Abstract
In this chapter, we investigate the routing problem in multi-hop wireless networks, whereby link qualities are unknown and time-varying due to fast-fading wireless channels.
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References
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Appendix
Appendix
The proof of Theorem 7.1 in Sect. 7.3 is based on some auxiliary results and Theorem 6.9 of [CL06], reproduced below as Theorem 7.3.
Theorem 7.3
Let \(\varPhi (U)=\psi (\sum _{i=1}^{N}\phi (u_i))\), where \(U=(u_1,\ldots ,u_N)\). Consider a selection strategy that selects action \(I_t\) at time t according to distribution \(P_t = \{p_{1,t}, p_{2,t},\ldots , P_{N,t}\}\), whose elements \(p_{i,t}\) are defined as:
where \(R_{i,t-1}=\sum _{s=1}^{t=1}(g(s,i)-g(s,I_s))\).
If the following assumptions hold:
-
1.
\(\sum _{t=1}^{n}\frac{1}{\gamma ^2_t}=o(\frac{n^2}{\ln {n}})\)
-
2.
For all vector \(V_t=(v_{1,t},\ldots ,v_{n,t})\) with \(\mid {v_{i,t}}\mid \le \frac{N}{\gamma _t}\), we have
$$\begin{aligned} \begin{aligned} \lim _{n\rightarrow \infty }\frac{1}{\psi (\phi (n))}\sum _{t=1}^{n}C(V_t)=0, \end{aligned} \end{aligned}$$(7.11)where
$$\begin{aligned} C(V_t)=\sup _{U\in {R^N}}\psi ^{\prime }\left( \sum _{i=1}^{N}\phi (u_i)\right) \sum _{i=1}^{N}\phi ^{\prime \prime }(u_i)v^2_{i,t}. \end{aligned}$$ -
3.
For all vectors \(U_t=(u_{1,t},\ldots ,u_{n,t})\), with \(u_{i,t}\le {t}\),
$$\begin{aligned} \begin{aligned} \lim _{n\rightarrow \infty }\frac{1}{\psi (\phi (n))}\sum _{t=1}^{n}\gamma _t\sum _{i=1}^{N}\nabla _i\varPhi (U_t)=0, \end{aligned} \end{aligned}$$(7.12) -
4.
For all vectors \(U_t=(u_{1,t},\ldots ,u_{n,t})\), with \(u_{i,t}\le {t}\),
$$\begin{aligned} \begin{aligned} \lim _{n\rightarrow \infty }\frac{\ln {n}}{\psi (\phi (n))}\sqrt{\sum _{t=1}^{n}\frac{1}{\gamma ^2_t}\left( \sum _{i=1}^{N}\nabla _i\varPhi (U_t)\right) ^2}=0, \end{aligned} \end{aligned}$$(7.13)
Then the selection strategy satisfies:
Proof of Theorem 7.1
In order to prove Theorem 7.1, we show that the selected parameters \(\gamma _t=t^{-\frac{1}{3}}\) and \(\eta _t=\frac{\gamma ^3_t}{N^2}\) satisfy (1)–(4) in Theorem 7.3.
-
1.
For \(\gamma _t=t^{-\frac{1}{3}}\), we have
$$\begin{aligned} \begin{aligned} \sum _{t=1}^{n}\frac{1}{\gamma ^2_t}=\sum _{t=1}^{n}t^{\frac{2}{3}}=H_n\left[ \frac{-2}{3}\right] . \end{aligned} \end{aligned}$$(7.15)Then,
$$\begin{aligned} \begin{aligned} \lim _{n\rightarrow \infty }\frac{\ln {n}}{n^2}\sum _{t=1}^{n}\gamma ^2_t=\lim _{n\rightarrow \infty }\frac{\ln {n}}{n^2}H_n\left[ \frac{-2}{3}\right] =0. \end{aligned} \end{aligned}$$(7.16) -
2.
For \(\psi (x)=\frac{1}{\eta _t}\ln {x}\) and \(\phi (x)=\exp (\eta _t{x})\), we obtain
$$\begin{aligned} \begin{aligned} C(V_t)=sup\left( \eta _t\sum _{i=1}{N}v^2_{i,t}\right) =\frac{\eta _tN^3}{\gamma ^2_t}. \end{aligned} \end{aligned}$$(7.17)Hence,
$$\begin{aligned} \begin{aligned} \lim _{n\rightarrow \infty }\frac{1}{\psi (\phi (n))}\sum _{t=1}^{n}C(V_t)&=\lim _{n\rightarrow \infty }\frac{1}{n} \sum _{t=1}^{n}t^{-\frac{1}{3}}\\&=\lim _{n\rightarrow \infty }\frac{1}{n}H_n\left[ \frac{1}{3}\right] =0. \end{aligned} \end{aligned}$$(7.18) -
3.
For \(\varPhi (U)=\frac{1}{\eta _t}\ln \left( \sum _{i=1}^{N}\exp (\eta _t{u_i})\right) \), \(\nabla _i\phi (U_t)\) is given by:
$$\begin{aligned} \begin{aligned} \nabla _i\phi (U_t)=\frac{\exp (\eta _t{u_i})}{\sum _{i=1}^{N}\exp (\eta _tu_i)}. \end{aligned} \end{aligned}$$(7.19)Therefore,
$$\begin{aligned} \begin{aligned}&\lim _{n\rightarrow \infty }\frac{1}{\psi (\phi (n))}\sum _{t=1}^{n}\gamma _t \sum _{i=1}^{N}\nabla _i\phi (U_t)=\\&\lim _{n\rightarrow \infty }\frac{1}{n}\sum _{t=1}^{n}t^{-\frac{1}{3}}\sum _{i=1}{N}\frac{\exp (\eta _tu_i)}{\sum _{i=1}^{N}\exp (\eta _tu_i)}=\\&\lim _{n\rightarrow \infty }\frac{1}{n}H_n\left[ \frac{1}{3}\right] =0. \end{aligned} \end{aligned}$$(7.20) - 4.
Therefore, (7.14) ensures since all assumptions (1)–(4) are held, which completes the proof of Theorem 7.1.
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Zheng, R., Hua, C. (2016). Online Routing in Multi-hop Wireless Networks. In: Sequential Learning and Decision-Making in Wireless Resource Management. Wireless Networks. Springer, Cham. https://doi.org/10.1007/978-3-319-50502-2_7
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