Patent Publication Number: US-7710919-B2

Title: Systems, methods, and apparatuses for spectrum-sensing cognitive radios

Description:
CROSS REFERENCE TO RELATED APPLICATIONS 
   This application claims priority to U.S. Provisional Application No. 60/729,036, filed Oct. 21, 2005, entitled “Systems, Methods, and Apparatuses for Spectrum-Sensing Cognitive Radios” and U.S. Provisional Application No. 60/729,035, filed Oct. 21, 2005, entitled “Systems, Methods, and Apparatuses for Spectrum-Sensing Modules,” which are both incorporated herein by reference in their entirety. In addition, this application is related to the following co-pending, commonly assigned U.S. applications, each of which is entirely incorporated herein by reference: “Systems, Methods, and Apparatuses for Coarse-Sensing Modules” filed Jul. 18, 2006, and accorded application Ser. No. 11/458,275 and “Systems, Methods, and Apparatuses for Fine-Sensing Modules,” filed Jul. 18, 2006, and accorded application Ser. No. 11/458,280. 

   FIELD OF THE INVENTION 
   The present invention relates generally to cognitive radios, and more particularly to spectrum-sensing cognitive radios. 
   BACKGROUND OF THE INVENTION 
   In the United States and in a number of other countries, a regulatory body like the FCC (Federal Communications Commission) oftentimes regulates and allocates the use of radio spectrum in order to fulfill the communications needs of entities such as businesses and local and state governments as well as individuals. More specifically, the FCC licenses a number of spectrum segments to entities and individuals for commercial or public use (“licensees”). These licensees may have an exclusive right to utilize their respective licensed spectrum segments for a particular geographical area for a certain amount of time. Such licensed spectrum segments are believed to be necessary in order to prevent or mitigate interference from other sources. However, if particular spectrum segments are not in use at a particular location at a particular time (“the available spectrum”), another device should be able to utilize such an available spectrum for communications. Such utilization of the available spectrum would make for a much more efficient use of the radio spectrum or portions thereof. 
   Previous spectrum-sensing techniques disclosed for determining the available spectrum have been met with resistance for at least two reasons: (1) they either do not work for sophisticated signal formats or (2) they require excessive hardware performances and/or computation power consumption. For example, a spectrum sensing technique has been disclosed where a non-coherent energy detector performs a computation of a Fast Fourier Transform (FFT) for a narrow-band input signal. The FFT provides the spectral components of the narrow-band input signal, which are then compared with a predetermined threshold level to detect a meaningful signal reception. However, this predetermined threshold level is highly-affected by unknown and varying noise levels. Moreover, the energy detector does not differentiate between modulated signals, noise, and interference signals. Thus, it does not work for sophisticated signal formats such as spread spectrum signal, frequency hopping, and multi-carrier modulation. 
   As another example, a cyclo-stationary feature detection technique has been disclosed as a spectrum-sensing technique that exploits the cyclic features of modulated signals, sinusoid carriers, periodic pulse trains, repetitive hopping patterns, cyclic prefixes, and the like. Spectrum correlation functions are calculated to detect the signal&#39;s unique features such as modulation types, symbol rates, and presence of interferers. Since the detection span and frequency resolution are trade-offs, the digital system upgrade is the only way to improve the detection resolution for the wideband input signal spectrum. However, such a digital system upgrade requires excessive hardware performances and computation power consumption. Further, flexible or scalable detection resolution is not available without any hardware changes. 
   Accordingly, there is a need in the industry for cognitive radios that allow the available spectrum to be utilized while minimizing hardware and power consumption requirements. 
   SUMMARY OF THE INVENTION 
   According to an embodiment of the present invention, there is a cognitive radio for utilizing available frequency spectrum resources. The cognitive radio may include spectrum sensing in conjunction with frequency-agile operation. The spectrum sensing, which may be comprised of coarse and/or fine spectrum sensing, may be performed in analog in accordance with an embodiment of the invention. The coarse-sensing technique may utilize wavelet transforms to provide a multi-resolution sensing feature known as Multi-Resolution Spectrum Sensing (MRSS), according to an embodiment of the present invention. The fine spectrum sensing technique may utilize the beneficial properties of the autocorrelation function to provide for Analog Auto-Correlation (AAC), according to an embodiment of the present invention. Spectrum sensing in accordance with an embodiment of the present invention may thus detect a variety of sophisticated signal formats adopted in the current and emerging wireless standards including IS-95, WCDMA, EDGE, GSM, Wi-Fi, Wi-MAX, Zigbee, Bluetooth, digital TV (ATSC, DVB), and the like. Moreover, the analog implementation of these spectrum sensing techniques in accordance with an embodiment of the present invention offers several features, including one or more of fast detection for a wideband frequency range, low power consumption, and low hardware complexity. 
   In accordance with an embodiment of the present invention, there is a cognitive radio system. The cognitive radio system includes at least one antenna, a radio front-end module in communication with the at least one antenna and configured to transmit and receive radio frequency signals via the at least one antenna, and a spectrum-sensing module in communication with the at least one antenna and configured to generate radio frequency (RF) spectrum usage information. The cognitive radio system further includes a medium access control (MAC) module operative to receive the RE spectrum usage information from the spectrum-sensing module, where the MAC module is operative to direct a frequency of operation of the radio front-end module based at least in part on the received spectrum usage information. 
