In this paper, the problem of maximizing the secondary user (SU) throughput under a primary user (PU) quality of service (QoS) delay requirement is studied. Moreover, the impact of having a fullduplex capability at the SU on the network performance is investigated compared to the case of a SU with half-duplex capability. We consider a cooperative cognitive radio (CR) network in which the receiving nodes have multi-packet reception (MPR) capabilities. In our proposed model, the SU not only benefits from the idle time slots (i.e. when PU is idle), but also chooses between sharing the channel or cooperating with the PU in a probabilistic manner. We formulate our optimization problem to maximize the SU throughput under a PU QoS, defined by a delay constraint; the optimization is performed over the transmission modes selection probabilities of the SU. The resultant optimization problem is found to be a non-convex quadratic constrained quadratic programming (QCQP) optimization problem, which is, generally, an NP-hard problem. We devise an efficient approach to solve it and to characterize the network stability region under a delay constraint set on the PU. Numerical results, surprisingly, reveal that the network performance when full-duplex capability exists at the SU is not always better compared to that of a half-duplex SU. In fact, we demonstrate that a full-duplex capability at the SU can, in some cases, adversely influence the network stability performance, especially if the direct channel conditions between the SU and the destinations are worse than that between the PU and the destinations. In addition, we formulate a multi-objective programming (MOP) optimization problem to investigate the trade-off between the PU delay and the SU throughput. Our MOP approach allows for assigning relative weights for our two conflicting performance metrics, i.e., PU delay and SU throughput. Numerical results also demonstrate that our cooperation policy outperforms conventional cooperative and non-cooperative policies presented in previous works. INDEX TERMS Cognitive radio (CR), throughput, quadratic constrained quadratic programming (QCQP), multi-objective programming (MOP). ALI GABER MOHAMED ALI received the B.Sc. (Hons.
Voice conversion (VC) is a process which modifies the speech signal produced by one source speaker so that it sounds like another target speaker. In this paper the transformation is determined by using equal Arabic utterances from source and target speakers; these utterances are time-aligned using dynamic time warping algorithm. A conversion function based on Gaussian mixture model (GMM) is used for transforming the spectral envelope described by line spectral frequencies (LSF) and the residuals are converted using three residual prediction techniques. We also compare between these techniques in the conversion of some Arabic utterances. The quality of the transformed utterances is measured using subjective and objective evaluations.
Voice conversion (VC) is a process which modifies the speech signal produced by one source speaker so that it sounds like another target speaker. In this paper the transformation is determined by using equal Arabic utterances from source and target speakers. A conversion function based on Gaussian mixture model (GMM) is used for transforming the spectral envelope described by Mel Frequency Cepstral Coefficients (MFCC). The quality of the transformed utterances is measured using subjective and objective evaluations.
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