Heuristic resource allocation for sum rate optimization in MIMO-OFDM systems using eigenvalue updates

Klotz, Johannes; Huppert, Carolin; Bossert, Martin

doi:10.1109/iswcs.2008.4726006

Cited by 3 publications

(3 citation statements)

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“…Our first algorithm, which is given in terms of pseudo code in Algorithm 1, uses the basic principles of the heuristic eigenvalue update algorithm for sum rate maximisation presented in Reference [5] and is, therefore, called 'extended eigenvalue update algorithm' (EEU). In Reference [5], the influence of a user k dis using DPC on subsequent users k in carrier l is estimated by updating their eigenvalues according to…”

Section: Extended Eigenvalue Update Algorithmmentioning

confidence: 99%

“…In Reference [5], the influence of a user k dis using DPC on subsequent users k in carrier l is estimated by updating their eigenvalues according to…”

Section: Extended Eigenvalue Update Algorithmmentioning

confidence: 99%

“…We use this method in the first step of the algorithm to maximise the sum rate. However, in contrast to Reference [5], where a water filling process is applied to distribute the available power to the different beams, we simply assign the same power to each beam. The number of served beams per carrier is restricted to B.…”

Section: Extended Eigenvalue Update Algorithmmentioning

confidence: 99%

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Resource allocation algorithms for minimum rates scheduling in MIMO‐OFDM systems

Klotz

Knabe

Huppert

2010

Trans Emerging Tel Tech

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SUMMARYIn this paper, we consider a multiple-input-multiple-output-orthogonal frequency division multiplexing (MIMO-OFDM) downlink scenario, where each receiving mobile station has quality of service requirements, namely minimum rate requirements. For this problem we propose three heuristic resource allocation algorithms, which have a much lower complexity than the existing optimal solution (opt). We compare and evaluate these algorithms according to sum rate performance and complexity. The first strategy is based on a heuristic sum rate maximisation algorithm using the so-called eigenvalue updates. In our second algorithm, we make use of the duality of uplink and downlink, which allows us to do the allocation in the dual uplink. Finally, our third algorithm is based on the well-known zero-forcing dirty paper coding (ZF-DPC) principles, which use the Gram-Schmidt process to orthogonalise the transmissions towards the different users.

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Section: Extended Eigenvalue Update Algorithmmentioning

confidence: 99%

“…In Reference [5], the influence of a user k dis using DPC on subsequent users k in carrier l is estimated by updating their eigenvalues according to…”

Section: Extended Eigenvalue Update Algorithmmentioning

confidence: 99%

Section: Extended Eigenvalue Update Algorithmmentioning

confidence: 99%

See 1 more Smart Citation