Base Stations In Cellular Networks Research Articles

Outdoor-to-Indoor mmWave Relaying with Massive MIMO: Impact of Imperfect Channel Estimation

Assuming incomplete knowledge of the channel state information (CSI), we investigate two scenarios involving millimeter wave (mmWave) relaying to support outdoor-to-indoor communications. We proceed to derive the average signal-to-noise ratio (SNR) expressions for two relaying scenarios and quantify the asymptotic SNR. The performance of the two relaying scenarios is evaluated using the outage probability—for which we have derived closed-form equations—the end-to-end channel capacity, and the energy efficiency. The obtained results are compared with those derived assuming complete knowledge of the CSI. The effect of the imperfect CSI is therefore assessed in relation to the reference of perfect CSI. In these scenarios, an outside base station (BS) in an urban cellular network serves several indoor users. In the context of a two-hop full-duplex (FD) relaying scheme, we initially suggest a method in which the base station (BS) utilizes zero-forcing (ZF) precoding, and we take into account the overall channel response. Furthermore, we make the assumption that the base station (BS) engages in precoding only depending on the response of the channel in the first hop; in this second design, the relay precodes (using the response of the second-hop channel), amplifies, and sends the signals. Both techniques utilize massive multiple-input–multiple-output (mMIMO) arrays to permit transmission. We also present Monte Carlo simulation results to assess the accuracy of our analytical results. Finally, the two systems are compared in terms of channel estimation and precoding complexity, the number of antennas, as well as the number of users. Practical deployment recommendations are formulated at the end of this work.

Offloading Decision and Resource Allocation in Mobile Edge Computing for Cost and Latency Efficiencies in Real-Time IoT

Internet of Things (IoT) devices can integrate with applications requiring intensive contextual data processing, intelligent vehicle control, healthcare remote sensing, VR, data mining, traffic management, and interactive applications. However, there are computationally intensive tasks that need to be completed quickly within the time constraints of IoT devices. To address this challenge, researchers have proposed computation offloading, where computing tasks are sent to edge servers instead of being executed locally on user devices. This approach involves using edge servers located near users in cellular network base stations, and also known as Mobile Edge Computing (MEC). The goal is to offload tasks to edge servers, optimizing both latency and energy consumption. The main objective of this paper mentioned in the summary is to design an algorithm for time- and energy-optimized task offloading decision-making in MEC environments. Therefore, we developed a Lagrange Duality Resource Optimization Algorithm (LDROA) to optimize for both decision offloading and resource allocation for tasks, whether to locally execute or offload to an edge server. The LDROA technique produces superior simulation outcomes in terms of task offloading, with improved performance in computation latency and cost usage compared to conventional methods like Random Offloading, Load Balancing, and the Greedy Latency Offloading scheme.

A Data-driven Base Station Sleeping Strategy Based on Traffic Prediction

Due to the rapidly increasing number of base stations (BSs) in the operational cellular networks, their energy consumption is escalating. In this paper, we propose an intelligent data-driven BS sleeping mechanism relying on a wireless traffic prediction model that measures the BSs' capacity in different regions. Firstly, a spatio-temporal cellular traffic prediction model is proposed, where a multi-graph convolutional network (MGCN) is developed to capture the associated spatial features. Furthermore, a multi-channel long short-term memory (LSTM) solution involving hourly, daily and weekly periodic data is used to capture the relevant temporal features. Secondly, the capacities of macro-cell BSs (MBSs) and small-cell BSs (SBSs) having different environment characteristics are modeled, where both clustering and transfer learning algorithms are adopted for quantifying the traffic supported by the MBSs and SBSs. Finally, an optimal BS sleeping strategy is proposed for minimizing the network's power consumption. Experimental results show that the proposed MGCN-LSTM model outperforms the existing models in terms of its cellular traffic prediction accuracy, and the proposed BS sleeping strategy using an approximated non-linear model of the associated capacity function achieves near-maximal energy-saving at a modest complexity.

