Large Number Of Base Stations Research Articles

Rotating Target Detection Using Commercial 5G Signal

Passive radar detection emerges as a pivotal method for environmental perception and target detection within radar applications. Through leveraging its advantages, including minimal electromagnetic pollution and efficient spectrum utilization, passive radar methodologies have garnered increasing interest. In recent years, there has been an increasing selection of passive radar signal sources, and the emerging 5G has the characteristics of a high-frequency band, high bandwidth, and a large number of base stations, which give it significant advantages for use in passive radar. Therefore, in this paper, we introduce a passive radar target detection method based on 5G signals and design a rotating target speed measurement experiment. In the experiment, this paper validated the method of detecting rotating targets using 5G signals and evaluated the measurement accuracy, providing a research foundation for passive radar target detection using 5G signals and detecting rotating targets such as drone rotors.

Algorithm-Unrolling-Based Distributed Optimization for RIS-Assisted Cell-Free Networks

The user-centric cell-free network has emerged as an appealing technology to improve the wireless communication’s capacity of the internet of things (IoT) networks thanks to its ability to eliminate inter-cell interference effectively. However, the cell-free network inevitably brings in higher hardware cost and backhaul overhead as a larger number of base stations (BSs) are deployed. Additionally, severe channel fading in high-frequency bands constitutes another crucial issue that limits the practical application of the cell-free network. In order to address the above challenges, we amalgamate the cell-free system with another emerging technology, namely reconfigurable intelligent surface (RIS), which can provide high spectrum and energy efficiency with low hardware cost by reshaping the wireless propagation environment intelligently. To this end, we formulate a weighted sum-rate (WSR) maximization problem for RIS-assisted cell-free systems by jointly optimizing the BS precoding matrix and the RIS reflection coefficient vector. Subsequently, we transform the complicated WSR problem to a tractable optimization problem and propose a distributed cooperative alternating direction method of multipliers (ADMM) to fully utilize parallel computing resources. Inspired by the model-based algorithm unrolling concept, we unroll our solver to a learning-based deep distributed ADMM (D-ADMM) network framework. To improve the efficiency of the D-ADMM in distributed BSs, we develop a monodirectional information exchange strategy with a small signaling overhead. In addition to benefiting from domain knowledge, D-ADMM adaptively learns hyper-parameters and non-convex solvers of the intractable RIS design problem through data-driven end-to-end training. Finally, numerical results demonstrate that the proposed D-ADMM achieve around 210% improvement in capacity compared with the distributed noncooperative algorithm and almost 96% compared with the centralized algorithm.

Spatio-Temporal Analysis and Prediction of Mass Telecommunication Base Station Failure Events

Large-scale telecommunication systems are lifeline infrastructure in modern society. A telecommunication system typically consists of a huge number of base stations with diverse geographical locations across a country, which highly complicates maintenance operations. To allocate maintenance resource properly, it is important to have a good understanding on the failure pattern of these base stations. Statistical inference of recurrent failures of these base stations is challenging because of the large number of base stations and the spatial correlation of their failure processes. Based on eight-month failure data of telecommunication base stations in Harbin, China, we propose a customized nonhomogeneous Poisson process (NHPP) model for recurrent failure data from telecommunication systems. The model consists of two layers, where the temporal layer applies an NHPP with station-specific frailty for failures of each base station, and the spatial layer uses a multivariate lognormal distribution to characterize the correlation among the frailties. The Monte Carlo EM (MCEM) algorithm is applied to estimate parameters included in the proposed model. We demonstrate the proposed model using the Harbin telecommunication system example with 7725 base stations and 4615 failure records.

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.

Design of AUVs based on 3D coils positioning and distributed base station control for nuclear spent fuel pools

An underwater magnetic induction positioning and communication system and an energy-efficient distributed control algorithm for underwater base stations are proposed in this paper. The positioning and communication system consists of a number of base stations and an Autonomous Underwater Vehicle (AUV) equipped with three-axis source coils respectively. The AUV receives this signal and based on its amplitude and phase information is able to locate the AUV and communicate with the base station. Due to the short positioning distance of magnetic induction positioning technology, a large number of base stations need to be installed underwater, which puts high demands on the control of the base stations. In this paper, an energy-efficient distributed control algorithm for underwater base stations is proposed to enable the AUV to meet the operational requirements while minimizing the total energy consumption of the base station. According to the simulation results, the design solves the problem that traditional underwater positioning and communication equipment cannot work stably for long periods of time in a high radiated environment, with a positioning error of no more than 10 cm within a preset operating range, and the algorithm proposed in this paper is able to reduce energy wastage by about 20%.

