WO2022052336A1 - 一种基于反向散射与空中计算的协作频谱检测方法 - Google Patents
一种基于反向散射与空中计算的协作频谱检测方法 Download PDFInfo
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- H—ELECTRICITY
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- Y—GENERAL TAGGING OF NEW TECHNOLOGICAL DEVELOPMENTS; GENERAL TAGGING OF CROSS-SECTIONAL TECHNOLOGIES SPANNING OVER SEVERAL SECTIONS OF THE IPC; TECHNICAL SUBJECTS COVERED BY FORMER USPC CROSS-REFERENCE ART COLLECTIONS [XRACs] AND DIGESTS
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- Y02D30/00—Reducing energy consumption in communication networks
- Y02D30/70—Reducing energy consumption in communication networks in wireless communication networks
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- the invention relates to the technical field of communications, in particular to a cooperative spectrum detection method based on backscattering and air computing.
- B5G cellular IoT networks need to enable ubiquitous smart devices to access available spectrum resources in real time. Due to the scarcity of spectrum resources and rapidly changing channel conditions, available wireless spectrum resources do not always exist. Most mobile users, especially those located in ultra-dense areas, require spectrum detection to discover available radio resources. However, the ultra-high frequency signals used by the B5G system will experience more severe deep fading when encountering various obstacles, which brings difficulties to a single user equipment with limited detection and processing capabilities to detect spectrum opportunities in a timely and accurate manner. Recently, sensing-based cooperative spectrum detection method, referred to as spectrum detection method, as a novel sensing paradigm with higher flexibility, can motivate ubiquitous smart devices to participate in the spectrum detection process, thereby effectively mitigating deep fading in B5G systems. and the impact of hidden terminals.
- spectrum detection method sensing-based cooperative spectrum detection method
- the detection assistant first needs to collect enough spectral data and report it over a large number of independent channels. After that, the fusion and decision-making process of a large amount of spectrum data is carried out at the spectrum requester. The above process will consume a lot of energy and network resources for the detection assistant and the entire detection system. At the same time, performing a separate fusion operation at the requester may lead to high information delay, especially when the amount of reported spectrum detection data is large. .
- the current research on cooperative spectrum detection methods can be divided into two categories according to data types reported by users or fusion rules: spectrum detection methods based on hard fusion and spectrum detection methods based on soft fusion.
- spectrum detection methods based on hard fusion For the first approach, the spectral data collected and reported by each detection assistant is the binary spectral decision after local decision. In this method, each perceptual assistant requires fewer transmission bits. However, due to the high subjectivity of local decision-making, it is easy to cause a high error rate and may lead to tampering of spectral data.
- For the second method more accurate spectral detection results can be achieved based on soft fusion. However, this method requires a lot of energy and network resources to transmit the locally sampled spectral signal.
- the fusion of multi-bit spectrum data is relatively complex, which is likely to cause long data processing delay and low computational efficiency to the spectrum sensing requester. The above two methods still need to be improved, especially when the amount of reported spectrum detection data is huge.
- the present invention solves the problem of optimal trade-off between spectrum detection accuracy and resource consumption, and directly backscatters the received spectrum signal to the requester through backscattering without local sensing, and then calculates based on the air , using the superposition feature of the wireless channel to realize the air fusion of spectrum detection data on the same channel, without occupying multiple independent channels, and finally, the requester can make the final detection decision by sampling the spectrum data after air fusion There is no need to perform additional fusion calculations, saving energy for perception and transmission and limited network, while ensuring the timeliness of information and the effectiveness of computing.
- the present invention provides a cooperative spectrum detection method based on backscattering and air calculation.
- the technical scheme of the present invention is: a cooperative spectrum sensing method based on backscattering and air computing, the specific steps include the following:
- step (1.1) the backscattering device in the cooperative spectrum detection system uses backscattering to reflect the received spectrum signal to the requester;
- Step (1.3) the requester obtains the finally received spectrum signal
- Step (1.4) the requester finally receives Perform M times of sampling to obtain the spectrum detection statistic T;
- Step (1.5) the requester determines the detection threshold
- step (1.6) the requester compares the obtained spectrum detection statistic T with the detection threshold to make a final spectrum decision.
