优化CRSN中SSN配置：节能谱 sensing 方法
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更新于2024-08-28
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本文主要探讨了在认知无线传感器网络（Cognitive Radio Sensor Networks, CRSNs）中如何实现能源效率提升的谱感测方法，通过优化谱感测节点（Spectrum Sensing Nodes, SSNs）的数量和配置。在CRSNs中，传感器设备为了动态地访问授权频谱，必须频繁地进行频道检测，这会消耗大量电池能量，从而缩短网络的寿命。研究者关注的核心问题是：为了确保准确的谱感测性能，如何确定所需的SSN数量，以及如何有效地分配这些节点以达到能源利用的最优平衡。
文章标题"EESS: An Energy-Efficient Spectrum Sensing Method by Optimizing Spectrum Sensing Node in Cognitive Radio Sensor Networks"明确提出了研究的目标，即提出一种节能的谱感测策略，通过调整SSN的数量和分布来减少能耗，同时维持或接近所需的谱感测准确性。研究团队由来自南京信息科技学院、密歇根州立大学和扬州大学的学者组成，Zilong Jin为主要作者，他们共同探讨了这一问题并提交了这篇于2018年5月接收、6月接受最终修订、7月公开的研究论文。
研究方法论可能涉及以下几个方面：
1. **理论模型**：研究者可能首先建立了一个数学模型，考虑了SSN的增加对谱感测精确度的影响，以及这如何与每个SSN的能耗成本相互作用。这可能包括信号噪声比、误报率和漏报率等关键指标。
2. **优化算法**：为了找到最佳的SSN配置，可能会应用优化算法，如遗传算法、粒子群优化或者线性规划，以寻找在给定条件下最小化能耗而保持足够准确性的SSN数目和位置。
3. **仿真和评估**：通过计算机仿真，研究人员可能评估了不同SSN配置下的网络性能，包括能源消耗、感知时间以及整体网络寿命。这有助于量化优化策略的效果。
4. **实证验证**：最后，为了证明理论发现的有效性，可能会进行实地试验或使用模拟器，在CRSN环境中测试优化后的SSN配置。
结论部分可能会讨论这项工作的实际意义，比如对延长CRSN网络寿命、降低运营成本以及对环境可持续性的影响。此外，论文还强调了开放获取出版的版权许可，使得其他研究者可以自由使用和传播他们的研究成果。
该研究为解决CRSN中能源效率与谱感测精度之间的权衡问题提供了创新的解决方案，对于设计能效更高的CRSN架构具有重要的理论和实践价值。
Wireless Communications and Mobile Computing
Primary User
CRSDCRSD
Fusion Center
F : e network model of cognitive radio sensor network.
where
2
is the noise power and is the total number of
CRSDs in the network.
2.2. Spectrum Sensing
2.2.1. Spectrum Sensing Hypothesis. In a CRSN, the received
signal sample of the CRSDs can be formulated as a binary
hypothesis testing; the hypothesis
0
represents that no PU
exists in the spectrum, and hypothesis
1
represents that the
PU exists in the spectrum; that is [],
(
)
=
(
)
,
0
(
)
+
(
)
,
1
,
()
where ()is the received signal at a CRSD, ()is the signal
of the PU and is assumed to be an iid random process with
zero mean and variance,
2
𝑠
.enoise,(), is assumed to
be Gaussian iid random process with zero mean and variance,
2
𝑛
,and()and ()are independent.
In the channel sensing, the probability of detection,
𝑑
, and the probability of false alarm,
𝑓
,aresignicant
indicators for measuring the spectrum sensing accuracy.
𝑑
and
𝑓
are dened as the probabilities of detecting the PU
under hypotheses
1
and
0
, respectively.
2.2.2. PU Detection. e energy detection [] method can
be utilized by the CRSDs as their channel sensing method.
e test statistics for energy detector is given by
=
1
𝑀
𝑚=1
(
)
2
,
()
where()isthesampleofreceivedsignalatCRSDsand=
2where TW represents the product of detection time, T,
and signal bandwidth, W.WhenM is relatively large (e.g.,
M >), ()can be approximated as a Gaussian random
variable under both hypotheses
0
and
1
,withmeans
0
,
1
and variances
2
0
,
2
1
respectively, which can be given by []
0
=
1
=
𝑗
+1
2
0
=2
2
1
=22
𝑗
+1,
()
where
𝑗
is SNR of the PU signal at the j-th CRSD.
For a given threshold, , the probability of false alarm and
detection probability of the j-th CRSD are given by
𝑓,𝑗
=>|
0
=
2
𝑛
−1
, ()
𝑑,𝑗
=>|
1
=
2
𝑛
−
𝑗
−1
2+1
,
()
where Q(⋅) denotes the Gaussian Q-function. Without loss of
generality, we set the detection threshold of all the CRSDs
to be the same; hence, the false alarm probability of all the
CRSDs becomes xed and is denoted by
𝑓
. erefore, () and
() can be represented by () and the probability was denoted
by []
𝑑,𝑗
=>|
1
=
−1
𝑓
−
𝑗
2
𝑗
+1
.
()
2.2.3. Cooperative Spectrum Sensing. In a densely deployed
network, we consider that the sensing results of neighboring
CRSDs are similar. And the network can be divided into
multiple clusters based on the method proposed in Section .
erefore, the sensing results in a cluster can be represented
by one node which is called SSN in this paper. To reduce
the communication overhead, each SSN sends the nal -bit
decision to the FC which is an energy-unconstrained center
for data aggregation and decision making and can decode
and combine all the decoded information from all SSNs using
the logic-OR-rule to make a nal decision []; that is to say,
the PU is considered to be occupying the channel if at least
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