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Selecting a sample generation scheme from multiple text-based generative models is typically addressed by choosing the model that maximizes an averaged evaluation score. However, this score-based selection overlooks the possibility that different models achieve the best generation performance for different types of text prompts. An online identification of the best generation model for various input prompts can reduce the costs associated with querying sub-optimal models. In this work, we explore the possibility of varying rankings of text-based generative models for different text prompts and propose an online learning framework to predict the best data generation model for a given input prompt. The proposed framework adapts the kernelized contextual bandit (CB) methodology to a CB setting with shared context variables across arms, utilizing the generated data to update a kernel-based function that predicts which model will achieve the highest score for unseen text prompts. Additionally, we apply random Fourier features (RFF) to the kernelized CB algorithm to accelerate the online learning process and establish a $\widetilde{\mathcal{O}}(\sqrt{T})$ regret bound for the proposed RFF-based CB algorithm over T iterations. Our numerical experiments on real and simulated text-to-image and image-to-text generative models show RFF-UCB performs successfully in identifying the best generation model across different sample types.

The conventional reconfigurable intelligent surface (RIS) assisted far-field communication systems can only implement angle beamforming, which actually limits the capability for reconfiguring the wireless propagation environment. To overcome this limitation, this paper proposes a newly designed frequency diverse RIS (FD-RIS), which can achieve joint distance-angle beamforming with the assistance of the time modulation technology. The signal processing model for FD-RIS-aided wireless communications is first derived. Then, an optimization problem aimed at maximizing the achievable rate is formulated where the frequency-time modulations are jointly optimized to achieve distance-angle beamforming. Furthermore, a novel iterative algorithm based on the cross-entropy optimization (CEO) framework is proposed to effectively handle the non-convex optimization problem. The numerical results validate that the proposed FD-RIS assisted communication scheme can achieve a notable performance improvement compared with the baseline scheme utilizing traditional RIS. In addition, the effectiveness of the proposed CEO algorithm is further verified by comparing with the benchmark using the genetic algorithm (GA).

Existing frameworks for evaluating and comparing generative models typically target an offline setting, where the evaluator has access to full batches of data produced by the models. However, in many practical scenarios, the goal is to identify the best model using the fewest generated samples to minimize the costs of querying data from the models. Such an online comparison is challenging with current offline assessment methods. In this work, we propose an online evaluation framework to find the generative model that maximizes a standard assessment score among a group of available models. Our method uses an optimism-based multi-armed bandit framework to identify the model producing data with the highest evaluation score, quantifying the quality and diversity of generated data. Specifically, we study the online assessment of generative models based on the Fr\'echet Inception Distance (FID) and Inception Score (IS) metrics and propose the FID-UCB and IS-UCB algorithms leveraging the upper confidence bound approach in online learning. We prove sub-linear regret bounds for these algorithms and present numerical results on standard image datasets, demonstrating their effectiveness in identifying the score-maximizing generative model., Comment: arXiv version

Constructive interference (CI) precoding, which converts the harmful multi-user interference into beneficial signals, is a promising and efficient interference management scheme in multi-antenna communication systems. However, CI-based symbol-level precoding (SLP) experiences high computational complexity as the number of symbol slots increases within a transmission block, rendering it unaffordable in practical communication systems. In this paper, we propose a symbol-level extrapolation (SLE) strategy to extrapolate the precoding matrix by leveraging the relationship between different symbol slots within in a transmission block, during which the channel state information (CSI) remains constant, where we design a closed-form iterative algorithm based on SLE for both PSK and QAM modulation. In order to further reduce the computational complexity, a sub-optimal closed-form solution based on SLE is further developed for PSK and QAM, respectively. Moreover, we design an unsupervised SLE-based neural network (SLE-Net) to unfold the proposed iterative algorithm, which helps enhance the interpretability of the neural network. By carefully designing the loss function of the SLE-Net, the time-complexity of the network can be reduced effectively. Extensive simulation results illustrate that the proposed algorithms can dramatically reduce the computational complexity and time complexity with only marginal performance loss, compared with the conventional SLP design methods.

