scholarly journals Simulation and Emulation Approach for the Performance Evaluation of Adaptive Modulation and Coding Scheme in Mobile WiMAX Network

2012 ◽  
Vol 44 (16) ◽  
pp. 26-32
Author(s):  
Thontadharya H.J ◽  
Shwetha D ◽  
Subramanya Bhat. M.S ◽  
Devaraju J.T
Keyword(s):  
Performance Evaluation ◽  
Adaptive Modulation ◽  
Adaptive Modulation And Coding ◽  
Mobile Wimax ◽  
Coding Scheme ◽  
Wimax Network

Mobile WiMAX Bandwidth Reservation Thresholds

2011 ◽  
Vol 1 (2) ◽  
pp. 42-61
Author(s):  
Sondes Khemiri ◽  
Khaled Boussetta ◽  
Nadjib Achir ◽  
Guy Pujolle
Keyword(s):  
Adaptive Modulation ◽  
Adaptive Modulation And Coding ◽  
Mobile Wimax ◽  
Service Class ◽  
Ieee 802.16E ◽  
Coding Scheme ◽  
A Cell ◽  
Bandwidth Capacity ◽  
Call Request ◽  
Bandwidth Partitioning

This paper addresses the issue of wireless bandwidth partitioning of a Mobile WiMAX cell. The authors consider a Complete Partitioning strategy, where the wireless bandwidth capacity of a cell is divided into trunks. Each partition is strictly reserved to a particular type of connection. Four IEEE 802.16e 2005 service classes are distinguished: UGS, rtPS, nrtPS, and ErtPS. The authors consider mobility and differentiate new call request from handoffs. In addition, the authors take into consideration the Adaptive Modulation and Coding (AMC) scheme, through the partition of the cell into different areas associated to a particular modulation and coding scheme. The purpose of the paper is to determine, using an analytical model and a heuristic approach, the nearly optimal sizes of the partition sizes dedicated to each type of connection, which is characterized by its service class, type of request and modulation, and coding scheme.

Mobile WiMAX Bandwidth Reservation Thresholds

2013 ◽  
pp. 133-153
Author(s):  
Sondes Khemiri ◽  
Khaled Boussetta ◽  
Nadjib Achir ◽  
Guy Pujolle
Keyword(s):  
Adaptive Modulation ◽  
Adaptive Modulation And Coding ◽  
Mobile Wimax ◽  
Service Class ◽  
Ieee 802.16E ◽  
Coding Scheme ◽  
A Cell ◽  
Bandwidth Capacity ◽  
Call Request ◽  
Bandwidth Partitioning

This paper addresses the issue of wireless bandwidth partitioning of a Mobile WiMAX cell. The authors consider a Complete Partitioning strategy, where the wireless bandwidth capacity of a cell is divided into trunks. Each partition is strictly reserved to a particular type of connection. Four IEEE 802.16e 2005 service classes are distinguished: UGS, rtPS, nrtPS, and ErtPS. The authors consider mobility and differentiate new call request from handoffs. In addition, the authors take into consideration the Adaptive Modulation and Coding (AMC) scheme, through the partition of the cell into different areas associated to a particular modulation and coding scheme. The purpose of the paper is to determine, using an analytical model and a heuristic approach, the nearly optimal sizes of the partition sizes dedicated to each type of connection, which is characterized by its service class, type of request and modulation, and coding scheme.

scholarly journals Towards More Reliable Deep Learning-Based Link Adaptation for WiFi 6

2021 ◽  
Author(s):  
Mostafa Hussien
Keyword(s):  
Deep Learning ◽  
Loss Function ◽  
Adaptive Modulation ◽  
Link Adaptation ◽  
Classification Problem ◽  
System Throughput ◽  
Adaptive Modulation And Coding ◽  
Coding Scheme ◽  
Proposed Model ◽  
Multi Class Classification

The problem of selecting the modulation and coding scheme (MCS) that maximizes the system throughput, known as link adaptation, has been investigated extensively, especially for IEEE 802.11 (WiFi) standards. Recently, deep learning has widely been adopted as an efficient solution to this problem. However, in failure cases, predicting a higher-rate MCS can result in a failed transmission. In this case, a retransmission is required, which largely degrades the system throughput. To address this issue, we model the adaptive modulation and coding (AMC) problem as a multi-label multi-class classification problem. The proposed modeling allows more control over what the model predicts in failure cases. We also design a simple, yet powerful, loss function to reduce the number of retransmissions due to higher-rate MCS classification errors. Since wireless channels change significantly due to the surrounding environment, a huge dataset has been generated to cover all possible propagation conditions. However, to reduce training complexity, we train the CNN model using part of the dataset. The effect of different subdataset selection criteria on the classification accuracy is studied. The proposed model adapts the IEEE 802.11ax communications standard in outdoor scenarios. The simulation results show the proposed loss function reduces up to 50% of retransmissions compared to traditional loss functions.

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