Wi-Fi Fine Timing Measurement (FTM) enables accurate indoor positioning by measuring round-trip propagation delay. However, its performance degrades significantly in congested networks due to the increased channel contention and protocol overhead. This paper quantitatively analyzes this performance degradation and proposes a one-way ranging (OWR)-based positioning method, which passively utilizes signals received from asynchronous transmitters to enable scalable and privacy-preserving positioning for the receiver. The proposed method employs an extended Kalman filter (EKF) to jointly perform clock synchronization with multiple asynchronous transmitters and estimate the position of the receiver. Simulation results demonstrate that while FTM performance degrades as network congestion increases, the proposed OWR approach maintains stable accuracy and thus outperforms FTM in highly congested environments.

Radar sensing is increasingly deployed across diverse Internet of Things (IoT) domains, from intelligent transportation and industrial automation to human-centric monitoring. With the growing density of radars in these environments, radar-to-radar interference has emerged as a critical challenge, particularly for conventional systems that lack waveform distinctiveness. This trend underscores the need for digital radars capable of source identification and interference resilience. In this work, we present a software-defined radio (SDR) platform for digital radar, implemented on a Xilinx Radio Frequency System-on-Chip (RFSoC) device that supports multi-gigahertz sampling. The proposed architecture provides flexible waveform generation and processing entirely in software, thereby accommodating arbitrary digital sequences, including but not limited to phase-modulated continuous wave (PMCW). While this paper focuses on PMCW radar as a representative case study, the system can be readily extended to alternative digital waveforms for broader sensing and communication scenarios. We validate the prototype through extensive indoor and outdoor experiments, demonstrating accurate range and velocity estimation under realistic conditions. The results confirm that SDR-based digital radar offers a practical and reconfigurable approach for future IoT-oriented radar deployments, paving the way toward interference-resilient and software-defined sensing platforms.

Range-based positioning systems require accurate locations of anchor nodes; however, obtaining these locations is often costly and time-consuming. In this paper, we propose a novel framework to automatically estimate the locations of Wi-Fi access points (APs) using unlabeled data passively collected from mobile users as they naturally navigate the environment. The proposed framework is primarily based on the observation that vertical movement within a building is constrained to a limited number of transition points, such as escalators and elevators. Accordingly, the framework first detects vertical transitions of users by analyzing barometer readings. By simultaneously considering the transitions of all users, it determines the absolute floor level of each user throughout the building. Next, user trajectories are reconstructed using inertial sensor measurements and partitioned into segments corresponding to individual floors. A cost function is designed to align each segmented trajectory on the determined floor level by selecting the most probable entry and exit points where vertical transitions are possible. The locations of APs are then estimated using Wi-Fi signal strengths measured along the aligned trajectories. The effectiveness of the proposed framework was evaluated in a large-scale, 10-floor shopping mall, where each floor spans up to 30,000 m. Using unlabeled datasets collected from multiple users, the locations of 1,214 APs operating on the 2.4 GHz band were estimated. Based on this database, user locations could be obtained during the online phase, demonstrating an average accuracy of less than 5 m. This result confirms the successful operation and practical applicability of the proposed framework.

Phase modulated continuous wave (PMCW) radar is one of promising options for the enhanced sensing capability and the multiple access availability. In this paper, we focus on the advantage of the high-resolution multi-input multi-output (MIMO) radar with fast-time code division multiplexing (CDM). First, we formulate the signal model of the fast-time CDM-MIMO PMCW radar by assigning different code sequences to each transmitter (Tx). We investigate the level of self-code interference induced by simultaneously transmitting Tx signals, as a function of the number of Tx and the type of sequence. For self-code interference cancellation, we propose compressive sensing-based greedy algorithms with significantly reduced computation complexity using the property of the circulant matrix. We show via simulation that the proposed methods can effectively mitigate self-code interference and generate radar images by implementing virtual antenna array with significantly enhanced peak to sidelobe ratio and range/angle estimation accuracy. Since the proposed method can fully leverage the advantages of CDM whose transmit signals share time-frequency resources, it can be utilized for high density Internet of Things networks and increase the fundamental performance of sensing while the reduced computations are suitable for the real-time operation.

