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Study on Dynamic Sensing Strategy of Military Electromagnetic Spectrum Resource
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Procedia Engineering
ProcediaProcedia Engineering 00 (2011) Engineering 29 000–000 (2012) 1362 – 1367 www.elsevier.com/locate/procedia
International Conference on Advances in Computational Modeling and Simulation
Study on Dynamic Sensing Strategy of Military Electromagnetic Spectrum Resource Lin Suna* , Hao Liub,c , Fangsheng Lid a
Modeling and Simulation Teaching and Reshearch Section, Department of Communication Command, Communication Command Academy, No. 45 Jiefang Park Road, Wuhan, 430010, China b Company 21, Department 6, Communication Command Academy, No. 45 Jiefang Park Road, Wuhan, 430010, China c Military Branches and Services Teaching and Research Section, Nanchang Military Academy, Nanchang, 330103, China d Studying institution of Communication Development Strategy, Communication Command Academy, No. 45 Jiefang Park Road, Wuhan, 430010, China
Abstract Dynamic sensing of Military spectrum resource (MSR) is the premise and foundation of dynamic management of MSR. In this paper, dynamic sensing dimensionalities of MSR are firstly introduced. And then cooperative and noncooperative sensing of MSR is demonstrated. Meanwhile, some key factors to be considered when choosing dynamic sensing way are also pointed out.
© 2011 Published by Elsevier Ltd. Selection and/or peer-review under responsibility of Harbin University of Science and Technology. Keywords: electromagnetic spectrum resource; dynamic sensing; frequency holes; wavelet detecting; cooperative sensing; hidden terminals
1. Introduction Dynamic sensing of Military spectrum resource (MSR) is the premise of dynamic management of MSR as well as the foundation of implementing the dynamic management strategies. By dynamic sensing, spectrum resources can be used more effectively and scientifically, which means that weapon
* Lin Sun. Tel.: 13971509831 . E-mail address: [email protected].
1877-7058 © 2011 Published by Elsevier Ltd. doi:10.1016/j.proeng.2012.01.141
al. / Procedia 29 (2012) 1362 – 1367 Lin Sun , Lin HaoSun Liuet, Fangsheng Li Engineering / Procedia Engineering 00 (2011) 000–000
2
performance and operation commanding effectiveness can be bettered to have the initiative in joint operation under the informationized condition. Table 1. MSR’s dynamic sensing dimensions Dimensionality
Sensing details
Annotation
Frequency
Chance of frequency domain
Frequency chance means that for all unused frequency band of a dimension (for example, at the same time), some can have the chance to be used.
Time
Chance of a specific frequency band at some point
If frequency band is not used continuously, there will be some time when it can have the chance to be used.
Geographical position
The distance from primary users to the position(longitude, latitude and elevation)
For space path loss, frequency spectrum can be used in some unoccupied area at some point. Observe the interference. No interference means that there is no primary users’ transmitting in the area. And hidden terminals should be bewared.
Coding
Primary users’ coding of SPSP, frequency hopping or time hopping; Timing information should be synchronized to primary users.
Frequency of a band can be used by SPSP or hopping at a certain point. Frequency using, using code and possible multipath parameters need to be detected.
Angle
Direction (azimuth and elevation angle) and position of primary users’ wave beam
According to the position or direction of primary users, chance of angle dimensionality is created.
Primary users’ signal polarization and wave shape
Subprime users can transmit an orthogonal wave shape using signal dimensionality without interfering primary users in a certain band and at a certain point.Frequency evaluation and wave shape recognition should be carried out.
