Radiation source (RS) localization is crucial in electromagnetic environmental monitoring. Existing methods require prior knowledge of the environment and have overlooked the impact of multipath effects. Their positioning accuracy is penalized in practical applications. This paper presents an environment cognition-based variational Bayesian positioning method, which can achieve precise multi-source localization in uncooperative multipath environments using only the measurements of received signal strength (RSS). We begin by leveraging the signal propagation properties to design a model-data integrated unsupervised learning network, which partitions the positioning area according to the transmission states of different multipath signals. To estimate the number of RSs and mitigate the multipath effect, we identify the data samples of multipath RSS and divide them into groups, each corresponding to an RS. Then, a new variational Bayesian positioning algorithm is developed to operate without prior channel information and locate multiple RSs accurately across varying signal propagation models. Simulations show that our positioning method can effectively improve the accuracy of multi-RS localization by 10% in uncooperative multipath environments, compared to the state-of-the-art RSS-based localization techniques.

Drones are expected to be promising aerial platforms in air-to-ground (A2G) integrated communication networks, where the A2G propagation channel is fundamental for reliable communication links. This paper proposes a hybrid parameter generation framework combining the deterministic and statistical methods for a cluster-based A2G channel model. In this framework, the map-based deterministic method is used to generate delay and angle parameters, which can achieve great scenario consistency. However, it is difficult for users to provide precise material information of scatterers, which would cause deviation of power parameters. To tackle this issue, a measurement-driven power generation method is proposed. Firstly, a bandwidth-dependent clustering method is developed to group the rays into clusters. Then, the cluster power is generated by measurement-driven statistical models with a power-decomposition idea. It decomposes the power parameter into several parts that are less dependent on the scenario. Moreover, it can avoid massive measurement campaigns and is more robust when applied in unmeasured scenarios. Finally, a new channel measurement campaign in a street canyon scenario is performed for validations. The proposed method is also compared with a ray-tracing (RT) method and the 3rd Generation Partnership Project (3GPP) channel model. It is shown that the proposed framework and power generation method are great alternatives for accurate and robust modeling requirements under specific A2G communication scenarios.

Spectrum mapping (SM) visualizes spectrum information across a geographical area, constructing radio environment maps (REMs), which serve as a foundation for spectrum monitoring, management, and security. Most existing SM schemes rely on spatially distributed sensors or vehicle-mounted equipment, and assume prior environmental knowledge, limiting their applicability in dynamic or unknown 3D environments. In this paper, we propose a Bayesian learning-based three-dimensional (3D) SM framework that enables accurate REM construction through adaptive UAV sampling in complex and unknown environments. First, a mutual-information-driven UAV path planner is designed by integrating an enhanced sampling-based optimization scheme, enabling efficient data collection according to the maximum mutual information criterion and recent sensing data. Second, a semi-deterministic channel dictionary, refined with sampled field data, is established to model the correlation between observed spectrum values and environmental features. Based on this dictionary, a Bayesian learning-based recovery algorithm reconstructs the spectrum distribution at unsampled positions, producing the corresponding 3D REM. Experimental results on open simulated and measured datasets demonstrate that the proposed framework reduces the mean absolute error by over 60% compared with CS-based methods and by 35% with data-driven interpolation. It also improves sampling efficiency by up to 70% for a given recovery accuracy, highlighting the effectiveness in unknown 3D environments.

Trust region optimization-based received signal strength indicator (RSSI) interference source localization methods have been widely used in low-altitude research. However, these methods often converge to local optima in complex environments, degrading the positioning performance. This paper presents a novel unmanned aerial vehicle (UAV)-aided progressive interference source localization method based on improved trust region optimization. By combining the Levenberg-Marquardt (LM) algorithm with particle swarm optimization (PSO), our proposed method can effectively enhance the success rate of localization. We also propose a confidence quantification approach based on the UAV-to-ground channel model. This approach considers the surrounding environmental information of the sampling points and dynamically adjusts the weight of the sampling data during the data fusion. As a result, the overall positioning accuracy can be significantly improved. Experimental results demonstrate the proposed method can achieve high-precision interference source localization in noisy and interference-prone environments.

