arXiv subjects

Huarui Yin

Publications and source records attributed to Huarui Yin.

At least 19 recordsLinked to original sources

arXiv2025

A Parallelization Strategy for GRAND with Optimality Guarantee by Exploiting Error Pattern Tree Representation

Parallelism has become a central concern in modern decoding frameworks aiming to meet stringent throughput and latency requirements. Guessing Random Additive Noise Decoding (GRAND) is a recently proposed decoding paradigm that tests candidate Error Patterns (EPs) until a valid codeword is found. Among its variants, Soft GRAND (SGRAND) achieves maximum-likelihood (ML) decoding but relies on real-time generation and likelihood ordering of EPs, making parallel execution nontrivial under the ML optimality constraint. In this work, we introduce a unified binary tree representation of EPs, termed the EP tree, which formalizes the hierarchical structure underlying SGRAND and Ordered Reliability Bits (ORB) GRAND algorithms, enabling structured organization of EPs and algorithmic-level parallel exploration. Building upon this unified framework, we propose a parallel design of SGRAND that preserves ML optimality while significantly reducing decoding complexity through pruning strategies and tree-based computation. Furthermore, we develop an enhanced ORBGRAND algorithm based on the same EP tree representation, improving decoding performance toward ML while retaining parallel efficiency. Numerical experiments show that the proposed parallel SGRAND achieves a $3.96\times$ reduction in decoding latency compared with its serial counterpart, while the enhanced ORBGRAND achieves a $4.21\times$ speedup, demonstrating the effectiveness of the unified tree-based framework and its strong potential for future algorithmic and hardware optimizations.

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arXiv2025

Fine-tuning ORBGRAND with Very Few Channel Soft Values

Guessing random additive noise decoding (GRAND) is a universal decoding paradigm that decodes by repeatedly testing error patterns until identifying a codeword, where the ordering of tests is generated by the received channel values. On one hand, while testing error patterns in a descending order of posterior probabilities leads to maximum likelihood decoding, its implementation complexity is prohibitive. On the other hand, testing error patterns with a prescribed set of error patterns permuted by the ranking among magnitudes of log-likelihood ratios (i.e., ordered reliability bits, ORB) enables efficient implementation, but results in performance loss for finite-length codes. Aiming at harnessing the strengths of these two approaches, this work proposes a fine-tuning method to improve ORBGRAND, adjusting the ordering of tests with the aid of very few exact channel soft values. This method is based on a metric for assessing the ``well-orderedness'' of error patterns. The metric is studied via the lens of the asymptotic theory of integer partitioning, which provides highly accurate estimation in numerical experiments. The metric then leads to an effective identification of fine-tuning to conduct, at the cost of a negligible increment of complexity. Numerical experiments demonstrate that the proposed fine-tuning method achieves a substantial performance enhancement compared with ORBGRAND.

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arXiv2024

Finite-Precision Arithmetic Transceiver for Massive MIMO Systems

Efficient implementation of massive multiple-input-multiple-output (MIMO) transceivers is essential for the next-generation wireless networks. To reduce the high computational complexity of the massive MIMO transceiver, in this paper, we propose a new massive MIMO architecture using finite-precision arithmetic. First, we conduct the rounding error analysis and derive the lower bound of the achievable rate for single-input-multiple-output (SIMO) using maximal ratio combining (MRC) and multiple-input-single-output (MISO) systems using maximal ratio transmission (MRT) with finite-precision arithmetic. Then, considering the multi-user scenario, the rounding error analysis of zero-forcing (ZF) detection and precoding is derived by using the normal equations (NE) method. The corresponding lower bounds of the achievable sum rate are also derived and asymptotic analyses are presented. Built upon insights from these analyses and lower bounds, we propose a mixed-precision architecture for massive MIMO systems to offset performance gaps due to finite-precision arithmetic. The corresponding analysis of rounding errors and computational costs is obtained. Simulation results validate the derived bounds and underscore the superiority of the proposed mixed-precision architecture to the conventional structure.

