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Youlong Wu

Publications and source records attributed to Youlong Wu.

54 records · Page 3Linked to original sources
arXiv2022

Optimal Probabilistic Constellation Shaping for Covert Communications

In this paper, we investigate the optimal probabilistic constellation shaping design for covert communication systems from a practical view. Different from conventional covert communications with equiprobable constellations modulation, we propose nonequiprobable constellations modulation schemes to further enhance the covert rate. Specifically, we derive covert rate expressions for practical discrete constellation inputs for the first time. Then, we study the covert rate maximization problem by jointly optimizing the constellation distribution and power allocation. In particular, an approximate gradient descent method is proposed for obtaining the optimal probabilistic constellation shaping. To strike a balance between the computational complexity and the transmission performance, we further develop a framework that maximizes a lower bound on the achievable rate where the optimal probabilistic constellation shaping problem can be solved efficiently using the Frank-Wolfe method. Extensive numerical results show that the optimized probabilistic constellation shaping strategies provide significant gains in the achievable covert rate over the state-of-the-art schemes.

cs.IT↗
arXiv2022

Optimal Power Allocation for Integrated Visible Light Positioning and Communication System with a Single LED-Lamp

In this paper, we investigate an integrated visible light positioning and communication (VLPC) system with a single LED-lamp. First, by leveraging the fact that the VLC channel model is a function of the receiver's location, we propose a system model that estimates the channel state information (CSI) based on the positioning information without transmitting pilot sequences. Second, we derive the Cramer-Rao lower bound (CRLB) on the positioning error variance and a lower bound on the achievable rate with on-off keying modulation. Third, based on the derived performance metrics, we optimize the power allocation to minimize the CRLB, while satisfying the rate outage probability constraint. To tackle this non-convex optimization problem, we apply the worst-case distribution of the Conditional Value-at-Risk (CVaR) and the block coordinate descent (BCD) methods to obtain the feasible solutions. Finally, the effects of critical system parameters, such as outage probability, rate threshold, total power threshold, are revealed by numerical results.

cs.IT↗
arXiv2022

Covert Beamforming Design for Integrated Radar Sensing and Communication Systems

We propose covert beamforming design frameworks for integrated radar sensing and communication (IRSC) systems, where the radar can covertly communicate with legitimate users under the cover of the probing waveforms without being detected by the eavesdropper. Specifically, by jointly designing the target detection beamformer and communication beamformer, we aim to maximize the radar detection mutual information (MI) (or the communication rate) subject to the covert constraint, the communication rate constraint (or the radar detection MI constraint), and the total power constraint. For the perfect eavesdropper's channel state information (CSI) scenario, we transform the covert beamforming design problems into a series of convex subproblems, by exploiting semidefinite relaxation, which can be solved via the bisection search method. Considering the high complexity of iterative optimization, we further propose a single-iterative covert beamformer design scheme based on the zero-forcing criterion. For the imperfect eavesdropper's CSI scenario, we develop a relaxation and restriction method to tackle the robust covert beamforming design problems. Simulation results demonstrate the effectiveness of the proposed covert beamforming schemes for perfect and imperfect CSI scenarios.

cs.IT↗
arXiv2022

Differentially Private Federated Learning via Reconfigurable Intelligent Surface

Federated learning (FL), as a disruptive machine learning paradigm, enables the collaborative training of a global model over decentralized local datasets without sharing them. It spans a wide scope of applications from Internet-of-Things (IoT) to biomedical engineering and drug discovery. To support low-latency and high-privacy FL over wireless networks, in this paper, we propose a reconfigurable intelligent surface (RIS) empowered over-the-air FL system to alleviate the dilemma between learning accuracy and privacy. This is achieved by simultaneously exploiting the channel propagation reconfigurability with RIS for boosting the receive signal power, as well as waveform superposition property with over-the-air computation (AirComp) for fast model aggregation. By considering a practical scenario where high-dimensional local model updates are transmitted across multiple communication blocks, we characterize the convergence behaviors of the differentially private federated optimization algorithm. We further formulate a system optimization problem to optimize the learning accuracy while satisfying privacy and power constraints via the joint design of transmit power, artificial noise, and phase shifts at RIS, for which a two-step alternating minimization framework is developed. Simulation results validate our systematic, theoretical, and algorithmic achievements and demonstrate that RIS can achieve a better trade-off between privacy and accuracy for over-the-air FL systems.