Specific emitter identification (SEI), known as radio frequency fingerprint (RFF) identification, is one of the key techniques to provide effective protection for the low-altitude security. However, most existing SEI methods cannot achieve satisfactory identification performance in low signal-to-noise ratio (SNR) environments. By fusing background information of the environment, this paper presents a new deep learning-based SEI method that can accurately identify emitters in the environments with severe noises. We first construct a dual convolutional neural network (DCNN) and a U-shaped Convolutional Network (UNet) to extract the RFFs and background information features, respectively. Then, a background-fingerprint attention fusion network (BFAFN) is designed to fuse the background information with RFF features. Using this network, we can obtain detailed emitters information through the fused signals, improving the identification accuracy. Experimental results show that our proposed SEI method outperforms other methods in performance on both open-source and collected unmanned aerial vehicles (UAVs) datasets, with an improvement in identification accuracy of 2% to 5%.

Constructing three-dimensional (3D) radio environment maps (REMs) has emerged as a promising solution to visualize the spectrum information over the geographical map. In this paper, we propose a novel online path planning method for unmanned aerial vehicle (UAV)-based 3D spectrum mapping in unknown environments. The UAV can effectively collect spectrum data along dynamically planned paths while adhering to budget constraints. We formulate the path planning problem by a surrogate objective that maximizes the information gain along the sampling path. Specifically, a Gaussian process (GP) is adopted to estimate the spatial distribution of received signal strength (RSS) based on observations. A goal location decision algorithm based on the negative integrated posterior variance (NIPV) criterion is developed, which identifies high-value locations by maximizing the reduction in uncertainty. Besides, an uncertainty-aware local path planner is introduced to optimize sampling paths during flight. Simulation results demonstrate that it achieves at least a 66.49% improvement in REM construction accuracy and 42.74% reduction in mapping uncertainty compared to traditional methods.

Due to the increasing demand for the accuracy of spectrum maps, fusing spectrum maps has gained attention as an effective method to improve the exactitude of spectrum map construction. However, most of the existing spectrum map fusion methods overlook the over-fitting problem and the correlation of spectral data in the fusion process, so the performance can hardly meet expectations. In this paper, a spectrum map fusion method based on differential ridge regression is proposed, which can construct accurate spectrum maps in the electromagnetic environment with strong-correlation data with high accuracy. First, we construct a spectrum map fusion model by exploiting the propagation characteristics of the spectrum signal. According to the path loss model, the differential ridge regression regularization term is designed to handle the correlation of spectral data and suppress anomalies from spectrum receivers. Finally, we construct a convex optimization problem for spectrum map fusion and obtain the lower bound of the problem by developing Lagrange duality. This method can ensure the convergence of the spectrum map fusion problem and accelerate the convergence speed under low complexity. Simulation results show that the proposed fusion method can effectively improve the accuracy of spectrum map construction compared with the state-of-the-art.

Radio environment maps (REMs) have been used to visualize the information of invisible electromagnetic spectrum. Although in the past there have been many research activities dealing with the reconstruction of static REMs, they did not consider the time variation of the dynamic spectrum operational environment. In this article, we present a novel time-variant REM reconstruction methodology based on sparsely distributed sensors which jointly considers sensor layout optimization, propagation model improvement, and missing spectrum data recovery. First, a low complexity and computationally efficient method is proposed to improve the sampling efficiency. The proposed method jointly employs the gradient descent method and an upgraded greedy matching algorithm to optimize the sensor positions even when large-scale scenarios are considered. Then, by using the sampled spectrum data obtained from these sensors, the accuracy of commonly employed propagation models is improved and subsequently used to construct a channel dictionary for such time-varying environments. By exploring the heterogeneity of dynamic spectrum operational environments, an improved optimal reconstruction method is designed to recover the spectrum data using their spatial-temporal correlation. By considering a typical university campus environment as a case study, simulation and measurement data are obtained to reconstruct the time-variant REM. Through the simulation data, the reconstruction performance results are compared with those obtained from other state-of-the-art methods showing that the proposed methodology outperforms the others with respect to the sampling scheme and missing rate. Additionally, field measurement results have demonstrated that the proposed approach can effectively reconstruct time-variant REMs under dynamic scenarios.

