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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%.

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.

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.

This paper proposes a non-overlapping planar cross-arranged ultra-wideband shared-aperture base station antenna array targeting the 2 to 6 GHz application bandwidth. The low-frequency module (double-layer parasitic coupling) and the high-frequency module (chamfered slotted patch) are independently designed, and metal baffles are introduced around the antenna elements to reshape the boundary conditions and physically block the electromagnetic coupling paths. Both simulation and experimental results demonstrate that the fabricated prototype successfully exceeds the targeted 2–6 GHz spectrum, achieving an actual continuous coverage from 1.84 to 6.3 GHz. Specifically, the antenna achieves a gain higher than 5.9 dBi in the measured low-frequency band (1.84–3.72 GHz) and higher than 6.1 dBi in the high-frequency band (3.63–6.3 GHz), with a voltage standing wave ratio (VSWR) below 2 across the entire band. The metal baffles successfully correct the high-frequency radiation pattern distortion and ensure stable directional radiation over the full operating bandwidth. This design provides an efficient, robust, and manufacturable solution for 5G offshore wind power multi-band base station antennas.

In the event of a major disaster, base stations in the disaster area will cease to function, making it impossible to obtain life-saving information. Therefore, it is necessary to provide a wireless communication infrastructure as soon as possible. To cope with this situation, we focus on NFV/SDN (Network Function Virtualization/Software-Defined Networking)-enabled UAVs equipped with a wireless communication infrastructure to provide services. The access link between the UAV and the user is assumed to be equipped with a millimeter-wave interface to achieve high throughput. However, the use of millimeter-waves increases the effect of attenuation, making the deployment of UAVs problematic. In addition, if multiple UAVs are deployed in a limited frequency band, co-channel interference will occur between the UAVs, resulting in a decrease in the data rate. Therefore, in this paper, we propose a method that combines UAV placement and frequency division for a non-uniform user distribution in an environment with multiple UAVs. As a result, it is found that the offered data rate is improved by using our specific placement method, in terms of not only the average but also the outage user rate.

Flying base stations, also known as aerial base stations, provide wireless connectivity to the user and utilize their aerial mobility to improve communication performance. Flying base stations depend on traditional stationary terrestrial base stations for connectivity, as stationary base stations act as the gateway to the backhaul/cloud via a wired connection. We introduce the flying base station channel capacity to build on the Shannon channel capacity, which quantifies the upper-bound limit of the rate at which information can be reliably transmitted using the communication channel regardless of the modulation and coding techniques used. The flying base station’s channel capacity assumes aerial mobility and ideal positioning for maximum channel capacity. Therefore, the channel capacity limit holds for any digital and signal processing technique used and for any location or positioning of the flying base station. Because of its inherent reliance on the stationary terrestrial base station, the flying base station channel capacity depends on the stationary base station’s parameters, such as its location and SNR performance to the user, in contrast to previous research, which focused on the link between the user and the flying base station without the stationary base station. For example, the beneficial region (where there is a positive flying base station capacity gain) depends on the stationary base station’s power and channel SNR in addition to the flying base station’s own transmission power and whether it has full duplex vs. half-duplex capability. We jointly study the mobility and the wireless communications of the flying base station to analyze its position, channel capacity, and beneficialness over the stationary terrestrial base station (capacity gain). As communication protocols and implementations for flying base stations undergo development for next-generation wireless networking, we focus on information-theoretical analyses and channel capacity to inform future research and development in flying base station networking.