Optimal Resource Allocation in OFDMA Broadcast Channels Using Dynamic Programming

Carretero, Jesús; Vía, Javier; Nazábal, Alfredo

doi:10.5772/18357

Cited by 1 publication

(3 citation statements)

References 14 publications

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“…an OFDM subcarrier, to each user of a multiuser orthogonal frequency-division multiple access (OFDMA) system. It should be interesting to combine such allocations with a resource allocation optimization [34], [35]. Finally, the multiuser case, with multipath propagation for each user, constitutes also an interesting perspective of this work.…”

Section: Discussionmentioning

confidence: 99%

“…Algorithms 4, 5, and 6 describe the semi-blind KALS, KSVD, and KALMS receivers for the TSTF system. The matrixỸ N K×RF M denotes a noisy version of Y N K×RF M defined in (35), with X N K×F P J replaced byX N K×F P J , an unfolding of the noisy received signal tensorX = X + σV, where V is an additive noise tensor, and σ is adjusted according to the desired SNR.…”

Section: Kronecker Receivers For the Tstf Systemmentioning

confidence: 99%

“…For TSTF, the identifiability condition is linked with the calculation of the LS estimate (35) of the Kronecker product, that is the full row-rank property of G RF M ×F P J . Premultiplying this expression by the permutation matrix…”

Section: Identifiability Conditionsmentioning

confidence: 99%

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Tensor modelling of MIMO communication systems with performance analysis and Kronecker receivers

2018

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The purpose of this paper is manifold. In a first part, we present a new alternating least squares (ALS)-based method for estimating the matrix factors of a Kronecker product, the so-called Kronecker ALS (KALS) method. Four other methods are also briefly described. In a second part, we consider the design of multiple-input multiple-output (MIMO) wireless communication systems using tensor modelling. Eight systems are presented in a unified way, and their theoretical performance is compared in terms of maximal diversity gain. Exploiting a Kronecker product of symbol and channel matrices, and applying the algorithms introduced in the first part, we propose three semi-blind and two supervised receivers, called Kronecker receivers, for jointly estimating the channel and the transmitted symbols. Necessary identifiability conditions are established. Finally, extensive Monte Carlo simulation results are provided to compare the performance of three tensor-based systems, on the one hand, and of the five proposed Kronecker receivers for the tensor space-time-frequency (TSTF) coding system, on the other hand.

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Section: Discussionmentioning

confidence: 99%

Section: Kronecker Receivers For the Tstf Systemmentioning

confidence: 99%