Accurately measuring the radiation directional pattern of base stations is crucial for performance evaluation of antenna radiation in fifth generation (5G) wireless communications. However, existing methods construct antenna directional pattern typically rely on the E-plane and H-plane data, limiting their applicability. This paper presents a new model-driven three-dimensional (3D) radiation directional pattern construction method based on data correction for 5G base stations. This method utilizes an unmanned aerial vehicle (UAV) to collect radiation characteristic data of the base station. We first propose a position correction method based on the 3D space transformation, and refine the radiation characteristic data accuracy according to a flat earth two-ray (FE2R) model. A improved close-in (CI) model driven inverse distance weighting (IDW) algorithm is then designed to accurately sense and depict the directional pattern of the base station. Experimental results demonstrate that the proposed radiation pattern construction method can achieve highly accurate radiation pattern of base stations.

Unmanned aerial vehicle (UAVs) are a key platform in the sixth generation (6G) communication networks, where integrated sensing and communication (ISAC) is also a promising technology that requires real-time channel estimation. This paper proposes a robust and efficient angle-of-arrival (AOA) estimation method based on a field-trained neural network (NN) for low-latency UAV ISAC applications. In this method, the NN is pre-trained quickly in the field with each receiving antenna element's channel state information (CSI) and the transceivers' locations. We extract the channel multi-paths from the CSI and calculate the path phases as the training data set in real time. Then the pre-trained NN is used for high-efficient AoA estimation in real time. A real-time UAV channel sounder is utilized to verify the proposed method. The measurement results show that the proposed field-trained estimation method is faster and more robust compared with the traditional method and fixed-trained NN. The proposed angle estimation method is valuable for UAV channel estimation and low-latency UAV ISAC applications.

Effective beam alignment is essential for vehicle-to-infrastructure (V2I) millimeter wave (mmWave) communication systems, particularly in high-mobility vehicle scenarios. This paper explores a three-dimensional (3D) vehicle environment and introduces a novel deep learning (DL)-based beam search method that incorporates an image-based coding (IBC) technique. The mmWave beam search is approached as an image processing problem based on situational awareness. We propose IBC to leverage the locations, sizes, and information of vehicles, and utilize convolutional neural network (CNN) to train the image dataset. Consequently, the optimal beam pair index(BPI)can be determined. Simulation results demonstrate that the proposed beam search method achieves satisfactory performance in terms of accuracy and robustness compared to conventional methods.

Dual-function-radar-communication (DFRC) is a promising candidate technology for next-generation networks. By integrating hybrid analog-digital (HAD) beamforming into a multi-user millimeter-wave (mmWave) DFRC system, we design a new reconfigurable subarray (RS) architecture and jointly optimize the HAD beamforming to maximize the communication sum-rate and ensure a prescribed signal-to-clutter-plus-noise ratio for radar sensing. Considering the non-convexity of this problem arising from multiplicative coupling of the analog and digital beamforming, we convert the sum-rate maximization into an equivalent weighted mean-square error minimization and apply penalty dual decomposition to decouple the analog and digital beamforming. Specifically, a second-order cone program is first constructed to optimize the fully digital counterpart of the HAD beamforming. Then, the sparsity of the RS architecture is exploited to obtain a low-complexity solution for the HAD beamforming. The convergence and complexity analyses of our algorithm are carried out under the RS architecture. Simulations corroborate that, with the RS architecture, DFRC offers effective communication and sensing and improves energy efficiency by 83.4% and 114.2% with a moderate number of radio frequency chains and phase shifters, compared to the persistently- and fully-connected architectures, respectively.

The radio environment map (REM) visually displays the spectrum information over the geographical map and plays a significant role in monitoring, management, and security of spectrum resources. In this paper, we present an efficient 3D REM construction scheme based on the sparse Bayesian learning (SBL), which aims to recover the accurate REM with limited and optimized sampling data. In order to reduce the number of sampling sensors, an efficient sparse sampling method for unknown scenarios is proposed. For the given construction accuracy and the priority of each location, the quantity and sampling locations can be jointly optimized. With the sparse sampled data, by mining the sparsity of the spectrum situation and channel propagation characteristics, a SBL-based spectrum data hierarchical recovery algorithm is developed to estimate the missing data of unsampled locations. Finally, the simulated three-dimensional (3D) REM data in the campus scenario are used to verify the proposed methods as well as to compare with the state-of-the-art. We also analyze the recovery performance and the impact of different parameters on the constructed REMs. Numerical results demonstrate that the proposed scheme can ensure the construction accuracy and improve the computational efficiency under the low sampling rate.

