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This paper deals with packet scheduling for Voiceover-IP (VoIP) traffic in the Long Term Evolution (LTE) E-UTRAN Downlink. The more specific target is to optimize the performance of dynamic scheduling for traffic mixes of VoIP and best effort users. To this end, we introduce the Required Activity Detection (RAD) packet scheduling algorithm with Delay Sensitivity (RAD-DS). With an appropriate delay sensitivity function, it is shown that a MACRO 1 cell with 5MHz transmission bandwidth can support up to 346 VoIP users. Furthermore, a novel delay sensitivity based soft prioritizing strategy is proposed for handling traffic mixes. It is shown that when 200 VoIP users are present in a cell along with best effort users, the proposed strategy can provide a cell throughput of up to 82% of the cell throughput with only best effort users.

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Traditionally, the dictionary matrices used in sparse wireless channel estimation have been based on the discrete Fourier transform, following the assumption that the channel frequency response (CFR) can be approximated as a linear combination of a small number of multipath components, each one being contributed by a specific propagation path. In practical communication systems, however, the channel response experienced by the receiver includes additional effects to those induced by the propagation channel. This composite channel embodies, in particular, the impact of the transmit (shaping) and receive (demodulation) filters. Hence, the assumption of the CFR being sparse in the canonical Fourier dictionary may no longer hold. In this work, we derive a signal model and subsequently a novel dictionary matrix for sparse estimation that account for the impact of transceiver filters. Numerical results obtained in an OFDM transmission scenario demonstrate the superior accuracy of a sparse estimator that uses our proposed dictionary rather than the classical Fourier dictionary, and its robustness against a mismatch in the assumed transmit filter characteristics.