arXiv subjects

Rong Li

Publications and source records attributed to Rong Li.

At least 73 records · Page 4Linked to original sources

arXiv2020

Toward Terabits-per-second Communications: Low-Complexity Parallel Decoding of $G_N$-Coset Codes

Recently, a parallel decoding framework of $G_N$-coset codes was proposed. High throughput is achieved by decoding the independent component polar codes in parallel. Various algorithms can be employed to decode these component codes, enabling a flexible throughput-performance tradeoff. In this work, we adopt SC as the component decoders to achieve the highest-throughput end of the tradeoff. The benefits over soft-output component decoders are reduced complexity and simpler (binary) interconnections among component decoders. To reduce performance degradation, we integrate an error detector and a log-likelihood ratio (LLR) generator into each component decoder. The LLR generator, specifically the damping factors therein, is designed by a genetic algorithm. This low-complexity design can achieve an area efficiency of $533Gbps/mm^2$ under 7nm technology.

cs.IT↗

arXiv2019

The production of $J/ψ$ associated with $c\bar{c}$ at ep colliders

We investigate the direct photoproduction of $J/ψ+c\bar{c}$ at $ep$ colliders with different center of mass energies in this paper. For the color-singlet part we take into account the contribution from QED subprocess as well as the QCD one. Our results show that the QED subprocess not only enhances the differential cross section in the large $p_t$ region but also gives a more transverse $J/ψ$ with $p_t$ increase, which is different from the almost unpolarized $J/ψ$ predicted by the QCD subprocess. The contribution from color octet subprocesses enhances the differential cross section by a factor of 5 in large $p_t$ region and predicts an even more transverse $J/ψ$ in the medium and large $p_t$ region. The sensitivity of this process to the color-octet matrix element $\langle O^{J/ψ}(^3S_1^8)\rangle$ may give great help in studying the matrix element.

hep-ph↗

arXiv2019

Buffer-aware Wireless Scheduling based on Deep Reinforcement Learning

In this paper, the downlink packet scheduling problem for cellular networks is modeled, which jointly optimizes throughput, fairness and packet drop rate. Two genie-aided heuristic search methods are employed to explore the solution space. A deep reinforcement learning (DRL) framework with A2C algorithm is proposed for the optimization problem. Several methods have been utilized in the framework to improve the sampling and training efficiency and to adapt the algorithm to a specific scheduling problem. Numerical results show that DRL outperforms the baseline algorithm and achieves similar performance as genie-aided methods without using the future information.

cs.IT↗

arXiv2019

Reinforcement Learning for Nested Polar Code Construction

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.

cs.IT↗

arXiv2019

AI Coding: Learning to Construct Error Correction Codes

In this paper, we investigate an artificial-intelligence (AI) driven approach to design error correction codes (ECC). Classic error correction code was designed upon coding theory that typically defines code properties (e.g., hamming distance, subchannel reliability, etc.) to reflect code performance. Its code design is to optimize code properties. However, an AI-driven approach doesn't necessarily rely on coding theory any longer. Specifically, we propose a constructor-evaluator framework, in which the code constructor is realized by AI algorithms and the code evaluator provides code performance metric measurements. The code constructor keeps improving the code construction to maximize code performance that is evaluated by the code evaluator. As examples, we construct linear block codes and polar codes with reinforcement learning (RL) and evolutionary algorithms. The results show that comparable code performance can be achieved with respect to the existing codes. It is noteworthy that our method can provide superior performances where existing classic constructions fail to achieve optimum for a specific decoder (e.g., list decoding for polar codes).

cs.IT↗

arXiv2019

On the Construction of $G_N$-coset Codes for Parallel Decoding

In this paper, we propose a type of $G_N$-coset codes for a highly parallel stage-permuted turbo-like decoder. The decoder exploits the equivalence between two stage-permuted factor graphs of $G_N$-coset codes. Specifically, the inner codes of a $G_N$-coset code consist of independent component codes, thus are decoded in parallel. The extrinsic information of the code bits is obtained and iteratively exchanged between the two graphs until convergence. Accordingly, we explore a heuristic and flexible code construction method (information set selection) for various information lengths and coding rates. Simulations show that the proposed $G_N$-coset codes could achieve a coding performance comparable with polar codes but enjoy higher decoding parallelism.

