arXiv subjects

Yulin Shao

Publications and source records attributed to Yulin Shao.

At least 73 records · Page 4Linked to original sources

arXiv2022

Semantic Communications with Discrete-time Analog Transmission: A PAPR Perspective

Recent progress in deep learning (DL)-based joint source-channel coding (DeepJSCC) has led to a new paradigm of semantic communications. Two salient features of DeepJSCC-based semantic communications are the exploitation of semantic-aware features directly from the source signal, and the discrete-time analog transmission (DTAT) of these features. Compared with traditional digital communications, semantic communications with DeepJSCC provide superior reconstruction performance at the receiver and graceful degradation with diminishing channel quality, but also exhibit a large peak-to-average power ratio (PAPR) in the transmitted signal. An open question has been whether the gains of DeepJSCC come from the additional freedom brought by the high-PAPR continuous-amplitude signal. In this paper, we address this question by exploring three PAPR reduction techniques in the application of image transmission. We confirm that the superior image reconstruction performance of DeepJSCC-based semantic communications can be retained while the transmitted PAPR is suppressed to an acceptable level. This observation is an important step towards the implementation of DeepJSCC in practical semantic communication systems.

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arXiv2022

AttentionCode: Ultra-Reliable Feedback Codes for Short-Packet Communications

Ultra-reliable short-packet communication is a major challenge in future wireless networks with critical applications. To achieve ultra-reliable communications beyond 99.999%, this paper envisions a new interaction-based communication paradigm that exploits feedback from the receiver. We present AttentionCode, a new class of feedback codes leveraging deep learning (DL) technologies. The underpinnings of AttentionCode are three architectural innovations: AttentionNet, input restructuring, and adaptation to fading channels, accompanied by several training methods, including large-batch training, distributed learning, look-ahead optimizer, training-test signal-to-noise ratio (SNR) mismatch, and curriculum learning. The training methods can potentially be generalized to other wireless communication applications with machine learning. Numerical experiments verify that AttentionCode establishes a new state of the art among all DL-based feedback codes in both additive white Gaussian noise (AWGN) channels and fading channels. In AWGN channels with noiseless feedback, for example, AttentionCode achieves a block error rate (BLER) of $10^{-7}$ when the forward channel SNR is 0 dB for a block size of 50 bits, demonstrating the potential of AttentionCode to provide ultra-reliable short-packet communications.

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arXiv2022

Learning-based Autonomous Channel Access in the Presence of Hidden Terminals

We consider the problem of autonomous channel access (AutoCA), where a group of terminals tries to discover a communication strategy with an access point (AP) via a common wireless channel in a distributed fashion. Due to the irregular topology and the limited communication range of terminals, a practical challenge for AutoCA is the hidden terminal problem, which is notorious in wireless networks for deteriorating the throughput and delay performances. To meet the challenge, this paper presents a new multi-agent deep reinforcement learning paradigm, dubbed MADRL-HT, tailored for AutoCA in the presence of hidden terminals. MADRL-HT exploits topological insights and transforms the observation space of each terminal into a scalable form independent of the number of terminals. To compensate for the partial observability, we put forth a look-back mechanism such that the terminals can infer behaviors of their hidden terminals from the carrier sensed channel states as well as feedback from the AP. A window-based global reward function is proposed, whereby the terminals are instructed to maximize the system throughput while balancing the terminals' transmission opportunities over the course of learning. Extensive numerical experiments verified the superior performance of our solution benchmarked against the legacy carrier-sense multiple access with collision avoidance (CSMA/CA) protocol.

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arXiv2022

Feedback is Good, Active Feedback is Better: Block Attention Active Feedback Codes

Deep neural network (DNN)-assisted channel coding designs, such as low-complexity neural decoders for existing codes, or end-to-end neural-network-based auto-encoder designs are gaining interest recently due to their improved performance and flexibility; particularly for communication scenarios in which high-performing structured code designs do not exist. Communication in the presence of feedback is one such communication scenario, and practical code design for feedback channels has remained an open challenge in coding theory for many decades. Recently, DNN-based designs have shown impressive results in exploiting feedback. In particular, generalized block attention feedback (GBAF) codes, which utilizes the popular transformer architecture, achieved significant improvement in terms of the block error rate (BLER) performance. However, previous works have focused mainly on passive feedback, where the transmitter observes a noisy version of the signal at the receiver. In this work, we show that GBAF codes can also be used for channels with active feedback. We implement a pair of transformer architectures, at the transmitter and the receiver, which interact with each other sequentially, and achieve a new state-of-the-art BLER performance, especially in the low SNR regime.