A Unmanned aerial vehicle (UAV)-assisted mobile edge computing (MEC) scheme with simultaneous wireless information and power transfer (SWIPT) is proposed in this paper. Unlike existing MEC-WPT schemes that disregard the downlink period for returning computing results to the ground equipment (GEs), our proposed scheme actively considers and capitalizes on this period. By leveraging the SWIPT technique, the UAV can simultaneously transmit energy and the computing results during the downlink period. In this scheme, our objective is to maximize the remaining energy among all GEs by jointly optimizing computing task scheduling, UAV transmit and receive beamforming, BS receive beamforming, GEs' transmit power and power splitting ratio for information decoding, time scheduling, and UAV trajectory. We propose an alternating optimization algorithm that utilizes the semidefinite relaxation (SDR), singular value decomposition (SVD), and fractional programming (FP) methods to effectively solve the nonconvex problem. Numerous experiments validate the effectiveness of the proposed scheme.

This paper introduces a novel multi-user mobile edge computing (MEC) scheme facilitated by the simultaneously transmitting and reflecting reconfigurable intelligent surface (STAR-RIS) and the unmanned aerial vehicle (UAV). Unlike existing MEC approaches, the proposed scheme enables bidirectional offloading, allowing users to concurrently offload tasks to the MEC servers located at the ground base station (BS) and UAV with STAR-RIS support. Specifically, we formulate an optimization problem aiming at maximizing the energy efficiency of the system while ensuring the quality of service (QoS) constraints by jointly optimizing the resource allocation, user scheduling, passive beamforming of the STAR-RIS, and the UAV trajectory. A block coordinate descent (BCD) iterative algorithm designed with the Dinkelbach's algorithm and the successive convex approximation (SCA) technique is proposed to effectively handle the formulated non-convex optimization problem with significant coupling among variables. Simulation results indicate that the proposed STAR-RIS enhanced UAV-enabled MEC scheme possesses significant advantages in enhancing the system energy efficiency over other baseline schemes including the conventional RIS-aided scheme.

Reinforcement learning (RL) problems where the learner attempts to infer an unobserved reward from some feedback variables have been studied in several recent papers. The setting of Interaction-Grounded Learning (IGL) is an example of such feedback-based RL tasks where the learner optimizes the return by inferring latent binary rewards from the interaction with the environment. In the IGL setting, a relevant assumption used in the RL literature is that the feedback variable $Y$ is conditionally independent of the context-action $(X,A)$ given the latent reward $R$. In this work, we propose Variational Information-based IGL (VI-IGL) as an information-theoretic method to enforce the conditional independence assumption in the IGL-based RL problem. The VI-IGL framework learns a reward decoder using an information-based objective based on the conditional mutual information (MI) between $(X,A)$ and $Y$. To estimate and optimize the information-based terms for the continuous random variables in the RL problem, VI-IGL leverages the variational representation of mutual information to obtain a min-max optimization problem. Also, we extend the VI-IGL framework to general $f$-Information measures leading to the generalized $f$-VI-IGL framework for the IGL-based RL problems. We present numerical results on several reinforcement learning settings indicating an improved performance compared to the existing IGL-based RL algorithm.

