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Physical layer deception (PLD) is a novel security mechanism that combines physical layer security (PLS) with deception technologies to actively defend against eavesdroppers. In this paper, we establish a novel semantic model for PLD that evaluates its performance in terms of semantic distortion. By analyzing semantic distortion at varying levels of knowledge on the receiver's part regarding the key, we derive the receiver's optimal decryption strategy, and consequently, the transmitter's optimal deception strategy. The proposed semantic model provides a more generic understanding of the PLD approach independent from coding or multiplexing schemes, and allows for efficient real-time adaptation to fading channels., Comment: Submitted to ICC 2025

This paper presents LiteVSR2, an enhanced version of our previously introduced efficient approach to Visual Speech Recognition (VSR). Building upon our knowledge distillation framework from a pre-trained Automatic Speech Recognition (ASR) model, we introduce two key improvements: a stabilized video preprocessing technique and feature normalization in the distillation process. These improvements yield substantial performance gains on the LRS2 and LRS3 benchmarks, positioning LiteVSR2 as the current best CTC-based VSR model without increasing the volume of training data or computational resources utilized. Furthermore, we explore the scalability of our approach by examining performance metrics across varying model complexities and training data volumes. LiteVSR2 maintains the efficiency of its predecessor while significantly enhancing accuracy, thereby demonstrating the potential for resource-efficient advancements in VSR technology.

The expansion of IoT devices and the demands of deep learning have highlighted significant challenges in distributed deep learning systems. Parallel split learning has emerged as a promising derivative of split learning well suited for distributed learning on resource-constrained devices. However, parallel split learning faces several challenges, such as large effective batch sizes, non-independent and identically distributed data, and the straggler effect. We view these issues as a sampling dilemma and propose to address them by orchestrating a mini-batch sampling process on the server side. We introduce a new method called uniform global sampling to decouple the effective batch size from the number of clients and reduce the mini-batch deviation. To address the straggler effect, we introduce a novel method called Latent Dirichlet Sampling, which generalizes uniform global sampling to balance the trade-off between batch deviation and training time. Our simulations reveal that our proposed methods enhance model accuracy by up to 34.1% in non-independent and identically distributed settings and reduce the training time in the presence of stragglers by up to 62%. In particular, Latent Dirichlet Sampling effectively mitigates the straggler effect without compromising model accuracy or adding significant computational overhead compared to uniform global sampling. Our results demonstrate the potential of our methods to mitigate common challenges in parallel split learning.

Physical layer security (PLS) is a promising technology to secure wireless communications by exploiting the physical properties of the wireless channel. However, the passive nature of PLS creates a significant imbalance between the effort required by eavesdroppers and legitimate users to secure data. To address this imbalance, in this article, we propose a novel framework of physical layer deception (PLD), which combines PLS with deception technologies to actively counteract wiretapping attempts. Combining a two-stage encoder with randomized ciphering and non-orthogonal multiplexing, the PLD approach enables the wireless communication system to proactively counter eavesdroppers with deceptive messages. Relying solely on the superiority of the legitimate channel over the eavesdropping channel, the PLD framework can effectively protect the confidentiality of the transmitted messages, even against eavesdroppers who possess knowledge equivalent to that of the legitimate receiver. We prove the validity of the PLD framework with in-depth analyses and demonstrate its superiority over conventional PLS approaches with comprehensive numerical benchmarks., Comment: Submitted to IEEE Transactions on Wireless Communications

Millimeter wave (mmWave) cell-free massive MIMO (CF mMIMO) is a promising solution for future wireless communications. However, its optimization is non-trivial due to the challenging channel characteristics. We show that mmWave CF mMIMO optimization is largely an assignment problem between access points (APs) and users due to the high path loss of mmWave channels, the limited output power of the amplifier, and the almost orthogonal channels between users given a large number of AP antennas. The combinatorial nature of the assignment problem, the requirement for scalability, and the distributed implementation of CF mMIMO make this problem difficult. In this work, we propose an unsupervised machine learning (ML) enabled solution. In particular, a graph neural network (GNN) customized for scalability and distributed implementation is introduced. Moreover, the customized GNN architecture is hierarchically permutation-equivariant (HPE), i.e., if the APs or users of an AP are permuted, the output assignment is automatically permuted in the same way. To address the combinatorial problem, we relax it to a continuous problem, and introduce an information entropy-inspired penalty term. The training objective is then formulated using the augmented Lagrangian method (ALM). The test results show that the realized sum-rate outperforms that of the generalized serial dictatorship (GSD) algorithm and is very close to the upper bound in a small network scenario, while the upper bound is impossible to obtain in a large network scenario.

