Due to the multi-dimensional search in the near-field (NF), the excessive computational burden has become one of the major problems. To address this issue, this paper proposes a computationally efficient angle and distance estimation algorithm for extremely large uniform planar array (UPA) systems. To reduce computation, the proposed algorithm decouples 3D search into a series of 2D search and 1D search. The 2D search estimates the azimuth and elevation, followed by the 1D search that estimates the distance. While the proposed algorithm brings significant improvement in computational complexity, the estimation of the proposed algorithm is guaranteed to be accurate as long as the distance between the receiver and transmitter (or scatterer) exceeds a specific threshold. For UPAs, we establish that this threshold is around a quarter of the Rayleigh distance. The simulation results demonstrate that the proposed algorithm has a superior accuracy-complexity trade-off compared to existing works.

This paper proposes a blocker-aware multicarrier integrated sensing and communication (ISAC)-non orthogonal multiple access (NOMA) system, leveraging hybrid beamforming and dynamic power allocation to enhance spectrum efficiency in 6G networks. Recognizing the performance degradation caused by environmental blockers, the system introduces a joint wave-form design that ensures robust operation under varying channel conditions. A channel switching mechanism is deployed to reroute communication through alternative non-line-of-sight paths when the primary line-of-sight links are obstructed. Moreover, a dynamic power allocation strategy enforces a minimum rate constraint for the weak NOMA user, ensuring consistent quality of service. Extensive simulations over multiple blockage scenarios and signal to noise (SNR) conditions validate the effectiveness of the proposed solution. Notably, under severe blockage, the system achieves up to a 400% sensing rate enhancement at 15 dB SNR, with only a 20% reduction in communication rate. These results corroborate the system’s ability to adapt and optimize joint sensing-communication performance in practical deployment environments.

Handover (HO) is essential for ensuring seamless connectivity but poses significant challenges, such as HO interruption time, frequent HO, and HO failures, especially in dense urban environments with millimeter wave small-cell deployments. To address these challenges, this paper proposes a novel HO procedure that leverages a digital twin of the radio access network (RAN Twin), which is compatible with the 3GPP New Radio standard, Release 18. The proposed RAN Twin integrates multimodal sensing data and historical network information to generate an optimal list of candidate cells and share user equipment context data. This enables early synchronization with the target HO cell, minimizing interruption time. Simulation results demonstrate that the proposed approach outperforms the baseline 3GPP HO in terms of latency and HO rate, underscoring its potential to enhance overall network performance.

Recently, large multimodal models (LMMs) have garned significant attention for interpreting multimodal inputs to generate desired outputs. With the exponential growth of the range of tasks performed by autonomous devices, the central unit (CU) needs to handle LMMs to control these devices. Ensuring seamless command delivery to these devices requires scheduling, a task to allocate resource blocks (RBs) and choose modulation and coding scheme (MCS) index. However, in 6G environments, sudden channel changes make this task difficult. In this paper, we propose a novel LMM-based scheduling technique to address this issue. The core idea is to use LMM to predict future channel parameters (e.g., distance and angles) by analyzing both the visual sensing information and pilot signals. By predicting the presence of reliable path and geometric information of users from the sensing information and then combining these with past channel estimates, we can predict future channel parameters accurately, using which we can proactively make channel-aware scheduling decisions. Numerical results demonstrate that the proposed technique outperforms the conventional scheduling techniques in terms of total system throughput.

Reconfigurable intelligent surface (RIS) is an emerging technique for robust millimeter-wave (mmWave) multiple-input multiple-output (MIMO) systems. In this paper, we study the channel estimation problem for extremely large-scale RIS (XL-RIS) assisted multi-user XL-MIMO systems with hybrid beamforming structures. In this system, we propose an unified channel estimation method that yields a notable estimation accuracy in the near-field BS-RIS and near-field RIS-User channels (in short, near-near field channels), far-near field channels, and far-far field channels. Our key idea is that the effective channels to be estimated can be each factorized as the product of low-rank matrices (i.e., the product of a common matrix and a user-specific coefficient matrix). The common matrix whose columns are the basis of the column space of the BS-RIS channel is efficiently estimated via a collaborative low-rank approximation (CLRA). Leveraging the hybrid beamforming structures, we develop an efficient iterative algorithm that jointly optimizes the user-specific coefficient matrices. Via experiments and complexity analysis, we verify the effectiveness of the proposed channel estimation method (named CLRA-JO) for the three categories of wireless channels.

