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dc.contributor.authorAlrashdi, Ayed
dc.contributor.authorKammoun, Abla
dc.contributor.authorMuqaibel, Ali H.
dc.contributor.authorAl-Naffouri, Tareq Y.
dc.date.accessioned2020-08-24T11:17:40Z
dc.date.available2020-08-24T11:17:40Z
dc.date.issued2020-08-16
dc.identifier.urihttp://hdl.handle.net/10754/664793
dc.description.abstractThis paper considers the problem of symbol detection in massive multiple-input multiple-output (MIMO) wireless communication systems. We consider hard-thresholding preceeded by two variants of the regularized least squares (RLS) decoder; namely the unconstrained RLS and the RLS with box constraint. For all schemes, we focus on the evaluation of the mean squared error (MSE) and the symbol error probability (SEP) for M-ary pulse amplitude modulation (M-PAM) symbols transmitted over a massive MIMO system when the channel is estimated using linear minimum mean squared error (LMMSE) estimator. Under such circumstances, the channel estimation error is Gaussian which allows for the use of the convex Gaussian min-max theorem (CGMT) to derive asymptotic approximations for the MSE and SER when the system dimensions and the coherence duration grow large with the same pace. The obtained expressions are then leveraged to derive the optimal power distribution between pilot and data under a total transmit energy constraint. In addition, we derive an asymptotic approximation of the goodput for all schemes which is then used to jointly optimize the number of training symbols and their associated power. Numerical results are presented to support the accuracy of the theoretical results.
dc.publisherarXiv
dc.relation.urlhttps://arxiv.org/pdf/2008.06993
dc.rightsArchived with thanks to arXiv
dc.titleOptimum M-PAM Transmission for Massive MIMO Systems with Channel Uncertainty
dc.typePreprint
dc.contributor.departmentElectrical Engineering Program
dc.contributor.departmentElectrical Engineering
dc.contributor.departmentComputer, Electrical and Mathematical Sciences and Engineering (CEMSE) Division
dc.eprint.versionPre-print
dc.contributor.institutionDepartment of Electrical Engineering, University of Hail, Hail 55476, Saudi Arabia.
dc.contributor.institutionDepartment of Electrical Engineering, King Fahd University of Petroleum and Minerals, Dhahran 3126, Saudi Arabia.
dc.identifier.arxivid2008.06993
kaust.personAlrashdi, Ayed Mofareh Bakheet
kaust.personKammoun, Abla
kaust.personAl-Naffouri, Tareq Y.
refterms.dateFOA2020-08-24T11:19:00Z


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