Abstract Spectrum resources are becoming harder to come by for wireless communications. The spectrum environment map (SEM), which depicts the electromagnetic environment's current state and future trend, is a valuable technique for managing and allocating spectrum resources. Most SEM construction approaches only take static SEMs into account and cannot forecast time‐domain changes and trends of SEMs in dynamic scenes. In this paper, a brand‐new temporal SEM prediction method for the high dynamic spectrum environment is proposed. This method is based on knowledge of radiation source and the optical flow driven by propagation channel models. First, a novel radiation source localization strategy is designed to obtain the radiation source movement information. Then, the optical flow field of the available SEMs is combined with the information regarding radiation source movement. In order to forecast future SEMs, a propagation model driven reconstruction technique is developed. Simulation findings demonstrate how well the suggested strategy is tailored to capture the spatiotemporal correlation of SEMs. This technique performs better than the state‐of‐the‐art in terms of single‐ and multiple‐step SEM predictions.

Path probability prediction is essential to describe the dynamic birth and death of propagation paths, and build the accurate channel model for air-to-ground (A2G) communications. The occurrence probability of each path is complex and time variant due to fast changeable altitudes of unmanned aerial vehicles and scattering environments. Considering the A2G channels under urban scenarios, this article presents three novel stochastic probability models for the Line-of-Sight (LoS) path, ground specular (GS) path, and building scattering (BS) path, respectively. By analyzing the geometric stochastic information of 3-D scattering environments, the proposed models are derived with respect to the width, height, and distribution of buildings. The effect of the Fresnel zone and altitudes of transceivers are also taken into account. Simulation results show that the proposed LoS path probability model has good performance at different frequencies and altitudes and is also consistent with existing models at the low or high altitude. Moreover, the proposed LoS and non-LoS path probability models show good agreement with the ray-tracing (RT) simulation method.

Line-of-sight (LoS) probability modeling plays an essential role in the reliability determination of millimeter wave (mmWave) communication systems. However, since most LoS probability models do not consider altitude information of communication terminals, they cannot be directly applied to air-to-ground (A2G) communication scenarios. In this paper, we propose a new multi-height LoS probability model for mmWave unmanned aerial vehicle (UAV) communication scenarios. A machine learning (ML)-based parameter estimation method is also developed, which trains the data from the constructed virtual urban scenes. We first propose a LoS/none-LoS (NLoS) identification method to recognize the LoS path and calculate the LoS probability. Then, we construct a two-layer single-input multiple-output back propagation neural network (BPNN) which trains the relationship between the model parameters and the altitude of UAVs. Simulation results show that the proposed LoS probability model has a good consistency with the ray tracing (RT) simulation data and the currently existing models when altitudes of UAVs are low. As the altitude increases, our model is still applicable and can achieve an excellent agreement with the RT data.

Line-of-sight (LoS) probability prediction is critical to the performance optimization of wireless communication systems. However, it is challenging to predict the LoS probability of air-to-ground (A2G) communication scenarios, because the altitude of unmanned aerial vehicles (UAVs) or other aircraft varies from dozens of meters to several kilometers. This paper presents an altitude-dependent empirical LoS probability model for A2G scenarios. Before estimating the model parameters, we design a K -nearest neighbor (KNN) based strategy to classify LoS and non-LoS (NLoS) paths. Then, a two-layer back propagation neural network (BPNN) based parameter estimation method is developed to build the relationship between every model parameter and the UAV altitude. Simulation results show that the results obtained using our proposed model has good consistency with the ray tracing (RT) data, the measurement data, and the results obtained using the standard models. Our model can also provide wider applicable altitudes than other LoS probability models, and thus can be applied to different altitudes under various A2G scenarios.

As a promising 5G cutting-edge communication technology, mobile edge computing (MEC) can improve the quality of computing by offloading computation-intensive tasks to base stations (BSs) or adjacent users through device-to-device (D2D) links. When mobile users offload tasks through the D2D links, using this technology, both transmission energy consumption and transmission delay can be reduced. In this paper, we propose a D2D-assisted MEC system, which can process a large number of independent computing tasks to reduce energy consumption and delay. Different from conventional binary computing offloading, we use partial computing offloading in this paper, which can reduce delay and energy consumption. In particular, we first propose a Knapsack problem-based pre-allocation (PA) algorithm to reduce the amount of offloaded tasks with known transmission power. By using this algorithm, we can obtain the offloading decisions of the tasks. We also apply the variable substitution technique to recast the constructed non-convex problem to be convex, so that the optimal transmission power can be obtained. We finally propose a new alternate optimization algorithm to alternately optimize tasks offloading decisions and transmission power. Simulations show that the proposed algorithm can significantly reduce the delay and energy consumption compared with the traditional offloading schemes.

