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Source publication
In this paper, we present an algorithm based on the Hidden Markov Models (HMM) theory to solve the problem of blind channel estimation and sequence detection in mobile digital communications. The environment in which the algorithm is tested is the Paneuropean Mobile Radio System, also known as GSM. In this system, a large part in each burst is devo...
Context in source publication
Context 1
... bandwidth (BT) equal to 0.3 is used, due to its good spectral properties. The access strategy is TDMA with 8 timeslots per carrier. At the chosen bit rate (270.8 kb/s), multipath propagation leads to deep fades and to uncontrolled Intersymbol Interference (ISI). Besides, receiver's mobile nature allows Doppler effect to show up. As depicted in Fig. 1, each timeslot in Normal Bursts reserves its 26 central bits to host a sounding sequence. This sequence is used at the receiver to obtain an estimate of the channel impulse response (CIR). This information is then used by the detector (i.e. a MLSE-type) to perform data detection. In contrast, a blind equalizer would make this sequence ...
Citations
We introduce several modifications to the Baum&Welch (BW) formulas used to reestimate the parameters of a hidden Markov model (HMM). The estimated parameter is the channel impulse response (CIR) of a communication system which is known to be time-varying. With these modifications, channel tracking properties of a BW-based algorithm are improved. The resulting algorithm is tested in a specific mobile radio environment (the GSM system), exhibiting good performance at expenses of higher computational complexity
