and insufficient positioning accuracy in indoor visible light positioning (VLP) systems, this paper proposes an integrated intelligent framework termed the obstructed environment based intelligent fingerprint positioning (OEIFP). This framework combines adaptive sampling, sparse expansion, and optimized positioning algorithms. The framework first employs the obstructed environment adaptive sampling (OEAS) algorithm to achieve highly representative sparse sampling by incorporating obstacle information, thereby significantly reducing the cost of fingerprint data collection. Subsequently, the variational autoencoder with convolutional encoding (VACE) model accomplishes high-quality reconstruction from sparse to dense fingerprint databases. On this basis, the firefly algorithm-based cross-variation optimized extreme learning machine (FA-CVO-ELM) positioning model is designed, integrating the firefly algorithm (FA) with cross-variation operation (CVO) to perform dual optimization of the input weights and biases of the extreme learning machine (ELM), which enhances positioning accuracy and generalization capability. Simulation and experimental results demonstrate superior performance across diverse obstacle scenarios. The simulation achieves a 99.75% reduction in database construction cost compared to the initial sampled fingerprint database, while maintaining an average positioning error (APE) as low as 0.0423 m. Experimental results show a 97.6% reduction in construction cost with a minimum average positioning error of 0.0503 m. The framework realizes synergistic optimization between adaptive sampling for low-cost dense database construction and high-precision positioning, offering a viable solution for deploying visible light fingerprint positioning systems in obstructed environments.

Visible light positioning (VLP) has emerged as a promising solution to address the requirements of indoor industrial localization services, such as the smart healthcare and indoor navigation. Increasing LEDs can boost positioning performance while leading to higher energy consumption, increased costs, and potential signal interference issues. To solve this problem, we propose an LED deployment algorithm that improves VLP performance by optimizing the placement of LEDs, thereby avoiding the introduction of excessive LEDs. The proposed algorithm uses the squared position error bound (SPEB) as the metric for deployment to assess the overall positioning performance. Additionally, we utilize Fisher information (FI) to quantify the information received from different LEDs and derive the fusion rules of information ellipses (IEs) to guide the deployment, aiming to maximize the overall received information in the system. To address the non-convex deployment problem, we introduce the confidence region for convex relaxation of the localization area, achieving deployment by minimizing the upper bound of SPEB within the confidence region. Moreover, we derive the localization error bounds to analyze the impact of various key parameters on positioning performance. Convincing simulation results demonstrate the significant improvement in VLP performance achieved with the proposed algorithm.

Visible light positioning (VLP) enables high-precision indoor localization, but single-sensor systems do not scale well to multi-user settings: camera-based methods suffer from motion jitter, while photodiode (PD)-based approaches are vulnerable to ambient light and multipath effects. This paper proposes a camera-photodiode fusion-based cooperative localization algorithm (C-CPD): cameras extract geometric features of a circular luminaire for coarse positioning, and PD-light-emitting diode (LED) modules measure distances to cooperative visible light communication (VLC) units via received signal strength (RSS) to refine estimates, mitigating motion distortions. We derive the Cram´er-Rao lower bound (CRLB) to characterize performance, revealing impacts of focal length, signal-to-noise ratio (SNR), and cooperative VLC units. Simulations show 0.41 cm average error, and experiments demonstrate 3.24 cm real-world error, outperforming baselines in dynamic scenarios. This work highlights multi-sensor fusion and geometric feature exploitation for robust, high-accuracy VLP, with CRLB insights guiding system design.

Visible light positioning (VLP) has become a popular indoor localization technology due to its low energy consumption and high security. Multi-target VLP systems typically utilize LEDs as anchors for localization, while overlooking the potential collaborative gains among targets. In this paper, we derive the Cramér-Rao lower bound (CRLB) for the received signal strength (RSS)-based collaborative VLP systems. It reveals the long-term performance mechanisms and the gains from both collaboration and prior information. Based on the derived CRLB and Fisher information (FI) fusion rules, we propose a collaborative target selection algorithm to mitigate the computational complexity caused by excessive collaborative targets. By integrating collaborative and prior information, we formulate the collaborative localization (CL) problem as an augmented Lagrangian function and solve it through the alternating direction method of multipliers (ADMM). Simulation and experimental results validate the effectiveness of the proposed algorithm and performance analysis.