Interference-Aware Deployment for Maximizing User Satisfaction in Multi-UAV Wireless Networks

In this letter, we study the deployment of Unmanned Aerial Vehicle mounted Base Stations (UAV-BSs) in multi-UAV cellular networks. We model the multi-UAV deployment problem as a user satisfaction maximization problem, that is, maximizing the proportion of served ground users (GUs) that meet a given minimum data rate requirement. We propose an interference-aware deployment (IAD) algorithm for serving arbitrarily distributed outdoor GUs. The proposed algorithm can alleviate the problem of overlapping coverage between adjacent UAV-BSs to minimize inter-cell interference. Therefore, reducing co-channel interference between UAV-BSs will improve user satisfaction and ensure that most GUs can achieve the minimum data rate requirement. Simulation results show that our proposed IAD outperforms comparative methods by more than 10% in user satisfaction in high-density environments.

The Energy Efficiency of Heterogeneous Cellular Networks Based on the Poisson Hole Process

In order to decrease energy consumption caused by the dense deployment of pico base stations (PBSs) in heterogeneous cellular networks (HetNets), this paper first analyzes the energy efficiency (EE) of two-tier HetNets and then proposes a method to maximize the network EE by adjusting the PBS transmit power. The two-tier HetNets are modeled by the Poisson point process (PPP) and the Poisson hole process (PHP), and then the coverage probability of the macro base station (MBS) and the PBS in the two-tier HetNets is derived based on the mean interference to signal ratio (MISR). According to the user association probability, the coverage probability of the PPP-PHP HetNets is obtained. Then, the tractable expression of the average achievable rate is deduced on the basis of the relationship between the coverage probability and the average achievable rate. Finally, the expression of EE is derived and the EE optimization algorithm is proposed based on the PBS transmit power. The simulation results show that the PPP-PHP network is superior to the PPP-PPP network in terms of coverage probability and EE, and the network EE can be effectively improved by setting an appropriate PBS transmit power.

Energy Cooperation Among Sustainable Base Stations in Multi-Operator Cellular Networks

Energy Harvesting technology contributes significantly to green cellular networking by ensuring self-sustainability and extinguishing environmental hazards. Due to the imbalance between the harvested energy and traffic load of the base stations (BSs), energy cooperation has become a crucial requirement. However, the decision of optimal energy cooperation among the BSs in a multi-operator cellular network is a challenging task due to the consideration of various factors, such as cost, loss of energy, future information of traffic load, and harvested energy of the BSs, etc. The two conflicting objectives are minimizing the energy buying cost and the loss of energy while transferring through the power links. In this work, we present an optimal energy cooperation framework, formulated as a multi-objective linear programming (MOLP) problem which brings a trade-off between the two above-mentioned conflicting objectives considering the harvested energy and load of the BSs at future time slots. For the prediction of harvested energy of the BSs, we develop a Deep Q-Learning-based prediction method that intelligently increases measurement accuracy through continuous exploration and exploitation. The results of simulation experiments carried out in MATLAB depict that the proposed multi-operator energy cooperation framework outperforms state-of-the-art works in terms of cost, performance, and energy-loss reduction.

Base Station Energy Management in 5G Networks Using Wide Range Control Optimization

The traffic activity of fifth generation (5G) networks demand for new energy management techniques that is dynamic deep and longer duration of sleep as compared to the fourth generation (4G) network technologies that demand always for varied control and data signalling based on control base station (CBS) and data base station (DBS). Hence, this paper discusses the energy management in wireless cellular networks using wide range of control for twice the reduction in energy conservation in non-standalone deployment of 5G network. As the new radio (NR) based 5G network is configured to transmit signal blocks for every 20 ms, the proposed algorithm implements withstanding capacity of on or off based energy switching, which in-turn operates in wide range control by carrying out reduced computational complexity. The proposed Wide range of control for base station in green cellular network using sleep mode for switch (WGCNS) algorithm toon and off the base station will work in heavy load with neighbouring base station. For reducing the overhead duration in air, heuristic versions of the algorithm are proposed at the base station. The algorithm operates based on the specification with suggested protocol-level to give best amount of energy savings. The proposed algorithm reduces 40% to 83% of residual energy based on the traffic pattern of the urban scenario.

