Compressive Spectrum Sensing for MIMO-OFDM Based Cognitive Radio Networks Conference Paper uri icon


  • © 2015 IEEE. Multiple-input multiple-output orthogonal frequency division multiplexing (MIMO-OFDM) is considered to be one of the most promising technologies for further generation mobile communication systems like 3GPP LTE in recent years. At the same time, as a smart spectrum sharing technology, Cognitive Radio (CR) was also proposed to enhance the utilization of the spectrum usage. Thus, the combination of MIMO-OFDM and Cognitive Radio, MIMO-OFDM based Cognitive Radio technology is treated as a prospect scheme for future dynamic spectrum access network or spectrum sharing system. Since only a finite number of subcarriers are occupied by the primary users (PUs) in CR networks, the secondary users (SUs) can detect the spectrum holes (the unoccupied subcarriers) and opportunistically access those unoccupied spectrum subcarriers. Thus, spectrum sensing or detection is an important component for the implementation of CR. However, in traditional MIMO-OFDM system, the signals received in each antenna are sampled by an individual analog-to-digital converter (ADC), which will lead to a significantly increase of front-end cost for the whole system since multiple ADCs need to be adopted by corresponding to the multiple receiving antennas. Thus, the problem is how to design efficient receiving scheme for reducing the power consume and hardware cost in MIMO system. Considering the sparsity property of the received signals, we proposed a novel spectrum sensing scheme for the MIMO-OFDM based CR network by exploiting compressive sensing technology in this paper. Different to traditional MIMO-OFDM system, by exploiting the sparsity model, the signals received in our receivers are mixed together from multiple antennas and then sampled by a single ADC. Thus, the hardware cost and energy consumption can be significantly reduced in our scheme. Besides, our proposed scheme can detect the spectrum usage without the prior information of sparsity, which is also suitable for the real wireless application environment. Simulation results also show the effectiveness of our proposed scheme.

author list (cited authors)

  • Jin, S., & Zhang, X.

citation count

  • 6

publication date

  • March 2015