Base Station Location Research Articles

Joint Base Station and IRS Deployment for Enhancing Network Coverage: A Graph-Based Modeling and Optimization Approach

Intelligent reflecting surface (IRS) can be densely deployed in complex environment to create cascaded line-of-sight (LoS) paths between multiple base stations (BSs) and users via tunable IRS reflections, thereby significantly enhancing the coverage performance of wireless networks. To achieve this goal, it is vital to optimize the deployed locations of BSs and IRSs in the wireless network, which is investigated in this paper. Specifically, we divide the coverage area of the network into multiple non-overlapping cells and decide whether to deploy a BS/IRS in each cell given a total number of BSs/IRSs available. We show that to ensure the network coverage/communication performance, i.e., each cell has a direct/cascaded LoS path with at least one BS, as well as such LoS paths have the average number of IRS reflections less than a given threshold, there is a fundamental trade-off with the deployment cost or the number of BSs/IRSs needed. To optimally characterize this trade-off, we formulate a joint BS and IRS deployment problem based on graph theory, which, however, is difficult to be optimally solved due to the combinatorial optimization involved. To circumvent this difficulty, we first consider a simplified problem with given BS deployment and propose the optimal as well as an efficient suboptimal IRS deployment solution to it, by applying the branch-and-bound method and iteratively removing IRSs from the candidate locations, respectively. Next, an efficient sequential update algorithm is proposed for solving the joint BS and IRS deployment problem. Numerical results are provided to show the efficacy of the proposed design approach and optimization algorithms for the joint BS and IRS deployment. The trade-off between the network coverage performance and the number of deployed BSs/IRSs with different cost ratios is also unveiled.

THE EUROSDR RPAS BENCHMARK: OPEN DATASET DESCRIPTION AND SUMMARY OF KEY RESULTS

Abstract. In 2021 EuroSDR initiated a benchmark study with the aim to evaluate the geometric quality of real-world survey data generated from state-of-practice commercial Remotely Piloted Aircraft System (RPAS) photogrammetry (including DJI P4 RTK and DJI P1) and lidar (including DJI L1 and Riegl MiniVUX). The particular benchmark focus was on achievable data quality from real-world network configurations in the absence of ground control, on-the-fly Real Time Kinematic (RTK) corrections, and/or local GNSS base station information. Successive custom datasets were released to registered benchmark participants who submitted individual outputs that were independently evaluated against reference surveys. Without the inclusion of any supporting ground information, DJI P4 RTK and DJI P1 RPAS solutions were found to deliver m- and dm-level accuracies, respectively, in both plan and height. RTK solutions were found to provide cm-level precisions and accuracies, with some outliers. The introduction of ground control points resulted in similar planimetric accuracy to the RTK solutions, but with slight improvements in height. In terms of lidar datasets, the Riegl MiniVUX solution, using corrections from a local base station, was found to provide smaller discrepancies than the DJI L1 RTK solution, when independently compared against terrestrial laser scanning surveys. This paper provides various quality statistics and demonstrates multiple ways of assessing the geometric quality of RPAS data. The EuroSDR RPAS benchmark datasets are now openly available online in order to support and facilitate further investigation by the community.

An Equivalent Model for Handover Probability Analysis of IRS-Aided Networks

Thanks to the reflecting beamforming gain, Intelligent Reflecting Surface (IRS) is expected to increase coverage range and reduce unnecessary handovers (HOs). However, <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$O({{N^{2}}})$</tex-math></inline-formula> gains from <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$N$</tex-math></inline-formula> elements and double path-loss change HO decision thus IRS cascaded channels need to be considered instead of the direct base station (BS)-user equipment (UE) channel, which makes existing Euclidean distance-based HO models not applicable. This paper proposes a uniform standard of equivalent UE-BS distance for deciding HOs with IRS cascaded channels, where the reference signal from the serving BS is reflecting beamformed while the reference signals from the neighboring BSs are randomly scattered due to the IRS being occupied. Specifically, the geometric point set of possible BS locations satisfying the HO condition is revised via the equivalent UE-BS distance. In addition, the IRS reselection affects the cascaded channel gain and HO decision, so the distribution of UE-IRS distance is modified with the correlation of UE location in unit time. The compact expression of HO probability in IRS-aided networks is obtained and the effect of the degree of IRS distributed deployment on HO is studied. The results show that there exists an optimal distributed deployment factor that minimizes the HO probability, which decreases by 59.6% compared to networks without IRSs when the density of BS is <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$10^{3}/\rm {km^{2}}$</tex-math></inline-formula> , the IRS serving distance is <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$20\rm {m}$</tex-math></inline-formula> and the number of IRS elements per cell is 100.

