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.

The present paper proposes an optimization design method for the Doherty output matching network (OMN) using impedance–phase hybrid objective function constraints, which possesses the capability of enhancing the efficiency consistency of the Doherty power amplifier (DPA) using integrated enhancing reactance (IER) during the back-off power (BOP) range. By calculating the desired reactance for an extended BOP range and combining it with the two-impedance matching method, the S -parameters of the OMN are obtained. Meanwhile, the impedance and phase constraints of the OMN are proposed to narrow the distribution range of the IER. Furthermore, a fragmenttype structure is employed in the OMN optimization so as to enhance the flexibility of the circuit optimization design. To validate the proposed method, a 1.7–2.5 GHz symmetric DPA with a large BOP range was designed and fabricated. Measurement results demonstrate that across the entire operating frequency band, the saturated output power is >44 dBm, and the efficiency ranges from 45% to 55% at a 9-dB BOP.

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.

With the combination of extra-large arrays and high frequencies, near-field transmissions have become prevalent, challenging the validity of classical channel representations typically derived under the plane wavefront assumption. In this paper, we investigate the angular-domain representation of line-of-sight (LoS) extra-large MIMO (XL-MIMO) channels, considering the impact of spherical wavefront effects. First, we demonstrate the structured sparsity of LoS XL-MIMO channels in the angular domain. Leveraging this sparsity, we propose an effective spatial bandwidth channel representation method, which characterizes near-field LoS XL-MIMO channels as a superposition of multiple plane wave components, enabling us to capture the spherical wavefront effect in a low-dimensional angular channel. Subsequently, we introduce an angular-domain transceiver architecture based on this low-dimensional channel representation. This architecture could significantly facilitate the implementation of LoS XL-MIMO systems. Finally, simulation results confirm the effectiveness of the effective spatial bandwidth identification method and analyze the impact of various array geometries on the effective spatial bandwidth. Additionally, the availability of the angular-domain processing architecture is validated.

In this work, we investigate the joint visibility region (VR) detection and channel estimation (CE) problem for extremely large-scale multiple-input-multiple-output (XL-MIMO) systems considering both the spherical wavefront effect and spatial non-stationary (SnS) property. Unlike existing SnS CE methods that rely on the statistical characteristics of channels in the spatial or delay domain, we propose an approach that simultaneously exploits the antenna-domain spatial correlation and the wavenumber-domain sparsity of SnS channels. To this end, we introduce a two-stage VR detection and CE scheme. In the first stage, the belief regarding the visibility of antennas is obtained through a VR detection-oriented message passing (VRDO-MP) scheme, which fully exploits the spatial correlation among adjacent antenna elements. In the second stage, leveraging the VR information and wavenumber-domain sparsity, we accurately estimate the SnS channel employing the belief-based orthogonal matching pursuit (BB-OMP) method. Simulations show that the proposed algorithms lead to a significant enhancement in VR detection and CE accuracy as compared to existing methods, especially in low signal-to-noise ratio (SNR) scenarios.

With the combination of extra-large arrays and high frequencies, near-field transmissions have become increasingly prevalent. In this paper, we investigate the angular-domain representation of near-field line-of-sight (LoS) extra-large multiple-input-multiple-output (XL-MIMO) channels. Specifically, we first demonstrate the structured sparsity of the near-field LoS channel in the angular domain. By leveraging this sparsity property, we propose an effective spatial bandwidth channel representation method. This method characterizes near-field LoS XL-MIMO channels as a superposition of multiple plane wave components within the effective spatial band between transceiver arrays. Finally, simulation results validate the equivalence between the proposed representation and the existing antenna domain channel model and demonstrate the effects of array geometries on the effective spatial bandwidth.

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.

This paper investigates the dynamic access point (AP) clustering and precoding problem in the downlink of user-centric virtual cell networks. The goal is to maximize the weighted sum spectral efficiency (SE) while satisfying the power constraints and AP clustering constraints in adjacent time slots (TSs). By adopting the random walk mobility to model the mobile user equipments' movement behaviors, we consider dynamic and time-varying channel conditions. Therefore, the weighted sum SE maximization programming takes the form of discrete-time sequence of mixed-integer non-convex optimization problems. In this paper, we propose to solve this sequential problem in two stages. In the first stage, a dynamic AP clustering approach based on discrete particle swarm optimization is developed. This approach takes the advantage of the channel correlation by exploiting the relationship between AP clustering solutions in adjacent TSs to improve the SE performance and reduce complexity. In the second stage, given the AP clustering solution obtained in the first stage, a distributed precoding algorithm is devised via applying the weighted minimum mean square error method. By combining these two stages, we propose a novel dynamic AP clustering and precoding algorithm (DAPC-Pre). The effectiveness of the proposed DAPC-Pre algorithm is verified by the simulation results. In particular, the proposed algorithm converges fast and significantly outperforms benchmark algorithms in terms of sum SE under different dynamic environments.

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.

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.