N-Polar growth of nitride semiconductors with MOVPE and its applications

All white LEDs that largely contribute to the energy saving as well as high electron mobility transistors (HEMTs) powering the base stations of cellular phone networks are made from only Ga-polar materials epitaxially grown along the c-axis. N-polar material, which has the opposite crystal-direction, is considered having high potential for expanding the application field. In this paper, the present status of N-polar epitaxial growth in our lab. is reviewed. Especially, the growth mechanism of N-polar GaN on the widely used sapphire substrate is investigated in detail by using transmission electron microscopy. Finally, as device applications, solar cells, red LEDs, and inverted HEMT are described.

Segmented Spline Curve Neural Network for Low Latency Digital Predistortion of RF Power Amplifiers

At present, we are the dawn of a new era for wireless communication systems. The new dynamic approach to the operation of cellular networks requires an extension of the essential input–output hardware mechanisms, to include intelligence. Traditional neural network structures can be used to embed artificial intelligence into cellular network base stations; however, standard network structures are not an optimal solution for these use cases. In this article, we present a neural network structure, which is specifically designed to provide a more accurate and computationally efficient solution compared with the previous neural network solutions for predistortion of RF power amplifiers (PAs). The proposed network structure is directly compared with alternative neural network solutions, which have been successfully employed for digital predistortion (DPD). The operation of this network is validated for DPD with experimental measurements with wideband signals using the latest generation of commercially available RF hardware. The novel network structure proposed in this work is demonstrated, in practice, to have better performance for a normalized mean square error (NMSE), an adjacent channel leakage ratio (ACLR), and an error vector magnitude (EVM) compared with the most popular previously published neural network.

Proactive Caching in D2D Assisted Multitier Cellular Network.

Cache-enabled networks suffer hugely from the challenge of content caching and content delivery. In this regard, cache-enabled device-to-device (D2D) assisted multitier cellular networks are expected to relieve the network data pressure and effectively solve the problem of content placement and content delivery. Consequently, the user can have a better opportunity to get their favored contents from nearby cache-enabled transmitters (CETs) through reliable and good-quality links; however, as expected, designing an effective caching policy is a challenging task due to the limited cache memory of CETs and uncertainty in user preferences. In this article, we introduce a joint content placement and content delivery technique for D2D assisted multitier cellular networks (D2DMCN). A support vector machine (SVM) is employed to predict the content popularity to determine which content is to be cached and where it is to be cached, thereby increasing the overall cache hit ratio (CHR). The content request is satisfied either by the neighboring node through the D2D link or by the cache-enabled base stations (BSs) of the multitier cellular networks (MCNs). Similarly, to solve the problem of optimal content delivery, the Hungarian algorithm is employed aiming to improve the quality of satisfaction. The simulation results indicate that the proposed content placement strategy effectively optimizes the overall cache hit ratio of the system. Similarly, an effective content delivery approach reduces the request content delivery delay and power consumption.

Spatial–Temporal Energy Management of Base Stations in Cellular Networks

The operations of base stations (BSs) contribute most of the energy consumption in the cellular wireless networks. Powering BSs by distributed energy resources (DERs), such as photovoltaic (PV) and energy storage, is an effective way to reduce on-grid power consumption and build green wireless networks. Optimal energy management of BSs helps to reduce electricity bills for the wireless network and provides flexibility to the power networks. This article proposes the concept of spatial–temporal energy management (ST-EM) for the energy management of BSs. On the one hand, the BSs manage their power consumption according to the real-time prices; on the other hand, the BSs adjust the user associations and change their power consumption according to the price differences among different BSs. The ST-EM of BSs is formulated as a large-scale mixed-integer nonlinear programming (MINLP) problem, which is proven to be NP-hard. We propose a heuristic approach to search for one suboptimal solution by decomposing the original problem into an energy network optimization subproblem and a communication network optimization subproblem. The two subproblems are solved alternatively until convergence. Numerical experiments are conducted to verify the effectiveness of our proposed method.

Drones in B5G/6G Networks as Flying Base Stations

Advances in the fields of networking, broadband communications and demand for high-fidelity low-latency last-mile communications have rendered as-efficient-as-possible relaying methods more necessary than ever. This paper investigates the possibility of the utilization of cellular-enabled drones as aerial base stations in next-generation cellular networks. Flying ad hoc networks (FANETs) acting as clusters of deployable relays for the on-demand extension of broadband connectivity constitute a promising scenario in the domain of next-generation high-availability communications. Matters of mobility, handover efficiency, energy availability, optimal positioning and node localization as well as respective multi-objective optimizations are discussed in detail, with their core ideas defining the structure of the work at hand. This paper examines improvements to the existing cellular network core to support novel use-cases and lower the operation costs of diverse ad hoc deployments.