The technological landscape is swiftly advancing towards large-scale systems, creating significant opportunities, particularly in the domain of Terahertz (THz) communications. Networks designed for massive connectivity, comprising numerous Internet of Things (IoT) devices, are at the forefront of this advancement. In this paper, we consider Wireless Power Transfer (WPT)-enabled networks that support these IoT devices with massive Ultra-Reliable and Low-Latency Communication (mURLLC) services.The focus of such networks is information freshness, with the Age-of-Information (AoI) serving as the pivotal performance metric. In particular, we aim to minimize the maximum AoI among IoT devices by optimizing the scheduling policy. Our analytical findings establish the convexity property of the problem, which can be solved efficiently. Furthermore, we introduce the concept of AoI-oriented cluster capacity, examining the relationship between the number of supported devices and the AoI performance in the network. Numerical simulations validate the advantage of our proposed approach in enhancing AoI performance, indicating its potential to guide the design of future THz communication systems for IoT applications requiring mURLLC services.

This paper proposes a novel, resource-efficient approach to Visual Speech Recognition (VSR) leveraging speech representations produced by any trained Automatic Speech Recognition (ASR) model. Moving away from the resource-intensive trends prevalent in recent literature, our method distills knowledge from a trained Conformer-based ASR model, achieving competitive performance on standard VSR benchmarks with significantly less resource utilization. Using unlabeled audio-visual data only, our baseline model achieves a word error rate (WER) of 47.4% and 54.7% on the LRS2 and LRS3 test benchmarks, respectively. After fine-tuning the model with limited labeled data, the word error rate reduces to 35% (LRS2) and 45.7% (LRS3). Our model can be trained on a single consumer-grade GPU within a few days and is capable of performing real-time end-to-end VSR on dated hardware, suggesting a path towards more accessible and resource-efficient VSR methodologies., Comment: Accepted for publication at ICASSP 2024

Semantic image segmentation, the process of classifying each pixel in an image into a particular class, plays an important role in many visual understanding systems. As the predominant criterion for evaluating the performance of statistical models, loss functions are crucial for shaping the development of deep learning-based segmentation algorithms and improving their overall performance. To aid researchers in identifying the optimal loss function for their particular application, this survey provides a comprehensive and unified review of $25$ loss functions utilized in image segmentation. We provide a novel taxonomy and thorough review of how these loss functions are customized and leveraged in image segmentation, with a systematic categorization emphasizing their significant features and applications. Furthermore, to evaluate the efficacy of these methods in real-world scenarios, we propose unbiased evaluations of some distinct and renowned loss functions on established medical and natural image datasets. We conclude this review by identifying current challenges and unveiling future research opportunities. Finally, we have compiled the reviewed studies that have open-source implementations on our GitHub page.

We propose a new security framework for physical layer security (PLS) in the finite blocklength (FBL) regime that incorporates deception technology, allowing for active countermeasures against potential eavesdroppers. Using a symmetric block cipher and power-domain non-orthogonal multiplexing (NOM), our approach is able to achieve high secured reliability while effectively deceiving the eavesdropper, and can benefit from increased transmission power. This work represents a promising direction for future research in PLS with deception technology., Comment: Accepted for presentation at SPAWC 2023. Appendices are omitted in the submitted version due to length limit. Minor corrections made in the latest arXiv version

How to provide information security while fulfilling ultra reliability and low-latency requirements is one of the major concerns for enabling the next generation of ultra-reliable and low-latency communications service (xURLLC), specially in machine-type communications. In this work, we investigate the reliability-security tradeoff via defining the leakage-failure probability, which is a metric that jointly characterizes both reliability and security performances for short-packet transmissions. We discover that the system performance can be enhanced by counter-intuitively allocating fewer resources for the transmission with finite blocklength (FBL) codes. In order to solve the corresponding optimization problem for the joint resource allocation, we propose an optimization framework, that leverages lower-bounded approximations for the decoding error probability in the FBL regime. We characterize the convexity of the reformulated problem and establish an efficient iterative searching method, the convergence of which is guaranteed. To show the extendability of the framework, we further discuss the blocklength allocation schemes with practical requirements of reliable-secure performance, as well as the transmissions with the statistical channel state information (CSI). Numerical results verify the accuracy of the proposed approach and demonstrate the reliability-security tradeoff under various setups., Comment: To appear in IEEE Journal on Selected Areas of Communications

