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5G communication systems operating above 24 GHz have promising properties for user localization and environment mapping. Existing studies have either relied on simplified abstract models of the signal propagation and the measurements, or are based on direct positioning approaches, which directly map the received waveform to a position. In this study, we consider an intermediate approach, which consists of four phases—downlink data transmission, multi-dimensional channel estimation, channel parameter clustering, and simultaneous localization and mapping (SLAM) based on a novel likelihood function. This approach can decompose the problem into simpler steps, thus leading to lower complexity. At the same time, by considering an end-to-end processing chain, we are accounting for a wide variety of practical impairments. Simulation results demonstrate the efficacy of the proposed approach.

In order to solve the problem that the location prediction rate of the base station is low due to the insufficient MR data within the coverage range of the base station, based on the inspiration of the grip rule, the longitude and latitude correction model of the base station in the main cell based on big data is established by using the coarse positioning data and fine positioning data of adjacent cells. Taking Guangdong Telecom as an example, the experimental results show that the prediction rate of base station location is significantly improved from 69% to 92%.

Passive radar (PR) systems use the target illumination by third-party transmitters, for example, from broadcast or cellular base stations, for target detection and localisation. Since PR does not use an own transmitter, it can be installed and operated at low cost and it is hard to detect and jam. These advantages and the increasing maturity of PR technology has led to growing interest in these systems over the last years. However, until now most PR systems have been rather experimental set-ups tailored to a single frequency band or implemented as laboratory test devices. This study in contrast describes the design, implementation and performance evaluation of a multi-band, multi-illuminator PR system developed at near-production stage. Starting out from a FM-broadcast-based approach, the step to DAB-based and DVB-T-based operation has already been made. As a result, a fully mobile FM/DAB/DVB-T multi-band PR system is now available, offering maximum flexibility for measurement campaigns with air, ground and sea targets. Experiments with a great variety of third-party transmitters and arbitrary transmitter-target-receiver geometries have been conducted. The design considerations and the resulting PR system concept are described, and the results of representative measurement campaigns with different types of ground and aerial targets are presented.

In this paper, we investigate the joint placement of aerial Base Stations (BSs) and power control of BSs and users’ association to maximize the sum of the receive rates of end users. We formulate this problem as a mixed integer nonlinear optimization problem and propose a heuristic algorithm based on clustering. The algorithm focuses on a set of users with the lowest rates and clusters them into k groups. The centroid of each group is considered as a candidate for the initial placement of an aerial BS. The optimization loop starts by locating an aerial BS and then proceeds to three subalgorithms: updating power control, users’ association update, and aerial BS location. The results show that adding a single aerial BS improves the average user rate by approximately 18%, while reducing the total transmission power by 44%. Moreover, our proposed algorithm outperforms baseline PSO (Particle Swarm Optimization) schemes in terms of average user rate.

The increasing frequency and intensity of forest fires demand innovative technologies to support firefighting and mitigate their impact on ecosystems and communities. This paper explores the application of untethered and tethered uncrewed aerial vehicles (UAVs) as aerial base station (ABS) in the context of forest fire management. We propose an ABS placement algorithm to serve UAV-user equipment (UE) in forest environments. The novelty of the proposed approach is to optimize the strategic ABS placement based on throughput requirements while serving multiple drone forest monitoring areas. We analyze and compare the performances of two aerial base stations (ABSs) such as an untethered ABS (UTABS) and a tethered ABS (TABS) for forest surveillance. We evaluate UTABS and TABS performances across single and multiple UAV-UE monitoring zones in forest environments under different network throughputs, wind effects, payload configurations, monitoring zone areas, communication frequencies, operating costs, etc. Our results indicate that while a UTABS offers superior flexibility, a TABS provides a cost-effective (78.2% reduction) and reliable solution to ensure long-term continuous forest fire surveillance operations.

5G base stations (BSs) are potential flexible resources for power systems due to their dynamic adjustable power consumption. However, the ever‐increasing energy consumption of 5G BSs places great pressure on electricity costs, and existing energy‐saving measures do not fully utilise BS wireless resources in accordance with dynamic changes in communication load, resulting in flexible resource waste and seriously limiting electricity cost savings for 5G BSs. A multi‐BS cooperation self‐optimising sleep strategy for 5G BSs that consists of an initial user association stage based on multi‐BS cooperation (MBSC) and a self‐optimising variable‐threshold sleep stage (SVTS). First, a heterogeneous cellular network (HCN) model is established. Then, a 5G BS economic optimisation model is constructed, which aims at minimising the electricity cost of the BSs and takes the BS and user equipment (UEs) states in the HCN model as constraints to clarify the optimisation objective and constraints for the proposed strategy. Furthermore, BSs are initially associated with UEs through MBSC, and idle and lightly loaded BSs are then maximally put to sleep through SVTS to reduce power and energy consumption and thereby realise economic optimisation of the BSs. Finally, simulations are conducted to validate the proposed strategy and illustrate the ability of 5G BSs to provide flexible resource regulation for power systems.

