Ant colony-based radio resource allocation for LTE-A systems with MIMO and CoMP transmission

Chung, Wen-Ching; Chen, Chien-Yuan; Huang, Chin-Ya; Chang, Chung‐Ju; Ren, Frank

doi:10.1109/iccchina.2013.6671215

Cited by 3 publications

(2 citation statements)

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“…The MVDR beamforming algorithm performance is affected by any change in the SOI signature vector, due to the fact that any imprecise change in the signature vector degrades the MVDR performance [1]. Therefore, the beamformer treats the SOI as interference and suppress it.…”

Section: Introductionmentioning

confidence: 99%

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Modifying MVDR Beamformer for Reducing Direction-of-Arrival Estimation Mismatch

Abdulrahman

Rahman

Hassnawi³

et al. 2015

Arab J Sci Eng

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The minimum variance distortionless response (MVDR) beamforming algorithm is used in smart antenna design for wireless communication. The operation of MVDR is based on finding the optimum weight to direct the main lobe beam to the desired user location with a unity gain. MVDR is very sensitive to signature vector mismatch. This mismatch occurs due to waveform deformation, local scattering, imperfect array element calibration and element shape distortion, which leads to errors in finding the direction of arrival (DOA) of the signal. In this paper, a new technique to modify the MVDR is presented, modelled and evaluated. The proposed algorithm is named modified MVDR (MMVDR) and is dependent on reconstructing the signature vector (steering vector) and the covariance matrix to introduce accurate beamformer weight by re-localization the reference element to be in the middle of ULA, rather than at one end side. The new reference position partitions the array's elements into two groups around this reference, which leads to treat received signals with identical phase along the array's elements, as well as increasing the degree of freedom to deals with different types of uniform arrays. The evaluation results show that MMVDR outperforms MVDR with respect to beamformer accuracy, system cost, processing time and signal classification to overcome the errors in DOA estimation which occur due to fabrication and calibration errors.

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Section: Introductionmentioning

confidence: 99%

“…These techniques depend on suppressing interference. The interference is an important factor that has an adverse impact on system capacity and system data rates [1]. Therefore, signal-to-interferenceplus-noise ratio (SINR) has become a significant factor in wireless communication systems.…”

Section: Introductionmentioning

confidence: 99%