cs.IT↗

arXiv2019

Relativistic effect of $J/ψ$ hadroproduction in large $p_T$ region

By combining NRQCD factorization and collinear factorization, we compute a series of relativistic corrections for $J/ψ$ hadroproduction to all orders in $v^2$ at large $p_T$ limit. The $v^2$ expansion converges well for all channels. We find that the ratio of relativistic correction term to the corresponding leading term is independent of kinematic variables for any channel, which generalizes the proportional relations found in previous works to all orders.

hep-ph↗

arXiv2019

Deep Reinforcement Learning for Scheduling in Cellular Networks

Integrating artificial intelligence (AI) into wireless networks has drawn significant interest in both industry and academia. A common solution is to replace partial or even all modules in the conventional systems, which is often lack of efficiency and robustness due to their ignoring of expert knowledge. In this paper, we take deep reinforcement learning (DRL) based scheduling as an example to investigate how expert knowledge can help with AI module in cellular networks. A simulation platform, which has considered link adaption, feedback and other practical mechanisms, is developed to facilitate the investigation. Besides the traditional way, which is learning directly from the environment, for training DRL agent, we propose two novel methods, i.e., learning from a dual AI module and learning from the expert solution. The results show that, for the considering scheduling problem, DRL training procedure can be improved on both performance and convergence speed by involving the expert knowledge. Hence, instead of replacing conventional scheduling module in the system, adding a newly introduced AI module, which is capable to interact with the conventional module and provide more flexibility, is a more feasible solution.

cs.IT↗

arXiv2019

Realistic Channel Models Pre-training

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.

eess.SP↗

arXiv2019

Predicting the Mumble of Wireless Channel with Sequence-to-Sequence Models

Accurate prediction of fading channel in the upcoming transmission frame is essential to realize adaptive transmission for transmitters, and receivers with the ability of channel prediction can also save some computations of channel estimation. However, due to the rapid channel variation and channel estimation error, reliable prediction is hard to realize. In this situation, an appropriate channel model should be selected, which can cover both the statistical model and small scale fading of channel, this reminds us the natural languages, which also have statistical word frequency and specific sentences. Accordingly, in this paper, we take wireless channel model as a language model, and the time-varying channel as talking in this language, while the realistic noisy estimated channel can be compared with mumbling. Furthermore, in order to utilize as much as possible the information a channel coefficient takes, we discard the conventional two features of absolute value and phase, replacing with hundreds of features which will be learned by our channel model, to do this, we use a vocabulary to map a complex channel coefficient into an ID, which is represented by a vector of real numbers. Recurrent neural networks technique is used as its good balance between memorization and generalization, moreover, we creatively introduce sequence-to-sequence (seq2seq) models in time series channel prediction, which can translates past channel into future channel. The results show that realistic channel prediction with superior performance relative to channel estimation is attainable.

eess.SP↗

arXiv2019

Delayed state synchronization of continuous-time multi-agent systems in the presence of unknown communication delays (including complete proofs)

This paper studies delayed synchronization of continuous-time multi-agent systems (MAS) in the presence of unknown nonuniform communication delays. A delay-free transformation is developed based on a communication network which is a directed spanning tree, which can transform the original MAS to a new one without delays. By using this transformation, we design a static protocol for full-state coupling and a dynamic protocol for delayed state synchronization for homogeneous MAS via full- and partial-state coupling. Meanwhile, the delayed output synchronization is also studied for heterogeneous MAS, which is achieved by using a low-gain and output regulation based dynamic protocol design via the delay-free transformation.

eess.SY↗

arXiv2019

Learning to Flip Successive Cancellation Decoding of Polar Codes with LSTM Networks

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.

cs.IT↗

arXiv2019

The complete study on the inclusive production of Υ + γ at the LHC

In this paper we investigate the inclusive associated production of Υ + γ at the hadron collider. We calculate the color-singlet sub-processes and all three color-octet sub-processes at the next-to-leading order. Seven sets of long distance matrix elements(LDMEs), which are extracted from the studies on the prompt production of Υ at hadron colliders, are used to give the numerical results and we find that there are three sets of LDMEs give the unphysical results on the yield and the polarization for this process. The yield is enhanced by several or two orders times comparing to that of color-singlet process and the polarization changes from longitudinal to slightly transverse or even mainly transverse in the large pt region. The estimation results indicate that to study the process at the large hadron collider (LHC) is difficult and it may be well investigated at the future Super Proton-Proton Collider(SPPC).

