In the real-time communication service over fifth-generation (5G) wireless network, e.g., FaceTime video call, the data packet loss and jitter, which increase the end - to-end packet delay and result in video stuck or glitching, influence the quality of experience (QoE) severely. In order to reduce packet delay and loss, an easily -configured enhancement scheme, called packet-level coding transmission optimization (PCTO), is proposed for user equipment, e.g., smart phone. In the proposed scheme, the data packets are firstly encoded by packet-level coding (PC) module in which some redundant packets are generated. Secondly, we differentiate the transmission priority for each packet by transmission optimization (TO) module, in which two principles are enforced. The first principle is that the transmission priority of data packet is higher than the redundant packet in order to overcome congestion cycle. The second principle is that the remaining uplink resource is allocated to code blocks uniformly. Simulation results show that the proposed scheme reduces 80 % packet loss and 22 % average delay at least and mean opinion score (MOS) can be improved by 33%. It can also be well adapted to different network conditions compared with existing schemes. Furthermore, the proposed scheme is compatible with the existing 5G standard. The procedures for configuring PCTO for an existing 5G network are also presented.

In this paper, reliable uplink transmission by 5G is investigated for task importance-oriented communication. A novel application-layer probabilistic constellation shaping (AL-PCS) scheme is proposed to improve the reliable transmission of data bits associated with more important task. Instead of physical layer (PL), the proposed scheme is implemented in the application layer (AL) of user equipment (UE), e.g., cell phone, in order to compatible with the existing 5G protocol. Firstly, the mapping table is constructed based on some related PL parameters. Then the output bitstream of applications are transformed to PCS bitstream by PCS module, in which a specific descrambling is preformed to counteract the scrambling operation in PL. Then after subsequent operations preformed by other layer, the obtained bitstream can be mapped to intended constellations with very high probability. Correspondingly, in the receiver, an PCS module is also implemented in application layer, in which the inverse operations are preformed to recover the original bitstream. For 256QAM and high code rate, the block error rate (BLER) performance can be improved by 13 dB.

Weak information signals can be detected with the assistance of stochastic perturbation, and nonlinear systems benefit from this phenomenon. Inspired by this, we propose a noise-assisted NMS list (Na-NMSL) algorithm for LDPC decoding. The algorithm consists of L independent NMS decoders, and the inputs of the NMS decoders are generated by adding artificial noise with different strengths to the received signal. When lifting size Z is small, list decoding is performed in the Na-NMSL decoder by utilizing the remaining hardware resources. Therefore, the Na-NMSL decoder enhances the decoding performance of NMS without additional hardware overhead. Simulation results show that the Na-NMSL decoder has a lower block error rate (BLER) and faster convergence than traditional NMS decoding.

Polar codes have been selected as the channel coding scheme for 5G new radio physical downlink control channel (NR PDCCH). In 5G NR PDCCH, blind detection is performed by user equipment (UE) to obtain its intended information. During blind detection, false alarm (FA) may happen which means that the UE can decode successfully even there is no intended information. FA can be caused by many factors. In this paper, we focus on the FA caused by radio network temporary identifier (RNTI), we call it RNTI-FA. RNTI-FA may happen even in high SNR range and results in abnormal network status. We firstly investigate the causes of RNTI-FA. Then a novel RNTIFA mitigate scheme, based on the Hamming distance metric, is proposed to reduce the RNTI-FA. The simulation results show that the RNTI-FA rate can be reduced from $10^{-1}$ to $10^{-5}$ or lower with negligible performance loss in the medium-to-high SNR range.

In this paper, a simplified successive cancellation (SC) decoding algorithm for polar codes on insertion/deletion error channels is proposed. First, the SC decoding is designed to decode polar codes on insertion/deletion channels and the joint weight distribution is derived to measure the occurrence probability of different scenarios. Some scenarios with small occurrence probability can be pruned to obtain lower decoding complexity with negligible performance loss. Inspired by this, a fixed pruning strategy (FPS) is proposed to reduce the decoding complexity, which can prune as many scenarios as possible with the given performance requirement. By exploiting the periodicity of the joint weight distribution, the upper bound of the block error rate of the pruned SC decoding is derived. Furthermore, according to the convergence of the upper bound, a dynamic self-adjusting pruning strategy is designed to further reduce the decoding complexity and improve the flexibility of the pruning algorithm. Simulation results show that the decoding complexity of the proposed pruning-based decoding algorithms is significantly reduced compared to the state-of-the-art scenario simplified SC decoding algorithm.

Skyrmion racetrack memory (Sk-RM) is a new storage technology in which skyrmions are used to represent data bits to provide high storage density. During the reading procedure, the skyrmion is driven by a current and sensed by a fixed read head. However, synchronization errors may happen if the skyrmion does not pass the read head on time. In this paper, a polar coding scheme is proposed to correct the synchronization errors in the Sk-RM. Firstly, we build two error correction models for the reading operation of Sk-RM. By connecting polar codes with the marker codes, the number of deletion errors can be determined. We also redesign the decoding algorithm to recover the information bits from the readout sequence, where a tighter bound of the segmented deletion errors is derived and a novel parity check strategy is designed for better decoding performance. Simulation results show that the proposed coding scheme can efficiently improve the decoding performance.