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arXiv2024

Joint Channel Estimation and Data Recovery for Millimeter Massive MIMO: Using Pilot to Capture Principal Components

Channel state information (CSI) is important to reap the full benefits of millimeter wave (mmWave) massive multiple-input multiple-output (MIMO) systems. The traditional channel estimation methods using pilot frames (PF) lead to excessive overhead. To reduce the demand for PF, data frames (DF) can be adopted for joint channel estimation and data recovery. However, the computational complexity of the DF-based methods is prohibitively high. To reduce the computational complexity, we propose a joint channel estimation and data recovery (JCD) method assisted by a small number of PF for mmWave massive MIMO systems. The proposed method has two stages. In Stage 1, differing from the traditional PF-based methods, the proposed PF-assisted method is utilized to capture the angle of arrival (AoA) of principal components (PC) of channels. In Stage 2, JCD is designed for parallel implementation based on the multi-user decoupling strategy. The theoretical analysis demonstrates that the PF-assisted JCD method can achieve equivalent performance to the Bayesian-optimal DF-based method, while greatly reducing the computational complexity. Simulation results are also presented to validate the analytical results.

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arXiv2021

Reliable Over-the-Air Computation by Amplify-and-Forward Based Relay

In typical sensor networks, data collection and processing are separated. A sink collects data from all nodes sequentially, which is very time consuming. Over-the-air computation, as a new diagram of sensor networks, integrates data collection and processing in one slot: all nodes transmit their signals simultaneously in the analog wave and the processing is done in the air. This method, although efficient, requires that signals from all nodes arrive at the sink, aligned in signal magnitude so as to enable an unbiased estimation. For nodes far away from the sink with a low channel gain, misalignment in signal magnitude is unavoidable. To solve this problem, in this paper, we investigate the amplify-and-forward based relay, in which a relay node amplifies signals from many nodes at the same time. We first discuss the general relay model and a simple relay policy. Then, a coherent relay policy is proposed to reduce relay transmission power. Directly minimizing the computation error tends to over-increase node transmission power. Therefore, the two relay policies are further refined with a new metric, and the transmission power is reduced while the computation error is kept low. In addition, the coherent relay policy helps to reduce the relay transmission power by half, to below the limit, which makes it one step ahead towards practical applications.

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arXiv2019

Polarized Low-Density Parity-Check Codes on the BSC

The connections between variable nodes and check nodes have a great influence on the performance of low-density parity-check (LDPC) codes. Inspired by the unique structure of polar code's generator matrix, we proposed a new method of constructing LDPC codes that achieves a polarization effect. The new code, named as polarized LDPC codes, is shown to achieve lower or no error floor in the binary symmetric channel (BSC)

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arXiv2019

An End-to-End Block Autoencoder For Physical Layer Based On Neural Networks

Deep Learning has been widely applied in the area of image processing and natural language processing. In this paper, we propose an end-to-end communication structure based on autoencoder where the transceiver can be optimized jointly. A neural network roles as a combination of channel encoder and modulator. In order to deal with input sequences parallelly, we introduce block scheme, which means that the autoencoder divides the input sequence into a series of blocks. Each block contains fixed number of bits for encoding and modulating operation. Through training, the proposed system is able to produce the modulated constellation diagram of each block. The simulation results show that our autoencoder performs better than other autoencoder-based systems under additive Gaussian white noise (AWGN) and fading channels. We also prove that the bit error rate (BER) of proposed system can achieve an acceptable range with increasing the number of symbols.

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arXiv2018

Hybrid Beamforming Design and Performance with Imperfect Phase Shifters in Multiuser Millimeter Wave Systems

Hybrid beamforming (HBF) includes analog beamforming with phase shifted array in RF domain and digital beamforming in baseband domain. Phase shifted array is usually made up with a large amount of phase shifters. Limited to the manufacturing techniques, phase shifters are unavoidable to have phaseshifting error and gain error. In the paper we study the influence of imperfect phase shifters upon the performance of multiuser mmWave massive MIMO systems1 with the wireless channel modeled by Rice fading. We derive the upper bound of the achievable sum rate with imperfect phase shifters, which can be very tight when the number of the antennas at the base station is much larger than the number of users. Results show that there is a performance ceiling due to phase-shifting error and gain error. Then we propose a novel channel estimation and hybrid beamforming method to settle this problem with low training overhead. It performs well when the channel paths are few and the channel Rician K-factor is large, which can be easily satisfied in outdoor mmWave communication environment. We further make a tradeoff between the performance and the training overhead so that the algorithm can work well in more kinds of propagation environment.

