Huazi Zhang

Huazi Zhang, Xianbin Wang, Jiajie Tong et al.

This paper introduces a novel framework for polar codes, designed for flexible Incremental Redundancy Hybrid Automatic Repeat Request (IR-HARQ). By generalizing the decoding order beyond the standard 1$\to$N sequence, we enable a capacity-aware scheduling strategy that prioritizes the decoding of reliable subblocks. The framework integrates nested parity-check polar construction and reverse bit-mapping to support continuous and arbitrary transmission lengths $E \in [N_{\min}, N_{\max}]$. Simulation results show that the proposed rateless codes match the coding gain of independently optimized fixed-rate codes across the entire range of rates and lengths. With a validated hardware implementation, this work provides a practical solution for next-generation wireless data channels.

Yourui Huangfu, Jian Wang, Chen Xu et al.

In this paper, we propose a neural-network-based realistic channel model with both the similar accuracy as deterministic channel models and uniformity as stochastic channel models. To facilitate this realistic channel modeling, a multi-domain channel embedding method combined with self-attention mechanism is proposed to extract channel features from multiple domains simultaneously. This 'one model to fit them all' solution employs available wireless channel data as the only data set for self-supervised pre-training. With the permission of users, network operators or other organizations can make use of some available user specific data to fine-tune this pre-trained realistic channel model for applications on channel-related downstream tasks. Moreover, even without fine-tuning, we show that the pre-trained realistic channel model itself is a great tool with its understanding of wireless channel.

Lingchen Huang, Huazi Zhang, Rong Li et al.

In this paper, we model nested polar code construction as a Markov decision process (MDP), and tackle it with advanced reinforcement learning (RL) techniques. First, an MDP environment with state, action, and reward is defined in the context of polar coding. Specifically, a state represents the construction of an $(N,K)$ polar code, an action specifies its reduction to an $(N,K-1)$ subcode, and reward is the decoding performance. A neural network architecture consisting of both policy and value networks is proposed to generate actions based on the observed states, aiming at maximizing the overall rewards. A loss function is defined to trade off between exploitation and exploration. To further improve learning efficiency and quality, an `integrated learning' paradigm is proposed. It first employs a genetic algorithm to generate a population of (sub-)optimal polar codes for each $(N,K)$, and then uses them as prior knowledge to refine the policy in RL. Such a paradigm is shown to accelerate the training process, and converge at better performances. Simulation results show that the proposed learning-based polar constructions achieve comparable, or even better, performances than the state of the art under successive cancellation list (SCL) decoders. Last but not least, this is achieved without exploiting any expert knowledge from polar coding theory in the learning algorithms.

Xianbin Wang, Huazi Zhang, Rong Li et al.

The key to successive cancellation (SC) flip decoding of polar codes is to accurately identify the first error bit. The optimal flipping strategy is considered difficult due to lack of an analytical solution. Alternatively, we propose a deep learning aided SC flip algorithm. Specifically, before each SC decoding attempt, a long short-term memory (LSTM) network is exploited to either (i) locate the first error bit, or (ii) undo a previous `wrong' flip. In each SC attempt, the sequence of log likelihood ratios (LLRs) derived in the previous SC attempt is exploited to decide which action to take. Accordingly, a two-stage training method of the LSTM network is proposed, i.e., learn to locate first error bits in the first stage, and then to undo `wrong' flips in the second stage. Simulation results show that the proposed approach identifies error bits more accurately and achieves better performance than the state-of-the-art SC flip algorithms.

Wei Lyu, Zhaoyang Zhang, Chunxu Jiao et al.

With the demand of high data rate and low latency in fifth generation (5G), deep neural network decoder (NND) has become a promising candidate due to its capability of one-shot decoding and parallel computing. In this paper, three types of NND, i.e., multi-layer perceptron (MLP), convolution neural network (CNN) and recurrent neural network (RNN), are proposed with the same parameter magnitude. The performance of these deep neural networks are evaluated through extensive simulation. Numerical results show that RNN has the best decoding performance, yet at the price of the highest computational overhead. Moreover, we find there exists a saturation length for each type of neural network, which is caused by their restricted learning abilities.

Kairan Sun, Huazi Zhang, Dapeng Wu

The explosive demand of on-line video from smart mobile devices poses unprecedented challenges to delivering high quality of experience (QoE) over wireless networks. Streaming high-definition video with low delay is difficult mainly due to (i) the stochastic nature of wireless channels and (ii) the fluctuating videos bit rate. To address this, we propose a novel delay-aware fountain coding (DAF) technique that integrates channel coding and video coding. In this paper, we reveal that the fluctuation of video bit rate can also be exploited to further improve fountain codes for wireless video streaming. Specifically, we develop two coding techniques: the time-based sliding window and the optimal window-wise sampling strategy. By adaptively selecting the window length and optimally adjusting the sampling pattern according to the ongoing video bit rate, the proposed schemes deliver significantly higher video quality than existing schemes, with low delay and constant data rate. To validate our design, we implement the protocols of DAF, DAF-L (a low-complexity version) and the existing delay-aware video streaming schemes by streaming H.264/AVC standard videos over an 802.11b network on CORE emulation platform. The results show that the decoding ratio of our scheme is 15% to 100% higher than the state of the art techniques.

Huazi Zhang, Yichao Jin, Weiwen Zhang et al.

Recently, multi-screen cloud social TV is invented to transform TV into social experience. People watching the same content on social TV may come from different locations, while freely interact with each other through text, image, audio and video. This crucial virtual living-room experience adds social aspects into existing performance metrics. In this paper, we parse social TV user experience into three elements (i.e., inter-user delay, video quality of experience (QoE), and resource efficiency), and provide a joint analytical framework to enhance user experience. Specifically, we propose a cloud-based optimal playback rate allocation scheme to maximize the overall QoE while upper bounding inter-user delay. Experiment results show that our algorithm achieves near-optimal tradeoff between inter-user delay and video quality, and demonstrates resilient performance even under very fast wireless channel fading.