Demo Data from EDBT 2026 / reviewers in the wild / expert
Costas Constantinou
dblp:17/8860
· DBLP profile ↗
2ranked-venue papers
0as first author
0since 2021 · last 2009
—ORCID · none
Domains — the database's venue-derived domains; a paper can count in several
Theory of computation · 1Applied, interdisciplinary, general and emerging computing · 1
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% | |
| Theoretical computer science
1 paper |
Information theory · 100% |
Topics — the 3 heaviest of 3, each with the papers that count most for it
| Topic | Weight | Papers | Last | Evidence papers |
|---|---|---|---|---|
Physical-layer communications › information theory › capacity analysis
channel capacity |
0.1 | 1 | 2009 | Capacity of the class of MIMO channels with incomplete CDI: properties of mutual information for a class of channels · IEEE Trans. Inf. Theory 2009 |
Physical-layer communications › MIMO
MIMO channel |
0.1 | 1 | 2009 | Capacity of the class of MIMO channels with incomplete CDI: properties of mutual information for a class of channels · IEEE Trans. Inf. Theory 2009 |
Information theory › channel capacity › state-dependent channel
compound channel |
0.1 | 1 | 2009 | Capacity of the class of MIMO channels with incomplete CDI: properties of mutual information for a class of channels · IEEE Trans. Inf. Theory 2009 |
Methods — techniques the papers use, named apart from their topics
relative entropy · 0.2maximin optimization · 0.2
| Year | Publication | Venue | Position |
|---|---|---|---|
| 2009 | Capacity of the class of MIMO channels with incomplete CDI: properties of mutual information for a class of channelsabstractThis paper is concerned with multiple-input multiple-output (MIMO) wireless channel capacity, when the probability distribution of the channel matrix p(H) is not completely known to the transmitter and the receiver. The partial knowledge of a true probability distribution of the channel matrix p(H) is modelled by a relative entropyD(middot||middot) such thatD(p||pnom) lesd,dges 0, wheredis the distance from the so-called nominal channel matrix distribution pnom(H). The capacity of this compound channel is equal to the maximin of the mutual information, where the minimum is with respect to the channel matrix distribution, and the maximum is with respect to the covariance matrix of a transmitted signal. The existence of a minimizing probability distribution is proved, and the explicit formula for the minimizing distribution is derived in terms of the nominal distribution pnom(H) and parameterd. A number of properties of the mutual information, minimized over the set of channel distributions, are derived. Specifically, upper and lower bounds are derived for the minimized mutual information, while its convexity with respect todis shown. In the case of the Rayleigh fading, an explicit formula for the capacity and the optimal transmit covariance matrix are derived. Charalambos D. Charalambous, Stojan Z. Denic, Costas Constantinou |
IEEE Trans. Inf. Theory | 3 |
| 2006 | Information Capacity of MIMO Channels with Relative Entropy ConstraintabstractThis paper addresses the issue of multiple-input multiple-output (MIMO) wireless channel capacity, when the probability distribution of the channel matrix p(H) is not completely known to the transmitter and the receiver. The partial knowledge of a true probability distribution of the channel matrix is modelled by using a relative entropy D(pparq). All possible channel matrix distributions p(H) satisfy D(pparpnom) les d, d ges 0, i.e., they lie within d distance from the so-called nominal channel matrix distribution pnom(H). The information channel capacity is defined as a maximin optimization problem, where the mutual information is a pay-off function. The minimum is with respect to the channel matrix distribution, and the maximum is with respect to the covariance matrix of a transmitted signal. Based on the derived characteristics of the pay-off function, the formula for the channel matrix distribution, which minimizes the mutual information, is derived. In the case of the Rayleigh fading, the formula for the information capacity and the optimal transmit covariance matrix are obtained. In addition, the existence of the saddle point of the maximin optimization problem is established for this particular case Charalambos D. Charalambous, Stojan Z. Denic, Costas Constantinou |
ISIT | 3 |