A Constructive Capacity Lower Bound of the Inhomogeneous Wireless Networks

Many research results in the direction of wireless network capacity are based on the homogeneous Poisson node process and random homogeneous traffic. However, most of the realistic wireless networks are inhomogeneous. And for this kind of networks, this paper gives a constructive capacity lower bound, which may be effective on network designing. To ensure significant inhomogeneities, we select both inhomogeneous node process and traffic. We divide the transmission into two parts: intra-cluster transmission and inter-cluster transmission. Within each distinct cluster, a circular percolation model is proposed and the highway system is established. Different with regular rectangle percolation model, the highway in our model is in the radial direction or around the circle. Based on this model, we propose a routing strategy and get the intra-cluster per-node rate. In the following, among these clusters, we set many “information pipes” connecting them. By getting the results of per-node transmission rate of each part, we can find that the bottleneck of the throughput capacity is caused by the difference of the node density all over the network region. Specially, the lower bound interval of the capacity can be easily obtained when the traffic is inhomogeneous.

[1]  Panganamala Ramana Kumar,et al.  A network information theory for wireless communication: scaling laws and optimal operation , 2004, IEEE Transactions on Information Theory.

[2]  R. Srikant,et al.  Optimal Delay–Throughput Tradeoffs in Mobile Ad Hoc Networks , 2008, IEEE Transactions on Information Theory.

[3]  David Tse,et al.  Mobility increases the capacity of ad hoc wireless networks , 2002, TNET.

[4]  Gabriel-Miro Muntean,et al.  CASHeW: Cluster-based Adaptive Scheme for Multimedia Delivery in Heterogeneous Wireless Networks , 2012, Wirel. Pers. Commun..

[5]  Stavros Toumpis,et al.  Capacity bounds for three classes of wireless networks: asymmetric, cluster, and hybrid , 2004, MobiHoc '04.

[6]  Rick S. Blum,et al.  Capacity of clustered ad hoc networks: how large is "Large"? , 2006, IEEE Transactions on Communications.

[7]  Emre Telatar,et al.  Information-theoretic upper bounds on the capacity of large extended ad hoc wireless networks , 2005, IEEE Transactions on Information Theory.

[8]  Michele Garetto,et al.  Capacity Scaling of Wireless Networks with Inhomogeneous Node Density: Lower Bounds , 2009, IEEE INFOCOM 2009.

[9]  Massimo Franceschetti,et al.  Closing the Gap in the Capacity of Wireless Networks Via Percolation Theory , 2007, IEEE Transactions on Information Theory.

[10]  Panganamala Ramana Kumar,et al.  Scaling Laws for Ad Hoc Wireless Networks: An Information Theoretic Approach , 2006, Found. Trends Netw..

[11]  Michele Garetto,et al.  Information-Theoretic Capacity of Clustered Random Networks , 2011, IEEE Trans. Inf. Theory.

[12]  J. Møller,et al.  Shot noise Cox processes , 2003, Advances in Applied Probability.

[13]  Sanjeev R. Kulkarni,et al.  A deterministic approach to throughput scaling in wireless networks , 2002, IEEE Transactions on Information Theory.

[14]  Eytan Modiano,et al.  Capacity and delay tradeoffs for ad hoc mobile networks , 2005, IEEE Trans. Inf. Theory.

[15]  Panganamala Ramana Kumar,et al.  RHEINISCH-WESTFÄLISCHE TECHNISCHE HOCHSCHULE AACHEN , 2001 .

[16]  Massimo Franceschetti,et al.  The Capacity of Wireless Networks: Information-Theoretic and Physical Limits , 2009, IEEE Transactions on Information Theory.

[17]  Devavrat Shah,et al.  Throughput-delay trade-off in wireless networks , 2004, IEEE INFOCOM 2004.