Optimal Interference for Device to Device Communication Underlaying Cellular Network

Device to Device (D2D) communication can effectively improve the spectrum eficiency and reduce the load of Base Station in cellular networks. However, when D2D users and cellular users share the wireless channels, it will bring signal interference for the cellular networks. In order to achieve mutual interference between users and base stations, and increase the spectrumutilization efficiency and energy utilization efficiency, this paper proposes a method control algorithm based on bearnforrning and interference alignment in D2D communication underlaying cellular networks. Simulation results show that the proposed algorithm can effectively reduce the interference of the BS transrnitting signal for D2D pais and significantly improve system capacity. Furthermore, D2D communication is more applicable to short-range links.

Optimum Sizing of Photovoltaic and Energy Storage Systems for Powering Green Base Stations in Cellular Networks

Satisfying the mobile traffic demand in next generation cellular networks increases the cost of energy supply. Renewable energy sources are a promising solution to power base stations in a self-sufficient and cost-effective manner. This paper presents an optimal method for designing a photovoltaic (PV)-battery system to supply base stations in cellular networks. A systematic approach is proposed for determining the power rating of the photovoltaic generator and battery capacity from a technical and economical point of view in order to minimize investment cost as well as operational expenditure, while the power autonomy of the PV-battery system is maximized in a multi-objective optimization framework. The proposed method is applied to optimally size a photovoltaic-battery system for three cases with different availability of solar power to investigate the effect of environmental conditions. Problem-solving using the proposed approach leads to a set of solutions at different costs versus different levels of power autonomy. According to the importance of each criterion and the preference of decision-makers, one of the achieved solutions can be selected for the implementation of the photovoltaic-battery system to supply base stations in cellular networks.

Performance Analysis of Millimeter Wave SWIPT System Using Ginibre Point Process

This paper deals with the coverage probability analysis of mmWave simultaneous wireless information and power transfer (SWIPT) system using stochastic geometry. mmWaves are higher frequency waves results in dense deployment of base stations (BS) leading to a higher data rate. Stochastic geometry provides a mathematical model to the randomly distributed nodes in the network. The Poisson point process (PPP) is the most analytically tractable and widely used model for the location of BSs, but it fails to capture the underlying separation between the BSs of cellular networks. Ginibre point process (GPP) model is used for the repulsion between the two nodes and provides a realistic model compared to PPP. In this paper, mmWave interfering base stations are considered to be GPP for deriving the coverage probability analysis. The proposed model considers the interfering base stations for analyzing the coverage probability. The closed form expression for the coverage probability is derived and is analyzed. The theoretical results are validated through simulation results.

On the Power of Microwave Communication Data to Monitor Rain for Agricultural Needs in Africa

Over the last two decades, prevalent technologies and Internet of Things (IoT) systems have been found to have potential for carrying out environmental monitoring. The data generated from these infrastructures are readily available and have the potential to provide massive spatial coverage. The costs involved in using these data are minimal since the records are already generated for the original uses of these systems. Commercial microwave links, which provide the underlying framework for data transfer between cellular network base stations, are one example of such a system and have been found useful for monitoring rainfall. Wireless infrastructure of this kind is deployed widely by communication providers across Africa and can thus be used as a rainfall monitoring device to complement the sparse proprietary resources that currently exist or to substitute for them where alternatives do not exist. Here we focus this approach’s potential to acquire valuable information required for agricultural needs across Africa using Kenya as an example.