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arXiv2018

TCP Decoupling for Next Generation Communication System

In traditional networks, interfaces of network nodes are duplex. But, emerging communication technologies such as visible light communication, millimeter-wave communications, can only provide a unidirectional interface when cost is limited. It's urgent to find effective solutions to utilize such new unidirectional communication skills. Decoupling implies separating one single resource to two independent resources. This idea can be applied at physical layer, link layer, network layer, even transport layer. TCP decoupling is an end to end solution provided at transport layer. With decoupled TCP, two distinct unidirectional path can be created to meet the requirements of reliable information transfer. However, it is not an easy task to decouple a bidirectional logical path at transport layer. In this paper, we dwell on the idea of TCP decoupling. Advantages of decoupling at transport layer are analyzed also. In addition, an experiment is carried out to figure out how to implement a decouple TCP. Our results show decoupling at transport layer is possible and the modified protocol is available.

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arXiv2017

Joint Domain Based Massive Access for Small Packets Traffic of Uplink Wireless Channel

The fifth generation (5G) communication scenarios such as the cellular network and the emerging machine type communications will produce massive small packets. To support massive connectivity and avoid signaling overhead caused by the transmission of those small packets, this paper proposes a novel method to improve the transmission efficiency for massive connections of wireless uplink channel. The proposed method combines compressive sensing (CS) with power domain NOMA jointly, especially neither the scheduling nor the centralized power allocation is necessary in the method. Both the analysis and simulation show that the method can support up to two or three times overloading.

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arXiv2016

Stopping Condition for Greedy Block Sparse Signal Recovery

For greedy block sparse recovery where the sparsity level is unknown, we derive a stopping condition to stop the iteration process. Focused on the block orthogonal matching pursuit (BOMP) algorithm, we model the energy of residual signals at each iteration from a probabilistic perspective. At the iteration when the last supporting block is detected, the resulting energy of residual signals is supposed to suffer an obvious decrease. Based on this, we stop the iteration process when the energy of residual signals is below a given threshold. Compared with other approaches, our derived condition works well for the BOMP recovery. What is more, we promote our approach to the interference cancellation based BOMP (ICBOMP) recovery in paper [1]. Simulation results show that our derived condition can save many unnecessary iterations and at the same time guarantees a favorable recovery accuracy, both for the BOMP and ICBOMP recoveries.

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arXiv2016

Many Access for Small Packets Based on Precoding and Sparsity-aware Recovery

Modern mobile terminals produce massive small data packets. For these short-length packets, it is inefficient to follow the current multiple access schemes to allocate transmission resources due to heavy signaling overhead. We propose a non-orthogonal many-access scheme that is well suited for the future communication systems equipped with many receive antennas. The system is modeled as having a block-sparsity pattern with unknown sparsity level (i.e., unknown number of transmitted messages). Block precoding is employed at each single-antenna transmitter to enable the simultaneous transmissions of many users. The number of simultaneously served active users is allowed to be even more than the number of receive antennas. Sparsity-aware recovery is designed at the receiver for joint user detection and symbol demodulation. To reduce the effects of channel fading on signal recovery, normalized block orthogonal matching pursuit (BOMP) algorithm is introduced, and based on its approximate performance analysis, we develop interference cancellation based BOMP (ICBOMP) algorithm. The ICBOMP performs error correction and detection in each iteration of the normalized BOMP. Simulation results demonstrate the effectiveness of the proposed scheme in small packet services, as well as the advantages of ICBOMP in improving signal recovery accuracy and reducing computational cost.

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arXiv2014

Multiple Access for Small Packets Based on Precoding and Sparsity-Aware Detection

Modern mobile terminals often produce a large number of small data packets. For these packets, it is inefficient to follow the conventional medium access control protocols because of poor utilization of service resources. We propose a novel multiple access scheme that employs block-spreading based precoding at the transmitters and sparsity-aware detection schemes at the base station. The proposed scheme is well suited for the emerging massive multiple-input multiple-output (MIMO) systems, as well as conventional cellular systems with a small number of base-station antennas. The transmitters employ precoding in time domain to enable the simultaneous transmissions of many users, which could be even more than the number of receive antennas at the base station. The system is modeled as a linear system of equations with block-sparse unknowns. We first adopt the block orthogonal matching pursuit (BOMP) algorithm to recover the transmitted signals. We then develop an improved algorithm, named interference cancellation BOMP (ICBOMP), which takes advantage of error correction and detection coding to perform perfect interference cancellation during each iteration of BOMP algorithm. Conditions for guaranteed data recovery are identified. The simulation results demonstrate that the proposed scheme can accommodate more simultaneous transmissions than conventional schemes in typical small-packet transmission scenarios.