   According to an aspect of the present invention, the at least one antenna may include a first antenna and a second antenna, where the radio front-end module may be in communication with the first antenna and the spectrum-sensing module may be in communication with the second antenna. According to another aspect of the present invention, the spectrum-sensing module may include at least one of a coarse-sensing module and a fine-sensing module, where the coarse-sensing module provides spectrum usage information associated with spectrum occupancy and the fine-sensing module provides spectrum usage information associated with signal identification. The spectrum usage information associated with signal identification may relate to at least one of modulation schemes and frame types. The spectrum sensing module may generate spectrum usage information associated with spectrum occupancy and upon a request by the MAC module, may generate spectrum usage information associated with signal identification. According to another aspect of the present invention, the cognitive radio system may further include an analog-to-digital converter that digitizes the generated spectrum usage information, where the digitized spectrum usage information may received by the medium access control module. According to yet another aspect of the present invention, the spectrum-sensing module may be an analog, wideband spectrum-sensing module. 
   According to another embodiment of the present invention, there is a method for implementing a cognitive radio system. The method includes receiving radio signals at a spectrum-sensing module and generating via the spectrum-sensing module, spectrum information based at least in part on the received radio signals. The method further includes receiving the spectrum information at a medium access control module, determining via the medium access control module, a non-occupied spectrum based at least in part upon the spectrum information, and reconfiguring a transceiver to operate in the non-occupied spectrum. 
   According to an aspect of the present invention, generating spectrum information may include generating information for determining spectrum occupancy. According to another aspect of the present invention, generating spectrum information may include generating information for signal identification upon a request from the medium access control module. In addition, the spectrum sensing module may include a module that generates the information for determining spectrum occupancy. The spectrum sensing module may also include a module that generates the information for signal identification. According to yet another aspect of the present invention, receiving radio signals at a spectrum-sensing module comprises receiving radio signals at an analog, wideband spectrum-sensing module. 
   According to yet another embodiment of the present invention, there is a spectrum sensing system. The spectrum sensing system includes a coarse-sensing module that utilizes a wavelet transform to generate a signal indicative of spectrum usage, a spectrum recognition module in communication with the coarse sensing module that determines an occupied spectrum segment based at least partially on the signal from the course sensing module, and a fine-sensing module that generates a signal indicative of a feature of a signal type for an occupied spectrum segment identified by the spectrum recognition module. 
   According to an aspect of the present invention, the system further includes at least one analog-to-digital converter for digitizing the signal indicative of spectrum usage and the signal indicative of a feature of the signal type, where the digitized signal indicative of spectrum usage and the digitized signal indicative of a feature of the signal type may be provided to the spectrum recognition module. According to another aspect of the present invention, the fine-sensing module may generate a signal indicative of a feature of a signal type based at least in part on a periodic nature of an input signal. The feature of the system type may be associated with at least one of a modulation format and frame structure of the input signal. According to another aspect of the present invention, the spectrum recognition module may be in communication with the fine-sensing module. Likewise, the spectrum recognition module may determine a non-occupied segment based at least partially on the signal from the fine-sensing module. According to yet another embodiment of the present invention, at least one of the coarse-sensing module and the fine-sensing module may operate in an analog domain. 

   
     BRIEF DESCRIPTION OF THE SEVERAL VIEWS OF THE DRAWING(S) 
     Having thus described the invention in general terms, reference will now be made to the accompanying drawings, which are not necessarily drawn to scale, and wherein: 
       FIG. 1  illustrates a functional block diagram of an exemplary cognitive radio system in accordance with an embodiment of the present invention. 
       FIG. 2  illustrates an exemplary flowchart of the cognitive radio system of  FIG. 1 . 
       FIG. 3  illustrates a tradeoff between a wavelet pulse width and a wavelet pulse frequency in accordance with an embodiment of the present invention. 
       FIG. 4A  illustrates a block diagram for an exemplary Multi-Resolution Spectrum Sensing (MRSS) implementation in accordance with an embodiment of the present invention. 
       FIG. 4B  illustrates an example of scalable resolution control in accordance with an embodiment of the present invention. 
       FIG. 5A  illustrates the waveform of a two-tone signal and  FIG. 5B  illustrates the corresponding spectrum to be detected with the MRSS implementation in accordance with an embodiment of the present invention. 
       FIG. 6  illustrates a waveform of the chain of wavelet pulses in accordance with an embodiment of the present invention. 
       FIG. 7A  illustrates the I-component waveform of the I-Q sinusoidal carrier, and  FIG. 7B  illustrates the Q-component waveform of the I-Q sinusoidal carrier in accordance with an embodiment of the present invention. 
       FIG. 8A  illustrates modulated wavelet pulses obtained from a wavelet generator with an I-component of an I-Q sinusoidal carrier in accordance with an embodiment of the present invention. 
       FIG. 8B  illustrates the modulated wavelet pulses obtained from a wavelet generator with a Q-component of an I-Q sinusoidal carrier in accordance with an embodiment of the present invention. 
       FIG. 9A  illustrates a correlation output signal waveform for the input signal with the I-component of the I-Q sinusoidal carrier in accordance with an embodiment of the present invention. 
       FIG. 9B  illustrates the correlation output signal waveform for the input signal with the Q-component of the I-Q sinusoidal carrier in accordance with an embodiment of the present invention. 
       FIG. 10A  illustrates sampled values via the integrator and the analog-to-digital converter for the correlation values with the I-component of the wavelet waveform within given intervals in accordance with an embodiment of the present invention. 
       FIG. 10B  illustrates sampled values via the integrator and the analog-to-digital converter for the correlation values with the Q-component of the wavelet waveform within given intervals in accordance with an embodiment of the present invention. 
       FIG. 11  illustrates an exemplary spectrum shape detected by the spectrum recognition module in the MAC module in accordance with an embodiment of the present invention. 
       FIGS. 12-17  illustrate simulations of various signal formats detected by MRSS implementations in accordance with embodiments of the present invention. 
