arXiv subjects

Yusheng Ji

Publications and source records attributed to Yusheng Ji.

At least 19 recordsLinked to original sources

arXiv2026

Formal Verification for Deep Learning-based Power Control in Massive MIMO

Deep learning is a promising approach to optimize wireless communication by simplifying the search for near-optimal solutions. Prior studies on deep learning-based wireless communication optimization have explored supervised learning approaches that map raw user information, such as location or channel state information, to optimal power allocation vectors. While this approach demonstrates competitive performance, it is susceptible to adversarial attacks via input perturbations. Current defense mechanisms primarily rely on empirical methods, which do not provide formal guarantees of robustness. We fill this gap by proposing a formal verification framework to evaluate the robustness of deep learning-based power allocation in multi-cell massive multiple-input multiple-output (MIMO) systems against a wide range of potential adversarial input manipulations. To the best of our knowledge, this is the first attempt to formally verify deep neural networks in a regression setting with non-linear output constraints. We model the adversary's capabilities using hyper-rectangle constraints on their perturbation, adopt the abstraction-based bound-propagation technique (DeepPoly) to bound the interval of potential allocated powers, and formulate the minimum performance requirements as a constrained program for numerical feasibility analysis. Evaluation on publicly available datasets for power allocation in multi-cell massive MIMO indicates that a well-trained model can guarantee the local robustness under location perturbation by +-1m while retaining a maximum 1% optimality gap.

cs.NI↗

arXiv2026

CaFTRA: Frequency-Domain Correlation-Aware Feedback-Free MIMO Transmission and Resource Allocation for 6G and Beyond

The fundamental designs of wireless systems toward AI-Native 6G and beyond are driven by the need for ever-increasing demand of mobile data traffic, extreme spectral efficiency, and adaptability across diverse service scenarios. To overcome the limitations posed by feedback-based multiple-input and multiple-output (MIMO) transmission, we propose a novel frequency-domain Correlation-aware Feedback-free MIMO Transmission and Resource Allocation (CaFTRA) framework tailored for fully-decoupled radio access networks (FD-RAN) to meet the emerging requirements of AI-Native 6G and beyond. By leveraging artificial intelligence (AI), CaFTRA effectively eliminates real-time uplink feedback by predicting channel state information (CSI) based solely on user geolocation. We introduce a Learnable Queries-driven Transformer Network for CSI mapping from user geolocation, which utilizes multi-head attention and learnable query embeddings to accurately capture frequency-domain correlations among resource blocks (RBs), thereby significantly improving the precision of CSI prediction. Once base stations (BSs) adopt feedback-free transmission, their downlink transmission coverage can be significantly expanded due to the elimination of frequent uplink feedback. To enable efficient resource scheduling under such extensive-coverage scenarios, we apply a low-complexity many-to-one matching theory-based algorithm for efficient multi-BS association and multi-RB resource allocation, which is proven to converge to a stable matching within limited iterations. Simulation results demonstrate that CaFTRA achieves stable matching convergence and significant gains in spectral efficiency and user fairness compared to 5G, underscoring its potential value for 6G standardization efforts.

eess.SY↗

arXiv2026

Leveraging Deep Reinforcement Learning for Clustered Cell-Free Networking Over User Mobility

Clustered cell-free networking paves a new way for enabling scalable joint transmission among access points (APs) by partitioning the whole network into non-overlapping subnetworks. Previous works adopted clustering algorithms, graph partitioning methods or conventional continuous optimization theories to partition a network based on the channels between all users and all APs, resulting in huge channel measurement and computational costs. This makes these methods difficult to be implemented in practical systems since the optimal network partition could vary frequently due to user mobility. In addition, existing methods were usually designed for specific clustered cell-free networking problems with different optimization algorithms employed. In this paper, we leverage deep reinforcement learning (DRL) for clustered cell-free networking so as to rapidly adapt to user movements in dynamic environments, and propose a deep deterministic policy gradient based clustered cell-free networking (DDPG-C$^{2}$F) framework that can be adapted in various application scenarios. Moreover, in our framework, only one single channel needs to be estimated at each AP as the input of the neural network, which greatly reduces the channel measurement costs for clustered cell-free networking, and the training and inference costs of our framework. The proposed DDPG-C$^{2}$F framework is then applied to various clustered cell-free networking problems with different objectives and constraints to demonstrate its performance. Simulation results show that our framework outperforms existing baselines in all scenarios. Moreover, we show that the proposed framework can reduce the handover cost over user mobility, and is robust to dynamic scenarios with random user joining or leaving.

