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Dynamic spectrum access with packet size adaptation and residual energy balancing for energy-constrained cognitive radio sensor networks

机译:具有数据包大小自适应和残差能量平衡的动态频谱访问,用于能量受限的认知无线电传感器网络

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We demonstrate an improvement in energy efficiency and network lifetime for the cluster-based multichannel cognitive radio sensor network (CRSN). The improvement roots from two techniques proposed in dynamic spectrum access. The first technique exploits packet size adaptation: varying the packet size to adapt the transmission over the state-varying channel. This is to efficiently utilize the battery of sensors by having the most appropriately sized packets successfully transmitted, in accordance with the instantaneous channel conditions. The second technique focuses on channel assignment with awareness of the residual energy of sensors, such that sensors can spend their energy in a balanced way. This helps to prolong the network lifetime, compared to the random channel pairing approach. As all those techniques rely on the estimates of channel states and their performance is tied with the estimation accuracy, we theoretically derive a polynomial-time resolvable expression for the maximum-likelihood (ML) estimator PMF function. In light of this expression, the impact of channel estimation accuracy on network performance is thereby illustrated.
机译:我们展示了基于群集的多通道认知无线电传感器网络(CRSN)的能效和网络寿命方面的改进。改进源自动态频谱访问中提出的两种技术。第一种技术利用分组大小自适应:改变分组大小以适应状态变化信道上的传输。通过根据瞬时信道条件成功发送最合适大小的数据包,可以有效地利用传感器的电池。第二种技术着眼于信道分配,了解传感器的剩余能量,以便传感器可以平衡地消耗能量。与随机信道配对方法相比,这有助于延长网络寿命。由于所有这些技术都依赖于信道状态的估计,并且它们的性能与估计精度有关,因此我们从理论上推导了最大似然(ML)估计器PMF函数的多项式时间可解析表达式。根据该表达式,由此示出了信道估计精度对网络性能的影响。

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