In this paper, we propose a two-dimensional signal path classification (2D-SPC) for reconfigurable intelligent surface (RIS)-assisted near-field (NF) localization. In the NF regime, multiple RIS-driven signal paths (SPs) can contribute to precise localization if these are decomposable and the reflected locations on the RIS are known, referred to as SP decomposition (SPD) and SP labeling (SPL), respectively. To this end, each RIS element modulates the incoming SP’s phase by shifting it by one of the values in the phase shift profile (PSP) lists satisfying resolution requirements. By interworking with a conventional orthogonal frequency division multiplexing (OFDM) waveform, the user equipment can construct a 2D spectrum map that couples each SP’s time-of-arrival (ToA) and PSP. Then, we design SPL by mapping the SPs with the corresponding reflected RIS elements when they share the same PSP. Given two unlabeled SPs, we derive a geometric discriminant by checking whether the current label is correct. It can be extended to more than three SPs by sorting them using pairwise geometric discriminants between adjacent ones. From simulation results, it has been demonstrated that the proposed 2D-SPC achieves consistent localization accuracy while poor accuracy in a benchmark, even if insufficient PSPs are given.

This paper presents a robust near-field (NF) beam tracking algorithm for terahertz communications based on deep Q-network (DQN). Traditional NF beam tracking methods relying on mobility models are fatal in ultra-massive MIMO systems, where even the slightest error could result in beam tracking failures. Thus, the proposed algorithm aims to maintain a stable beamforming gain by tracking the mobile station through the analysis of received signals without requiring mobile dynamics. By utilizing DQN, the proposed algorithm strengthens its tracking capability from online experiences and updates the combining beam towards positions expected to maximize beamforming gain. Throughout simulations, we compare the proposed algorithm with the Bayesian filter-based NF beam tracking algorithm. The simulation results confirm the robustness of the proposed algorithm for NF beam tracking, especially for abrupt changes in mobile dynamics.

In this paper, we derive the Cramér-Rao bounds (CRBs) on the positioning errors for narrow-band low earth orbit positioning, navigation, and timing (LEO-PNT) systems. Fisher information analysis is performed to characterize the CRBs for errors in Doppler and carrier phase measurements. In addition, we analyze the effect of the carrier phase and Doppler on the CRB for positioning. Numerical simulations were conducted in different settings. To show the relevance of CRB, we compare our results to the maximum likelihood (ML) estimator. Our findings show that using the carrier phase significantly improves the positioning in LEO-PNT systems.

Radio simultaneous localization and mapping (SLAM) with active sensing, such as radar and LiDAR, faces difficulty in detecting mirror-like walls that cause specular reflection. To solve this problem, the proposed radio SLAM algorithm merges active and passive sensing. Passive sensing exploits low-frequency radio signals that are specularly reflected from objects. However, maps created by active and passive sensing have different characteristics. Thus, the proposed algorithm fuses heterogeneous maps using Dirichlet process-based clustering to create one integrated map and improve mapping accuracy. Simulation results demonstrate that the proposed radio SLAM algorithm outperforms the classical methods only with active or passive sensing in mixed reflection type environments.

Recent progress of deep learning (DL) and computer vision (CV) have paved the way for the application of DL-based CV technologies in 6G wireless communications. DL-based CV is data-hungry, and thus it is important to collect a massive vision dataset designed for wireless applications. An aim of this paper is to propose a vision dataset called Vision Objects for Millimeter and Terahertz Communications (VOMTC) consisting of 20,232 pairs of RGB and depth images, each of which is manually annotated with three object classes (person, mobile, and laptop) and their corresponding boxes. To demonstrate the efficacy of VOMTC, we develop a CV-aided beamforming technique called VOMTC-based beam management (VBM). In VBM, the location of the mobile is extracted via the VOMTC-trained object detector and then a beam heading toward the extracted location is generated. Due to the use of this special object detector tailored for identifying mobiles, VBM enhances the chance of transmitting directional beams to the mobile location. Using the VOMTC test dataset, we show that VBM achieves 15% improvement in the data rate over the conventional CV-aided beam management.