       FIG. 18  illustrates an exemplary circuit diagram of the coarse sensing module in accordance with an embodiment of the present invention. 
       FIG. 19  illustrates a functional block diagram of an exemplary fine-sensing technique utilizing the AAC function in accordance with an embodiment of the present invention. 
       FIG. 20A  illustrates an exemplary data OFDM symbol followed by a preamble in accordance with an embodiment of the present invention. 
       FIG. 20B  illustrates the spectrum of an input IEEE802.11a signal to be detected with an AAC implementation in accordance with an embodiment of the present invention. 
       FIG. 21A  illustrates an input IEEE802.11a signal and  FIG. 21B  illustrates a delayed IEEE 802.11a signal in accordance with an embodiment of the present invention. 
       FIG. 22  illustrates a waveform of a correlation between the original input signal and the delayed signal in accordance with an embodiment of the present invention. 
       FIG. 23  illustrates a waveform produced by an integrator in accordance with an embodiment of the present invention. 
       FIG. 24  illustrates an exemplary configuration for a frequency-agile radio front-end in accordance with an embodiment of the present invention. 
   

   DETAILED DESCRIPTION OF THE INVENTION 
   The present invention now will be described more fully hereinafter with reference to the accompanying drawings, in which some, but not all embodiments of the invention are shown. Indeed, these inventions may be embodied in many different forms and should not be construed as limited to the embodiments set forth herein; rather, these embodiments are provided so that this disclosure will satisfy applicable legal requirements. Like numbers refer to like elements throughout. 
   Embodiments of the present invention relate to cognitive radio systems, methods, and apparatuses for exploiting limited spectrum resources. The cognitive radios may allow for negotiated and/or opportunistic spectrum sharing over a wide frequency range covering a plurality of mobile communication protocols and standards. In accordance with the present invention, embodiments of the cognitive radio may be able to intelligently detect the usage of a segment in the radio spectrum and to utilize any temporarily unused spectrum segment rapidly without interfering with communications between other authorized users. The use of these cognitive radios may allow for a variety of heterogeneous wireless networks (e.g., using different communication protocols, frequencies, etc.) to coexist with each other. These wireless networks may include cellular networks, wireless personal area networks (PAN), wireless local area networks (LAN), and wireless metro area networks (MAN). These wireless networks may also coexist with television networks, including digital TV networks. Other types of networks may be utilized in accordance with the present invention, as known to one of ordinary skill in the art. 
   A. System Overview of Cognitive Radios 
     FIG. 1  illustrates a functional block diagram of an exemplary cognitive radio system in accordance with an embodiment of the present invention. In particular,  FIG. 1  illustrates a cognitive radio  100  that includes an antenna  116 , a transmit/receive switch  114 , a radio front end  108 , an analog wideband spectrum-sensing module  102 , an analog to digital converter  118 , a signal processing module  126 , and a medium access control (MAC) module  124 . 
   During operation of the cognitive radio system of  FIG. 1 , which will be discussed in conjunction with the flowchart of  FIG. 2 , radio frequency (RF) input signals may be received by the antenna  116 . In an exemplary embodiment of the present invention, the antenna  116  may be a wideband antenna operable over a wide frequency range, perhaps from several megahertz (MHz) to the multi-gigahertz (GHz) range. The input signals received by the antenna  116  may be passed or otherwise provided to the analog wideband spectrum-sensing module  102  via the transmit/receive switch  114  (block  202 ). The spectrum-sensing module  102  may include one or both of a coarse-sensing module  104  and a fine-sensing module  106 . As their names suggest, the coarse-sensing module  104  may detect the existence or presence of suspicious spectrum segments (e.g., potentially utilized spectrum segments) while the fine-sensing module  106  may scrutinize or otherwise analyze the detected suspicious spectrum segments to determine the particular signal types and/or modulation schemes utilized therein. 
   Referring back to  FIG. 2 , the coarse-sensing module  104  may initially determine the spectrum occupancy for the received input signal (block  204 ). The spectrum occupancy information may be converted from analog form to digital form by the analog-to-digital (A/D) converter  118 , which may be a low-speed A/D converter (ADC) in an exemplary embodiment of the present invention. The digital spectrum occupancy information provided by the A/D converter  118  may be received by the spectrum recognition module  120  in the medium access control (MAC) module  124 . The spectrum recognition module  120  may perform one or more calculations on the digital spectrum occupancy information to recognize whether one or more spectrum segments are currently in use or occupied by others. The spectrum recognition module  120  may be implemented in hardware, software, or a combination thereof. 
   In some instances, based on the recognized spectrum segments, the MAC module  124  may request a more refined search of the spectrum occupancy (block  206 ). In such a case, the fine-sensing module  106  may be operative to identify the particular signal types and/or modulation schemes utilized within at least a portion of the spectrum occupancy (block  208 ). The information identifying the signal types and/or modulation schemes may then be digitized by the A/D converter  118 , and provided to the spectrum recognition module  120 . Information about the signal type and/or modulation scheme may be necessary to determine the impact of interferers within the detected suspicious spectrum segments. 
   In accordance with an embodiment of the present invention, the spectrum recognition module  120  may compare information from the coarse-sensing module  104  and/or fine-sensing module  106  with a spectrum usage database (block  210 ) to determine an available (e.g., non-occupied or safe) spectrum slot (block  212 ). The spectrum usage database may include information regarding known signal types, modulation schemes, and associated frequencies. Likewise, the spectrum usage database may include one or more thresholds for determining whether information from the coarse-sensing module  104  and/or fine-sensing module  106  is indicative of one or more occupied spectrum. According to an exemplary embodiment of the present invention, the spectrum usage database may be updated based upon information received from an external source, including periodic broadcasts form a base station or other remote station, removable information stores (e.g., removable chips, memory, etc.), Internet repositories. Alternatively, the spectrum usage database may be updated based upon internally, perhaps based upon adaptive learning techniques that may involve trial and error, test configurations, statistical calculations, etc. 
