Abstract:
The cognitive radio system and method uses a wideband chirp signal for characterization of the spectra that a mobile radio may use. A cognitive radio base station broadcasts the low power reference wideband chirp signal with bandwidth covering the sensed spectrum. At the receiver, spectral resolution in the presence of white noise is achieved by cross-correlating the chirp signal with a locally generated copy of itself (i.e., matched filtering). A Fast Fourier Transform (FFT) is applied to the output of this matched filtering. The FFT output is fed to a decision circuitry, where a threshold value is set to decide the minimum amplitude of utilized frequencies. This process eases sensing computational complexity and improves the quality of sensing, thereby offering enhanced cognition at the cognitive radio receiver.

Description:
CROSS-REFERENCE TO RELATED APPLICATION 
       [0001]    This application is a continuation-in-part of my prior U.S. patent application, Ser. No. 13/562,133, filed Jul. 30, 2012, which is a continuation of my prior U.S. patent application Ser. No. 12/662,656, filed Apr. 27, 2010. 
     
    
     BACKGROUND OF THE INVENTION 
       [0002]    1. Field of the Invention 
         [0003]    The present invention relates to radio systems providing wireless voice and data communications, and more particularly, to a cognitive radio sensing method and system that is capable of changing its operating parameters responsive to a changing and unanticipated radio environment, and performs such cognitive functions using a wideband chirp signal to reduce computational complexity. 
         [0004]    2. Description of the Related Art 
         [0005]    Most traditional radios have their technical characteristics set at the time of manufacture. More recently, radios have been built that self-adapt to one of several preprogrammed radio frequency (RF) environments that might be encountered. The main idea of cognitive radio is to improve the utilization of the scarce radio resources. A cognitive radio can sense its environment and alter radio resources such as time and frequency and operational behavior to benefit both itself and its geographical and spectral neighbors. The ability to sense and respond intelligently to changes in radio environment distinguishes cognitive radios from fixed radios. A cognitive radio can respond intelligently in order to utilize scarce and unused radio resources. The result is enhanced communications at the least costly radio resources. The Oxford English Dictionary (OED) defines “cognitive” as: “pertaining to cognition, or to the action or process of knowing”. “Cognition” is defined as “the action or faculty of knowing taken in its widest sense, including sensation, perception, conception, etc., as distinguished from feeling and volition”. Given these definitions, the process of sensing an existing wireless channel, evolving a radio&#39;s operation to accommodate the perceived wireless channel, and evaluating what happens is appropriately termed a cognitive process. Most cognitive computing systems to date have been based on sensing methodologies, which result in high computational complexity. 
         [0006]    The success of cognitive transmission strategies relies on the quality and quantity of the cognition systems at the receiver. Such cognition is acquired through rigorous sensing of the radio channel and an ability to characterize the interference. Based on the sensing functionality, the transmission facilities should adapt their transmissions accordingly. 
         [0007]    The problem of spectrum sensing and characterization is a typical trade-off problem where accuracy and the simplicity are inversely related. The most widely known sensing techniques are match filtering, energy detection, and cyclostationary features detection. While match filtering is the optimal detection technique, a practical implementation is difficult due to system requirements. At a lower level of difficulty, the performance of cyclostationary features detection is near optimal. However, system complexity is non-trivial. Energy detection is the least complex and most inaccurate among the three methods. 
         [0008]    Mobile Next Generation Networks (MNGN) are characterized as heterogeneous networks where varieties of access technologies are meant to coexist. Decisions on choosing an air interface that meets a particular need at a particular time should be shifted from the network&#39;s side to a (more intelligent) user&#39;s side. Moreover, network operators and regulators have come to the realization that assigned spectrum bands are not utilized as they should be. Cognitive radio stands out as a candidate technology to address many emerging issues in MNGN, such as capacity, quality of service and spectral efficiency. As a transmission strategy, cognitive radio systems depend greatly on sensing the radio environment. This strategy requires a novel approach towards interference characterization in cognitive radio networks. 
         [0009]    Thus, a cognitive radio sensing method solving the aforementioned problems is desired. 
       SUMMARY OF THE INVENTION 
       [0010]    In the cognitive radio sensing method, a cognitive radio base station broadcasts a low power reference wideband chirp signal with bandwidth covering a wide portion of the sensed spectrum. Cross-correlation characteristics of the chirp signal in time and frequency domains are exploited to enhance the sensing capabilities of the receiver. At the receiver, spectral resolution in the presence of channel interference is achieved by cross-correlating the chirp signal with a locally generated copy of itself (i.e., matched filtering). A Fast Fourier Transform (FFT) is applied to the output of this matched filtering. The FFT output is fed to a decision circuit, where a threshold value is set to decide the minimum amplitude of the utilized frequencies. This process improves the quality of sensing by offering enhanced cognition at the cognitive radio terminals at low computational complexity cost. 
         [0011]    These and other features of the present invention will become readily apparent upon further review of the following specification and drawings. 
     
