As CMOS technology scales down, digital supply voltage and digital power consumption goes down. However due to dynamic range limitations, power supply and power consumption of the RF front-ends and analog sections do not scale in the same fashion. In fact, in scaled systems, the RF section of a wireless transceiver consumes more energy than the digital part. For better understanding of the design trade offs, we first develop an accurate and comprehensive energy model for the analog front-end of wireless transceivers. Next, we evaluate a single user point-to-point wireless data communication system and a multi-user CDMA based system with respect to RF front end energy consumption and communication quality. We demonstrate the effect of occupied signal bandwidth, peak-to-average ratio (PAR), symbol rate, constellation size, and pulseshaping roll-off factor on single user system, and the effect of number of users and multiple access interference (MAI) on CDMA based multi-user system. For a given quality specification, we show how the energy consumption can be reduced by adjusting one or more of these parameters.
Instantaneous frequency (IF) estimation of signals with nonlinear phase is challenging, especially for online processing. In this paper, we propose IF estimation using sequential Bayesian techniques, by combining the particle filtering method with the Markov chain Monte Carlo (MCMC) method. Using this approach, a nonlinear IF of unknown closed form is approximated as a linear combination of the IFs of nonoverlapping waveforms with polynomial phase. Simultaneously applying parameter estimation and model selection, the new technique is extended to the IF estimation of multicomponent signals. Using simulations, the performance of this sequential MCMC approach is demonstrated and compared with an existing IF estimation technique using the Wigner distribution.
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