- the cooperative spectrum detection system is composed of N backscattering devices, a user IU that is using the current frequency band, and a spectrum detection requester;
- the specific operation steps for accumulating the N backscattered spectral signals are as follows: the spectral signals from the N scattering devices are transmitted through the same backscattering channel. , using the superposition characteristics of the wireless channel to realize the natural accumulation of the spectrum signal in the wireless channel, and obtain the accumulated backscattered spectrum signal
- pi a ⁇ p IU
- 2 +vi represents the power of zi
- vi is the power of ni
- R represents the channel between the i -th scattering device and the requester fading coefficient.
- the specific operation method for the requester to obtain the finally received spectrum signal is as follows:
- the spectrum signal received by the requester includes N backscattered spectrum signals superimposed through the same wireless channel and receive the signal directly from the IU
- hi ,R represents the channel fading coefficient between the ith scattering device and the requester
- pi a ⁇ p IU
- vi is n i
- the power of , h R, IU represents the channel fading coefficient between IU and the requester
- n R is the additive white Gaussian noise at the requester;
- the spectrum signal finally received by the requester is When the IU sends the signal, the spectrum signal finally received by the requester is
- z R, Af represents the accumulated backscattered spectral signal
- z R represents the directly received signal of the IU.
- the specific operation method for obtaining the spectrum detection statistic T is as follows: Take M samples, where, Represents the lth sampling result, and then squares the modulo of the M sampling results respectively, and then accumulates to obtain the average value, so as to obtain the spectrum detection statistic as
- the specific operation method for the requester to determine the detection threshold is as follows: the requester determines the target false alarm probability according to the preset The detection threshold ⁇ is obtained as
- v R is the power of n R
- Q -1 ( ) is the inverse function of the Q function
- the Q function is the complementary distribution function of the standard Gaussian, which is expressed as
- the final spectrum judgment is: when the spectrum detection statistic T is greater than the detection threshold, it is judged that the current spectrum is occupied; when the spectrum detection statistic T is less than the detection threshold, it is judged that the current spectrum is idle.
- the purpose of the present invention is to realize a green cooperative spectrum detection method through backscattering and air calculation.
- the performance of spectrum detection needs to be guaranteed at the cost of a large amount of sensing energy consumption and network resource occupation.
- Widely distributed smart devices as detection assistants firstly use backscatter to directly reflect the received spectrum signal to the requester without local sensing; then, based on over-the-air calculation and the superposition characteristics of wireless channels, the spectrum detection data can be stored in the same Over-the-air fusion on the backscatter channel does not need to occupy multiple independent channels; finally, the requester only needs to sample the spectrum data after over-the-air fusion to make a final detection decision without additional data processing operations, and finally achieve energy and computational efficiency, as well as the saving of limited network resources.
- the beneficial effects of the present invention are as follows: 1. Collect and report a large amount of spectrum data by using backscattering, without the need for local spectrum sensing, without consuming sensing energy, reducing the sensing cost, and at the same time helping the requester to recruit with less total compensation Sufficient detection assistant.
- over-the-air computing to achieve over-the-air fusion of a large amount of spectrum data on the same backscatter channel, saving limited network channel resources, and simultaneous backscatter and data fusion further ensures information timeliness and computational effectiveness.
- Fig. 1 is the schematic flow sheet of the present invention
- Fig. 2 is the functional module schematic diagram of the present invention
- 3 is a graph showing the variation of the cooperative detection probability with the false alarm probability of the hard-fused spectrum detection method and the method of the present invention in the embodiment of the present invention
- Fig. 4 is the variation diagram of the cooperative detection probability with the false alarm probability of the soft fusion spectrum detection method and the method of the present invention in the embodiment of the present invention
- FIG. 5 is a graph showing the variation of the number of global transmission bits and the global energy with the number of detection assistants in an embodiment of the present invention.
- the present invention realizes a green cooperative spectrum detection method through backscattering and air calculation. Under the circumstance that the spectrum detection performance needs to be guaranteed at the expense of a large amount of sensing energy consumption and network resources, a reverse-based method is provided.
- a method for spectral detection based on scattering and aerial computing First, the excited broad smart device acts as a spectrum detection assistant, using backscatter to directly backscatter the received spectrum signal to the requester, without the need for local sensing or data processing procedures. Then, using over-the-air computing, all reflected spectrum signals are reported to the requester through the same backscatter channel. Using the superposition feature of the wireless channel, the fusion of spectrum detection data is realized in the air instead of the requester. Finally, the perception requester only needs to sample and analyze the received spectrum data after air fusion to make a final detection decision.
