5,747 research outputs found
Comparative Study of SVD and QRS in Closed-Loop Beamforming Systems
We compare two closed-loop beamforming algorithms, one based on singular
value decomposition (SVD) and the other based on equal diagonal QR
decomposition (QRS). SVD has the advantage of parallelizing the MIMO channel,
but each of the sub-channels has different gain. QRS has the advantage of
having equal diagonal value for the decomposed channel, but the subchannels are
not fully parallelized, hence requiring successive interference cancellation or
other techniques to perform decoding. We consider a closed-loop system where
the feedback information is a unitary beamforming matrix. Due to the discrete
and limited modulation set, SVD may have inferior performance to QRS when no
modulation set selection is performed. However, if the selection of modulation
set is performed optimally, we show that SVD can outperform QRS.Comment: Milcom 200
On-line Search History-assisted Restart Strategy for Covariance Matrix Adaptation Evolution Strategy
Restart strategy helps the covariance matrix adaptation evolution strategy
(CMA-ES) to increase the probability of finding the global optimum in
optimization, while a single run CMA-ES is easy to be trapped in local optima.
In this paper, the continuous non-revisiting genetic algorithm (cNrGA) is used
to help CMA-ES to achieve multiple restarts from different sub-regions of the
search space. The CMA-ES with on-line search history-assisted restart strategy
(HR-CMA-ES) is proposed. The entire on-line search history of cNrGA is stored
in a binary space partitioning (BSP) tree, which is effective for performing
local search. The frequently sampled sub-region is reflected by a deep position
in the BSP tree. When leaf nodes are located deeper than a threshold, the
corresponding sub-region is considered a region of interest (ROI). In
HR-CMA-ES, cNrGA is responsible for global exploration and suggesting ROI for
CMA-ES to perform an exploitation within or around the ROI. CMA-ES restarts
independently in each suggested ROI. The non-revisiting mechanism of cNrGA
avoids to suggest the same ROI for a second time. Experimental results on the
CEC 2013 and 2017 benchmark suites show that HR-CMA-ES performs better than
both CMA-ES and cNrGA. A positive synergy is observed by the memetic
cooperation of the two algorithms.Comment: 8 pages, 9 figure
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