Reconfigurable Intelligent Surfaces (RIS) and Un-manned Aerial Vehicles (UAVs) have emerged as promising tech-nologies for the 6th-Generation (6G) network. The integration of RIS with the UAV (RIS-UAV) can enhance ground communication by providing a 360°panoramic reflection. Existing RIS-UAV mainly considers passive elements which suffer from double path loss problems. This motivates the use of the hybrid RIS-UAV equipped with both active and passive RIS elements. This paper investigates the energy efficiency maximisation problem for the hybrid RIS-UAV by optimising the placement of the UAV, subject to the UAV's permitted altitude range. The non-convex optimisation problem is addressed using Particle Swarm Optimisation (PSO) tool and distributed learning algorithm. The numerical results show that the proposed distributed learning algorithm is preferred when optimising the energy efficiency of the hybrid RIS-UAV system. In addition, hybrid RIS-UAV outperforms the fully passive RIS-UAV and the active amplify-and-forward (AF) relay in terms of energy efficiency of the system.

In this paper, we propose a graph convolutional network (GCN)-based cooperative localization method for 5G mobile networks using round-trip time (RTT) and angle of arrival (AoA) measurements. Implemented in an Urban Macrocell (UMa) scenario with non-line-of-sight (NLOS) conditions and high measurement noise, our approach enhances localization accuracy and reduces computational complexity. Compared with existing cooperative and non-cooperative methods, our simulation results demonstrate that the proposed method satisfies the 3GPP Release 17 requirements, achieving a horizontal accuracy of less than 0.94 m for 90% of user equipments (UEs) and a computational time of 0.60 ms even in non-line of sight (NLOS) environments. This method leverages sidelink-positioning reference signals (SLPRS) to facilitate device-to-device positioning without base station communication, proving effective in challenging environments.

This paper presents an efficient channel estimation algorithm for multi-user reconfigurable intelligent surface (RIS)-aided millimeter-wave (mmWave) systems. In this paper, the concept of low rank matrix completion (LRMC) is exploited to reduce beam training overhead for channel estimation. The proposed beam training samples part of each channel matrix in a special pattern that is suitable for LRMC with less beam training overhead. Then, the beam training is followed by multi-user channel estimation. For computationally efficient channel estimation, the proposed algorithm exploits the property that all the channel matrices share the same low-rank subspace in multi-user RIS-aided systems. The shared subspace is derived by combining candidate subspaces, which are estimated by fast alternating least squares (FALS) from partially observed channels. With the shared subspace, all the missing entries of channels are recovered via computationally efficient linear estimation. The simulations and complexity analysis demonstrate that the proposed algorithm shows a superior accuracy-complexity trade-off compared to existing works.

In this paper, we propose location-aware beam training and multi-dimensional atomic norm minimization (ANM)-based channel estimation for reconfigurable intelligent surface (RIS)-aided millimeter-wave systems. The use of both location information and RIS beamwidth adaptation allows a significant reduction of beam training overhead. However, considering a trade-off between accuracy and beam training overhead, this may induce inaccurate channel estimation. Nevertheless, superior channel estimation performance is achieved by multi-dimensional ANM techniques, which have been shown to be effective in capturing cascaded structures such as the channel in RIS-aided systems. In the proposed work, a cascade of BS-to-RIS channel and RIS-to-BS channel is represented as a linear combination of either steering vectors, 2D steering vectors, or 3D steering vectors, and ANM with appropriate dimension is applied to estimate the channel. From simulation results, it has been demonstrated that location-aware channel estimation via 2D ANM and 3D ANM achieves excellent estimation accuracy along with a reduced beam training overhead.