To meet the high data rate demands of 5 th generation mobile communication (5G), the millimeter-wave (mmWave) frequency band is crucial for securing extensive bandwidth. As cellular systems evolve toward 6th generation mobile communication (6G), the sub-terahertz (sub-THz) frequency band has received growing research attention, and comparative studies of the two aforementioned bands are underway. However, a significant frequency band gap between the two bands and absence of a third generation partnership project reference model present challenges in their direct comparison. These issues can be addressed by comparing their one-to-one mapped data when conditions are identical in both bands. To this end, we conducted a comparative analysis of the propagation characteristics of mmWave 28 GHz and sub-THz 140 GHz bands under identical conditions. This analysis encompassed multi-path phenomena, indoor and outdoor pathlosses, outdoor-to-indoor pathloss, as well as penetration losses, including those related to human body and glass. Experimental results show that line of sight and non-line of sight at the sub-THz band have sparse multi-path characteristics and experience much higher loss than the mmWave band. This study provides valuable insights into predicting performance when sub-THz equipment is installed near existing mmWave equipment.

Modern autonomous driving vehicles are equipped with a number of sensors to perceive the surrounding environment and enable automation of various systems. Especially, the passenger occupancy detection system can be utilized to detect a child left unattended in a parked vehicle, and efficiently manage the energy inside the vehicle. In this study, we propose a method of detecting the occupancy of passengers inside a vehicle using multichannel 60-GHz frequency-modulated continuous-wave (FMCW) radar. The received signal from the radar is converted into a range-angle map, and clutter suppression is performed to eliminate reflections from stationary objects. Then, by applying classification algorithms, such as the support vector machine (SVM), multilayer perceptron (MLP), and convolutional neural network (CNN), various arrangements of passengers inside a vehicle are identified. The classification results demonstrated that the proposed method can detect the location and number of passengers with an accuracy of 97.68%.

Precise indoor positioning requires building databases depending on applied location estimation techniques in general. This paper studies an automated framework to determine the locations of Wi-Fi access points (APs) using data from multiple mobile users. As mobile users only move vertically in certain points (e.g., stairs), the proposed framework detects vertical movements from air pressure measurements, extracts user trajectories on a target floor, and places the extracted trajectories between a pair of vertically movable points. In order to detect vertical movement, we assume the indoor trajectory starts on one floor and moves to another floor, resulting in more than 2 vertical movements. Finally, the AP locations are estimated using Wi-Fi signal strength measured along the placed user trajectories. The effectiveness of the proposed framework is verified under a practical indoor environment using an Android application.

Because each indoor site has its own radio propagation characteristics, a site survey process is essential to optimize a Wi-Fi ranging strategy for range-based positioning solutions. This study examines an unsupervised learning technique that autonomously learns an optimal ranging strategy for each site using Wi-Fi and sensor data accumulated while users access a positioning application. Using the collected sensor data, the device trajectory is regenerated, and a Wi-Fi ranging module is optimized to generate the shape of the estimated trajectory using Wi-Fi, similar to that obtained from sensors. In this process, the ranging module learns the way to identify the channel conditions from each Wi-Fi access point (AP) and produces ranging results accordingly. Furthermore, we collect the channel state information (CSI) from beacon frames to investigate the benefit of using CSI in addition to received signal strength measurements. With the CSI, the ranging module can identify diverse channel conditions from each AP and more accurately generate the reliability of each distance estimate to achieve accurate positioning results. The effectiveness of the proposed learning technique is verified using a real-time positioning application implemented on a PC platform.

