arXiv subjects

Pei Peng

Publications and source records attributed to Pei Peng.

14 recordsLinked to original sources

arXiv2026

Dual Fluid Antenna-Assisted UAV MIMO Networks

Fluid Antennas (FAs)-assisted Unmanned Aerial Vehicle (UAV) networks leverage the FA position adaptivity and flexible beamforming to overcome the limitations of Fixed-Positioned Antennas (FPAs) in dynamic UAV channels and Multi-User (MU) interference. This letter investigates a dual FA-assisted UAV network for MU-Multiple-Input-Multiple-Output (MIMO) downlink communications, aiming to maximize the average achievable rate through the joint optimization of UAV trajectory, the transmit/receive FA positions, and beamforming. The formulated problem is highly coupled and non-convex. Accordingly, an efficient Alternating Optimization (AO)-based algorithm is developed for decomposed subproblems, yielding a suboptimal solution. Numerical results demonstrate significant performance gains of 120% and 110% over conventional FPA-based and existing FA-based baselines, respectively.

eess.SP↗

arXiv2025

Advancing Fluid Antenna-Assisted Non-Terrestrial Networks in 6G and Beyond: Fundamentals, State of the Art, and Future Directions

With the surging demand for ultra-reliable, low-latency, and ubiquitous connectivity in Sixth-Generation (6G) networks, Non-Terrestrial Networks (NTNs) emerge as a key complement to terrestrial networks by offering flexible access and global coverage. Despite the significant potential, NTNs still face critical challenges, including dynamic propagation environments, energy constraints, and dense interference. As a key 6G technology, Fluid Antennas (FAs) can reshape wireless channels by reconfiguring radiating elements within a limited space, such as their positions and rotations, to provide higher channel diversity and multiplexing gains. Compared to fixed-position antennas, FAs can present a promising integration path for NTNs to mitigate dynamic channel fading and optimize resource allocation. This paper provides a comprehensive review of FA-assisted NTNs. We begin with a brief overview of the classical structure and limitations of existing NTNs, the fundamentals and advantages of FAs, and the basic principles of FA-assisted NTNs. We then investigate the joint optimization solutions, detailing the adjustments of FA configurations, NTN platform motion modes, and resource allocations. We also discuss the combination with other emerging technologies and explore FA-assisted NTNs as a novel network architecture for intelligent function integrations. Furthermore, we delve into the physical layer security and covert communication in FA-assisted NTNs. Finally, we highlight the potential future directions to empower broader applications of FA-assisted NTNs.

cs.NI↗

arXiv2025

Representation-Level Counterfactual Calibration for Debiased Zero-Shot Recognition

Object-context shortcuts remain a persistent challenge in vision-language models, undermining zero-shot reliability when test-time scenes differ from familiar training co-occurrences. We recast this issue as a causal inference problem and ask: Would the prediction remain if the object appeared in a different environment? To answer this at inference time, we estimate object and background expectations within CLIP's representation space, and synthesize counterfactual embeddings by recombining object features with diverse alternative contexts sampled from external datasets, batch neighbors, or text-derived descriptions. By estimating the Total Direct Effect and simulating intervention, we further subtract background-only activation, preserving beneficial object-context interactions while mitigating hallucinated scores. Without retraining or prompt design, our method substantially improves both worst-group and average accuracy on context-sensitive benchmarks, establishing a new zero-shot state of the art. Beyond performance, our framework provides a lightweight representation-level counterfactual approach, offering a practical causal avenue for debiased and reliable multimodal reasoning.

cs.CV↗

arXiv2025

Uncertain Location Transmitter and UAV-Aided Warden Based LEO Satellite Covert Communication Systems

We propose a novel covert communication system in which a ground user, Alice, transmits unauthorized message fragments to Bob, a low-Earth orbit satellite (LEO), and an unmanned aerial vehicle (UAV) warden (Willie) attempts to detect these transmissions. The key contribution is modeling a scenario where Alice and Willie are unaware of each other's exact locations and move randomly within a specific area. Alice utilizes environmental obstructions to avoid detection and only transmits when the satellite is directly overhead. LEO satellite technology allows users to avoid transmitting messages near a base station. We introduce two key performance metrics: catch probability (Willie detects and locates Alice during a message chunk transmission) and overall catch probability over multiple message chunks. We analyze how two parameters impact these metrics: 1) the size of the detection window and 2) the number of message chunks. The paper proposes two algorithms to optimize these parameters. The simulation results show that the algorithms effectively reduce the detection risks. This work advances the understanding of covert communication under mobility and uncertainty in satellite-aided systems.

cs.CR↗

arXiv2024

Counterfactual Reasoning for Multi-Label Image Classification via Patching-Based Training

