A Bayesian matching pursuit based scheduling algorithm for feedback reduction in MIMO broadcast channels

Hussain J. Shibli, Mohammed E. Eltayeb, Tareq Y. Al-Naffouri

Research output: Chapter in Book/Report/Conference proceedingConference contribution


Opportunistic schedulers rely on the feedback of all users in order to schedule a set of users with favorable channel conditions. While the downlink channels can be easily estimated at all user terminals via a single broadcast, several key challenges are faced during uplink transmission. First of all, the statistics of the noisy and fading feedback channels are unknown at the base station (BS) and channel training is usually required from all users. Secondly, the amount of network resources (air-time) required for feedback transmission grows linearly with the number of users. In this paper, we tackle the above challenges and propose a Bayesian based scheduling algorithm that 1) reduces the air-time required to identify the strong users, and 2) is agnostic to the statistics of the feedback channels and utilizes the a priori statistics of the additive noise to identify the strong users. Numerical results show that the proposed algorithm reduces the feedback air-time while improving detection in the presence of fading and noisy channels when compared to recent compressed sensing based algorithms. Furthermore, the proposed algorithm achieves a sum-rate throughput close to that obtained by noiseless dedicated feedback systems. © 2013 IEEE.
Original languageEnglish (US)
Title of host publication2013 Third International Conference on Communications and Information Technology (ICCIT)
PublisherInstitute of Electrical and Electronics Engineers (IEEE)
Number of pages5
ISBN (Print)9781467353076
StatePublished - Jun 2013

Bibliographical note

KAUST Repository Item: Exported on 2020-10-01


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