## 11 Vision and Future Work  
There is a trend to integrate cognitive radio with cognitive radar, together with anti-jamming capabilities. The advent of multi-GHz arbitrary waveform generators and the need for cognitive radio make this integration attractive. The multi-GHz waveform provides super anti-jamming capabilities. The objective of this proposal is to investigate a novel paradigm of integrating the three ingredients; the multi-GHz waveform (through the use of revolutionary compressive sampling) is jointly considered with the dynamic spectrum access (through a novel system architecture for spectrum sensing). The primary challenge is caused by the wideband (multi-GHz) nature of the problem at hand.  
One of the central tenets of communications is the Shannon/Nyquist sampling theory, which states that the number of samples required to capture a signal is dictated by its bandwidth. It is well known today, however, the Nyquist rate is a sufficient but by no means necessary condition. Compressive sampling or compressed sensing (CS) enables of the faithful recovery of signals, images, and other data, from what appear to be highly sub-Nyquist-rate samples. At the heart of the new approach are two crucial observations. (1) The Shannon/Nyquist signal representation exploits only minimal prior knowledge about the signal being sampled, namely its bandwidth. Most objects of our interest, however, are structured and depend upon a smaller number of _degrees of freedom_ than the bandwidth suggests. In other words, most objects are _sparse_ or _compressible_ in the sense that they can be encoded with just a few numbers without much numerical or perceptual loss. (2) The useful information content in compressible signals can be captured via sampling protocols that directly condense signals into a small amount of data. In short, and in stark contrast with conventional wisdom, the theory of CS asserts that one can combine \"low-rate sampling\" with computation power for efficient and accurate signal acquisition.  
On the other hand, at the heart of this cognitive radio, there is spectrum sensing: narrowband and wideband. The narrowband spectrum sensing--represented by IEEE 802.22--is mature and adopted by the Federal Communication Commission (FCC). The wideband spectrum sensing, in particular multi-GHz, seems be in its infancy. The FCC has abandoned the concept of interference temperature that may be a candidate. As a result, the proposed research may have potential impact on the future policy on spectrum sharing for wideband cognitive radio.  
Roughly speaking, if there is _a prior_ information of the primary radio such as modulation format, pilot, symbol rate, etc., spectrum sensing--that enables the secondary radio for dynamic spectrum access (DSA)--can be implemented using approaches such as matched filter, energy detection, cylcostationary sensing, eigenvalue based sensing, etc. For wideband (multi-GHz) spectrum sensing, however, there is no practical way to locate unused white spectra. Another critical challenge is wideband RF front-end capable of simultaneous sensing of several GHz wide spectrum.  
It is critical to test key system components in different system settings. Three system models are proposed: (1) MATLAB/C simulation model, (2) waveform model, and (3) real-time FPGA system model. The majority of the research results are obtained in the domain of MATLAB/C simulation model. This approach is simple. But many real-world limitations cannot be simulated. The unique approach of this proposal is to combine these three models. Real-time FPGA model is the ultimate test, but time-consuming. We will, thus, use this model when the system concept is very stable. As a result, most system emulations are based on the waveform model. This waveform model is made available only recently, with the latest A/D conversion for 9.6 GHz signals with 10-bit resolution. The TTU's lab is fortunate to be awarded the NSF MRI grant that makes this possible.