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arXiv2022

Decentralized Channel Management in WLANs with Graph Neural Networks

Wireless local area networks (WLANs) manage multiple access points (APs) and assign scarce radio frequency resources to APs for satisfying traffic demands of associated user devices. This paper considers the channel allocation problem in WLANs that minimizes the mutual interference among APs, and puts forth a learning-based solution that can be implemented in a decentralized manner. We formulate the channel allocation problem as an unsupervised learning problem, parameterize the control policy of radio channels with graph neural networks (GNNs), and train GNNs with the policy gradient method in a model-free manner. The proposed approach allows for a decentralized implementation due to the distributed nature of GNNs and is equivariant to network permutations. The former provides an efficient and scalable solution for large network scenarios, and the latter renders our algorithm independent of the AP reordering. Empirical results are presented to evaluate the proposed approach and corroborate theoretical findings.

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arXiv2022

Vision Transformer for Adaptive Image Transmission over MIMO Channels

This paper presents a vision transformer (ViT) based joint source and channel coding (JSCC) scheme for wireless image transmission over multiple-input multiple-output (MIMO) systems, called ViT-MIMO. The proposed ViT-MIMO architecture, in addition to outperforming separation-based benchmarks, can flexibly adapt to different channel conditions without requiring retraining. Specifically, exploiting the self-attention mechanism of the ViT enables the proposed ViT-MIMO model to adaptively learn the feature mapping and power allocation based on the source image and channel conditions. Numerical experiments show that ViT-MIMO can significantly improve the transmission quality cross a large variety of scenarios, including varying channel conditions, making it an attractive solution for emerging semantic communication systems.

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arXiv2022

Bayesian Over-The-Air Computation

As an important piece of the multi-tier computing architecture for future wireless networks, over-the-air computation (OAC) enables efficient function computation in multiple-access edge computing, where a fusion center aims to compute a function of the data distributed at edge devices. Existing OAC relies exclusively on the maximum likelihood (ML) estimation at the fusion center to recover the arithmetic sum of the transmitted signals from different devices. ML estimation, however, is much susceptible to noise. In particular, in the misaligned OAC where there are channel misalignments among received signals, ML estimation suffers from severe error propagation and noise enhancement. To address these challenges, this paper puts forth a Bayesian approach by letting each edge device transmit two pieces of statistical information to the fusion center such that Bayesian estimators can be devised to tackle the misalignments. Numerical and simulation results verify that, 1) For the aligned and synchronous OAC, our linear minimum mean squared error (LMMSE) estimator significantly outperforms the ML estimator. In the low signal-to-noise ratio (SNR) regime, the LMMSE estimator reduces the mean squared error (MSE) by at least 6 dB; in the high SNR regime, the LMMSE estimator lowers the error floor of MSE by 86.4%; 2) For the asynchronous OAC, our LMMSE and sum-product maximum a posteriori (SP-MAP) estimators are on an equal footing in terms of the MSE performance, and are significantly better than the ML estimator. Moreover, the SP-MAP estimator is computationally efficient, the complexity of which grows linearly with the packet length.

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arXiv2022

All you need is feedback: Communication with block attention feedback codes

Deep learning based channel code designs have recently gained interest as an alternative to conventional coding algorithms, particularly for channels for which existing codes do not provide effective solutions. Communication over a feedback channel is one such problem, for which promising results have recently been obtained by employing various deep learning architectures. In this paper, we introduce a novel learning-aided code design for feedback channels, called generalized block attention feedback (GBAF) codes, which i) employs a modular architecture that can be implemented using different neural network architectures; ii) provides order-of-magnitude improvements in the probability of error compared to existing designs; and iii) can transmit at desired code rates.