hep-ph↗

arXiv2018

A Flip-Syndrome-List Polar Decoder Architecture for Ultra-Low-Latency Communications

We consider practical hardware implementation of Polar decoders. To reduce latency due to the serial nature of successive cancellation (SC), existing optimizations improve parallelism with two approaches, i.e., multi-bit decision or reduced path splitting. In this paper, we combine the two procedures into one with an error-pattern-based architecture. It simultaneously generates a set of candidate paths for multiple bits with pre-stored patterns. For rate-1 (R1) or single parity-check (SPC) nodes, we prove that a small number of deterministic patterns are required to guarantee performance preservation. For general nodes, low-weight error patterns are indexed by syndrome in a look-up table and retrieved in O(1) time. The proposed flip-syndrome-list (FSL) decoder fully parallelizes all constituent code blocks without sacrificing performance, thus is suitable for ultra-low-latency applications. Meanwhile, two code construction optimizations are presented to further reduce complexity and improve performance, respectively.

cs.IT↗

arXiv2018

Polarization Weight Family Methods for Polar Code Construction

Polar codes are the first proven capacity-achieving codes. Recently, they are adopted as the channel coding scheme for 5G due to their superior performance.A polar code for encoding length-K information bits in length-N codeword could be specified by the polar code construction method. Most construction methods define a polar code related to channel parameter set, e.g. designed signal-to-noise ratio. Polarization weight (PW) is a channel-independent approximation method, which estimates the subchannel reliability as a function of its index. In this paper, we generalize the PW method by including higher-order bases or extended bases. The proposed methods have robust performance while preserving the computational and mathematical simplicity as PW.

cs.IT↗

arXiv2018

Reed-Muller Sequences for 5G Grant-free Massive Access

We propose to use second order Reed-Muller (RM) sequence for user identification in 5G grant-free access. The benefits of RM sequences mainly lie in two folds, (i) support of much larger user space, hence lower collision probability and (ii) lower detection complexity. These two features are essential to meet the massive connectivity ($10^7$ links/km$^2$), ultra-reliable and low-latency requirements in 5G, e.g., one-shot transmission ($\leq 1$ms) with $\leq 10^{-4}$ packet error rate. However, the non-orthogonality introduced during sequence space expansion leads to worse detection performance. In this paper, we propose a noise-resilient detection algorithm along with a layered sequence construction to meet the harsh requirements. Link-level simulations in both narrow-band and OFDM-based scenarios show that RM sequences are suitable for 5G.

cs.IT↗

arXiv2018

Parity-Check Polar Coding for 5G and Beyond

In this paper, we propose a comprehensive Polar coding solution that integrates reliability calculation, rate matching and parity-check coding. Judging a channel coding design from the industry's viewpoint, there are two primary concerns: (i) low-complexity implementation in application-specific integrated circuit (ASIC), and (ii) superior \& stable performance under a wide range of code lengths and rates. The former provides cost- \& power-efficiency which are vital to any commercial system; the latter ensures flexible and robust services. Our design respects both criteria. It demonstrates better performance than existing schemes in literature, but requires only a fraction of implementation cost. With easily-reproducible code construction for arbitrary code rates and lengths, we are able to report "1-bit" fine-granularity simulation results for thousands of cases. The released results can serve as a baseline for future optimization of Polar codes.

cs.IT↗

arXiv2017

Nonlinear transport by vortex tangles in cuprate high-temperature superconductors

A unified model of vortex tangles is proposed to describe unconventional transport in cuprate high-temperature superconductors, which not only captures the fast vortices scenario at low density, but also predicts a novel mechanism of core-core collisions in dense vortex fluid regime. The theory clarifies the nature of vortex fluctuations being the quantum fluctuations of holes and then resolves a discrepancy of two orders of magnitude of Anderson's damping model $\hbar n_v$, with right prediction of the nonlinear field dependence of the resistivity $ρ=ρ_n(B+B_T)/(B_0+B+B_T)$ and the Nernst effect, validated by data of several samples. Consequently, Anderson's vortex tangles concept and phase fluctuation scenario of pseudogap are verified quantitatively.

cond-mat.supr-con↗