We study risk-sensitive Reinforcement Learning (RL), where we aim to maximize the Conditional Value at Risk (CVaR) with a fixed risk tolerance $\tau$. Prior theoretical work studying risk-sensitive RL focuses on the tabular Markov Decision Processes (MDPs) setting. To extend CVaR RL to settings where state space is large, function approximation must be deployed. We study CVaR RL in low-rank MDPs with nonlinear function approximation. Low-rank MDPs assume the underlying transition kernel admits a low-rank decomposition, but unlike prior linear models, low-rank MDPs do not assume the feature or state-action representation is known. We propose a novel Upper Confidence Bound (UCB) bonus-driven algorithm to carefully balance the interplay between exploration, exploitation, and representation learning in CVaR RL. We prove that our algorithm achieves a sample complexity of $\tilde{O}\left(\frac{H^7 A^2 d^4}{\tau^2 \epsilon^2}\right)$ to yield an $\epsilon$-optimal CVaR, where $H$ is the length of each episode, $A$ is the capacity of action space, and $d$ is the dimension of representations. Computational-wise, we design a novel discretized Least-Squares Value Iteration (LSVI) algorithm for the CVaR objective as the planning oracle and show that we can find the near-optimal policy in a polynomial running time with a Maximum Likelihood Estimation oracle. To our knowledge, this is the first provably efficient CVaR RL algorithm in low-rank MDPs., Comment: The first three authors contribute equally and are ordered randomly

We describe explicitly the geometric compactifications, obtained by adding slc surfaces $X$ with ample canonical class, of moduli spaces of Burniat surfaces of degrees $K^2=5$, $4$ and $3$.

We study constructive interference based block-level precoding (CI-BLP) in the downlink of multi-user multiple-input single-output (MU-MISO) systems. Specifically, our aim is to extend the analysis on CI-BLP to the case where the considered number of symbol slots is smaller than that of the users. To this end, we mathematically prove the feasibility of using the pseudo-inverse to obtain the optimal CI-BLP precoding matrix in a closed form. Similar to the case when the number of users is small, we show that a quadratic programming (QP) optimization on simplex can be constructed. We also design a low-complexity algorithm based on the alternating direction method of multipliers (ADMM) framework, which can efficiently solve large-scale QP problems. We further analyze the convergence and complexity of the proposed algorithm. Numerical results validate our analysis and the optimality of the proposed algorithm, and further show that the proposed algorithm offers a flexible performance-complexity tradeoff by limiting the maximum number of iterations, which motivates the use of CI-BLP in practical wireless systems.

This paper investigates the utilization of simultaneously transmitting and reflecting RIS (STAR-RIS) in supporting joint physical layer security (PLS) and covert communications (CCs) in a multi-antenna millimeter wave (mmWave) system, where the base station (BS) communicates with both covert and security users while defeating eavesdropping by wardens with the help of a STAR-RIS. Specifically, analytical derivations are performed to obtain the closed-form expression of warden's minimum detection error probability (DEP). Furthermore, the asymptotic result of the minimum DEP and the lower bound of the secure rates are derived, considering the practical assumption that BS only knows the statistical channel state information (CSI) between STAR-RIS and the wardens. Subsequently, an optimization problem is formulated with the aim of maximizing the average sum of the covert rate and the minimum secure rate while ensuring the covert requirement and quality of service (QoS) for legal users by jointly optimizing the active and passive beamformers. Due to the strong coupling among variables, an iterative algorithm based on the alternating strategy and the semi-definite relaxation (SDR) method is proposed to solve the non-convex optimization problem. Simulation results indicate that the performance of the proposed STAR-RIS-assisted scheme greatly surpasses that of the conventional RIS scheme, which validates the superiority of STAR-RIS in simultaneously implementing PLS and CCs.

The offset quadrature phase-shift keying (OQPSK) modulation is a key factor for the technique of ZigBee, which has been adopted in IEEE 802.15.4 for wireless communications of Internet of Things (IoT) and Internet of Vehicles (IoV), etc. In this paper, we propose the general conditions of pulse shaping filters (PSFs) with constant envelope (CE) property for OQPSK modulation, which can be easily leveraged to design the PSFs with CE property. Based on these conditions, we further design an advanced PSF called $\alpha$-half-sine PSF. It is verified that the newly designed $\alpha$-half-sine PSF can not only keep the CE property for OQPSK but also achieve better performance than the traditional PSFs in certain scenarios. Moreover, the $\alpha$-half-sine PSF can be simply adjusted to achieve a flexible performance tradeoff between the transition roll-off speed and out-of-band leakage., Comment: 5 pages, 5 figures, journal paper

