协同频谱感知中的空间复用媒体访问控制策略研究

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"无线传感器网络,2017,9,205-237\nhttp://www.scirp.org/journal/wsn\nISSNOnline:1945-3086\nISSNPrint:1945-3078\nDOI:10.4236/wsn.2017.97012 July 31, 2017 Media Access with Spatial Reuse for Cooperative Spectrum Sensing\n Xiao Shao, Harry Leib\n Department of Electrical and Computer Engineering, McGill University, Montreal, Canada" 协同频谱感测是认知无线电(Cognitive Radio, CR)技术中的核心功能，旨在高效利用有限的无线电频谱资源。随着无线通信服务需求的持续增长和频谱资源的日益紧张，认知无线电作为一种创新解决方案，允许次级用户在不干扰主要用户的情况下共享频谱。本文聚焦于一个特定的检测转发无线传感器网络(Detect-and-Forward Wireless Sensor Network, DetF WSN)，它采用k-out-of-n决策融合策略，以应对报告通道错误时的协作频谱感知。空间复用是一种有效的频谱效率提升手段，尤其在无线传感器网络(WSN)中。通过空间复用，多个传感器节点可以在同一时隙内同时传输数据，从而增加网络的吞吐量和整体性能。文章探讨了基于时分多址(TDMA)和正交频分多址(OFDMA)的空间复用媒体访问控制(MAC)协议设计，这些协议旨在减少冲突，节省用于WSN内部通信的资源。在MAC协议设计中，作者考虑了两种方法：贪婪算法和自适应模拟退火(Adaptive Simulated Annealing, ASA)算法。贪婪算法通常追求局部最优解，而ASA算法则是一种全局优化工具，能够探索更广泛的解决方案空间，特别是在复杂的优化问题中。这两种方法被应用于在Rician衰落环境下的网格网络，以评估它们对频谱感测性能的影响。通过模拟和分析，文章展示了在不同条件下，两种设计方法如何影响WSN的频谱感测性能。Rician衰落模型考虑了多径传播引起的信号衰减，这是无线通信中常见的现象。这些结果对于理解WSN在实际环境中的表现以及优化空间复用MAC协议具有重要意义。总结而言，本文深入研究了在WSN中实现协同频谱感测的空间复用MAC协议设计，探讨了两种不同的优化算法，并在Rician衰落环境中进行了性能评估。这些研究成果为提高WSN的频谱效率和可靠性提供了理论支持和实践指导，对于未来智能无线网络的设计和优化具有参考价值。

X. Shao, H. Leib

210

Without loss of generality, in our work we choose binary phase shift keying

(BPSK). The average BEP for BPSK in Rician fading over reporting channel

ss→

is ([18], p. 126)

( )

( ) ( )

π 2

1 sin

exp d

1 sin 1 sin

ji ji

εφ

φγ φγ



= −



++ ++





∫

(8)

where

is the Rician parameter, and

,ji

is the average received SNR at

determined by the path loss as well as both AWGN and additive interference.

Hence from (3),

( )

{ }

( )

ij T

N kj T

Ad P

P Ad P

−

∈

∑



If an error occurs when

( )

s ∈

transmits its decision to

, a



(or



)

decision of

will be turned into



(or



) when received by

. Thus, as

shown in [8],

( )

s∀∈

, the equivalent probabilities of detection and false

alarm received by

are

( )

{ } ( ) ( )

, 1 1 , , ,,

prob receives from | 1 1 ,

dj i j dj ji dj ji

P s sP P

εε

= − +−  

(9)

(

)

{

}

(

) (

)

, 1 0 , , ,,

prob receives from | 1 1 .

fj i j fj ji fj ji

P s sP P

εε

= − +−



 

(10)

We assume that each sensor experiences i.i.d. Rician fading, and performs

spectrum sensing independently. In this paper, we employ combinatorial nota-

tions as in [23]. We use

( )









to represent the set of all

-combinations of

( )



having

(

)

mn m





−



members,

( )

(

)



∈ ≤≤







repre-

sents one

-combination of

( )



(

( )



are arranged in lexicographic order),

and

( ) ( )



denotes the relative complement of

( )



(

)



. Then from

([16], Equation (11)) we obtain the probabilities of detection and false alarm at

sensor

after

-out-of-

decision fusion,

( )

(

)

(

)

( )

()

, ,,

ml ml

di i dj dj

mkl

Qk P P







= =

∈∈





= −





∑∑

∏∏

 

(11)

( )

(

)

()

, ,,

ml ml

fi i fj fj

mkl

Qk P P







= =

∈∈





= −





∑∑

∏∏

 

(12)

where

( )

and

( )

are given in (9) and (10), and

kn≤≤

‘s

decision fusion threshold.

Figure 2 we present the impacts of reporting channel errors on cooperative

spectrum sensing performance by illustrating ROC curves of

( )

,di i

versus

( )

,fi i

. There are 5 sensors employing identical energy detection thresholds

, and

makes a global decision. Thus, we omit the second subscript

( )

,di i

and

( )

fi i

. The Rician fading parameter is

7K =

, and the average

received SNR of the primary signal

is identical at each sensors. In Figure

2(a), the vertical separation between curves indicates the degradation introduced

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