- step (1.1) the backscattering device in the cooperative spectrum detection system uses backscattering to reflect the received spectrum signal to the requester;
- Step (1.3) the requester obtains the finally received spectrum signal
- Step (1.4) the requester finally receives Perform M times of sampling to obtain the spectrum detection statistic T;
- Step (1.5) the requester determines the detection threshold
- step (1.6) the requester compares the obtained spectrum detection statistic T with the detection threshold to make a final spectrum decision.
- the cooperative spectrum detection system is composed of N backscattering devices, a user IU that is using the current frequency band, and a spectrum detection requester; the i-th scattering device participating in the cooperative spectrum detection Received signal from IU
- S IU and p IU respectively represent the transmitted signal and its power of the IU
- hi IU is the distance between the IU and the i-th scattering device Channel fading coefficient
- n i is the impedance noise of the ith scattering device; then, the ith scattering device backscatters the received zi, and the backscattered spectrum signal is
- the specific operation steps for accumulating the N backscattered spectral signals are as follows: the spectral signals from the N scattering devices are transmitted through the same backscattering channel. , using the superposition characteristics of the wireless channel to realize the natural accumulation of the spectrum signal in the wireless channel, and obtain the accumulated backscattered spectrum signal
- pi a ⁇ p IU
- 2 +vi represents the power of zi
- vi is the power of ni
- R represents the channel between the i -th scattering device and the requester fading coefficient.
- the specific operation method for the requester to obtain the finally received spectrum signal is as follows:
- the supplicant receives the signal from the IU directly from the transmission channel
- n R represents the additive white Gaussian noise at the requester
- h R, IU is the fading coefficient of the transmission channel between the IU and the requester; further obtain the spectral signal finally received by the requester
- H 0 and H 1 respectively represent the state of the IU not sending a signal and sending a signal
- the spectrum signal received by the requester includes N backscattered spectrum signals superimposed through the same wireless channel and receive the signal directly from the IU
- hi ,R represents the channel fading coefficient between the ith scattering device and the requester
- pi a ⁇ p IU
- vi is n i
- the power of , h R, IU represents the channel fading coefficient between IU and the requester
- n R is the additive white Gaussian noise at the requester;
- the spectrum signal finally received by the requester is When the IU sends the signal, the spectrum signal finally received by the requester is
- z R, Af represents the accumulated backscattered spectral signal
- z R represents the directly received signal of the IU.
- the specific operation method for obtaining the spectrum detection statistic T is as follows: Take M samples, where, Represents the lth sampling result, and then squares the modulus of the M sampling results respectively, and then accumulates to obtain the average value, thereby obtaining the spectrum detection statistic
- the specific operation method for the requester to determine the detection threshold is as follows: the requester determines the target false alarm probability according to the preset The detection threshold ⁇ is obtained as
- v R is the power of n R
- Q -1 ( ) is the inverse function of the Q function
- the Q function is the complementary distribution function of the standard Gaussian, which is expressed as
- the final spectrum judgment is: when the spectrum detection statistic T is greater than the detection threshold, it is judged that the current spectrum is occupied; when the spectrum detection statistic T is less than the detection threshold, it is judged that the current spectrum is idle.
- a collaborative spectrum detection method based on backscattering and aerial computing is presented.
- a large number of smart devices stimulated by spectrum sensing requests act as detection assistants to directly backscatter the received spectrum signals to the requester without local sensing or data preprocessing.
- all the backscattered spectrum signals are reported through a backscatter channel to realize the air fusion of spectrum data.
- the requester receives the backscattered radio frequency signal after air fusion, and performs sampling to make the final spectrum. Judgment decision.
- Accompanying drawing 3 is the method of the present invention and three kinds of typical hard fusion cooperative spectrum detection methods, namely based on and fusion, or fusion, and the method of voting fusion, the obtained detection probability is compared with the change of false alarm probability; It can be seen that when the total number of sampling points and the number of detection assistants are the same, given the target false alarm probability, the spectrum detection method based on backscattering and air calculation in the present invention can obtain higher than the three hard fusion detection mechanisms.
- the spectrum data reported by the detection assistant is a binary spectrum decision that is highly subjective and may be tampered locally, while the backscatter-based collaborative device can receive
- the spectrum signal is directly backscattered to the requester, and the spectrum data has high objectivity, so high cooperative spectrum detection performance can be achieved.