    
     
       BRIEF DESCRIPTION OF THE DRAWINGS 
         [0012]      FIG. 1A  is a plot showing the time domain spectrum of an exemplary cognitive radio sensing chirp signal used in a cognitive radio sensing system according to the present invention. 
           [0013]      FIG. 1B  is a plot showing the frequency domain spectrum of the exemplary cognitive radio sensing chirp signal of  FIG. 1  with processing gain selected to provide a rectangular spectrum. 
           [0014]      FIG. 2  is a block diagram showing a system architecture utilizing a cognitive radio sensing method according to the present invention. 
           [0015]      FIG. 3  is a block diagram showing the frequency sensing process of a cognitive radio sensing method according to the present invention. 
           [0016]      FIG. 4  is an exemplary frequency domain spectrum of an exemplary received signal after match filtering and Fast Fourier Transform in a cognitive radio sensing system according to the present invention. 
           [0017]      FIG. 5  is a plot showing SINR versus normalized d. 
           [0018]      FIG. 6  is a plot showing delay versus normalized d. 
           [0019]      FIG. 7A  is a block diagram showing the system function of the matched filter in the time domain in a cognitive radio sensing method according to the present invention. 
           [0020]      FIG. 7B  is a block diagram showing the system function of the matched filter in the frequency domain in a cognitive radio sensing method according to the present invention. 
           [0021]      FIG. 7C  are plots showing graphical representations of the frequencies of the chirp signal, the interference, and the chirp signal&#39;s complex conjugate being processed by the matched filter to provide the matched filter output G (ω) in a cognitive radio sensing method according to the present invention. 
           [0022]      FIG. 8  is a block diagram showing the temporal sensing process of a cognitive radio sensing method according to the present invention. 
           [0023]      FIG. 9A  is a plot showing amplitude vs. time for a wideband chirp with no interference. 
           [0024]      FIG. 9B  is a plot showing amplitude vs. time for the wideband chirp signal of  FIG. 9A  in the presence of primary user interference. 
           [0025]      FIG. 10  is a block diagram showing a cognitive radio system using both frequency and temporal sensing in a cognitive radio sensing method according to the present invention. 
       
    
    
