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An enhanced-MDP based vertical handoff algorithm for QoS support over heterogeneous wireless networks

- Sharna, Shusmita, Amin, Mohammad, Murshed, Manzur

  • Authors: Sharna, Shusmita , Amin, Mohammad , Murshed, Manzur
  • Date: 2011
  • Type: Text , Conference proceedings
  • Relation: Proceedings of 2011 IEEE International Symposium on Network Computing and Applications (NCA 2011),Cambridge, MA, 25-27th Aug, 2011
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  • Description: Vertical handoff plays an important role in guaranteeing users to be always connected in an overlapped multi-network environment. During the vertical handoff procedure, handoff decision is the most important step that affects the normal working of communication. An incorrect handoff decision or selection of a non-optimal network may result in undesirable effects such as higher costs, poor quality of service (QoS) experience, and even dropped communication. Among the existing vertical handoff decision algorithms, the Markov Decision Process (MDP) based algorithm by Stevens-Navarro et al. is promising due to its ability to achieve the optimal expected reward. However, the reward function used by this algorithm is flawed as it favors reducing expected number of vertical handoffs at the expense of diminished expected values of other QoS parameters. This paper presents an extended MDP based algorithm (EMDP) with novel reward function formulation. Analysis shows that EMDP outperforms the MDP based algorithm in terms of improved expected values of all QoS parameters considered while keeping the vertical handoff number reasonably low.

Time-frequency filter bank: A simple approach for audio and music separation

- Yang, Ning, Usman, Muhammad, He, Xiangjian, Jan, Mian, Zhang, Liming

  • Authors: Yang, Ning , Usman, Muhammad , He, Xiangjian , Jan, Mian , Zhang, Liming
  • Date: 2017
  • Type: Text , Journal article
  • Relation: IEEE access Vol. 5, no. (2017), p. 27114-27125
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  • Description: Blind Source Separation techniques are widely used in the field of wireless communication for a very long time to extract signals of interest from a set of multiple signals without training data. In this paper, we investigate the problem of separation of the human voice from a mixture of human voice and sounds from different musical instruments. The human voice may be a singing voice in a song or may be a part of some news, broadcast by a channel with background music. This paper proposes a generalized Short Time Fourier Transform (STFT)-based technique, combined with filter bank to extract vocals from background music. The main purpose is to design a filter bank and to eliminate background aliasing errors with best reconstruction conditions, having approximated scaling factors. Stereo signals in time-frequency domain are used in experiments. The input stereo signals are processed in the form of frames and passed through the proposed STFT-based technique. The output of the STFT-based technique is passed through the filter bank to minimize the background aliasing errors. For reconstruction, first an inverse STFT is applied and then the signals are reconstructed by the OverLap-Add method to get the final output, containing vocals only. The experiments show that the proposed approach performs better than the other state-of-the-art approaches, in terms of Signal-to-Interference Ratio (SIR) and Signal-to-Distortion Ratio (SDR), respectively.
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