Federated learning (FL) is a privacy-preserving technique that enables local model training on devices without raw data sharing. However, a critical challenge in FL lies in the communication requirement of uploading the trained models to servers, which can be hindered by interference from ambient devices, particularly in unreliable wireless environments. To address this, hierarchical FL (HFL) introduces an additional intermediate layer where the edge server performs work aggregation from the devices nearby, aiming at reducing the communication load and improving the efficiency of model training. However, existing approaches suffer from two critical limitations. First, they fail to fully quantify the impact of device competition-induced interference on transmission performance, which leads to unacceptably high upload latency and low success upload probability (SUP). Second, they lack a targeted optimization strategy to balance model accuracy and transmission efficiency under dynamic interference conditions. To address these critical limitations and mitigate their adverse impacts on FL performance, we take these gaps as the core motivation of our work and propose a targeted solution. Specifically, we first model the network as a two-layer binomial point process (BPP), which allows us to analyze the network-layer performance and calculate the SUP for the trained model. Based on this model, we propose optimizing cluster selection to balance accuracy and latency, thereby enhancing overall FL performance. We formulate this optimization as a Markov decision process (MDP) and solve it using a twin-delayed deep deterministic policy gradient (TD3)-based cluster selection algorithm (CS-TD3). In addition, to guarantee network-layer performance and enhance the efficiency of HFL, we employ an experimental exhaustive search algorithm to find the best solution within a limited range. The experimental results show that our algorithm overperforms other commonly-used algorithms in terms of HFL accuracy and model transmission latency, achieving a 10.95% improvement over the other methods.

This manuscript introduces a novel energy-efficient optimization strategy for a zero-energy reconfigurable intelligent reflecting surface (Ze-RIS) supported backscatter communication system employing non-orthogonal multiple access (NOMA). The central objective is to maximize the energy-efficiency of the system by optimizing the several key parameters, including the amplitude reflection coefficient of Ze-RIS, the reflection coefficients of the backscatter tags, transmit beamforming at the base station, and passive beamforming at the Ze-RIS node, while incorporating a practical non-linear energy harvesting model both for the Ze-RIS and backscatter nodes. The proposed algorithm addresses the complex non-convex problem through three stages. Firstly, the transmit beamforming vectors are determined by leveraging the semi-definite programming and successive-convex approximation, while handling the rank-1 constraint with the semi-definite relaxation. Secondly, we determine the amplitude reflection coefficient of Ze-RIS by leveraging the monotonicity property of the objective function. Simultaneously, we compute the reflection coefficients of backscatter tags using the Dinkelbach algorithm, Lagrange duality, and the sub-gradient method. Thirdly, we compute passive beamforming using successive-convex approximation and semi-definite programming techniques, achieving a rank-1 solution through the penalty-based method. Finally, the numerical simulations confirm the effectiveness of the proposed approach, demonstrating its superiority over the benchmark competitors with rapid convergence within a few iterations.

In this paper, we aim at exploring the performance limits of passive indoor target localization utilizing multiple light-emitting diode (LED) transmitters embedded into the ceiling and multiple photodiode (PD) sensors anchored on the floor, where the precise locations of the PDs are not predetermined. This work enriches our understanding of error evolution in the time-domain localization cooperation. We propose a novel solution to estimate the target position by identifying the PDs occluded by the target, through measuring the changes in received signal strength (RSS) caused by the shadowing effects induced by this indoor target. Concurrently, these LEDs are used for the positional calibration of the PDs involved in the localization process. Specifically, we derive the Cramér-Rao lower bound (CRLB) on the estimation errors for both target localization and sensor position calibration, and then conduct the convergence and asymptotic performance analyses based on the obtained error bounds. Finally, simulation results corroborate the performance analysis, elucidating how the localization error evolves until it converges, and identifying the factors that influence this intrinsic convergence mechanism. The derived CRLBs and the long-term localization performance from these bounds provide a theoretical basis for optimizing passive positioning algorithms without requiring any auxiliary receiver.

Integrated sensing and communication (ISAC) is emerging as a key technology and research focus for future vehicular communication networks (VCN). It achieves efficient reuse of wireless infrastructure and spectrum resources through the collaborative design of sensing and communication functionalities. However, this integration leads to inevitable mutual interference, and the complexity of channel conditions further complicates the coordination of these functionalities. This paper primarily focuses on the joint sensing-communication performance analysis of ISAC-enabled VCN. Specifically, we model the spatial distribution of roads using a Poisson line process, while the locations of vehicles and roadside units (RSUs) are represented by two one-dimensional Poisson point processes. We characterize the dynamic interference distribution caused by RSUs during the sensing and communication phases and calculate the probability of successful perception (PSP) for a typical pair. Furthermore, for this typical pair, we meticulously derive the communication coverage probability based on the derived PSP for such a pair. To provide a detailed analysis of the interaction between communication and sensing functionalities, we evaluate their trade-off relationship and derive the joint probability of ISAC coverage. Moreover, we perform comprehensive simulations to verify the theoretical results. Additionally, the numerical results demonstrate how different parameters impact the network performance, providing guidance for network deployment and resource allocation under certain performance requirements.