Base station location generation algorithm for satellite‐terrestrial co‐existence studies

SummaryThe enormous growth of mobile broadband traffic is a catalyst for expanding terrestrial communication systems and their integration with satellite communication systems. This results in sharing frequencies allocated to satellite communication systems with terrestrial systems. As this can lead to interference between the incumbent and the upcoming service, coexistence studies are conducted. In the case of sharing satellite uplink frequencies with terrestrial communication systems, interference from base stations (BSs) is dominant. While there are methods to estimate the number of BSs that may be deployed, it is unclear where the BSs will be located. The BS locations play a pivotal role in interference calculation, as the values of link budget parameters are highly dependent on the longitude and latitude of interfering transmitters. This article presents an intelligent method which balances network capacity and coverage for simulating base stations (BSs) in coexistence studies. A realistic BS distribution is generated using weighted K‐means clustering (WKMC) algorithm and incorporating population density along with Haversine distance. The paper provides a case study of co‐frequency, co‐coverage interference in the satellite uplink in the C‐band to demonstrate the impact of BS distribution on the results of coexistence studies. This paper will find applications in upcoming frequency sharing and coexistence studies and the design of integrated satellite‐terrestrial communication networks.

Energy efficient and coverage aware grey wolf optimizer based clustering process for Software-defined wireless sensor networks

Clustering has been adopted as a critical technique for reducing energy consumption in wireless sensor networks (WSNs). Furthermore, the selected group of Cluster Heads (CHs) should enhance the network’s coverage while minimizing any impact on its energy efficiency. Although distributed CHs election processes have improved the network’s lifespan, they have struggled to provide adequate coverage. Moreover, they usually use impractical assumptions like unlimited transmission range and location cognition. On the other hand, centralized meta-heuristic-based CHs selection algorithms have shown remarkable improvement in clustering problems using centralized decisions. However, software-defined wireless sensor networks (SDWSN) have gained a prevalent use in recent years. SDWSN consists of stripping control from nodes and centralizing it in an external controller. This paper introduces an energy-efficient and coverage-aware Grey wolf optimizer (GWO) based clustering process for SDWSN. Based on a novel fitness function, our proposed algorithm aims to maximize coverage, decrease blind spots, and preserve energy in the network. Our algorithm is widely evaluated on various homogeneous and heterogeneous network scenarios, with varied network sizes and base station (BS) locations, versus prominent clustering algorithms. The obtained results testify that our proposed algorithm shows better performance in energy efficiency and CHs distribution. Our algorithm improved coverage up to a mean of 31.42%, 27.72%, 18.69%, 21.12%, 13.81%, 4.14%, 3.8% and 8.31% compared to CREEP, DEEC, I-LEACH, HEESR, BRE-LEACH, PSO-ECHS, EPSO-CEO, and GWO-C, respectively, in all simulation cases.

