Unmanned aerial vehicles (UAVs) in wireless communication systems offer rapid deployment, flexible reconfiguration, and superior communication channels, thanks to their short-range line-of-sight links, making them more efficient and cost-effective than terrestrial or high-altitude platform networks. As a promising technique, integrated sensing and communication (ISAC) enhances UAV networks by integrating sensing and communication functionalities. This concurrent design improves spectrum efficiency and reduces hardware costs. To ensure reliable communication for their high mobility, a novel modulation technique, the orthogonal time-frequency space (OTFS) waveform, has been proposed, which leverages the delay-Doppler domain for efficient information transmission. In this paper, we investigate ISAC-based multi-target sensing with distributed OTFS radars to deliver reliable performance for UAV networks. To achieve that, we propose to leverage the delay and Doppler information featured by OTFS signals to determine the range and radial velocity, accomplishing successful sensing tasks. Moreover, to address the challenge of unassociated measurements and targets, we propose a novel optimization framework to concurrently perform data association and target sensing tasks. This framework is developed by formulating a mixed-integer optimization problem, which is then solved with polynomial complexity through convex approximation. Additionally, we propose an iterative maximum likelihood estimator (MLE) to further enhance sensing performance by accounting for target-measurement errors. Extensive simulation results verify the superiority of our proposed work to state-of-the-art methods.

Wireless sensing enables contactless and accurate recognition of human activities and physiological states by using electromagnetic signals. As a promising enabler for sixth-generation (6 G) multi-functional networks, orthogonal time frequency space (OTFS) modulation exhibits strong resilience to high Doppler shifts in high-mobility environments, while also supporting precise human sensing in low-mobility scenarios. In this work, we propose a novel two-dimensional (2D) delay-Doppler motion profiling framework based on the OTFS waveform to extract distinctive features of human activities. To enhance recognition performance, a fractional-Doppler enhancement network is integrated with a convolutional neural network (CNN)-aided encoder-only Transformer architecture. Extensive experiments are conducted to assess the cross-domain generalization capability of the proposed OTFSensi system. Compared with existing classification models based on CNN, gated recurrent unit (GRU), and long short-term memory (LSTM) networks, OTFSensi demonstrates substantial improvements in adaptability across diverse environments and observation angles. Furthermore, a comparative analysis with various radio frequency (RF) sensing technologies confirms the superior classification performance achieved by OTFSensi.

In this paper, we propose to exploit movable elements of intelligent reflecting surface (IRS) to enhance the overall performance of integrated sensing and communication (ISAC) systems. Firstly, focusing on a single-user scenario, we reveal the function of movable elements by performance analysis, and then design a joint beamforming and element position optimization scheme. Further, we extend it to a general multi-user scenario, and also propose an element position optimization scheme according to the derived performance expressions. Finally, simulation results confirm that the movement of IRS elements can improve the communication rate and the sensing accuracy, and especially broaden the coverage of ISAC.

Pose-based isolated sign language recognition (ISLR) demonstrates strong resilience to background noise and maintains computational efficiency. Existing methods typically use raw pose data, which are sensitive to camera angles and positioning, leading to reduced recognition accuracy. Additionally, they often fail to track inter-frame trajectories essential for accurate sign interpretation. To address these limitations, we propose an end-to-end ISLR framework incorporating sign language semantic mapping and CorrFormer: the proposed sign language semantic mapping leverages intra-frame spatial invariance of human pose, enhancing stability, while CorrFormer tracks adjacent temporal inter-frame trajectories for more precise recognition. We evaluated our framework on WLASL and AUTSL, with results and ablation studies confirming its efficacy, achieving Top-1 accuracies of 67.72%, 47.51 %, and 85.74% on WLASL100, WLASL300, and AUTSL, respectively.

To further enhance the performance of intelligent reflecting surface (IRS) aided integrated sensing and communication (ISAC) system, the conception of movable reflection elements is introduced to IRS. We derive the performance bound of both communication and sensing in the context of movable reflection elements, and then design a joint transmit beamforming and element position design algorithm. Numerical results demonstrate that the proposed algorithm, using angular information, aligns with the performance of both communicationonly systems and sensing-only systems with perfect channel state information (CSI).

In practical intelligent reflecting surface (IRS)-assisted multiuser communication systems, inevitable imperfections such as hardware impairments, imperfect channel state information (CSI), and the limited resolution of the IRS phase shifts would introduce interference and thus cause significant performance degradation. As an interference management strategy, rate-splitting multiple access (RSMA) employs the rate-splitting (RS) principle to partition user information into common and private parts, thereby offering enhanced robustness. Accounting for practical imperfections, this study investigates robust beamforming design in IRS-assisted multiuser systems under the RSMA architecture. First, we introduce a system model that captures these non-ideal factors and evaluate their impacts on communication performance. To enhance the performance of the considered system, a weighted sum rate maximization problem is formulated, for which a sample average approximation (SAA)-based robust algorithm is proposed to jointly optimize the IRS phase shifts and the beamforming matrix at the base station (BS). Simulation results demonstrate that the IRS-assisted RSMA system exhibits superior robustness compared to the IRS-assisted space division multiple access (SDMA) system in the presence of inevitable imperfections. Furthermore, the proposed SAA-based robust algorithm outperforms existing benchmark algorithms, highlighting its effectiveness and robustness.

