A better signal recovery method for an urban environment

Abstract

Communication systems have had such a profound impact on every aspect of humanity. Wireless broadband infrastructures with high throughput and improved spectral efficiency should ensure greater networking and services throughout all areas. Multiple input multiple output orthogonal frequency division multiplexing (MIMO-OFDM) has excessive bandwidth capacity compared with other techniques; also it can be used in high-frequency applications and fast wireless environments. However, accurate channel estimation is a major problem. Compressive sensing is one of the latest techniques that is paying an overwhelming response to wireless communication networks. This can overcome the channel estimation drawbacks of MIMO-OFDM. Compressed sensing's extraordinary qualities have prompted scholars to use it in a variety of certain other domains, and its innumerable applications have a key role in wireless systems. In Compressive sensing, a priori aided compressive sampling matching pursuit (PA-CoSaMP) algorithm is used here to ensure good performance and to optimize the exact mean squared error rate information. This paper gives an overview of MIMO-OFDM, compressive sensing, and the performance evaluation of the proposed algorithm with OFDM. Simulation results gives the recovered signal details and comparison of error rate performance of PA-CoSaMP and conventional OFDM techniques.