Reduced state MAP algorithm with modified branch metric

Chavali, N K and Bade, D T and Kilari, A and Kuchi, Kiran (2016) Reduced state MAP algorithm with modified branch metric. In: International Conference on Signal Processing and Communications (SPCOM), 12-15 June 2016.

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Abstract

In this paper, we develop a detection algorithm at the receiver, for a wireless system employing multiple antennas at both the transmitter and receiver, where the knowledge of channel is not available at the transmitter (a.k.a open-loop system). The proposed algorithm offers a very good trade-off between implementation complexity and bit-error-rate (BER) performance. In the literature, the fixed complexity sphere decoding (FSD) is known to be state-of-the art decoding algorithm for open-loop multiple input and multiple output (MIMO) systems. The reduced state maximum aposteriori (RS-MAP) algorithm is an extension of FSD where reduced state (RS) receiver delivers bit level soft decisions using maximum aposteriori (MAP) Bahl, Cocke, Jelinek and Raviv (BCJR) recursions that are implemented on the reduced state tree. While RS-MAP employs state dependent hard decision feedback, the proposed algorithm uses a) a state dependent soft/hard symbol feedback b) a scaling of the branch metric with the assumption that soft decision feedback is employed, to obtain a performance improvement over standard RS-MAP algorithm. The proposed modifications to the branch metric are general and can be applied to a number of other sequence estimators such as maximum likelihood soft output viterbi algorithm (ML-SOVA), FSD-SOVA, RS-MAP with forward only recursion etc.

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