EDBT 2026 Demo / reviewers in the wild / expert
Kangning Ma
dblp:251/3249
· DBLP profile ↗
3ranked-venue papers
3as first author
3since 2021 · last 2026
0000-0002-3310-4637ORCID · corroborated
Domains — the database's venue-derived domains; a paper can count in several
Applied, interdisciplinary, general and emerging computing · 2 · 2 first-author · 2 since 2021Computer networks · 1 · 1 first-author · 1 since 2021
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 · 75% Network optimization and economics · 25% | |
| Theoretical computer science
1 paper |
Mathematical optimization · 100% |
Topics — the 6 heaviest of 6, each with the papers that count most for it
| Topic | Weight | Papers | Last | Evidence papers |
|---|---|---|---|---|
Physical-layer communications › MIMO › MIMO channel
MIMO fading channels |
0.9 | 1 | 2025 | Hierarchical Rate Splitting to General MIMO Fading Channels · IEEE Trans. Commun. 2025 |
Physical-layer communications
power allocation |
0.9 | 1 | 2025 | Hierarchical Rate Splitting to General MIMO Fading Channels · IEEE Trans. Commun. 2025 |
Network optimization and economics › resource allocation
rate allocation |
0.9 | 1 | 2025 | Hierarchical Rate Splitting to General MIMO Fading Channels · IEEE Trans. Commun. 2025 |
Physical-layer communications › multiple access
rate splitting |
0.9 | 1 | 2025 | Hierarchical Rate Splitting to General MIMO Fading Channels · IEEE Trans. Commun. 2025 |
Mathematical optimization
iterative methods |
0.3 | 1 | 2025 | Hierarchical Rate Splitting to General MIMO Fading Channels · IEEE Trans. Commun. 2025 |
Mathematical optimization
nonconvex optimization |
0.3 | 1 | 2025 | Hierarchical Rate Splitting to General MIMO Fading Channels · IEEE Trans. Commun. 2025 |
Methods — techniques the papers use, named apart from their topics
rate splitting · 1.7layered broadcast approach · 1.7iterative optimization · 1.7
| Year | Publication | Venue | Position |
|---|---|---|---|
| 2026 | Resilient Distributed Joint Source-Channel Coding with Non-Stationary Side Information
Kangning Ma, Shuo Shao 0001, Jincheng Dai, Zhou Zhong, Wenrui Dai, Hongkai Xiong |
ISIT | 1 |
| 2025 | Hierarchical Rate Splitting to General MIMO Fading ChannelsabstractWe consider a general point-to-point fading multiple-input multiple-output (MIMO) Gaussian channel. The channel suffers block fading and is with finite channel states, among which there is no degraded order due to the multi-antenna deployment. To guarantee reliable transmissions under the arbitrary unpredictable fading state, we generalize the layered broadcast approach in the channel by inducing the general rate splitting scheme. It assigns the specific sub-message layers for every possible state set and splits the transmission rate by allocating power to each layer. Under this scheme, a generalized broadcast approach is designed to be suitable for multi-antenna transmission. The maximum average rate subject to a transmit power constraint can be determined by a non-convex optimization problem over power allocation and rate tuple. We propose an iterative algorithm based on certain properties that the optimal solution would meet under some necessary conditions. It can output one local optimum for the optimization and thus assist in obtaining a scheme with a decent performance. Kangning Ma, Yinfei Xu, Shuo Shao 0001 |
IEEE Trans. Commun. | 1 |
| 2022 | On Broadcast Approach to MIMO Fading ChannelsabstractWe consider a general point-to-point fading multiple-input multiple-output (MIMO) Gaussian channel. The channel suffers block fading and with finite channel states, among which there is no degraded order on state information due to multi-antenna deployment. To guarantee reliable transmissions under arbitrary unpredictable fading states, we generalize the layered broadcast approach in the channel by inducing a rate splitting scheme. It assigns a specific sub-message layer for every possible state set and splits the transmission rate by allocating power to each layer. Under this scheme, a generalized broadcast approach is designed to be suitable for multi-antenna transmission. The maximum average rate under total power constraint can be characterized by an optimization problem. Numerical examples are provided to show the optimality of our transmission scheme within degradedness among different channel state information. Meanwhile, such generalization can also provide a decent performance under the general non-degraded case. Kangning Ma, Yinfei Xu, Shuo Shao 0001 |
ISIT | 1 |