arXiv subjects

Chengjian Sun

Publications and source records attributed to Chengjian Sun.

11 recordsLinked to original sources

arXiv2025

Optimizing QoE-Privacy Tradeoff for Proactive VR Streaming

Proactive virtual reality (VR) streaming requires users to upload viewpoint-related information, raising significant privacy concerns. Existing strategies preserve privacy by introducing errors to viewpoints, which, however, compromises the quality of experience (QoE) of users. In this paper, we first delve into the analysis of the viewpoint leakage probability achieved by existing privacy-preserving approaches. We determine the optimal distribution of viewpoint errors that minimizes the viewpoint leakage probability. Our analyses show that existing approaches cannot fully eliminate viewpoint leakage. Then, we propose a novel privacy-preserving approach that introduces noise to uploaded viewpoint prediction errors, which can ensure zero viewpoint leakage probability. Given the proposed approach, the tradeoff between privacy preservation and QoE is optimized to minimize the QoE loss while satisfying the privacy requirement. Simulation results validate our analysis results and demonstrate that the proposed approach offers a promising solution for balancing privacy and QoE.

cs.MM↗

arXiv2022

Privacy Leakage in Proactive VR Streaming: Modeling and Tradeoff

Proactive tile-based virtual reality (VR) video streaming employs the viewpoint of a user to predict the tiles to be requested, renders and delivers the predicted tiles before playback. Recently, it has been found that the identity and preference of the user can be inferred from the trace of viewpoint uploaded for proactive streaming, which indicates that viewpoint leakage incurs privacy leakage. In this paper, we strive to answer the following questions regarding viewpoint leakage during proactive VR video streaming. When is the viewpoint leaked? Can privacy-preserving approaches (e.g., federated or individual training, using predictors with no need for training, or predicting locally) avoid viewpoint leakage? We find that if the prediction error or the quality of experience (QoE) metric is uploaded for adaptive streaming, the real viewpoint can be inferred even with the privacy-preserving approaches. Then, we define viewpoint leakage probability to characterize the accuracy of the inferred viewpoint, and respectively derive the probability when uploading prediction error and QoE metric. We find that the viewpoint leakage probability can be reduced by sacrificing QoE or increasing resources. Simulation with the state-of-the-art predictor over a real dataset shows that such a tradeoff does not exist only in rare cases.

cs.MM↗

arXiv2021

Improving Learning Efficiency for Wireless Resource Allocation with Symmetric Prior

Improving learning efficiency is paramount for learning resource allocation with deep neural networks (DNNs) in wireless communications over highly dynamic environments. Incorporating domain knowledge into learning is a promising way of dealing with this issue, which is an emerging topic in the wireless community. In this article, we first briefly summarize two classes of approaches to using domain knowledge: introducing mathematical models or prior knowledge to deep learning. Then, we consider a kind of symmetric prior, permutation equivariance, which widely exists in wireless tasks. To explain how such a generic prior is harnessed to improve learning efficiency, we resort to ranking, which jointly sorts the input and output of a DNN. We use power allocation among subcarriers, probabilistic content caching, and interference coordination to illustrate the improvement of learning efficiency by exploiting the property. From the case study, we find that the required training samples to achieve given system performance decreases with the number of subcarriers or contents, owing to an interesting phenomenon: "sample hardening". Simulation results show that the training samples, the free parameters in DNNs and the training time can be reduced dramatically by harnessing the prior knowledge. The samples required to train a DNN after ranking can be reduced by $15 \sim 2,400$ folds to achieve the same system performance as the counterpart without using prior.

eess.SP↗

arXiv2021

A Tutorial on Ultra-Reliable and Low-Latency Communications in 6G: Integrating Domain Knowledge into Deep Learning