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arXiv2022

Channel-Adaptive Wireless Image Transmission with OFDM

We present a learning-based channel-adaptive joint source and channel coding (CA-JSCC) scheme for wireless image transmission over multipath fading channels. The proposed method is an end-to-end autoencoder architecture with a dual-attention mechanism employing orthogonal frequency division multiplexing (OFDM) transmission. Unlike the previous works, our approach is adaptive to channel-gain and noise-power variations by exploiting the estimated channel state information (CSI). Specifically, with the proposed dual-attention mechanism, our model can learn to map the features and allocate transmission-power resources judiciously based on the estimated CSI. Extensive numerical experiments verify that CA-JSCC achieves state-of-the-art performance among existing JSCC schemes. In addition, CA-JSCC is robust to varying channel conditions and can better exploit the limited channel resources by transmitting critical features over better subchannels.

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arXiv2022

Phase Code Discovery for Pulse Compression Radar: A Genetic Algorithm Approach

Discovering sequences with desired properties has long been an interesting intellectual pursuit. In pulse compression radar (PCR), discovering phase codes with low aperiodic autocorrelations is essential for a good estimation performance. The design of phase code, however, is mathematically non-trivial as the aperiodic autocorrelation properties of a sequence are intractable to characterize. In this paper, we put forth a genetic algorithm (GA) approach to discover new phase codes for PCR with the mismatched filter (MMF) receiver. The developed GA, dubbed GASeq, discovers better phase codes than the state of the art. At a code length of 59, the sequence discovered by GASeq achieves a signal-to-clutter ratio (SCR) of 50.84, while the best-known sequence has an SCR of 45.16. In addition, the efficiency and scalability of GASeq enable us to search phase codes with a longer code length, which thwarts existing deep learning-based approaches. At a code length of 100, the best phase code discovered by GASeq exhibit an SCR of 63.23.

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arXiv2022

Dynamic gNodeB Sleep Control for Energy-Conserving 5G Radio Access Network

5G radio access network (RAN) is consuming much more energy than legacy RAN due to the denser deployments of gNodeBs (gNBs) and higher single-gNB power consumption. In an effort to achieve an energy-conserving RAN, this paper develops a dynamic on-off switching paradigm, where the ON/OFF states of gNBs can be dynamically configured according to the evolvements of the associated users. We formulate the dynamic sleep control for a cluster of gNBs as a Markov decision process (MDP) and analyze various switching policies to reduce the energy expenditure. The optimal policy of the MDP that minimizes the energy expenditure can be derived from dynamic programming, but the computation is expensive. To circumvent this issue, this paper puts forth a greedy policy and an index policy for gNB sleep control. When there is no constraint on the number of gNBs that can be turned off, we prove the dual-threshold structure of the greedy policy and analyze its connections with the optimal policy. Inspired by the dual-threshold structure and Whittle index, we develop an index policy by decoupling the original MDP into multiple one-dimensional MDPs -- the indexability of the decoupled MDP is proven and an algorithm to compute the index is proposed. Extensive simulation results verify that the index policy exhibits close-to-optimal performance in terms of the energy expenditure of the gNB cluster. As far as the computational complexity is concerned, on the other hand, the index policy is much more efficient than the optimal policy, which is computationally prohibitive when the number of gNBs is large.

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arXiv2022

Uncertainty-of-Information Scheduling: A Restless Multi-armed Bandit Framework

This paper proposes using the uncertainty of information (UoI), measured by Shannon's entropy, as a metric for information freshness. We consider a system in which a central monitor observes multiple binary Markov processes through a communication channel. The UoI of a Markov process corresponds to the monitor's uncertainty about its state. At each time step, only one Markov process can be selected to update its state to the monitor; hence there is a tradeoff among the UoIs of the processes that depend on the scheduling policy used to select the process to be updated. The age of information (AoI) of a process corresponds to the time since its last update. In general, the associated UoI can be a non-increasing function, or even an oscillating function, of its AoI, making the scheduling problem particularly challenging. This paper investigates scheduling policies that aim to minimize the average sum-UoI of the processes over the infinite time horizon. We formulate the problem as a restless multi-armed bandit (RMAB) problem, and develop a Whittle index policy that is near-optimal for the RMAB after proving its indexability. We further provide an iterative algorithm to compute the Whittle index for the practical deployment of the policy. Although this paper focuses on UoI scheduling, our results apply to a general class of RMABs for which the UoI scheduling problem is a special case. Specifically, this paper's Whittle index policy is valid for any RMAB in which the bandits are binary Markov processes and the penalty is a concave function of the belief state of the Markov process. Numerical results demonstrate the excellent performance of the Whittle index policy for this class of RMABs.