eess.SP↗

arXiv2026

FGGM: Formal Grey-box Gradient Method for Attacking DRL-based MU-MIMO Scheduler

In 5G mobile communication systems, MU-MIMO has been applied to enhance spectral efficiency and support high data rates. To maximize spectral efficiency while providing fairness among users, the base station (BS) needs to selects a subset of users for data transmission. Given that this problem is NP-hard, DRL-based methods have been proposed to infer the near-optimal solutions in real-time, yet this approach has an intrinsic security problem. This paper investigates how a group of adversarial users can exploit unsanitized raw CSIs to launch a throughput degradation attack. Most existing studies only focused on systems in which adversarial users can obtain the exact values of victims' CSIs, but this is impractical in the case of uplink transmission in LTE/5G mobile systems. We note that the DRL policy contains an observation normalizer which has the mean and variance of the observation to improve training convergence. Adversarial users can then estimate the upper and lower bounds of the local observations including the CSIs of victims based solely on that observation normalizer. We develop an attacking scheme FGGM by leveraging polytope abstract domains, a technique used to bound the outputs of a neural network given the input ranges. Our goal is to find one set of intentionally manipulated CSIs which can achieve the attacking goals for the whole range of local observations of victims. Experimental results demonstrate that FGGM can determine a set of adversarial CSI vector controlled by adversarial users, then reuse those CSIs throughout the simulation to reduce the network throughput of a victim up to 70\% without knowing the exact value of victims' local observations. This study serves as a case study and can be applied to many other DRL-based problems, such as a knapsack-oriented resource allocation problems.

cs.NI↗

arXiv2026

Modality-Tailored Age of Information for Multimodal Data in Edge Computing Systems

As Internet of Things (IoT) systems scale and device heterogeneity grows, multimodal data have become ubiquitous. Meanwhile, evaluating the freshness of multimodal data is essential, as stale updates would delay task execution, degrade decision accuracy, and undermine safety in latency-sensitive services. However, existing freshness metrics such as Age of Information (AoI) are not suitable for multimodal data, as they do not capture modality-specific characteristics. In this paper, we propose a metric, namely, Modality-Tailored Age of Information (MAoI), to provide a unified and decision-relevant evaluation of freshness for resource management and policy optimization for multimodal data. This metric integrates modality-specific semantic and temporal characteristics, reflecting both age evolution and content importance for multimodal data in multi-access edge computing (MEC) systems. Then, the closed-form expression of the average MAoI is derived, and an MAoI minimization problem is formulated, where sampling intervals and offloading decisions are optimized with practical energy constraints. To effectively solve this problem, a Joint Sampling Offloading Optimization (JSO) algorithm is proposed to jointly optimize the sampling intervals and offloading decisions. It is a block coordinate descent-based algorithm where an optimal sampling-interval subalgorithm is used to update the sampling intervals, and an interference-aware best-response offloading subalgorithm is proposed to update the offloading decisions alternately. Finally, a comprehensive simulation is performed, confirming that the MAoI metric effectively quantifies multimodal freshness compared to traditional AoI, and the JSO algorithm significantly minimizes the average MAoI compared to state-of-the-art algorithms.

cs.NI↗

arXiv2025

Trajectory Planning for UAV-Based Smart Farming Using Imitation-Based Triple Deep Q-Learning

Unmanned aerial vehicles (UAVs) have emerged as a promising auxiliary platform for smart agriculture, capable of simultaneously performing weed detection, recognition, and data collection from wireless sensors. However, trajectory planning for UAV-based smart agriculture is challenging due to the high uncertainty of the environment, partial observations, and limited battery capacity of UAVs. To address these issues, we formulate the trajectory planning problem as a Markov decision process (MDP) and leverage multi-agent reinforcement learning (MARL) to solve it. Furthermore, we propose a novel imitation-based triple deep Q-network (ITDQN) algorithm, which employs an elite imitation mechanism to reduce exploration costs and utilizes a mediator Q-network over a double deep Q-network (DDQN) to accelerate and stabilize training and improve performance. Experimental results in both simulated and real-world environments demonstrate the effectiveness of our solution. Moreover, our proposed ITDQN outperforms DDQN by 4.43\% in weed recognition rate and 6.94\% in data collection rate.

cs.LG↗

arXiv2025

FM-EAC: Feature Model-based Enhanced Actor-Critic for Multi-Task Control in Dynamic Environments

