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Abstract

Under the scenario of an underlay cognitive radio network, we introduce the concept of minimum-selection maximum ratio transmission (MS-MRT). Inspired by the mode of operation of the minimum-selection generalized selection combining (MS-GSC) technique, the main idea behind MS-MRT is to present an adaptive variation of the existing maximum ratio transmission (MRT) technique. While in the MRT scheme, all the transmit antennas are used for transmission, and only a subset of antennas verifying the interference constraint to the primary receiver in MS-MRT are adaptively selected and optimally beamformed in order to meet a given modulation requirement. The main goal of these schemes is to maximize the capacity of the secondary link while satisfying the bit error rate (BER) requirement and a peak interference constraint to the primary link. The performance of the proposed schemes is analyzed in terms of the average spectral efficiency, the average number of antennas used for transmission, the average delay, and the average BER performance. These results are then compared to the existing bandwidth efficient and switching efficient schemes (BES and SES, respectively). The obtained analytical results are then verified with selected numerical examples obtained via Monte-Carlo simulations. We demonstrate through these examples that the proposed schemes improve the spectral and the delay performance of the SES and BES schemes and fit better to delay sensitive applications. The proposed schemes also offer better processing-power consumption than the MRT schemes since a minimum number of antennas is used for communication in the MS-MRT schemes. The MS-MRT techniques represent power and spectral efficient schemes that can be extended to more practical scenarios. As an example, these schemes can be studied in the context of Long term Evolution (LTE) Networks where adaptive modulation, beamforming, and interference management are of the major enabling Techniques.

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/content/papers/10.5339/qfarf.2012.CSPS14
2012-10-01
2019-11-16
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http://instance.metastore.ingenta.com/content/papers/10.5339/qfarf.2012.CSPS14
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