Signal
Graphical representation
2. Dynamic Sensing Dimensionalities of Military Electromagnetic Spectrum Resource Spectrum space can be defined as a multi-dimension space occupied theoretically by radio signal. And the dimensions include geographical position, arriving angle, frequency, time, signal, coding and others. In the Table 1, different dimensionalities with their own sensing parameters and relative measuring/sensing demands are expressed. And typical schematic diagram of each dimensionality is also
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listed in the table. Dynamic sensing of Military spectrum resource (MSR) includes full-dimension recognizing spectrum occupation and the whole process of finding spectrum holes. 3. Non-cooperative Sensing of MSR Non-cooperative sensing of MSR is also called local sensing, which means that each subprime user itself analyzes the local received signal, detects spectrum holes, and determines whether there is primary user’s signal in the detected frequency band according to the detecting results. There are 4 common ways of non-cooperative sensing, which are energy detector, matched filter, cyclic stable feature detecting and wavelet detecting. 3.1. Energy Detector If the primary users’ signal is unknown, energy detector is the best way to detect any unknown signal, which determines whether the channel is occupied by measuring radio frequency energy in the channel at a period of time or receiving signal strength. It is very easy to realize such way, commonly in time dimension, and sometimes in frequency dimension. Figure 1(a) is the diagram representing how energy detector realizes frequency sensing. Its binary decision can be expressed in Formula 1. N ⎧ 2 ⎪ H 0 , if ∑ Y ( f ) ≤ λ ⎨ n =1 ⎪⎩ H1 , other
(1)
In Formula 1, N is sampling points, and λ is threshold value relying on device noise. XG users in DARPA program realizes frequency sensing by energy detector. 3.2. Matched Filter If the primary users’ signal is known, matched filter is the best detecting way. Even though additive Gaussian white noise exists, matched filter can have the largest SNR of receiving signal. It detects unknown signal by correlating a known signal with an unknown signal, which is equivalent to convolution of a known signal and an unknown signal. By comparing its output and threshold value, whether primary users’ signal exists or not can be known. Figure 1(b) is the diagram of matched filter. Its binary decision can be expressed in Formula 2, in which y[n] represents known signal. N ⎧ ⎪H 0 , if ∑ x[n] y[n]∗ ≤ λ ⎨ n =1 ⎪⎩ H 1 , other
(2)
3.3. Cyclic Stable Feature Detecting In cyclic stable feature detecting, inherent periodicity of cyclic signal such as sinusoidal wave carrier and pulse train is used. Such periodicity can be used for timing and channel evaluation, also can be used to detect primary users. If correlation of a signal is the periodic function of time t at a certain period, then (generally speaking) it is cyclic stable. The process of cyclic stable feature detecting is as the following: Firstly, cyclic self-correlating function Rxα (τ ) of receiving signal x(t ) is calculated with Formula 3, among which α is periodic
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frequency. And then, discrete Fourier transform of Rxα (τ ) is calculated with Formula 4 to get spectrum correlation function S ( f , α ) , which is also called periodic frequency, and 2-D function of frequency f and periodic frequency α . Finally, special periodic frequency α corresponding to peak value of spectrum correlation function is sought to finish the detecting (When α is equal to the basic frequency of transmitting signal, spectrum correlation function has the peak value. And α can be regarded as the feature to recognize the transmitting signal). To be noticed, when there is a = 0 , spectrum correlation function is equivalent with power spectrum density generated by energy detector. And naturally, cyclic stable feature detecting can have more information than energy detector. Figure 1(c) is the diagram representing how cyclic stable feature detecting realizes frequency sensing.
= Rxα (τ ) lim
T →∞
1 T2 τ τ x(t + )x(t − )e− j 2πα t dt ∫ 2 − T 2 2 T
∞
S xα ( f ) = ∫ Rxα (τ )e − j 2π f τ dτ −∞
(3) (4)
3.4. Wavelet Detecting Wavelet transform is the way to analyze time-frequency of signal, which can carry out multiple resolution ratio analysis, and has the ability to represent partial signal feature both in time domain and frequency domain. In lower frequency band, it has higher frequency resolution and lower time resolution. While in higher frequency band, it has the reverse result. Unlike Fourier transform using sine or cosine as its basic function, wavelet transform uses wavelet with irregular shape as its basic function. Thus it can provide a tool which can represent shape changing and position feature with a better effect. When detecting signal in wideband channel, compared with traditional band-pass filter using several narrow bands, wavelet detecting has the obvious advantages of low cost and flexible adapting to dynamic frequency spectrum. In order to recognize the position of spectrum holes, it models the whole frequency band as a sequential sub-band string. Power spectrum feature in each sub-band is smooth. Junction power spectrum feature of two close sub-bands will mutate. By carrying out wavelet transform of power spectrum density of receiving signal x(t ) , the abnormal in power spectrum density S ( f ) will be found, and then spectrum holes will be found and