135 research outputs found
Multi-Armed Bandits for Spectrum Allocation in Multi-Agent Channel Bonding WLANs
While dynamic channel bonding (DCB) is proven to boost the capacity of wireless local area networks (WLANs) by adapting the bandwidth on a per-frame basis, its performance is tied to the primary and secondary channel selection. Unfortunately, in uncoordinated high-density deployments where multiple basic service sets (BSSs) may potentially overlap, hand-crafted spectrum management techniques perform poorly given the complex hidden/exposed nodes interactions. To cope with such challenging Wi-Fi environments, in this paper, we first identify machine learning (ML) approaches applicable to the problem at hand and justify why model-free RL suits it the most. We then design a complete RL framework and call into question whether the use of complex RL algorithms helps the quest for rapid learning in realistic scenarios. Through extensive simulations, we derive that stateless RL in the form of lightweight multi-armed-bandits (MABs) is an efficient solution for rapid adaptation avoiding the definition of broad and/or meaningless states. In contrast to most current trends, we envision lightweight MABs as an appropriate alternative to the cumbersome and slowly convergent methods such as Q-learning, and especially, deep reinforcement learning
An Adaptive Common Control Channel MAC with Transmission Opportunity in IEEE 802.11ac
Spectral utilization is a major challenge in wireless ad hoc networks due in part to using limited network resources. For ad hoc networks, the bandwidth is shared among stations that can transmit data at any point in time. It ย is important to maximize the throughput to enhance the network service. In this paper, we propose an adaptive multi-channel access with transmission opportunity protocol for multi-channel ad hoc networks, called AMCA-TXOP. For the purpose of coordination, the proposed protocol uses an adaptive common control channel over which the stations negotiate their channel selection based on the entire available bandwidth and then switch to the negotiated channel. AMCA-TXOP requires a single radio interface so that each station can listen to the control channel, which can overhear all agreements made by the other stations. This allows parallel transmission to multiple stations over various channels, prioritizing data traffic to achieve the quality-of-service requirements. The proposed approach can work with the 802.11ac protocol, which has expanded the bandwidth to 160 MHz by channel bonding. Simulations were conducted to demonstrate the throughput gains that can be achieved using the AMCA-TXOP protocol. Moreover, we compared our protocol with ย the IEEE 802.11ac standard protocols
Reinforcement Learning Approaches to Improve Spatial Reuse in Wireless Local Area Networks
The ubiquitous deployment of IEEE 802.11 based Wireless Local Area Networks (WLANs) or WiFi networks has resulted in dense deployments of Access Points (APs) in an effort to provide wireless links with high data rates to users. This, however, causes APs and users/stations to experience a higher interference level. This is because of the limited spectrum in which WiFi networks operate, resulting in multiple APs operating on the same channel. This in turn affects the signal-tonoise-plus interference ratio (SINR) at APs and users, leading to low data rates that limit their quality of service (QoS).