In this manuscript, we introduce an efficient resource allocation strategy to enhance the security of an intelligent omni-surface (IOS) assisted secure Internet-of-things (IoT) enabled non-orthogonal multiple access (NOMA) network under residual hardware impairments (RHIs) resulting from imperfect hardware design. In particular, the goal is to maximize the sum secrecy rate of the considered multi-cluster based secure NOMA system assisted by an IOS node. This is achieved by optimizing both the active beamforming vectors of NOMA users within the transmission and reflection regions of the system, and the transmission and reflection coefficients of the IOS node, while adhering to quality-of-service, successive interference cancellation, power budget, and energy conservation constraints. Moreover, the presented alternating optimization framework tackles the significantly non-convex optimization problem through a two-stage process. Firstly, the active beamforming vectors are obtained using successive convex approximation (SCA) and second-order conic programming (SOCP) techniques. Secondly, based on the determined active beamforming vectors, the transmission and reflection coefficients of the IOS node are computed utilizing SCA and semi-definite relaxation (SDR) techniques, where rank-1 solution is achieved through Gaussian randomization method. Ultimately, the numerical simulations validate the efficacy of the suggested method over competing benchmarks, in terms of sum secrecy rate, showcasing its superiority in achieving fast convergence within a limited number of iterations.

The study of molecular communication (MC) receivers with ligand receptors has attracted increasing interest. This receiver design is the most physically relevant, as the ligand–receptor interactions are common in biological systems, and thus suitable for MC devices and system architectures supported by biology. In this article, we consider a ligand–receptor diffusion channel with several types of ligands, and they can react reversibly with the receptors. Under this scenario, competitions among different types of ligands for receptors may exist and largely affect the channel characteristics. In order to solve the above problems in channel modeling, we first utilize Fick’s law to obtain a mathematical model of this channel. And then we propose a finite-receptor iterative (FRI) algorithm to achieve the channel impulse response (CIR), i.e., the expected number of receptors activated at the receiver surface by a particular type of ligand. It also reflects the competitions between different types of ligands for receptors by deriving the time-varying modified forward reaction rate for each type of ligand in each time slot. In addition, we analyze the competition mechanism of ligands for receptors and derive an interference element expression for describing it. We then present an approach to extend the analysis for a system with$\alpha $ligands. Finally, we verify the accuracy of the analytical results by proposing a simulation framework for the considered environment.

The Internet of Nano Things (IoNT) is a nanonetwork comprised of numerous nano devices capable of data computation, storage, and actuation. IoNT has broad applications, including medicine and environmental protection. Monitoring specific molecular concentrations in the environment is necessary for practical tasks, such as disease monitoring and pollution tracking. However, sensors can only be sparsely arranged due to the limited space and high sensor costs. Consequently, it leads to the loss of monitoring data and seriously affects the performance of the IoNT. Therefore, it is indispensable to address the problem of how to effectively and accurately recover the missing data from the measurements of sparse sensors. To solve this problem, we propose a spatio-temporal constraint tensor completion (SCTC) algorithm based on CANDECOMP/PARAFAC (CP) decomposition. Specifically, we divide the entire environment into uniform grids and model the measurements of all grid positions over a period of time as a tensor. The sparse arrangement of sensors resulted in missing entire columns of data in the tensor, corresponding to the positions without sensors. Our objective is to recover these missing data utilizing the available measurements in the tensor. To effectively and accurately recover the missing data, the spatial and temporal constraint matrices are introduced to leverage the spatio-temporal correlations among the data. Due to the nonconvex optimization problem, CP decomposition is introduced to transform the problem of tensor recovery into solving multiple-factor matrices. The performances of the proposed SCTC algorithm are confirmed via simulation and experiment data.