   The sensing results determined by the spectrum recognition module  120  may be reported to the controller (e.g., spectrum allocation module) of the MAC module  124 , and permission may be requested for a particular spectrum use (block  214 ). Upon approval from the controller (block  216 ), the reconfiguration block of the MAC module  124  may provide reconfiguration information to the radio front end  108  via the signal processing module  126  (block  218 ). In an exemplary embodiment of the present invention, the radio front-end  108  may be reconfigurable to operate at different frequencies (“frequency-agile”), where the particular frequency or frequencies may depend upon the selected spectrum segments for use in communications by the cognitive radio  100 . In conjunction with the frequency-agile front-end  108 , the signal processing module  126 , which may be a physical layer signal processing block in an exemplary embodiment, may enhance the cognitive radio&#39;s  100  performance with adaptive modulation and interference mitigation technique. 
   Many modifications can be made to the cognitive radio  100  without departing from embodiments of the present invention. In an alternative embodiment, the antenna  116  may comprise at least two antennas. A first antenna may be provided for the radio front end  108  while a second antenna may be provided for the spectrum sensing module  102 . The use of at least two antennas may remove the necessity of a transmit/receive switch  114  between the radio front end  108  and the spectrum-sensing module  102  according to an exemplary embodiment. However, in another embodiment of the present invention, a transmit/receive switch  114  may still be needed between the transmitter  110  and the receiver  112  of the radio front end  108 . In addition, the spectrum sensing module  102 , the A/D converter  118 , and the MAC module  124  may remain in operation even where the radio front end  108  and signal processing module  126  are not operational or are on standby. This may reduce the power consumption of the cognitive radio  100  while still allowing the cognitive radio  100  to determine the spectrum occupancy. 
   Having described the cognitive radio  100  generally, the operation of the components of the cognitive radio  100  will now be described in further detail. 
   B. Spectrum-Sensing Components 
   Still referring to  FIG. 1 , the spectrum-sensing module  102  may include the coarse-sensing module  104  and a fine-sensing module  106 , according to an exemplary embodiment of the present invention. However, other embodiments of the present invention may utilize only one of the spectrum-sensing module  102  or the coarse-sensing module  104  as necessary. In addition, while the spectrum-sensing module  102  has been illustrated as a component of an exemplary cognitive radio  100 , such a spectrum-sensing module  102  may be embodied in a different device and utilized as an efficient method for determining the available spectrum in alternative applications. These alternative applications may include wireless personal area networks (PANs), wireless local area networks (LANs), wireless telephones, cellular phones, digital televisions, mobile televisions, and global positioning systems. 
   Now referring to the spectrum-sensing module  102  of  FIG. 1 , spectrum-sensing module  102  may include the coarse the coarse-sensing module  104  and the fine-sensing module  106 , which may be utilized together to enhance the accuracy of the spectrum detection performance by the MAC module  124 . In addition, according to an embodiment of the present invention, the spectrum-sensing module  102  may be implemented in an analog domain, which may offer several features. For example, such a spectrum-sensing module  102  implemented in the analog domain may provide for fast detection for a wideband frequency range, low power consumption, and low hardware complexity. The coarse-sensing module  104  and the fine-sensing module  106  of the spectrum-sensing module  102  will now be discussed in further detail below. 
   1. Coarse-Sensing Module 
   In accordance with an exemplary embodiment of the present invention, the coarse-sensing module  104  may utilize wavelet transforms in providing a multi-resolution sensing feature known as Multi-Resolution Spectrum Sensing (MRSS). The use of MRSS with the coarse-sensing module  104  may allow for a flexible detection resolution without requiring an increase in the hardware burden. 
   With MRSS, a wavelet transform may be applied to a given time-variant signal to determine the correlation between the given time-variant signal and the function that serves as the basis (e.g., a wavelet pulse) for the wavelet transform. This determined correlation may be known as the wavelet transform coefficient, which may be determined in analog form according to an embodiment of the present invention. The wavelet pulse described above that serves as the basis for the wavelet transform utilized with MRSS may be varied or configured, perhaps via the MAC module  124 , according to an embodiment of the present invention. In particular, the wavelet pulses for the wavelet transform may be varied in bandwidth, carrier frequency, and/or period. By varying the wavelet pulse width, carrier frequency, and/or period, the spectral contents provided through the wavelet transform coefficient for the given signal may be represented with a scalable resolution or multi-resolution. For example, by varying the wavelet pulse width and/or carrier frequency after maintaining them within a certain interval, the wavelet transform coefficient may provide an analysis of the spectral contents of the time-variant signals in accordance with an exemplary embodiment of the present invention. Likewise, the shape of the wavelet pulse may be configurable according to an exemplary embodiment of the present invention. 
   a. Wavelet Pulse Selection 
   The selection of the appropriate wavelet pulse, and in particular the width and carrier frequency for the wavelet pulse, for use in MRSS will now be described in further detail.  FIG. 3  illustrates the tradeoff between the wavelet pulse width (Wt)  302  and the wavelet pulse frequency (Wf)  304  (e.g., also referred herein as the “resolution bandwidth”) that may be considered when selecting an appropriate wavelet pulse. In other words, as the wavelet pulse width  302  increases, the wavelet pulse frequency  304  generally decreases. As shown in  FIG. 3 , the wavelet pulse width  302  may be inversely proportional to the wavelet pulse frequency  304 . 