In the upcoming sixth generation (6G) of wireless communication systems, reconfigurable intelligent surfaces (RISs) are regarded as one of the promising technological enablers, which can provide programmable signal propagation. Therefore, simultaneous radio localization and mapping (SLAM) with RISs appears as an emerging research direction within the 6G ecosystem. In this paper, we propose a novel framework of RIS-enabled radio SLAM for wireless operation without the intervention of access points (APs). We first design the RIS phase profiles leveraging prior information for the user equipment (UE), such that they uniformly illuminate the angular sector where the UE is probabilistically located. Second, we modify the marginal Poisson multi-Bernoulli SLAM filter and estimate the UE state and landmarks, which enables efficient mapping of the radio propagation environment. Third, we derive the theoretical Cramér-Rao lower bounds on the estimators for the channel parameters and the UE state. We finally evaluate the performance of the proposed method under scenarios with a limited number of transmissions, taking into account the channel coherence time. Our results demonstrate that the RIS enables solving the radio SLAM problem with zero APs, and that the consideration of the Doppler shift contributes to improving the UE speed estimates.

Crowdsourcing is considered an efficient and promising paradigm for constructing large-scale signal fingerprint radio maps due to the proliferation of Wi-Fi-enabled devices. However, a crowdsourced indoor positioning system (IPS) has to handle diverse devices and the inherent heterogeneity in received signal strength (RSS) measurements. To address the device heterogeneity problem, differential fingerprinting methods have been explored, which mitigate the device characteristics that cause RSS from different commercial devices to report differently. In this article, we focus on mean differential fingerprinting (MDF) that produces the differential fingerprints by subtracting the mean RSS value of all access points from the original RSS fingerprints. We study the localization performance of the MDF method by means of the Cramér–Rao lower bound (CRLB) and show analytically that it outperforms another method that addresses device diversity. Furthermore, we evaluate the localization accuracy of existing solutions using real-life Wi-Fi RSS data sets collected by multiple consumer devices. The experimental results confirm our analytical findings and demonstrate the effectiveness of the MDF method to mitigate device diversity, as well as other factors that affect the RSS readings, including the device carrying mode and power control schemes of the Wi-Fi infrastructure, thus contributing to the wider adoption of crowdsourced IPS.

In this paper, we present a beam tracking algorithm based on the deep Q-network (DQN) for mobile millimeter-wave (mmWave) communications. The proposed algorithm determines the receive beam angle from the received signals without knowing the channel model and dynamics. It uses the received signals of the current and previous time slots to design the state and reward of the DQN. Our goal is to maximize the signal-to-noise ratio by the actions of the designed DQN. A significant computational complexity reduction is achieved since the receiver does not need to run complicated signal processing algorithms once the DQN is properly trained. Therefore a practical implementation of mmWave beam tracking with a very large number of antennas under harsh mobile environments becomes feasible. Through the extensive simulations, we verified the performance of the proposed algorithm and demonstrated robustness to the system uncertainty and low computational complexity in comparison with particle filter and the Q-learning.

Reconfigurable intelligent surface (RIS), one core element in 6G, opens a new opportunity to design near-field (NF) localization since a signal's propagation distance can be different depending on the reflected points on large RIS. For the design of NF localization to be effective, signals reflected on distinct RIS points should be profiled without interfering with the others, called signal classification (SC). In this paper, we propose a simple yet novel SC technique, called linear phase modulation (LPM), where sequences of distant RIS elements' phases are linearly modulated with different rates. Along with the conventional or-thogonal frequency division multiplexing waveform, LPM makes it possible to classify the reflected signals by distant RIS elements as well as estimate their propagation distances using a two-dimensional Fourier transform (2D-FT) technique. It exploits one more signal dimension for performance improvement than conventional one-dimensional FT (1D-FT) based SC. Through analytic and numerical studies, we verify the effectiveness of the proposed SC using LPM by comparing its localization accuracy with several benchmarks designed based on 1D-FT.

This paper proposes a two-stage beam training and a channel estimation based on fast alternating least squares (FALS) for reconfigurable intelligent surface (RIS)-aided millimeter-wave systems. To reduce the beam training overhead, only selected columns and rows of the channel matrix are observed by two-stage beam training. This beam training produces a partly observed channel matrix with low coherence, which enables the low rank matrix completion technique to recover unobserved entries. Unobserved entries are recovered by FALS, which alternatingly updates the left and the right singular vectors that comprise the channel. Simulation results and analysis show that the proposed algorithm is computationally efficient and has superior accuracy to existing algorithms.