Car-mounted measurements of radiofrequency electromagnetic exposure levels were carried out in a large area around Tokyo. Prior to the electric field (E-field) measurements using a car, the effect of the car body was evaluated in an anechoic chamber. The measurements between May 2021 and February 2022 were carried out within a radius of 100 km centering on Nihonbashi, Tokyo, with a measurement distance of about 13,800 km. The measurement results were averaged in the reference area mesh (1 km2). It was found that the E-field strengths of FM/TV frequency bands are lower than that of mobile phone base stations. It was also found that the E-field strength of only the 5G frequency band is approximately 20–30 dB lower than that of all mobile phone systems. However, note that it is possible to depend on the data traffic of 5G. The E-field strength of all bands is higher in Tokyo than in other prefectures. Additionally, repeated measurements were carried out to investigate the reproducibility of the measured E-field. The standard deviation is less than 3 dB along the same route, and a similar tendency of E-field strength by the car to the time-averaged results of spot measurements in the past was confirmed. Finally, the relationship of E-field strength with population density was investigated. It was found that the E-field strength from mobile phone base stations has a positive relationship with population density.

The amount of internet traffic generated during mass public events is significantly growing in a way that requires methods to increase the overall performance of the wireless network service. Recently, legacy methods in form of mobile cell sites, frequently called cells on wheels, were used. However, modern technologies are allowing the use of unmanned aerial vehicles (UAV) as a platform for network service extension instead of ground-based techniques. This results in the development of flying base stations (FBS) where the number of deployed FBSs depends on the demanded network capacity and specific user requirements. Large-scale events, such as outdoor music festivals or sporting competitions, requiring deployment of more than one FBS need a method to optimally distribute these aerial vehicles to achieve high capacity and minimize the cost. In this paper, we present a mathematical model for FBS deployment in large-scale scenarios. The model is based on a location set covering problem and the goal is to minimize the number of FBSs by finding their optimal locations. It is restricted by users’ throughput requirements and FBSs’ available throughput, also, all users that require connectivity must be served. Two meta-heuristic algorithms (cuckoo search and differential evolution) were implemented and verified on a real example of a music festival scenario. The results show that both algorithms are capable of finding a solution. The major difference is in the performance where differential evolution solves the problem six to eight times faster, thus it is more suitable for repetitive calculation. The obtained results can be used in commercial scenarios similar to the one used in this paper where providing sufficient connectivity is crucial for good user experience. The designed algorithms will serve for the network infrastructure design and for assessing the costs and feasibility of the use-case.

In many real-time kinematic (RTK) positioning applications, reference observations are transmitted over wireless links that can experience frequent interruptions or substantial delays. This results in large differential ages between base and rover observations, which, in turn, leads to a deterioration in positioning performance. To bridge the significant age difference, in this work, we propose a simple and effective scheme for modeling and compensating for such errors. Firstly, the overall differential age error was modeled using truncated Taylor expansion. Then, a time-differenced carrier phase (TDCP)-based observation model was established to estimate the errors with the Kalman framework. Since estimating the receiver’s clock error is unnecessary, equivalent transformation and sequential filtering technology were adopted to significantly reduce the computational complexity. Furthermore, a predictor performance monitor was introduced to mitigate the integrity risks that may occur due to model mismatches. The effectiveness of this scheme was validated by static and dynamic field experiments. The static experiment results showed that when the differential age was 60 s, the GPS and BDS satellites’ overall root mean square error (RMSE) with the asynchronous RTK (ARTK) prediction method was 2.8 and 5.5 times that of the proposed method, respectively. Moreover, when the differential age was 120 s, these values were 3.3 and 5.4 times that of the proposed method, respectively. The field experiment results showed that when the differential age was 60 s, the integer ambiguity fixed rate and false fixed rate of the ARTK method were 0.90 and 1.63 times that of the proposed method, respectively. Finally, at a 120 s differential age, these values were 0.78 and 4.78 times that of the proposed, respectively.

In the current era of exponentially growing demand for user connectivity, spectral efficiency (SE), and high throughput, the performance goals have become even more challenging in ultra-dense 5G networks. The conventional orthogonal frequency division multiple access (OFDMA) tech-niques are mature but have not proven sufficient to address the growing user demand for high data rates and increased capacity. Therefore, to achieve an improved throughput in an ultra-dense 5G network with an expanded network capacity, the unified non-orthogonal multiple access (NOMA) technique is considered to be a more promising and effective solution. Throughput can be im-proved by implementing PD-NOMA, as the interference is managed with the successive inter-ference cancellation (SIC) technique, but the issue of increased complexity and capacity with compromised data rate persists. This study implements the clustered PD-NOMA algorithm to enhance user association and network performance by managing the users in clusters with fewer users per cluster with the implementation of the cooperative PD-NOMA within the clusters. In this study, we enhanced the user association in a network and ultimately improved the throughput, sum rate, and system capacity in an ultra-dense heterogeneous network (HetNet). By imple-menting the proposed clustered PD-NOMA scheme, the system throughput has improved by 23% when compared to the unified PD-NOMA scheme and 65% when compared to the OFDMA scheme with a varied number of randomly deployed users, along with an improvement in system capacity of 8% as compared to the unified PD-NOMA and almost 80% as compared to the conventional OFDMA scheme in a randomly deployed ultra-dense multi-tier heterogeneous network. Thus, we improved the network performance with the proposed algorithm and achieved increased capacity, throughput, and sum rate by outperforming the unified PD-NOMA scheme in an ultra-dense heterogeneous network.