       [0026]    Similar reference characters denote corresponding features consistently throughout the attached drawings. 
       DETAILED DESCRIPTION OF THE PREFERRED EMBODIMENTS 
       [0027]    The cognitive radio sensing method employs wideband chirp signal frequency modulation for a digital signal, which is used in sensing the operable spectrum of the cognitive radio. The chirp signal is inherently wideband, as its bandwidth spread over a range of frequencies exceeds the signaling frequency of the cognitive radio. The chirp signal is generated by linear frequency modulation of a digital signal. Thus, the instantaneous frequency of the chirp signal increases or decreases linearly with time. As shown in  FIG. 1A , the bandwidth of a chirp signal  100   a  extends from the starting frequency sweep f 1  to the final frequency sweep f 2 . With a proper choice for processing gain, i.e., the FT product, where T is the bit period, is such that the spectrum of the chirp signal has a distinctive, nearly square shape  100   b , as shown in  FIG. 1B . 
         [0028]    As shown in  FIG. 2 , the system architecture is a hybrid network  200 , comprising a primary radio network  312   a  and a cognitive “adaptive” radio network  313 . The two networks  312   a  and  313  coexist, i.e., have overlapping service areas, without being physically connected. 
         [0029]    The primary radio network  312   a  comprises a primary base station  304  serving primary “licensed” users  312   b  over the primary coverage area. The primary base station  304  performs normal functions of a base station. 
         [0030]    The cognitive radio network  313  of the cognitive radio sensing system is adaptive and comprises a cognitive radio base station  206 , which serves cognitive radio user devices  315 . The coverage area of cognitive radio network  313  overlaps with the coverage area of the primary network  312   a , which serves primary network user devices  312   b . The idea of cognitive radio transmission strategy is to sense the radio spectrum, looking for available carrier frequencies to be used for an opportunistic transmission. The idea is to avoid using a frequency being instantly (instantaneously, at that precise moment) used by another radio. Thus, the sensing spectrum is the range of frequencies that a cognitive radio monitors in order to assess its radio resources for opportunistic transmission. In order to accomplish this, the cognitive radio user devices  315  have a transmission algorithm that allows devices  315  to transmit only after the devices  315  sense the availability of the required radio resources, i.e., if the cognitive radio user devices  315  detect that a particular channel is in use, the cognitive radio user device  315  automatically switches to an unused channel before transmitting. The transmission algorithm makes sure that no excessive interference from cognitive radio user devices  315  occurs at the primary user devices  312   b.    
         [0031]      FIG. 3  shows a block diagram of the cognitive radio sensing system  300 , which is simulated using Matlab. The simulation of the cognitive radio system  300  includes the step of the cognitive radio base station  206  broadcasting a low power reference chirp signal  100   a  using a chirp generator  305 . The reference chirp signal  100   a  is preferably a low power signal in order to avoid causing excessive interference to other transmissions in the vicinity sharing the spectrum. The parameters (linear chirp rate, bandwidth, etc.) of the reference linear chirp signal are previously agreed upon between the base station and the cognitive radio receiving stations, or are previously communicated by the base station to the cognitive radio receiving stations, or are predetermined by the protocol of the cognitive radio system so that the receiving stations can generate a local copy of the reference chirp signal and be equipped with matching filters for the reference chirp signal. Along with transmissions from primary user devices  312   b  and electromagnetic additive white Gaussian noise (AWGN  310 ), the reference chirp signal  100   a  is received by the CR user device  315 , which employs a chirp signal matched filter  317  in the cognitive radio receiver having an impulse response that produces a replica (or conjugate) of the reference chirp signal  100   a . An exemplary CR user device  315  may be a cellular telephone, smart phone, or other radiotelephone transceiver. 
         [0032]    With respect to frequency sensing, spectral resolution is obtained by cross-correlating the chirp signal with a locally generated copy of itself, (i.e., auto-correlation). This auto-correlation is achieved using a chirp matched filter  317 . The result of this is optimal reception of the chirp signal where excessive noise components are removed.  FIG. 7A  shows a block diagram of the matched filter&#39;s system transfer function  700   a  expressed in the time domain. The inputs to the chirp signal matched filter is the reference chirp signal f(t), and the interfering carrier signals (tones) of primary users m 1 ( t ), m 2 ( t ), etc. For simplicity, the problem can be addressed in the frequency domain because time convolution is transferred into frequency multiplication, as shown in  FIG. 7B . Therefore: 
         [0000]        G (ω)= F   2 (ω)+ F (ω) M 1(ω)+ F (ω) M 2(ω)  (1)
 
         [0033]    Thus, the spectral resolution sought for spectral sensing is obtained by match filtering the received reference chirp. By definition, match filtering includes the process of correlating the signal with a locally generated version of itself. The procedure, as mentioned above, is known as autocorrelation because this procedure correlates the signal with itself. In the frequency domain, the correlation is achieved by multiplying the spectral of the signal with the spectral of its mathematical conjugate. 
         [0034]    To obtain spectral resolution, the output of the chirp matched filter should be transferred to the frequency domain. Thus, the auto-correlation signal is fed to a Fast Fourier Transform (FFT)  319 . What we mean by resolution is having a flat top that enable us to better set a threshold to perform the sensing function. The good thing about a chirp signal matched-filter is that it first optimally removes noise from a received signal and provides this interesting resolution. Since the frequency representation of a sine wave is the unit impulse function in the frequency domain shifted to its corresponding frequency, equation (1) can be further simplified as: 
         [0000]        G (ω)= F   2 (ω)+ M 1(ω)+ M 2(ω).  (2)
 