Model-based reinforcement learning (MBRL) and model-free reinforcement learning (MFRL) evolve along distinct paths but converge in the design of Dyna-Q [1]. However, modern RL methods still struggle with effective transferability across tasks and scenarios. Motivated by this limitation, we propose a generalized algorithm, Feature Model-Based Enhanced Actor-Critic (FM-EAC), that integrates planning, acting, and learning for multi-task control in dynamic environments. FM-EAC combines the strengths of MBRL and MFRL and improves generalizability through the use of novel feature-based models and an enhanced actor-critic framework. Simulations in both urban and agricultural applications demonstrate that FM-EAC consistently outperforms many state-of-the-art MBRL and MFRL methods. More importantly, different sub-networks can be customized within FM-EAC according to user-specific requirements.

cs.LG↗

arXiv2024

Communication and Control Co-Design in 6G: Sequential Decision-Making with LLMs

This article investigates a control system within the context of six-generation wireless networks. The control performance optimization confronts the technical challenges that arise from the intricate interactions between communication and control sub-systems, asking for a co-design. Accounting for the system dynamics, we formulate the sequential co-design decision-makings of communication and control over the discrete time horizon as a Markov decision process, for which a practical offline learning framework is proposed. Our proposed framework integrates large language models into the elements of reinforcement learning. We present a case study on the age of semantics-aware communication and control co-design to showcase the potentials from our proposed learning framework. Furthermore, we discuss the open issues remaining to make our proposed offline learning framework feasible for real-world implementations, and highlight the research directions for future explorations.

eess.SY↗

arXiv2024

Know in AdVance: Linear-Complexity Forecasting of Ad Campaign Performance with Evolving User Interest

Real-time Bidding (RTB) advertisers wish to \textit{know in advance} the expected cost and yield of ad campaigns to avoid trial-and-error expenses. However, Campaign Performance Forecasting (CPF), a sequence modeling task involving tens of thousands of ad auctions, poses challenges of evolving user interest, auction representation, and long context, making coarse-grained and static-modeling methods sub-optimal. We propose \textit{AdVance}, a time-aware framework that integrates local auction-level and global campaign-level modeling. User preference and fatigue are disentangled using a time-positioned sequence of clicked items and a concise vector of all displayed items. Cross-attention, conditioned on the fatigue vector, captures the dynamics of user interest toward each candidate ad. Bidders compete with each other, presenting a complete graph similar to the self-attention mechanism. Hence, we employ a Transformer Encoder to compress each auction into embedding by solving auxiliary tasks. These sequential embeddings are then summarized by a conditional state space model (SSM) to comprehend long-range dependencies while maintaining global linear complexity. Considering the irregular time intervals between auctions, we make SSM's parameters dependent on the current auction embedding and the time interval. We further condition SSM's global predictions on the accumulation of local results. Extensive evaluations and ablation studies demonstrate its superiority over state-of-the-art methods. AdVance has been deployed on the Tencent Advertising platform, and A/B tests show a remarkable 4.5\% uplift in Average Revenue per User (ARPU).

cs.IR↗

arXiv2023

ReSup: Reliable Label Noise Suppression for Facial Expression Recognition

Because of the ambiguous and subjective property of the facial expression recognition (FER) task, the label noise is widely existing in the FER dataset. For this problem, in the training phase, current FER methods often directly predict whether the label of the input image is noised or not, aiming to reduce the contribution of the noised data in training. However, we argue that this kind of method suffers from the low reliability of such noise data decision operation. It makes that some mistakenly abounded clean data are not utilized sufficiently and some mistakenly kept noised data disturbing the model learning process. In this paper, we propose a more reliable noise-label suppression method called ReSup (Reliable label noise Suppression for FER). First, instead of directly predicting noised or not, ReSup makes the noise data decision by modeling the distribution of noise and clean labels simultaneously according to the disagreement between the prediction and the target. Specifically, to achieve optimal distribution modeling, ReSup models the similarity distribution of all samples. To further enhance the reliability of our noise decision results, ReSup uses two networks to jointly achieve noise suppression. Specifically, ReSup utilize the property that two networks are less likely to make the same mistakes, making two networks swap decisions and tending to trust decisions with high agreement. Extensive experiments on three popular benchmarks show that the proposed method significantly outperforms state-of-the-art noisy label FER methods by 3.01% on FERPlus becnmarks. Code: https://github.com/purpleleaves007/FERDenoise

cs.CV↗

arXiv2023

Wital: A COTS WiFi Devices Based Vital Signs Monitoring System Using NLOS Sensing Model

