arXiv subjects

Jintao Wang

Publications and source records attributed to Jintao Wang.

At least 91 records · Page 5Linked to original sources

arXiv2021

Tradeoff between Diversity and Multiplexing Gains in Block Fading Optical Wireless Channels

The diversity-multiplexing tradeoff (DMT) provides a fundamental performance metric for different multiple-input multiple-output (MIMO) schemes in wireless communications. In this paper, we explore the block fading optical wireless communication (OWC) channels and characterize the DMT in the presence of both optical peak- and average-power constraints. Three different fading distributions are considered, which reflect different channel conditions. In each channel condition, we obtain the optimal DMT when the block length is sufficiently large, and we also derive the lower and upper bounds of the DMT curve when the block length is small. These results are dramatically different from the existing DMT results in radio-frequency (RF) channels. These differences may be due to the fact that the optical input signal is real and bounded, while its RF counterpart is usually complex and unbounded.

cs.IT↗

arXiv2021

On the Generative Utility of Cyclic Conditionals

We study whether and how can we model a joint distribution $p(x,z)$ using two conditional models $p(x|z)$ and $q(z|x)$ that form a cycle. This is motivated by the observation that deep generative models, in addition to a likelihood model $p(x|z)$, often also use an inference model $q(z|x)$ for extracting representation, but they rely on a usually uninformative prior distribution $p(z)$ to define a joint distribution, which may render problems like posterior collapse and manifold mismatch. To explore the possibility to model a joint distribution using only $p(x|z)$ and $q(z|x)$, we study their compatibility and determinacy, corresponding to the existence and uniqueness of a joint distribution whose conditional distributions coincide with them. We develop a general theory for operable equivalence criteria for compatibility, and sufficient conditions for determinacy. Based on the theory, we propose a novel generative modeling framework CyGen that only uses the two cyclic conditional models. We develop methods to achieve compatibility and determinacy, and to use the conditional models to fit and generate data. With the prior constraint removed, CyGen better fits data and captures more representative features, supported by both synthetic and real-world experiments.

cs.LG↗

arXiv2021

Triple-Structured Compressive Sensing-based Channel Estimation for RIS-aided MU-MIMO Systems

Reconfigurable intelligent surface (RIS) has been recognized as a potential technology for 5G beyond and attracted tremendous research attention. However, channel estimation in RIS-aided system is still a critical challenge due to the excessive amount of parameters in cascaded channel. The existing compressive sensing (CS)-based RIS estimation schemes only adopt incomplete sparsity, which induces redundant pilot consumption. In this paper, we exploit the specific triple-structured sparsity of the cascaded channel, i.e., the common column sparsity, structured row sparsity after offset compensation and the common offsets among all users. Then a novel multi-user joint estimation algorithm is proposed. Simulation results show that our approach can significantly reduce pilot overhead in both ULA and UPA scenarios.

cs.IT↗

arXiv2021

Joint Link Rate Selection and Channel State Change Detection in Block-Fading Channels

In this work, we consider the problem of transmission rate selection for a discrete time point-to-point block fading wireless communication link. The wireless channel remains constant within the channel coherence time but can change rapidly across blocks. The goal is to design a link rate selection strategy that can identify the best transmission rate quickly and adaptively in quasi-static channels. This problem can be cast into the stochastic bandit framework, and the unawareness of time-stamps where channel changes necessitates running change-point detection simultaneously with stochastic bandit algorithms to improve adaptivity. We present a joint channel change-point detection and link rate selection algorithm based on Thompson Sampling (CD-TS) and show it can achieve a sublinear regret with respect to the number of time steps $T$ when the channel coherence time is larger than a threshold. We then improve the CD-TS algorithm by considering the fact that higher transmission rate has higher packet-loss probability. Finally, we validate the performance of the proposed algorithms through numerical simulations.