We study a multi-source wireless power transfer (WPT) enabled network supporting multi-sensor transmissions. Activated by energy harvesting (EH) from multiple WPT sources, sensors transmit short packets to a destination with finite blocklength (FBL) codes. This work for the first time characterizes the FBL reliability for such multi-source WPT enabled network and provides reliability-oriented resource allocation designs, while a practical nonlinear EH model is considered. For scenario with a fixed frame structure, we maximize the FBL reliability via optimally allocating the transmit power among multi-source. In particular, we first investigate the relationship between the FBL reliability and multiple WPT source power, based on which a power allocation problem is formulated. To solve the formulated non-convex problem, we introduce auxiliary variables and apply successive convex approximation (SCA) technique to the non-convex component. Consequently, a sub-optimal solution can be obtained. Moreover, we extend our design into a dynamic frame structure scenario, i.e., the blocklength allocated for WPT phase and short-packet transmission phase are adjustable, which introduces more flexibility and new challenges to the system design. We provide a joint power and blocklength allocation design to minimize the system overall error probability under the total power and blocklength constraints. To address the high-dimensional optimization problem, auxiliary variables introduction, multiple variable substitutions and SCA technique utilization are exploited to reformulate and efficiently solve the problem. Finally, through numerical results, we validate our analytical model and evaluate the system performance, where a set of guidelines for practical system design are concluded.

The extreme requirements for high reliability and low latency in the upcoming Sixth Generation (6G) wireless networks are challenging the design of multi-hop wireless transport networks. Inspired by the advent of the virtualization concept in the wireless networks design and openness paradigm as fostered by the O-RAN Alliance, we target a revolutionary resource allocation scheme to improve the overall transmission efficiency. In this paper, we investigate the problem of multi-hop decode-and-forward (DF) relaying in the finite blocklength (FBL) regime, and propose a DMH-HARQ scheme, which maximizes the end-to-end (E2E) communication reliability in the wireless transport network. We also propose an integer dynamic programming (DP) algorithm to efficiently solve the optimal DMH-HARQ strategy. Constrained within a certain time frame to accomplish E2E transmission, our proposed approach is proven to outperform the conventional listening-based cooperative ARQ, as well as any static HARQ strategy, regarding the E2E reliability. It is applicable without dependence on special delay constraint, and is particularly competitive for long-distance transport network with many hops., Comment: Submitted to IEEE Transactions on Mobile Computing

Enabling ultra-reliable and low-latency communication services while providing massive connectivity is one of the major goals to be accomplished in future wireless communication networks. In this paper, we investigate the performance of a hybrid multi-access scheme in the finite blocklength (FBL) regime that combines the advantages of both non-orthogonal multiple access (NOMA) and time-division multiple access (TDMA) schemes. Two latency-sensitive application scenarios are studied, distinguished by whether the queuing behaviour has an influence on the transmission performance or not. In particular, for the latency-critical case with one-shot transmission, we aim at a certain physical-layer quality-of-service (QoS) performance, namely the optimization of the reliability. And for the case in which queuing behaviour plays a role, we focus on the link-layer QoS performance and provide a design that maximizes the effective capacity. For both designs, we leverage the characterizations in the FBL regime to provide the optimal framework by jointly allocating the blocklength and transmit power of each user. In particular, for the reliability-oriented design, the original problem is decomposed and the joint convexity of sub-problems is shown via a variable substitution method. For the effective-capacity-oriented design, we exploit the method of Lagrange multipliers to formulate a solvable dual problem with strong duality to the original problem. Via simulations, we validate our analytical results of convexity/concavity and show the advantage of our proposed approaches compared to other existing schemes., Comment: 32pages, 9 figures. Accepted by IEEE Transactions on Wireless Communications

To support ultra-reliable and low-latency services for mission-critical applications, transmissions are usually carried via short blocklength codes, i.e., in the so-called finite blocklength (FBL) regime. Different from the infinite blocklength regime where transmissions are assumed to be arbitrarily reliable at the Shannon's capacity, the reliability and capacity performances of an FBL transmission are impacted by the coding blocklength. The relationship among reliability, coding rate, blocklength and channel quality has recently been characterized in the literature, considering the FBL performance model. In this paper, we follow this model, and prove the joint convexity of the FBL error probability with respect to blocklength and transmit power within a region of interest, as a key enabler for designing systems to achieve globally optimal performance levels. Moreover, we apply the joint convexity to general use cases and efficiently solve the joint optimization problem in the setting with multiple users. We also extend the applicability of the proposed approach by proving that the joint convexity still holds in fading channels, as well as in relaying networks. Via simulations, we validate our analytical results and demonstrate the advantage of leveraging the joint convexity compared to other commonly-applied approaches., Comment: 33 pages, 7 figures, to be published in IEEE Transactions on Wireless Communications

