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Nt1330 Unit 1 Algorithm Paper

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The complexity and memory requirements of the algorithm are in the order of $D_\mathcal{D}.{\rm N}$, denoted as $O(D_\mathcal{D}{\rm N})$. The algorithm become more effective when the minimum difference $\Delta Y_k$ is large between the transmission probability gains $Y_k$. Consequently, our algorithm finds an optimal solution coupled with linear complexity, when the network become more heterogeneous due to small ${\rm N}$. Our algorithm also finds the optimal solution with an increased complexity, when the network becomes more homogeneous or less heterogeneous due to increasing ${\rm N}$. The quantization precision ${\rm N}$ in Algorithm 1, is a physical quantity specified by the underlying network that elaborate the designing of quantization step. The total content size $H$ depends on the required content transmission rate $r_{c_i, d_i}$ and $Y_k$, whereas $Y_k \sigma$ are defined by the contact dynamics of the nodes in a network. However, if the network is difficult by the required level of transmission ratio, the values of $Y_k$ and the value of $\Delta Y_k$, such that $\rm N$ is too large, then the designer may have to comprise by reducing the desired level of transmission ratio to reduce $\rm N$. In results, the sub-optimal …show more content…

The transmission rate and contact rates between D2D pairs are often quasi-statistic information. In cellular network, all the nodes of the network can be known by their contact rates and transmission rate to the source node, as they can access the services provided by the network. In D2D communication, where the base station is not taking part of the data transmission, the D2D pair can transmit the contact rates and transmission rate by opportunistic contacts. Therefore, the information for the source node for content transmission and resource allocation of D2D pairs are distributed in the

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