   In accordance with an embodiment of the present invention, an uncertainty inequality may be applied to the selection of a wavelet pulse width (Wt)  302  and resolution bandwidth (Wf)  304 . Generally, the uncertainty inequality provides bounds for the wavelet pulse width (Wt)  302  and resolution bandwidth (Wf)  304  for particular types of wavelet pulses. An uncertainty inequality may be utilized where the product of the wavelet pulse width (Wt)  302  and the resolution bandwidth (Wf)  304  may be greater than or equal to 0.5 (i.e., Wt*Wf≧0.5). Equality may be reached where the wavelet pulse is a Gaussian wavelet pulse. Thus, for a Gaussian wavelet pulse, the wavelet pulse width (Wt)  302  and the resolution bandwidth (Wf)  304  may be selected for use in the wavelet transform such that their product is equal to 0.5 according to the uncertainty inequality. 
   While the Gaussian wavelet pulses have been described above for an illustrative embodiment, other shapes of wavelet pulses may be utilized, including from the Hanning, Haar, Daubechies, Symlets, Coifets, Bior Splines, Reverse Bior, Meyer, DMeyer, Mexican hat, Morlet, Complex Gaussian, Shannon, Frequency B-Spline, and Complex Morlet wavelet families. 
   b. Block Diagram for MRSS Implementation 
     FIG. 4A  illustrates a block diagram for an exemplary Multi-Resolution Spectrum Sensing (MRSS) implementation that includes a coarse-sensing module  104 . In particular, the coarse-sensing module may receive a time-variant RF input signal x(t) from the antenna  116 . According to an exemplary embodiment of the present invention, this RF input signal x(t) may be amplified by an amplifier  402  before being provided to the coarse sensing module  104 . For example, the amplifier  402  may be a driver amplifier, which may be operative to provide for consistent gain across a wide frequency range. 
   Referring to the coarse-sensing module  104  of  FIG. 4A , the coarse-sensing module  104  may be comprised of an analog wavelet waveform generator  404 , an analog multiplier  406 , an analog integrator  408 , and a timing clock  410 . The timing clock  410  may provide timing signals utilized by the wavelet generator  404  and the analog integrator  408 . Analog correlation values may be provided at the output of the analog integrator  408 , which may in turn be provided to an analog-to-digital converter (ADC)  118 , which may be low-speed according to an exemplary embodiment of the present invention. The digitized correlation values at the output of the ADC  118  may be provided to the medium access control (MAC) module  124 . 
   Still referring to  FIG. 4A , the wavelet generator  404  of the coarse-sensing module  104  may be operative to generate a chain of wavelet pulses v(t) that are modulated to form a chain of modulated wavelet pulses w(t). For example, the chain of wavelet pulses v(t) may be modulated with I- and Q-sinusoidal carriers f LO (t) having a given local oscillator (LO) frequency. With the I- and Q-sinusoidal carriers f LO (t), the I-component signal may be equal in magnitude but 90 degrees out of phase with the Q-component signal. The chain of modulated wavelet pulses w(t) output by the wavelet generator  404  may then be multiplied or otherwise combined with the time-variant input signal x(t) by the analog multiplier  406  to form an analog correlation output signal z(t) that is input into the analog integrator  408 . The analog integrator  408  determines and outputs the analog correlation values y(t). 
   These analog correlation values y(t) at the output of the analog integrator  408  are associated with wavelet pulses v(t) having a given spectral width that is based upon the pulse width and the resolution bandwidth discussed above. Referring back to the coarse-sensing module  104  of  FIG. 4A , the wavelet pulse v(t) is modulated using the I- and Q-sinusoidal carriers f LO (t) to form the modulated wavelet pulses w(t). The local oscillator (LO) frequency of the I- and Q-sinusoidal carriers f LO (t) can then be swept or adjusted. By sweeping the I- and Q-sinusoidal carriers f LO (t), the signal power magnitudes and the frequency values within the time-variant input signal x(t) may be detected in the analog correlation values y(t) over a spectrum range, and in particular, over the spectrum range of interest, thereby providing for scalable resolution. 
   For example, by applying a narrow wavelet pulse v(t) and a large tuning step size of the LO frequency f LO (t), an MRSS implementation in accordance with an embodiment of the present invention may examine a very wide spectrum span in a fast and sparse manner. By contrast, very precise spectrum searching may be realized with a wide wavelet pulse v(t) and the delicate adjusting of the LO frequency f LO (t). Moreover, in accordance with an exemplary embodiment of the present invention, this MRSS implementation may not require any passive filters for image rejection due to the bandpass filtering effect of the window signal (e.g., modulated wavelet pulses w(t)). Likewise, the hardware burdens, including high-power consuming digital hardware burdens, of such an MRSS implementation may be minimized.  FIG. 4B  illustrates an example of such scalable resolution control in the frequency domain with the use of wavelet pulses W(ω). In particular,  FIG. 4B  illustrates that an input signal W(ω) can be multiplied  406  with wavelet pulse W(ω) having varying resolution bandwidths to achieve scalable resolution control of the various output correlation values Y(ω). 