In this work, we present a deep neural network (DNN)-based indoor fingerprinting localization method with WiFi fine time measurements (FTM). The proposed method leverages the WiFi FTM and its variance as environment features to provide accurate location estimation. An $i$ -th layer DNN structure used in this paper is implemented by back propagation using an Adam optimizer. The weights and the bias of the $l-\text{th}$ layer that minimize the loss function is computed in order to minimize the positioning mean squared error (MSE). Experimental results using real-world data obtained in a typical office setting proves the efficiency of the proposed solution. The performance of the system is remarkably improved, using the $600\times 600$ hidden layer size of the DNN, we achieved an average positioning accuracy of 0.7 m and 0.9 m for the 68-th percentiles $(1-\sigma)$ and 95-th percentiles $(2-\sigma)$ respectively.

This paper presents the first real-world demonstration of a vision-aided 28 GHz mmWave transmission with a joint Tx-Rx beam tracking for 5G communications. This demonstration employs the architecture of the joint Tx-Rx beam tracking which is designed to update Tx and Rx beams simultaneously based on computer vision with deep learning model. For the demonstration setup, we build a complete end-to-end real-time 5G 28 GHz system testbed. Through this demonstration, we showcase how the presented vision-aided mmWave transmission can effectively sustain a high-throughput link despite Tx-Rx misalignment induced by user mobility. A demo video can be found at https://youtu.be/Mu6pxEYuvbY.

Millimeter wave (mmWave) signals are useful for simultaneous localization and mapping (SLAM), due to their inherent geometric connection to the propagation environment and the propagation channel. To solve the SLAM problem, existing approaches rely on sigma-point or particle-based approximations, leading to high computational complexity, precluding real-time execution. We propose a novel low-complexity SLAM filter, based on the Poisson multi-Bernoulli mixture (PMBM) filter. It utilizes the extended Kalman (EK) first-order Taylor series based Gaussian approximation of the filtering distribution, and applies the track-oriented marginal multi-Bernoulli/Poisson (TOMB/P) algorithm to approximate the resulting PMBM as a Poisson multi-Bernoulli (PMB). The filter can account for different landmark types in radio SLAM and multiple data association hypotheses. Hence, it has an adjustable complexity/performance trade-off. Simulation results show that the developed SLAM filter can greatly reduce the computational cost, while it keeps the good performance of mapping and user state estimation.

5G new radio will provide a new paradigm in high accurate vehicle localization, reinforced by the use of large antenna arrays along with carefully designed broadband radio technology. However, a high computational load still remains unravelled in the context of cooperative localization, albeit with its advantages of high-precision localization. To alleviate such the computational burden, we develop a reliable technique of cooperative localization, addressed as a constraint satisfaction problem. A constraint satisfaction formalism for cooperative localization readily enables to recast a formulation of the decentralized optimization. Its efficient solution of the optimization is developed in a distributed manner. Simulation results demonstrate that the proposed approach saves the requested computational loads significantly while sustaining the satisfactory accuracy of cooperative localization.

In this article, we propose a novel deep reinforcement learning (DRL) approach for controlling multiple unmanned aerial vehicles (UAVs) with the ultimate purpose of tracking multiple first responders (FRs) in challenging 3-D environments in the presence of obstacles and occlusions. We assume that the UAVs receive noisy distance measurements from the FRs which are of two types, i.e., Line of Sight (LoS) and non-LoS (NLoS) measurements and which are used by the UAV agents in order to estimate the state (i.e., position) of the FRs. Subsequently, the proposed DRL-based controller selects the optimal joint control actions according to the Cramér–Rao lower bound (CRLB) of the joint measurement likelihood function to achieve high tracking performance. Specifically, the optimal UAV control actions are quantified by the proposed reward function, which considers both the CRLB of the entire system and each UAV's individual contribution to the system, called global reward and difference reward, respectively. Since the UAVs take actions that reduce the CRLB of the entire system, tracking accuracy is improved by ensuring the reception of high quality LoS measurements with high probability. Our simulation results show that the proposed DRL-based UAV controller provides a highly accurate target tracking solution with a very low runtime cost.

