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

Bazco-Nogueras, Antonio; Kerret, Paul de; Gesbert, David; Gresset, Nicolas

doi:10.1109/tit.2020.2993958

Cited by 8 publications

(8 citation statements)

References 47 publications

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“…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%

“…Although the importance of cooperative transmission schemes based on limited CSIT sharing has been acknowledged in the literature, a satisfactory understanding of systems with distributed CSIT is still missing. Most of the available information theoretical results rely on asymptotic signal-to-noise ratio (SNR) tools [7], [9], or focus on simple settings with a single receiver (RX) [21]. However, [7], [9], [21] do not lead to practical schemes for complex settings such as cell-free massive MIMO networks.…”

Section: B Summary Of Contributions and Related Workmentioning

confidence: 99%

“…Most of the available information theoretical results rely on asymptotic signal-to-noise ratio (SNR) tools [7], [9], or focus on simple settings with a single receiver (RX) [21]. However, [7], [9], [21] do not lead to practical schemes for complex settings such as cell-free massive MIMO networks. On the other hand, the available practical schemes are essentially based on heuristic adaptations of known centralized precoding designs such as maximum-ratio transmission (MRT), zero-forcing (ZF), or minimum mean-square error (MMSE) precoding [10], [11], [18].…”

Section: B Summary Of Contributions and Related Workmentioning

confidence: 99%

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Team MMSE Precoding with Applications to Cell-free Massive MIMO

Miretti¹,

Björnson²,

Gesbert³

2021

Preprint

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This article studies a novel distributed precoding design, coined team minimum mean-square error (TMMSE) precoding, which rigorously generalizes classical centralized MMSE precoding to distributed operations based on transmitter-specific channel state information (CSIT). Building on the so-called theory of teams, we derive a set of necessary and sufficient conditions for optimal TMMSE precoding, in the form of an infinite dimensional linear system of equations. These optimality conditions are further specialized to cell-free massive MIMO networks, and explicitly solved for two important examples, i.e., the classical case of local CSIT and the case of unidirectional CSIT sharing along a serial fronthaul. The latter case is relevant, e.g., for the recently proposed radio stripe concept and the related advances on sequential processing exploiting serial connections. In both cases, our optimal design outperforms the heuristic methods that are known from the previous literature. Duality arguments and numerical simulations validate the effectiveness of the proposed team theoretical approach in terms of ergodic achievable rates under a sum-power constraint. I. INTRODUCTIONInter-cell interference is a major limiting factor of wireless communication systems capitalizing on aggressive spectrum reuse and network densification to increase capacity. To mitigate this effect, future generation systems are expected to implement advanced cooperative communication techniques, in particular by letting geographically distributed base stations jointly serve their users. However, the practical deployment of cooperative wireless networks is currently prevented by the severe scalability issue arising from network-wide processing [2]. Specifically, the excessive amount of data and channel state information (CSI) to be timely A preliminary version of this work has been submitted to IEEE SPAWC 2021 [1].

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Section: A Cooperative Transmission With Distributed Csitmentioning

confidence: 99%

Section: A Cooperative Transmission With Distributed Csitmentioning

confidence: 99%

Section: B Summary Of Contributions and Related Workmentioning

confidence: 99%

Section: B Summary Of Contributions and Related Workmentioning

confidence: 99%

See 2 more Smart Citations

Team MMSE Precoding with Applications to Cell-free Massive MIMO

Miretti¹,

Björnson²,

Gesbert³

2021

Preprint

Self Cite

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“…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%

Exploring Aligned-Images Bounds: Robust Secure GDoF of 3-to-1 Interference Channel

Chan

Jafar

2021

ICC 2021 - IEEE International Conference on Communications

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Sum-set inequalities based on Aligned-Images bounds have been recently introduced as essential elements of converse proofs for asymptotic/approximate wireless network capacity characterizations under robust assumptions, i.e., assumptions that limit channel knowledge at the transmitters to finite precision. While these sum-set inequalities have produced robust Generalized Degrees of Freedom (GDoF) results for various wireless networks, their scope and limitations in general are not well understood. To explore these limitations, in this work we study the robust secure GDoF of a symmetric 3-user manyto-one interference channel. We identify regimes where existing sum-set inequalities are sufficient, settling the GDoF for those settings. For the remaining regime we conjecture the form of new sum-set inequalities that may be needed, whose validity remains an open problem for future work.

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