Deployment Of Base Stations Research Articles

Optimizing the stochastic deployment of small base stations in an interleave division multiple access‐based heterogeneous cellular networks

SummaryThe use of small base stations (SBSs) to improve the throughput of cellular networks gave rise to the advent of heterogeneous cellular networks (HCNs). Still, the interleave division multiple access (IDMA) performance in sleep mode active HCNs has not been studied in the existing literature. This research examines the 24‐h throughput, spectral efficiency (SE), and energy efficiency (EE) of an IDMA‐based HCN and compares the result with orthogonal frequency division multiple access (OFDMA). An energy‐spectral‐efficiency (ESE) model of a two‐tier HCN was developed. A weighted sum modified particle swarm optimization (PSO) algorithm simultaneously maximized the SE and EE of the IDMA‐based HCN. The result obtained showed that the IDMA performs at least 68% better than the OFDMA on the throughput metric. The result also showed that the particle swarm optimization algorithm produced the Pareto optimal front at moderate traffic levels for all varied network parameters of SINR threshold, SBS density, and sleep mode technique. The IDMA‐based HCN can improve the throughput, SE, and EE via sleep mode techniques. Still, the combination of network parameters that simultaneously maximize the SE and EE is interference limited. In sleep mode, the performance of the HCN is better if the SBSs can adapt to spatial and temporal variations in network traffic.

A study of risk perception of radiofrequency electromagnetic field (RF-EMF) exposure from mobile phones and base stations in India

ABSTRACT Due to the ever-increasing use of mobile communications in India, health risks associated with exposure to radiofrequency electromagnetic field (RF-EMF) transmitted by mobile phones and base stations has become the focus of research. Internationally, there have been numerous studies on RF-EMF risk perception, but this issue is scarcely addressed in India. The present study evaluates the level of risk perception of Indians associated with RF-EMF exposure from mobile phones and base stations and assesses the underlying reasons for their concerns. We conducted a qualitative study on a small group of Indians (n = 25) from varied socioeconomic backgrounds, followed by a large-scale population (n = 500) survey to identify the primary issues of concern. The results show that many participants, irrespective of their social group, have a positive attitude towards mobile phones and are willing to accept the risks of using them, but are against the installation of base stations. Our study confirms that lack of knowledge about mobile communications technology, health concerns about mobile phones and base stations, community concern regarding base station deployment and media influence have a significant impact on risk perception among Indians. Carefully planned communication techniques are required to decrease risk perceptions and increase acceptance of mobile communications infrastructure.

Aerial Base Station Placement: A Tutorial Introduction

The deployment of aerial base stations (ABSs) mounted onboard unmanned aerial vehicles is emerging as a promising technology to provide connectivity in areas where terrestrial infrastructure is insufficient or absent. This may occur, for example, in remote areas, large events, emergency situations, or areas affected by natural disasters such as wildfires or tsunamis. To successfully materialize this goal, it is required that ABSs are placed at locations in 3D space that ensure a high quality of service to the ground terminals. This article provides a tutorial introduction to this ABS placement problem where the fundamental challenges and trade-offs are first investigated by means of a toy application example. Next, the different approaches in the literature to address the aforementioned challenges in both 2D or 3D space are introduced, and a discussion on adaptive placement is provided. The article is concluded by discussing future research directions.

Energy efficiency-driven mobile base station deployment strategy for shopping malls using modified improved differential evolution algorithm

Energy efficiency-driven mobile base station deployment strategy for shopping malls using modified improved differential evolution algorithm

“Cellular Network Densification Increases Radio-Frequency Pollution”: True or False?