Abstract This paper presents a novel variational Bayesian learning‐based channel estimation scheme for hybrid pre‐coding‐employed wideband multiuser millimetre wave multiple‐input multiple‐output communication systems. We first propose a frequency variational Bayesian algorithm, which leverages common sparsity of different sub‐carriers in the frequency domain. The algorithm shares all the information of the support sets from the measurement matrices, significantly improving channel estimation accuracy. To enhance robustness of the frequency variational Bayesian algorithm, we develop a hierarchical Gaussian prior channel model, which employs an identify‐and‐reject strategy to deal with random outliers imposed by hardware impairments. A support selection frequency variational Bayesian channel estimation algorithm is also proposed, which adaptively selects support sets from the measurement matrices. As a result, the overall computational complexity can be reduced. Validated by the Bayesian Cramér‐Rao bound, simulation results show that, both frequency variational Bayesian and support selection‐frequency variational Bayesian algorithms can achieve higher channel estimation accuracy than existing methods. Furthermore, compared with frequency variational Bayesian, support selection‐frequency variational Bayesian requires significantly lower computational complexity, and hence, it is more practical for channel estimation applications.

Reducing cost and power consumption while maintaining high network access capability is a key physical-layer requirement of massive Internet of Things (mIoT) networks. Deploying a hybrid array is a cost- and energy-efficient way to meet the requirement, but would penalize system degree of freedom (DoF) and channel estimation accuracy. This is because signals from multiple antennas are combined by a radio frequency (RF) network of the hybrid array. This article presents a novel hybrid uniform circular cylindrical array (UCyA) for mIoT networks. We design a nested hybrid beamforming structure based on sparse array techniques and propose the corresponding channel estimation method based on the second-order channel statistics. As a result, only a small number of RF chains are required to preserve the DoF of the UCyA. We also propose a new tensor-based two-dimensional (2-D) direction-of-arrival (DoA) estimation algorithm tailored for the proposed hybrid array. The algorithm suppresses the noise components in all tensor modes and operates on the signal data model directly, hence improving estimation accuracy with an affordable computational complexity. Corroborated by a Cramér-Rao lower bound (CRLB) analysis, simulation results show that the proposed hybrid UCyA array and the DoA estimation algorithm can accurately estimate the 2-D DoAs of a large number of IoT devices.

This paper proposes to deploy multiple reconfigurable intelligent surfaces (RISs) in device-to-device (D2D)-underlaid cellular systems. The uplink sum-rate of the system is maximized by jointly optimizing the transmit powers of the users, the pairing of the cellular users (CUs) and D2D links, the receive beamforming of the base station (BS), and the configuration of the RISs, subject to the power limits and quality-of-service (QoS) of the users. To address the non-convexity of this problem, we develop a new block coordinate descent (BCD) framework which decouples the D2D-CU pairing, power allocation and receive beamforming, from the configuration of the RISs. Specifically, we derive closed-form expressions for the power allocation and receive beamforming under any D2D-CU pairing, which facilitates interpreting the D2D-CU pairing as a bipartite graph matching solved using the Hungarian algorithm. We transform the configuration of the RISs into a quadratically constrained quadratic program (QCQP) with multiple quadratic constraints. A low-complexity algorithm, named Riemannian manifold-based alternating direction method of multipliers (RM-ADMM), is developed to decompose the QCQP into simpler QCQPs with a single constraint each, and solve them efficiently in a decentralized manner. Simulations show that the proposed algorithm can significantly improve the sum-rate of the D2D-underlaid system with a reduced complexity, as compared to its alternative based on semidefinite relaxation (SDR).

