| Reference : Spectral efficient compressive transmission framework for wireless communication systems |
| Scientific journals : Article | |||
| Engineering, computing & technology : Electrical & electronics engineering | |||
| http://hdl.handle.net/10993/14603 | |||
| Spectral efficient compressive transmission framework for wireless communication systems | |
| English | |
Sharma, Shree Krishna  [University of Luxembourg > Interdisciplinary Centre for Security, Reliability and Trust (SNT) > >] | |
| Patwary, Mohammad [Staffordshire University > Faculty of Computing, Engineering & Sciences] | |
| Abdel-Maguid, Mohamed [University Campus Suffolk > School of Science, Technology and Health] | |
| 2013 | |
| IET Signal Processing | |
| Institution of Engineering & Technology | |
| Yes | |
| International | |
| 1751-9675 | |
| [en] Spectral Efficient System ; Compressive Sensing ; Quality of Service ; MIMO-OFDM | |
| [en] Increasing demand of high-speed data rate is leading to a challenging task to provide services to the users within exponentially growing market for wireless multimedia services. Subsequently, the available radio resources are becoming scarce because of different factors such as spectrum segmentation and dedicated frequency allocation to existing wireless
standards. Exploring new techniques for enhancing the spectral efficiency in wireless communication has been an important research challenge. In this study, the enhancement of spectral efficiency of wireless communication systems is considered. A framework is proposed to implement the concept of compressive sampling (CS) for compressing the natural random signals. The performance of proposed framework is evaluated in the context of multiple input multiple output orthogonal frequency division multiplexing system. Simulation-based results show that 25% of resources can be saved by marginal trade-off with the quality of service (QoS) requirement applying CS to the natural random signals. Furthermore, it can be claimed that this QoS trade-off can be optimised with dynamic selection of random measurement matrices. | |
| Researchers ; Professionals ; Students | |
| http://hdl.handle.net/10993/14603 |
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