Achievable Capacity Limit of High Performance Nodes for Wireless Mesh Networks

Achievable Capacity Design for Irregular and Clustered High Performance Mesh Networks

“…Then, one obtains network capacity by summing over all bits in the network as have provided in (2). Therefore, using similar arguments, steps (3), (7) through (10) as provided in the Proof of Theorem 1, the interference constraint protocol holds for any networks [6]. Consequently, the derived E2E capacity limit will be upper bound according to:…”

“…To illustrate this concept further, suppose node A is transmitting a bit to node B, while node C on one side of node B, is simultaneously transmitting a bit to node D furthest from node A and both the sessions are over a common frequency channel, W. Then, using the interference protocol model and the geometry sufficient for successful reception, node E which is between nodes B and C, cannot transmit at the same time with nodes A and C [6]. That is,…”

“…Proof: We assume a clustered placement of the mesh network as a special case of regular or uniform HPNs placement [6]. However, in this case the node densities are: δ 1 = n 1 /A 1 as the density of nodes within a cluster consisting of n 1 nodes in A 1 of an area and as the density of clusters consisting of n 2 clusters in A 2 of an area.…”

“…The increasing question is how the end to end (E2E) theoretical capacity of such multi-radio network scales with the node density, irregularity of node placement and link obstructions [6]. In their seminal work, Gupta and Kumar [7] determined the capacity of single radio single channel networks.…”

“…Lastly, nodes are randomly located on the surface of a torus of unit area to avoid technicalities arising out of edge effects. However, in realistic networks, location of nodes is determined by the irregularity of the terrain, the presence of tree foliage [6], and needs and locations of terminal users [10]. Moreover, typical rural based wireless networks can be described by (i) long single hop links, (ii) limited and unreliable energy sources, and (iii) clustered distribution of Internet users [11].…”