Random Access Transport Capacity



This project develop a new metric for quantifying end-to end throughput in Multi hop wireless networks, which we term random access transport capacity, since the interference model presumes uncoordinated transmissions. The metric quantifies the average maximum rate of successful end-to-end transmissions, multiplied by the communication distance, and normalized by the network area. We show that a simple upper bound on this quantity is computable in closed-form in terms of key network parameters when the number of retransmissions is not restricted and the hops are assumed to be equally spaced on a line between the source and destination. We also derive the optimum number of hops and optimal per hop success probability and show that our result follows the well-known square root scaling law while providing exact expressions for the preconstants, which contain most of the design-relevant network parameters. Numerical results demonstrate that the upper bound is accurate for the purpose of determining the optimal hop count and success (or outage) probability.


Existence System:-


Complementary to the transport capacity research, some recent work has formulated the multi hop capacity problem as a line network without additional network interference. Both of these papers agree that numerous hops are helpful only in the “power-limited" regime, that is where the spectral efficiency is low and overcoming noise is the primary concern. Both also find that in the “bandwidth-limited regime".


Proposed System:-


The primary goal of this project is to address this limitation, and we develop a related metric which we term the random access transport capacity because it presumes packets are transported end-to-end over some distance, but assume independent locations and transmissions for the interferers. In that sense, one contribution of the paper is to find a middle ground between the transport and transmission capacity approaches into the capacity of multi hop wireless networks. As one might expect, the results of this paper follow the scaling law, but provide exact expressions for the “preconstants”, which is where nearly all the impact of any network design resides



The key distinction in this work is that we focus on achievable end-to end throughput and optimal transmission strategies and hop count rather than the stability of queues. We also consider noise whereas they do not, which is important since we show that it is in the power-limited regime where multi hop is beneficial.


The capacity of distributed wireless networks (i.e., ad hoc networks) is one of the most general and challenging open problems in information theory. Straightforward applications of known information theoretic tools and inequalities become intractable almost immediately and have hence yielded little in the way of results. This motivates the exploration of approaches to describing ad hoc network throughput that, while falling short of strict information theory upper bound standards, do provide insight into the fundamental trends on achievable throughput.



Project Implementation Module:-


1. Single Hop, Single Transmission


In this project we develop a new, quite general model for end-to-end throughput in a multi hop wireless network. We term the resulting metric random access transport capacity since the analysis requires all transmissions to be independent, which precludes cooperative transmission scheduling among the nodes, since this would generally couple transmissions and the active transmitter locations would no longer be independent. However, that the model does not preclude cooperative or multi packet reception, although we do not consider such approaches in this paper. The general model includes arbitrary paths of hops and an end-to-end delay/


2.Multiple Hops, Multiple Transmissions per Hop Module:


The random access transport capacity fora multi hop wireless network is the maximum average source to destination rate that can be sustained reliably over a distance withat most transmissionattempts per packet, normalized by the area of

the network. Formally,this paper is to move beyond the single-hop,single-transmission model to a network that allows multiplehops and multiple transmissions per hop, while retaining someof the tractability of the transmission capacity model.


3.Random access transport capacity Module:


That Module, only nodes that compose network detect other nodes of network and

Form network without alternative system management. Such as existing network, alternative base station, wire, cable, router and bridge are possible to compose network without infrastructure for composing network, see Fig below.



Node S checks the table that there is any information of node D, which can’t communicate with infrastructure. If it doesn’t have information about node D, node S sends RREQ messages, with sequence number to countermeasure loops, to the neighboring nodes via broadcast and starts path discovery process. RREQ messages are sent throughout the network within the predefined time period until it reaches to the destination node D,




In emergency situation, a node that can’t communicate with infrastructure turns on Ad hoc mode and communicate with a node who can communicate with infrastructure. However suppose one of the node in the path moves and path is broken. Then a surrounding node which is the nearest to the node S broadcasts RREQ_rp message to recover path. Using reserve part in the message, it notices to receive nodes that it is for path. See fig given below... Recovery


4. End-to-End Guaranteed Delivery Module:


Each transmission per hop to have independent success probability requires sufficient diversity in the interference and signal strength per transmission attempt, which could possibly be achieved through diversity techniques such as frequency hopping.


It follows the square-root scaling law for source-destination transmissions in large wireless networks. That is, unscheduled, channel-blind transmissions can achieve – given well-positioned relays – a transport capacity that scales the same as optimally scheduled nearest neighbor routing.







This project introduced a metric called the random access transport capacity, which is similar in spirit to the well-knowntransport capacity metric but made more tractable (andadmittedly, less general) with three admittedly strong assumptions:


(i) Uncoordinated transmissions, allowing a Poisson interference model,


(ii) Equally spaced relays on a line between the source and destination, which allows identical statistics for each hop,


(iii) An iid interference and signal sample for each retransmission, which allows a geometric distribution to model the number of transmissions required per hop. The primary benefit of these assumptions is that they allow a closed-form and reasonably tight upper bound to be derived for the end-to-end throughput in terms of the key network parameters, which is notoriously difficult to accomplish in a general model. Alternatively, the approach in this paper can be viewed as a nontrivial extension of the more recent transmission capacity line of work – all of which is single hop, single transmission, and not end-to-end – to a multi hop, end-to-end setting where retransmissions are allowed.


H/W System Configuration

        Processor                           -    Pentium –III


Speed                                -    1.1 Ghz

RAM                                 -    256 MB (min)

Hard Disk                          -   20 GB

Floppy Drive                     -    1.44 MB

Key Board                         -    Standard Windows Keyboard

Mouse                                -    Two or Three Button Mouse

Monitor                              -    SVGA



Software Requirements:-


          Language:             Java RMI, SWING, J2ME


          Mobile toolkit           : J2ME Wireless Toolkit 2.5.2


          Development Tool   : My Eclipse 3.0


           O/S                           :WIN2000/XP





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