Characterization of base station deployment distribution and coverage in heterogeneous networks

Considering different types of base stations (BSs) in future cellular networks are overlapping deployment with the status of dense, multi-tier and heterogeneous in general, how to optimize the real BS deployment becomes a complicated problem. Based on it, repulsive BS dataset and clustering BS dataset are statically characterized with various types of spatial point processes. It shows that the improvement of coverage probability between 1-tier and 2-tier network fluctuates as the signal-to-interference ratio (SIR) threshold increases for different datasets. The authors' proposed hybrid model fits well with repulsive BS dataset, while the log-Gaussian Cox point process (LGCP) and Cauchy model are reasonable models for clustering BS dataset. Besides, in order to dynamically analyze the coverage problem affected by adding new BSs, a cell boundary constructed by an irregular circle is introduced under an equal SIR constraint, and a BS placement scheme is proposed to place new BSs at the points of minimum interference. Numerical results show that the coverage probability may increase after adding BSs in the target area of heterogeneous network by using the proposed scheme. However, as the density of femto BS reaches a certain value, its coverage may remain unchanged even after adding more femto BSs.

Exploiting Secrecy Performance of Uplink NOMA in Cellular Networks

We study the secrecy transmission of uplink non-orthogonal multiple access (NOMA) with single antenna and multi-antenna users in presence of an eavesdropper. Two phases are required for communications during each time frame between the users and the base station in cellular networks. We study the case where an eavesdropper overhears the relay and direct links from the users to the base stations. In terms of the secure performance analysis, we focus on two main metrics including secrecy outage probability (SOP) and strictly positive secrecy capacity (SPSC) with the assumption that the eavesdropper is able to detect the signals. Analytical closed-form expressions for the SOP and SPSC are derived to evaluate the system secure performance achieved by the proposed schemes. Furthermore, the asymptotic analysis is presented to gain further insights. The analytical and numerical results indicate that the proposed schemes can realize better secrecy performance once we improve the channel condition and signal-to-noise ratio (SNR) at the base station. Our results confirms that the secrecy performance gaps exist among the two users since different power allocation factors are assigned to these users.

ATOMIC: Adaptive Transmission Power and Message Interval Control for C-V2X Mode 4

To support the emerging vehicle-to-everything (V2X) communication for autonomous vehicles and smart transportation services, 3rd Generation Partnership Project (3GPP) has recently introduced cellular V2X (C-V2X) standards. In C-V2X, radio resources can be managed not only centrally by the cellular network base station, but also in a completely distributed manner ( <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Mode 4</i> ) without any cellular support. However, <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Mode 4</i> may suffer significant collisions and interference in a dense environment since each vehicle selects its own resources to transmit V2X messages autonomously without adapting appropriately to vehicle density. To address this challenge, we propose <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">ATOMIC</i> , an <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Adaptive Transmission pOwer and Message Interval Control scheme for C-V2X Mode 4</i> , in which each vehicle utilizes real-time channel sensing and neighbor information to reduce channel contention for improved reliability and latency. Through analysis and extensive simulations, we show that <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">ATOMIC</i> outperforms the standard <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Mode 4</i> in both urban and highway scenarios especially in highly dense environments.

Heuristic Edge Server Placement in Industrial Internet of Things and Cellular Networks

Rapid developments in industry 4.0, machine learning, and digital twins have introduced new latency, reliability, and processing restrictions in Industrial Internet of Things (IIoT) and mobile devices. However, using current information and communications technology (ICT), it is difficult to optimally provide services that require high computing power and low latency. To meet these requirements, mobile-edge computing is emerging as a ubiquitous computing paradigm that enables the use of network infrastructure components such as cluster heads/sink nodes in IIoT and cellular network base stations to provide local data storage and computation servers at the edge of the network. However, optimal location selection for edge servers within a network out of a very large number of possibilities, such as to balance workload and minimize access delay, is a challenging problem. In this article, the edge server placement problem is addressed within an existing network infrastructure obtained from Shanghai Telecom's base station data set that includes a significant amount of call data records and locations of actual base stations. The problem of edge server placement is formulated as a multiobjective constraint optimization problem that places edge servers strategically to balance between the workloads of edge servers and reduce access delay between the industrial control center/cellular base stations and edge servers. To search randomly through a large number of possible solutions and selecting those that are most descriptive of optimal solution can be a very time-consuming process, therefore, we apply the genetic algorithm and local search algorithms (hill climbing and simulated annealing) to find the best solution in the least number of solution space explorations. Experimental results are obtained to compare the performance of the genetic algorithm against the above-mentioned local search algorithms. The results show that the genetic algorithm can quickly search through the large solution space as compared to local search optimization algorithms to find an edge placement strategy that minimizes the cost function.