Performance-Complexity Analysis for MAC ML-Based Decoding With User Selection
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Cites background or methods from "Performance-Complexity Analysis for..."
...However, it can be seen from [32, 36] that at high SNR regime the matrix R – afterMMSE-GDFE preprocessing – is ill-conditioned with Kont(Knt−nr) number of singular values arbitrarily close to zero....
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...It was shown in [32, 36] that the complexity exponent for decoding the DMT optimal code [26] is given by...
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...The complexity exponent of existing MIMOMAC DMT optimal codes [26, 32, 36] is also given to further motivate our design objectives that are presented in Section 3 and to serve as a baseline for comparing the complexity of the first proposed scheme given in Section 4....
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...Theorem 1: For the K-user MAC, the minimum over all ML-based decoders (all SD implementations, all halting and all decoding order policies) complexity exponent cmac,d(r) required to achieve a certain DMT d(r) ≤ d∗mac(r), is upper bounded by c̄mac,d(r) = sup µ∈B(r) (K − nr)rT +T ∑ν i=1 (r − (1−…...
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...…d(r) ≤ d∗mac(r), is upper bounded by c̄mac,d(r) = sup µ∈B(r) (K − nr)rT +T ∑ν i=1 (r − (1− µi)+) + , if K > nr, sup µ∈B(r) T ∑ν i=1 [min {r, r + µi − 1}] + , if K ≤ nr, (8) where µ = [µ1 · · ·µν ]>, ν = min{K,nr} and B(r) := { µ : µ1 ≥ · · · ≥ µν , 0 ≤ µi ∈ R∑ν i=1 (|K − nr|+ 2i− 1)µi ≤ d(r) } ....
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...…implementations, all halting and all decoding order policies) complexity exponent cmac,d(r) required to achieve a certain DMT d(r) ≤ d∗mac(r), is upper bounded by c̄mac,d(r) = sup µ∈B(r) (K − nr)rT +T ∑ν i=1 (r − (1− µi)+) + , if K > nr, sup µ∈B(r) T ∑ν i=1 [min {r, r + µi − 1}] + , if K ≤ nr,…...
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