To improve QoS, interference management is critical. To this end, a key metric of interest is spatial reuse. A high spatial reuse means multiple transmissions are able to transmit concurrently, which leads to a high network capacity. One approach to optimize spatial reuse is by tuning the clear channel access (CCA) threshold employed by the carrier sense multiple access with collision avoidance (CSMA/CA) medium access control (MAC) protocol. Specifically, the CCA threshold of a node determines whether it is allowed to transmit after sensing the channel. A node may increase its CCA threshold, causing it to transmit even when there are other ongoing transmissions. Another parameter to be tuned is transmit power. This helps a transmitting node lower its interference to neighboring cells, and thus allows nodes in these neighboring cells to transmit as well. Apart from that, channel bonding can be applied to improve transmission rate. In particular, by combining/aggregating multiple channels together, the resulting channel has a proportionally higher data rate than the case without channel bonding. However, the issue of spatial reuse remains the same whereby the focus is to maximize the number of concurrent transmissions across multiple channels
IEEE 802.11 ๊ธฐ๋ฐ Enterprise ๋ฌด์ LAN์ ์ํ ์์ ๊ด๋ฆฌ ๊ธฐ๋ฒ
ํ์๋
ผ๋ฌธ (๋ฐ์ฌ)-- ์์ธ๋ํ๊ต ๋ํ์ : ๊ณต๊ณผ๋ํ ์ ๊ธฐยท์ปดํจํฐ๊ณตํ๋ถ, 2019. 2. ์ ํ์.IEEE 802.11์ด ๋ฌด์ LAN (wireless local area network, WLAN)์ ์ค์ง์ ์ธ ํ์ค์ด ๋จ์ ๋ฐ๋ผ ์ ๋ง์ ์์ธ์ค ํฌ์ธํธ(access points, APs)๊ฐ ๋ฐฐ์น๋์๊ณ , ๊ทธ ๊ฒฐ๊ณผ WLAN ๋ฐ์ง ํ๊ฒฝ์ด ์กฐ์ฑ๋์๋ค. ์ด๋ฌํ ํ๊ฒฝ์์๋, ์ด์ํ AP๋ค์ ๋์ผํ ์ฑ๋์ ํ ๋นํ๋ ๋ฌธ์ ๋ฅผ ํผํ ์ ์์ผ๋ฉฐ, ์ด๋ ํด๋น AP๋ค์ด ๊ฐ์ ์ฑ๋์ ๊ณต์ ํ๊ฒ ํ๊ณ ๊ทธ๋ก ์ธํ ๊ฐ์ญ์ ์ผ๊ธฐํ๋ค. ๊ฐ์ญ์ผ๋ก ์ธํ ์ฑ๋ฅ ์ ํ๋ฅผ ์ค์ด๊ธฐ ์ํด ์ฑ๋ ํ ๋น(channelization) ๊ธฐ๋ฒ์ด ์ค์ํ๋ค. ๋ํ, ํ ์กฐ์ง์ด ํน์ ์ง์ญ์ ๋ฐ์ง ๋ฐฐ์น๋ AP๋ค์ ๊ด๋ฆฌํ๋ค๋ฉด ํน์ ์ฌ์ฉ์๋ฅผ ์๋น์คํ ์ ์๋ AP๊ฐ ์ฌ๋ฟ์ผ ์ ์๋ค. ์ด ๊ฒฝ์ฐ, ์ฌ์ฉ์ ์ ์(user association, UA) ๊ธฐ๋ฒ์ด ์ค์ ์ (quasi-static) ํ๊ฒฝ๊ณผ ์ฐจ๋ ํ๊ฒฝ ๋ชจ๋์์ ๋คํธ์ํฌ ์ฑ๋ฅ์ ํฐ ์ํฅ์ ๋ฏธ์น๋ค. ๋ณธ ๋
ผ๋ฌธ์์๋ ๋ฐ์ง ๋ฐฐ์น๋ WLAN ํ๊ฒฝ์์ ์์ดํ์ด(WiFi) ์ฑ๋ฅ ํฅ์์ ์ํด ์ฑ๋ ํ ๋น ๊ธฐ๋ฒ์ ์ ์ํ๋ค. ๋จผ์ , ์ ์ํ ๊ธฐ๋ฒ์์๋ ๊ฐ๊ฐ์ AP์ ์ฑ๋์ ํ ๋นํ๊ธฐ ์ํด ๊ฐ์ญ ๊ทธ๋ํ(interference graph)๋ฅผ ์ด์ฉํ๋ฉฐ ์ฑ๋ ๊ฒฐํฉ(channel bonding)์ ๊ณ ๋ คํ๋ค. ๋ค์์ผ๋ก, ์ฃผ์ด์ง ์ฑ๋ ๊ฒฐํฉ ๊ฒฐ๊ณผ๋ฅผ ๊ธฐ๋ฐ์ผ๋ก ํด๋น AP๊ฐ ๋์ ์ฑ๋ ๊ฒฐํฉ์ ์ง์ํ๋์ง ์ฌ๋ถ์ ๋ฐ๋ผ ์ฃผ ์ฑ๋(primary channel)์ ๊ฒฐ์ ํ๋ค.
