Large-scale Base Station Research Articles

LCP-Based Low-Cost Base Station Antenna for 3.7 GHz 5G Band

This paper presents a large-scale base station antenna assembly structure that is low-cost, reliable, and easy to manufacture. The antenna element is composed of a low-loss liquid crystal polymer based on a plastic molded module and a modified wideband stacked patch antenna. The base station antenna consists of a 4 × 8 antenna module, with each module comprising 3 × 1 subarrays along with dual-polarized antenna elements. The antenna element achieved an efficiency of 91% and an impedance bandwidth of 1.14 GHz within a height of 10 mm at 3.7 GHz. Furthermore, the fabricated array antenna structure was tested and verified to have an effective isotropic radiated power of 75.6 dBm at boresight and a steering range of less than ±60°. Therefore, the proposed structure meets the required antenna and beam-forming performance for commercial 5G active antenna systems.

Optimization of Communication Base Station Battery Configuration Considering Demand Transfer and Sleep Mechanism under Uncertain Interruption Duration

In the communication power supply field, base station interruptions may occur due to sudden natural disasters or unstable power supplies. This work studies the optimization of battery resource configurations to cope with the duration uncertainty of base station interruption. We mainly consider the demand transfer and sleep mechanism of the base station and establish a two-stage stochastic programming model to minimize battery configuration costs and operational costs. To transform the uncertainty expression in the first stage into a deterministic model, we design the K-Means-SAA algorithm to accelerate problem-solving and to compare it with the SAA algorithm. The case study results indicate that the proposed two-stage stochastic programming model can save 17.02% of the total cost compared to the expected value model. The proposed demand transfer and sleep mechanism can reduce the total cost by 41.92% compared to no mechanism. The results of numerical experiments and sensitivity analysis also verify the superiority of the designed algorithm in terms of running efficiency and solving time. Therefore, the model and algorithm proposed in this work provide valuable application guidance for large-scale base station configuration optimization of battery resources to cope with interruptions in practical scenarios.

Optimization of Distribution Network and Mobile Network With Interactive Balance of Flexibility and Power

High proportions of renewable energy place higher requirements on the flexibility of the distribution network. Meanwhile, the communication network is striving to seek ways to save energy. In this regard, this paper proposes a novel collaborative optimization method with interactive balance of flexibility and power for distribution network operator (DNO) and mobile network operator (MNO). First, the flexibility balance mechanism is analyzed and the optimization model integrating flexibility balance and power balance is established for the distribution network. Secondly, a flexibility aggregation method of large-scale base stations with energy storage (ES) devices is proposed, and a virtual ES-based optimization model is built for the mobile network. Then, a Nash bargaining-based optimization method is proposed for the collaboration of DNO and MNO, and a decentralized solution algorithm is designed to obtain the Nash bargaining solution, preserving the personal privacy. Finally, through a comprehensive case study we can draw that, compared with the independent approach, the proposed collaborative optimization method with interactive balance of flexibility and power can improve the operating flexibility of the distribution network while reducing the electricity bills of the mobile network effectively, thus achieving a win-win result.

Graph neural network-based cell switching for energy optimization in ultra-dense heterogeneous networks

The development of ultra-dense heterogeneous networks (HetNets) will cause a significant rise in energy consumption with large-scale base station (BS) deployments, requiring cellular networks to be more energy efficient to reduce operational expense and promote sustainability. Cell switching is an effective method to achieve the energy efficiency goals, but traditional heuristic cell switching algorithms are computationally demanding with limited generalization abilities for ultra-dense HetNet applications, motivating the usage of machine learning techniques for adaptive cell switching. Graph neural networks (GNNs) are powerful deep learning models with strong generalization abilities but receive little attention for cell switching. This paper proposes a GNN-based cell switching solution (GBCSS) that has a smaller computational complexity than existing heuristic algorithms. The presented performance evaluation uses the Milan telecommunication dataset based on real-world call detail records, comparing GBCSS with a traditional exhaustive search (ES) algorithm, a state-of-the-art learning-based algorithm, and the baseline without cell switching. Results indicate that GBCSS achieves a 10.41% energy efficiency gain when compared with the baseline and achieves 75.76% of the optimal performance obtained with ES algorithm. The results also demonstrate GBCSS’ significant scalability and generalization abilities to differing load conditions and the number of BSs, suggesting this approach is well-suited to ultra-dense HetNet deployment.

Hybrid Decoupling Structure Based on Neutralization and Partition Schemes for Compact Large-Scale Base Station Arrays

This letter proposes a hybrid decoupling structure consisting of a neutralization dielectric board, metal baffle, and feeding network to enhance the isolation in a compact large-scale base station array. Both the neutralization- and partition-based schemes are utilized to reduce the mutual coupling between antennas. The neutralization dielectric board provides a new signal path to cancel the original mutual coupling, and the metal baffle is employed to suppress the propagation of the space electromagnetic wave. The antenna elements in the vertical direction are excited by a feeding network. A 4×4 dual-polarized antenna array with a horizontal edge-to-edge distance of 0.38 free-space wavelength is designed and fabricated to demonstrate the effectiveness of the proposed structure for large-scale arrays for base station application. Simulated and measured results show that the voltage standing wave ratio is less than 1.7, and the isolation between any two ports in the array is better than 20 dB within the entire operating frequency band. Stable radiation patterns with a good front-to-back ratio and cross-polarization ratio are achieved.

