Improving Cross Layer Design for Multimedia Applications Over Distributed Radio Network using Adaptive Equalizer

— Delay in the passage of piece of information from the transmitting point to the receiving point has become a very big problem in our communication network. This can be overcome by improving cross layer design for multimedia applications over distributed radio network using adaptive equalizer. This is done in this manner by characterizing the network understudy, determining the bit error rate and congestion from the characterized network that could cause the passage of information from sender to receiver difficult, determining the shortest route from the characterized network, designing a Simulink model that will reduce interference and congestion in improving cross layer design for multimedia applications over distributed radio network using adaptive equalizer and Comparing conventional and optimized adaptive equalizer. The result obtained from the analysis shows that using adaptive equalizer gives a better communication network than using conventional approach.


I. INTRODUCTION
Over the last years a number of new protocols have been developed for multimedia applications in the whole OSI layer's scale. The RTP and RTCP protocols (Schulzrinne, 2003), which operate on the transport layer usually on top of the UDP protocol, have been especially designed for multimedia data transmission. The RTSP(Schulzrinne, 1998) protocol offers control mechanisms over real time multimedia transmission whereas SIP (Schulzrinne, 2002) and H.323 are used in multimedia conferencing. Apart from the above developments there have been a number of proposals for improving QoS in multimedia applications through cross layer adaptation strategies. In Van der (Schaar, 2005) the need of a cross-layer optimization is examined and an adaptation framework is proposed amongst the APP, the MAC and the Physical (PHY) layers. In Shakkottai, 2003 the issue of cross-layer design in wireless networks is addressed. The focus is on the way that higher layers share knowledge of the PHY and MAC layers conditions in order to provide efficient methods to allocate network resources over the Internet. In Van der Schaar, 2003 a joined APP and MAC adaptation is proposed with the use of MPEG-4 and the latest Fine Granularity Scalability (FGS) extension. In this work, packets containing multimedia data are classified into different classes and in the light of poor network conditions only packets with high value are transmitted. The network conditions are jointly measuredby combining the information obtained by the retransmission number of a lost MACframes (ARQ) and the information provided by the RTCP protocol. Signaling issues between the layers for cross-layer optimization over wireless networks are examined in Wang, 2003.

To characterize the network understudy.
Distributed Radio Systems include distributed antenna systems, distributed MIMO, distributed wireless networks and distributed spectrum sensing. ... a) is the traditional tree structure of a cellular system. Mobile terminals or antennas send the signal to base transceiver station (BTS).
An hourly measured data of packet loss was collected from GLO network in Enugu metropolis for eight days as shown in table 1.
Where L is packet loss W is the network congestion Then, make W the subject formula in equation 1 The mathematical model for congestion in the network is as shown in equation

To determine an ideal bit error rate convenient for the characterized network
The bit error rate that caused the collected packet loss in the communication network understudy is calculated with equation 3 Taking into consideration the worst-case scenario, the linear relationship between BER and packet error rate (PER) is expressed as: Where the MTU is the maximum transmission unit, and using the Ethernet standards it is set to 1500 bytes for the simulations and then the MTU is increased to improve performance. A conversion from 8 bits to 1 byte is shown, Recall 1 byte = 8bits, 1500bytes = 8 x 1500 = 12000bits MTU = 12000bits PER is packet loss and BER is bit error rate to evaluate the bit error rate in day one when the packet loss is 452.
Make BER the subject formula in equation 3 In graph theory, the shortest path problem is the problem of finding a path between two vertices (or nodes) in a graph such that the sum of the weights of its constituent edges is minimized.
The problem of finding the shortest path between two intersections on a road map (the graph's vertices correspond to intersections and the edges correspond to road segments, each weighted by the length of its road segment) may be modeled by a special case of the shortest path problem in graphs. Traffic is routed through the middle link (4, 6), congestion occurs.
If, instead, paths (I --> 3 --> 6) and (2 --> 5 --> 6) are used, the average delay is small. equal 10. Thus, the total throughput can be no more than 15 units. The shortest distance gotten is 4 that is the distance when bit error rate reduces thereby minimizing congestion in improving cross layer design for multimedia applications over distributed radio network using adaptive equalizer.