In this paper, we investigate the robust resource allocation design for secure communication in an integrated sensing and communication (ISAC) system. A multi-antenna dual-functional radar-communication (DFRC) base station (BS) serves multiple single-antenna legitimate users and senses for targets simultaneously, where already identified targets are treated as potential single-antenna eavesdroppers. The DFRC BS scans a sector with a sequence of dedicated beams, and the ISAC system takes a snapshot of the environment during the transmission of each beam. Based on the sensing information, the DFRC BS can acquire the channel state information (CSI) of the potential eavesdroppers. Different from existing works that focused on the resource allocation design for a single snapshot, in this paper, we propose a novel optimization framework that jointly optimizes the communication and sensing resources over a sequence of snapshots with adjustable durations. To this end, we jointly optimize the duration of each snapshot, the beamforming vector, and the covariance matrix of the AN for maximization of the system sum secrecy rate over a sequence of snapshots while guaranteeing a minimum required average achievable rate and a maximum information leakage constraint for each legitimate user. The resource allocation algorithm design is formulated as a non-convex optimization problem, where we account for the imperfect CSI of both the legitimate users and the potential eavesdroppers. To make the problem tractable, we derive a bound for the uncertainty region of the potential eavesdroppers' small-scale fading based on a safe approximation, which facilitates the development of a block coordinate descent-based iterative algorithm for obtaining an efficient suboptimal solution., Comment: 38 pages, 12 figures

The deployment of multi-access edge computing (MEC) is paving the way towards pervasive intelligence in future 6G networks. This new paradigm also proposes emerging requirements of dependable communications, which goes beyond the ultra-reliable low latency communication (URLLC), focusing on the performance of a closed loop instead of that of an unidirectional link. This work studies the simple but efficient one-shot transmission scheme, investigating the closed-loop-reliability-optimal policy of blocklength allocation under stringent time and energy constraints., Comment: Accepted for publication at CSCN 2021 V1: accepted version V2: minor correction in the modulation order V3: corrections to resolve chaos caused by different normalizations of the FBL PER equation, model figure file updated in HQ

In this work, we propose a novel approach for reinforcement learning driven by evolutionary computation. Our algorithm, dubbed as Evolutionary-Driven Reinforcement Learning (evo-RL), embeds the reinforcement learning algorithm in an evolutionary cycle, where we distinctly differentiate between purely evolvable (instinctive) behaviour versus purely learnable behaviour. Furthermore, we propose that this distinction is decided by the evolutionary process, thus allowing evo-RL to be adaptive to different environments. In addition, evo-RL facilitates learning on environments with rewardless states, which makes it more suited for real-world problems with incomplete information. To show that evo-RL leads to state-of-the-art performance, we present the performance of different state-of-the-art reinforcement learning algorithms when operating within evo-RL and compare it with the case when these same algorithms are executed independently. Results show that reinforcement learning algorithms embedded within our evo-RL approach significantly outperform the stand-alone versions of the same RL algorithms on OpenAI Gym control problems with rewardless states constrained by the same computational budget., Comment: 9 pages, 7 figures

We consider a fully-loaded ground wireless network supporting unmanned aerial vehicle (UAV) transmission services. To enable the overload transmissions to a ground user (GU) and a UAV, two transmission schemes are employed, namely non-orthogonal multiple access (NOMA) and relaying, depending on whether or not the GU and UAV are served simultaneously. Under the assumption of the system operating with infinite blocklength (IBL) codes, the IBL throughputs of both the GU and the UAV are derived under the two schemes. More importantly, we also consider the scenario in which data packets are transmitted via finite blocklength (FBL) codes, i.e., data transmission to both the UAV and the GU is performed under low-latency and high reliability constraints. In this setting, the FBL throughputs are characterized again considering the two schemes of NOMA and relaying. Following the IBL and FBL throughput characterizations, optimal resource allocation designs are subsequently proposed to maximize the UAV throughput while guaranteeing the throughput of the cellular user.Moreover, we prove that the relaying scheme is able to provide transmission service to the UAV while improving the GU's performance, and that the relaying scheme potentially offers a higher throughput to the UAV in the FBL regime than in the IBL regime. On the other hand, the NOMA scheme provides a higher UAV throughput (than relaying) by slightly sacrificing the GU's performance., Comment: 13 pages, 13 figures