In this paper, we propose a deep learning-based beam tracking method for millimeter-wave (mmWave) communications. Beam tracking is employed for transmitting the known symbols using thesounding beamsand tracking time-varying channels to maintain a reliable communication link. When the pose of a user equipment (UE) device varies rapidly, the mmWave channels also tend to vary fast, which hinders seamless communication. Thus, models that can capture temporal behavior of mmWave channels caused by the motion of the device are required, to cope with this problem. Accordingly, we employ a deep neural network to analyze the temporal structure and patterns underlying in the time-varying channels and the signals acquired by inertial sensors. We propose a model based on long short term memory (LSTM) that predicts the distribution of the future channel behavior based on a sequence of input signals available at the UE. This channel distribution is used to 1) control the sounding beams adaptively for the future channel state and 2) update the channel estimate through themeasurement update stepunder a sequential Bayesian estimation framework. Our experimental results demonstrate that the proposed method achieves a significant performance gain over the conventional beam tracking methods under various mobility scenarios.

5G millimeter wave (mmWave) signals can be used to jointly localize the receiver and map the propagation environment in vehicular networks, which is a typical simultaneous localization and mapping (SLAM) problem. Mapping the environment is challenging, due to measurements comprising both specular and diffuse multipath components, where diffuse multipath is usually considered as a perturbation. We here propose a novel method to utilize all available multipath signals from each landmark for mapping and incorporate this into a Poisson multi-Bernoulli mixture for the 5G SLAM problem. Simulation results demonstrate the efficacy of the proposed scheme.

In 5G mmWave, simultaneous localization and mapping (SLAM) allows devices to exploit map information to improve their position estimate. Even the most basic SLAM filter based on a Rao-Blackwellized particle filter (RBPF) combined with a probability hypothesis density (PHD) map representation exhibits high complexity. This paper proposes a new implementation method for the 5G SLAM using message passing (MP) and the cubature Kalman filter (CKF). We demonstrate that the proposed method significantly reduces the complexity while retaining the SLAM accuracy of the RBPF-PHD approach.

5G is expected to enable simultaneous vehicle localization and environment mapping (SLAM). Furthermore, vehicular networks will be covered with 5G small cells, wherein the map information is collected at each base station (BS) and then fused so as to promote the overall performance of SLAM. In 5G multi-cell SLAM, there are challenges such as the unknown number of targets, uncertainty regarding the association between the targets and the measurements, unknown types of targets, as well as map management among BSs. To address those challenges, we propose a new method for 5G multi-cell SLAM which comprises a joint cubature Kalman filter and multi-model probability hypothesis density, and a map fusion routine. Simulation results demonstrate that the proposed method solves the aforementioned challenges and also improves vehicle state and map estimates.

In this paper, we propose an efficient beam training technique for millimeter-wave (mmWave) communications. Beam training should be performed frequently when some mobile users are under high mobility to ensure the accurate acquisition of the channel state information. To reduce the resource overhead caused by frequent beam training, we introduce a dedicated beam training strategy which sends the training beams separately to a specific high mobility user (called a target user) without changing the periodicity of the conventional beam training. The dedicated beam training requires a small amount of resources because the training beams can be optimized for the target user. To satisfy the performance requirement with a low training overhead, we propose the optimal training beam selection strategy which finds the best beamforming vectors yielding the lowest channel estimation error based on the target user's probabilistic channel information. This dedicated beam training is combined with the greedy channel estimation algorithm that accounts for sparse characteristics and temporal dynamics of the target user's channel. Our numerical evaluation demonstrates that the proposed scheme can maintain good channel estimation performance with significantly less training overhead compared to the conventional beam training protocols.

5G millimeter wave (mmWave) signals can enable accurate positioning in vehicular networks when the base station and vehicles are equipped with large antenna arrays. However, radio-based positioning suffers from multipath signals generated by different types of objects in the physical environment. Multipath can be turned into a benefit, by building up a radio map (comprising the number of objects, object type, and object state) and using this map to exploit all available signal paths for positioning. We propose a new method for cooperative vehicle positioning and mapping of the radio environment, comprising a multiple-model probability hypothesis density filter and a map fusion routine, which is able to consider different types of objects and different fields of views. Simulation results demonstrate the performance of the proposed method.