As one of the key communication scenarios in the 5th and also the 6th generation (6G) of mobile communication networks, ultra-reliable and low-latency communications (URLLC) will be central for the development of various emerging mission-critical applications. State-of-the-art mobile communication systems do not fulfill the end-to-end delay and overall reliability requirements of URLLC. In particular, a holistic framework that takes into account latency, reliability, availability, scalability, and decision making under uncertainty is lacking. Driven by recent breakthroughs in deep neural networks, deep learning algorithms have been considered as promising ways of developing enabling technologies for URLLC in future 6G networks. This tutorial illustrates how domain knowledge (models, analytical tools, and optimization frameworks) of communications and networking can be integrated into different kinds of deep learning algorithms for URLLC. We first provide some background of URLLC and review promising network architectures and deep learning frameworks for 6G. To better illustrate how to improve learning algorithms with domain knowledge, we revisit model-based analytical tools and cross-layer optimization frameworks for URLLC. Following that, we examine the potential of applying supervised/unsupervised deep learning and deep reinforcement learning in URLLC and summarize related open problems. Finally, we provide simulation and experimental results to validate the effectiveness of different learning algorithms and discuss future directions.

eess.SP↗

arXiv2020

Unsupervised Deep Learning for Optimizing Wireless Systems with Instantaneous and Statistic Constraints

Deep neural networks (DNNs) have been introduced for designing wireless policies by approximating the mappings from environmental parameters to solutions of optimization problems. Considering that labeled training samples are hard to obtain, unsupervised deep learning has been proposed to solve functional optimization problems with statistical constraints recently. However, most existing problems in wireless communications are variable optimizations, and many problems are with instantaneous constraints. In this paper, we establish a unified framework of using unsupervised deep learning to solve both kinds of problems with both instantaneous and statistic constraints. For a constrained variable optimization, we first convert it into an equivalent functional optimization problem with instantaneous constraints. Then, to ensure the instantaneous constraints in the functional optimization problems, we use DNN to approximate the Lagrange multiplier functions, which is trained together with a DNN to approximate the policy. We take two resource allocation problems in ultra-reliable and low-latency communications as examples to illustrate how to guarantee the complex and stringent quality-of-service (QoS) constraints with the framework. Simulation results show that unsupervised learning outperforms supervised learning in terms of QoS violation probability and approximation accuracy of the optimal policy, and can converge rapidly with pre-training.

cs.IT↗

arXiv2020

Proactive Optimization with Machine Learning: Femto-caching with Future Content Popularity

Optimizing resource allocation with predicted information has shown promising gain in boosting network performance and improving user experience. Earlier research efforts focus on optimizing proactive policies under the assumption of knowing the future information. Recently, various techniques have been proposed to predict the required information, and the prediction results were then treated as the true value in the optimization, i.e., "first-predict-then-optimize". In this paper, we introduce a proactive optimization framework for anticipatory resource allocation, where the future information is implicitly predicted under the same objective with the policy optimization in a single step. An optimization problem is formulated to integrate the implicit prediction and the policy optimization, based on the conditional distribution of the future information given the historical observations. To solve such a problem, we transform it equivalently to a problem depending on the joint distribution of future and historical information. Then, we resort to unsupervised learning with neural networks to learn the proactive policy as a function of the past observations via stochastic optimization. We take proactive caching and bandwidth allocation at base stations as a concrete example, where the objective function is the conditional expectation of successful offloading probability taken over the future popularity given the historically observed popularity. We use simulation to validate the proposed framework and compare it with the "first-predict-then-optimize" strategy and a heuristic "end-to-end" optimization strategy with supervised learning.

eess.SY↗

arXiv2020

Optimizing Wireless Systems Using Unsupervised and Reinforced-Unsupervised Deep Learning