cs.NI↗

arXiv2021

On the forward dynamical behavior of nonautonomous lattice dynamical systems

In this article, we study the forward dynamical behavior of nonautonomous lattice systems. We first construct a family of sets $\{\mathcal{A}_\varepsilon(σ)\}_{σ\in Σ}$ in arbitrary small neighborhood of a global attractor of the skew-product flow generated by a general nonautonomous lattice system, which is forward invariant and uniformly forward attracts any bounded subset of the phase space. Moreover, under some suitable conditions, we further construct a family of sets $\{\mathcal{B}_\varepsilon(σ)\}_{σ\in Σ}$ such that it uniformly forward exponentially attracts bounded subsets of the phase space. As an application, we study the discrete Gray-Scott model in detail and illustrate how to apply our abstract results to some concrete lattice system.

math.DS↗

arXiv2021

Multi-resolution CSI Feedback with deep learning in Massive MIMO System

In massive multiple-input multiple-output (MIMO) system, user equipment (UE) needs to send downlink channel state information (CSI) back to base station (BS). However, the feedback becomes expensive with the growing complexity of CSI in massive MIMO system. Recently, deep learning (DL) approaches are used to improve the reconstruction efficiency of CSI feedback. In this paper, a novel feedback network named CRNet is proposed to achieve better performance via extracting CSI features on multiple resolutions. An advanced training scheme that further boosts the network performance is also introduced. Simulation results show that the proposed CRNet outperforms the state-of-the-art CsiNet under the same computational complexity without any extra information. The open source codes are available at https://github.com/Kylin9511/CRNet

cs.IT↗

arXiv2021

Aggregated Network for Massive MIMO CSI Feedback

In frequency division duplexing (FDD) mode, it is necessary to send the channel state information (CSI) from user equipment to base station. The downlink CSI is essential for the massive multiple-input multiple-output (MIMO) system to acquire the potential gain. Recently, deep learning is widely adopted to massive MIMO CSI feedback task and proved to be effective compared with traditional compressed sensing methods. In this paper, a novel network named ACRNet is designed to boost the feedback performance with network aggregation and parametric RuLU activation. Moreover, valid approach to expand the network architecture in exchange of better performance is first discussed in CSI feedback task. Experiments show that ACRNet outperforms loads of previous state-of-the-art feedback networks without any extra information.

cs.IT↗

arXiv2021

Binarized Aggregated Network with Quantization: Flexible Deep Learning Deployment for CSI Feedback in Massive MIMO System

Massive multiple-input multiple-output (MIMO) is one of the key techniques to achieve better spectrum and energy efficiency in 5G system. The channel state information (CSI) needs to be fed back from the user equipment to the base station in frequency division duplexing (FDD) mode. However, the overhead of the direct feedback is unacceptable due to the large antenna array in massive MIMO system. Recently, deep learning is widely adopted to the compressed CSI feedback task and proved to be effective. In this paper, a novel network named aggregated channel reconstruction network (ACRNet) is designed to boost the feedback performance with network aggregation and parametric rectified linear unit (PReLU) activation. The practical deployment of the feedback network in the communication system is also considered. Specifically, the elastic feedback scheme is proposed to flexibly adapt the network to meet different resource limitations. Besides, the network binarization technique is combined with the feature quantization for lightweight and practical deployment. Experiments show that the proposed ACRNet outperforms loads of previous state-of-the-art networks, providing a neat feedback solution with high performance, low cost and impressive flexibility.

cs.IT↗

arXiv2021

A Remark on Attractor Bifurcation

In this paper we present some local dynamic bifurcation results in terms of invariant sets of nonlinear evolution equations. We show that if the trivial solution is an isolated invariant set of the system at the critical value $λ=λ_0$, then either there exists a one-sided neighborhood $I^-$ of $λ_0$ such that for each $λ\in I^-$, the system bifurcates from the trivial solution to an isolated nonempty compact invariant set $K_λ$ with $0\not\in K_λ$, or there is a one-sided neighborhood $I^+$ of $λ_0$ such that the system undergoes an attractor bifurcation for $λ\in I^+$ from $(0,λ_0)$. Then we give a modified version of the attractor bifurcation theorem. Finally, we consider the classical Swift-Hohenberg equation and illustrate how to apply our results to a concrete evolution equation.

