KAUST DepartmentComputer, Electrical and Mathematical Sciences and Engineering (CEMSE) Division
Electrical Engineering Program
Young Talent Development
Communication Theory Lab
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AbstractDue to their ability to provide high data rates, multiple-input multiple-output (MIMO) systems have become increasingly popular. Decoding of these systems with acceptable error performance is computationally very demanding. In this paper, we employ the Sequential Decoder using the Fano Algorithm for large MIMO systems. 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. Numerical results are done that show moderate bias values result in a decent performance-complexity trade-off. We also attempt to bound the error by bounding the bias, using the minimum distance of a lattice. The variations in complexity with SNR have an interesting trend that shows room for considerable improvement. Our work is compared against linear decoders (LDs) aided with Element-based Lattice Reduction (ELR) and Complex Lenstra-Lenstra-Lovasz (CLLL) reduction. © 2014 IFIP.
Journal2014 12th International Symposium on Modeling and Optimization in Mobile, Ad Hoc, and Wireless Networks (WiOpt)
Conference/Event name2014 12th International Symposium on Modeling and Optimization in Mobile, Ad Hoc, and Wireless Networks, WiOpt 2014