- Accompanying drawing 4 is the method of the present invention and three kinds of typical soft fusion cooperative spectrum detection methods, namely based on equal gain combining, maximum ratio combining, and the method of likelihood ratio combining, the obtained detection probability is compared with the change of false alarm probability; It can be seen from the figure that when the total number of sampling points and the number of detection assistants are the same, when the target false alarm probability is given, the spectrum detection method based on backscattering and air calculation in the present invention can obtain more soft fusion than the three The detection mechanism has a higher detection probability; this is because in the method of the present invention, the spectral data backscattered by each detection assistant is the original received spectral signal without local sensing, sampling or quantization; this kind of spectral data is better than soft fusion.
- the reported spectrum data after local processing is more objective and accurate; in addition, by using over-the-air calculation, all backscattered spectrum signals are naturally fused together on the wireless channel, and there is no co-channel interference during the data reporting process; Therefore, the requester can obtain the spectral data after air fusion with higher accuracy, thereby achieving better detection performance.
- Accompanying drawing 5 is the method of the present invention, the hard fusion method and the soft fusion method, the global transmission bit required in the spectrum data reporting process and the global energy comparison diagram; it can be seen that in the hard fusion and soft fusion spectrum detection methods, the global The number of transmitted bits increases with the increase of the number of detection assistants, and far exceeds the method of the present invention; this is because, based on backscattering, the detection assistant can directly reflect the spectral signal without quantization; in addition, when the transmitted bits The more the number, the more energy required for data reporting. Therefore, compared with the hard fusion and soft fusion methods, the cooperative spectrum detection method based on backscattering and air calculation of the present invention improves the detection performance and realizes more Good energy saving effect.
