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22nd Wireless and Optical Communications Conference (WOCC)

作者： Zhou, Yuan Luo, Zezhou Zhuang, Hongcheng Huawei Technol Co Ltd Commun Technol Lab Shenzhen Peoples R China

ISBN: (纸本)9781467356978;9781467356992

coverage optimization is a big challenge for wireless local area network (WLAN) deployment. The most common problems in coverage are coverage hole and interference. Traditional approaches exploit knowledge of the propagation environment to obtain static network planning and optimization, thus are not adaptable to the changes of environment and network topology. In this paper, a coverage self-optimization solution is proposed for WLAN, which adjusts the power of each beam of access points based on sensor measurement, and the Received Signal Strength (RSS) and Signal-to-Interference ratio (SIR) targets of the sensors to self-optimize coverage performance. Simulation shows that by implementing the proposed solution, coverage problems can be identified and solved adaptively.

关键词： coverage optimization self-optimization wireless local access network sensor

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Sensor-assisted coverage Self-optimization for Wireless Local Area Network

Sensor-assisted Coverage Self-Optimization for Wireless Loca...

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2013 22nd Wireless and Optical Communication Conference

作者： Yuan Zhou Zezhou Luo Hongcheng Zhuang Communication Technologies Lab.Huawei Technologies Co. Ltd.

ISBN: (纸本)9781467356985

coverage optimization is a big challenge for wireless local area network(WLAN) *** most common problems in coverage are coverage hole and *** approaches exploit knowledge of the propagation environment to obtain static network planning and optimization,thus are not adaptable to the changes of environment and network *** this paper,a coverage self-optimization solution is proposed for WLAN,which adjusts the power of each beam of access points based on sensor measurement,and the Received Signal Strength(RSS) and Signal-to-Interference ratio(SIR) targets of the sensors to self-optimize coverage *** shows that by implementing the proposed solution,coverage problems can be identified and solved adaptively.

关键词： coverage optimization self-optimization wireless local access network sensor

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Comparison of Wireless Channels for Antenna Tilt based coverage and Capacity optimization

Comparison of Wireless Channels for Antenna Tilt based Cover...

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Global Wireless Summit

作者： Nikolay Dandanov Soumya Ranjan Samal Shuvabrata Bandopadhaya Vladimir Poulkov Krasimir Tonchev Pavlina Koleva Faculty of Telecommunications Technical University of Sofia Sofia Bulgaria BML Munjal University Gurgaon India

ISBN: (纸本)9781538642894;9781538642887

Radio coverage and capacity optimization is an important challenge for mobile network operators in deploying future generations of cellular networks. One key factor influencing the coverage in mobile networks is related to the configuration of antennas and especially the antenna tilt setting. The power of the received useful signal in a cell can be increased with proper antenna tilt, causing a significant improvement in signal-to-interference-plus-noise ratio (SINR) which also leads to reduction of interference towards other cells. Dynamic antenna tilt optimization is even more important for the next fifth generation (5G) of wireless communications especially in ultra-dense scenarios for the provision of sufficient coverage and quality of service (QoS). In this work, the effect of wireless channel model selection for the goal of self-optimization of the base station (BS) antenna electrical tilt is investigated. A comparative analysis of the application of different propagation models is performed using an existing method for dynamic and adaptive tilt adjustment based on reinforcement learning (RL) methodology. Simulation experiments considering a cellular network with multiple cells in an urban scenario with randomly distributed users have been carried out and the achieved results show that the careful choice of channel model and related parameters is crucial for the antenna tilt self-optimization process.