positioned. Figure 1(d) is the diagram representing wavelet detecting way. 4. Cooperative Sensing of MSR Calculation and realization of cooperative sensing of MSR is relatively easy, but it has two problems. One is about sensing accuracy, which may result in false-alarm and mis-detecting. The other is about hidden terminal. Both can be shown in Figure 2(a). The terminal which is not been detected will cause disastrous interference to subprime users. Uncertainty, in terms of channel, noise, and accumulating interference, is the key factor causing the above two problems. Using cooperative sensing of MSR, damages such problems may cause will be relatively relieved. And probability of false-alarm and misdetecting will be lowered. Meanwhile, interference to primary users will be reduced. The above can be represented in Figure 2(b). There are 3 common cooperative sensing ways of MSR, which are centralized sensing, distributed sensing and external sensing. 4.1. Centralized Sensing
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Lin ,Sun et al. / Procedia Engineering 29 (2012) 1362 000–000 – 1367 Lin Sun , Hao Liu Fangsheng Li / Procedia Engineering 00 (2011)
The process of centralized sensing is as the following: Firstly, each subprime user carries out local spectrum sensing independently, and makes a binary decision whether there is primary user. Then, all the subprime users submit their binary decisions or sensing results to public receiver, which may be an access point (AP), or a base station (BS) in mobile communication. And finally, all the decisions or sensing results are fused by the public receiver, and final decision is made to determine whether there is primary user in the detected frequency band. The final decision is broadcasted in the network, or it directly controls the action of subprime users. To be noticed, if what the public receiver fuses is binary decisions of subprime users, it is called decision fusion (or hard grouping), which may be realized by OR logic. However, if what the public receiver fuses is sensing results of subprime users, it is called data fusion (or soft grouping). For decision fusion, it needs smaller controlling channel bandwidth, while it can have almost the same detecting performance as data fusion.
Fig. 1. MSR’s non-cooperative sensing ( x(t ) and X ( f ) denotes the time domain and frequency domain of the receiving signal respectively. S ( f ) denotes the power spectrum density of x(t ) . And S ( f ,α ) represents the cyclic spectrum.)
Fig. 2. (a) Problems existing in on-cooperative sensing of MSR; (b) Cooperative sensing of MSR solving the problems of shadowing and hidden terminal
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4.2. Distributed Sensing For centralized sensing, it needs public receiver and controlling channel to realize decision fusion or data fusion to make the final decision, which has two problems. One is that at present it is very difficult to accumulate all the receiving data or decision to a point. The other is that requirement of controlling channel may cause wasting of spectrum resources, and will make it not being able to adapt to dynamic change of the environment. For distributed sensing, it can solve the two problems. For distributed sensing, subprime users exchange sensing results, share the information, and make their own decision according to the frequency spectrum they may use, not needing the infrastructure network that centralized sensing should have. Thus, the cost is lowered. And it adapts to the practical dynamic management system of MSR. 4.3. External Sensing Another cooperative sensing way to have spectrum information is external sensing, which uses a foreign agent (FA) to carry out channel sensing, and broadcasts the occupation information of the channel to each subprime user. Its advantages are as the following: Firstly, it overcomes the problem of hidden terminal and the problem of uncertainty caused by shielding and fading effect. Secondly, because there is no need for subprime user to spend time carrying out sensing, the spectrum efficiency is improved. And finally, because there is no requirement of mobility and battery supplying, power dissipation of inner sensing is reduced. While its disadvantage is that there should be specific infrastructure network in target area. In practical application, there may be no such specific infrastructure network, and the deploying cost may be very high. 5. Conclusion Ways of dynamic sensing of Military spectrum resource (MSR) have its advantages and disadvantages. When choosing frequency sensing way, comprehensive consideration is needed and key performance indexes should be focused. In the future, dynamic sensing development will have a tendency of being multi-level, multi-user and multi-frequency band. And uncertainty influence of channel, noise, and interference will be overcome to improve the detecting probability and lower false-alarm and falsedetecting probability. References [1] Aiqing Zhang. Frequency management technology in cognitive radio[J]. Mobile Communication, 2009.3, 3: 26-28. [2] Danijela Branislav Cabric. Cognitive Radios: System Design Perspective [D]. Berkeley: University of California, Fall 2007. [3] Shared Spectrum Company. XG Dynamic Spectrum Sharing Field Test Results [J]. DySPAN 2007, April 2007: 676-684. [4] Serena chan. Sharing frequency spectrum access of US DOD [J]. Chinese Radio, 2008.3, 3: 10-14. [5] Peyman Setoodeh and Simon Haykin. Robust transmit power control for cognitive radio [J]. Proceedings of the IEEE, May 2009, 97(5): 915-939.