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arXiv2014

A Novel Uplink Data Transmission Scheme For Small Packets In Massive MIMO System

Intelligent terminals often produce a large number of data packets of small lengths. For these packets, it is inefficient to follow the conventional medium access control (MAC) protocols because they lead to poor utilization of service resources. We propose a novel multiple access scheme that targets massive multiple-input multiple-output (MIMO) systems based on compressive sensing (CS). We employ block precoding in the time domain to enable the simultaneous transmissions of many users, which could be even more than the number of receive antennas at the base station. We develop a block-sparse system model and adopt the block orthogonal matching pursuit (BOMP) algorithm to recover the transmitted signals. Conditions for data recovery guarantees are identified and numerical results demonstrate that our scheme is efficient for uplink small packet transmission.

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arXiv2014

Maximizing Mobile Coverage via Optimal Deployment of Base Station and Relays

Deploying relays and/or mobile base stations is a major means of extending the coverage of a wireless network. This paper presents models, analytical results, and algorithms to answer two related questions: The first is where to deploy relays in order to extend the reach from a base station to the maximum; the second is where to deploy a mobile base station and how many relays are needed to reach any point in a given area. Simple time-division and frequency-division scheduling schemes as well as an end-to-end data rate requirement are assumed. An important use case of the results is in the Public Safety Broadband Network, in which deploying relays and mobile base stations is often crucial to provide coverage to an incident scene.

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arXiv2013

Monobit Digital Receivers for QPSK: Design, Analysis and Performance

Future communication system requires large bandwidth to achieve high data rate up to multigigabit/ sec, which makes analog-to-digital (ADC) become a key bottleneck for the implementation of digital receivers due to its high complexity and large power consumption. Therefore, monobit receivers for BPSK have been proposed to address this problem. In this work, QPSK modulation is considered for higher data rate. First, the optimal receiver based on monobit ADC with Nyquist sampling is derived, and its corresponding performance in the form of deflection ratio is calculated. Then a suboptimal but more practical monobit receiver is obtained, along with iterative demodulation and small sample removal. The effect of the imbalances between the In-phase (I) and Quadrature-phase (Q) branches, including the amplitude and phase imbalances, is carefully investigated too. To combat the performance loss caused by IQ imbalances, monobit receivers based on double training sequences are proposed. Numerical simulations show that the low-complexity suboptimal receiver suffers only 3dB signal to noise ratio (SNR) loss in AWGN channels and 1dB SNR loss in multipath static channels compared with the matched filter based monobit receiver with full channel state information (CSI). The impact of the phase difference between the transmitter and receiver is presented. It is observed that the performance degradation caused by the amplitude imbalance is negligible. Receivers based on double training sequences can efficiently compensate the performance loss in AWGN channel. Thanks to the diversity offered by the multipath, the effect of imbalances on monobit receivers in fading channels is slight. I

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arXiv2012

Joint Viterbi Decoding and Decision Feedback Equalization for Monobit Digital Receivers

In ultra-wideband (UWB) communication systems with impulse radio (IR) modulation, the bandwidth is usually 1GHz or more. To process the received signal digitally, high sampling rate analog-digital-converters (ADC) are required. Due to the high complexity and large power consumption, monobit ADC is appropriate. The optimal monobit receiver has been derived. But it is not efficient to combat intersymbol interference (ISI). Decision feedback equalization (DFE) is an effect way dealing with ISI. In this paper, we proposed a algorithm that combines Viterbi decoding and DFE together for monobit receivers. In this way, we suppress the impact of ISI effectively, thus improving the bit error rate (BER) performance. By state expansion, we achieve better performance. The simulation results show that the algorithm has about 1dB SNR gain compared to separate demodulation and decoding method and 1dB loss compared to the BER performance in the channel without ISI. Compare to the full resolution detection in fading channel without ISI, it has 3dB SNR loss after state expansion.

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arXiv2012

One-sided Precoder Designs on Manifolds for Interference Alignment

Interference alignment (IA) is a technique recently shown to achieve the maximum degrees of freedom (DoF) of $K$-user interference channel. In this paper, we focus on the precoder designs on manifolds for IA. By restricting the optimization only at the transmitters' side, it will alleviate the overhead induced by alternation between the forward and reverse links significantly. Firstly a classical steepest descent (SD) algorithm in multi-dimensional complex space is proposed to achieve feasible IA. Then we reform the optimization problem on Stiefel manifold, and propose a novel SD algorithm based on this manifold with lower dimensions. Moreover, aiming at further reducing the complexity, Grassmann manifold is introduced to derive corresponding algorithm for reaching the perfect IA. Numerical simulations show that the proposed algorithms on manifolds have better convergence performance and higher system capacity than previous methods, also achieve the maximum DoF.

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