关键词： Antenna tilt Cellular networks Channel models coverage optimization Mobile networks Ultra-Dense networks

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Deployment optimization of Self-Powered Sensors for coverage Enhancement in the Internet of Things Sensing Layer

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IEEE SENSORS JOURNAL 2023年第18期23卷 20773-20781页

作者： Su, Shengchao Yang, Chen Ju, Xiang Xu, Chaojie Shanghai Univ Engn Sci Sch Elect & Elect Engn Shanghai 201620 Peoples R China

coverage is an important indicator of the data acquisition and transmission of the Internet of Things IoT sensing layer. However, the sensor nodes are equipped with battery units, which results in limited coverage performance. Self-powered sensors provide a solution to handle energy constraint problems. In this article, self-powered sensors are used as the sensing nodes, which could collect energy from the environment to supplement the battery unit while performing coveragetasks. Adeploymentoptimizationmethod of self-powered sensors is first proposed based on an improved particle swarm optimization (PSO). In this method, the population is divided into homogeneous and heterogeneous clusters. Homogeneous clusters use the same search strategy as the PSO, while the heterogeneous clusters are further divided into multiple subgroups by the topology structure and using the improved communication strategy. On this basis, the position of self-powered sensors is obtained, which could achieve the desired coverage performance. Then, a local information-based critical node identification method is proposed. When the failed critical nodes are found, the redundant nodes around the critical nodes will work in place. The tolerance performance of coverage is enhanced. The results of simulation experiments show that the self-powered nodes are more uniformly distributed;the coverage is significantly improved, and the coverage could achieve 95.95%.

关键词： coverage optimization critical node identification Internet of Things (IoT) particle swarm optimization (PSO) self-powered sensors

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optimization of coverage in 5G Self-Organizing Small Cell Networks

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MOBILE NETWORKS & APPLICATIONS 2018年第6期23卷 1502-1512页

作者： Chen, Yu Guo, Zhi Yang, Xiuqing Hu, Yi Zhu, Qinghua Beijing Polytech Sch Telecommun Engn Beijing 100176 Peoples R China State Radio Monitoring Ctr Beijing 100037 Peoples R China

Heterogeneous two-tier network with hybrid deployed small cells and macrocells is a promising solution for fifth generation (5G) wireless networks. However, with the higher and higher spectrum band used in 5G, the coverage and capacity of indoor environment is not good enough for the users' increasing demand. In this paper, we proposed a self-organizing capacity and coverage optimization scheme using power adaptation to enhance the capacity and improve the coverage. Simulation results show that the proposed self-organizing scheme can effectively improve the capacity and coverage.

关键词： Small cells coverage optimization Capacity optimization Self-Organizing network

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coverage Analysis and Chance-Constrained optimization for HSR Communications With Carrier Aggregation

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IEEE TRANSACTIONS ON INTELLIGENT TRANSPORTATION SYSTEMS 2022年第9期23卷 15107-15120页

作者： Lin, Sheng-Hong Xu, Youyun Wang, Lei Wang, Jin-Yuan Nanjing Univ Posts & Telecommun Natl Local Joint Engn Res Ctr Commun & Network Te Nanjing 210003 Peoples R China Nanjing Inst Mechatron Technol Dept Automat Engn Nanjing 211135 Peoples R China Nanjing Inst Railway Technol Sch Locomot & Vehicle Nanjing 210031 Peoples R China Nanjing Univ Posts & Telecommun Key Lab Broadband Wireless Commun & Sensor Networ Nanjing 210003 Peoples R China

This paper investigates coverage analysis and performance optimization for a high-speed railway (HSR) communication system with carrier aggregation (CA). Initially, the system model is established. By using CA technology, the statistical characteristic of the received signal-to-noise ratio (SNR) is obtained. Then, for the systems with or without CA, unified theoretical expressions for the edge coverage probability (ECP) and the percentage of cell coverage area (CCA) are obtained, respectively. After that, a chance-constrained coverage optimization is proposed to improve the coverage performance, which is effectively solved by using a heuristic algorithm. Numerical results show that the derived expressions of the ECP and the percentage of CCA are accurate and can be directly used to evaluate the coverage performance without time-intensive simulations. Moreover, the impacts of CA, transmit power, cell radius, received SNR threshold, HSR scenario on coverage performance have been discussed. Furthermore, the effectiveness of the proposed coverage optimization scheme is also verified.