A very popular theory circulating among non-scientific communities claims that the massive deployment of Base Stations (BSs) over the territory, a.k.a. cellular network densification, always triggers an uncontrolled and exponential increase of human exposure to Radio Frequency “Pollution” (RFP). To face such concern in a way that can be understood by the layman, in this work we develop a very simple model to compute the RFP, based on a set of worst-case and conservative assumptions. We then provide closed-form expressions to evaluate the RFP variation in a pair of candidate 5G deployments, subject to different densification levels. Results, obtained over a wide set of representative 5G scenarios, dispel the myth: cellular network densification triggers an RFP decrease (up to three orders of magnitude) when the radiated power from the BS is adjusted to ensure a minimum sensitivity at the cell edge. Eventually, we analyze the conditions under which the RFP may increase when the network is densified (e.g., when the radiated power does not scale with the cell size), proving that the amount of RFP is always controlled. Finally, the results obtained by simulation confirm the outcomes of the RFP model.

User-Centric Cooperative Transmissions-Enabled Handover for Ultra-Dense Networks

The user-centric cooperative transmission provides a compelling way to alleviate frequent handovers caused by an ever-increasing number of randomly deployed base stations (BSs) in ultra-dense networks (UDNs). This paper proposes a new user-centric cooperative transmissions-based handover scheme, i.e., the group-cell handover (GCHO) scheme, with the aim of reducing the handover rate in UDNs. In the proposed scheme, the boundary of the cooperating cluster depends on the distance among the user equipment (UE) and cooperating BSs. The new scheme captures the dynamicity and irregularity of the cooperating cluster topology resulting from randomly distributed BSs. Based on stochastic-geometry tools where BSs locations are modeled as the Poison point process (PPP), we derive an analytical expression of the handover rate for the UE with an arbitrary movement trajectory. Furthermore, a GCHO skipping (GCHO-S) scheme is proposed to minimize the handover cost, i.e., the percentage of time wasted in handover signaling in user-centric cooperative transmissions scenarios. The numerical results show that the GCHO scheme decreases the handover rate by 42.3% and 72.7% compared with the traditional single BS association and fixed-region cooperative network topology handover approaches, respectively. Moreover, under the same group-cell size and constant velocity, the GCHO-S scheme diminishes the handover cost by 50% against the GCHO scheme.

Effectiveness of Handover Control Parameters on Handover Performance in 5G and beyond Mobile Networks

Mobility management is essential in mobile communication networks to provide a smooth connection during users’ mobility. Handover control parameters (HCPs), such as handover margin (HOM) and time-to-trigger (TTT), are major and essential factors in mobility management that must be defined carefully to make efficient handover (HO) procedures. Their impact becomes more critical with the deployment of fifth-generation (5G) mobile networks and beyond (B5G). This is due to the different characterizations of next mobile networks, such as the use of millimeter-wave (mmWave) bands, the ultradense deployment of small base stations (BSs), large mobile connection traffic growth, and other more critical factors. The case becomes more sensitive with the high mobility speed scenarios. This study proposes different HCP system settings to be investigated and analyzed over B5G networks. They will be investigated with various mobile speed scenarios to illustrate their impact on the network performance. Various key performance indicators (KPIs) are considered to evaluate and validate system performance, such as reference signal received power (RSRP), HO probability (HOP), HO ping-pong (HOPP), radio link failure (RLF), HO interruption time (HIT), and HO failure (HOF). Results show that the various system settings provide different and significant impacts on the performance of B5G networks. Furthermore, the setting of HCP1 obtained the best performance in RSRP and RLF with -69.7 dBm and 4.8%, respectively, while the optimum performance of HOPP, HIT, HOP, and HOF is achieved in the HCP6 setting with 0%, 0.02 ms, 0.05%, and 0%, respectively. Moreover, the overall outcome of all HCP settings is 54.94%. These results indicate a tradeoff between RLF and HOPP with various HCP settings in B5G mobile networks. Thus, the HCP system settings must be adjusted carefully considering other factors, such as mobile environment and use case.