ํํธ, ์ค์ ์ ํ๊ฒฝ๊ณผ ์ฐจ๋ ํ๊ฒฝ์์์ UA ๋ฌธ์ ๋ ๋ค์ ์ฐจ์ด๊ฐ ์๋ค. ๋ฐ๋ผ์ ๋ณธ ๋
ผ๋ฌธ์์๋ ๊ฐ๊ฐ์ ํ๊ฒฝ์ ๋ฐ๋ผ ์๋ก ๋ค๋ฅธ UA ๊ธฐ๋ฒ์ ์ค๊ณํ์๋ค. ์ค์ ์ ํ๊ฒฝ์์์ UA ๊ธฐ๋ฒ์ ๋ฉํฐ์บ์คํธ ์ ์ก, ๋ค์ค ์ฌ์ฉ์ MIMO (multi-user multiple input multiple output), ๊ทธ๋ฆฌ๊ณ AP ์๋ฉด๊ณผ ๊ฐ์ ๋ค์ํ ๊ธฐ์ ๊ณผ ํจ๊ป AP๊ฐ์ ๋ถํ ๋ถ์ฐ(load balancing)๊ณผ ์๋์ง ์ ์ฝ์ ๊ณ ๋ คํ๋ค. ์ ์ํ๋ ๊ธฐ๋ฒ์์ UA ๋ฌธ์ ๋ ๋ค๋ชฉ์ ํจ์ ์ต์ ํ ๋ฌธ์ ๋ก ์ ์ํํ์๊ณ ๊ทธ ํด๋ฅผ ๊ตฌํ์๋ค. ์ฐจ๋ ํ๊ฒฝ์์์ UA ๊ธฐ๋ฒ์ ํธ๋์ค๋ฒ(handover, HO) ์ค์ผ์ค ๋ฌธ์ ๋ก ๊ท๊ฒฐ๋๋ค. ๋ณธ ๋
ผ๋ฌธ์์๋ ๋๋ก์ ์งํ์ ๊ณ ๋ คํ์ฌ ์ฌ์ฉ์๊ฐ ์ ์ํ AP๋ฅผ ๊ฒฐ์ ํ๋ HO ์ค์ผ์ค ๊ธฐ๋ฒ์ ์ ์ํ๋ค. ์ฌ์ฉ์๋ ๋จ์ง ๋ค์ AP๋ก ์ฐ๊ฒฐ์ ๋งบ์ ์๊ธฐ๋ง ๊ฒฐ์ ํ๋ฉด ๋๊ธฐ ๋๋ฌธ์, ์ฐจ๋ ํ๊ฒฝ์์์ ๋งค์ฐ ๋น ๋ฅด๊ณ ํจ์จ์ ์ธ HO ๊ธฐ๋ฒ์ ๊ตฌํํ ์ ์๋ค. ์ด๋ฅผ ์ํด, ๊ทธ๋ํ ๋ชจ๋ธ๋ง ๊ธฐ๋ฒ(graph modeling technique)์ ํ์ฉํ์ฌ ๋๋ก๋ฅผ ๋ฐ๋ผ ๋ฐฐ์น๋ AP์ฌ์ด์ ๊ด๊ณ๋ฅผ ํํํ๋ค. ํ์ค์ ์ธ ์๋๋ฆฌ์ค๋ฅผ ์ํด ์ง์ ๊ตฌ๊ฐ, ์ฐํ ๊ตฌ๊ฐ, ๊ต์ฐจ๋ก, ๊ทธ๋ฆฌ๊ณ ์ ํด ๊ตฌ๊ฐ ๋ฑ์ ํฌํจํ๋ ๋ณต์กํ ๋๋ก ๊ตฌ์กฐ๋ฅผ ๊ณ ๋ คํ๋ค. ๋๋ก ๊ตฌ์กฐ๋ฅผ ๊ณ ๋ คํ์ฌ ๊ฐ ์ฌ์ฉ์์ ์ด๋ ๊ฒฝ๋ก๋ฅผ ์์ธกํ๊ณ , ๊ทธ์ ๊ธฐ๋ฐํ์ฌ ๊ฐ ์ฌ์ฉ์ ๋ณ HO์ ๋ชฉ์ AP ์งํฉ์ ์ ํํ๋ค. ์ ์ํ๋ HO ์ค์ผ์ค ๊ธฐ๋ฒ์ ์ค๊ณ ๋ชฉ์ ์ HO ์ง์ฐ ์๊ฐ์ ํฉ์ ์ต์ํํ๊ณ ๊ฐ AP์์ ํด๋น ์ฑ๋์ ์ฌ์ฉํ๋ ค๋ ์ฌ์ฉ์ ์๋ฅผ ์ค์ด๋ฉด์ WiFi ์ฐ๊ฒฐ ์๊ฐ์ ์ต๋ํํ๋ ๊ฒ์ด๋ค. ๋ง์ง๋ง์ผ๋ก, ๋ณธ ๋
ผ๋ฌธ์์๋ ์ค์ ์ ํ๊ฒฝ์์ ์ ์ํ ์ฑ๋ ํ ๋น ๊ธฐ๋ฒ๊ณผ UA ๊ธฐ๋ฒ์ ํ์ค์ฑ์ ์ฆ๋ช
ํ๊ธฐ ์ํ ์ํ๋(testbed)๋ฅผ ๊ตฌ์ฑํ์๋ค. ๋ํ, ๊ด๋ฒ์ํ ์๋ฎฌ๋ ์ด์
์ ํตํด ์ค์ ์ ํ๊ฒฝ๊ณผ ์ฐจ๋ ํ๊ฒฝ์์ ์ ์ํ ๊ธฐ๋ฒ๋ค๊ณผ ๊ธฐ์กด์ ๊ธฐ๋ฒ๋ค์ ์ฑ๋ฅ์ ๋น๊ตํ์๋ค.As the IEEE 802.11 (WiFi) becomes the defacto global standard for wireless local area network (WLAN), a huge number of WiFi access points (APs) are deployed. This condition leads to a densely deployed WLANs.
In such environment, the conflicting channel allocation between the neighboring access points (APs) is unavoidable, which causes the channel sharing and interference between APs. Thus, the channel allocation (channelization) scheme has a critical role to tackle this issue. In addition, when densely-deployed APs covering a certain area are managed by a single organization, there can exist multiple candidate APs for serving a user. In this case, the user association (UA), i.e., the selection of serving