The key to multi-label image classification (MLC) is to improve model performance by leveraging label correlations. Unfortunately, it has been shown that overemphasizing co-occurrence relationships can cause the overfitting issue of the model, ultimately leading to performance degradation. In this paper, we provide a causal inference framework to show that the correlative features caused by the target object and its co-occurring objects can be regarded as a mediator, which has both positive and negative impacts on model predictions. On the positive side, the mediator enhances the recognition performance of the model by capturing co-occurrence relationships; on the negative side, it has the harmful causal effect that causes the model to make an incorrect prediction for the target object, even when only co-occurring objects are present in an image. To address this problem, we propose a counterfactual reasoning method to measure the total direct effect, achieved by enhancing the direct effect caused only by the target object. Due to the unknown location of the target object, we propose patching-based training and inference to accomplish this goal, which divides an image into multiple patches and identifies the pivot patch that contains the target object. Experimental results on multiple benchmark datasets with diverse configurations validate that the proposed method can achieve state-of-the-art performance.

cs.CV↗

arXiv2023

Redundancy Management for Fast Service (Rates) in Edge Computing Systems

Edge computing operates between the cloud and end users and strives to provide low-latency computing services for simultaneous users. Redundant use of multiple edge nodes can reduce latency, as edge systems often operate in uncertain environments. However, since edge systems have limited computing and storage resources, directing more resources to some computing jobs will either block the execution of others or pass their execution to the cloud, thus increasing latency. This paper uses the average system computing time and blocking probability to evaluate edge system performance and analyzes the optimal resource allocation accordingly. We also propose blocking probability and average system time optimization algorithms. Simulation results show that both algorithms significantly outperform the benchmark for different service time distributions and show how the optimal replication factor changes with varying parameters of the system.

cs.DC↗

arXiv2021

Covert, Low-Delay, Coded Message Passing in Mobile (IoT) Networks

We introduce a gossip-like protocol for covert message passing between Alice and Bob as they move in an area watched over by a warden Willie. The area hosts a multitude of Internet of (Battlefield) Things (Io\b{eta}T) objects. Alice and Bob perform random walks on a random regular graph. The Io\b{eta}T objects reside on the vertices of this graph, and some can serve as relays between Alice and Bob. The protocol starts with Alice splitting her message into small chunks, which she can covertly deposit to the relays she encounters. The protocol ends with Bob collecting the chunks. Alice may encode her data before the dissemination. Willie can either perform random walks as Alice and Bob do or conduct uniform surveillance of the area. In either case, he can only observe one relay at a time. We evaluate the system performance by the covertness probability and the message passing delay. In our protocol, Alice splits her message to increase the covertness probability and adds (coded) redundancy to reduce the transmission delay. The performance metrics depend on the graph, communications delay, and code parameters. We show that, in most scenarios, it is impossible to find the design parameters that simultaneously maximize the covertness probability and minimize the message delay.

cs.IT↗

arXiv2021

Diversity/Parallelism Trade-off in Distributed Systems with Redundancy

As numerous machine learning and other algorithms increase in complexity and data requirements, distributed computing becomes necessary to satisfy the growing computational and storage demands, because it enables parallel execution of smaller tasks that make up a large computing job. However, random fluctuations in task service times lead to straggling tasks with long execution times. Redundancy, in the form of task replication and erasure coding, provides diversity that allows a job to be completed when only a subset of redundant tasks is executed, thus removing the dependency on the straggling tasks. In situations of constrained resources (here a fixed number of parallel servers), increasing redundancy reduces the available resources for parallelism. In this paper, we characterize the diversity vs. parallelism trade-off and identify the optimal strategy, among replication, coding and splitting, which minimizes the expected job completion time. We consider three common service time distributions and establish three models that describe scaling of these distributions with the task size. We find that different distributions with different scaling models operate optimally at different levels of redundancy, and thus may require very different code rates.

cs.DC↗

arXiv2021

Distributed Storage Allocations for Optimal Service Rates

Redundant storage maintains the performance of distributed systems under various forms of uncertainty. This paper considers the uncertainty in node access and download service. We consider two access models under two download service models. In one access model, a user can access each node with a fixed probability, and in the other, a user can access a random fixed-size subset of nodes. We consider two download service models. In the first (small file) model, the randomness associated with the file size is negligible. In the second (large file) model, randomness is associated with both the file size and the system's operations. We focus on the service rate of the system. For a fixed redundancy level, the systems' service rate is determined by the allocation of coded chunks over the storage nodes. We consider quasi-uniform allocations, where coded content is uniformly spread among a subset of nodes. The question we address asks what the size of this subset (spreading) should be. We show that in the small file model, concentrating the coded content to a minimum-size subset is universally optimal. For the large file model, the optimal spreading depends on the system parameters. These conclusions hold for both access models.