In molecular communication via diffusion (MCvD) systems with multiple reactive receivers (RXs), mutual interference renders universal application of single RX channel characteristics infeasible. This letter presents a single-input-multiple-output (SIMO) MC system featuring a point transmitter and two RXs. Based on the above system, we propose the multiple RXs iterative (MRI) algorithm to model the channel and derive the expected received signal (ERS) of each RX. The proposed model is validated through numerous particle-based simulations, and the mechanism of the impact between the RXs is revealed in terms of both reaction rate and variables of angle.

In diffusion-based molecular communication, the impact of the transmitter has often been ignored in previous studies, and assuming simultaneous release of all molecules, which neglects system saturation. This letter models the signal reception by a reversible reaction receiver during the transmitter's continuous release state, incorporating initial conditions into the system's equations to derive the received signal and obtain an expression for it. To analyze the state where receptor saturation occurs due to an excessive release of molecules, we introduce saturation conditions to amend the reaction rate. We validate the effectiveness of this method through simulation. During the continuous release of ligands, receptors reach a dynamic equilibrium between the absorption and dissociation of these ligands. This equilibrium indicates that the system can maintain a certain level of signal reception capability even when in a state of saturation.

Recently, anomaly detection or localization in molecular communication (MC) system has become a research hot topic that can be used to distinguish the abnormal cells in the human body. Due to no or very few molecules the target released, it is difficult to detect the silent target in the MC system. In this article, we construct a three-node MC (TNMC) system, including a point source transmitter, a passive receiver, and a silent absorbing target (SAT), the SAT cannot release but has the ability to absorb the molecules. Based on the above system, we propose a probability equivalent modeling (PEM) method to model the channel of the TNMC system and derive the channel impulse response (CIR) of the receiver. Besides, we propose a localization algorithm based on maximum-likelihood estimation (MLE) and Newton–Raphson method to spot the relative location of the SAT. Simulation results verify the correctness of the PEM method and effectiveness for localizing the target.

Vehicular communication is an important application of the fifth generation of mobile communication systems (5G). Due to its low cost and energy efficiency, intelligent reflecting surface (IRS) has been envisioned as a promising technique that can enhance the coverage performance significantly by passive beamforming. In this paper, we analyze the outage probability performance in IRS-assisted vehicular communication networks. We derive the expression of outage probability by utilizing series expansion and central limit theorem. Numerical results show that the IRS can significantly reduce the outage probability for vehicles in its vicinity. The outage probability is closely related to the vehicle density and the number of IRS elements, and better performance is achieved with more reflecting elements.

Hardware-efficient low-complexity precoding is very important in the downlink of Massive MIMO systems for mitigating interference and optimizing performance. In this paper, we propose a correlation-driven optimized Taylor expansion (CD-OTE) precoding scheme to simplify linear minimum mean square error (MMSE) precoding. In order to simplify the hardware-expensive matrix inversion involved in the linear MMSE pre-coder, a Taylor expansion with optimized polynomial coefficients and selection of the most relevant correlation coefficients is proposed. We take into consideration the correlation between different users' channels and develop a general design criterion. Both convergence and complexity analyses are carried out. Simulation results show that the proposed CD-OTE precoder is significantly better than previously reported techniques, while requiring a similar cost.

The performance of the existing sparse Bayesian learning (SBL) methods for off-grid direction-of-arrival (DOA) estimation is dependent on the tradeoff between the accuracy and the computational workload. To speed up the off-grid SBL method while remain a reasonable accuracy, this letter describes a computationally efficient root SBL method for off-grid DOA estimation, which adopts a coarse grid and considers the sampled locations in the coarse grid as the adjustable parameters. We utilize an expectation-maximization algorithm to iteratively refine this coarse grid and illustrate that each updated grid point can be simply achieved by the root of a certain polynomial. Simulation results demonstrate that the computational complexity is significantly reduced, and the modeling error can be almost eliminated.

In this paper, we study widely linear precoding techniques to mitigate in-phase/quadrature-phase (IQ) imbalance (IQI) in the downlink of large-scale multiple-input multiple-output (MIMO) systems. We adopt a real-valued signal model, which considers the IQI at the transmitter, and then develop widely linear zero-forcing (WL-ZF), widely linear matched filter, widely linear minimum mean-squared error, and widely linear block-diagonalization (WL-BD) type precoding algorithms for both single- and multiple-antenna users. We also present a performance analysis of WL-ZF and WL-BD. It is proved that without IQI, WL-ZF has exactly the same multiplexing gain and power offset as ZF, while when IQI exists, WL-ZF achieves the same multiplexing gain as ZF with ideal IQ branches, but with a minor power loss, which is related to the system scale and the IQ parameters. We also compare the performance of WL-BD with BD. The analysis shows that with ideal IQ branches, WL-BD has the same data rate as BD, while when IQI exists, WL-BD achieves the same multiplexing gain as BD without IQ imbalance. Numerical results verify the analysis and show that the proposed widely linear type precoding methods significantly outperform their conventional counterparts with IQI and approach those with ideal IQ branches.