AP, holds a key role in the network performance both in quasi-static and vehicular environments. To improve the performance of WiFi in a densely deployed WLANs environment, we propose a channelization scheme. The proposed channelization scheme utilizes the interference graph to assign the channel for each AP and considers channel bonding.
Then, given the channel bonding assignment, the primary channel location for each AP is determined by observing whether the AP supports the static or dynamic channel bonding.
Meanwhile, the UA problem in the quasi-static and vehicular environments are slightly different. Thus, we devise UA schemes both for quasi-static and vehicular environments. The UA schemes for quasi-static environment takes account the load balancing among APs and energy saving, considering various techniques for performance improvement, such as multicast transmission, multi-user MIMO, and AP sleeping, together.
Then, we formulate the problem into a multi-objective optimization and get the solution as the UA scheme.
On the other hand, the UA scheme in the vehicular environment is realized through handover (HO) scheduling mechanism. Specifically, we propose a HO scheduling scheme running on a server, which determines the AP to which a user will be handed over, considering the road topology. Since a user only needs to decide when to initiate the connection to the next AP, a very fast and efficient HO in the vehicular environment can be realized. For this purpose, we utilize the graph modeling technique to map the relation between APs within the road. We consider a practical scenario where the structure of the road is complex, which includes straight, curve, intersection, and u-turn area. Then, the set of target APs for HO are selected for each user moving on a particular road based-on its moving path which is predicted considering the road topology. The design objective of the proposed HO scheduling is to maximize the connection time on WiFi while minimizing the total HO latency and reducing the number of users which contend for the channel within an AP.