In this paper, we consider the problem of maximizing the weighted sum energy efficiency (WS-EE) for multi-input single-output (MISO) interference channels (ICs), which are well acknowledged as general models of heterogeneous networks (HetNets), multicell networks, etc. To address this problem, we develop an efficient distributed beamforming algorithm based on a pricing mechanism. Specifically, we carefully introduce a price metric for distributed beamforming design, which fortunately allows efficient closed-form solutions to the per-user beam-vector optimization problem. The convergence of the distributed pricing-based beamforming design is theoretically proven. Furthermore, we present an implementation strategy of the proposed distributed algorithm with limited information exchange. Numerical results show that our algorithm converges much faster than existing algorithms, while yielding comparable, sometimes even better performance in terms of the WS-EE. Finally, by taking the backhaul power consumption into account, it is interesting to show that the proposed algorithm with limited information exchange achieves better WS-EE than the full information exchange-based algorithm in some special cases.

In this paper, we consider the problem of maximizing the weighted sum energy efficiency (WS-EE) of a multi-input single-output (MISO) ad hoc network. To solve this problem, we develop one low-complexity distributed beamforming algorithm based on pricing mechanism. Specifically, each node updates its price information and broadcasts it to all the other nodes. Having collected all the information, each node selects its beam-vector in closed-form with low computational complexity. The convergence of this algorithm is strictly proved. Compared with the existing two-layer optimization algorithm, our algorithm has lower computational complexity without performance loss. Simulation results show that the proposed algorithm performs slightly worse than the centralized algorithm, but requires much less information exchange overhead.

In this paper, we propose widely-linear blockdiagonalization (BD) type precoding techniques to alleviate the impact of IQ imbalance in the downlink Massive multi-input multi-output (MIMO) systems. We first introduce a real-valued signal model and then develop widely-linear BD (WL-BD) type precoding algorithms, i.e., WL-BD, widely linear regularized BD (WL-RBD) and widely linear simplified generalized MMSE channel inversion (WL-S-GMI). We also present analysis of the sum-rate and multiplexing gain achieved by the proposed WLBD for scenarios with and without IQ imbalance. Numerical results verify the analysis and show that WL-BD type precoding methods significantly outperform their conventional counterparts with IQ imbalance and approach the ideal case.

The joint estimation and compensation of IQ imbalance (IQI) parameters at both transmitter (TX) and receiver (RX) is studied in this paper. We develop a generalized system model with a reduced number of parameters (RNP) that covers a wide range of mobile communications scenarios. We devise efficient direct least-squares (DLS) and alternating least-squares (ALS) techniques for IQI parameter estimation based on the generalized system model. For the ALS based method, we prove that the algorithm will converge to a local optimal solution of the optimization problem. Numerical results show that compared with a previously reported method, the proposed DLS-RNP achieves similar performance with a reduced computational complexity, and the proposed ALS-RNP algorithm has significantly better performance with comparable complexity, with a gain over 5 dB for QPSK and 10 dB for 64QAM in the high signal-to-noise-ratio (SNR) region.

This paper studies the impact of hardware mismatch (11M) between the base station (BS) and the user equipment (UE) in the downlink (DL) of large-scale antenna systems. Analytical expressions to predict the achievable rates are derived for different precoding methods, i.e., matched filter (MF) and regularized zero-forcing (RZF), using large system analysis techniques. Furthermore, the upper bounds on achievable rates of MF and RZF with 11M are investigated, which are related to the statistics of the circuit gains of the mismatched hardware. Moreover, we present a study of 11M calibration, where we take zero-forcing (ZF) precoding as an example to compare two 11M calibration schemes, i.e., Pre-precoding Calibration (Pre-Cal) and Post-precoding Calibration (Post-Cal). The analysis shows that Pre-Cal outperforms Post-Cal schemes. Monte-Carlo simulations are carried out, and numerical results demonstrate the correctness of the analysis.

In this paper, we consider the problem of maximizing the energy efficiency (EE) for multiple-input-multiple-output (MIMO) interference channels (ICs), subject to the per-link power constraint. To avoid extensive information exchange among all links, the optimization problem is formulated as a noncooperative game, where each link maximizes its own EE. We show that this game always admits a Nash equilibrium (NE) and the sufficient condition for the uniqueness of the NE is derived for the case of large enough maximum transmit power constraint. To reach the NE of this game, we develop a totally distributed EE algorithm, in which each link updates its own transmit covariance matrix in a completely distributed and asynchronous way. Some players may update their solutions more frequently than others or even use the outdated interference information. The sufficient conditions that guarantee the global convergence of the proposed algorithm to the NE of the game have been given as well. We also study the impact of the circuit power consumption on the sum EE performance of the proposed algorithm in the case when the links are separated sufficiently far away. Moreover, the tradeoff between the sum EE and the sum spectral efficiency (SE) is investigated with the proposed algorithm under two special cases: 1) low transmit power constraint regime; and 2) high transmit power constraint regime. Finally, extensive simulations are conducted to evaluate the impact of various system parameters on the system performance.