The opportunistic spectrum access in cognitive radio networks has always played a key role in improving the performance of secondary users. In this paper, we propose an optimal spectrum access strategy for multi-channel access of secondary users under multiple physical considerations. Firstly we integrate path loss and Nakagami-m fading of primary and secondary links with imperfect spectrum sensing into our model to obtain the probability that data packet is successfully transmitted. Then we introduce the M/M/1/K queueing to deduce the average packet delay under the assumption of finite-size buffer. In order to minimize the total average packet delay, the optimal probability vector for spectrum access is confirmed by genetic algorithm. Result proves that our proposed spectrum access strategy outperforms the strategies of equal probability and inverse ratio. Furthermore, according to the optimal probability vector, the total average packet loss rate is derived with one more consideration that the number of transmission attempts for automatic repeat request is fixed. Finally, numerical results illustrate the effects of referred lower-layer parameters on total average packet delay and loss rate.

The cognitive cooperative system with coexisting scenario of multiple primary users and one secondary capable of relaying is considered in this study. The primary users transmit packets in orthogonal subchannels. According to the cognitive principle, the secondary activity cannot interfere with the primary performance. Therefore in this study, the secondary user makes use of the spectrum when sensed idle. Based on the proposed media access control (MAC) protocol, the authors derive the stable throughput and delay slots expressions of each primary user with secondary relaying. They also achieve the stable network constraints of relaying probability ɛ, the feasible range of primary arrival rates and the maximum allowed secondary transmitting power which is to make a tradeoff between the stable throughput of the primary and secondary user. Simulation results show that secondary relaying can increase the primary and secondary throughput and also reduce the delay slots when designing an appropriate ɛ.

The concept of cognitive radio is to exploit efficiently the spectrum resources by allowing the coexistence of the primary and secondary users in the same bandwidth without interfering the performance of primary users. Three coexisting models (overlay, underlay and interleave) were presented in recent literature. In this study, the authors propose a hybrid cognitive network model with overlay and underlay models, whereby a primary link leases its fractions of transmission time to the secondary users for their cooperation (i.e. these nodes form as a virtual multiple input multiple output (VMIMO) group) under the outage constraints of the primary and secondary systems. A new cooperative protocol between primary and secondary users is presented. The authors attempt to achieve the maximum transmission capacity of the secondary users under the primary and secondary outage constraints, which depend on the secondary density in the cognitive network. This study gives the upper bound density of the secondary transmitters that are modelled as a homogeneous marked Poisson point process. The maximum density is achieved by computing an optimal set of system parameters such as power control factor of the secondary user and the dirty paper coding (DPC) parameter. Simulation results illustrate that the maximum secondary density obtained using VMIMO is superior to that obtained by direct primary transmission.

The cognitive cooperative system with coexisting scenario of multiple primary users and one secondary capable of relaying is considered. The primary users transmit packets in orthogonal sub-channels. According to the cognitive principle, the secondary activity cannot interfere with the primary performance. Therefore, in this paper, the secondary user makes use of the spectrum when sensed idle. Based on the proposed MAC protocol, we derive the stable throughput expressions of each primary user with secondary relaying. We also achieve the stable network constraints of relaying probability ε, the feasible range of primary arrival rates and the maximum allowed secondary transmitting power which is to make a tradeoff between the stable throughput of the primary and secondary user. Numerical simulations show that secondary relaying can increase the primary and secondary throughput when designing an appropriate ε.

A novel selection cooperation scheme for cognitive nodes (Cog-Sel scheme) in multiple sources cooperative networks is presented and the outage probability is derived. In the proposed scheme, the source chooses a single optimal relay or transmits directly to the destination in terms of the SNR between the source-destination pair with some feedback. Each node can smartly utilize the idle frequency band to transmit signal using cognitive radio technology. Therefore, no extra channel resources are allocated for cooperation and the system encounters no bandwidth losses. The outage probability of the proposed scheme is analyzed and the result reveals that it outperforms the conventional relaying strategies such as simple selection and opportunistic relaying schemes. These benefits contribute to the efficient use of power and channel resources. Theoretic analysis and numerical simulation results are presented to verify our analysis.