Indoor location systems that rely on WiFi signal strength values from the existing building infrastructure deliver adequate accuracy with no installation cost at the expense of time and effort to populate the signal database. To this end, crowdsourcing has been widely explored to leverage on large volumes of user-collected data; however, mobile devices that report signal strength differently, known as device diversity, limit its applicability in practice. We consider crowdsourced location systems and present a qualitative (in terms of analytical results) and a quantitative (with respect to real-life experimental data) evaluation of several approaches that are robust to diverse devices while focusing on differential signal strength methods.

Due to the millimeter wave's high path loss, beamforming is required to provide higher gain. Conventional beamformer is widely used for its simplicity in many other studies. However, it is vulnerable to unexpected interferences since it has high level of sidelobes. This paper proposes a new beamformer which can be used to make an interference-robust codebook for receivers. The idea of the new method is to null out sidelobes of conventional beamformer by using the linear constraint minimum variance (LCMV) beamformer algorithm. It modifies the original LCMV beamformer to be applicable to the analog beamformer. To compare its performance with conventional beamformer, an empirical millimeter wave channel model was used. Results show a significant increase in signal to interference plus noise ratio (SINR) on the general millimeter wave environment.

5G new radio (NR) provides new opportunities for accurate positioning from a single reference station: large bandwidth combined with multiple antennas, at both the base station and user sides, allows for unparalleled angle and delay resolution. Nevertheless, positioning quality is affected by multipath and clock biases. We study, in terms of performance bounds and algorithms, the ability to localize a vehicle in the presence of multipath and unknown user clock bias. We find that when a sufficient number of paths is present, a vehicle can still be localized thanks to redundancy in the geometric constraints. Moreover, the 5G NR signals enable a vehicle to build up a map of the environment.

In this paper, we propose an optimal power allocation scheme for multiple unmanned aerial vehicles (UAVs) communications that maximizes the sum rates at the UAVs. The proposed scheme exploits the location information of multiple UAVs and adapts the transmit power based on the UAV distance from the transmitting base station (BS). The location information enables the BS to dynamically adapt the transmit power over the UAVs entire flight time to maximize the average sum rate. The average sum rate and power allocated to the UAVs during the flight time are used as the performance metrics. In addition, the proposed scheme is compared with the fixed power allocation scheme, and the results give useful insight into UAV location-aware communication. The simulation results show that at low transmit power, a sum rate improvement can be achieved when the adaptive power transmission scheme is applied as compared to a fixed transmit power schemes.

This paper presents a cooperative localization strategy via a distributed optimization technique known as the alternating direction method of multipliers (ADMM). In a Vehicular Ad hoc Network (VANET) where a vehicle communicates with neighboring vehicles via vehicle-to-vehicle (V2V) and vehicle-to-infrastructure (V2I) communication, the developed algorithm utilizes three types of measurements, which are the pairwise relative distance, angle of arrival, and absolute positions for a subset of vehicles in cooperative localization. The proposed algorithm is designed to provide an attractive solution for the localization of autonomous driving vehicle in the GPS-denied (urban) environment. Simulation results confirm the potency of distributed ADMM-based cooperative localization for autonomous driving in 5G-based VANETs.

We study, via combinatorial enumeration, the probability of k-hop connection between two nodes in a wireless multihop network. This addresses the difficulty of providing an exact formula for the scaling of hop counts with Euclidean distance without first making a sort of mean field approximation, which in this case assumes all nodes in the network have uncorrelated degrees. We therefore study the mean and variance of the number of k-hop paths between two vertices x, y in the random connection model, which is a random geometric graph where nodes connect probabilistically rather than deterministically according to a critical connection range. In the example case where Rayleigh fading is modeled, the variance of the number of three hop paths is in fact composed of four separate decaying exponentials, one of which is the mean, which decays slowest as Iix - yIi → ∞. These terms each correspond to one of exactly four distinct substructures which can form when pairs of paths intersect in a specific way, for example at exactly one node. Using a sum of factorial moments, this relates to the path existence probability. We also discuss a potential application of our results in bounding the broadcast time.