eess.SP↗
arXiv2021

Wyner-Ziv Gradient Compression for Federated Learning

Due to limited communication resources at the client and a massive number of model parameters, large-scale distributed learning tasks suffer from communication bottleneck. Gradient compression is an effective method to reduce communication load by transmitting compressed gradients. Motivated by the fact that in the scenario of stochastic gradients descent, gradients between adjacent rounds may have a high correlation since they wish to learn the same model, this paper proposes a practical gradient compression scheme for federated learning, which uses historical gradients to compress gradients and is based on Wyner-Ziv coding but without any probabilistic assumption. We also implement our gradient quantization method on the real dataset, and the performance of our method is better than the previous schemes.

cs.LG↗
arXiv2021

Coded Caching for Broadcast Networks with User Cooperation

In this paper, we investigate the transmission delay of cache-aided broadcast networks with user cooperation. Novel coded caching schemes are proposed for both centralized and decentralized caching settings, by efficiently exploiting time and cache resources and creating parallel data delivery at the server and users. We derive a lower bound on the transmission delay and show that the proposed centralized coded caching scheme is \emph{order-optimal} in the sense that it achieves a constant multiplicative gap within the lower bound. Our decentralized coded caching scheme is also order-optimal when each user's cache size is larger than the threshold $N(1-\sqrt[{K-1}]{ {1}/{(K+1)}})$ (approaching 0 as $K\to \infty$), where $K$ is the total number of users and $N$ is the size of file library. Moreover, for both the centralized and decentralized caching settings, our schemes obtain an additional \emph{cooperation gain} offered by user cooperation and an additional \emph{parallel gain} offered by the parallel transmission among the server and users. It is shown that in order to reduce the transmission delay, the number of users parallelly sending signals should be appropriately chosen according to user's cache size, and alway letting more users parallelly send information could cause high transmission delay.

cs.IT↗
arXiv2021

Improved Communication Efficiency for Distributed Mean Estimation with Side Information

In this paper, we consider the distributed mean estimation problem where the server has access to some side information, e.g., its local computed mean estimation or the received information sent by the distributed clients at the previous iterations. We propose a practical and efficient estimator based on an r-bit Wynzer-Ziv estimator proposed by Mayekar et al., which requires no probabilistic assumption on the data. Unlike Mayekar's work which only utilizes side information at the server, our scheme jointly exploits the correlation between clients' data and server' s side information, and also between data of different clients. We derive an upper bound of the estimation error of the proposed estimator. Based on this upper bound, we provide two algorithms on how to choose input parameters for the estimator. Finally, parameter regions in which our estimator is better than the previous one are characterized.

cs.IT↗
arXiv2021

Optimal Coding Scheme and Resource Allocation for Distributed Computation with Limited Resources

A central issue of distributed computing systems is how to optimally allocate computing and storage resources and design data shuffling strategies such that the total execution time for computing and data shuffling is minimized. This is extremely critical when the computation, storage and communication resources are limited. In this paper, we study the resource allocation and coding scheme for the MapReduce-type framework with limited resources. In particular, we focus on the coded distributed computing (CDC) approach proposed by Li et al.. We first extend the asymmetric CDC (ACDC) scheme proposed by Yu et al. to the cascade case where each output function is computed by multiple servers. Then we demonstrate that whether CDC or ACDC is better depends on system parameters (e.g., number of computing servers) and task parameters (e.g., number of input files), implying that neither CDC nor ACDC is optimal. By merging the ideas of CDC and ACDC, we propose a hybrid scheme and show that it can strictly outperform CDC and ACDC. Furthermore, we derive an information-theoretic converse showing that for the MapReduce task using a type of weakly symmetric Reduce assignment, which includes the Reduce assignments of CDC and ACDC as special cases, the hybrid scheme with a corresponding resource allocation strategy is optimal, i.e., achieves the minimum execution time, for an arbitrary amount of computing servers and storage memories.

cs.IT↗
arXiv2020

Coded Computing for Master-Aided Distributed Computing Systems

We consider a MapReduce-type task running in a distributed computing model which consists of ${K}$ edge computing nodes distributed across the edge of the network and a Master node that assists the edge nodes to compute output functions. The Master node and the edge nodes, both equipped with some storage memories and computing capabilities, are connected through a multicast network. We define the communication time spent during the transmission for the sequential implementation (all nodes send symbols sequentially) and parallel implementation (the Master node can send symbols during the edge nodes' transmission), respectively. We propose a mixed coded distributed computing scheme that divides the system into two subsystems where the coded distributed computing (CDC) strategy proposed by Songze Li \emph{et al.} is applied into the first subsystem and a novel master-aided CDC strategy is applied into the second subsystem. We prove that this scheme is optimal, i.e., achieves the minimum communication time for both the sequential and parallel implementation, and establish an {\emph{optimal}} information-theoretic tradeoff between the overall communication time, computation load, and the Master node's storage capacity. It demonstrates that incorporating a Master node with storage and computing capabilities can further reduce the communication time. For the sequential implementation, we deduce the approximately optimal file allocation between the two subsystems, which shows that the Master node should map as many files as possible in order to achieve smaller communication time. For the parallel implementation, if the Master node's storage and computing capabilities are sufficiently large (not necessary to store and map all files), then the proposed scheme requires at most 1/2 of the minimum communication time of system without the help of the Master node.

