A Soft-Input Soft-Output Polar Decoding Algorithm for Turbo-Detection in MIMO-Aided 5G New Radio

Abstract

Soft-Input Soft-Output (SISO) polar decoding algorithms, such as Belief Propagation (BP) and Soft Cancellation (SCAN) polar decoding, offer iteration capability for facilitating turbo-style detection. However, at lower Signal-to-Noise Ratios (SNRs), the performance of the BP and SCAN decoders is about 1.5 dB and 0.5 dB worse than that of the state-of-the-art harddecision Successive Cancellation List (SCL) decoding algorithm, respectively, despite iteratively exchanging information with a Multiple Input Multiple Output (MIMO) detector. Motivated by this gap, we conceive a novel G-SCAN polar decoder, which generates both soft-decision and hard-decision outputs. This is achieved by intrinsically amalgamating a list decoder with a novel SISO decoder. These soft-decision outputs may be used for turbo-detection, but they also support the hard-decision outputs of the SCL algorithm for achieving superior block error rate (BLER) performance. As a result of these benefits, the proposed G-SCAN algorithm using a list size of L = 2 offers around 1dB BLER gain compared to the conventional hard-decision SCL decoder relying on L = 32. Furthermore, we have carried out its Extrinsic Information Transfer (EXIT) chart analysis, and characterized the performance vs. the complexity of the proposed G-SCAN algorithm, and compared it to various softand hard-decision output benchmarks for a wide variety of different rate-matching modes and block lengths. Furthermore, in order to reduce the complexity of the proposed algorithm, a novel Cyclic Redundancy Check (CRC)-aided G-SCAN algorithm is also proposed, which facilitates early termination and improves the BLER performance.

Soft-In Soft-Out Polar Decoding Aided Three-Stage Concatenated Iterative MIMO-Turbo Transceivers

“…In this section, a novel hybrid polar decoder is proposed, which has two main components that operate simultaneously, namely an upper SCAN polar decoder [12], and a lower G-SCAN polar decoder [21], as shown in Fig. 1.…”

“…Here, the G-SCAN polar decoder comprises a conventional SCL decoder [11] which outputs a hard-decision bit vector û, as well as a modified-SCAN decoder, which is capable of exploiting û in order to improve the error correction capability [21]. In this way, the G-SCAN decoder achieves superior harddecision performance over the SCL decoder and outperforms the soft-in soft-out SCAN decoder [21]. In the proposed hybrid decoder the upper SCAN decoder and the lower G-SCAN decoder beneficially complement each other.…”

“…It has been shown that the SCAN polar decoder allows the iterative exchange of extrinsic information with an inner decoder, enabling joint decoding and detection for polar codes concatenated with an inner component such as a Multiple Input Multiple Output (MIMO)-detector [18]- [20]. However, our previous work [21] showed that even when iterating with an inner detector, the SCAN decoder may not perform better than the SCL decoder, which is incapable of exchanging extrinsic information with a MIMO detector and hence can only benefit from 'one-shot' MIMO detection. Motivated by this, our previous work [21] introduced a novel soft-in and soft-out G-SCAN polar decoder which is particularly suitable for iterations with an inner decoder.…”

“…However, our previous work [21] showed that even when iterating with an inner detector, the SCAN decoder may not perform better than the SCL decoder, which is incapable of exchanging extrinsic information with a MIMO detector and hence can only benefit from 'one-shot' MIMO detection. Motivated by this, our previous work [21] introduced a novel soft-in and soft-out G-SCAN polar decoder which is particularly suitable for iterations with an inner decoder. This offers the best of both worlds by simultaneously generating soft outputs for attaining iterative gains, as well as hard outputs that can improve upon the SCL performance.…”

Deep Learning Aided LLR Correction Improves the Performance of Iterative MIMO Receivers

“…only what the most likely value of each bit is, but also how likely it is. This soft information is typically represented using Logarithmic Likelihood Ratios (LLRs) processed by in probabilistic detection [1], [2]. However, in some practical systems, these LLRs may not accurately express true confidence in the values of the bits, owing to deficiencies in channel estimation or the adoption of reduced-complexity algorithms.…”