User Association for Reconfigurable Intelligent Surfaces Aided Cell-Free Networks

Cell-free (CF) network can efficiently suppress inter-cell interference and enable multiple base stations (BSs) to serve users cooperatively without being affected by cell boundaries. However, to further increase the network capacity, a large number of BSs need to be deployed, resulting in high cost and power consumption. To tackle this problem, reconfigurable intelligent surface (RIS) has emerged as a cost-effective technology to enhance energy and spectral efficiency of wireless communications. In this paper, we consider an RIS-aided CF network by exploiting their complementary advantages where each user assisted by a dedicated RIS can associate with multiple BSs. We first characterize each user's average signal-to-interference-plus-noise ratio (SINR) in the RIS-CF network. Then, we formulate an SINR balancing problem to maximize the minimum SINR among all users by designing the BS-user associations. Despite of the non-convexity and combinatorial feature of this problem, a two-step fractional linearization algorithm is developed to solve it optimally. Specifically, we transform a nested mixed-integer linear fractional program (MILFP) into a solvable mixed-integer linear programming (MILP) problem through the generalized fractional programming and reformulation-linearization method. Finally, we provide numerical results to validate the effectiveness of our proposed algorithm compared to other benchmark schemes.

Physical-Layer Security of Uplink mmWave Transmissions in Cellular V2X Networks

In this paper, we investigate physical-layer security of the uplink millimeter wave communications for a cellular vehicle-to-everything (C-V2X) network comprised of a large number of base stations (BSs) and different categories of V2X nodes, including vehicles, pedestrians, and road side units. Considering the dynamic change and randomness of the topology of the C-V2X network, we model the roadways, the V2X nodes on each roadway, and the BSs by a Poisson line process, a 1D Poisson point process (PPP), and a 2D PPP, respectively. We propose two uplink association schemes for a typical vehicle, namely, the smallest-distance association (SDA) scheme and the largest-power association (LPA) scheme, and we establish a tractable analytical framework to comprehensively assess the security performance of the uplink transmission, by leveraging the stochastic geometry theory. Specifically, for each association scheme, we first obtain new expressions for the association probability of the typical vehicle, and then derive the overall connection outage probability and secrecy outage probability by calculating the Laplace transform of the aggregate interference power. Numerical results are presented to validate our theoretical analysis, and we also provide interesting insights into how the security performance is influenced by various system parameters, including the densities of V2X nodes and BSs. Moreover, we show that the LPA scheme outperforms the SDA scheme in terms of secrecy throughput.

Robust and energy-efficient user association and traffic routing in B5G HetNets

Next-generation cellular networks, i.e., beyond fifth generation (B5G) and sixth generation (6G) networks, aim at significantly increasing capacity by deploying a massive number of small cells (SCs) and leveraging millimeter wave (mmWave) links that have large bandwidth availability. However, as optical connections to a large number of base stations (BSs) are not cost effective, wireless backhaul (BH) links may be used to connect SCs to the core network using mmWave frequencies and forming multihop mesh BH paths. While very flexible in deployment, the uncertainty in user demand, which varies over time and place, makes network planning and management challenging. In this work, we propose novel algorithms to solve the joint problem of energy-efficient user association, BH traffic routing and BS/BH link on/off switching. We first formulate an exact model assuming user traffic demand uncertainty leveraging Γ-robustness theory. As the model is intractable for large-scale instances, we develop a novel greedy robust heuristic (P-HEUR), which includes a robustifying step to effectively cope with demand uncertainty. Our evaluation demonstrates that P-HEUR provides robust and energy-efficient solutions quickly for different scenarios and traffic demand uncertainty levels, and it significantly outperforms state-of-the-art approaches while achieving up to 83% of the optimal capacity, even in the most demanding scenarios in terms of traffic load and demand uncertainty, with up to 200k times lower complexity.

