Impact of transform coding in compressive sensing based wideband cognitive radios

In the sprouting paradigm of interoperable radio networks, wideband spectrum sensing is a challenging task for analog-to-digital converters (ADC) incorporated at the prevailing wireless radio systems because of the necessities of high sampling rates functioning at or above Nyquist frequencies. In order to cope with current ADCs, compressive sampling (CS), a promising scheme in signal processing arena, can be employed to search for the spectrum holes in the sparse wideband signals which are then opportunistically used by the cognitive radios (CR). In CS, transform coding as well as measurement matrix selection is an essential tool and it plays a vital role in the acquisition of wideband signals which comes out with a few number of random measurements. In this paper, two types of transform coding (Discrete Cosine Transform, DCT and Discrete Walsh-Hadamard Transform, WHT) is analyzed in the context of sparse wideband estimation via a well-known CS approach e.g., l₁-norm optimization problem which could be used for spectrum sensing in wideband CR. Through the engagement of those measurement matrices, detection performance, execution time to sense PU bands and achievable capacity are investigated and analyzed at a single CR node. Finally, WHT coded CS scheme has been proposed for the wideband CR spectrum sensing as the simulation results arrange for the validation of our choice.