         [0035]    As shown graphically in the plots  700   c  of  FIG. 7C , the output of the matched filter has the spectrum G(ω) where the frequency domain spectrum of the chirp signal is a square wave function and M1 and M2 appear as spikes above the square wave chirp signal. The threshold value should be set just above the flat top of the received waveform. 
         [0036]    The output of the FFT algorithm  319  is fed into a decision circuit  321 , which is set at the aforementioned threshold value to decide whether the signal of a primary user device  312   b  interferes with the reference chirp signal  100   a . The decision circuit  321  implements an algorithm to detect the peaks representing primary users&#39; frequencies. This algorithm belongs to the search algorithms family and could be implemented either using sequential or binary search. Either algorithm should return the frequency values at which the FFT magnitude values exceed the threshold. 
         [0037]    As shown in  FIG. 4 , interfering frequencies (signals from primary user devices  312   b ) appear as spikes, i.e., peaks, rising above the flat-top  100   c  of the received chirp reference  100   a  spectrum in the presence of AWGN, simulated by an AWGN generator  310 . The Signal to Interference plus Noise Ratio (SINR) for an interfering (primary user) signal is 20 dB. 
         [0038]    As shown in  FIG. 5 , plot  500  displays the carrier&#39;s SINR versus d, normalized to a value of d at SINR=10 dB, which measures the distance between the peak of the carrier&#39;s spike and the threshold value set just above the noisy flat top of the FFT algorithm  319  output. The value of d=0 dB signifies that the spike is no longer distinguishable from the noise, and therefore the probability of a false alarm is great. Preferably, the noise floor determines the threshold value for the decision circuit  315 , since d decreases as SINR decreases. For the MATLAB™ simulation, it is shown that d=0 at SINR=−25 dB, which is an extremely low SINR value. The SINR values were calculated assuming that transmissions of primary user devices  312   b  are in synchronization with the referenced chirp signal  100   a.    
         [0039]    Plot  600  of  FIG. 6  shows Delay versus normalized d, which investigates the effect assuming both networks (primary  312   a  and cognitive  313 ) are synchronized. The plot  600  shows how d changes with respect to the delay between primary user&#39;s signal and the reference chirp signal. For example, if the primary user&#39;s signal is delayed by 0.5T seconds, where T is the bit period, d drops by 25%. Nonetheless, the system is tolerant to delay up to 0.25T seconds in both SINR cases shown. Moreover, additional research has shown that in the event when the primary and cognitive networks are not synchronized, d is dependent on the overlapping duration between the chirp duration (Tc) and the bit duration (Tb). Therefore, even in worst case scenarios when the overlapping between the first chirp duration and the carrier bit is not enough, the consecutive overlapping should be enough to make a better decision. This may lead to the conclusion that the observation interval must be at least twice as much as the chirp signal period to avoid this drawback. 
         [0040]    With respect to temporal resolution, this resolution is obtained by the process of correlation. But in this case the received chirp is correlated (in the time domain) with its mathematical conjugate. This procedure is distinguished from the spectral sensing in that the correlation is with the conjugate, not the signal itself. Moreover, this is called cross-correlation, not autocorrelation. Actually, when the signal is correlated with any other signal apart from itself, this is called cross-correlation. As shown in  FIG. 8 , the reference chirp  305   a , primary user  812 , and noise  310  are additively received into the cognitive receiver  315 . Within the cognitive receiver  315 , a cross-correlator  817  cross-correlates the received signal with the locally generated conjugate  305   b  of the reference chirp  305   a.    
         [0041]    Primary user interference times are discerned by feeding the output of the cross-correlator  817  to a time estimator  315 , which estimates the interference time via, e.g., a timer that starts counting the tone delay referenced to the starting time of the chirp signal reception. The timer is re-set as soon as the flat top of received chirp signal has begun to deform. The deformation corresponds to the moment a primary user starts to transmit. To sense this moment, received samples must be compared against a threshold value. The threshold value should be set just above the flat top of the received waveform. 
         [0042]    The output of the temporal sensing cross-correlation process is shown in  FIGS. 9A and 9B , where plot  900   a  shows a flat top with no interfering primary users and plot  900   b  shows a flat top, except during the time a primary user is transmitting. It should be understood that this signal is in the time domain in order to facilitate temporal sensing.  FIG. 10  shows a CR system  1000  utilizing both frequency sensing  1003  and time sensing  1008  to assist an interference characterizer  1010 , which controls the cognitive radio. 
         [0043]    It should be understood by one of ordinary skill in the art that embodiments of the present method can comprise software or firmware code executing on a computer, a microcontroller, a microprocessor, or a DSP processor; state machines implemented in application specific or programmable logic; or numerous other forms without departing from the spirit and scope of the method. The present method can be provided as a computer program, which includes a non-transitory machine-readable medium having stored thereon instructions that can be used to program a computer (or other electronic devices) to perform a process according to the method. The machine-readable medium can include, but is not limited to, floppy diskettes, optical disks, CD-ROMs, and magneto-optical disks, ROMs, RAMs, EPROMs, EEPROMs, magnetic or optical cards, flash memory, or other type of media or machine-readable medium suitable for storing electronic instructions. 
         [0044]    It is to be understood that the present invention is not limited to the embodiments described above, but encompasses any and all embodiments within the scope of the following claims.