Detecting format of a transport channel

1. A method comprising:

receiving, at a receiver, a guiding stream on a guiding transport channel and a guided stream on a guided transport channel;

convolutionally decoding the guided stream to produce a plurality of Viterbi path metrics; and

selecting a transport format for the guided transport channel from a predefined plurality of possible transport formats that are determined by information provided on the guiding transport channel, based at least in part on a state metric computed from a combination of the Viterbi path metrics, wherein

the state metric comprises a difference between a first operand and a second operand, the first operand being computed using at least one intermediate Viterbi path metric and the second operand being computed using a corresponding final Viterbi path metric.

2. The method of claim 1 , wherein the plurality of Viterbi path metrics include at least one of a minimum path metric, a maximum path metric, or a zero path metric.

3. The method of claim 1 , wherein:

the plurality of Viterbi path metrics comprise a plurality of intermediate Viterbi path metrics and a plurality of final Viterbi path metrics; and

the convolutionally decoding further comprises convolutionally decoding an initial number of bits in the guided stream to produce the intermediate Viterbi path metrics, the initial number of bits determined using information provided on the guiding transport channel; and

convolutionally decoding a final number of bits in the guided stream to produce the final Viterbi path metrics, the final number of bits determined using information provided on the guiding transport channel.

4. The method of claim 3 , wherein the first operand comprises a difference between a zero state metric in the intermediate Viterbi path metrics and a minimum state metric in the intermediate Viterbi path metrics.

5. The method of claim 3 , wherein the first operand comprises a ratio of a numerator to a denominator, the numerator using a subset of the intermediate Viterbi path metrics and the denominator using an overlapping subset of the intermediate Viterbi path metrics.

6. The method of claim 1 , wherein selecting the transport format for the guided transport channel further comprises:

selecting, as the transport format for the guided transport channel, a first one of the possible transport formats if the state metric does not exceed a predefined threshold.

7. The method of claim 6 , wherein selecting the transport format for the guided transport channel further comprises:

selecting, as the transport format for the guided transport channel, a second one of the possible transport formats if the state metric equals or exceeds the predefined threshold.

8. The method of claim 1 , further comprising convolutionally decoding the guiding stream to produce another plurality of Viterbi path metrics, wherein the selecting of the transport format for the guided transport channel is further based on another state metric computed from a combination of the another Viterbi path metrics.

9. The method of claim 8 , wherein selecting the transport format for the guided transport channel further comprises:

if the state metric does not exceed a predefined threshold, comparing the another state metric to another predefined threshold; and

selecting, as the transport format for the guided transport channel, a first one of the possible transport formats if the another state metric exceeds the another predefined threshold and a second one of the possible transport formats if the another state metric does not exceed the another predefined threshold.

10. A device comprising:

a radio frequency (RF) transceiver; and

a baseband processor communicatively coupled to the RF transceiver, the baseband processor configured to:

receive a guiding stream of bits on a guiding transport channel and a guided stream on a guided transport channel;

convolutionally decode the guided stream in a plurality of stages;

generate, at each of the plurality of stages, corresponding sets of Viterbi path metrics V i , where i corresponds to a respective one of the plurality of stages;

compute a state metric from a combination of metrics in the sets of Viterbi path metrics V i ; and

select, based on the state metric, a transport format for the guided transport channel from a predefined plurality of possible transport formats, the predefined plurality of possible transport formats being inferred from information on the guiding transport channel, wherein:

the sets of Viterbi path metrics V i , comprise a set of intermediate Viterbi path metrics associated with an intermediate stage of the plurality of stages and a set of final Viterbi path metrics associated with a final stage of the plurality of stages; and

the state metric is computed from at least one of the intermediate Viterbi path metrics and the final Viterbi path metrics.

11. The device of claim 10 , wherein the baseband processor is further configured to:

convolutionally decode an initial number of bits in the guided stream to produce the set of intermediate Viterbi path metrics, the initial number of bits being inferred from information on the guiding transport channel; and

convolutionally decode up to a remaining number of bits in the guided stream to produce the set of final Viterbi path metrics, the remaining number of bits being inferred from information on the guiding transport channel.

12. The device of claim 10 , wherein the set of intermediate Viterbi path metrics and the set of final Viterbi path metrics each comprise a V(min) path metric, a V(max) path metric, and a V(0) path metric.

13. The device of claim 12 , wherein the state metric is computed using a zero_max value and a max_min value, wherein zero_max=V(0)−V(min) and wherein max_min=V(max)−V(min).

14. The device of claim 13 , wherein the state metric is computed as a difference between a first ratio and a second ratio, wherein the first ratio is a ratio of values of the set of intermediate Viterbi path metrics, and the second ratio is a ratio of values of the set of final Viterbi path metrics.

15. The device of claim 10 , wherein the baseband processor is further configured to:

convolutionally decode the guiding stream to produce another set of Viterbi path metrics;

compute another state metric computed from the another set of Viterbi path metrics; and

compute the state metric further based on the another state metric.

16. The device of claim 15 , wherein the another set of Viterbi path metrics comprises V(0), V(max), and V(min) path metrics, and the baseband processor is further configured to:

compute a max_min value of the another set of Viterbi path metrics, wherein max_min=V(max)−V(min);

compute a max_min value of the set of intermediate Viterbi path metrics; and

compute a quality measure from a sum of the max_min value of the another set of Viterbi path metrics and the max_min value of the set of intermediate Viterbi path metrics; and

select the transport format further based on the quality measure.

17. A system comprising:

means for receiving a guiding stream of bits on a guiding transport channel and a guided stream of bits on a guided transport channel;

means for mapping a detected transport format of the guiding stream to a plurality of possible transport formats for the guided stream, each of the possible transport formats associated with a coded block size;

means for convolutionally decoding the guided stream for an initial number of bits specified by the one of the possible transport formats having a shortest coded block size;

means for storing a plurality of intermediate Viterbi path metrics upon convolutionally decoding the initial number of bits;

means for continuing to convolutionally decode the guided stream up to a final number of bits specified by the one of the possible transport formats having a longest coded block size;

means for storing a plurality of final Viterbi path metrics upon convolutionally decoding the final number of bits; and

means for selecting a transport format for the guided transport channel from the possible transport formats, based at least in part on a quality measure computed from at least one of the intermediate Viterbi path metrics and at least one of the final Viterbi path metrics.

18. The system of claim 17 , wherein the intermediate Viterbi path metrics and the final Viterbi path metrics each comprise a V(min) path metric, a V(max) path metric, and a V(0) path metric.

19. The system of claim 17 , further comprising:

means for computing a max_min value of the intermediate Viterbi path metrics; and

means for computing a max_min value of the final Viterbi path metrics.

20. The system of claim 19 , further comprising:

means for computing a quality measure from a sum of the max_min value of the intermediate Viterbi path metrics and the max_min value of the final Viterbi path metrics; and

means for selecting the transport format further based on the quality measure.