Interference Analysis of Coexisting 5G Networks and NGSO FSS Receivers in the 12-GHz Band

Despite the promising attributes of the 12 GHz band for expanding terrestrial 5G network’s capacity and coverage, interference between coexisting networks remains a major issue. This paper develops a simulation-based evaluation framework and investigates the harmful interference between the 5G radio links and incumbent fixed non-geostationary satellite orbit (NGSO) fixed satellite services (FSS) receivers of the 12 GHz band. A variety of features including actual deployment locations of 5G base stations (BSs) and fixed NGSO FSS receivers, industry-standardized beamforming at BSs, directional signal reception at FSS receivers, realistic propagation channels with obstruction from buildings, and channel scheduling at 5G BSs are incorporated in the interference study. Simulation results conducted in a realistic urban-micro deployment scenario confirm that the terrestrial 5G networks with directional BSs can coexist in the 12GHz band by suitably selecting exclusion zone’s radius around the FSS receiver. Simulation results also show that interference in the coexisting network can be notably reduced by appropriately activating BSs in the 12 GHz band based on their locations and surroundings.

Intelligent Base Station Placement in Urban Areas With Machine Learning

This paper presents a time-reliable machine learning model for accurate and rapid base station placement in urban areas. Several real-world city data are used for training the machine learning model to predict parameters such as path loss values. It is developed using supervised training and multiple regressions with the use of artificial intelligence. Parameters such as coverage dimensioning, bandwidth of frequencies, building's density and height, propagation model tuning and path loss values are used to predict the locations of base stations. Once the predicted base station position is obtained, the average percentage error is calculated against the real-world data of base station that shows an accuracy of 84%. This proves that our model is a reliable tool to predict future base station locations. This model will benefit the telecommunication industries to reduce time and cost for rapid base station planning in urban areas that see frequent changes to their landscapes.

BSLPSDN: Base station location privacy via software‐defined networking (SDN) in wireless sensor networks

SummaryBase station's location privacy in a wireless sensor network (WSN) is critical for information security and operational availability of the network. A key part of securing the base station from potential compromise is to secure the information about its physical location. This paper proposes a technique called base station location privacy via software‐defined networking (SDN) in wireless sensor networks (BSLPSDN). The inspiration comes from the architecture of SDN, where the control plane is separated from the data plane, and where control plane decides the policy for the data plane. BSLPSDN uses three categories of nodes, namely, a main controller to instruct the overall operations, a dedicated node to buffer and forward data, and lastly, a common node to sense and forward the packet. We employ three kinds of nodes to collaborate and achieve stealth for the base station and thus protecting it against the traffic‐analysis attacks. Different traits of the WSN including energy status and traffic density can actively be monitored by BSLPSDN, which positively affects the energy goals, expected life of the network, load on common nodes, and the possibility of creating diversion in the wake of an attack on the base station. We incorporated multiple experiments to analyze and evaluate the performance of our proposed algorithm. We use single controller with multiple sensor nodes and multiple controllers with multiple sensor nodes to show the level of anonymity of BS. Experiments show that providing BS anonymity via multiple controllers is the best method both in terms of energy and privacy.

The optimal 5G base station location of the wireless sensor network considering timely reliability

The optimal 5G base station location of the wireless sensor network considering timely reliability

Research on IMU-Assisted UWB-Based Positioning Algorithm in Underground Coal Mines.

The application of an ultra-wideband (UWB) positioning system in a Global Positioning System (GPS) denial environment such as an underground coal mine, mainly focuses on position information and rarely involves information such as direction attitude. Position accuracy is often affected by multipath, non-visible ranges, base station layout, and more. We proposed an IMU-assisted UWB-based positioning system for the provision of positioning and orientation services to coal miners in underground mines. The Error-State Kalman Filter (ESKF) is used to filter the errors in the measured data from the IMU-assisted UWB positioning system to obtain the best estimate of the error for the current situation and correct for inaccuracies due to approximations. The base station layout of the IMU-assisted UWB positioning system was also simulated. The reasonable setting of the reference base station location can suppress multi-access interference and improve positioning accuracy to a certain extent. Numerous simulation experiments have been conducted in GPS denial environments, such as underground coal mines. The experimental results show the effectiveness of the method for determining the position, direction, and attitude of the coal miner under the mine, which provides a better reference value for positioning and orientation in a GPS rejection environment such as under the mine.