As positioning solutions integrate multiple components to improve accuracy, the number of parameters that require calibration has increased. This article studies a calibration-free positioning technique using Wi-Fi ranging and pedestrian dead reckoning (PDR), where every parameter in the system is optimized in real time. This significantly decreases the time and effort required to perform manual calibration procedures and enables the positioning solution to achieve robust performance in various situations. Additionally, this article studies an efficient way of performing irregular Wi-Fi ranging procedures to improve battery life and network performance of mobile devices. The positioning performance of the proposed method was verified using a real-time Android application on several mobile devices under a large indoor office environment. Without any calibration, the proposed method achieved up to 1.38-m average positioning accuracy for received signal strength (RSS)-based ranging scenarios, which differs only by 30 cm from the benchmark assuming perfect calibration. In addition, the proposed method achieved up to 1.04-m accuracy for round trip time (RTT)-based ranging scenarios with a 40-MHz bandwidth configuration, which differs only by 10 cm from the benchmark.

Given that the accuracy of range-based positioning techniques generally increases with the number of available anchor nodes, it is important to secure more of these nodes. To this end, this paper studies an unsupervised learning technique to obtain the coordinates of unknown nodes that coexist with anchor nodes. As users use the location services in an area of interests, the proposed method automatically discovers unknown nodes and estimates their coordinates. In addition, this method learns an appropriate calibration curve to correct the distortion of raw distance measurements. As such, the positioning accuracy can be greatly improved using more anchor nodes and well-calibrated distance measurements. The performance of the proposed method was verified using commercial Wi-Fi devices in a practical indoor environment. The experiment results show that the coordinates of unknown nodes and the calibration curve are simultaneously determined without any ground truth data.

This study demonstrates a signal‐to‐noise ratio (SNR) analysis and estimation algorithm for efficient phase noise mitigation that can be practically applied to single‐carrier frequency‐domain‐equalisation (SC‐FDE) systems that operate in millimetre‐wave bands. First, the effect of phase noise in SC‐FDE systems is investigated on each of the packet reception processes, namely, channel estimation, SNR estimation, and data‐field reception. According to the analysis, an SNR estimation algorithm is proposed. The performance of minimum‐mean‐square‐error equalisation and conventional phase noise mitigation algorithm can be enhanced using the proposed SNR estimation. The effectiveness of the proposed analysis and SNR estimation algorithm is verified through the link‐level simulation. Compared with the conventional SNR estimation and the iterative phase noise mitigation algorithms, the proposed algorithm provides a lower packet‐error rate without any iterative decoding process.

Inter-cell interference coordination (ICIC) is a promising technique to improve the performance of frequency-domain packet scheduling (FDPS) in downlink LTE/LTE-A networks. However, it is difficult to maximize the performance of FDPS using static ICIC schemes because of insufficient consideration of signal-to-interference-and-noise ratio distribution and user fairness. On the other hand, dynamic ICIC schemes based on channel state information (CSI) also have difficulty presented in the excessive signaling overhead and X2 interface latency. In order to overcome these drawbacks, we introduce a new concept of ICIC problem based on geometric network information (GNI) and propose an adaptive sector coloring game (ASCG) as a decentralized solution of the GNI-based ICIC problem. Furthermore, we develop an ASCG with a dominant strategy space noted as ASCG-D to secure a stable solution through proving the existence of Nash equilibrium. The proposed scheme provides better performance in terms of system throughput gain of up to about 44.1%, and especially of up to about 221% for the worst 10% users than static ICIC schemes. Moreover, the performance of the CSI-based ICIC, which require too much computational load and signaling overhead, is only 13.0% and 5.6% higher than that of ASCG-D regarding the total user throughput and the worst 10% user throughput, respectively. The most interesting outcome is that the signaling overhead of ASCG-D is 1/144 of dynamic ICIC schemes' one.

