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In this paper, a new algorithm is presented for estimating mobile speed for handoff in hierarchical cellular systems. The proposed algorithm contains six steps. Firstly, the instantaneous power of the received baseband signal is calculated to remove the fkequency offset and datalspeech information-bearing signals, while keeping the doppler Requency information. Secondly, the calculated power signal is filtered using lowpass linear phase flnite impulse response (FIR) filter to suppress interference and noise. Thirdly, the filtered power signal is decimated to ease the computational burden, while the decimation factor is properly chosen t o avoid frequency aliasing. Fourthly, autocorrelation values of t h e decimated filtered power signals are calculated on shifting slot by slot t o suppress the 'slot burst frequency" interference. Fifthly, the calculated autocorrelation values are normalized t o suppress the power fluctuation of the received signals. Finally, the normalized autocorrelation values are compared with thresholds t o estimate mobile speed. The simulation results indicate that the new algorithm works very well for both non-dispersive channels and dispersive channels to distinguish fast and slow moving mobiles. The method has very low latency, with results being available typically within one second after communication is established, and it can report estimation result every second or less.

Abstract-In this paper, a new algorithm is presented for estimating mobile speed for handoff in hierarchical cellular systems. The proposed algorithm is based on normalized autocorrelation values of received signals to estimate mobile speed; it contains six steps. First, the instantaneous power of the received baseband signal is calculated to remove the frequency offset and data/speech information-bearing signals, while keeping the Doppler frequency information. Second, the calculated power signal is filtered using a low-pass linear phase finite impulse response filter to suppress interference and noise. Third, the filtered power signal is decimated to ease the computational burden, while the decimation factor is properly chosen to avoid frequency aliasing.

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