Cross-layer resource allocation based on equivalent bandwidth in OFDMA systems

A quality of service（QoS） guaranteed cross-layer resource allocation algorithm with physical layer, medium access control（MAC） layer and call admission control（CAC） considered simultaneously is proposed for the full IP orthogonal frequency division multiple access（OFDMA） communication system, which can ensure the quality of multimedia services in full IP networks.The algorithm converts the physical layer resources such as subcarriers, transmission power, and the QoS metrics into equivalent bandwidth which can be distributed by the base station in all three layers. By this means, the QoS requirements in terms of bit error rate（BER）, transmission delay and dropping probability can be guaranteed by the cross-layer optimal equivalent bandwidth allocation. The numerical results show that the proposed algorithm has higher spectrum efficiency compared to the existing systems.

JOINT RESOURCE ALLOCATION FOR WLAN&WCDMA INTEGRATED NETWORKS BASED ON SPECTRAL BANDWIDTH MAPPING

Next wireless network aims to integrate heterogeneous wireless access networks by sharing wireless resource.The spectral bandwidth mapping concept is proposed to uniformly describe the resource in heterogeneous wireless networks.The resources of codes and power levels in WCDMA system as well as statistical time slots in WLAN are mapped into equivalent bandwidth which can be allocated in different networks and layers.The equivalent bandwidth is jointly distributed in call admission and vertical handoff control process in an integrated WLAN/WCDMA system to optimize the network utility and guarantee the heterogeneous QoS required by calls.Numerical results show that,when the incoming traffic is moderate,the proposed scheme could receive 5%-10% increase of system revenue compared to the MDP based algorithms.

Voice to Face Recognition Using Spectral ERB-DMLP Algorithms

Designing an authentication system for securing the power plants are important to allow only specific staffs of the power plant to access the certain blocks so that they can be restricted from using high risk-oriented equipment.This authentication is also vital to prevent any security threats or risks like compromises of business server,release of confidential data etc.Though conventional works attempted to accomplish better authentication,they lacked with respect to accuracy.Hence,the study aims to enhance the recognition rate by introducing a voice recognition system as a personal authentication based on Deep Learning(DL)due to its ability to perform effective learning.The study proposes Equivalent Rectangular Bandwidth and Deep Multi-Layer Perceptron(ERB-DMLP)as it has the ability to perform efficient and relevant feature extraction and faster classification.This algorithm also has the ability to establish effective correlation between voices and images and achieve the semantic relationship between them.Voice preprocessing is initially performed to make it suitable for further processing by removing the noise and enhancing the quality of signal.This process is also vital to minimize the extra computations so that the overall efficacy of the system can be made flexible by considering the audio files as features and the images as labels to identify a person’s voice by classifying the extracted features from the ERB Feature Extraction.This is then passed as the input into DMLP model to classify the persons,and trained the model to make an accurate classification of audio with corresponding image labels,and perform the performance test based on the trained model.Flexibility,relevant feature extraction and faster classification ability of the proposed work has made it explore better outcomes that is confirmed through results.