Accurate channel parameter estimation is challenging for wideband millimeter-wave (mmWave) large-scale hybrid arrays, due to beam squint and much fewer radio frequency (RF) chains than antennas. This article presents a novel joint angle and delay estimation (JADE) approach for wideband mmWave fully-connected hybrid uniform cylindrical arrays. We first design a new hybrid beamformer to reduce the dimension of received signals on the horizontal plane by exploiting the convergence of the Bessel function, and to reduce the active beams in the vertical direction through preselection. The important recurrence relationship of the received signals needed for subspace-based angle and delay estimation is preserved, even with substantially fewer RF chains than antennas. Then, linear interpolation is generalized to reconstruct the received signals of the hybrid beamformer, so that the signals can be coherently combined across the whole band to suppress the beam squint. As a result, efficient subspace-based algorithm algorithms can be developed to estimate the angles and delays of multipath components. The estimated delays and angles are further matched and correctly associated with different paths in the presence of non-negligible noises, by putting forth perturbation operations. Simulations show that the proposed approach can approach the Cramér-Rao lower bound (CRLB) of the estimation with a significantly lower computational complexity than existing techniques.

Highly accurate localization is important for wire-less communications. In this paper, we propose a new tensor-based positioning method for 5G wideband mmWave massive MIMO systems. We first develop an extended multidimensional interpolation (E-MI)-based method as the preprocessing step to suppress the frequency-dependence of the array steering vectors. By using this method, the data across the whole frequency band can be processed jointly, and the high temporal resolution offered by wideband mmWave signals can be exploited. Then, we propose a parameter decoupling (PD)-based tensor multiparameter estimation algorithm. This algorithm can suppress the noises in all of temporal, spatial and frequency domains, and thus all the parameters can be precisely estimated. A simplified perturbation term (S-PT)-based method is also presented to match the estimated parameters at low complexity. Based on the quasi-optical property of mmWave signals, we propose a novel method to compute the 3D coordinates of the target. Simulation results demonstrate the effectiveness of the proposed positioning method in the end.

To enable ultrareliable and low-latency communications (URLLCs) in the Internet of Things (IoT), a sparse-code multiple-access (SCMA)-enhanced full-duplex (FD) scheme (FD-SCMA) is proposed in this article. FD-SCMA can support short-packet transmissions of several SCMA users in the uplink (UL) and downlink (DL) simultaneously by an FD next generation node B (gNB). First, the gNB and UL users can generate and superpose signals according to the preconfigured SCMA codebooks, and simultaneously transmit the signals via occupied subcarriers in a joint SCMA pattern. The receivers at the gNB and DL users can demodulate and decode the signals with multiuser detection (MUD). With the imperfect self-interference suppression (SIS) of FD considered, the effective signal-to-noise ratio (SNR) of FD-SCMA at the gNB and DL users is formulated. The error probability of FD-SCMA in the UL and DL is also derived under a given transmission latency constraint of short-packet transmissions. In the stationary flat-fading channel, it is proved that FD-SCMA can achieve better reliability than the existing FD and SCMA schemes. In the time-invariant frequency-selective fading channel, the upper bounds for error probability of the UL and DL users in FD-SCMA are derived, respectively. Through the theoretical calculation and Monte Carlo simulation, it is verified that the superiority of FD-SCMA in supporting ultrareliable and low-latency short-packet transmissions in IoT.

Channel estimation is challenging for hybrid millimeter wave (mmWave) large-scale antenna arrays which are promising in 5G/B5G applications. The challenges are associated with angular resolution losses resulting from hybrid front-ends, beam squinting, and susceptibility to the receiver noises. Based on tensor signal processing, this paper presents a novel multi-dimensional approach to channel parameter estimation with large-scale mmWave hybrid uniform circular cylindrical arrays (UCyAs) which are compact in size and immune to mutual coupling but known to suffer from infinite-dimensional array responses and intractability. We design a new resolution-preserving hybrid beamformer and a low-complexity beam squinting suppression method, and reveal the existence of shift-invariance relations in the tensor models of received array signals at the UCyA. Exploiting these relations, we propose a new tensor-based subspace estimation algorithm to suppress the receiver noises in all dimensions (time, frequency, and space). The algorithm can accurately estimate the channel parameters from both coherent and incoherent signals. Corroborated by the Cramér-Rao lower bound (CRLB), simulation results show that the proposed algorithm is able to achieve substantially higher estimation accuracy than existing matrix-based techniques, with a comparable computational complexity.