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arXiv2022

Federated Spatial Reuse Optimization in Next-Generation Decentralized IEEE 802.11 WLANs

As wireless standards evolve, more complex functionalities are introduced to address the increasing requirements in terms of throughput, latency, security, and efficiency. To unleash the potential of such new features, artificial intelligence (AI) and machine learning (ML) are currently being exploited for deriving models and protocols from data, rather than by hand-programming. In this paper, we explore the feasibility of applying ML in next-generation wireless local area networks (WLANs). More specifically, we focus on the IEEE 802.11ax spatial reuse (SR) problem and predict its performance through federated learning (FL) models. The set of FL solutions overviewed in this work is part of the 2021 International Telecommunication Union (ITU) AI for 5G Challenge.

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arXiv2022

Denoising Noisy Neural Networks: A Bayesian Approach with Compensation

Deep neural networks (DNNs) with noisy weights, which we refer to as noisy neural networks (NoisyNNs), arise from the training and inference of DNNs in the presence of noise. NoisyNNs emerge in many new applications, including the wireless transmission of DNNs, the efficient deployment or storage of DNNs in analog devices, and the truncation or quantization of DNN weights. This paper studies a fundamental problem of NoisyNNs: how to reconstruct the DNN weights from their noisy manifestations. While all prior works relied on the maximum likelihood (ML) estimation, this paper puts forth a denoising approach to reconstruct DNNs with the aim of maximizing the inference accuracy of the reconstructed models. The superiority of our denoiser is rigorously proven in two small-scale problems, wherein we consider a quadratic neural network function and a shallow feedforward neural network, respectively. When applied to advanced learning tasks with modern DNN architectures, our denoiser exhibits significantly better performance than the ML estimator. Consider the average test accuracy of the denoised DNN model versus the weight variance to noise power ratio (WNR) performance. When denoising a noisy ResNet34 model arising from noisy inference, our denoiser outperforms ML estimation by up to 4.1 dB to achieve a test accuracy of 60%.When denoising a noisy ResNet18 model arising from noisy training, our denoiser outperforms ML estimation by 13.4 dB and 8.3 dB to achieve test accuracies of 60% and 80%, respectively.

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arXiv2022

Efficient FFT Computation in IFDMA Transceivers

Interleaved Frequency Division Multiple Access (IFDMA) has the salient advantage of lower Peak-to-Average Power Ratio (PAPR) than its competitors like Orthogonal FDMA (OFDMA). A recent research effort put forth a new IFDMA transceiver design significantly less complex than conventional IFDMA transceivers. The new IFDMA transceiver design reduces the complexity by exploiting a certain correspondence between the IFDMA signal processing and the Cooley-Tukey IFFT/FFT algorithmic structure so that IFDMA streams can be inserted/extracted at different stages of an IFFT/FFT module according to the sizes of the streams. Although the prior work has laid down the theoretical foundation for the new IFDMA transceiver's structure, the practical realization of the transceiver on specific hardware with resource constraints has not been carefully investigated. This paper is an attempt to fill the gap. Specifically, this paper puts forth a heuristic algorithm called multi-priority scheduling (MPS) to schedule the execution of the butterfly computations in the IFDMA transceiver with the constraint of a limited number of hardware processors. The resulting FFT computation, referred to as MPS-FFT, has a much lower computation time than conventional FFT computation when applied to the IFDMA signal processing. Importantly, we derive a lower bound for the optimal IFDMA FFT computation time to benchmark MPS-FFT. Our experimental results indicate that when the number of hardware processors is a power of two: 1) MPS-FFT has near-optimal computation time; 2) MPS-FFT incurs less than 44.13\% of the computation time of the conventional pipelined FFT.

