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This paper proposes a turbo equalizer for intersymbol interference channels (ISI) that uses coarsely quantized messages across all receiver components. Lookup tables (LUTs) carry out compression operations designed with the information bottleneck method aiming to maximize relevant mutual information. The turbo setup consists of an equalizer and a decoder that provide extrinsic information to each other over multiple turbo iterations. We develop simplified LUT structures to incorporate the decoder feedback in the equalizer with significantly reduced complexity. The proposed receiver is optimized for selected ISI channels. A conceptual hardware implementation is developed to compare the area efficiency and error correction performance. A thorough analysis reveals that LUT-based configurations with very coarse quantization can achieve higher area efficiency than conventional equalizers. Moreover, the proposed turbo setups can outperform the respective non-turbo setups regarding area efficiency and error correction capability.

We enhance coarsely quantized LDPC decoding by reusing computed check node messages from previous iterations. Typically, variable and check nodes generate and replace old messages in every iteration. We show that, under coarse quantization, discarding old messages involves a significant loss of mutual information. The loss is avoided with additional memory, improving performance up to 0.36 dB. We propose a modified information bottleneck algorithm to design node operations taking messages from the previous iteration(s) into account as side information. Finally, we reveal a 2-bit row-layered decoder that can operate within 0.25 dB w.r.t. 32-bit belief propagation., Comment: The paper has been submitted to IEEE Communications Letters and is currently under review

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., Comment: This paper has been submitted to IEEE Transactions on Communications

An implementation-efficient finite alphabet decoder for polar codes relying on coarsely quantized messages and low-complexity operations is proposed. Typically, finite alphabet decoding performs concatenated compression operations on the received channel messages to aggregate compact reliability information for error correction. These compression operations or mappings can be considered as lookup tables. For polar codes, the finite alphabet decoder design boils down to constructing lookup tables for the upper and lower branches of the building blocks within the code structure. A key challenge is to realize a hardware-friendly implementation of the lookup tables. This work uses the min-sum implementation for the upper branch lookup table and, as a novelty, a computational domain implementation for the lower branch lookup table. The computational domain approach drastically reduces the number of implementation parameters. Furthermore, a restriction to uniform quantization in the lower branch allows a very hardware-friendly compression via clipping and bit-shifting. Its behavior is close to the optimal non-uniform quantization, whose implementation would require multiple high-resolution threshold comparisons. Simulation results confirm excellent performance for the developed decoder. Unlike conventional fixed-point decoders, the proposed method involves an offline design that explicitly maximizes the preserved mutual information under coarse quantization., Comment: This work has been submitted to IEEE GLOBECOM 2023 and is currently under review

We investigate iterative low-resolution message-passing algorithms for quasi-cyclic LDPC codes with horizontal and vertical layered schedules. Coarse quantization and layered scheduling are highly relevant for hardware implementations to reduce the bit width of messages and the number of decoding iterations. As a novelty, this paper compares the two scheduling variants in combination with mutual information maximizing compression operations in variable and check nodes. We evaluate the complexity and error rate performance for various configurations. Dedicated hardware architectures for regular quasi-cyclic LDPC decoders are derived on a conceptual level. The hardware-resource estimates confirm that most of the complexity lies within the routing network operations. Our simulations reveal similar error rate performance for both layered schedules but a slightly lower average iteration count for the horizontal decoder., Comment: This paper has been presented at the Asilomar Conference 2022. The updated version involves improvements in section III d)

For improving coarsely quantized decoding of LDPC codes, we propose a check node aware design of the variable node update. In contrast to previous works, we optimize the variable node to explicitly maximize the mutual information preserved in the check-to-variable instead of the variable-to-check node messages. The extended optimization leads to a significantly different solution for the compression operation at the variable node. Simulation results for regular LDPC codes confirm that the check node aware design, especially for very coarse quantization with 2- or 3-bit messages, achieves performance gains of up to 0.2 dB - without additional hardware costs. We also show that the 2-bit message resolution enables a very efficient implementation of the check node update, which requires only 2/9 of the 3-bit check node's transistor count and reduces the signal propagation delay by a factor of 4., Comment: This work has been submitted to IEEE GLOBECOM 2022

Recently, low-resolution LDPC decoders have been introduced that perform mutual information maximizing signal processing. However, the optimal quantization in variable and check nodes requires expensive non-uniform operations. Instead, we propose to use uniform quantization with a simple hardware structure, which reduces the complexity of individual node operations approximately by half and shortens the decoding delay significantly. Our analysis shows that the loss of preserved mutual information resulting from restriction to uniform quantization is very small. Furthermore, the error rate simulations with regular LDPC codes confirm that the uniformly quantized decoders cause only minor performance degradation within 0.01 dB compared to the non-uniform alternatives. Due to the complexity reduction, especially the proposed 3-bit decoder is a promising candidate to replace 4-bit conventional decoders., Comment: This work has been submitted to IEEE GLOBECOM 2022