5.
To design a Simulink model that will reduce interference and congestion in improving cross layer design for multimedia applications over distributed radio network without using adaptive equalizer The equalizer is a device that attempts to reverse the distortion incurred by a signal transmitted through a channel. In digital communication its purpose is to reduce inter symbol interference to allow recovery of the transmit symbols. It can be a simple linear filter or a complex algorithm. vi. Adaptive Equalizer: It is typically a linear equalizer or a DFE, which updates the equalizer parameters (such as the filter coefficients) as it is processes the data. It uses the MSE cost function and it assumes that it makes the correct symbol decisions and uses its estimate of the symbols to compute e which is defined above. vii.
Viterbi Equalizer: It Finds the optimal solution to the equalization problem. It is having a goal to minimize the probability of making an error over the entire sequence.
viii. BCJR Equalizer: It uses the BCJR algorithm whose goal is to minimize the probability that a given bit was incorrectly estimated. ix.
Turbo Equalizer: It applies turbo decoding while treating the channel as a convolutional code.
The equalizer used in this work is an adaptive equalizer because it uses the MSE cost function and it assumes that it makes the correct symbol decisions. Figure 2 shows designed Simulink model in improving cross layer design for multimedia applications over distributed radio network without using adaptive equalizer.

Fig.2: Designed Simulink Model in Improving Cross Layer Design for Multimedia Applications Over Distributed Radio Network Without Using Adaptive Equalizer
The detailed result is as shown in tables 4, 5 and figures 4 and 5 respectively. Figure 3 shows designed Simulink model that will reduce interference and congestion in improving cross layer design for multimedia applications over distributed radio network using adaptive equalizer. Meanwhile the simulated results are shown in tables 6 and 7, figures 6 and 7 respectively.  Figure 4 shows result for conventional bit error rate in improving cross layer design for multimedia applications over distributed radio network without using adaptive equalizer. Figure4 shows that the highest bit error rate occurred at bit error rate and time coordination of (0.2116, 6) at this bit error rate the transmission of data from sender to receiver will become apparently impossible.

Fig.5: Result for conventional congestion in improving cross layer design for multimedia applications over distributed radio network without using adaptive equalizer
Figure5 shows result for conventional congestion in improving cross layer design for multimedia applications over distributed radio network without using adaptive equalizer. In figure5, the highest congestion occurred at coordination of congestion and time of (0.0741, 4). At this congestion stage data will be delayed to reach its destination in time.  Figure 6 shows result for bit error rate in improving cross layer design for multimedia applications over distributed radio network using adaptive equalizer. Figure 6 shows that the highest bit error rate to time coordination occurred at (0.1058,6). At this point the passage of data from sender to receiver is fast.

Fig.7: Congestion in improving cross layer design for multimedia applications over distributed radio network using adaptive equalizer
Figure7 shows that the lowest congestion verse time coordination is at (0.0228, 1). This shows that the passage of piece of information from source to sink in cross layer design for multimedia applications over distributed radio network using adaptive equalizer is fast and reliable.  Figure 8 shows that the highest bit error rate verse time without using and using adaptive equalizer (0.07681, 1).On the other hand, the highest bit error rate coordination with time using adaptive equalizer falls at (0.03841, 1).
With these results, it shows that without having adaptive equalizer has high bit error rate which equally means that it has an interference that make it not to transfer data from source to sink. While the one that adaptive equalizer was incorporated in it has reduced bit error rate that does not contain interference thereby passes data conveniently.  Figure 9 shows the comparing conventional congestion with congestion when adaptive equalizer is used. It shows that in the congestion verse time coordinate the highest congestion when adaptive equalizer is used occurred at (0.0741, 4) while that when adaptive equalizer is used occurred at (0.03705, 4). With these results it shows that with congestion in improving cross layer design for multimedia applications over distributed radionetwork without using adaptive equalizer the transmitting of data from one point to the other become practically impossible while when adaptive equalizer is used the congestion is reduced with quick transmission of information.

IV. CONCLUSION
The difficulty of transmitting data from one point to the other or passing piece of information from source to sink is caused by high bit error rate which constitutes interference and congestion in the communication network. The difficulty of passing piece of information in communication network is overcome by improving cross layer design for multimedia applications over distributed radio network using adaptive equalizer. This is done in this manner, by characterizing the network understudy, determining the bit error rate and congestion from the characterized network that could cause the passage of information from sender to receiver difficult, determining the shortest route from the characterized network, designing a Simulink model that will reduce interference and congestion in improving cross layer design for multimedia applications over distributed radio network using adaptive equalizer and Comparing conventional and optimized adaptive equalizer