Vital sign (breathing and heartbeat) monitoring is essential for patient care and sleep disease prevention. Most current solutions are based on wearable sensors or cameras; however, the former could affect sleep quality, while the latter often present privacy concerns. To address these shortcomings, we propose Wital, a contactless vital sign monitoring system based on low-cost and widespread commercial off-the-shelf (COTS) Wi-Fi devices. There are two challenges that need to be overcome. First, the torso deformations caused by breathing/heartbeats are weak. How can such deformations be effectively captured? Second, movements such as turning over affect the accuracy of vital sign monitoring. How can such detrimental effects be avoided? For the former, we propose a non-line-of-sight (NLOS) sensing model for modeling the relationship between the energy ratio of line-of-sight (LOS) to NLOS signals and the vital sign monitoring capability using Ricean K theory and use this model to guide the system construction to better capture the deformations caused by breathing/heartbeats. For the latter, we propose a motion segmentation method based on motion regularity detection that accurately distinguishes respiration from other motions, and we remove periods that include movements such as turning over to eliminate detrimental effects. We have implemented and validated Wital on low-cost COTS devices. The experimental results demonstrate the effectiveness of Wital in monitoring vital signs.

cs.HC↗

arXiv2022

TinyQMIX: Distributed Access Control for mMTC via Multi-agent Reinforcement Learning

Distributed access control is a crucial component for massive machine type communication (mMTC). In this communication scenario, centralized resource allocation is not scalable because resource configurations have to be sent frequently from the base station to a massive number of devices. We investigate distributed reinforcement learning for resource selection without relying on centralized control. Another important feature of mMTC is the sporadic and dynamic change of traffic. Existing studies on distributed access control assume that traffic load is static or they are able to gradually adapt to the dynamic traffic. We minimize the adaptation period by training TinyQMIX, which is a lightweight multi-agent deep reinforcement learning model, to learn a distributed wireless resource selection policy under various traffic patterns before deployment. Therefore, the trained agents are able to quickly adapt to dynamic traffic and provide low access delay. Numerical results are presented to support our claims.

cs.NI↗

arXiv2021

An Online Orchestration Mechanism for General-Purpose Edge Computing

In recent years, the fast development of mobile communications and cloud systems has substantially promoted edge computing. By pushing server resources to the edge, mobile service providers can deliver their content and services with enhanced performance, and mobile-network carriers can alleviate congestion in the core networks. Although edge computing has been attracting much interest, most current research is application-specific, and analysis is lacking from a business perspective of edge cloud providers (ECPs) that provide general-purpose edge cloud services to mobile service providers and users. In this article, we present a vision of general-purpose edge computing realized by multiple interconnected edge clouds, analyzing the business model from the viewpoint of ECPs and identifying the main issues to address to maximize benefits for ECPs. Specifically, we formalize the long-term revenue of ECPs as a function of server-resource allocation and public data-placement decisions subject to the amount of physical resources and inter-cloud data-transportation cost constraints. To optimize the long-term objective, we propose an online framework that integrates the drift-plus-penalty and primal-dual methods. With theoretical analysis and simulations, we show that the proposed method approximates the optimal solution in a challenging environment without having future knowledge of the system.

cs.NI↗

arXiv2020

Computation Offloading in Beyond 5G Networks: A Distributed Learning Framework and Applications

Facing the trend of merging wireless communications and multi-access edge computing (MEC), this article studies computation offloading in the beyond fifth-generation networks. To address the technical challenges originating from the uncertainties and the sharing of limited resource in an MEC system, we formulate the computation offloading problem as a multi-agent Markov decision process, for which a distributed learning framework is proposed. We present a case study on resource orchestration in computation offloading to showcase the potentials of an online distributed reinforcement learning algorithm developed under the proposed framework. Experimental results demonstrate that our learning algorithm outperforms the benchmark resource orchestration algorithms. Furthermore, we outline the research directions worth in-depth investigation to minimize the time cost, which is one of the main practical issues that prevent the implementation of the proposed distributed learning framework.

cs.DC↗

arXiv2020

Information Freshness-Aware Task Offloading in Air-Ground Integrated Edge Computing Systems