Covert communications assisted by simultaneously transmitting and reflecting reconfigurable intelligent surface (STAR-RIS) in non-orthogonal multiple access (NOMA) systems have been explored in this paper. In particular, the access point (AP) transmitter adopts NOMA to serve a downlink covert user and a public user. The minimum detection error probability (DEP) at the warden is derived considering the uncertainty of its background noise, which is used as a covertness constraint. We aim at maximizing the covert rate of the system by jointly optimizing APs transmit power and passive beamforming of STAR-RIS, under the covertness and quality of service (QoS) constraints. An iterative algorithm is proposed to effectively solve the non-convex optimization problem. Simulation results show that the proposed scheme significantly outperforms the conventional RIS-based scheme in ensuring system covert performance., Comment: arXiv admin note: text overlap with arXiv:2305.04930, arXiv:2305.03991

This paper investigates the multi-antenna covert communications assisted by a simultaneously transmitting and reflecting reconfigurable intelligent surface (STAR-RIS). In particular, to shelter the existence of communications between transmitter and receiver from a warden, a friendly full-duplex receiver with two antennas is leveraged to make contributions to confuse the warden. Considering the worst case, the closed-form expression of the minimum detection error probability (DEP) at the warden is derived and utilized as a covert constraint. Then, we formulate an optimization problem maximizing the covert rate of the system under the covertness constraint and quality of service (QoS) constraint with communication outage analysis. To jointly design the active and passive beamforming of the transmitter and STAR-RIS, an iterative algorithm based on globally convergent version of method of moving asymptotes (GCMMA) is proposed to effectively solve the non-convex optimization problem. Simulation results show that the proposed STAR-RIS-assisted scheme highly outperforms the case with conventional RIS.

This paper investigates the multi-antenna covert communications assisted by a simultaneously transmitting and reflecting reconfigurable intelligent surface (STAR-RIS). In particular, to shelter the existence of covert communications between a multi-antenna transmitter and a single-antenna receiver from a warden, a friendly full-duplex receiver with two antennas is leveraged to make contributions where one antenna is responsible for receiving the transmitted signals and the other one transmits the jamming signals with a varying power to confuse the warden. Considering the worst case, the closed-form expression of the minimum detection error probability (DEP) at the warden is derived and utilized in a covert constraint to guarantee the system performance. Then, we formulate an optimization problem maximizing the covert rate of the system under the covertness constraint and quality of service (QoS) constraint with communication outage analysis. To jointly design the active and passive beamforming of the transmitter and STAR-RIS, an iterative algorithm based on semi-definite relaxation (SDR) method and Dinkelbachs algorithm is proposed to effectively solve the non-convex optimization problem. Simulation results show that the proposed STAR-RIS-assisted scheme highly outperforms the case with conventional RIS, which validates the effectiveness of the proposed algorithm as well as the superiority of STAR-RIS in guaranteeing the covertness of wireless communications., Comment: arXiv admin note: substantial text overlap with arXiv:2305.03991

Uncertainty and unknown nonlinearity are often inevitable in the suspension systems, which were often solved using fuzzy logic system (FLS) or neural networks (NNs). However, these methods are restricted by the structural complexity of the controller and the huge computing cost. Meanwhile, the estimation error of such approximators is affected by adopted adaptive laws and learning gains. Thus, in view of the above problem, this paper proposes the approximation-free control based on the bioinspired reference model for a class of uncertain suspension systems with unknown nonlinearity. The proposed method integrates the superior vibration suppression of the bioinspired reference model and the structural advantage of the prescribed performance function (PPF) in approximation-free control. Then, the vibration suppression performance is improved, the calculation burden is relieved, and the transient performance is improved, which is analyzed theoretically in this paper. Finally, the simulation results validate the approach, and the comparisons show the advantages of the proposed control method in terms of good vibration suppression, fast convergence, and less calculation burden.