The convex property of a nonlinear wireless power transfer (WPT) is characterized in this work. Following a nonlinear energy harvesting model, we express the relationship between the harvested direct current (DC) power and the power of the received radio-frequency signal via an implicit function, based on which the convex property is further proved. In particular, for a predefined rectifier's input signal distribution, we show that the harvested DC power of the nonlinear model is convex in the reciprocal of the rectifier's input signal power. Finally, we provide an example to show the advantages of applying the convex property in WPT network designs., Comment: 5 pages, 2 figures

In this paper, we study an unmanned aerial vehicle (UAV)-enabled wireless power transfer (WPT) network, where a UAV flies at a constant altitude in the sky to provide wireless energy supply for a set of ground nodes with a linear topology. Our objective is to maximize the minimum received energy among all ground nodes by optimizing the UAV's one-dimensional (1D) trajectory, subject to the maximum UAV flying speed constraint. Different from previous works that only provided heuristic and locally optimal solutions, this paper is the first work to present the globally optimal 1D UAV trajectory solution to the considered min-energy maximization problem. Towards this end, we first show that for any given speed-constrained UAV trajectory, we can always construct a maximum-speed trajectory and a speed-free trajectory, such that their combination can achieve the same received energy at all these ground nodes. Next, we transform the original UAV-speed-constrained trajectory optimization problem into an equivalent UAV-speed-free problem, which is then optimally solved via the Lagrange dual method. The obtained optimal 1D UAV trajectory solution follows the so-called successive hover-and-fly (SHF) structure, i.e., the UAV successively hovers at a finite number of hovering points each for an optimized hovering duration, and flies among these hovering points at the maximum speed. Numerical results show that our proposed optimal solution significantly outperforms the benchmark schemes in prior works under different scenarios., Comment: 7 pages, 4 figures

Under the assumption of outdated channel state information (CSI) at the source, we consider the finite blocklength (FBL) throughput of a two-hop relaying system. Previous work has considered this setting so far only for the infinite blocklength case, where decoding can be arbitrarily reliable as long as operating below the Shannon limit. In contrast, in the FBL regime residual decoding errors can not be avoided even when transmitting below the Shannon limit. This makes the scheduling problem at the source more vulnerable to transmission errors, where we investigate the trade-off between the choice of so called scheduling weights to avoid transmission errors and the resulting coding rate. We show that the corresponding maximization of the throughput under a reliability constraint can be solved efficiently by iterative algorithms. Nevertheless, the optimal solution requires a recomputation of the scheduling weights prior to each transmission. Thus, we also study heuristics relying on choosing the scheduling weights only once. Through numerical analysis, we first provide insights on the structure of the throughout under different scheduling weights and channel correlation coefficients. We then turn to the comparison of the optimal scheduling with the heuristic and show that the performance gap between them is only significant for relay systems with high average signal-to-noise ratios (SNR) on the backhaul and relaying link. In particular, the optimal scheduling scheme provides most value in case that the data transmission is subject to strict reliability constraints, justifying the significant additional computational burden., Comment: 24 pages, 8 figures, IEEE TWC submission

In this paper, the blocklength-limited performance of a relaying system is studied, where channels are assumed to experience quasi-static Rayleigh fading while at the same time only the average channel state information (CSI) is available at the source. Both the physical-layer performance (blocklength-limited throughput) and the link-layer performance (effective capacity) of the relaying system are investigated. We propose a simple system operation by introducing a factor based on which we weight the average CSI and let the source determine the coding rate accordingly. In particular, we prove that both the blocklength-limited throughput and the effective capacity are quasi-concave in the weight factor. Through numerical investigations, we show the appropriateness of our theoretical model. In addition, we observe that relaying is more efficient than direct transmission. Moreover, this performance advantage of relaying under the average CSI scenario is more significant than under the perfect CSI scenario. Finally, the speed of convergence (between the blocklength-limited performance and the performance in the Shannon capacity regime) in relaying system is faster in comparison to the direct transmission under both the average CSI scenario and the perfect CSI scenario., Comment: 12 figures, submitted to IEEE Transactions on Wireless Communications