   Referring back to  FIG. 4A , once the analog correlation values y(t) have been generated by the analog integrator  408 , the magnitudes of the coefficient values y(t) may be digitized by the analog-to-digital converter  118  and provided to the MAC module  124 . More specifically, the resulting analog correlation values y(t) associated with each of the I- and Q-components of the wavelet waveforms may be digitized by the analog-to-digital converter  118 , and their magnitudes are recorded by the MAC module  124 . If the magnitudes are greater than a certain threshold level, then the sensing scheme, perhaps utilizing the spectrum recognition module  120  in the MAC module  124 , may determine a meaningful interferer reception (e.g., a particular detected spectrum occupancy) in accordance with an embodiment of the present invention. 
   c. Simulation of MRSS Implementation 
   An Multi-Resolution Spectrum Sensing (MRSS) implementation in accordance with an embodiment of the present invention will now be described with respect to several computer simulations. In particular, a computer simulation was performed using a two-tone signal x(t), where each tone was set at the same amplitude but at a different frequency. The sum of the two tone signals with different frequencies and the phases can be expressed as x(t)=A 1  cos(ω 1 t+θ 1 )+A 2  cos(ω 2 t+θ 2 ).  FIG. 5A  illustrates the waveform of the two-tone signal x(t), and  FIG. 5B  illustrates the corresponding spectrum to be detected with the MRSS implementation in accordance with an embodiment of the present invention. 
   In accordance with the exemplary simulated MRSS implementation, the Manning window function (e.g., Wt*Wf=0.513) for this exemplary simulated MRSS implementation was chosen as the wavelet window function that bounds the selection of wavelet pulse width Wt and the resolution bandwidth Wf for the wavelet pulses v(t). The Manning window function was used in this simulation because of its relative simplicity in terms of the practical implementation. The uncertainty inequality of Wt*Wf=0.513 discussed above may be derived from the calculations of the wavelet pulse width (Wt)  302  and the resolution bandwidth (Wf)  304  for Hanning wavelet pulses as shown below: 
   
     
       
         
           
             
               
                 
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     FIG. 6  illustrates the waveform of the exemplary chain of wavelet pulses v(t). Accordingly, a chain of modulated wavelet pulses w(t) may be obtained from the wavelet generator  404  by modulating the I- and Q-sinusoidal carriers f LO (t) with a window signal comprised of a chain of wavelet pulses v(t) in an exemplary embodiment of the present invention. In particular, the modulated wavelet pulses w(t) may be obtained by w(t)=v(t)·f LO (t), where v(t)=I+m cos(ω p t+θ p ) and 
                 f   LO     ⁡     (   t   )       =       ∑     k   =   1     K     ⁢     cos   ⁡     (       k   ⁢           ⁢     ω   LO     ⁢   t     +   Φ     )           ,     Φ   =     0   ⁢           ⁢   or   ⁢           ⁢   90   ⁢     °   .                 FIG. 7A  illustrates the I-component waveform of the T-Q sinusoidal carrier f LO (t), and  FIG. 7B  illustrates the Q-component waveform of the I-Q sinusoidal carrier f LO (t).  FIG. 8A  illustrates the modulated wavelet pulses w(t) obtained from the wavelet generator  404  with the I-component of the I-Q sinusoidal carrier f LO (t). Likewise,  FIG. 8B  illustrates the modulated wavelet pulses w(t) obtained from the wavelet generator  404  with the Q-component of the I-Q sinusoidal carrier f LO (t).
 
   Each modulated wavelet pulse w(t) is then multiplied by the time-variant signal x(t) by an analog multiplier  406  to produce the resulting analog correlation output signals z(t), as illustrated in  FIGS. 9A and 9B . In particular,  FIG. 9A  illustrates the correlation output signal z(t) waveform for the input signal x(t) with the I-component of the I-Q sinusoidal carrier f LO (t) while  FIG. 9B  illustrates the correlation output signal z(t) waveform for the input signal x(t) with the Q-component of the I-Q sinusoidal carrier f LO (t). The resulting waveforms in  FIGS. 9A and 9B  are then integrated by the analog integrator  408  to obtain the correlation values y(t) of the input signal x(t) with the I- and the Q-component of the wavelet waveform w(t). 
   The correlation values y(t) can then be integrated by the analog integrator  408  and sampled by the analog-to-digital converter  118 .  FIG. 10A  shows the sampled values y t  provided by the analog-to-digital converter  118  for these correlation values y(t) with the I-component of the wavelet waveform w(t) within the given intervals.  FIG. 10B  shows the sampled values y Q  via the analog integrator  408  and the analog-to-digital converter  118  for the correlation values with the Q-component of the wavelet waveform w(t) within the given intervals. The MAC module  124 , and perhaps its constituent spectrum recognition module  120 , then calculates the magnitudes of those sampled values by taking the square-root for those values, y I  and y Q , as shown in by |y|=√{square root over (y I   2 (t)+y Q   2  (t))}{square root over (y I   2 (t)+y Q   2  (t))} according to an exemplary embodiment of the present invention. The spectrum shape detected by the spectrum recognition module  120  in the MAC module  124  is shown in  FIG. 11 . As shown in  FIG. 11 , the detected spectrum shape is well-matched with the expected spectrum shown in  FIG. 5B , thereby signifying good detection and recognition of the expected spectrum. 
