摘要:
A symbol detector (30) converts initial symbol estimates of received symbols to soft estimates for decoding. The symbol detector (30) computes spreading waveform correlations between a spreading waveform for a symbol of interest and spreading waveforms for one or more interfering symbols. Interference rejection terms are computed by scaling the spreading waveform correlations by corresponding signal powers and compensating for noise. A soft scaling factor for the symbol of interest is computed from the interference rejection terms. The soft scaling factors are then applied to the initial symbol estimates to generate the soft estimates.
摘要:
Teachings presented herein offer the performance advantages of sequence estimation for received signal symbol detection, while simultaneously providing potentially significant reductions in computational overhead. Initial demodulation of a received signal (12) identifies a reduced number of candidate symbol values (24) for all or a subset of a sequence (14) of symbols (16) represented in the received signal (12). A sequence estimation process, e.g., an MLSE process, constrains its state spaces (62-1, 62-2, 62-3) to the reduced number of candidate symbols values (24), rather than considering all possible symbol values.
摘要:
According to the teachings presented herein, 'spreading code' knowledge is used in forming amplitude references for QAM demodulation in a DS-CDMA receiver. Here, 'spreading code' broadly refers to spreading/channelization codes, scrambling codes, or the product of such codes. Further, these teachings apply to any linear DS-CDMA demodulator, such as Rake, Generalized Rake (G-Rake), or chip equalizer, and to nonlinear demodulators that employ linear filtering, such as decision feedback equalizers (DFEs). Advantageously, the determination of symbol-specific amplitude references relies on shared correlation estimates and/or shared combining weights that are common to two or more symbols of interest, thereby significantly reducing processing requirements as compared to the use of symbol-specific impairment correlation estimates.
摘要:
Teachings presented herein offer the performance advantages of sequence estimation for received signal symbol detection, while simultaneously providing potentially significant reductions in computational overhead. Initial demodulation of a received signal (12) identifies a reduced number of candidate symbol values (24) for all or a subset of a sequence (14) of symbols (16) represented in the received signal (12). A sequence estimation process, e.g., an MLSE process, constrains its state spaces (62-1, 62-2, 62-3) to the reduced number of candidate symbols values (24), rather than considering all possible symbol values.