Finally, we develop a WLAN testbed to demonstrate the practicality and feasibility of the proposed channelization and UA scheme in a quasi-static environment. Furthermore, through extensive simulations, we compare the performance of the proposed schemes with the existing schemes both in quasi-static and vehicular environments.1 Introduction
1.1 Background and Motivation
1.2 Related Works
1.3 Research Scope and Proposed Schemes
1.3.1 Centralized Channelization Scheme for Wireless LANs Exploiting Channel Bonding
1.3.2 User Association for Load Balancing and Energy Saving in Enterprise WLAN
1.3.3 A Graph-Based Handover Scheduling for Heterogenous Vehicular Networks
1.4 Organization
2 Centralized Channelization Scheme for Wireless LANs Exploiting Channel Bonding
2.1 System Model
2.2 Channel Sharing and Bonding
2.2.1 Interference between APs
2.2.2 Channel Sharing
2.2.3 Channel Bonding
2.3 Channelization Scheme
2.3.1 Building Interference Graph
2.3.2 Channel Allocation
2.3.3 Primary Channel Selection
2.4 Implementation
3 User Association for Load Balancing and Energy Saving in Enterprise Wireless LANs
3.1 System Model
3.1.1 IEEE 802.11 ESS-based Enterprise WLAN
3.1.2 Downlink Achievable Rate for MU-MIMO Groups
3.1.3 Candidate MU-MIMO Groups
3.2 User Association Problem
3.2.1 Factors of UA Objective
3.2.2 Problem Formulation
3.3 User Association Scheme
3.3.1 Equivalent Linear Problem
3.3.2 Solution Algorithm
3.3.3 Computational Complexity (Execution Time)
3.4 Implementation
4 A Graph-Based Handover Scheduling for Heterogenous Vehicular Networks
4.1 System Model
4.2 Graph-Based Modeling
4.2.1 Division of Road Portion into Road Segments
4.2.2 Relation between PoAs on a Road Segment
4.2.3 Directed Graph Representation
4.3 Handover Scheduling Problem
4.3.1 Problem Formulation
4.3.2 Weight of Edge
4.3.3 HO Scheduling Algorithm
4.4 Handover Scheduling Operation
4.4.1 HO Schedule Delivery
4.4.2 HO Triggering and Execution
4.4.3 Communication Overhead
5 Performance Evaluation
5.1 CentralizedChannelizationSchemeforWirelessLANsExploitingChannel Bonding
5.1.1 Experiment Settings
5.1.2 Comparison Schemes
5.1.3 Preliminary Experiment for Building Interference Graph
5.1.4 Experiment Results
5.2 User Association for Load Balancing and Energy Saving in Enterprise Wireless LANs
5.2.1 Performance Metrics
5.2.2 Experiment Settings
5.2.3 Experiment Results
5.2.4 Simulation Settings
5.2.5 Comparison Schemes
5.2.6 Simulation Results
5.2.7 Simulation for MU-MIMO System
5.3 A Graph-BasedHandover Scheduling for Heterogenous Vehicular Networks
5.3.1 Performance Metrics
5.3.2 Simulation Settings
5.3.3 Simulation Results
6 Conculsion
Bibliography
AcknowledgementsDocto
IEEE 802.11ax: challenges and requirements for future high efficiency wifi
The popularity of IEEE 802.11 based wireless local area networks (WLANs) has increased significantly in recent years because of their ability to provide increased mobility, flexibility, and ease of use, with reduced cost of installation and maintenance. This has resulted in massive WLAN deployment in geographically limited environments that encompass multiple overlapping basic service sets (OBSSs). In this article, we introduce IEEE 802.11ax, a new standard being developed by the IEEE 802.11 Working Group, which will enable efficient usage of spectrum along with an enhanced user experience. We expose advanced technological enhancements proposed to improve the efficiency within high density WLAN networks and explore the key challenges to the upcoming amendment.Peer ReviewedPostprint (author's final draft
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