• Journal of the Institute of Electronics Engineers of Korea TC
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• v.45 no.9
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• pp.39-48
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• 2008

Recently, many multimedia services have emerged in Internet such as real-time and non- real time services. However, in this Internet environment, we have some limitations to satisfy each service feature. To guarantee the service features in Measurement-Based Admission Control(MBAC) based system on the flow-aware network, there is the method applying Dynamic Priority Scheduling(DPS) algorithm that gives a higher priority to an earlier incoming flow in all of the link bandwidth. This paper classifies all flows under several groups according to flow characteristics on per-flow MBAC algorithm based system. In each flow group, DPS algorithm is applied. This paper proposes two methods that are a DPS based bandwidth borrowing method and a bandwidth dynamic allocation method. The former is that if low priority class has available bandwidths, the flow of high priority class borrows the bandwidth of low priority class when high priority flow has insufficient bandwidth to connect a flow call. The later is that the each group has a minimum bandwidth and is allocated the bandwidth dynamically according to the excess rate for available bandwidth. We compare and analyze the characteristics of the two proposed methods through the simulation experiments. As the results of the experiment, the proposed methods are more effective than existing DPS based method on the packet loss and delay characteristics. Consequently the proposed two methods are very useful in various multimedia network environments.

• Journal of KIISE:Information Networking
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• v.33 no.4
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• pp.333-342
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• 2006

The cdma2000 lxEV - DO mobile communication system provides broadcast and multicast services (BCMCS) to meet an increasing demand from multimedia data services. The servicing of video streams over a BCMCS network must, however, face a challenge from the unreliable and error-prone nature of the radio channel. The BCMCS network uses Reed-Solomon coding integrated with the MAC protocol for error recovery. We analyze this coding technique and show that it is not effective in the case of slowly moving mobiles. To improve the playback quality of an MPEG-4 FGS video stream, we propose the Hybrid error recovery scheme, which combines Reed-Solomon with ARQ, using slots which are saved by reducing the Reed-Solomon coding overhead. The target packets to be retransmitted are prioritized by a utility function to reduce the packet error rate in the application layer within a fixed retransmission budget. This is achieved by considering of the map of the error control block at each mobile node. The proposed Hybrid error recovery scheme also uses the characteristics of MPEG-4 FGS (fine granularity scalability) to improve the video quality even when conditions are adverse: slow-moving nodes and a high error rate in the physical channel.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.17 no.7
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• pp.1951-1975
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• 2023

Recent advances in Cognitive Radio Networks (CRN) have elevated them to the status of a critical instrument for overcoming spectrum limits and achieving severe future wireless communication requirements. Collaborative spectrum sensing is presented for efficient channel selection because spectrum sensing is an essential part of CRNs. This study presents an innovative cooperative spectrum sensing (CSS) model that is built on the Firefly Algorithm (FA), as well as machine learning artificial neural networks (ANN). This system makes use of user grouping strategies to improve detection performance dramatically while lowering collaboration costs. Cooperative sensing wasn't used until after cognitive radio users had been correctly identified using energy data samples and an ANN model. Cooperative sensing strategies produce a user base that is either secure, requires less effort, or is faultless. The suggested method's purpose is to choose the best transmission channel. Clustering is utilized by the suggested ANN-FA model to reduce spectrum sensing inaccuracy. The transmission channel that has the highest weight is chosen by employing the method that has been provided for computing channel weight. The proposed ANN-FA model computes channel weight based on three sets of input parameters: PU utilization, CR count, and channel capacity. Using an improved evolutionary algorithm, the key principles of the ANN-FA scheme are optimized to boost the overall efficiency of the CRN channel selection technique. This study proposes the Artificial Neural Network with Firefly Algorithm (ANN-FA) for cognitive radio networks to overcome the obstacles. This proposed work focuses primarily on sensing the optimal secondary user channel and reducing the spectrum handoff delay in wireless networks. Several benchmark functions are utilized We analyze the efficacy of this innovative strategy by evaluating its performance. The performance of ANN-FA is 22.72 percent more robust and effective than that of the other metaheuristic algorithm, according to experimental findings. The proposed ANN-FA model is simulated using the NS2 simulator, The results are evaluated in terms of average interference ratio, spectrum opportunity utilization, three metrics are measured: packet delivery ratio (PDR), end-to-end delay, and end-to-average throughput for a variety of different CRs found in the network.