     FIGS. 12-17  illustrate simulations of various signal formats detected by exemplary MRSS implementations in accordance with embodiments of the present invention. These signal formats may include GSM, EDGE, wireless microphone (FM), ATDC (VSB), 3G cellular—WCDMA, IEEE802.11a—WLAN (OFDM). In particular,  FIG. 12A  illustrates the spectrum of a GSM signal and  FIG. 12B  illustrates the corresponding detected signal spectrum. Likewise,  FIG. 13A  illustrates the spectrum of an EDGE signal and  FIG. 13B  illustrates the corresponding detected signal spectrum.  FIG. 14A  illustrates the spectrum of a wireless microphone (FM) signal and  FIG. 14B  illustrates the corresponding detected signal spectrum.  FIG. 15A  illustrates the spectrum of an ATDC (VSB) signal and  FIG. 15B  illustrates the corresponding detected signal spectrum.  FIG. 16A  illustrates the spectrum of a 3G-cellular (WCDMA) signal and  FIG. 16B  illustrates the corresponding detected signal spectrum.  FIG. 17A  illustrates the spectrum of an IEEE 802.11a—WLAN (OFDM) signal and  FIG. 17B  illustrates the corresponding detected signal spectrum. One of ordinary skill in the art will recognize that other signal formats may be detected in accordance with MRSS implementations in accordance with embodiments of the present invention. 
   d. Circuit Diagram for Coarse-Sensing Block 
   An exemplary circuit diagram of the coarse sensing module  104  shown in  FIG. 4  is illustrated in  FIG. 18 . More specifically,  FIG. 18  illustrates a wavelet generator  454 , multipliers  456   a  and  456   b , and integrators  458   a  and  458   b . The wavelet generator  454  may be comprised of a wavelet pulse generator  460 , a local oscillator (LO)  462 , a phase shifter  464  (e.g., a 90° phase shifter), and multipliers  466   a  and  466   b . The wavelet pulse generator  460  may provide envelope signals that determines the width and/or shape of the wavelet pulses v(t). Using multiplier  466   a , the wavelet pulse v(t) is multiplied by the I-component of the LO frequency provided by the LO  462  to generate the I-component modulated wavelet pulse w(t). Likewise, using multiplier  466   b , the wavelet pulse v(t) is multiplied by the Q-component of the LO frequency, as shifted 90° by the phase shifter  464 , to generate the Q-component modulated wavelet pulse w(t). 
   The respective I- and Q-components of the modulated wavelet pulse w(t) are then multiplied by the respective multipliers  456   a  and  456   b  to generate the respective correlation output signals z I (t) and z Q (t). The correlation output signals z I (t) and z Q (t) are then integrated by the respective integrators  458   a  and  458   b  to yield respective correlation values y I (t) and y Q (t). While  FIG. 18  illustrates a specific embodiment, one of ordinary skill in the art will recognize that many variations of the circuit diagram in  FIG. 18  are possible. 
   2. Fine-Sensing Module 
   In accordance with an exemplary embodiment of the present invention, the fine-sensing module  106  of  FIG. 1  may be operative to recognize the periodic features of the input signals unique for each suspect modulation format or frame structure. These periodic features may include sinusoidal carriers, periodic pulse trains, cyclic prefixes, and preambles. More specifically, the fine-sensing module  106  may implement one or more correlation functions for recognizing these periodic features of the input signals. The recognized input signals may include a variety of sophisticated signal formats adopted in the current and emerging wireless standards, including IS-95, WCDMA, EDGE, GSM, Wi-Fi, Wi-MAX, Zigbee, Bluetooth, digital TV (ATSC, DVB), and the like. 
   According to an embodiment of the present invention, the correlation function implemented for the fine-sensing module  106  may be an Analog Auto-Correlation (AAC) function. The AAC function may derive the amount of the similarity (i.e., the correlation) between two signals. In other words, the correlation between the same waveforms produces the largest value. However, because the data modulated waveform has a random feature because the underlying original data includes random values, the correlation between the periodic signal waveform and the data modulated signal waveform may be ignored. Instead, the periodic feature of a given signal (e.g., modulation format or frame structure) has a high correlation that may be utilized by the AAC function as the signature for the specific signal type. The specific signal type identified by the AAC function in the fine-sensing module  106  may be provided to the signal processing module  126  for mitigation of interference effects. 
   a. Block Diagram of AAC Implementation 
     FIG. 19  illustrates a functional block diagram of an exemplary fine-sensing module  106  utilizing the AAC function in accordance with an embodiment of the present invention. In particular, the fine-sensing module  106  may include an analog delay module  502 , an analog multiplier  504 , an analog integrator  506 , and a comparator  508 . The analog correlation values provided at an output of the fine-sensing module  106  may be digitized by an analog-to-digital converter  118 , which may be low-speed according to an embodiment of the present invention. 
   Now referring to the fine-sensing module  106  of  FIG. 19 , an input RF signal x(t) from the antenna  116  is delayed by a certain delay value T d  by the analog delay module  502 . The delay value T d  provided by the analog delay block  502  may be a predetermined and unique value for each periodic signal format. For example, an IEEE 802.11a—WLAN (OFDM) signal may be associated with a first delay value T d1  while a 3G-cellular (WCDMA) signal may be associated with a second delay value T d2  different from the first delay value T d1 . 
   The analog correlation between the original input signal x(t) and the corresponding delayed signal x(t−T d ) may be performed by multiplying or otherwise combining these two signals—the original input signal x(t) and the delayed signal x(t−T d )—with an analog multiplier  504  to form a correlation signal. The correlation signal is then integrated with an analog integrator  506  to yield correlation values. The analog integrator  506  may be a sliding-window integrator according to an exemplary embodiment of the present invention. When correlation values from the integrator  506  are greater than a certain threshold as determined by the comparator  508 , the specific signal type for the original input signal may be identified by the spectrum recognition module  120  of the MAC module  124 . According to an embodiment of the present invention, the threshold may be predetermined for each signal type. These signal types can include IS-95, WCDMA, EDGE, GSM, Wi-Fi, Wi-MAX, Zigbee, Bluetooth, digital TV (ATSC, DVB), and the like. 