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Abstract
本发明是一种基于反向散射与空中计算的协作频谱检测方法。该方法考虑在5G蜂窝物联网场景中，广泛分布的移动用户设备作为频谱检测助手，共同协作帮助请求者获取当前频谱状态。具体步骤如下：每个检测用户首先利用反向散射，直接将接收到的频谱信号以请求者为目标进行反向散射，无需进行本地感知与数据处理程序。然后，利用空中计算，将所有经过检测助手反射的频谱信号通过同一个反向散射信道上报给请求者，利用无线信道的累加特性，实现反向散射频谱信号的叠加。最后，请求者对接收到的空中融合后的频谱信号进行采样分析，做出最终的频谱检测判决。
Description
本发明涉及通信技术领域，具体涉及一种基于反向散射与空中计算的协作频谱检测方法。
B5G蜂窝物联网网络需要使无处不在的智能设备可以实时接入可用的频谱资源。由于频谱资源的稀缺以及快速变化的信道条件，可用的无线频谱资源并非随时存在。大多数移动用户，特别是那些位于超密集区域的用户，需要进行频谱检测以发现可用的无线电资源。然而，B5G系统采用的超高频信号在遇到各种障碍物时会经历更严重的深衰落，给具有有限检测与处理能力的单个用户设备及时准确地检测频谱机会带来了困难。近来，基于感知的协作频谱检测方法，简称为频谱检测方法，作为一种具有更高灵活性的新颖感知范例，可以激励普遍存在的智能设备共同参与频谱检测过程，从而有效减轻B5G系统中深衰落和隐终端带来的影响。为了提高检测准确性，检测助手首先需要收集足够的频谱数据，并通过大量独立信道进行报告。之后，在频谱请求者处进行大量频谱数据的融合与决策过程。以上流程对检测助手以及整个检测系统而言均会消耗大量的能源和网络资源，同时，在请求者处进行单独的融合操作可能导致较高的信息延迟，尤其当上报的频谱检测数据数量庞大时。
目前对协作频谱检测方法的研究，根据用户上报的数据类型或融合规则，可被分为两类：基于硬融合的频谱检测方法和基于软融合的频谱检测方法。对于第一种方法，每个检测助手收集和报告的频谱数据是本地决策后的二进制频谱决策。该方法中，每个感知助手需要较少的传输比特。但是，由于本地决策的主观性高，因此很容易引起较高的错误率，并且可能会导致频谱数据被篡改。对于第二种方法，基于软融合可以实现更准确的频谱检测结果。然而，该方法需要大量的能量和网络资源来传输本地采样后的频谱信号。此外，多位频谱数据的融合相对复杂，极有可能给频谱感知请求者造成较长的数据处理延迟以及较低的计算效率。以上两种方法在仍然有待提高，尤其当上报的频谱检测数据量巨大时。
鉴于上述挑战，本发明解决了频谱检测准确度与资源消耗之间的最佳权衡问题，通过反向散射直接将接收到的频谱信号反向散射给请求者，无需进行本地感知，然后基于空中计算，利用无线信道的叠加特性，实现频谱检测数据在同一信道上的空中融合，无需占用多个独立信道，最后，请求者对空中融合后的频谱数据进行采样，即可做出最终的检测判决，无需进行额外的融合计算，实现感知与传输能量以及有限网络的节约，同时保证信息的时效性与计算有效性。
发明内容
针对上述问题，本发明提供了一种基于反向散射与空中计算的协作频谱检测方法。
本发明的技术方案是：一种基于反向散射与空中计算的协作频谱感知方法，具体步骤包括如下：
步骤(1.1)、协作频谱检测系统中的反向散射设备利用反向散射向请求者反射接收到的频谱信号；
步骤(1.2)、基于空中计算，对N个被反向散射的频谱信号进行累加；
步骤(1.3)、请求者得到最终接受到的频谱信号；
步骤(1.4)、请求者对最终接收到的
进行M次采样，得到频谱检测统计量T；
步骤(1.5)、请求者确定检测门限；
步骤(1.6)、请求者将得到的频谱检测统计量T与检测门限进行比较，做出最终频谱判决。
进一步的，在步骤(1.1)中，所述协作频谱检测系统由N个反向散射设备，正在使用当前频段的用户IU，以及一个频谱检测请求者组成；
参与协作频谱检测的第i个散射设备接收到来自IU的信号
其中，a＝1表示IU正发送信号，a＝0表示IU不发送信号，S
IU和p
IU分别表示IU的发送信号及其功率，h
i,IU是IU与第i个散射设备之间的信道衰落系数，n
i是第i个散射设备的阻抗噪声；然后，第i个散射设备将接收到的z