Deletion errors are notoriously difficult to correct. The capacity of the binary deletion channel is not yet fully known, and there is no completely satisfactory solution even to the specific problem of correctingonly twodeletion errors. In this paper, we show that polarization theory, along with the powerful methods of polar coding and successive-cancellation decoding, can be extended to channels with deletion errors. Our results further generalize to channels corrupted by insertion errors and/or combinations of insertions and deletions. It was recently proposed to correct deletion errors using polar codes designed for the BEC, precoded with a sufficiently powerful CRC code. In this approach, in order to correct${d}$deletions in a codeword of length${n}$, successive-cancellation decoding is run separately for each of the$\binom {n}{d}$possible deletion patterns, while treating the deleted symbols as erasures. The resulting complexity of decoding is${O}\bigl ({n}^{d+1}\log {n}\bigr)$. In contrast, we develop herein a natural generalization of successive-cancellation decoding to channels with deletion errors. Our algorithm is based on the recursive structure of polar codes, processing the channel output in${m} = \log _{2} {n}$decoding layers, just like the conventional successive-cancellation decoder. Every node in each decoding layer propagates its uncertainty about the deletion pattern to the nodes in the next layer and, eventually, the correct deletion pattern becomes visible at the decoder output, with high probability. This makes it possible to consider at most$\binom {d+2}{2}$scenarios at each node, and the overall decoding complexity is only${O}\bigl ({d}^{3} {n} \log {n}\bigr)$, which scales polynomially rather than exponentially with the number of deletions. Based on the proposed algorithm, we also investigate the channel polarization phenomenon. We are able to prove weak polarization where the number of deletions${d}$is small,i.e.${d}={o}({n})$. We also conjecture the strong polarization for when${d}={O}(1)$and provide some evidence to support this conjecture. The strong polarization, if proven, implies that our coding scheme achieves the capacity of any binary-input symmetric channel which furthermore introduces a constant number of deletions.

In this paper, we propose a polar coding scheme for binary deletion channels. We also present an implementation of low-complexity polar SC decoder for deletion channels. The modified decoding algorithm requires only O(d2nlogn) computational complexity, where d and n respectively denote the number of deletions and the code-length. This is a huge improvement over naive implementation of the SC decoder for channels with deletion with O(nd+1logn) computation complexity that was recently proposed by Thomas et al. in [21], and is based on running individual instances of SC decoder for every deletion pattern while treating the deleted symbols as erasures. We also prove polarization theorems for the polar bit-channels in presence of deletions when d = o(n), which implies that our coding scheme is capable of achieving the symmetric information rate for this concatenated scheme with diminishing error probabilities as n becomes large. The same framework, in both theory and implementation, is also applicable to channels formed as a concatenation between binary discrete memoryless channels and the d-deletion channel, which marks our coding scheme as the first family of practical codes that is capable of decoding noisy channels with deletions at the optimal code rate.

Polar codes provably achieve the capacity of memoryless symmetric channels with low encoding and decoding complexity. Nonetheless, for short and moderate blocklengths, polar codes fail to deliver competitive performance under successive cancellation decoding. Consequently, much effort has been devoted to improving the performance of polar codes, either through enhanced decoding algorithms or by modifying the code structure, or both. CRC-aided list decoding of polar codes is the most successful approach along this line of research. However, list decoding requires following L decoding paths, which leads to a significant increase in decoding complexity if L is large. As noted by Arikan shortly after the invention of polar codes, their performance could be improved dramatically if we had a genie that intervenes in the successive cancellation decoding process only a few times to reverse incorrect decisions on information bits. In fact, the Tal-Vardy list-decoding algorithm for polar codes can be regarded as an attempt to implement such a genie. Herein, we introduce an alternative implementation of Arikan's genie, which has much lower complexity than list decoding. Our approach is based on precoding some of the information bits with a short convolutional code that provides local error- correction capability at the expense of a small rate loss. This structure allows the successive cancellation polar decoder to verify its output by running it through a Viterbi decoder for the convolutional code. In contrast to conventional CRC-aided list decoding, wherein incorrect decoding paths are rejected detected only after reaching the last information bit, the Viterbi decoder detects incorrect decisions "on the fly'' after a short delay. Whenever an incorrect decision is detected, the successive cancellation decoder is set back to the corresponding bit-channel, and then restarts its computation using the correct bit value provided by the Viterbi decoder.

Combining the polar coded hybrid automatic repeat request (HARQ) with higher-order modulation has been proposed recently. To further improve the throughput of HARQ, a novel polar coded HARQ cheme with higher-order modulation is proposed. Wherein the proposed HARQ scheme, the unequal rror protection (UEP), caused by bit-mapping, in bit- interleaved polar coded modulation (BIPCM) is considered. Based on UEP, a non-linear integer programming (NLIP) problem is constructed to determine the optimal retransmitted sequence. Furthermore, a low-complexity suboptimal algorithm is proposed to solve the resulting NLIP problem. Simulation results show that the throughput of the proposed HARQ scheme outperforms the existing polar coded HARQ for higher-order modulation; in particular, our scheme easily accommodates arbitrarily puncturing methods. This makes it possible to use recently proposed puncturing methods for polar codes to further improve the performance of our HARQ scheme.