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    On Lattice Sequential Decoding for Large MIMO Systems

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    Type
    Thesis
    Authors
    Ali, Konpal S.
    Advisors
    Alouini, Mohamed-Slim cc
    Committee members
    Al-Naffouri, Tareq Y. cc
    Sultan Salem, Ahmed Kamal
    Turkiyyah, George
    Program
    Electrical Engineering
    KAUST Department
    Computer, Electrical and Mathematical Science and Engineering (CEMSE) Division
    Date
    2014-04
    Permanent link to this record
    http://hdl.handle.net/10754/316732
    
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    Abstract
    Due to their ability to provide high data rates, Multiple-Input Multiple-Output (MIMO) wireless communication systems have become increasingly popular. Decoding of these systems with acceptable error performance is computationally very demanding. In the case of large overdetermined MIMO systems, we employ the Sequential Decoder using the Fano Algorithm. A parameter called the bias is varied to attain different performance-complexity trade-offs. Low values of the bias result in excellent performance but at the expense of high complexity and vice versa for higher bias values. We attempt to bound the error by bounding the bias, using the minimum distance of a lattice. Also, a particular trend is observed with increasing SNR: a region of low complexity and high error, followed by a region of high complexity and error falling, and finally a region of low complexity and low error. For lower bias values, the stages of the trend are incurred at lower SNR than for higher bias values. This has the important implication that a low enough bias value, at low to moderate SNR, can result in low error and low complexity even for large MIMO systems. Our work is compared against Lattice Reduction (LR) aided Linear Decoders (LDs). Another impressive observation for low bias values that satisfy the error bound is that the Sequential Decoder's error is seen to fall with increasing system size, while it grows for the LR-aided LDs. For the case of large underdetermined MIMO systems, Sequential Decoding with two preprocessing schemes is proposed – 1) Minimum Mean Square Error Generalized Decision Feedback Equalization (MMSE-GDFE) preprocessing 2) MMSE-GDFE preprocessing, followed by Lattice Reduction and Greedy Ordering. Our work is compared against previous work which employs Sphere Decoding preprocessed using MMSE-GDFE, Lattice Reduction and Greedy Ordering. For the case of large systems, this results in high complexity and difficulty in choosing the sphere radius. Our schemes, particularly 2), perform better in terms of complexity and are able to achieve almost the same error curves, depending on the bias used.
    Citation
    Ali, K. S. (2014). On Lattice Sequential Decoding for Large MIMO Systems. KAUST Research Repository. https://doi.org/10.25781/KAUST-PK1HO
    DOI
    10.25781/KAUST-PK1HO
    ae974a485f413a2113503eed53cd6c53
    10.25781/KAUST-PK1HO
    Scopus Count
    Collections
    MS Theses; Electrical and Computer Engineering Program; Computer, Electrical and Mathematical Science and Engineering (CEMSE) Division

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