i进行反向散射，得到被反向散射后的频谱信号为
其中，f
e表示反向散射系数。
进一步的，在所述步骤(1.2)中，基于空中计算，对N个被反向散射的频谱信号进行累加的具体操作步骤如下：通过同一个反向散射信道传输来自N个散射设备的频谱信号，利用无线信道的叠加特性，实现频谱信号在无线信道中的自然累加，得到累加后的反向散射频谱信号
其中，p
i＝a·p
IU|h
i,IU|
2+v
i表示z
i的功率，v
i为n
i的功率，h
i,R表示第i个散射设备与请求者之间的信道衰落系数。
进一步的，在所述步骤(1.3)中，请求者得到最终接受到的频谱信号的具体操作方法如下：
请求者接收到的频谱信号包括，经过同一无线信道叠加后的N个被反向散 射的频谱信号
及直接接收到来自IU的信号
其中，h
i,R表示第i个散射设备与请求者之间的信道衰落系数，p
i＝a·p
IU|h
i,IU|
2+v
i为z
i的功率，v
i为n
i的功率，h
R,IU表示IU与请求者之间的信道衰落系数，n
R为请求者处的加性高斯白噪声；
由此可得，当IU不发送信号时，请求者最终接收到的频谱信号为
当IU发送信号时，请求者最终接收到的频谱信号为
其中，z
R,Af表示累加后的反向散射频谱信号，z
R表示直接接收到的IU的信号。
进一步的，在所述步骤(1.4)中，得到频谱检测统计量T的具体操作方法如下：请求者对最终接收到的频谱信号
进行M次采样，其中，
表示第l个采样结果，然后对M次采样结果分别求模的平方，随后进行累加求取平均值，从而得到频谱检测统计量为
进一步的，在所述步骤(1.5)中，请求者确定检测门限的具体操作方法如下：求者根据预先设定的目标虚警概率
得到检测门限ε为
其中，v
R为n
R的功率，Q
-1(·)为Q函数的逆函数，Q函数为标准高斯的互补分布函数，表示为
进一步的，在所述步骤(1.6)中，最终频谱判决是：当频谱检测统计量T大于检测门限时，判决当前频谱被占用；当频谱检测统计量T小于检测门限时，判决当前频谱空闲。
本发明的目的是通过反向散射与空中计算实现一种绿色的协作频谱检测方法。针对当前频谱检测过程中，频谱检测性能需要以大量的感知能耗和网络资源的占用为代价才得以保证的问题，构建了一种基于反向散射与空中计算实现频谱检测的方案。广泛分布的智能设备作为检测助手首先利用反向散射直接将接收到的频谱信号反射给请求者，无需进行本地感知；然后，然后基于空中计算，利用无线信道的叠加特性，实现频谱检测数据在同一反向散射信道上的空中融合，无需占用多个独立信道；最后，请求者仅需对空中融合后的频谱数据进行采样，做出最终的检测判决，无需进行额外的数据处理操作，最终实现能量与计算有效性，以及有限网络资源的节约。
本发明的有益效果是：1.利用反向散射收集并报告大量的频谱数据，不需要进行本地频谱感知，无需消耗感知能量，减少了感知代价，同时有利于请求者以较少的总报酬招募足够的检测助手。
2.利用空中计算，实现大量频谱数据在同一反向散射信道上的空中融合，节省了有限的网络信道资源，反向散射与数据融合同时进行进一步保证了信息时效性以及计算有效性。
3.由于每个检测助手报告的频谱数据是直接接收到的原始频谱信号，而非本地二值频谱决策或具有强烈主观性的预处理频谱数据，因此，在节约能耗与网络资源的同时，实现了更高的检测准确度。
图1是本发明的流程示意图；
图2是本发明的功能模块示意图；
图3是本发明实施例中硬融合频谱检测方法与本发明方法的协作检测概率随虚警概率变化图；
图4是本发明实施例中软融合频谱检测方法与本发明方法的协作检测概率随虚警概率变化图；
图5是本发明实施例中全局传输比特数与全局能量随检测助手数量的变化图。
本发明是通过反向散射与空中计算实现一种绿色的协作频谱检测方法，在频谱检测性能需要以大量感知能耗和网络资源为代价才得以保证的情况下，给出了一种基于反向散射与空中计算实现频谱检测的方法。首先，被激励的广泛智能设备作为频谱检测助手，利用反向散射直接将接收到的频谱信号反向散射给请求者，无需进行本地感知或数据处理程序。然后，利用空中计算，将所有被反射的频谱 信号通过同一个反向散射通道上报给请求者，利用无线信道的叠加特性，频谱检测数据的融合在空中而非请求者处实现。最后，感知请求者仅需对接收到的空中融合后的频谱数据进行采样分析，即可做出最终的检测判决。
为了更清楚地说明本发明的技术方案，下面结合附图对本发明的技术方案做进一步的详细说明：
如图1及图2所述；一种基于反向散射与空中计算的协作频谱感知方法，具体步骤包括如下：
步骤(1.1)、协作频谱检测系统中的反向散射设备利用反向散射向请求者反射接收到的频谱信号；
步骤(1.2)、基于空中计算，对N个被反向散射的频谱信号进行累加；
步骤(1.3)、请求者得到最终接受到的频谱信号；
步骤(1.4)、请求者对最终接收到的
进行M次采样，得到频谱检测统计量T；
步骤(1.5)、请求者确定检测门限；