Guest Editorial: Evolution of 5G NR MIMO Standards

The multi-input multi-output (MIMO) technology is proven salient in cellular wireless communications standards for improving end-user experience by increasing the overall spectral efficiency. With the advent of antenna technologies such as active antennas and two-dimensional arrays, deploying base stations with tens or hundreds of antenna elements has become feasible and it is an essential component for successful deployments of 5G New Radio (NR) technologies standardized by the 3rd Generation Partnership Project (3GPP). Also included in 5G NR is the use of mmWave bands where an analog-beam-based transmission and reception paradigm, termed the beam management, poses a new set of challenges and requirements. The initial release of 5G NR, i.e., Rel-15 includes the support of high-resolution channel state information (CSI) to facilitate efficient multi-user MIMO operation, multiple transmit/receive points (multi-TRP) operation, and beam management. Such features are enabled by basic components such as reference signals for facilitating demodulation and CSI measurements, CSI reporting mechanisms for link adaptation and scheduling, transmission schemes for downlink and uplink, and control signaling. 5G NR MIMO has been enhanced in 3GPP Rel-16 and Rel-17 standards. Following the recent completion of Rel-17 standards, 5G NR MIMO would be further enhanced in Rel-18 taking into account various requests to enable new products and more flexible network deployments.

A Comprehensive Evaluation of Generating a Mobile Traffic Data Scheme without a Coarse-Grained Process Using CSR-GAN

Large-scale mobile traffic data analysis is important for efficiently planning mobile base station deployment plans and public transportation plans. However, the storage costs of preserving mobile traffic data are becoming much higher as traffic increases enormously population density of target areas. To solve this problem, schemes to generate a large amount of mobile traffic data have been proposed. In the state-of-the-art of the schemes, generative adversarial networks (GANs) are used to transform a large amount of traffic data into a coarse-grained representation and generate the original traffic data from the coarse-grained data. However, the scheme still involves a storage cost, since the coarse-grained data must be preserved in order to generate the original traffic data. In this paper, we propose a scheme to generate the mobile traffic data by using conditional-super-resolution GAN (CSR-GAN) without requiring a coarse-grained process. Through experiments using two real traffic data, we assessed the accuracy and the amount of storage data needed. The results show that the proposed scheme, CSR-GAN, can reduce the storage cost by up to 45% compared to the traditional scheme, and can generate the original mobile traffic data with 94% accuracy. We also conducted experiments by changing the architecture of CSR-GAN, and the results show an optimal relationship between the amount of traffic data and the model size.

Modeling and Analysis of Data and Coverage Energy Efficiency for Different Demographic Areas in 5G Networks

It is expected that fifth-generation (5G) mobile networks will need to accommodate vast data traffic volumes. The practical implementation of 5G networks is further challenged by the presence of versatile demographic areas differing in user density and expected transmission rates. Accommodating the increased traffic demands in such versatile demographic areas mandates the installation of different in size, number, and capacity 5G base stations (BSs), which will have an impact on the energy consumption of the 5G network. In this article, for the square kilometer area of the radio part of the 5G network, standardized network energy-efficiency (EE) metrics are presented. The EE analysis of the 5G network was performed for four different demographic areas (indoor hotspot, dense urban, urban, and rural) and two standardized data (bit/J) and coverage (m <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup> /J) EE metrics. To determine how the different 5G BSs deployment strategies affect the network's EE, five different 5G BSs installation and operation strategies have been analyzed for each demographic area. Interpolation functions expressing the impact of the data volume (DV) on the data and coverage EE metrics have been developed for each demographic area and network installation and operation scenario. The obtained results show that the DVs of the different demographic areas significantly impact the standardized EE metrics.

Large-Scale Fading Coefficients Classification-Aided Power Control Strategy for Mitigating the Pilot Contamination in Massive MIMO Systems

Deploying base stations with a massive number of antennas give birth to massive multi-input multi-output technology, which can boost the quality of the wireless communication; however, the limitations imposed by the length of the coherence interval has, directly, limited the number of the orthogonal pilot sequences (OPSs) that can be exploited for acquiring such accurate channel state information at each base station (BS). Hence, the available pilot resources are reused among several cells, which causes the problem of pilot contamination (PC). In this paper, the PC problem is analyzed, and by exploiting the large-scale fading coeffificients of the users’ equipment (UEs), we have classifified the users within each cell; therefore, the same OPS is, solely, reused by the far-distant UEs; hence, the inflfluence of the PC problem is made as small as possible. Since the UEs are subject to different channel conditions, we have consolidated our proposal by a power control algorithm, which boosts the performance of our proposal. Simulation results prove the effectiveness of our proposed decontaminating strategy.

