USRP2 IMPLEMENTATION OF COMPRESSIVE SENSING BASED CHANNEL ESTIMATION IN OFDM

Radio channel impairment is a major concern in any wireless system. Channel estimation is performed at the receiver to obtain the channel response in order to calculate the multipath channel effects. However, the traditional way of using pilots for channel estimation has a tradeoff between spectral efficiency and estimation accuracy. An increasing amount of research is being done on a novel signal processing technique called compressive sensing and its applications in the modern day wireless systems for channel estimation. In this thesis, we can exploit the sparsity of the time domain channel by choosing the pilots randomly and building a random projection measurement matrix. This approach improves the channel estimation accuracy by conserving the bandwidth. This thesis investigates various modulation schemes in an open source software based radio development kit, GNU Radio for a wireless system and build a compressed sensing based channel estimator for the OFDM module on a Universal Software Radio Peripheral 2 (USRP2). Simulations for compressed channel sensing are conducted to prove the effectiveness over traditional channel estimation. The time domain based compressed channel estimator is implemented as a signal processing package in GNU Radio, and performance studies are done in the real-time system.