FSO communication system for high-speed trains under varying visibility conditions

High-speed train systems are rapidly growing in complexity and coverage, demanding efficient high-speed internet services. Existing radio frequency technology struggles to meet these demands due to handover mechanisms and limited connectivity times. Furthermore, current free-space optics (FSO) techniques require a large number of base stations (BSs), resulting in high costs. This study presents a novel high-speed train communication technique, using FSO, that significantly reduces the number of required BSs while maintaining sufficient coverage and data speeds. The proposed technique can be adapted for fixed or variable representations of transmitted and received power, providing a fixed or variable data rate. The research also investigates the impact of divergence angle on transmitted and received power. By employing the proposed communication technique, a system with a fixed data rate, such as 1.25 Gbps, can be achieved. The results demonstrate the potential of this approach in high-speed train applications, reducing the number of BSs from 113, as reported in the best existing literature, to less than 10. Weather conditions, including clear, haze, thin fog, light fog, and heavy fog, are considered when calculating the required BS locations to achieve a target data rate. Additionally, this work introduces a new handover mechanism and mathematical formulation, providing a more comprehensive solution for high-speed train communication systems. The simulations and evaluations conducted in this study are based on parameters and equations from previous works to ensure a fair comparison.

Low-Cost GNSS and PPP-RTK: Investigating the Capabilities of the u-blox ZED-F9P Module

GNSS has become ubiquitous in high-precision applications, although the cost of high-end GNSS receivers remains a major obstacle for many applications. Recent advances in GNSS receiver technology have led to the development of low-cost GNSS receivers, making high-precision positioning available to a wider range of users. One such technique for achieving high-precision positioning is Precise Point Positioning-Real Time Kinematic (PPP-RTK). It is a GNSS processing technique that combines the PPP and RTK approaches to provide high-precision positioning in real time without the need for a base station. In this work, we aim to assess the performance of the low-cost u-blox ZED-F9P GNSS module in PPP-RTK mode using the low-cost u-blox ANN-MB antenna. The experiment was designed to investigate both the time it takes the receiver to resolve the phase ambiguity and to determine the positioning accuracies achievable. Results showed that the u-blox ZED-F9P GNSS module could achieve centimeter-level positioning accuracy in about 60 s in PPP-RTK mode. These results make the PPP-RTK technique a good candidate to fulfill the demand for mass-market accurate and robust navigation since uses satellite-based corrections to provide accurate positioning information without the need for a local base station or network. Furthermore, due to its rapid acquisition capabilities and accurate data georeferencing, the technique has the potential to serve as a valuable method to improve the accuracy of the three-S techniques (GIS, remote sensing, and GPS/GNSS).

Modeling blockages and link dependency in millimeter wave communication networks using Matérn Hard-Core Point Process

With the pursuit of higher transmission rate, millimeter wave (mmWave) communication has become one of the propelling technologies in wireless communications. Due to its short wavelength, a mmWave signal suffers from significant path loss and is heavily affected by obstructions and reflections caused by blockages. Moreover, the interference links are dependent as the locations of base stations are not independent in practical deployment. And the link dependency in the densely deployed mmWave communication networks cannot be ignored, which makes coverage analysis even more complicated. In this paper, we investigate the coverage model incorporating the Line-of-Sight (LOS) and Non-Line-of-Sight (NLOS) links of both signal and interference, to comprehensively portray the performance of mmWave networks with randomly distributed blockages. Further, we capture the dependency between interfering links with the copula function to model the received aggregate interference more accurately. The probability density function of the received power, Signal-to-Interference-plus-Noise Ratio (SINR), and outage probability are characterized to theoretically quantify the impact of blockages and interference. The proposed analytical model perfectly models the impact of blockages and link dependency, and obtains the cumulative distribution function (CDF) of the SINR with a gap less than 0.2% compared to the Monte-Carlo simulation.