Multi-Zone Authentication and Privacy-Preserving Protocol (MAPP) Based On the Bilinear Pairing Cryptography for 5G-V2X.

5G-Vehicle-to-Everything (5G-V2X) supports high-reliability and low latency autonomous services and applications. Proposing an efficient security solution that supports multi-zone broadcast authentication and satisfies the 5G requirement is a critical challenge. In The 3rd Generation Partnership Project (3GPP) Release 16 standard, for Cellular- Vehicle-to-Everything (C-V2X) single-cell communication is suggested to reuse the IEEE1609.2 security standard that utilizes the Public Key Infrastructure (PKI) cryptography. PKI-based solutions provide a high-security level, however, it suffers from high communication and computation overhead, due to the large size of the attached certificate and signature. In this study, we propose a light-weight Multi-Zone Authentication and Privacy-Preserving Protocol (MAPP) based on the bilinear pairing cryptography and short-size signature. MAPP protocol provides three different authentication methods that enable a secure broadcast authentication over multiple zones of large-scale base stations, using a single message and a single short signature. We also propose a centralized dynamic key generation method for multiple zones. We implemented and analyzed the proposed key generation and authentication methods using an authentication simulator and a bilinear pairing library. The proposed methods significantly reduce the signature generation time by 16 times–80 times, as compared to the previous methods. Additionally, the proposed methods significantly reduced the signature verification time by 10 times–16 times, as compared to the two previous methods. The three proposed authentication methods achieved substantial speed-up in the signature generation time and verification time, using a short bilinear pairing signature.

Min-SINR Maximization for mmWave Massive MISO-NOMA System With Randomly Directional Beamforming

Based on the high-direction characteristic of millimeter wave (mmWave) transmission, randomly directional beamforming (RDB) can be used for the mmWave massive multiple-input single-output (MISO) non-orthogonal multiple access (NOMA) system to reduce the number of radio frequency (RF) chains. However, the impact of RDB on the signal-to-interference-plus-noise ratio (SINR) of each user is related to the corresponding beamforming gain and interference from other users. Thus, in this paper, we investigate the max-min SINR among all users to evaluate user fairness. In particular, we focus on the single-cell downlink mmWave MISO-NOMA system with RDB, where single-antenna users are divided into multiple NOMA clusters according to their azimuth angles. We formulate the minimum achievable SINR maximization problem associated with power allocation and propose the sum of power allocation coefficients based iterative algorithm (SPACIA) to find the max-min SINR. We also prove that the max-min SINR monotonically decreases as the number of paired users in an NOMA cluster increases as well as the number of beams in the cell with large-scale base station (BS) antenna array increases. Moreover, we derive the upper bound of the max-min SINR. Simulation results verify our theoretical analyses and demonstrate that the proposed algorithm guarantees user fairness, thus outperforming existing schemes.

Improved 3-D Indoor Positioning Based on Particle Swarm Optimization and the Chan Method

Time of arrival (TOA) measurement is a promising method for target positioning based on a set of nodes with known positions, with high accuracy and low computational complexity. However, most positioning methods based on TOA (such as least squares estimation, maximum-likelihood, and Chan, etc.) cannot provide desirable accuracy while maintaining high computational efficiency in the case of a non-line of sight (NLOS) path between base stations and user terminals. Therefore, in this paper, we proposed a creative 3-D positioning system based on particle swarm optimization (PSO) and an improved Chan algorithm to greatly improve the positioning accuracy while decreasing the computation time. In the system, PSO is used to estimate the initial location of the target, which can effectively eliminate the NLOS error. Based on the initial location, the improved Chan algorithm performs iterative computations quickly to obtain the final exact location of the target. In addition, the proposed methods will have computational benefits in dealing with the large-scale base station positioning problems while has highly positioning accuracy and lower computational complexity. The experimental results demonstrated that our algorithm has the best time efficiency and good practicability among stat-of-the-art algorithms.

A rolling window optimization method for large-scale WCDMA base stations planning problem

The large-scale base station planning problem for wideband code division multiple access (WCDMA) wireless networks is studied in this paper. A new rolling window optimization method is presented, where the global optimization problem is decomposed into small optimization sub-problems, which are defined on a series of successive rolling windows. Effective rolling strategies are designed in the rolling optimization method based on the prediction of the interference among the base stations in the WCDMA wireless network. We show that the proposed method has the property that the global objective is non-increasing in the successive optimization procedure. Simulations are carried out to analyze the performance of the proposed optimization method, which show the importance of the rolling strategy.