This paper studies the problem of information freshness-aware task offloading in an air-ground integrated multi-access edge computing system, which is deployed by an infrastructure provider (InP). A third-party real-time application service provider provides computing services to the subscribed mobile users (MUs) with the limited communication and computation resources from the InP based on a long-term business agreement. Due to the dynamic characteristics, the interactions among the MUs are modelled by a non-cooperative stochastic game, in which the control policies are coupled and each MU aims to selfishly maximize its own expected long-term payoff. To address the Nash equilibrium solutions, we propose that each MU behaves in accordance with the local system states and conjectures, based on which the stochastic game is transformed into a single-agent Markov decision process. Moreover, we derive a novel online deep reinforcement learning (RL) scheme that adopts two separate double deep Q-networks for each MU to approximate the Q-factor and the post-decision Q-factor. Using the proposed deep RL scheme, each MU in the system is able to make decisions without a priori statistical knowledge of dynamics. Numerical experiments examine the potentials of the proposed scheme in balancing the age of information and the energy consumption.

eess.SP↗

arXiv2019

Resource Awareness in Unmanned Aerial Vehicle-Assisted Mobile-Edge Computing Systems

This paper investigates an unmanned aerial vehicle (UAV)-assisted mobile-edge computing (MEC) system, in which the UAV provides complementary computation resource to the terrestrial MEC system. The UAV processes the received computation tasks from the mobile users (MUs) by creating the corresponding virtual machines. Due to finite shared I/O resource of the UAV in the MEC system, each MU competes to schedule local as well as remote task computations across the decision epochs, aiming to maximize the expected long-term computation performance. The non-cooperative interactions among the MUs are modeled as a stochastic game, in which the decision makings of a MU depend on the global state statistics and the task scheduling policies of all MUs are coupled. To approximate the Nash equilibrium solutions, we propose a proactive scheme based on the long short-term memory and deep reinforcement learning (DRL) techniques. A digital twin of the MEC system is established to train the proactive DRL scheme offline. Using the proposed scheme, each MU makes task scheduling decisions only with its own information. Numerical experiments show a significant performance gain from the scheme in terms of average utility per MU across the decision epochs.

cs.NI↗

arXiv2019

EmoSense: Computational Intelligence Driven Emotion Sensing via Wireless Channel Data

Emotion is well-recognized as a distinguished symbol of human beings, and it plays a crucial role in our daily lives. Existing vision-based or sensor-based solutions are either obstructive to use or rely on specialized hardware, hindering their applicability. This paper introduces EmoSense, a first-of-its-kind wireless emotion sensing system driven by computational intelligence. The basic methodology is to explore the physical expression of emotions from wireless channel response via data mining. The design and implementation of EmoSense {face} two major challenges: extracting physical expression from wireless channel data and recovering emotion from the corresponding physical expression. For the former, we present a Fresnel zone based theoretical model depicting the fingerprint of the physical expression on channel response. For the latter, we design an efficient computational intelligence driven mechanism to recognize emotion from the corresponding fingerprints. We prototyped EmoSense on the commodity WiFi infrastructure and compared it with main-stream sensor-based and vision-based approaches in the real-world scenario. The numerical study over $3360$ cases confirms that EmoSense achieves a comparable performance to the vision-based and sensor-based rivals under different scenarios. EmoSense only leverages the low-cost and prevalent WiFi infrastructures and thus constitutes a tempting solution for emotion sensing.

cs.HC↗

arXiv2019

Multi-Tenant Cross-Slice Resource Orchestration: A Deep Reinforcement Learning Approach

With the cellular networks becoming increasingly agile, a major challenge lies in how to support diverse services for mobile users (MUs) over a common physical network infrastructure. Network slicing is a promising solution to tailor the network to match such service requests. This paper considers a system with radio access network (RAN)-only slicing, where the physical infrastructure is split into slices providing computation and communication functionalities. A limited number of channels are auctioned across scheduling slots to MUs of multiple service providers (SPs) (i.e., the tenants). Each SP behaves selfishly to maximize the expected long-term payoff from the competition with other SPs for the orchestration of channels, which provides its MUs with the opportunities to access the computation and communication slices. This problem is modelled as a stochastic game, in which the decision makings of a SP depend on the global network dynamics as well as the joint control policy of all SPs. To approximate the Nash equilibrium solutions, we first construct an abstract stochastic game with the local conjectures of channel auction among the SPs. We then linearly decompose the per-SP Markov decision process to simplify the decision makings at a SP and derive an online scheme based on deep reinforcement learning to approach the optimal abstract control policies. Numerical experiments show significant performance gains from our scheme.

cs.NI↗