1 research outputs found
Improved Multiple Feedback Successive Interference Cancellation Algorithm for Near-Optimal MIMO Detection
In this article, we propose an improved multiple feedback successive
interference cancellation (IMF-SIC) algorithm for symbol vector detection in
multiple-input multiple-output (MIMO) spatial multiplexing systems. The
multiple feedback (MF) strategy in successive interference cancellation (SIC)
is based on the concept of shadow area constraint (SAC) where, if the decision
falls in the shadow region multiple neighboring constellation points will be
used in the decision feedback loop followed by the conventional SIC. The best
candidate symbol from multiple neighboring symbols is selected using the
maximum likelihood (ML) criteria. However, while deciding the best symbol from
multiple neighboring symbols, the SAC condition may occur in subsequent layers
which results in inaccurate decision. In order to overcome this limitation, in
the proposed algorithm, SAC criteria is checked recursively for each layer.
This results in successful mitigation of error propagation thus significantly
improving the bit error rate (BER) performance. Further, we also propose an
ordered IMF-SIC (OIMF-SIC) where we use log likelihood ratio (LLR) based
dynamic ordering of the detection sequence. In OIMF-SIC, we use the term
dynamic ordering in the sense that the detection order is updated after every
successful decision. Simulation results show that the proposed algorithms
outperform the existing detectors such as conventional SIC and MF-SIC in terms
of BER, and achieves a near ML performance