## 1 Introduction  
In the most general sense, cognitive radio takes advantage of the Moore's law to capitalize on the computational power of the semiconductor industry. When information is accessible in digital domain, the force driver behind this novel radio is computationally intelligent algorithms. Machine learning and artificial intelligence have become the new frontier toward this vision--analogy of robotics. Converting information from analog domain to digital domain plays a central role in this vision: revolutionary compressed sensing is, therefore, critical to expanding the territory of this new system paradigm. The agile, software defined radios that can perform according to algorithms are basic building blocks. When each node is computationally intelligent, wireless networking faces a novel revolution. At the system level, functions such as cognitive radio, cognitive radar and anti-jamming (even electronic warfare) have no fundamental difference and are unified into a single framework that requires inter-disciplinary knowledge. Radar and communications should be unified since both require dynamic spectrum access--the bottleneck. Spectrum agile/cognitive radio is a new paradigm in wireless communications--a special application of the above general radio. Spectrum agile/cognitive radio is a new paradigm in wireless communications [1], as illustrated by DARPA XG radio [2] in Figure 1. Cognitive radios can opportunistically use spectrum white space and increase usage by ten times [3]. One ingredient of this paper is to investigate a novel, wideband (multi-GHz) system architecture enabled by compressive sampling (or compressed sensing)--a revolutionary breakthrough in applied mathematics and signal processing. The other is to design multi-GHz spectrum sensing and experimental system testbeds. These ingredients share the same goal of bringing together three separate system paradigms: cognitive radio, cognitive radar and electronic warfare. The Department of Defense (DoD) is transforming the military into a more responsive digitized force capable of of rapidly deploying and effectively operating in all types of military operations, which makes an intensive information network critical [4; 5; 6]. Wireless sensor networks in Figure 2 is such an example [7]. A 2003 Congressional Budget Office report [8] concluded: \"current demand within the Army is larger than the supply by _an order of magnitude_ and these shortfalls will continue into and after 2010 with shortage as high as _30 times_ at some command levels.\" To solve this bandwidth shortage, improvements in spectrum usage are required. These bandwidth shortages take place even though a vast amount of the allocated spectrum is virtually unused or under-used. This paradox results from the current static and inefficient allocation process. In response, the Federal Communication Commission (FCC) [9] and US DoD [10] recently issued separate challenges to address the poor efficiency of static spectrum assignment in licensed bands. A recent study conducted by Shared Spectrum shows that average spectrum occupancy in the frequency band from 300 MHz to 3000 MHz over multiple locations is merely 5.2 \(\%\). The maximum occupancy is about 13\(\%\) in New York City [11; 12]. It can be found that the spectrum scarcity is mostly caused by the fixed assignment to the wireless service operators, and there exist spectrum opportunities both spatially and temporally. Therefore, the interest in allowing access to unutilized spectrum by unlicensed user (second user) has been growing in several regulatory bodies and standardization groups, e.g., the FCC and IEEE 802.22--the first complete cognitive radio-based international standard [13]. In particular, the spectrum scarcity is the most severe problem for US for wireless services, partially due to the fact that US has the densest spectrum usage. There is a common belief that we are running out of usable radio frequencies.