cs.IT↗
arXiv2020

Reduce Transmission Delay for Cache-Aided Two-Layer Network

In this paper, we consider a cache-aided relay network, where a single server consisting of a library of N files connects with K1 relays through a shared noiseless link, and each relay connects with K2 users through a shared noiseless link. Each relay and user are equipped with a cache memory of M1 and M2 files, respectively. We propose a centralized and a decentralized coded caching scheme that exploit the spared transmission time resource by allowing concurrent transmission between the two layers. It is shown that both caching schemes are approximately optimal, and greatly reduce the transmission delay compared to the previously known caching schemes. Surprisingly, we show that when the relay's caching size is equal to a threshold that is strictly smaller than N (e.g. M1=0.382N under the decentralized setup and (K1-1)N/K1 under the centralized setup, when K1=2), our schemes achieve the same delay as if each relay had access to the full library. To our best knowledge, this is the first result showing that even the caching size is strictly smaller than the library's size, increasing the caching size is wasteful in reducing the transmission latency.

cs.IT↗
arXiv2020

On the Achievable Rate Region of the $ K $-Receiver Broadcast Channels via Exhaustive Message Splitting

This paper focuses on $ K $-receiver discrete-time memoryless broadcast channels (DM-BCs) with private messages, where the transmitter wishes to convey $K$ private messages to $K$ receivers respectively. A general inner bound on the capacity region is proposed based on an exhaustive message splitting and a $K$-level modified Marton's coding. The key idea is to split every message into $ \sum_{j=1}^K {K\choose j} $ submessages each corresponding to a set of users who are assigned to recover them, and then send these submessages through codewords that are jointly typical with each other. To guarantee the joint typicality among all transmitted codewords, a sufficient condition on the subcodebooks sizes is derived through a newly establishing hierarchical covering lemma, which extends the 2-level multivariate covering lemma to the $K$-level case including $(2^{K}-1)$ random variables with more intricate dependence. As the number of auxiliary random variables and rate constraints both increase linearly with $(2^{K}-1)$, the standard Fourier-Motzkin elimination procedure becomes infeasible when $K$ is large. To tackle this problem, we obtain the final form of achievable rate region with a special observation of disjoint unions of sets that constitute the power set of $ \{1,\dots,K\}$. The proposed achievable rate region allows arbitrary input probability mass functions (pmfs) and improves over all previously known ones for $ K$-receiver ($K\geq 3$) BCs whose input pmfs should satisfy certain Markov chain(s).

cs.IT↗
arXiv2019

Centralized Coded Caching with User Cooperation

In this paper, we consider the coded-caching broadcast network with user cooperation, where a server connects with multiple users and the users can cooperate with each other through a cooperation network. We propose a centralized coded caching scheme based on a new deterministic placement strategy and a parallel delivery strategy. It is shown that the new scheme optimally allocate the communication loads on the server and users, obtaining cooperation gain and parallel gain that greatly reduces the transmission delay. Furthermore, we show that the number of users who parallelly send information should decrease when the users' caching size increases. In other words, letting more users parallelly send information could be harmful. Finally, we derive a constant multiplicative gap between the lower bound and upper bound on the transmission delay, which proves that our scheme is order optimal.

cs.IT↗
arXiv2018

The Capacity on Degraded Relay Broadcast Channel

The relay broadcast channel (RBC) is considered, in which a transmitter communicates with two receivers with the assistance of a relay. Based on different degradation orders among the relay and the receivers' outputs, three types of physically degraded RBCs (PDRBCs) are introduced. Inner bounds and outer bounds are derived on the capacity region of the presented three types. The bounds are tight for two types of PDRBCs: 1) one receiver's output is a degraded form of the other receiver's output, and the relay's output is a degraded form of the weaker receiver's output; 2) one receiver's output is a degraded form of the relay's output, and the other receiver's output is a degraded form of the relay's output. For the Gaussian PDRBC, the bounds match, i.e., establish its capacity region.

cs.IT↗
arXiv2016

Coding Schemes for Discrete Memoryless Multicast Networks with Rate-limited Feedback