In this paper, we propose a new recursive sparse channel recovery algorithm which can track time-varying support of angular domain channel response vector in mobility scenario for millimeter wave-band communications. We model the angle of departure (AoD) and the angle of arrival (AoA) using discrete state Markov random process and derive joint estimation of the time-varying support and amplitude of the angular domain channel vector. Using sequential Monte Carlo (SMC) method, the proposed channel estimation scheme tracks the support by drawing the samples from a posteriori distribution of the support indices while capturing the dynamics of time-varying amplitude using Kalman filter. Our simulation results show that the proposed algorithm yields significantly better tracking performance than the existing compressed sensing schemes.

We study the effect of anisotropic radiation on wireless network complexity. To this end, we model a wireless network as a random geometric graph where nodes have random antenna orientations as well as random positions, and communication is affected by Rayleigh fading. Complexity is quantified by computing the Shannon entropy of the underlying graph model. We use this formalism to develop analytic scaling results that describe how complexity can be controlled by varying key system parameters such as the transmit power and the directivity of transmissions in large-scale networks. Our results point to striking contrasts between power scaling and directivity scaling in the large connection range regime.

This paper presents a multihop range-free localization algorithm that tolerates network anisotropy with a small number of anchors. A detoured path detection is proposed which measures the deviation in the hop count between the direct and shortest paths of a node pair. A novel distance estimation method is introduced to approximate the shortest path based on the path deviation and to estimate their Euclidean distance by taking into account the extent of the detour of the approximate shortest path. Compared to other range-free localization algorithms, the proposed algorithm requires fewer anchors while achieving higher localization accuracy in anisotropic networks. We demonstrated its superiority over existing range-free localization algorithms through extensive computer simulations.

This paper presents a nearest neighbor selection algorithm for real-time WiFi fingerprinting positioning with the assist of inertial measurement unit (IMU) measurements. The WiFi fingerprinting positioning using received signal strength (RSS) measurements suffers from the RSS variation problem. Due to this problem, reference points that are irrelevant to the user's position are selected, and the positioning accuracy decreases. To overcome the RSS variation problem, we propose an IMU-assisted nearest neighbor selection algorithm that filters out irrelevant reference points based on the position prediction with IMU measurements. The proposed algorithm was evaluated and compared with the conventional ii-nearest neighbors (KNN) selection and the IMU-based dead-reckoning positioning in a real indoor environment. The experimental results showed that the average positioning error of the proposed algorithm was 2.41 m, whereas those of the KNN-based fingerprinting algorithm and the IMU-based dead-reckoning positioning were 3.57 m and 15.27 m.

Due to the received signal strength (RSS) variation, WiFi indoor positioning techniques using RSS have difficulties to provide good location estimates. To mitigate the effect of the RSS variation, this paper presents a Kalman filter-based positioning algorithm that is combined with pedestrian dead reckoning and RSS-based fingerprinting positioning. The RSS recovery and clustering methods are also introduced to enhance the accuracy of the fingerprinting positioning. Unlike other existing algorithms, the proposed algorithm estimates biases accumulated in RSS measurements based on the recursive least square estimation and removes them from the measurements. Reference points are effectively selected with clustering using the recovered RSS measurements. Hence, a more accurate location estimate can be obtained in the existence of the RSS variation. The proposed algorithm is implemented into an Android-based smartphone for test.

This paper presents a multihop range-free localization algorithm to enhance the localization accuracy in anisotropic networks with a small number of anchors. We derive potential locations of a normal node and corresponding probabilities in terms of the average one-hop internodal distance, the average hop progress and the hop counts to anchors. A novel distance estimation based on the potential locations is proposed to tolerate network anisotropy from nonuniform node deployments, irregular regions, and irregular radio simultaneously. Compared to other range-free algorithms, the proposed algorithm requires fewer anchors while achieving higher localization accuracy. The superiority of the proposed algorithm against other algorithms is demonstrated through computer simulations.