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arXiv2021

Broadband Digital Over-the-Air Computation for Asynchronous Federated Edge Learning

This paper presents the first broadband digital over-the-air computation (AirComp) system for phase asynchronous OFDM-based federated edge learning systems. Existing analog AirComp systems often assume perfect phase alignment via channel precoding and utilize uncoded analog modulation for model aggregation. In contrast, our digital AirComp system leverages digital modulation and channel codes to overcome phase asynchrony, thereby achieving accurate model aggregation in the asynchronous multi-user OFDM systems. To realize a digital AirComp system, we propose a non-orthogonal multiple access protocol that allows simultaneous transmissions from multiple edge devices, and present a joint channel decoding and aggregation (Jt-CDA) decoder (i.e., full-state joint decoder). To reduce the computation complexity, we further present a reduced-complexity Jt-CDA decoder (i.e., reduced-state joint decoder), and its arithmetic sum bit error rate performance is similar to that of the full-state joint decoder for most signal-to-noise ratio (SNR) regimes. Simulation results on test accuracy (of CIFAR10 dataset) versus SNR show that: 1) analog AirComp systems are sensitive to phase asynchrony under practical setup, and the test accuracy performance exhibits an error floor even at high SNR regime; 2) our digital AirComp system outperforms an analog AirComp system by at least 1.5 times when SNR 9dB, demonstrating the advantage of digital AirComp in asynchronous multi-user OFDM systems.

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arXiv2021

Partially Observable Minimum-Age Scheduling: The Greedy Policy

This paper studies the minimum-age scheduling problem in a wireless sensor network where an access point (AP) monitors the state of an object via a set of sensors. The freshness of the sensed state, measured by the age-of-information (AoI), varies at different sensors and is not directly observable to the AP. The AP has to decide which sensor to query/sample in order to get the most updated state information of the object (i.e., the state information with the minimum AoI). In this paper, we formulate the minimum-age scheduling problem as a multi-armed bandit problem with partially observable arms and explore the greedy policy to minimize the expected AoI sampled over an infinite horizon. To analyze the performance of the greedy policy, we 1) put forth a relaxed greedy policy that decouples the sampling processes of the arms, 2) formulate the sampling process of each arm as a partially observable Markov decision process (POMDP), and 3) derive the average sampled AoI under the relaxed greedy policy as a sum of the average AoI sampled from individual arms. Numerical and simulation results validate that the relaxed greedy policy is an excellent approximation to the greedy policy in terms of the expected AoI sampled over an infinite horizon.

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arXiv2021

Federated Edge Learning with Misaligned Over-The-Air Computation

Over-the-air computation (OAC) is a promising technique to realize fast model aggregation in the uplink of federated edge learning. OAC, however, hinges on accurate channel-gain precoding and strict synchronization among the edge devices, which are challenging in practice. As such, how to design the maximum likelihood (ML) estimator in the presence of residual channel-gain mismatch and asynchronies is an open problem. To fill this gap, this paper formulates the problem of misaligned OAC for federated edge learning and puts forth a whitened matched filtering and sampling scheme to obtain oversampled, but independent, samples from the misaligned and overlapped signals. Given the whitened samples, a sum-product ML estimator and an aligned-sample estimator are devised to estimate the arithmetic sum of the transmitted symbols. In particular, the computational complexity of our sum-product ML estimator is linear in the packet length and hence is significantly lower than the conventional ML estimator. Extensive simulations on the test accuracy versus the average received energy per symbol to noise power spectral density ratio (EsN0) yield two main results: 1) In the low EsN0 regime, the aligned-sample estimator can achieve superior test accuracy provided that the phase misalignment is non-severe. In contrast, the ML estimator does not work well due to the error propagation and noise enhancement in the estimation process. 2) In the high EsN0 regime, the ML estimator attains the optimal learning performance regardless of the severity of phase misalignment. On the other hand, the aligned-sample estimator suffers from a test-accuracy loss caused by phase misalignment.

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