cs.IT↗

arXiv2019

Solving inverse problems via auto-encoders

Compressed sensing (CS) is about recovering a structured signal from its under-determined linear measurements. Starting from sparsity, recovery methods have steadily moved towards more complex structures. Emerging machine learning tools such as generative functions that are based on neural networks are able to learn general complex structures from training data. This makes them potentially powerful tools for designing CS algorithms. Consider a desired class of signals $\cal Q$, ${\cal Q}\subset{R}^n$, and a corresponding generative function $g:{\cal U}^k\rightarrow {R}^n$, ${\cal U}\subset {R}$, such that $\sup_{{\bf x}\in {\cal Q}}\min_{{\bf u}\in{\cal U}^k}{1\over \sqrt{n}}\|g({\bf u})-{\bf x}\|\leq δ$. A recovery method based on $g$ seeks $g({\bf u})$ with minimum measurement error. In this paper, the performance of such a recovery method is studied, under both noisy and noiseless measurements. In the noiseless case, roughly speaking, it is proven that, as $k$ and $n$ grow without bound and $δ$ converges to zero, if the number of measurements ($m$) is larger than the input dimension of the generative model ($k$), then asymptotically, almost lossless recovery is possible. Furthermore, the performance of an efficient iterative algorithm based on projected gradient descent is studied. In this case, an auto-encoder is used to define and enforce the source structure at the projection step. The auto-encoder is defined by encoder and decoder (generative) functions $f:{R}^n\to{\cal U}^k$ and $g:{\cal U}^k\to{R}^n$, respectively. We theoretically prove that, roughly, given $m>40k\log{1\over δ}$ measurements, such an algorithm converges to the vicinity of the desired result, even in the presence of additive white Gaussian noise. Numerical results exploring the effectiveness of the proposed method are presented.

cs.IT↗

arXiv2019

Straggling for Covert Message Passing on Complete Graphs

We introduce a model for mobile, multi-agent information transfer that increases the communication covertness through a protocol which also increases the information transfer delay. Covertness is achieved in the presence of a warden who has the ability to patrol the communication channels. Furthermore we show how two forms of redundancy can be used as an effective tool to control the tradeoff between the covertness and the delay.

cs.CR↗

arXiv2018

On Distributed Storage Allocations of Large Files for Maximum Service Rate

Allocation of (redundant) file chunks throughout a distributed storage system affects important performance metrics such as the probability of file recovery, data download time, or the service rate of the system under a given data access model. This paper is concerned with the service rate under the assumption that the stored data is large and its download time is not negligible. We focus on quasi-uniform storage allocations and provide a service rate analysis for two common data access models. We find that the optimal allocation varies in accordance with different system parameters. This was not the case under the assumption that the download time does not scale with the size of data, where the minimal spreading allocation was previously found to be universally optimal.

cs.DC↗

arXiv2017

Effective Straggler Mitigation: Which Clones Should Attack and When?

Redundancy for straggler mitigation, originally in data download and more recently in distributed computing context, has been shown to be effective both in theory and practice. Analysis of systems with redundancy has drawn significant attention and numerous papers have studied pain and gain of redundancy under various service models and assumptions on the straggler characteristics. We here present a cost (pain) vs. latency (gain) analysis of using simple replication or erasure coding for straggler mitigation in executing jobs with many tasks. We quantify the effect of the tail of task execution times and discuss tail heaviness as a decisive parameter for the cost and latency of using redundancy. Specifically, we find that coded redundancy achieves better cost vs. latency and allows for greater achievable latency and cost tradeoff region compared to replication and can yield reduction in both cost and latency under less heavy tailed execution times. We show that delaying redundancy is not effective in reducing cost.

cs.PF↗

arXiv2017

Straggler Mitigation by Delayed Relaunch of Tasks

Redundancy for straggler mitigation, originally in data download and more recently in distributed computing context, has been shown to be effective both in theory and practice. Analysis of systems with redundancy has drawn significant attention and numerous papers have studied pain and gain of redundancy under various service models and assumptions on the straggler characteristics. We here present a cost (pain) vs. latency (gain) analysis of using simple replication or erasure coding for straggler mitigation in executing jobs with many tasks. We quantify the effect of the tail of task execution times and discuss tail heaviness as a decisive parameter for the cost and latency of using redundancy. Specifically, we find that coded redundancy achieves better cost vs. latency tradeoff than simple replication and can yield reduction in both cost and latency under less heavy tailed execution times. We show that delaying redundancy is not effective in reducing cost and that delayed relaunch of stragglers can yield significant reduction in cost and latency. We validate these observations by comparing with the simulations that use empirical distributions extracted from Google cluster data.

cs.PF↗