Resource allocation and transceivers in wireless networks are usually designed by solving optimization problems subject to specific constraints, which can be formulated as variable or functional optimization. If the objective and constraint functions of a variable optimization problem can be derived, standard numerical algorithms can be applied for finding the optimal solution, which however incur high computational cost when the dimension of the variable is high. To reduce the on-line computational complexity, learning the optimal solution as a function of the environment's status by deep neural networks (DNNs) is an effective approach. DNNs can be trained under the supervision of optimal solutions, which however, is not applicable to the scenarios without models or for functional optimization where the optimal solutions are hard to obtain. If the objective and constraint functions are unavailable, reinforcement learning can be applied to find the solution of a functional optimization problem, which is however not tailored to optimization problems in wireless networks. In this article, we introduce unsupervised and reinforced-unsupervised learning frameworks for solving both variable and functional optimization problems without the supervision of the optimal solutions. When the mathematical model of the environment is completely known and the distribution of environment's status is known or unknown, we can invoke unsupervised learning algorithm. When the mathematical model of the environment is incomplete, we introduce reinforced-unsupervised learning algorithms that learn the model by interacting with the environment. Our simulation results confirm the applicability of these learning frameworks by taking a user association problem as an example.

cs.LG↗

arXiv2019

Model-Free Unsupervised Learning for Optimization Problems with Constraints

In many optimization problems in wireless communications, the expressions of objective function or constraints are hard or even impossible to derive, which makes the solutions difficult to find. In this paper, we propose a model-free learning framework to solve constrained optimization problems without the supervision of the optimal solution. Neural networks are used respectively for parameterizing the function to be optimized, parameterizing the Lagrange multiplier associated with instantaneous constraints, and approximating the unknown objective function or constraints. We provide learning algorithms to train all the neural networks simultaneously, and reveal the connections of the proposed framework with reinforcement learning. Numerical and simulation results validate the proposed framework and demonstrate the efficiency of model-free learning by taking power control problem as an example.

cs.LG↗

arXiv2019

Unsupervised Deep Learning for Ultra-reliable and Low-latency Communications

In this paper, we study how to solve resource allocation problems in ultra-reliable and low-latency communications by unsupervised deep learning, which often yield functional optimization problems with quality-of-service (QoS) constraints. We take a joint power and bandwidth allocation problem as an example, which minimizes the total bandwidth required to guarantee the QoS of each user in terms of the delay bound and overall packet loss probability. The global optimal solution is found in a symmetric scenario. A neural network was introduced to find an approximated optimal solution in general scenarios, where the QoS is ensured by using the property that the optimal solution should satisfy as the "supervision signal". Simulation results show that the learning-based solution performs the same as the optimal solution in the symmetric scenario, and can save around 40% bandwidth with respect to the state-of-the-art policy.

cs.NI↗

arXiv2019

Learning to Optimize with Unsupervised Learning: Training Deep Neural Networks for URLLC

Learning the optimized solution as a function of environmental parameters is effective in solving numerical optimization in real time for time-sensitive applications. Existing works of learning to optimize train deep neural networks (DNN) with labels, and the learnt solution are inaccurate, which cannot be employed to ensure the stringent quality of service. In this paper, we propose a framework to learn the latent function with unsupervised deep learning, where the property that the optimal solution should satisfy is used as the "supervision signal" implicitly. The framework is applicable to both functional and variable optimization problems with constraints. We take a variable optimization problem in ultra-reliable and low-latency communications as an example, which demonstrates that the ultra-high reliability can be supported by the DNN without supervision labels.

cs.LG↗

arXiv2017

Energy-Efficient Resource Allocation for Ultra-reliable and Low-latency Communications

Ultra-reliable and low-latency communications (URLLC) is expected to be supported without compromising the resource usage efficiency. In this paper, we study how to maximize energy efficiency (EE) for URLLC under the stringent quality of service (QoS) requirement imposed on the end-to-end (E2E) delay and overall packet loss, where the E2E delay includes queueing delay and transmission delay, and the overall packet loss consists of queueing delay violation, transmission error with finite blocklength channel codes, and proactive packet dropping in deep fading. Transmit power, bandwidth and number of active antennas are jointly optimized to maximize the system EE under the QoS constraints. Since the achievable rate with finite blocklength channel codes is not convex in radio resources, it is challenging to optimize resource allocation. By analyzing the properties of the optimization problem, the global optimal solution is obtained. Simulation and numerical results validate the analysis and show that the proposed policy can improve EE significantly compared with existing policy.

cs.IT↗