We consider the problem of maximizing the energy efficiency (EE) for a MIMO interference channel (IC), with the power constraint on each link. To obtain totally distributed solutions, this problem is formulated as a noncooperative game. We show that this game always admits a Nash equilibra (NE). Importantly, the sufficient condition that one can check to guarantee the uniqueness of the NE is derived. To reach the NE of this game, we provide a totally distributed EE algorithm, in which each player employs the fractional programming to update his own solution. These updates can be performed in a completely distributed and asynchronous fashion. Sufficient conditions that guarantee the convergence of the algorithm have been given as well. Simulation results show that the proposed algorithm converges fast and significantly outperforms the existing algorithms in terms of the sum-EE or the sum-rate.

This paper studies the impact of hardware mismatch (HM) between base station (BS) and user equipment in the downlink of large scale antenna systems. Analytical expressions to predict the achievable rates are derived for different precoding methods, i.e. matched filter (MF) and regularized zero-forcing (RZF), using large system analysis techniques. Furthermore, the asymptotic downlink signal to interference plus noise ratio (SINR) under HM is investigated, which is only related to the variances of circuit gains in most practical scenarios. Monte-Carlo simulations are carried out, and numerical results demonstrate the correctness of the analysis.

This paper aims to design an optimal set of beam-vectors for multi-band multi-antenna cognitive radio networks that jointly allocate power over both space and frequency, so that the sum power of secondary users (SUs) is minimized, subject to rate demands at the SUs, as well as the interference constraints imposed by primary users. Unlike the rate maximization problems, which are always feasible, this power minimization (PM) problem may be infeasible due to the rate constraints. Therefore, we provide a complete analysis of the PM problem by splitting the solution into two separate phases. In phase I, a novel method is developed to check the feasibility of the PM problem by considering an alternative problem, where one additional variable is introduced. This alternative problem is always feasible and one algorithm based on network duality and geometric programs is developed to solve it. In phase II, a novel algorithm is developed to solve the PM problem. This algorithm can be implemented in an online fashion. Furthermore, this algorithm is proved to converge to a Karush-Kuhn-Tucker point of the PM problem. Simulation results show that the proposed algorithms converge in only a few iterations and significantly outperform the existing single-band method in terms of both the feasibility probabilities and power savings.

In this paper, the downlink signal-to-interference-plus-noise ratio (SINR) distribution in multiuser large scale antenna systems with conjugate beamforming and Rayleigh fading is investigated. The probability density function (PDF) is derived and the distribution in high signal-to-noise ratio (SNR) regime is studied. Results indicate that the PDF of downlink SINR converges to F distribution when the interference is dominant over noise. It is interesting that the asymptotic SINR is just the reciprocal of the ratio of the number of users U to the number of transmit antennas N, and is irrelevant to the average transmit power when N and U grow with fixed ratio. However, when U is a large constant, the transmit power could be proportional to equation to maintain a specified quality of service (QoS), as a result of the large scale antenna system effect. Simulation results validate the derived PDF and analytical results.

In this paper, a novel optimization objective namely weighted sum rate is proposed for beamformer design in single group multicast systems. As an upper bound and special case of maximizing minimum rate, it can overcome previous disadvantages of computation complexity and sensitivity to channel state when the weights are chosen properly. Although the corresponding optimization problem is nonconvex, some properties of the optimal solution and closed-form solutions under some usual special cases are derived. In addition, an iterative algorithm with low computation complexity for general case is proposed. Simulation results show that the beamformer obtained by the iterative algorithm not only improves the minimum rate and average rate, but also gets better fairness and overall performance compared with previous schemes.

The problem of energy-efficient joint beamforming and antenna selection for multicast systems is consider in this paper. Under the performance objective of maximizing the number of bits per joule of energy consumed, beamformer design, optimal transmit power and antenna selection is discussed respectively. The beamforming problem is converted to an equivalent form of the max-min problem and we propose a suboptimal solution with low complexity whose validity is verified by the numerical results. The existence and uniqueness of the optimal transmit power is proved. Then, a simple antenna selection scheme is presented. In the end, we combine the above algorithms into a joint beamforming and antenna selection algorithm. Simulation results show the performance gain of the proposed scheme. A great deal of energy can be saved at the expense of acceptable throughput loss especially with low number of users or short distance. In addition, antenna selection is necessary for energy-efficient design in multi-antenna systems through the performance comparison.

In this paper, the impact of path loss exponents on antenna location design in generalized istributed antenna system (GDAS) is investigated, with the goal of maximizing a lower bound of the average uplink capacity. A simple case when antenna ports (AP) are uniformly placed along a circle is studied first. Both the upper and lower bounds for the optimal placement radius are given. An approximate expression of the optimal radius with sufficient accuracy is derived using Newton-Cotes integration formula with a degree of 5. The given expression indicates that with different path loss exponents the optimal locations of APs differ. The interesting thing is that the optimal radius decreases as the path loss exponent grows when the number of APs is large, while it is the opposite when the number of APs is small. Analytical results are then extended to more general scenarios and a new criterion is proposed for antenna location design. Simulation results show that the given criterion outperforms those in previous literature, and the achieved capacity gain can be as much as 10%.