Energy-Efficient and QoS Guaranteed BBU Aggregation in CRAN Based on Heuristic- Assisted Deep Reinforcement Learning

The surging mobile traffic poses serious challenges for mobile operators, one of which is the unsustainable growth caused by the high energy consumption of the massively deployed base stations (BSs). Cloud radio access network (CRAN), as a new architecture, is proposed to confront this challenge. By isolating baseband unit (BBU) from its remote radio head (RRH) in BS, the BBUs are consolidated into a common place (i.e., BBU pool). Since the “any-to-any” connection between BBUs and RRHs is realized in CRAN, low utilized BBUs can be switched to sleep mode to save energy during traffic valley, which can effectively reduce the energy consumption of CRAN. However, when a BBU enters into the sleep mode, RRHs connected with this BBU must be switched to another BBU, which would degrade the quality of service (QoS) for the RRHs. In this paper, to simultaneously guarantee low BBU energy consumption and low RRH traffic migration, both of which are interrelated and mutual restraint, we propose a deep reinforcement learning (DRL) based BBU aggregation scheme. Furthermore, to train the DRL fast and well, we introduce several heuristic algorithms to assist the DRL training. Extensive numerical evaluations show that our proposed heuristic-assisted DRL (HA-DRL) can guarantee both the low power consumption and the less traffic migration. When compared with the baselines, HA-DRL can achieve up to 18.3% cost reduction and 32.8% migrated traffic reduction with at most 8.4% higher energy consumption and attains the lowest cost for all cases considered.

Drone-Base-Station for Next-Generation Internet-of-Things: A Comparison of Swarm Intelligence Approaches

The emergence of next-generation Internet-of-Things (NG-IoT) applications introduces several challenges for the sixth-generation (6G) mobile networks, such as massive connectivity, increased network capacity, and extremely low-latency. To countermeasure the aforementioned challenges, ultra-dense networking has been widely identified as a possible solution. However, the dense deployment of base stations (BSs) is not always possible or cost-efficient. Drone-base-stations (DBSs) can facilitate network expansion and efficiently address the requirements of NG-IoT. In addition, due to their flexibility, they can provide on-demand connectivity in emergency scenarios or address temporary increases in network traffic. Nevertheless, the optimal placement of a DBS is not a straightforward task due to the limited energy reserves and the increased signal quality degradation in air-to-ground links. To this end, swarm intelligence approaches can be attractive solutions for determining the optimal position of the DBS in the three-dimensional (3D) space. In this work, we explore well-known swarm intelligence approaches, namely the Cuckoo Search (CS), Elephant Herd Optimization (EHO), Grey Wolf Optimization (GWO), Monarch Butterfly Optimization (MBO), Salp Swarm Algorithm (SSA), and Particle Swarm Optimization (PSO) and investigate their performance and efficiency in solving the aforementioned problem. In particular, we investigate the performance of three scenarios in the presence of different swarm intelligence approaches. Additionally, we carry out non-parametric statistical tests, namely the Friedman and Wilcoxon tests, in order to compare the different approaches.

