Wideband spectrum sensing for cognitive radios requires very demanding analog-to-digital conversion (ADC) speed and dynamic range. In this paper, a mixed-signal parallel compressive sensing architecture is developed to realize wideband spectrum sensing for cognitive radios at sub-Nqyuist rates by exploiting the sparsity in current frequency usage. Overlapping windowed integrators are used for analog basis expansion, that provides flexible filter nulls for clock leakage spur rejection. A low-speed experimental system, built with off-the-shelf components, is presented. The impact of circuit nonidealities is considered in detail, providing insight for a future integrated circuit implementation. 1. Introduction Cognitive Radio (CR), first proposed in [1], provides a new paradigm to improve spectrum efficiency by enabling Dynamic Spectrum Access (DSA). In CR, spectrum holes that are unoccupied by primary users can be assigned to appropriate secondary users as long as the interference introduced by secondary users is not harmful to the primary users [2–4]. The design of cognitive radio networks is a complicated cross-layer procedure [5]. In this paper, we focus on the spectrum sensing problem in CR, in which sensing and detection of primary users is done in order to realize Dynamic Spectrum Access. Spectrum sensing can be a very challenging task for CR due to many factors. First, for the sake of improving the frequency usage efficiency, the sensing bandwidth for CR can expand from hundreds of MHz to several GHz. Second, the sensing radio should be able to detect very weak primary users, which arise due to fading and the hidden terminal problem [5]. With traditional time-domain Nyquist sampling, sensors are needed with both wide bandwidth and high dynamic range, stressing technology, and demanding higher power [6, 7]. Conventional wideband sensing with a high-speed and high-resolution ADC becomes less appealing as the bandwidth becomes significant. Alternative approaches, such as a fixed bank of analog filters followed by parallel ADCs, impose strict requirements on the filter design. It has been observed that today's spectrum usage presents some sparsity in the sense that only a small portion of the available frequency bands are heavily loaded while others are partially or rarely occupied [5]. This frequency usage sparsity can be exploited under the framework of Compressed Sensing (CS) [8, 9] to effectively reduce the sampling rate. The sparse signal can be captured via projection over a random basis that is incoherent with respect to the signal basis, and
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