math.DS↗

arXiv2021

Bifurcation from Infinity of the Schrödinger Equation via Invariant Manifolds

This paper is concerned with the bifurcation from infinity of the nonlinear Schrödinger equation $$-Δu+V(x)u=λu+f(x,u),\hspace{0.4cm} x\in \mathbb{R}^N.$$ We treat this problem in the framework of dynamical systems by considering the corresponding parabolic equation on unbounded domains. Firstly, we establish a global invariant manifold for the parabolic equation on $\mathbb{R}^N$. Then, we restrict the parabolic equation to this invariant manifold, which generates a system of finite dimension. Finally, we use the Conley index theory and the shape theory of attractors to establish some new results on bifurcations from infinity and multiplicity of solutions of the Schrödinger equation under an appropriate Landesman-Lazer type condition.

math.DS↗

arXiv2021

Cache Updating Strategy Minimizing the Age of Information with Time-Varying Files' Popularities

We consider updating strategies for a local cache which downloads time-sensitive files from a remote server through a bandwidth-constrained link. The files are requested randomly from the cache by local users according to a popularity distribution which varies over time according to a Markov chain structure. We measure the freshness of the requested time-sensitive files through their Age of Information (AoI). The goal is then to minimize the average AoI of all requested files by appropriately designing the local cache's downloading strategy. To achieve this goal, the original problem is relaxed and cast into a Constrained Markov Decision Problem (CMDP), which we solve using a Lagrangian approach and Linear Programming. Inspired by this solution for the relaxed problem, we propose a practical cache updating strategy that meets all the constraints of the original problem. Under certain assumptions, the practical updating strategy is shown to be optimal for the original problem in the asymptotic regime of a large number of files. For a finite number of files, we show the gain of our practical updating strategy over the traditional square-root-law strategy (which is optimal for fixed non time-varying file popularities) through numerical simulations.

cs.IT↗

arXiv2020

Upper semi-continuity of random attractors and existence of invariant measures for nonlocal stochastic Swift-Hohenberg equation with multiplicative noise

In this paper, we mainly study the long-time dynamical behaviors of 2D nonlocal stochastic Swift-Hohenberg equations with multiplicative noise from two perspectives. Firstly, by adopting the analytic semigroup theory, we prove the upper semi-continuity of random attractors in the Sobolev space $H_0^2(U)$, as the coefficient of the multiplicative noise approaches zero. Then, we extend the classical "stochastic Gronwall's lemma", making it more convenient in applications. Based on this improvement, we are allowed to use the analytic semigroup theory to establish the existence of ergodic invariant measures.

math.PR↗

arXiv2020

Binary Neural Network Aided CSI Feedback in Massive MIMO System

In massive multiple-input multiple-output (MIMO) system, channel state information (CSI) is essential for the base station to achieve high performance gain. Recently, deep learning is widely used in CSI compression to fight against the growing feedback overhead brought by massive MIMO in frequency division duplexing system. However, applying neural network brings extra memory and computation cost, which is non-negligible especially for the resource limited user equipment (UE). In this paper, a novel binarization aided feedback network named BCsiNet is introduced. Moreover, BCsiNet variants are designed to boost the performance under customized training and inference schemes. Experiments shows that BCsiNet offers over 30$\times$ memory saving and around 2$\times$ inference acceleration for encoder at UE compared with CsiNet. Furthermore, the feedback performance of BCsiNet is comparable with original CsiNet. The key results can be reproduced with https://github.com/Kylin9511/BCsiNet.