This paper proposes a novel 3D localization method for wideband mmWave massive MIMO systems. A high dimensional linear interpolation (HDLI)-based preprocessing is first proposed to transform the frequency-associated dynamical array response vectors into the common counterparts at the reference frequency. Through this method, the received data in all frequency bands can be processed jointly, and thus the high temporal resolution provided by wideband mmWave systems can be fully exploited for position estimation. To reduce the computational complexity in the process of the parameter estimation, we then present a wideband beamspace (WBS)-based parameter estimation algorithm to estimate the angle and delay in the low-dimensional beamspace. By exploiting the quasi- optical propagation at the mmWave frequencies, a novel positioning scheme is also designed to determine the 3D location of the target. According to our analysis and simulation results, the proposed method is capable of achieving significantly reduced computational complexity, while maintaining high localization accuracy.

In this paper, a frequency-distributed variational Bayesian (F-DVB) channel estimation algorithm is proposed for wideband multiuser millimeter wave (mmWave) multiple-input multiple-output (MIMO) systems, where hybrid precoding architectures are adopted and frequency selective fading channels are assumed. First, a distributed compressed sensing-based method is employed by leveraging the joint sparsity of different subcarriers in the frequency domain, reducing the required pilot overhead significantly. Next, a hierarchical channel model, which adopts an identify-and-reject strategy to deal with hardware impairments, is designed to enhance the robustness of the proposed algorithm. Finally, the channel information is estimated by a modified variational Bayesian method, which improves the channel estimation accuracy dramatically. Simulation results verify that the proposed algorithm outperforms the state-of-the-art channel estimation strategies at low SNR and pilot overhead.

In this paper, we propose a fast channel estimation algorithm for wideband millimeter wave (mmWave) massive MIMO systems, where the hybrid precoding architectures are adopted. Stimulated by the joint sparsity of different subcarriers, a distributed compressed sensing-based strategy is presented to reduce the required pilot overhead. Based on a hierarchical channel model, a fast sparse Bayesian learning method, which can drastically release the relaxed evidence low bound, is designed to accelerate the convergence rate. Simulation results verify that the proposed algorithm is capable of achieving substantially higher estimation accuracy and convergence rate as compared to other existing Bayesian channel estimation strategies.

To meet the requirements of high energy efficiency (EE) and large system capacity for the fifth-generation Internet of Things (IoT), the use of massive multiple-input multiple-output technology has been launched in the massive IoT (mIoT) network, where a large number of devices are connected and scheduled simultaneously. This paper considers the energy-efficient design of a multipair decode-and-forward relay-based IoT network, in which multiple sources simultaneously transmit their information to the corresponding destinations via a relay equipped with a large array. In order to obtain an accurate yet tractable expression of the EE, first, a closed-form expression of the EE is derived under an idealized simplifying assumption, in which the location of each device is known by the network. Then, an exact integral-based expression of the EE is derived under the assumption that the devices are randomly scattered following a uniform distribution and transmit power of the relay is equally shared among the destination devices. Furthermore, a simple yet efficient lower bound of the EE is obtained. Based on this, finally, a low-complexity energy-efficient resource allocation strategy of the mIoT network is proposed under the specific quality-of-service constraint. The proposed strategy determines the near-optimal number of relay antennas, the near-optimal transmit power at the relay, and near-optimal density of active mIoT device pairs in a given coverage area. Numerical results demonstrate the accuracy of the performance analysis and the efficiency of the proposed algorithms.

In this paper, a novel three-dimensional (3-D) indoor positioning scheme is proposed for millimeter-wave (mmWave) massive multiple-input multiple-output (MIMO) systems. Its operation is based upon a hybrid received signal strength and angle of arrival (RSS-AoA) positioning scheme, which employs only a single access point equipped with a large-scale uniform cylindrical array. To reduce the high computational complexity imposed by the large number of antennas used in mmWave massive MIMO (M3-MIMO) systems, we firstly propose a novel channel compression method. By proper quantization and selection of the received mmWave signals, which exhibit quasioptical and sparse multipath characteristics, the channel compression method reduces the dimension of the received signal space while maintaining the accuracy of the position estimation. Then, we propose a beamspace transformation approach to transform signal vectors in the element space to the beamspace, and thus the computational complexity of the angle estimation is significantly reduced. Finally, a novel hybrid RSS-AoA positioning scheme is designed for the computations of the 3-D coordinates of the target mobile terminal. Simulation results have shown that the proposed indoor positioning scheme is capable of achieving high accuracy as well as significantly lower computational complexity as compared to other previously known indoor positioning techniques.