关键词： Computer architecture Microprocessors optimization Rail transportation Signal to noise ratio Communication systems Probability density function HSR communications cell coverage carrier aggregation ECP percentage of CCA coverage optimization

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An efficient coverage method for SEMWSNs based on adaptive chaotic Gaussian variant snake optimization algorithm

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MATHEMATICAL BIOSCIENCES AND ENGINEERING 2023年第2期20卷 3191-3215页

作者： Liu, Xiang Tian, Min Zhou, Jie Liang, Jinyan Shihezi Univ Coll Mech & Elect Engn Shihezi 832000 Peoples R China Shihezi Univ Coll informat Sci & technol Shihezi 832000 Peoples R China

Soil element monitoring wireless sensor networks (SEMWSNs) are widely used in soil element monitoring agricultural activities. SEMWSNs monitor changes in soil elemental content during agriculture products growing through nodes. Based on the feedback from the nodes, farmers adjust irrigation and fertilization strategies on time, thus promoting the economic growth of crops. The critical issue in SEMWSNs coverage studies is to achieve maximum coverage of the entire monitoring field by adopting a smaller number of sensor nodes. In this study, a unique adaptive chaotic Gaussian variant snake optimization algorithm (ACGSOA) is proposed for solving the above problem, which also has the advantages of solid robustness, low algorithmic complexity, and fast convergence. A new chaotic operator is proposed in this paper to optimize the position parameters of individuals, enhancing the convergence speed of the algorithm. Moreover, an adaptive Gaussian variant operator is also designed in this paper to effectively avoid SEMWSNs from falling into local optima during the deployment process. Simulation experiments are designed to compare ACGSOA with other widely used metaheuristics, namely snake optimizer (SO), whale optimization algorithm (WOA), artificial bee colony algorithm (ABC), and fruit fly optimization algorithm (FOA). The simulation results show that the performance of ACGSOA has been dramatically improved. On the one hand, ACGSOA outperforms other methods in terms of convergence speed, and on the other hand, the coverage rate is improved by 7.20%, 7.32%, 7.96%, and 11.03% compared with SO, WOA, ABC, and FOA, respectively.

关键词： coverage optimization snake optimizer chaotic operator Gaussian variant power consumption

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An Efficient 6G Federated Learning-Enabled Energy-Efficient Scheme for UAV Deployment

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IEEE TRANSACTIONS ON VEHICULAR TECHNOLOGY 2025年第2期74卷 2057-2066页

作者： Raja, Kathiroli Kottursamy, Kottilingam Ravichandran, Vishal Balaganesh, Sivaganesh Dev, Kapal Nkenyereye, Lewis Raja, Gunasekaran Anna Univ NGNLab MIT Campus Chennai 600044 India Munster Technol Univ Dept Comp Sci Bishopstown T12P928 Cork Ireland Munster Technol Univ CONNECT Ctr Bishopstown T12P928 Cork Ireland Univ Johannesburg Dept Inst intelligent Syst ZA-2006 Johannesburg South Africa Lebanese American Univ Dept Elect & Comp Engn Byblos 1111 Lebanon Sejong Univ Dept Comp & Informat Secur Seoul 05006 South Korea

Unmanned Aerial Vehicles (UAVs) are widely used for commercial transportation and data collection in many applications. Recently, UAVs have been used as flying relays to support terrestrial cellular networks for enhancing coverage and connectivity. Multiple UAVs serve as data collectors at a common altitude in a UAV-aided Internet of Things (IoT) network. Data collected is processed by Federated Learning (FL) before being sent to central servers to protect users' privacy and reduce communication costs. Batteries in UAVs are tiny and efficient only for a short duration, making FL challenging to execute for many iterations. Therefore, a Multi-UAV Energy Efficient coverage Deployment algorithm based on a Spatial Adaptive Play (MUECD-SAP) is proposed in this article. MUECD uses a modified Particle Swarm optimization (PSO) to optimize the accurate location of deployed UAVs based on the Signal-to-Interference Ratio (SINR) to increase the data collection rate. Also, to make the FL run for multiple iterations, SAP dynamically allocates resources using Deep Deterministic Policy Gradient (DDPG) to optimize the energy consumed and the link latency between the user and the UAV system. The proposed MUECD algorithm outperforms all the state-of-the-art algorithms by achieving an improved data rate and balanced SINR value. The proposed SAP resource allocation strategy has improved the FL execution by 66.67% and attains a very low latency and energy consumed compared to other resource allocation techniques.