Energy optimization for optimal location in 5G networks using improved Barnacles Mating Optimizer

In recent years, wireless sensor networks have been studied in many applications. One of the important issues studied in these networks is the optimal placement of cells in order to achieve optimal energy consumption. Therefore, intelligent algorithms have been used in most research to achieve this goal. In order to properly roll out 5G, the network must be able to handle the increase in data traffic. The essential aspects of resource allocation systems, such as scalability and throughput, are under too much stress due to the rising amount of traffic. In the present work, network planning is characterized by the optimal selection of the base station location and transmission power in 5G networks. Here, an Amended design of the Barnacles Mating Optimizer (ABMO) Algorithm is proposed and is employed to get higher efficiency in both terms of total connected users and transmit power saving for 5G networks. Therefore, the novelty of this study is in the use of an amended design of the Barnacles Mating Optimizer (ABMO) Algorithm. This amended design improves the accuracy and performance of the ABMO and makes it more effective in finding the best locations for base stations and changing the power level for 5G networks. The amended design also offers several advantages over some other state-of-the-art algorithms and makes the ABMO useful for improving 5G networks Simulations of the proposed ABMO algorithm are then compared with some other state-of-the-art algorithms and the results showed the right location and changing the range of the power level for the proposed method. The outcomes show that the suggested Amended Barnacles Mating Optimizer Algorithm statistically outperforms the original BMO, and some other methods based on DE, and RGA. The findings show that the t-value for BMO and the modified BMO is 4.339e−35 which is so better than that of DE and ABMOO with 1.926e−22, and for the RGA and ABMOO with 4.826e−32.

Cooperative Beamforming With Nonlinear Power Amplifiers: A Deep Learning Approach for Distributed Networks

Network densification is essential to enhance the areal capacity of future wireless networks, for which the user-centric network is a promising solution. In the perspective of network scalability, user association and cooperative beamforming (BF) among the base stations (BSs) should be designed elaborately to manage strong interference from nearby BSs. However, practical systems with hardware impairments including nonlinearity of power amplifiers may disturb these techniques and make the network difficult to be scalable. The nonlinearity of power amplifiers affects not only radio frequency (RF)-level signal but also performance according to the number of user equipments (UEs), resulting in necessity of re-designing network-level operations including BS-UE association. This paper proposes a deep learning-based cooperative BF framework for distributed networks with nonlinear power amplifiers. To optimize the cooperative BF for complicated nonlinear systems, we adopt unsupervised learning approach with constraints. To reduce the communication overhead of the network, we propose a novel neural network structure, from which the BSs can perform the cooperative BF in distributed manner. In particular, the information exchange between the central unit and the local BSs is reduced by designing neural network so that the local channel state information are utilized only in the local BSs while the central unit utilizes only covariance matrices. We show that the proposed scheme achieves a higher effective sum rate compared to the baseline schemes by adjusting the user association, BF, and power allocation to control interference and nonlinear distortion with reduced communication overhead.

Base Station Planning Based on Region Division and Mean Shift Clustering

The problem of insufficient signal coverage of 5G base stations can be solved by building new base stations in areas with weak signal coverage. However, due to construction costs and other factors, it is not possible to cover all areas. In general, areas with high traffic and weak coverage should be given priority. Although many scientists have carried out research, it is not possible to make the large-scale calculation accurately due to the lack of data support. It is necessary to search for the central point through continuous hypothesis testing, so there is a large systematic error. In addition, it is difficult to give a unique solution. In this paper, the weak signal coverage points were divided into three categories according to the number of users and traffic demand. With the lowest cost as the target, and constraints such as the distance requirement of base station construction, the proportion of the total signal coverage business, and so on, a single objective nonlinear programming model was established to solve the base station layout problem. Through traversal search, the optimal threshold of the traffic and the number of base stations was obtained, and then, a kernel function was added to the mean shift clustering algorithm. The center point of the new macro station was determined in the dense area, the location of the micro base station was determined from the scattered and abnormal areas, and finally the unique optimal planning scheme was obtained. Based on the assumptions made in this paper, the minimum total cost is 3752 when the number of macro and micro base stations were determined to be 31 and 3442 respectively, and the signal coverage rate can reach 91.43%. Compared with the existing methods, such as K-means clustering, K-medoids clustering, and simulated annealing algorithms, etc., the method proposed in this paper can achieve good economic benefits; when the traffic threshold and the number of base stations threshold are determined, the unique solution can be obtained.