Distributed Offloading in Overlapping Areas of Mobile-Edge Computing for Internet of Things

With the maturity of 5G cellular communication systems and mobile-edge computing (MEC), a large number of base stations (BSs) with edge-computing servers are densely deployed. There are extensive overlapping coverage areas among the BSs in which some heavy computational tasks from Internet of Things (IoT) devices can be divided and offloaded to multiple BSs via the coordinated multipoint (CoMP) technique for parallel processing. However, it is a challenging issue about how to make proper task offloading decisions among multiple connected BSs while satisfying delay requirements of multiple devices. To address this challenge, this article presents an efficient multidevice and multi-BSs task offloading scheme with the goal of minimizing the delay for completing the tasks of the devices. By conducting quantitative analysis of local delay and offloading delay, a nonlinear and nonconvex delay optimization offloading problem, which is based on the theory of noncooperative game, is formulated. We prove the existence of Nash equilibrium by analyzing the feature of the proposed offloading problem and further propose a distributed task offloading algorithm called DOLA. Finally, simulation experiments based on real-world data set from the Melbourne CBD area of Australia are conducted to validate the efficacy of our DOLA algorithm. Comparison experiments are also carried out to demonstrate the superiority of DOLA in comparison with some existing schemes.

Uplink coverage enhancements for extremely large-cell sites

In urban areas, the network operators deploy small-cell sites and install a large number of base stations to support densely populated cellular users. A similar small-cell deployment in rural areas is not feasible for the operators, as it will significantly increase the capital expenditure, and the return on the investment will also be comparatively lower. Hence, the most feasible way to provide cellular connectivity to rural areas is the deployment of large-cell sites. However, the technology developed for International Mobile Telecommunications (IMT)-Advanced standard (or 4G) was evaluated for an inter-site distance of 1.732 km, making the large-cell connectivity required for rural areas an afterthought. For cellular last-mile rural connectivity to be a reality through IMT-2020 (or 5G), the key is to have future technologies supporting large cells. In large-cell sites, the users experience comparatively more path loss, and thus, have poor signal coverage. Further, compared to downlink, the transmission power in uplink is limited, and therefore, uplink transmissions define the coverage of the communication system. Motivated by this, we propose several coverage solutions for uplink, considering an extremely large-cell site scenario. We initially analyze the received signal-to-noise ratio with 4G/5G systems at various distances based on the link budget evaluation. At larger inter-site distances, we show that it is infeasible to achieve cellular coverage with the current 4G/5G specifications. We then propose enhancements related to the waveform, modulation and coding schemes, resource allocation, and power control mechanisms. We evaluate the proposed enhancements through system- and link-level simulations, and show a significant improvement in the coverage for cell-edge users. We also show that our proposed enhancements achieve close to two times the improvement in the network capacity when compared to the currently available 5G specifications.

Carbon emissions and mitigation potentials of 5G base station in China

The emergence of fifth-generation (5G) telecommunication would change modern lives, however, 5G network requires a large number of base stations, which may lead to greater carbon emissions. Since 2020, over 700,000 5G base stations are in operation in China. This study aims to understand the carbon emissions of 5G network by using LCA method to divide the boundary of a single 5G base station and discusses the carbon emission of 5G base station from the perspective of the whole life cycle. Also, we considered China's 5G base station as an example to calculate carbon emission at a national scale. The results indicated that the carbon emissions of one micro/macro base station were 6.2 ± 0.4 and 30.2 ± 1.5 tons, respectively, subject to one-year operation time. At national level, the total carbon emissions were 17±5 million metric tons (Mt) in 2020, and may increase to 145±7 Mt by 2030. However, a significant reduction of ca. 42.8% can be achieved by optimizing the power structure and base station layout strategy and reducing equipment power consumption. Overall, this study provides a clear approach to assess the environmental impact of the 5G base station and will promote the green development of mobile communication facilities.

A lightweight cell switching and traffic offloading scheme for energy optimization in ultra-dense heterogeneous networks

One of the major capacity boosters for 5G networks is the deployment of ultra-dense heterogeneous networks (UDHNs). However, this deployment results in a tremendous increase in the energy consumption of the network due to the large number of base stations (BSs) involved. In addition to enhanced capacity, 5G networks must also be energy efficient for it to be economically viable and environmentally friendly. Dynamic cell switching is a very common way of reducing the total energy consumption of the network, but most of the proposed methods are computationally demanding, which makes them unsuitable for application in ultra-dense network deployment with massive number of BSs. To tackle this problem, we propose a lightweight cell switching scheme also known as Threshold-based Hybrid cEll swItching Scheme (THESIS) for energy optimization in UDHNs. The developed approach combines the benefits of clustering and exhaustive search (ES) algorithm to produce a solution whose optimality is close to that of the ES (which is guaranteed to be optimal), but is computationally more efficient than ES and as such can be applied for cell switching in real networks even when their dimension is large. The performance evaluation shows that THESIS significantly reduces the energy consumption of the UDHN and can reduce the complexity of finding a near-optimal solution from exponential to polynomial complexity.