Achieving Reliable Underground Positioning With Visible Light

Visible light technology shows great potential in the underground mine positioning today. Most visible light-based underground positioning technology deploy the receiver, such as photodiodes (PDs), on the mine tunnel ground and consider the miner’s lamp on the ceiling as the transmitter. This incurs the unreliable reception of visible light signals and the difficult implementation of VLP systems due to the complex underground mine environment, such as the receiver being obscured or trampled by objects (e.g., Minecarts and Ore). We present a reliable underground positioning algorithm with visible light considering the actual mine environment. The key innovation is that we design a novel reverse mechanism, i.e., utilizing the existing miner’s headlamp as the transmitter for broadcasting the specific light beacon representing the mine’s identity information, and the PDs deployed on the ceiling of the mine tunnel as the receiver for optical signal reception. We can infer the current locations of all miners using a series of base stations constructed by PDs. If some special mine environment limits the large-scale deployment of base stations and causes the positioning blind spots, the inertial navigation is combined to achieve all miners’ real-time positioning. We overcome some technical challenges for this new mechanism and the underground mine environment, such as optimal frequency selection, robust frequency identification, and accurate base station positioning. We conduct extensive experiments on our algorithm in a simulated mine tunnel, and the experimental results verify that our algorithm exhibits good performance.

Influence of Power Frequency Magnetic Field Interference in Substation on 5G Base Station Deployment

Influence of Power Frequency Magnetic Field Interference in Substation on 5G Base Station Deployment

Distributed Edge System Orchestration for Web-based Mobile Augmented Reality Services

The emergence of edge computing and 5G networks has fueled the growth of mobile Web AR. Although efforts have been made to improve the edge system efficiency for Web AR applications, efficient edge-assisted mobile Web AR services remain technically challenging. This paper presents EARNet, a distributed edge system orchestration approach for mobile Web AR in 5G networks. The design of EARNet makes three novel contributions. First, EARNet manages the edge network dynamics with respect to user mobility and their Web AR service requests by employing landmarks and grid index based edge node localization mechanisms. Second, EARNet takes into account both request serving performance and offloading cost in managing workload balance and quality of service and leverages dynamic hash and max heap mechanisms for efficient Web AR service lookup and AR computations. Third, EARNet designs the service migration schemes by optimizing several performance factors, such as message efficiency, scheduling latency, request density and locality of mobile users and edge nodes, and accuracy of Web AR services after migration. Experimental evaluations are conducted using the real base station deployment data in the Melbourne Central Business District (CBD) area. The results shows the effectiveness of the EARNet edge orchestration approach compared to several baseline approaches.

Analysis of Wave Propagation Models With Radio Network Planning Using Dual Polarized MIMO Antenna for 5G Base Station Applications

Dual polarized printed multiple input multiple output (MIMO) antenna for Band 42 (3.4 - 3.6 GHz) with wave propagation models is presented. Polarization and spatial diversity are achieved by utilizing two printed bow-tie antennas in orthogonal orientation. The designed dual polarized antenna element with <inline-formula> <tex-math notation="LaTeX">$2\times 2$ </tex-math></inline-formula>, <inline-formula> <tex-math notation="LaTeX">$4\times 4$ </tex-math></inline-formula> and <inline-formula> <tex-math notation="LaTeX">$8\times 8$ </tex-math></inline-formula> massive MIMO antenna configuration radiation patterns are deployed in selected geographical situation for detailed radio network planning using FEKO-WinProp platform. Knife edge diffraction, extended walfisch-ikegami and dominant path wave propagation models are implemented with designed MIMO antenna configurations. Modulation schemes of QPSK and QAM with corresponding data rates and throughput for all propagation models are presented. The signal strength and quality reflecting parameters reference signal received power (RSRP), received signal strength indicator (RSSI), reference signal received quality (RSRQ), and signal to noise plus interference ratio (SNIR) are also evaluated for each model. From the simulation results dominant path model provides data rate and throughput of 3.827, 995 MBit/s and 3.577, 930.1 MBit/s for single stream of data in uplink and downlink respectively. The maximum data rate of 1.37 GBits/s is achieved for deployed base stations with <inline-formula> <tex-math notation="LaTeX">$8\times 8$ </tex-math></inline-formula> massive MIMO antenna configuration effectively covering the entire geographical site.

How Trend of Increasing Data Volume Affects the Energy Efficiency of 5G Networks.