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Procedia Engineering
ProcediaProcedia Engineering 00 (2011) Engineering 29 000–000 (2012) 1362 – 1367 www.elsevier.com/locate/procedia
International Conference on Advances in Computational Modeling and Simulation
Study on Dynamic Sensing Strategy of Military Electromagnetic Spectrum Resource Lin Suna* , Hao Liub,c , Fangsheng Lid a
Modeling and Simulation Teaching and Reshearch Section, Department of Communication Command, Communication Command Academy, No. 45 Jiefang Park Road, Wuhan, 430010, China b Company 21, Department 6, Communication Command Academy, No. 45 Jiefang Park Road, Wuhan, 430010, China c Military Branches and Services Teaching and Research Section, Nanchang Military Academy, Nanchang, 330103, China d Studying institution of Communication Development Strategy, Communication Command Academy, No. 45 Jiefang Park Road, Wuhan, 430010, China
Abstract Dynamic sensing of Military spectrum resource (MSR) is the premise and foundation of dynamic management of MSR. In this paper, dynamic sensing dimensionalities of MSR are firstly introduced. And then cooperative and noncooperative sensing of MSR is demonstrated. Meanwhile, some key factors to be considered when choosing dynamic sensing way are also pointed out.
© 2011 Published by Elsevier Ltd. Selection and/or peer-review under responsibility of Harbin University of Science and Technology. Keywords: electromagnetic spectrum resource; dynamic sensing; frequency holes; wavelet detecting; cooperative sensing; hidden terminals
1. Introduction Dynamic sensing of Military spectrum resource (MSR) is the premise of dynamic management of MSR as well as the foundation of implementing the dynamic management strategies. By dynamic sensing, spectrum resources can be used more effectively and scientifically, which means that weapon
* Lin Sun. Tel.: 13971509831 . E-mail address: [email protected].
1877-7058 © 2011 Published by Elsevier Ltd. doi:10.1016/j.proeng.2012.01.141
al. / Procedia 29 (2012) 1362 – 1367 Lin Sun , Lin HaoSun Liuet, Fangsheng Li Engineering / Procedia Engineering 00 (2011) 000–000
2
performance and operation commanding effectiveness can be bettered to have the initiative in joint operation under the informationized condition. Table 1. MSR’s dynamic sensing dimensions Dimensionality
Sensing details
Annotation
Frequency
Chance of frequency domain
Frequency chance means that for all unused frequency band of a dimension (for example, at the same time), some can have the chance to be used.
Time
Chance of a specific frequency band at some point
If frequency band is not used continuously, there will be some time when it can have the chance to be used.
Geographical position
The distance from primary users to the position(longitude, latitude and elevation)
For space path loss, frequency spectrum can be used in some unoccupied area at some point. Observe the interference. No interference means that there is no primary users’ transmitting in the area. And hidden terminals should be bewared.
Coding
Primary users’ coding of SPSP, frequency hopping or time hopping; Timing information should be synchronized to primary users.
Frequency of a band can be used by SPSP or hopping at a certain point. Frequency using, using code and possible multipath parameters need to be detected.
Angle
Direction (azimuth and elevation angle) and position of primary users’ wave beam
According to the position or direction of primary users, chance of angle dimensionality is created.
Primary users’ signal polarization and wave shape
Subprime users can transmit an orthogonal wave shape using signal dimensionality without interfering primary users in a certain band and at a certain point.Frequency evaluation and wave shape recognition should be carried out.