The Efficient Mobile Management Based on Metaheuristic Algorithm for Internet of Vehicle.

With the low latency, high transmission rate, and high reliability provided by the fifth-generation mobile communication network (5G), many applications requiring ultra-low latency and high reliability (uRLLC) have become a hot research topic. Among these issues, the most important is the Internet of Vehicles (IoV). To maintain the safety of vehicle drivers and road conditions, the IoV can transmit through sensors or infrastructure to maintain communication quality and transmission. However, because 5G uses millimeter waves for transmission, a large number of base stations (BS) or lightweight infrastructure will be built in 5G, which will make the overall environment more complex than 4G. The lightweight infrastructure also has to be considered together. For these reasons, in 5G, there are two mechanisms for handover, horizontal, and vertical handover; hence, it must be discussed how to handle handover to obtain the best performance for the whole network. In this paper, to address handover selection, we consider delay time, energy efficiency, load balancing, and energy consumption and formulate it as a multi-objective optimization (MOO) problem. At the same time, we propose the handover of the mobile management mechanism based on location prediction combined with heuristic algorithms. The results show that our proposed mechanism is better than the distance-based one for energy efficiency, load, and latency. It optimizes by more than about 20% at most.

Wideband, Millimeter Wave Domain SI Canceling (>50dB) In-Band Full-Duplex Circulator Receiver

This paper describes a 26-GHz fully integrated In-band Full-duplex Circulator (IBFD) Receiver (RX), which employs passive and active Self-interference Cancellation (SIC) techniques in the mm-wave domain. Coverage of wireless networks at mm-wave frequencies can be enhanced by deploying a large number of base stations economically based on Integrated Access and Backhaul (IAB) relays and repeaters. However, to retain the channel capacity, IAB needs to be implemented using full-duplex schemes that suffer from a strong Transmitter (TX) to RX SI. This SI leakage can significantly impact the receiver sensitivity and increase the baseband/ADC dynamic range requirements. Canceling SI at mm-wave applications is particularly challenging given the high frequency of operation, wide bandwidth, and antenna (ANT) impedance sensitivity to the surroundings. Proposed mm-wave RX with a shared ANT interface based on a circulator with active SI cancelers provide ~53 dB SIC over 400 MHz and ~40 dB SIC over 400 MHz in mm-wave domain to meet the link budget requirements. Proposed architecture achieves SIC by (i) introducing a shared ANT interface based on a hybrid-coupler and a non-reciprocal delay line that provides wideband SIC and additionally creates a SI replica (ii) subsequent active cancellation using SI replica processed with variable gain amplifiers and phase shifters. This system also accommodates SI channel variations due to surroundings. Proposed 26-GHz circulator RX has >100x better SIC at high TX power (>10dBm) levels in comparison to the state-of-the-art and it consumes ~111 mW power. The system is implemented in 45nm SOI CMOS and has an active area of <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$4.54\, mm^{2}$ </tex-math></inline-formula> . Stand-alone RX NF is ~5.8 dB and TX to ANT insertion loss (IL) is ~3.1 dB. Over-the-air measurements with modulated TX (128 QAM 2.1 Gb/s) and RX (128 QAM 4.2 Gb/s) signals show an EVM of 3.3%.

On Energy Efficiency of Wideband RIS-Aided Cell-Free Network

Cell-free (CF) network is a favorable technique against inter-cell interferences to improve the network capacity. However, to further improve the network capacity, a large number of base stations (BSs) are required to be deployed with high cost and power consumption. To tackle this problem, an energy-efficient technique called reconfigurable intelligent surface (RIS)-aided CF network has been recently proposed. By replacing some of the required power-hungry BSs with low-power RISs, the energy efficiency of CF network can be enhanced with guaranteed performance. To achieve this goal, in this paper, we first formulate a joint active and passive beamforming problem to maximize the energy efficiency of RIS-aided CF networks. Then, we propose an alternate optimization algorithm to solve this problem. Specifically, we decompose the original energy efficiency maximization problem into multiple subproblems and solve them alternatively. Particularly, we adopt the zero-forcing (ZF) beamforming scheme to optimize the active beamforming at BSs, while the sequential programming (SP) method is adopted to realize the passive beamforming at RISs. Moreover, a realistic energy-consumption model for wideband RIS-aided CF networks is provided, and the effectiveness of the proposed scheme is evaluated by simulations. Finally, simulation results verify that, the proposed scheme is able to achieve a higher energy efficiency than the existing benchmark solution.