As the rapid growth of mobile users and Internet-of-Everything devices will continue in the upcoming decade, more and more network capacity will be needed to accommodate such a constant increase in data volumes (DVs). To satisfy such a vast DV increase, the implementation of the fifth-generation (5G) and future sixth-generation (6G) mobile networks will be based on heterogeneous networks (HetNets) composed of macro base stations (BSs) dedicated to ensuring basic signal coverage and capacity, and small BSs dedicated to satisfying capacity for increased DVs at locations of traffic hotspots. An approach that can accommodate constantly increasing DVs is based on adding additional capacity in the network through the deployment of new BSs as DV increases. Such an approach represents an implementation challenge to mobile network operators (MNOs), which is reflected in the increased power consumption of the radio access part of the mobile network and degradation of network energy efficiency (EE). In this study, the impact of the expected increase of DVs through the 2020s on the EE of the 5G radio access network (RAN) was analyzed by using standardized data and coverage EE metrics. An analysis was performed for five different macro and small 5G BS implementation and operation scenarios and for rural, urban, dense-urban and indoor-hotspot device density classes (areas). The results of analyses reveal a strong influence of increasing DV trends on standardized data and coverage EE metrics of 5G HetNets. For every device density class characterized with increased DVs, we here elaborate on the process of achieving the best and worse combination of data and coverage EE metrics for each of the analyzed 5G BSs deployment and operation approaches. This elaboration is further extended on the analyses of the impact of 5G RAN instant power consumption and 5G RAN yearly energy consumption on values of standardized EE metrics. The presented analyses can serve as a reference in the selection of the most appropriate 5G BS deployment and operation approach, which will simultaneously ensure the transfer of permanently increasing DVs in a specific device density class and the highest possible levels of data and coverage EE metrics.

OBTAINING CONDITIONS FOR TECHNOLOGY NEUTRALITY OF COMMUNICATION SYSTEMS WITH MINIMUM COUPLING LOSS METHOD

Background. Technology neutrality is widely used in frequency bands, where communication systems are evolving, but there are strict restrictions on the parameters and deployment of base stations of different technologies using adjacent channels. Ways to mitigate this effect have not been sufficiently studied and require further analysis and development.&#x0D; Objective. The purpose of this article is to investigate the methodology for obtaining technical conditions of technological neutrality with minimum coupling loss method to determine the value of additional filtering requirements and present the results of practical implementation of this technique.&#x0D; Methods. The method of detailed power analysis of frequency characteristics of filters for base stations’ transmitter and receiver is applied.&#x0D; Results. The article presents the results of obtaining minimal guard band and additional filtering requirements in the adjacent channels of transmitter and receiver belonging to different technologies. Examples of practical implementation of the minimum guard band and frequency characteristics of additional filters are given.&#x0D; Conclusions. The general method of determining the technical conditions for ensuring technology neutrality is presented and the value of the minimal required guard band between the adjacent transmitter and receiver channels is obtained.

Energy Efficient Optimal Resource Allocation in Multi-RAT Heterogeneous Network

ABSTRACT In this paper, we address the frequent problem associated with user association and resource allocation along with optimal deployment of base station (BS) in multiple radio access technology (Multi-RAT)-assisted heterogeneous network (Het-Net). Considering real time user scenarios, optimal resource allocation in Het-Net while ensuring each user’s minimum required data rate is a challenging task to be performed. Here, we propose a novel algorithm with a well-known and efficient meta-heuristic optimization technique to resolve the aforementioned problem. We use hybrid memory-based dragonfly algorithm with differential evolution (DADE) for its excellent convergence characteristics. Extensive simulations are performed to determine the optimal network utility under the consideration of nonuniform user distribution and fine-tuning their respective service class and contract of association parameters. Simulation results depict that the proposed algorithm improves the overall network utility in terms of radio resource utilization and energy consumption while satisfying the user demands. Comparative analysis of the proposed technique with the other state-of-the-art algorithm depicts the superiority of the proposed algorithm in terms of accuracy and consistency. We also perform optimal multi-RAT cell planning under the above constraints including a network blackout scenario. The algorithm ensures each user coverage by optimally allocating the available resources.