步骤(1.6)、请求者将得到的频谱检测统计量T与检测门限进行比较，做出最终频谱判决。
进一步的，在步骤(1.1)中，所述协作频谱检测系统由N个反向散射设备，正在使用当前频段的用户IU，以及一个频谱检测请求者组成；参与协作频谱检测的第i个散射设备接收到来自IU的信号
其中，a＝1表示IU正发送信号，a＝0表示IU不发送信号，S
IU和p
IU分别表示IU的发送信号及其功率，h
i,IU是IU与第i个散射设备之间的信道衰落系数，n
i是第i个散射设备的阻抗噪声；然后，第i个散射设备将接收到的z
i进行反向散射，得到被反向散射后的频谱信号为
其中，f
e表示反向散射系数。
进一步的，在所述步骤(1.2)中，基于空中计算，对N个被反向散射的频谱信号进行累加的具体操作步骤如下：通过同一个反向散射信道传输来自N个散射设备的频谱信号，利用无线信道的叠加特性，实现频谱信号在无线信道中的自然累加，得到累加后的反向散射频谱信号
其中，p
i＝a·p
IU|h
i,IU|
2+v
i表示z
i的功率，v
i为n
i的功率，h
i,R表示第i个散射设备与请求者之间的信道衰落系数。
进一步的，在所述步骤(1.3)中，请求者得到最终接受到的频谱信号的具 体操作方法如下：
除了从反向散射信道接收到的空中融合后的信号z
R,Af之外，请求者还会从传输信道直接接收到来自IU的信号
其中，n
R表示请求者处的加性高斯白噪声，h
R,IU是IU与请求者之间传输信道的衰落系数；进一步得到请求者最终接收到的频谱信号
其中，H
0和H
1分别表示IU不发送信号与发送信号的状态；
请求者接收到的频谱信号包括，经过同一无线信道叠加后的N个被反向散射的频谱信号
及直接接收到来自IU的信号
其中，h
i,R表示第i个散射设备与请求者之间的信道衰落系数，p
i＝a·p
IU|h
i,IU|
2+v
i为z
i的功率，v
i为n
i的功率，h
R,IU表示IU与请求者之间的信道衰落系数，n
R为请求者处的加性高斯白噪声；
由此可得，当IU不发送信号时，请求者最终接收到的频谱信号为
当IU发送信号时，请求者最终接收到的频谱信号为
其中，z
R,Af表示累加后的反向散射频谱信号，z
R表示直接接收到的IU的信号。
进一步的，在所述步骤(1.4)中，得到频谱检测统计量T的具体操作方法如下：请求者对最终接收到的频谱信号
进行M次采样，其中，
表示第l个采样结果，然后对M次采样结果分别求模的平方，随后进行累加求取平 均值，从而得到频谱检测统计量
进一步的，在所述步骤(1.5)中，请求者确定检测门限的具体操作方法如下：求者根据预先设定的目标虚警概率
得到检测门限ε为
其中，v
R为n
R的功率，Q
-1(·)为Q函数的逆函数，Q函数为标准高斯的互补分布函数，表示为
进一步的，在所述步骤(1.6)中，最终频谱判决是：当频谱检测统计量T大于检测门限时，判决当前频谱被占用；当频谱检测统计量T小于检测门限时，判决当前频谱空闲。
综上所述，基于反向散射与空中计算的协作频谱检测方法。首先，被频谱感知请求激励的大量智能设备作为检测助手，将接收到的频谱信号直接反向散射给请求者，无需进行本地感知或数据预处理，然后，通过空中计算，利用无线信道的叠加特性，将所有被反向散射的频谱信号通过一个反向散射信道上报，实现频谱数据的空中融合，最后，请求者接收到空中融合后的反向散射频频信号，并进行采样，做出最终的频谱判决决策。
附图3是本发明方法和三种典型的硬融合协作频谱检测方法，即基于与融合，或融合，以及表决融合的方法，获得的检测概率随虚警概率的变化对比图；从图中可以看出，在总采样点数和检测助手数量相同的情况下，给定目标虚警概率时，本发明中基于反向散射与空中计算的频谱检测方法，可以获得比三种硬融合检测机制更高的检测概率；这是由于基于硬融合的频谱检测方法中，检测助手上报的频谱数据是具有强烈主观性且可能被本地篡改的二值频谱决策，而基于反向散射的协作设备可以将接收到的频谱信号直接反向散射给请求者，频谱数据具有高客观性，因此可以实现较高的协作频谱检测性能。
附图4是本发明方法和三种典型的软融合协作频谱检测方法，即基于等增益合并，最大比合并，以及似然比合并的方法，获得的检测概率随虚警概率的变化对比图；从图中可以看出，在总采样点数和检测助手数量相同的情况下，给定目标虚警概率时，本发明中基于反向散射与空中计算的频谱检测方法，可以获得比三种软融合检测机制更高的检测概率；这是由于本发明方法中，每个检测助手反向散射的频谱数据是原始接收的频谱信号，而没有经过本地感知、采样或量化； 这种频谱数据比软融合频谱检测方法中进行局部处理后报告的频谱数据更加客观和准确；此外，利用空中计算，所有反向散射的频谱信号在无线信道上自然地融合在一起，数据报告过程中不存在同频干扰；因此，请求者能够以更高的准确度获得空中融合后的频谱数据，进而实现更好的检测性能。
