Abstract
To design an efficient wireless local-area network (WLAN), we have studied the throughput estimation model for the IEEE 802.11n link both in indoor and outdoor environments. This model first estimates the received signal strength (RSS) at the receiving node using the log distance path loss model. Then, it converts RSS to the throughput using the sigmoid function. The parameters in these functions are optimized by applying the parameter optimization tool to measurement results. In this paper, we examine the throughput estimation model for the Multiple Input Multiple Output (MIMO) link in IEEE 802.11n. MIMO link can increase the transmission capacity using multiple streams. First, throughput measurements for MIMO links are conducted using commercial devices and software in three network fields. Then, using the results, the parameters in the model are optimized by the tool. The evaluation results show that this model can estimate throughputs with considerably high accuracy in any field when compared with the ns-3 network simulator. Besides, the model is modified to improve the accuracy when multiple hosts are communicating with one access point (AP) simultaneously.
Original language | English |
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Title of host publication | 2018 3rd International Conference on Computer and Communication Systems, ICCCS 2018 |
Publisher | Institute of Electrical and Electronics Engineers Inc. |
Pages | 421-425 |
Number of pages | 5 |
ISBN (Print) | 9781538663509 |
DOIs | |
Publication status | Published - Sept 11 2018 |
Event | 3rd International Conference on Computer and Communication Systems, ICCCS 2018 - Nagoya, Japan Duration: Apr 27 2018 → Apr 30 2018 |
Other
Other | 3rd International Conference on Computer and Communication Systems, ICCCS 2018 |
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Country/Territory | Japan |
City | Nagoya |
Period | 4/27/18 → 4/30/18 |
Keywords
- IEEE 802.11n
- MIMO
- ns-3
- throughput estimation model
- throughput measurement
- WIMNET simulator
ASJC Scopus subject areas
- Computer Networks and Communications
- Computer Vision and Pattern Recognition
- Hardware and Architecture
- Artificial Intelligence