2020
DOI: 10.1109/tit.2020.2993958
|View full text |Cite
|
Sign up to set email alerts
|

On the Degrees-of-Freedom of the K-User Distributed Broadcast Channel

Help me understand this report
View preprint versions

Search citation statements

Order By: Relevance

Paper Sections

Select...
2
2
1

Citation Types

0
8
0

Year Published

2020
2020
2022
2022

Publication Types

Select...
4
1
1

Relationship

1
5

Authors

Journals

citations
Cited by 8 publications
(8 citation statements)
references
References 47 publications
0
8
0
Order By: Relevance
“…In this work we explore a downlink (DL) cooperation regime with full data sharing and general distributed CSI at the TXs (CSIT) [7]- [9], that is, we let each TX operate on the basis of possibly different estimates of the global channel state obtained through some arbitrary CSIT acquisition and sharing mechanism. This assumption is relevant, e.g., for all service situations where the CSIT sharing burden dominates the fronthaul overhead.…”
Section: A Cooperative Transmission With Distributed Csitmentioning
confidence: 99%
See 3 more Smart Citations
“…In this work we explore a downlink (DL) cooperation regime with full data sharing and general distributed CSI at the TXs (CSIT) [7]- [9], that is, we let each TX operate on the basis of possibly different estimates of the global channel state obtained through some arbitrary CSIT acquisition and sharing mechanism. This assumption is relevant, e.g., for all service situations where the CSIT sharing burden dominates the fronthaul overhead.…”
Section: A Cooperative Transmission With Distributed Csitmentioning
confidence: 99%
“…This assumption is relevant, e.g., for all service situations where the CSIT sharing burden dominates the fronthaul overhead. For instance, it is suitable in case of rapidly varying channels due to user mobility, or when delay-tolerant data is proactively made available at the TXs using caching techniques (see [9] and reference therein for a detailed discussion). As an extreme example, a cooperation regime with full data sharing and no CSIT sharing (a configuration here referred to as local CSIT) is perhaps the leading motivation behind the early development of the now popular cell-free massive MIMO paradigm [10].…”
Section: A Cooperative Transmission With Distributed Csitmentioning
confidence: 99%
See 2 more Smart Citations
“…AI bounds are based on a combinatorial accounting of the number of codewords that can cast resolvable images [11] at one receiver while casting 'aligned images' at another receiver. These bounds have been instrumental in settling important conjectures [12], closing large GDoF gaps [9], establishing new DoF characterizations [13], [14], identifying new parameter regimes for optimality of robust schemes such as treating interference as noise and rate-splitting [15]- [18], shedding new light on the significance of network coherence times [19], and quantifying extremal behaviors [20], [21]. The significance of AI bounds is underscored by their surprisingly large advantage over the best known alternatives; for example AI bounds show that under finite precision CSIT a K user interference channel has no more than 1 DoF [9], but no alternative approach thus far has produced an outer bound under finite precision CSIT that is better (smaller) than the trivial K/2 DoF bound (which corresponds to perfect CSIT [22]).…”
Section: Introductionmentioning
confidence: 99%