Downlink multi-user MIMO scheduling with performance guarantees

Prasad, Narayan; Qi, Xiao Feng

doi:10.23919/wiopt.2018.8362843

Cited by 1 publication

(2 citation statements)

References 27 publications

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“…is a submodular set function over Ω. Then, from (17) we can deduce that h (.) is a linear combination of K submodular set functions with non-negative combining coefficients, which proves that h (.)…”

Section: A Proof Of Propositionmentioning

confidence: 99%

“…These include sensor placement, single-user scheduling (that schedules users on orthogonal time-frequency resources) with fixed transmit powers [12] as well as optimized powers [13]. Submodularity has also been shown to hold in formulations considering the user and base-station association problem [14], [15], caching [16] and to some extent even multi-user MIMO scheduling (that schedules multiple users on same time-frequency resource) [17]. The main motivation for these works is the availablitly of increasingly effective approximation algorithms for constrained submodular set function maximation [18]- [20].…”

Section: Introductionmentioning

confidence: 99%

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Optimizing Beams and Bits: A Novel Approach for Massive MIMO Base-Station Design

Prasad¹,

Qi²,

Gatherer³

2019

2019 International Conference on Computing, Networking and Communications (ICNC)

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We consider the problem of jointly optimizing ADC bit resolution and analog beamforming over a frequencyselective massive MIMO uplink. We build upon a popular model to incorporate the impact of low bit resolution ADCs, that hitherto has mostly been employed over flat-fading systems. We adopt weighted sum rate (WSR) as our objective and show that WSR maximization under finite buffer limits and important practical constraints on choices of beams and ADC bit resolutions can equivalently be posed as constrained submodular set function maximization. This enables us to design a constantfactor approximation algorithm. Upon incorporating further enhancements we obtain an efficient algorithm that significantly outperforms state-of-the-art ones.

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Section: A Proof Of Propositionmentioning

confidence: 99%