Coding schemes for discrete memoryless multicast networks (DM-MN) with rate-limited feedback from the receivers and relays to the transmitter are proposed. The schemes improve over the noisy network coding proposed by Lim et al.. For the single relay channel with relay-transmitter feedback, our coding schemes recover Gabbai and Bross's results, and strictly improve on noisy network coding, distributed decode-forward coding proposed by Lim et al., and all known lower bounds on the achievable rates proposed for the setup without feedback. The coding schemes are based on block-Markov coding, superposition coding, sliding-window/backward decoding and hybrid relaying strategies. In our Scheme 1A, the relays and receivers use compress-forward strategy and send the compression indices to the transmitter through the feedback pipes. After obtaining the compression indices through feedback, the transmitter sends them together with the source message. Each receiver uses backward decoding to jointly decode the source message and all compression indices. Our Scheme 1B is similar, except that here each relay not only uses compress forward to compress its channel outputs, but also performs partial decode forward to decode a common part of source message. Our Scheme 1C allows different relays to decode different parts of the source message (in Scheme 1A, no relay decodes any part of the source message; in Scheme 1B, all relays decode the same part of source message), which can achieve higher rates than Scheme 1A and Scheme 1B. Motivated by the feedback coding scheme, we propose a new coding scheme for DM-MN without feedback, where the transmitter decodes the compression indices instead of obtaining them directly through feedback. It is shown that the scheme still improves noisy network coding and distributed decode-forward coding for some channels.

cs.IT↗
arXiv2016

Achievable Rate Regions for Cooperative Relay Broadcast Channels with Rate-limited Feedback

Achievable rate regions for cooperative relay broadcast channels with rate-limited feedback are proposed. Specifically, we consider two-receiver memoryless broadcast channels where each receiver sends feedback signals to the transmitter through a noiseless and rate-limited feedback link, and meanwhile, acts as relay to transmit cooperative information to the other receiver. It's shown that the proposed rate regions improve on the known regions that consider either relaying cooperation or feedback communication, but not both.

cs.IT↗
arXiv2016

Coding Schemes with Rate-Limited Feedback that Improve over the Nofeedback Capacity for a Large Class of Broadcast Channels

We propose two coding schemes for the two-receiver discrete memoryless broadcast channel (BC) with rate-limited feedback from one or both receivers. They improve over the nofeedback capacity region for a large class of channels, including the class of \emph{strictly essentially less-noisy BCs} that we introduce in this article. Examples of strictly essentially less-noisy BCs are the binary symmetric BC (BSBC) or the binary erasure BC (BEBC) with unequal cross-over or erasure probabilities at the two receivers. When the feedback rates are sufficiently large, our schemes recover all previously known capacity results for discrete memoryless BCs with feedback. In both our schemes, we let the receivers feed back quantization messages about their receive signals. In the first scheme, the transmitter simply \emph{relays} the quantization information obtained from Receiver 1 to Receiver 2, and vice versa. This provides each receiver with a second observation of the input signal and can thus improve its decoding performance unless the BC is physically degraded. Moreover, each receiver uses its knowledge of the quantization message describing its own outputs so as to attain the same performance as if this message had not been transmitted at all. In our second scheme the transmitter first \emph{reconstructs and processes} the quantized output signals, and then sends the outcome as a common update information to both receivers. A special case of our second scheme applies also to memoryless BCs without feedback but with strictly-causal state-information at the transmitter and causal state-information at the receivers. It recovers all previous achievable regions also for this setup with state-information.

cs.IT↗
arXiv2013

Insufficiency of Linear-Feedback Schemes In Gaussian Broadcast Channels with Common Message

We consider the $K\geq 2$-user memoryless Gaussian broadcast channel (BC) with feedback and common message only. We show that linear-feedback schemes with a message point, in the spirit of Schalkwijk & Kailath's scheme for point-to-point channels or Ozarow & Leung's scheme for BCs with private messages, are strictly suboptimal for this setup. Even with perfect feedback, the largest rate achieved by these schemes is strictly smaller than capacity $C$ (which is the same with and without feedback). In the extreme case where the number of receivers $K\to \infty$, the largest rate achieved by linear-feedback schemes with a message point tends to 0. To contrast this negative result, we describe a scheme for \emph{rate-limited} feedback that uses the feedback in an intermittent way, i.e., the receivers send feedback signals only in few channel uses. This scheme achieves all rates $R$ up to capacity $C$ with an $L$-th order exponential decay of the probability of error if the feedback rate $R_{\textnormal{fb}}$ is at least $(L-1)R$ for some positive integer $L$.

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