Name
Title | ||
|---|---|---|
A Flexible Framework Based On Reinforcement Learning For Adaptive Modulation And Coding In Ofdm Wireless Systems |
Abstract | ||
|---|---|---|
This paper presents a machine learning approach for link adaptation in orthogonal frequency-division multiplexing systems through adaptive modulation and coding. Although machine learning techniques have attracted attention for link adaptation, most of the the schemes proposed so far are based on off-line training algorithms, which make them not well suited for real time operation. The proposed solution, based on the reinforcement learning technique, learns the best modulation and coding scheme for a given signal-to-noise ratio by interacting with the radio channel and it does not rely on an off-line training mode. Simulation results show that under specific conditions, the proposed technique can outperform the well-known solution based on look-up tables for adaptive modulation and coding, and it can potentially adapt itself to distinct characteristics of the radio environment. |
Year | DOI | Venue |
|---|---|---|
2012 | 10.1109/WCNC.2012.6214482 | 2012 IEEE WIRELESS COMMUNICATIONS AND NETWORKING CONFERENCE (WCNC) |
Keywords | Field | DocType |
reinforcement learning,ofdm modulation,modulation,look up table,look up tables,adaptive modulation,encoding,ofdm,learning artificial intelligence,wireless communication,link adaptation,signal to noise ratio,machine learning | Link adaptation,Wireless,Computer science,Theoretical computer science,Real-time computing,Coding (social sciences),Modulation,Multiplexing,Computer engineering,Orthogonal frequency-division multiplexing,Encoding (memory),Reinforcement learning | Conference |
ISSN | Citations | PageRank |
1525-3511 | 9 | 0.52 |
References | Authors | |
5 | 3 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Joao P. Leite | 1 | 9 | 0.86 |
| Paulo Henrique Portela De Carvalho | 2 | 10 | 2.62 |
| Robson D. Vieira | 3 | 213 | 22.42 |