Demo Data from EDBT 2026 / reviewers in the wild / expert
Sudharsan Parthasarathy
dblp:180/5841
· DBLP profile ↗
2ranked-venue papers
2as first author
0since 2021 · last 2018
0000-0003-3236-9016ORCID · corroborated
Domains — the database's venue-derived domains; a paper can count in several
Computer networks · 2 · 2 first-author
Expertise — from the expertise taxonomy: the topics of the expert's papers under the CCF categories. A weight counts papers with recency: 1 for a paper about the topic, 0.3 when the topic is its context, halved every five years.
| Computer networks
1 paper |
Physical-layer communications · 100% |
Topics — the 4 heaviest of 4, each with the papers that count most for it
| Topic | Weight | Papers | Last | Evidence papers |
|---|---|---|---|---|
Physical-layer communications › interference suppression
cochannel interference |
0.3 | 1 | 2018 | Error Vector Magnitude Analysis in Generalized Fading With Co-Channel Interference · IEEE Trans. Commun. 2018 |
Physical-layer communications › modulation
error vector magnitude |
0.3 | 1 | 2018 | Error Vector Magnitude Analysis in Generalized Fading With Co-Channel Interference · IEEE Trans. Commun. 2018 |
Physical-layer communications
fading channels |
0.3 | 1 | 2018 | Error Vector Magnitude Analysis in Generalized Fading With Co-Channel Interference · IEEE Trans. Commun. 2018 |
Physical-layer communications › fading channels › fading models
κ-μ shadowed fading |
0.1 | 1 | 2018 | Error Vector Magnitude Analysis in Generalized Fading With Co-Channel Interference · IEEE Trans. Commun. 2018 |
Methods — techniques the papers use, named apart from their topics
special function analysis · 0.3nakagami fading model · 0.3
| Year | Publication | Venue | Position |
|---|---|---|---|
| 2018 | Error Vector Magnitude Analysis in Generalized Fading With Co-Channel InterferenceabstractIn this paper, we derive the data-aided error vector magnitude (EVM) in an interference limited system when both the desired channel and interferers experience independent and nonidentically distributed κ-μ shadowed fading. Then, it is analytically shown that the EVM is equal to the square root of number of interferers when the desired channel and interferers do not experience fading. Furthermore, the EVM is derived in the presence of interference and noise, when the desired channel experiences κ-μ shadowed fading and the interferers experience independent and identical Nakagami fading. Moreover, using the properties of the special functions, the derived EVM expressions are also simplified for various special cases. Sudharsan Parthasarathy, Suman Kumar 0001, Radha Krishna Ganti, Sheetal Kalyani, Krishnamurthy Giridhar |
IEEE Trans. Commun. | 1 |
| 2017 | Impact of Shadowing in D2D CommunicationabstractDevice-to-device (D2D) communication is expected to increase the coverage probability by coexisting along with the conventional cellular systems in 5G technology. As the user equipments are often held closer to the human body, D2D links experience shadowing modelled by the κ-μ shadowed fading model. Though the cellular links do not experience κ-μ shadowed fading, the same is assumed here as it generalizes many popular fading distributions such as Rayleigh, Rician, Nakagami, κ-μ, η-μ, Hoyt etc. Thus the above assumption makes the results obtained here generic. We show that by neglecting shadowing in the D2D link, coverage probability of D2D user is overestimated at low SIR thresholds and under-estimated at high SIR thresholds. Though cellular links are interfered with by the D2D links, we observe that there is negligible impact on cellular coverage probability due to any change of fading parameters in the D2D links. Also, when all the links are Rayleigh faded, the D2D distance below which D2D mode is preferred to cellular mode of communication is obtained analytically. Sudharsan Parthasarathy, Radha Krishna Ganti |
WCNC | 1 |