   Because the exemplary AAC implementation in  FIG. 19  processes all the signals in the analog domain, this may allow not only for real-time operation but also low-power consumption. By applying a delay T d  and thus a correlation to the input signal, a blind detection may achieved with no need of any known reference signals. This blind detection may drastically reduce the hardware burden and/or power consumption for the reference signal recovery. Moreover, in accordance with an embodiment of the present invention, the AAC implementation of  FIG. 19  may enhance the spectrum-sensing performance when provided in conjunction with the MRSS implementation described above. In particular, once the MRSS implementation detects the reception of a suspicious interferer signal, the AAC implementation may examine the signal and identify its specific signal type based upon its signature. 
   b. Simulation of the AAC Implementation 
   In accordance with an embodiment of the present invention, the AAC implementation of  FIG. 19  may be simulated for a variety of signal types. As an example, an IEEE 802.11a—OFDM (Orthogonal Frequency Division Multiplexing) signal may always have synchronization preambles at the beginning of a frame structure. For the simplicity, only one exemplary data OFDM symbol  552  may be followed by an exemplary preamble  551  as shown in  FIG. 20A .  FIG. 20B  illustrates the spectrum of the input IEEE 802.11a signal to be detected with an AAC implementation in accordance with an embodiment of the present invention. 
     FIG. 21A  illustrates the input IEEE 802.11a signal x(t) and  FIG. 21B  illustrates the delayed IEEE 802.11a signal x(t−T d ).  FIG. 22  illustrates a waveform of a correlation between the original input signal x(r) and the delayed signal x(t−T d ), as provided at an output of the multiplier  504 . The resulting correlation waveform shown in  FIG. 22  may have consecutive positive values  554  for the preambles  551 . The result of the integrator  506  as shown in  FIG. 23  may have peaks  602 ,  604  for the preamble  551  locations within the IEEE 802.11a frame structure. Meanwhile, the correlation for the modulated data symbols  552  has random values  556 , which can be ignored after integration by the analog integrator  506 . By comparing the predetermined threshold Vth utilizing a comparator  508  with the resulting waveform shown in  FIG. 23 , the exemplary AAC implementation of  FIG. 19  may determine the reception of the IEEE 802.11a—OFDM signal. 
   Many variations of the AAC implementation described with respect to  FIG. 19  are possible. In an alternative embodiment, the output of the integrator  506  may be digitized by the analog-to-digital converter  118  before a comparison to the threshold Vth is performed by comparator  508 . While the analog-to-digital converter  118  may be shared between the coarse-sensing module  104  and the fine-sensing module  106  in one embodiment, separate analog-to-digital converters may be provided for both the coarse-sensing module  104  and the fine-sensing module  106  in other embodiments. Likewise, the multiplier  504  and the integrator  506  of the fine-sensing module  106  may either be the same as or distinct from the multiplier  406  and the integrator  408  in the coarse-sensing module  104 . Many other variations will be known to one of ordinary skill in the art. 
   C. Signal Processing Block 
   Referring back to  FIG. 1 , a signal processing module  126  is disclosed, which may be a physical layer block according to an exemplary embodiment of the present invention. The signal processing module  126  may provide baseband processing, including one or more modulation and demodulation schemes. In addition, the signal processing module  126  may also provide interference mitigation, perhaps based upon any identified interferer signals. Furthermore, the signal processing module  126  may be operative to reconfigure the radio front-end, including the transmitter  110  and/or receiver  112 , perhaps based at least in part upon the available spectrum. For example, the signal processing block may adjust the transmission power control for the transmitter  110  or tune a filter for the receiver  112  to operate within a particular frequency range. One of ordinary skill in the art will readily recognize that other baseband processing may be provided by the signal processing module  126  as necessary or desirable. 
   D. Frequency-Agile Radio Front End 
     FIG. 24  illustrates an exemplary configuration for a frequency-agile radio front-end  108  in accordance with an embodiment of the present invention. In particular, the receive portion of the radio front-end  108  may include one or more tunable filters  702 , a wideband receiver  704 , and one or more low pass filters  706 . The tunable filter  702  may comprise a wavelet generator and a multiplier according to an exemplary embodiment of the present invention. The wideband receiver  704  may include one or more frequency stages and one or more downconverters as necessary. In addition, the transmit portion of the radio front-end  108  may include one or more low-pass filters  708 , a wideband transmitter  710 , and one or more power amplifiers  712 . The wideband transmitter  710  may also include one or more frequency stages and one or more upconverters as necessary. Furthermore, the wideband receiver  704  and transmitter  710  may be in communication with a tunable signal generator  714 . One of ordinary skill in the art will recognize that the components of the frequency-agile front-end  108  may be varied without departing from embodiments of the present invention. 
   As stated previously with respect to  FIGS. 1 and 2 , the MAC module  124  processes the digitized data (e.g., via ADC  118 ) from the spectrum sensing module  102  to allocate the available spectrum for a safe (e.g., unoccupied or non-interfering) cognitive radio  100  link. Additionally, the MAC module  124  provides the reconfiguration control signal to the radio front-end  108  for the optimal radio link in the allocated frequencies. Then, the radio front-end  108  changes the operating RF frequency to the corresponding frequency value in accordance with its frequency agile operation. More specifically, either or both of the tunable filter  702  and the tunable signal generator  714  may change their operating frequencies to select the signals within the corresponding frequency region. In the meantime, based upon the MAC module  124  control information, the PHY signal processing module  126  may enhance the link performance with the adaptive modulation and interference mitigation technique. 
   Many modifications and other embodiments of the inventions set forth herein will come to mind to one skilled in the art to which these inventions pertain having the benefit of the teachings presented in the foregoing descriptions and the associated drawings. Therefore, it is to be understood that the inventions are not to be limited to the specific embodiments disclosed and that modifications and other embodiments are intended to be included within the scope of the appended claims. Although specific terms are employed herein, they are used in a generic and descriptive sense only and not for purposes of limitation.