Resource Allocation and Multisession Routing Algorithms in Coordinated Multipoint Wireless Communication Networks

Lin, Frank Yeong-Sung; Wen, Yean‐Fu; Fang, Liwen; Hsiao, Chiu-Han

doi:10.1109/jsyst.2017.2687102

Cited by 12 publications

(11 citation statements)

References 28 publications

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“…The authors implement the proposal and show the feasibility of frequency hopping for IEEE 802.11 networks with industry-standard equipment. Other proposals perform channel allocation through interference observed between access points [23] and between clients and access points [20].…”

Section: Radio Environment Network Topology and Characterizationmentioning

confidence: 99%

A survey on data analysis on large-Scale wireless networks: online stream processing, trends, and challenges

Medeiros

Neto

Lopez

et al. 2020

J Internet Serv Appl

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In this paper we focus on knowledge extraction from large-scale wireless networks through stream processing. We present the primary methods for sampling, data collection, and monitoring of wireless networks and we characterize knowledge extraction as a machine learning problem on big data stream processing. We show the main trends in big data stream processing frameworks. Additionally, we explore the data preprocessing, feature engineering, and the machine learning algorithms applied to the scenario of wireless network analytics. We address challenges and present research projects in wireless network monitoring and stream processing. Finally, future perspectives, such as deep learning and reinforcement learning in stream processing, are anticipated.

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Section: Radio Environment Network Topology and Characterizationmentioning

confidence: 99%

A survey on data analysis on large-Scale wireless networks: online stream processing, trends, and challenges

Medeiros

Neto

Lopez

et al. 2020

J Internet Serv Appl

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“…In this context, machine learning is one of the most promising articial intelligence paradigm, designed to support intelligent radio terminals [40]. [12] Connected Objects Fingerprint Platform to analyze WiFi trace [13] Crowd Behavior Data Analysis [14] User Association WiFi SDN [15] Channel Allocation Metric Based Algorithm [16] Channel Allocation Wi-Fi SDN [17] Vehicles Location Survey [18] Trac Jam Prediction Combine data from dierent wireless sources [21] Optimal Channel Allocation [22] Heuristic Channel Allocation Dynamic channel selection [23] Channel Allocation Interference observed between APs [20] Channel Allocation Interference observed between clients and APs [24] Decrease Latency during hand-o…”

Section: Next-generation Networkmentioning

confidence: 99%

A Survey on Data Analysis on Large-Scale Wireless Networks: Online Stream Processing, Trends, and Challenges

Medeiros

Neto

Lopez

et al. 2020

Preprint

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“…The decision variable η uv is used to indicate whether MD v is in the service area of AP u or not by using (4) and (5). If the device v is associated with the AP u, then δ uv and η uv are estimated to be equal to 1 by using (9). Otherwise, η uv and δ uv are equal to 0, which implies that the MD v is not in the service area of AP u.…”

Section: Problem Descriptionmentioning

confidence: 99%

“…Quality of experience (QoE) is used to evaluate the QoS on the basis of the user perspective, and it differs from conventional network side performance evaluation [9]. Not all applications require the same level of QoS and the same extent of delay minimization.…”

Section: Introductionmentioning

confidence: 99%

Lagrangian-Relaxation-Based Self-Repairing Mechanism for Wi-Fi Networks

2019

Self Cite

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Wi-Fi was developed to support software-defined networks that are crucial for achieving high quality of service for next generation wireless networks. In this software-defined networking technology, network resources are utilized to reduce the effects of several influential factors such as the limited number of nonoverlapping channels, co-channel interference, rapidly changing distribution of customers, and access point (AP) failure. These factors cause irregular network traffic and service unavailability in some users. In this paper, we address the problems causing the AP failure and interference. The studied problems were modeled as linear and nonlinear mathematical programming problems. The Lagrangian relaxation approach, which is a type of divide-and-conquer method, was used to solve the load distribution problems near optimally. A self-repairing heuristic multiple-level load-balancing traffic adjustment was proposed to manage the problems pertaining to the AP failure. The delay difference metric, which is defined as the difference between tolerable delay and transmission delay, was used to evaluate the difference between the upper bound and lower bound of the delay. Moreover, the metric was used to estimate the improvement ratio between the existing methods and the proposed method. Modifiable transmission power ranges (which involve the cell breathing method) and association managements are adjusted to minimize the narrow gap and improve the self-repairing performance. Thus, resources are appropriately allocated to users, and adequate QoS is attained.

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Resource Allocation and Multisession Routing Algorithms in Coordinated Multipoint Wireless Communication Networks

Cited by 12 publications

References 28 publications

A survey on data analysis on large-Scale wireless networks: online stream processing, trends, and challenges

A survey on data analysis on large-Scale wireless networks: online stream processing, trends, and challenges

A Survey on Data Analysis on Large-Scale Wireless Networks: Online Stream Processing, Trends, and Challenges

Lagrangian-Relaxation-Based Self-Repairing Mechanism for Wi-Fi Networks

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