Name
Title | ||
|---|---|---|
Recurrent Neural Network-Based Received Signal Strength Estimation Using Depth Images For Mmwave Communications |
Abstract | ||
|---|---|---|
Camera-assisted millimeter-wave (mmWave) network is a new paradigm for mmWave communications where mobility of obstacles is captured by using RGB and depth cameras and conducts network operations by considering the captured information. For camera-assisted mmWave networks, this paper proposes a recurrent neural network (RNN)-based received signal strength (RSS) estimation scheme using depth camera images. This scheme enables us to estimate the RSS of any mmWave links, including links where nodes are not transmitting frames. An RNN enables us to model the relationship between current RSS and an image time series, which includes information regarding the mobility of nodes and obstacles. Simulation results demonstrate that the RNN-based estimation scheme achieves higher accuracy than that of a multi-layer perceptron. |
Year | Venue | Field |
|---|---|---|
2018 | 2018 15TH IEEE ANNUAL CONSUMER COMMUNICATIONS & NETWORKING CONFERENCE (CCNC) | Time series,Computer science,Computer network,Recurrent neural network,Network operations center,Real-time computing,RGB color model,Throughput,Artificial neural network,Perceptron,RSS |
DocType | ISSN | Citations |
Conference | 2331-9852 | 2 |
PageRank | References | Authors |
0.39 | 0 | 4 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Hironao Okamoto | 1 | 6 | 1.47 |
| Takayuki Nishio | 2 | 106 | 38.21 |
| Masahiro Morikura | 3 | 184 | 63.42 |
| Koji Yamamoto | 4 | 135 | 45.58 |