Optimization of Drone Base Station Location for the Next-Generation Internet-of-Things Using a Pre-Trained Deep Learning Algorithm and NOMA

Next-generation Internet-of-Things applications pose challenges for sixth-generation (6G) mobile networks, involving large bandwidth, increased network capabilities, and remarkably low latency. The possibility of using ultra-dense connectivity to address the existing problem was previously well-acknowledged. Therefore, placing base stations (BSs) is economically challenging. Drone-based stations can efficiently address Next-generation Internet-of-Things requirements while accelerating growth and expansion. Due to their versatility, they can also manage brief network development or offer on-demand connectivity in emergency scenarios. On the other hand, identifying a drone stations are a complex procedure due to the limited energy supply and rapid signal quality degradation in air-to-ground links. The proposed method uses a two-layer optimizer based on a pre-trained VGG-19 model to overcome these issues. The non-orthogonal multiple access protocol improves network performance. Initially, it uses a powerful two-layer optimizer that employs a population of micro-swarms. Next, it automatically develops a lightweight deep model with a few VGG-19 convolutional filters. Finally, non-orthogonal multiple access is used to schedule radio and power resources to devices, which improves network performance. We specifically examine how three scenarios execute when various Cuckoo Search, Grey Wolf Optimization, and Particle Swarm Optimization techniques are used. To measure the various methodologies, we also run non-parametric statistical tests, such as the Friedman and Wilcoxon tests. The proposed method also evaluates the accuracy level for network performance of DBSs using number of Devices. The proposed method achieves better performance of 98.44% compared with other methods.

Enhancement of Rural Connectivity by Recycling TV Towers with Massive MIMO Techniques

Nowadays, the digital divide is one of the major issues facing the global community. Around 3 billion people worldwide are still not-connected or under-connected. In this article, we investigate the use of TV towers with multi user (MU) massive multiple input multiple output (mMIMO) techniques to offer connectivity in rural areas. Specifically, the coverage range is assessed for a MU mMIMO base station (BS) mounted on a high tower as a TV tower, and compared with a legacy mMIMO BS. The obtained results show that one high tower BS can cover an area at least 25 times larger than the area covered by a legacy BS. This is of high interest as recycling TV towers can enhance rural connectivity with low expenditures. We apply the proposed solution to a realistic case study in an Ethiopian rural area, based on population densities and locations of current BS and TV towers. Our study shows that a high number of people can be covered by existing TV towers. Non-technological challenges and additional possible solutions to enhance rural connectivity are also discussed.

Research on the Model of Rectifying the Longitude and Latitude of the Base Station in the Main Cell Based on the Adjacent Cell Data

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 Location for 5G OFDM Radiation Sources Based on Virtual Synthetic Aperture

Passive location technology has been greatly developed because of its low power consumption, long detection distance, good concealment, and strong anti-interference ability. Orthogonal frequency-division multiplexing (OFDM) is an efficient multi-carrier transmission technology, which is an important signal form of 5G communication. Researching passive locations for OFDM signals can realize the location of base stations, which is of great significance in the military. Space-borne passive location technology has a contradiction between wide coverage and high precision. Therefore, a single-satellite passive location algorithm for OFDM radiation sources based on the virtual synthetic aperture is proposed. The algorithm introduces virtual synthetic aperture technology, using antenna movement to accumulate data coherently over a long time period and synthesizing a long azimuth virtual aperture. In addition, it utilizes fast Fourier transform (FFT) to extract phase information at a specific frequency based on the multi-carrier modulation technology of the OFDM signal. Pilot technology of the communication system is used for phase compensation and noise reduction. Thus, the azimuth linear frequency modulation (LFM) signal containing the location information of the radiation source is obtained. The radiation source location can be obtained by range searching and azimuth focusing. Simulation results verify the effectiveness of the algorithm and show that the algorithm can realize high-precision and wide-coverage location for the OFDM radiation sources using a single antenna, turning the hardware structure into software to reduce the cost and complexity of the system.