To evaluate the technical feasibility of the millimeter-wave (mmWave) band for cellular network, several channel measurement experiments have been performed in recent years. To reduce the burden of repetitive channel measurement campaigns, ray tracing simulations have been widely utilized to evaluate the characteristics of wireless propagation channels for outdoor environments. However, existing simulation studies have not addressed the permittivity effects of building materials on the mmWave band propagation channel because it is difficult to precisely model the overall building surfaces. In this paper, we investigate the variation of a propagation channel at non-line of sight (NLOS) points in urban micro-cell environments at the 28 GHz mmWave band using a three- dimensional (3D) ray tracing tool. The simulation results are compared with actual field measurement data. It is demonstrated that the path loss and channel impulse responses in the NLOS region fluctuate approximately 10-20 dB in urban environments owing to high-rise building blocks. Further, the impulse responses of the NLOS points indicate an increasing trend of the number of multipath components and the received signal strength as the permittivity increases. Therefore, the dielectric models of building materials must be considered for simulation analysis on mmWave channel properties such as path loss, mean excess delay, and delay spread.

In heterogeneous cellular networks, users are sometimes forcibly redirected into a low power base station (BS) for the purpose of data offloading. In order to guarantee an acceptable performance for such users, who suffer from severe inter-tier interference, enhanced inter-cell interference coordination (eICIC) was proposed. Using this technique, macro BSs periodically mute their data transmission by using an almost blank subframe (ABS). In this paper, we formulate a joint optimization problem incorporating user association and ABS portion tuning to increase network-wide utility. In the development of our algorithms, we particularly consider the time-varying characteristics of wireless propagation channels in order to reflect practical signal transmission environments, and we derive a throughput estimation equation that is compatible with the eICIC operation. Based on this analysis, we separately develop algorithms for user association and ABS tuning, and the performance enhancement achieved by our proposed methods is verified through extensive system-level simulations.

Small cells are considered an emerging technology for increasing the potential capacity of cellular networks. However, as the density of infrastructure increases, users have many choices for connection, and therefore, selecting an appropriate base station (BS) becomes an important issue. This study aims to provide an improved user association rule, where each user autonomously chooses the best among all the BSs in the vicinity, while considering their congestion levels. As the first step, an optimization problem is formulated, which emphasizes both the time-varying nature of the wireless channel and fairness among users. On the basis of this formulation, the influence of a specific handover event on system performance is investigated, and then, two versions of the handover frameworks are developed. Simulation results show that the proposed algorithms increase the throughput of every user in the network by 2.8–12% compared to the best conventional scheme. Furthermore, these schemes especially enhance the performance of users having low service quality by 5.0–85%, through efficient utilization of the pre-installed infrastructures.

In the recent cellular network environment, data traffic has increased rapidly by virtue of the development of a wide variety of wireless technology, such that it is hard for mobile service providers to satisfy the QoS (Quality of Service) requirement. In the real-world, users are not evenly distributed throughout cells; the base stations with multiple users have a great deal of difficulty of limited resources. In order to resolve this load imbalance problem, relay stations which can increase the transmission rates of cell boundary users by service area expansion have been an attractive technique in current networks. In addition, since users at the cell edge suffer from low throughput because of the inter-cell interference (ICI), we adopt additional scheme to mitigate this problem. In this paper, we propose an adaptive relay scheme which can improve the performance of the overall network by utilizing relay stations to distribute heavy traffic to adjacent cells. Each user calculates the network utility function with the neighboring relay stations and base stations, and each relay station also calculates the utility with adjacent cells. The central node controls the associations of users and relay stations through the utility information gathered from base stations. Simulation results show that our proposed algorithm improves the throughput and utility of the lower 5\% users without loss of the total network throughput.

In this paper, we propose an effective user association rule for resolving the unbalanced loading problem in multi-cell cellular networks. We first derive an improved user throughput estimation formula for the proportional fair (PF) scheduling algorithm under the Rayleigh fading environment. Our simple and accurate throughput estimation can be regarded as an extended version of the conventional scheme which is only valid when the data rate function is linear. Based on this result, we also develop a centralized user association framework, where a central node periodically determines the overall association of the system. The simulation results show that our load balancing algorithm achieves almost 90% of the upper limit of the system.