Signal
Graphical representation
2. Dynamic Sensing Dimensionalities of Military Electromagnetic Spectrum Resource Spectrum space can be defined as a multi-dimension space occupied theoretically by radio signal. And the dimensions include geographical position, arriving angle, frequency, time, signal, coding and others. In the Table 1, different dimensionalities with their own sensing parameters and relative measuring/sensing demands are expressed. And typical schematic diagram of each dimensionality is also
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Lin ,Sun et al. / Procedia Engineering 29 (2012) 1362 000–000 – 1367 Lin Sun , Hao Liu Fangsheng Li / Procedia Engineering 00 (2011)
3
listed in the table. Dynamic sensing of Military spectrum resource (MSR) includes full-dimension recognizing spectrum occupation and the whole process of finding spectrum holes. 3. Non-cooperative Sensing of MSR Non-cooperative sensing of MSR is also called local sensing, which means that each subprime user itself analyzes the local received signal, detects spectrum holes, and determines whether there is primary user’s signal in the detected frequency band according to the detecting results. There are 4 common ways of non-cooperative sensing, which are energy detector, matched filter, cyclic stable feature detecting and wavelet detecting. 3.1. Energy Detector If the primary users’ signal is unknown, energy detector is the best way to detect any unknown signal, which determines whether the channel is occupied by measuring radio frequency energy in the channel at a period of time or receiving signal strength. It is very easy to realize such way, commonly in time dimension, and sometimes in frequency dimension. Figure 1(a) is the diagram representing how energy detector realizes frequency sensing. Its binary decision can be expressed in Formula 1. N ⎧ 2 ⎪ H 0 , if ∑ Y ( f ) ≤ λ ⎨ n =1 ⎪⎩ H1 , other
(1)
In Formula 1, N is sampling points, and λ is threshold value relying on device noise. XG users in DARPA program realizes frequency sensing by energy detector. 3.2. Matched Filter If the primary users’ signal is known, matched filter is the best detecting way. Even though additive Gaussian white noise exists, matched filter can have the largest SNR of receiving signal. It detects unknown signal by correlating a known signal with an unknown signal, which is equivalent to convolution of a known signal and an unknown signal. By comparing its output and threshold value, whether primary users’ signal exists or not can be known. Figure 1(b) is the diagram of matched filter. Its binary decision can be expressed in Formula 2, in which y[n] represents known signal. N ⎧ ⎪H 0 , if ∑ x[n] y[n]∗ ≤ λ ⎨ n =1 ⎪⎩ H 1 , other
(2)
3.3. Cyclic Stable Feature Detecting In cyclic stable feature detecting, inherent periodicity of cyclic signal such as sinusoidal wave carrier and pulse train is used. Such periodicity can be used for timing and channel evaluation, also can be used to detect primary users. If correlation of a signal is the periodic function of time t at a certain period, then (generally speaking) it is cyclic stable. The process of cyclic stable feature detecting is as the following: Firstly, cyclic self-correlating function Rxα (τ ) of receiving signal x(t ) is calculated with Formula 3, among which α is periodic
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al. / Procedia 29 (2012) 1362 – 1367000–000 Lin Sun , Lin HaoSun Liuet, Fangsheng Li Engineering / Procedia Engineering 00 (2011)
4
frequency. And then, discrete Fourier transform of Rxα (τ ) is calculated with Formula 4 to get spectrum correlation function S ( f , α ) , which is also called periodic frequency, and 2-D function of frequency f and periodic frequency α . Finally, special periodic frequency α corresponding to peak value of spectrum correlation function is sought to finish the detecting (When α is equal to the basic frequency of transmitting signal, spectrum correlation function has the peak value. And α can be regarded as the feature to recognize the transmitting signal). To be noticed, when there is a = 0 , spectrum correlation function is equivalent with power spectrum density generated by energy detector. And naturally, cyclic stable feature detecting can have more information than energy detector. Figure 1(c) is the diagram representing how cyclic stable feature detecting realizes frequency sensing.
= Rxα (τ ) lim
T →∞
1 T2 τ τ x(t + )x(t − )e− j 2πα t dt ∫ 2 − T 2 2 T
∞
S xα ( f ) = ∫ Rxα (τ )e − j 2π f τ dτ −∞
(3) (4)
3.4. Wavelet Detecting Wavelet transform is the way to analyze time-frequency of signal, which can carry out multiple resolution ratio analysis, and has the ability to represent partial signal feature both in time domain and frequency domain. In lower frequency band, it has higher frequency resolution and lower time resolution. While in higher frequency band, it has the reverse result. Unlike Fourier transform using sine or cosine as its basic function, wavelet transform uses wavelet with irregular shape as its basic function. Thus it can provide a tool which can represent shape changing and position feature with a better effect. When detecting signal in wideband channel, compared with traditional band-pass filter using several narrow bands, wavelet detecting has the obvious advantages of low cost and flexible adapting to dynamic frequency spectrum. In order to recognize the position of spectrum holes, it models the whole frequency band as a sequential sub-band string. Power spectrum feature in each sub-band is smooth. Junction power spectrum feature of two close sub-bands will mutate. By carrying out wavelet transform of power spectrum density of receiving signal x(t ) , the abnormal in power spectrum density S ( f ) will be found, and then spectrum holes will be