关键词： federated learning coverage optimization internet of things internet of things internet of things deep reinforcement lear- ning deep reinforcement lear- ning deep reinforcement lear- ning deep reinforcement lear- ning deep reinforcement lear- ning resource allocation resource allocation unmanned aerial vehicles unmanned aerial vehicles resource allocation unmanned aerial vehicles

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Brain Storm optimization Algorithm for Full Area coverage of Wireless Sensor Networks 8

Brain Storm Optimization Algorithm for Full Area Coverage of...

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8th International Conference on Advanced Computational Intelligence (ICACI)

作者： Zhu, Haoyu Shi, Yuhui Xian Jiaotong Liverpool Univ Dept Elect & Elect Engn Suzhou 215123 Peoples R China

ISBN: (纸本)9781467377829

coverage problem is a fundamental issue in designing efficient wireless sensor networks, in which both coverage rate and energy consumption should be considered. A brain storm optimization algorithm is a swarm intelligence algorithm which is inspired by the human brainstorming process. This paper will focus on the application of the brain storm optimization algorithm in full coverage problems of wireless sensor networks. The full coverage problems are divided into two types: problems either with fixed or flexible number of activated sensor nodes. Binary detection model and grid based strategy will be used in describing the mathematic model of the full coverage problem, which will be applied to test the effectiveness of the brain storm optimization algorithm for solving coverage problems of wireless sensor networks in different areas. Experimental results on irregular areas even with obstacles illustrate the efficiency and effectiveness of the brain storm optimization algorithm for solving full coverage problems of wireless sensor networks. In addition, if the number of activated sensor nodes is flexible, with an appropriate weight coefficient, the brain storm optimization algorithm can obtain a reasonable number of sensor nodes to realize full coverage.

关键词： brain storm optimization coverage optimization wireless sensor networks

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A Grey Wolf optimization Algorithm for Multi Strategy Network coverage Based on Roulette Wheel 24

A Grey Wolf Optimization Algorithm for Multi Strategy Networ...

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11th International Conference on Wireless Communication and Sensor Networks (icWCSN)

作者： Ma, Qianli Qi, Xin Qian, Huimeng Cui, Zhiheng Shanxi Univ Sch Comp & Informat Technol Taiyuan Peoples R China

ISBN: (纸本)9798400709005

The key to wireless sensor network coverage lies in how to achieve full coverage of the region with as few sensor nodes as possible. The research on wireless sensor network coverage often faces problems such as low optimization accuracy, slow convergence speed, and susceptibility to premature convergence. To address these issues, A multi strategy network coverage grey wolf optimization algorithm (MSGWO) based on roulette wheel is proposed. Firstly, a mixed mapping of Tent and Circle maps is used to map the initial distribution of wireless sensor nodes, so that the initial distribution of sensor nodes is as evenly distributed as possible in the space. Secondly, as the iteration cycle changes, multi strategy position updates are performed on the gray wolves that meet the conditions in the Grey Wolf optimization Algorithm (GWO) to improve the ability to jump out of local convergence. Finally, in the iterative optimization of the Grey Wolf optimization Algorithm, a leadership strategy based on the roulette wheel method was used to expand the global search range of the algorithm. Simulation experiments have shown that MSGWO has higher coverage and less time consumption.

关键词： wireless sensor network coverage optimization grey wolf optimization algorithm coverage rate

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