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Link scheduling for throughput maximization in multihop wireless networks under physical interference

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Abstract

We consider the problem of link scheduling for throughput maximization in multihop wireless networks. Majority of previous methods are restricted to graph-based interference models. In this paper we study the link scheduling problem using a more realistic physical interference model. Through some key observations about this model, we develop efficient link scheduling algorithms by exploiting the intrinsic connections between the physical interference model and the graph-based interference model. For one variant of the problem where each node can dynamically adjust its transmission power, we design a scheduling method with O(g(E)) approximation to the optimal throughput capacity where g(E) denotes length diversity. For the other variant where each node has a fixed but possible different transmission powers for different nodes, we design a method with O(g(E))-approximation ratio when the transmission powers of all nodes are within a constant factor of each other, and in general with an approximation ratio of \(O(g(E)\log \rho )\) where \(\log \rho\) is power diversity. We further prove that our algorithm for fixed transmission power case retains O(g(E)) approximation for any length-monotone, sub-linear fixed power setting. Furthermore, all these approximation factors are independent of network size .

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Notes

  1. Other constant approximation algorithms on the MWISD problem are also applicable, depending on the desired tradeoff between computation complexity and approximation ratio. For example, we can also use other algorithms with lower complexity and smaller constant approximation ratios.

  2. For convenience, we let \(3^{\kappa } > \sigma\), but we do not necessarily assume \(3^{\kappa } > \sigma\), we later show that the lemma also holds when \(3^{\kappa } \le \sigma\).

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Acknowledgments

The research of Xiang-Yang Li is partially supported by NSF CNS-1035894, NSF ECCS-1247944, NSF CMMI 1436786. The research of Min Liu is supported by the National Natural Science Foundation of China (Nos. 61132001, 61120106008, 61472402, 61472404, 61272474 and 61202410).

Author information

Authors and Affiliations

  1. Institute of Computing Technology, Chinese Academy of Sciences, Beijing, China

    Yaqin Zhou, Min Liu & Zhongcheng Li

  2. School of Computer Science and Technology, University of Science and Technology of China, Hefei, 230026, China

    Xiang-Yang Li

  3. Department of Computer Science, Illinois Institute of Technology, Chicago, IL, 60616-3793, USA

    Xiang-Yang Li

  4. Center of Cybersecurity and Wireless Innovations, University of Toledo, Toledo, OH, USA

    Xiaohua Xu

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  1. Yaqin Zhou
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  2. Xiang-Yang Li
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  3. Min Liu
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  4. Zhongcheng Li
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Corresponding author

Correspondence to Xiang-Yang Li.

Appendix

Appendix

Proof

Our proof bases on the fact of fading metrics [13]. In fading metrics the path loss exponent \(\kappa\) must be strictly greater than the doubling dimension of the metric, and the doubling dimension \(A=n\) for the \(n-\)dimensional Euclidean space. We have assumed the Euclidean plane and the path loss exponent \(\kappa > 2\), therefore these assumptions construct a fading metric of doubling dimension \(A=2\). For the fading metric of doubling dimension A, there are at most \(Cg^A\) balls of radius Z inside a ball of radius gZ for any \(g > 0\). Here \(C=\frac{1}{6}\pi \sqrt{3} \approx 0.907\) for the Euclidean plane. A ball of radius \(\mu\), centered at v is defined by \(B(v,\mu )\).

Let \(X_g = \{w \in V(L) | d(w,v) < gd \slash 2 \}\) for \(g>0\). The distance between any two nodes in V(L) is at least d. It implies \(B(v,(g+1)d \slash 2)\) contains all balls of radius of \(d \slash 2\) centered at the nodes in \(X_g\) and these balls do not intersect. We have \(|X_2|=0\) for the smallest mutual distance between any pair of nodes is d. Then for each node \(v \in V(L),\) it holds that,

$$\begin{aligned} \sum _{w \in V(L)} {\frac{R^{\kappa }}{d(w,v)^{\kappa }}} &\le \sum _{g=3}^{\infty }|X_{g} \backslash X_{g-1}| \frac{R^{\kappa }}{[(g-1) d \slash 2]^{\kappa }} \\ &\le \frac{R^{\kappa }}{(d \slash 2)^{\kappa }} \cdot \sum _{g=3}^{\infty } |X_{g}| \left( \frac{1}{(g-1)^{\kappa }}-\frac{1}{g^{\kappa }}\right) \\ &\le \frac{R^{\kappa }}{(d \slash 2)^{\kappa }} \cdot \sum _{g=3}^{\infty } |X_{g}| \frac{\kappa }{(g-1)^{\kappa +1}} \\ & \le \frac{R^{\kappa }}{(d \slash 2)^{\kappa }} \sum _{g=3}^{\infty } C \cdot (g+1)^{A} \frac{\kappa }{(g-1)^{\kappa +1}} \\ & < \frac{ 2^{2\kappa +1}\kappa C}{\theta ^{\kappa } (\kappa -A)} = \frac{ 2^{2\kappa +1} \sqrt{3} \pi \kappa }{6 (\kappa -2) \theta ^{\kappa } } = O(1/\theta ^{\kappa }) \end{aligned}$$

\(\square\)

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Zhou, Y., Li, XY., Liu, M. et al. Link scheduling for throughput maximization in multihop wireless networks under physical interference. Wireless Netw 23, 2415–2430 (2017). https://doi.org/10.1007/s11276-016-1276-1

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