附图5是本发明方法与硬融合方法和软融合方法，在频谱数据上报过程中所需的全局传输比特与全局能量对比图；可以看出，在硬融合和软融合频谱检测方法中，全局传输比特数均随着检测助手数量的增加而增加，并且远远超过了本发明方法；这是由于，基于反向散射，检测助手可直接将频谱信号进行反射，无需量化；此外，当传输比特数越多，数据上报所需的能量就越多，因此，与硬融合和软融合方法相比，本发明基于反向散射与空中计算的协作频谱检测方法在提高检测性能的同时，实现了更好的节能效果。
实施例：
在一个B5G蜂窝物联网覆盖的办公楼中，一个移动用户在接入一段频谱之前，首先需要在其他设备的协助下检测该频谱是否空闲。因此，利用本专利发明的方法，该移动用户作为频谱检测请求者，N＝10个智能设备在请求者周围随机分布，作为反向散射设备。首先，根据步骤(1.1)所有反向散射设备利用反向散射向请求者反射接收到的该频谱的信号；然后基于空中计算，根据步骤(1.2)对10个被反向散射的频谱信号进行累加；其次，请求者根据步骤(1.3)得到最终接收到的频谱信号，并根据步骤(1.4)进行M＝200次采样，得到频谱检测统计量T；随后，请求者根据步骤(1.5)确定检测门限；最后，根据检测统计量与检测门限的比较结果，确定该频谱是否空闲:当检测统计量大于检测门限，判决该频谱被占用，请求者无法接入；当检测统计量小于检测门限，判决该频谱空闲，请求者可以接入。
Claims (7)
- 一种基于反向散射与空中计算的协作频谱感知方法，其特征在于，具体步骤包括如下：步骤(1.1)、协作频谱检测系统中的反向散射设备利用反向散射向请求者反射接收到的频谱信号；步骤(1.2)、基于空中计算，对N个被反向散射的频谱信号进行累加；步骤(1.3)、请求者得到最终接受到的频谱信号；步骤(1.4)、请求者对最终接收到的进行M次采样，得到频谱检测统计量T；
步骤(1.5)、请求者确定检测门限；步骤(1.6)、请求者将得到的频谱检测统计量T与检测门限进行比较，做出最终频谱判决。 - 根据权利要求1所述的一种基于反向散射与空中计算的协作频谱感知方法，其特征在于，在步骤(1.1)中，所述协作频谱检测系统由N个反向散射设备，正在使用当前频段的用户IU，以及一个频谱检测请求者组成；参与协作频谱检测的第i个散射设备接收到来自IU的信号其中，a＝1表示IU正发送信号，a＝0表示IU不发送信号，S IU和p IU分别表示IU的发送信号及其功率，h i,IU是IU与第i个散射设备之间的信道衰落系数，n i是第i个散射设备的阻抗噪声；然后，第i个散射设备将接收到的z i进行反向散射，得到被反向散射后的频谱信号为
其中，f e表示反向散射系数。
- 根据权利要求1所述的一种基于反向散射与空中计算的协作频谱感知方法，其特征在于，在所述步骤(1.2)中，基于空中计算，对N个被反向散射的频谱信号进行累加的具体操作步骤如下：通过同一个反向散射信道传输来自N个散射设备的频谱信号，利用无线信道的叠加特性，实现频谱信号在无线信道中的自然累加，得到累加后的反向散射频谱信号
其中，p i＝a·p IU|h i,IU| 2+v i表示z i的功率，v i为n i的功率，h i,R表示第i个散射设备与请求者之间的信道衰落系数。 - 根据权利要求1所述的一种基于反向散射与空中计算的协作频谱感知方法，其特征在于，在所述步骤(1.3)中，请求者得到最终接受到的频谱信号的具体操作方法如下：请求者接收到的频谱信号包括，经过同一无线信道叠加后的N个被反向散射的频谱信号及直接接收到来自IU的信号
其中，h i,R表示第i个散射设备与请求者之间的信道衰落系数，p i＝a·p IU|h i,IU| 2+v i为z i的功率，v i为n i的功率，h R,IU表示IU与请求者之间的信道衰落系数，n R为请求者处的加性高斯白噪声；由此可得，当IU不发送信号时，请求者最终接收到的频谱信号为当IU发送信号时，请求者最终接收到的频谱信号为
其中，z R,Af表示累加后的反向散射频谱信号，z R表示直接接收到的IU的信号。 - 根据权利要求1所述的一种基于反向散射与空中计算的协作频谱感知方法，其特征在于，在所述步骤(1.4)中，得到频谱检测统计量T的具体操作方法如下：请求者对最终接收到的频谱信号进行M次采样，其中，
表示第l个采样结果，然后对M次采样结果分别求模的平方，随后进行累加求取平均值，从而得到频谱检测统计量为
- 根据权利要求1所述的一种基于反向散射与空中计算的协作频谱感知方法，其特征在于，在所述步骤(1.5)中，请求者确定检测门限的具体操作方法如下：求者根据预先设定的目标虚警概率得到检测门限ε为
其中，v R为n R的功率，Q -1(·)为Q函数的逆函数，Q函数为标准高斯的互补分布函数，表示为
- 根据权利要求1所述的一种基于反向散射与空中计算的协作频谱感知方法，其特征在于，在所述步骤(1.6)中，最终频谱判决是：当频谱检测统计量T大于检测门限时，判决当前频谱被占用；当频谱检测统计量T小于检测门限时，判决当前频谱空闲。
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