cs.IT↗

arXiv2020

Delay Optimal Cross-Layer Scheduling Over Markov Channels with Power Constraint

We consider a scenario where a power constrained transmitter delivers randomly arriving packets to the destination over Markov time-varying channel and adapts different transmission power to each channel state in order to guarantee successful transmission. To minimize the expected average transmission delay of each packet, we formulate the problem into a constrained Markov decision process (CMDP). We reveal the queue-length threshold structure of the optimal policy, i.e., the transmitter sends packets if and only if the queue length surpasses a threshold and obtain the optimal cross-layer scheduling strategy through linear programming (LP). Numerical results validate the performance of the proposed strategy and illustrate a delay-power tradeoff in such scenario.

cs.NI↗

arXiv2020

On Relative Category and Morse Decompositions for Infinite-Dimensional Dynamical Systems

We employ the relative category to develop relations between the Ważewski pair $(N,E)$ and the Morse decomposition of the maximal invariant set in $\ol{N\sm E}$ for infinite-dimensional dynamical systems. Via these relations, we can detect connecting trajectories between Morse sets and obtain a dynamical-system version of critical point theorem with relative category.

math.DS↗

arXiv2020

Minimizing Age of Information with Power Constraints: Multi-user Opportunistic Scheduling in Multi-State Time-Varying Channels

This work is motivated by the need of collecting fresh data from power-constrained sensors in the industrial Internet of Things (IIoT) network. A recently proposed metric, the Age of Information (AoI) is adopted to measure data freshness from the perspective of the central controller in the IIoT network. We wonder what is the minimum average AoI the network can achieve and how to design scheduling algorithms to approach it. To answer these questions when the channel states of the network are Markov time-varying and scheduling decisions are restricted to bandwidth constraint, we first decouple the multi-sensor scheduling problem into a single-sensor constrained Markov decision process (CMDP) through relaxation of the hard bandwidth constraint. Next we exploit the threshold structure of the optimal policy for the decoupled single sensor CMDP and obtain the optimum solution through linear programming (LP). Finally, an asymptotically optimal truncated policy that can satisfy the hard bandwidth constraint is built upon the optimal solution to each of the decoupled single-sensor. Our investigation shows that to obtain a small AoI performance: (1) The scheduler exploits good channels to schedule sensors supported by limited power; (2) Sensors equipped with enough transmission power are updated in a timely manner such that the bandwidth constraint can be satisfied.

cs.IT↗

arXiv2020

Scheduling to Minimize Age of Synchronization in Wireless Broadcast Networks with Random Updates

In this work, a wireless broadcast network with a base station (BS) sending random time-sensitive information updates to multiple users with interference constraints is considered. The Age of Synchronization (AoS), namely the amount of time elapsed since the information stored at the network user becomes desynchronized, is adopted to measure data freshness from the perspective of network users. Compared with the more widely used metric---the Age of Information (AoI), AoS accounts for the freshness of the randomly changing content. The AoS minimization scheduling problem is formulated into a discrete time Markov decision process and the optimal solution is approximated through structural finite state policy iteration. An index based heuristic scheduling policy based on restless multi-arm bandit (RMAB) is provided to further reduce computational complexity. Simulation results show that the proposed index policy can achieve compatible performance with the MDP and close to the AoS lower bound. Moreover, theoretic analysis and simulations reveal the differences between AoS and AoI. AoI minimization scheduling policy cannot guarantee a good AoS performance.

cs.IT↗

arXiv2020

Recurrent Solutions of a Nonautonomous Modified Swift-Hohenberg Equation

We consider recurrent solutions of the nonautonomous modified Swift-Hohenberg equation $$u_t+Δ^2u+2Δu+au+b|\nabla u|^2+u^3=g(t,x).$$ We employ Conley index theory to show that, if the forcing $g:\mathbb{R}\rightarrow L^2(Ω)$ is a recurrent function, then there are at least two recurrent solutions in $H_0^2(Ω)$ under appropriate assumptions on the parameters $a$, $b$ and $g$.

math.AP↗