found and positioned. Figure 1(d) is the diagram representing wavelet detecting way. 4. Cooperative Sensing of MSR Calculation and realization of cooperative sensing of MSR is relatively easy, but it has two problems. One is about sensing accuracy, which may result in false-alarm and mis-detecting. The other is about hidden terminal. Both can be shown in Figure 2(a). The terminal which is not been detected will cause disastrous interference to subprime users. Uncertainty, in terms of channel, noise, and accumulating interference, is the key factor causing the above two problems. Using cooperative sensing of MSR, damages such problems may cause will be relatively relieved. And probability of false-alarm and misdetecting will be lowered. Meanwhile, interference to primary users will be reduced. The above can be represented in Figure 2(b). There are 3 common cooperative sensing ways of MSR, which are centralized sensing, distributed sensing and external sensing. 4.1. Centralized Sensing
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Lin ,Sun et al. / Procedia Engineering 29 (2012) 1362 000–000 – 1367 Lin Sun , Hao Liu Fangsheng Li / Procedia Engineering 00 (2011)
The process of centralized sensing is as the following: Firstly, each subprime user carries out local spectrum sensing independently, and makes a binary decision whether there is primary user. Then, all the subprime users submit their binary decisions or sensing results to public receiver, which may be an access point (AP), or a base station (BS) in mobile communication. And finally, all the decisions or sensing results are fused by the public receiver, and final decision is made to determine whether there is primary user in the detected frequency band. The final decision is broadcasted in the network, or it directly controls the action of subprime users. To be noticed, if what the public receiver fuses is binary decisions of subprime users, it is called decision fusion (or hard grouping), which may be realized by OR logic. However, if what the public receiver fuses is sensing results of subprime users, it is called data fusion (or soft grouping). For decision fusion, it needs smaller controlling channel bandwidth, while it can have almost the same detecting performance as data fusion.
Fig. 1. MSR’s non-cooperative sensing ( x(t ) and X ( f ) denotes the time domain and frequency domain of the receiving signal respectively. S ( f ) denotes the power spectrum density of x(t ) . And S ( f ,α ) represents the cyclic spectrum.)
Fig. 2. (a) Problems existing in on-cooperative sensing of MSR; (b) Cooperative sensing of MSR solving the problems of shadowing and hidden terminal
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al. / Procedia 29 (2012) 1362 – 1367000–000 Lin Sun , Lin HaoSun Liuet, Fangsheng Li Engineering / Procedia Engineering 00 (2011)
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4.2. Distributed Sensing For centralized sensing, it needs public receiver and controlling channel to realize decision fusion or data fusion to make the final decision, which has two problems. One is that at present it is very difficult to accumulate all the receiving data or decision to a point. The other is that requirement of controlling channel may cause wasting of spectrum resources, and will make it not being able to adapt to dynamic change of the environment. For distributed sensing, it can solve the two problems. For distributed sensing, subprime users exchange sensing results, share the information, and make their own decision according to the frequency spectrum they may use, not needing the infrastructure network that centralized sensing should have. Thus, the cost is lowered. And it adapts to the practical dynamic management system of MSR. 4.3. External Sensing Another cooperative sensing way to have spectrum information is external sensing, which uses a foreign agent (FA) to carry out channel sensing, and broadcasts the occupation information of the channel to each subprime user. Its advantages are as the following: Firstly, it overcomes the problem of hidden terminal and the problem of uncertainty caused by shielding and fading effect. Secondly, because there is no need for subprime user to spend time carrying out sensing, the spectrum efficiency is improved. And finally, because there is no requirement of mobility and battery supplying, power dissipation of inner sensing is reduced. While its disadvantage is that there should be specific infrastructure network in target area. In practical application, there may be no such specific infrastructure network, and the deploying cost may be very high. 5. Conclusion Ways of dynamic sensing of Military spectrum resource (MSR) have its advantages and disadvantages. When choosing frequency sensing way, comprehensive consideration is needed and key performance indexes should be focused. In the future, dynamic sensing development will have a tendency of being multi-level, multi-user and multi-frequency band. And uncertainty influence of channel, noise, and interference will be overcome to improve the detecting probability and lower false-alarm and falsedetecting probability. References [1] Aiqing Zhang. Frequency management technology in cognitive radio[J]. Mobile Communication, 2009.3, 3: 26-28. [2] Danijela Branislav Cabric. Cognitive Radios: System Design Perspective [D]. Berkeley: University of California, Fall 2007. [3] Shared Spectrum Company. XG Dynamic Spectrum Sharing Field Test Results [J]. DySPAN 2007, April 2007: 676-684. [4] Serena chan. Sharing frequency spectrum access of US DOD [J]. Chinese Radio, 2008.3, 3: 10-14. [5] Peyman Setoodeh and Simon Haykin. Robust transmit power control for cognitive radio [J]. Proceedings of the IEEE, May 2009, 97(5): 915-939.
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