  This paper presents novel techniques for improving the error correction
performance and reducing the complexity of coarsely quantized 5G-LDPC decoders.
The proposed decoder design supports arbitrary message-passing schedules on a
base-matrix level by modeling exchanged messages with entry-specific discrete
random variables. Variable nodes (VNs) and check nodes (CNs) involve
compression operations designed using the information bottleneck method to
maximize preserved mutual information between code bits and quantized messages.
We introduce alignment regions that assign the messages to groups with aligned
reliability levels to decrease the number of individual design parameters.
Group compositions with degree-specific separation of messages improve
performance by up to 0.4 dB. Further, we generalize our recently proposed
CN-aware quantizer design to irregular LDPC codes and layered schedules. The
method optimizes the VN quantizer to maximize preserved mutual information at
the output of the subsequent CN update, enhancing performance by up to 0.2 dB.
A schedule optimization modifies the order of layer updates, reducing the
average iteration count by up to 35 %. We integrate all new techniques in a
rate-compatible decoder design by extending the alignment regions along a
rate-dimension. Our complexity analysis for 2-bit decoding estimates up to 64 %
higher throughput versus 4-bit decoding at similar performance.