Editorial for Wiley's IJSCN Special Issue “Satellite networks integration with 5G”

Editorial for Wiley's IJSCN Special Issue “Satellite networks integration with 5G”

Licensed shared access field trial and a testbed for satellite‐terrestrial communication including research directions for 5G and beyond

SummaryThis paper describes a licensed shared access (LSA) testbed and field trials using a live network. The testbed includes real 4G base stations and up to 1000 virtual base stations, in the spectrum sharing scenario between satellite and cellular systems. The trials focus on 5G pioneer bands 3.4–3.8 GHz and 24.25–27.5 GHz where a satellite system is operating in the downlink direction and a cellular system is accessing the same band. The designed testbed supports both frequency bands. The performance evaluation concerns evacuation and frequency change times using different types of base stations in 3.6 GHz, that is, how fast the system relinquishes the shared band to the primary user and continues transmission using another band. We show that our LSA system is scalable and able to support large number of base stations. In addition, we investigate how satellite systems could reuse International Mobile Telecommunication (IMT) bands to offer enhanced satellite communication services for land, maritime, and aeronautical applications. Preliminary simulations and analysis confirm the possibility to reuse IMT spectrum for satellite systems without causing harmful interference to the terrestrial system.

Few-Shot Learning and Self-Training for eNodeB Log Analysis for Service-Level Assurance in LTE Networks

With the increasing network topology complexity and continuous evolution of the new wireless technology, it is challenging to address the network service outage with traditional methods. In the long-term evolution (LTE) networks, a large number of base stations called eNodeBs are deployed to cover the entire service areas spanning various kinds of geographical regions. Each eNodeB generates a large number of key performance indicators (KPIs). Hundreds of thousands of eNodeBs are typically deployed to cover a nation-wide service area. Operators need to handle hundreds of millions of KPIs to cover the areas. It is impractical to handle manually such a huge amount of KPI data, and automation of data processing is therefore desired. To improve network operation efficiency, a suitable machine learning technique is used to learn and classify individual eNodeBs into different states based on multiple performance metrics during a specific time window. However, an issue with supervised learning requires a large amount of labeled dataset, which takes costly human-labor and time to annotate data. To mitigate the cost and time issues, we propose a method based on few-shot learning that uses Prototypical Networks algorithm to complement the eNodeB states analysis. Using a dataset from a live LTE network that consists of thousand of eNodeB, our experiment results show that the proposed technique provides high performance while using a low number of labeled data.

Evaluation of D2D Communications in 5G Networks

5G networks are being tested today. 5G networks are capable of improving existing services and delivering new quality of service through low latency, such as tactile internet. Existing technologies do not meet the requirements of 5G, which necessitates the development of new technologies. These include new connectivity methods that depend on the quality of the network's functioning. One of the most famous D2D communications. 5G networks use millimeter-band technologies. As the radio signal energy of the millimeter range is strongly absorbed by the environment, this range has a short communication range, so a large number of base stations must be installed in the network. But this is not always effective as their use decreases. D2D device-to-device technology is used to solve this problem. In order to ensure a high quality of network service, attention must be paid to the structure of the D2D organization. The basic structural parameters are the communication distance, the communication radius and the number of repeaters. This can be solved by selecting the relay node correctly. For this purpose it is necessary to take into account the change of the allowed data rate in the channel due to the allocation of its resources by the network node, used as a repeater. In order for the route to provide the necessary bandwidth, it is necessary to change the distance between the network nodes. This ensures network connectivity and the required level of quality of service. This is achieved by choosing the optimal algorithm that is considered in the article.

Big data security access control algorithm based on memory index acceleration in WSNs

The access control is used to ensure these data security when WSN (wireless sensor network) with a large number of base stations transmits huge amount of data to a data center server. Meanwhile big data systems are used to efficiently store, manage, and use data from large-scale WSNs. In big data systems for WSNs, the traditional access control technology will greatly affect the system performance. This paper first analyzes the data processing flow of the traditional access control strategy in big data systems, analyzes its time complexity, and explores how it affects system performance. Then, we propose the big data security access control algorithm based on memory index acceleration in WSNs which has better performance over the traditional ones. In our experiments, under the same test environment and security strategy, the performance has been greatly improved with the proposed algorithm.