Name
Abstract | ||
|---|---|---|
In this paper, a novel approach is proposed based on the probability density function (PDF) concept to achieve the capacity of a correlated ergodic multi-input multi-output (MIMO) channel with Nakagami-m fading. In our proposed method, channel parameters are unknown, and they are initially estimated by using the PDF of the received samples in the receiving antennas. The copula theory is employed to estimate the parameters of the channel in the proposed PDF-based approach. By appealing to copula, the notion of PDF estimation is simplified in the computation technique when we are faced with correlated signals. Since we are working on a correlated channel, the copula concept results in a powerful estimation approach for the PDF of the signals in the receivers. Accurate PDF estimation leads to having a precise calculation for channel parameters. Hence, the new approach guarantees that the capacity of a correlated ergodic channel is predicted reliably. In the previous works, either the capacity of simple uncorrelated Nakagami-m channels is presented or an asymptotic formulation is suggested for a correlated Nakagami-m channel. However, our proposed method introduces an analytic expression for the capacity of the MIMO correlated Nakagami-m fading channel relying on copula. All the results in both channel parameter estimation and channel capacity prediction are validated with some simulations. |
Year | DOI | Venue |
|---|---|---|
2015 | 10.1186/s13638-015-0369-3 | EURASIP Journal on Wireless Communications and Networking |
Keywords | Field | DocType |
MIMO correlated Nakagami-m fading channel, PDF estimation, Copula theory, Channel capacity | Computer science,Copula (linguistics),Fading,MIMO,Algorithm,Communication channel,Real-time computing,Nakagami distribution,Statistics,Channel capacity,Probability density function,Precoding | Journal |
Volume | Issue | ISSN |
2015 | 1 | 1687-1499 |
Citations | PageRank | References |
2 | 0.36 | 8 |
Authors | ||
3 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Mohammad Hossein Gholizadeh | 1 | 9 | 1.25 |
| hamidreza amindavar | 2 | 215 | 36.34 |
| James A. Ritcey | 3 | 87 | 6.69 |