In symbiotic radio (SR), the secondary system not only shares the spectrum and power of the primary system but also enhances its performance by providing multipath gains, fostering a cooperative mutualism between the two systems. However, in high-mobility channels, time-frequency selective fading presents significant challenges for reliable SR communications. The recently introduced orthogonal time-frequency space (OTFS) technique, which processes signals in the delay-Doppler (DD) domain, is expected to improve SR communication performance in high-speed mobile scenarios. In this paper, we propose embedding primary information symbols in the DD domain using amplitude-phase modulation, while employing a combinatorial frequency (CF) modulation strategy for secondary information transmission. To obtain channel state information (CSI) and detect secondary symbols, for some special scenarios, we propose an off-grid sparse Bayesian learning (SBL)-based method. This method first estimates the equivalent CSI and then detects the symbols by leveraging the highly structured Doppler shifts. For more general scenarios, we introduce a model-driven equivalent CSI estimation-net (ECSIEst-Net) and a data-driven secondary symbol detection-Net (SSymDet-Net). Numerical results are provided to guide parameter selection and demonstrate the effectiveness of the proposed methods.

This paper investigates an intelligent reflecting surface (IRS) assisted integrated sensing and communication (ISAC) with high-mobility systems, where the orthogonal time frequency space (OTFS) modulation is employed to leverage the Delay- Doppler (DD) spread. We propose a subspace-based beamforming design algorithm, which optimizes the phase shifts at the IRS and the combining vector at the base station (BS) to enhance the communication performance subject to the constraint on the sensing accuracy. Moreover, we derived closed-form solutions for the optimization problems. Numerical results affirm the effectiveness of our proposed beamforming design algorithm in high-mobility scenarios.

In this paper, we investigate integrated sensing and communication (ISAC) in high-mobility systems with the aid of an intelligent reflecting surface (IRS). To exploit the benefits of Delay-Doppler (DD) spread caused by high mobility, orthogonal time frequency space (OTFS)-based frame structure and transmission framework are proposed. In such a framework, we first design a low-complexity ratio-based sensing algorithm for estimating the velocity of mobile user. Then, we analyze the performance of sensing and communication in terms of achievable mean square error (MSE) and achievable rate, respectively, and reveal the impact of key parameters. Next, with the derived performance expressions, we jointly optimize the phase shift matrix of IRS and the receive combining vector at the base station (BS) to improve the overall performance of integrated sensing and communication. Finally, extensive simulation results confirm the effectiveness of the proposed algorithms in high-mobility systems.

This paper presents a study on the integrated sensing and communication (ISAC) for symbiotic radio systems in mobile scenarios, where the receiver shared by the primary and secondary systems is capable of locating the moving object of secondary systems via estimating the direction of arrival (DoA), and detecting the symbols for both systems. For the considered symbiotic radio systems, the strong interference from the primary systems and moving object pose significant challenges for location sensing. Moreover, the symbol detection for symbiotic radio systems typically relies on channel state information (CSI), which would reduce the spectrum efficiency substantially. To address these challenges, this paper proposes a low-complexity and high-accuracy two-dimension DoA estimator based on the ratio of dual points on the main lobe using a single snapshot. The theoretical performance in terms of effective sensing probability and mean square error for the proposed estimator are provided in closed-form. To eliminate the dependence of symbol detection on CSI, we propose to fully capture the characteristic of the ISAC systems and introduce two types of DoA-assisted detectors to detect both the primary and secondary symbols. Specifically, the reciprocal linear detectors that use heuristic linear combiners and iterative detection to achieve mutual benefit for both transmissions, as well as a Manchester detector that can separate the primary and secondary signals, are proposed. Finally, the numerical results are provided to validate the correctness of the theoretical analysis, and demonstrate the superiority of the proposed DoA estimator and DoA-assisted detectors.

Orthogonal time frequency space (OTFS) modulation is a promising technique for the next-generation communications in high-mobility scenarios. However, in the delay-Doppler (DD) domain, the received signals suffer from a decrease in power due to the non-coherent superposition of all symbols transmitted through wireless channels. To address this issue, this paper proposes the incorporation of an intelligent reflecting surface (IRS) to assist the transmission for the OTFS systems, and jointly designs the OTFS frame structure and IRS phase shifts to achieve a coherent combination of the received signals. To address the problem of outdated channel state information in high-mobility systems, we propose a location-aided channel estimation strategy at the IRS. Additionally, to mitigate the adverse effects of the fractional Doppler shifts, a delay and shifted-Doppler domain-based channel estimation method is designed at the base station. By utilizing the well-designed OTFS frame structure and IRS phase shifts, we propose a low-complexity iterative interference cancellation (IIC) detector, and analyze the lower bound for its symbol error probability. To provide a clear understanding for the process of the considered systems, we describe a two-stage transmission protocol. Finally, the numerical results are provided to evaluate the effectiveness and superiority of the proposed estimation methods and IIC detector.

Symbiotic radio (SR) offers potential benefits for large-scale Internet of Things networks through its spectrum and resource-sharing capabilities between primary and secondary systems. However, in high-mobility scenarios, rapidly changing channels present significant challenges for reliable and low-latency SR communications. In response to this challenge, this paper proposes employing orthogonal time-frequency space (OTFS) modulation for SR systems in hostile environments. By processing signals in the delay-Doppler (DD) domain, the fast-varying channels can be efficiently characterized using a few quasi-static DD domain parameters. To enable joint primary and secondary transmissions in SR, we propose modulating primary information using phase-shift keying modulation, while the secondary information is modulated into the frequencies of the periodic rectangular wave. However, the time-varying secondary signals are intertwined with unknown DD-domain channels, making it difficult to differentiate between secondary information and the original channel state information (CSI). To overcome this challenge, we introduce coherent detection methods for secondary information using both amplitude-based and sparsity-based techniques, leveraging the spectral characteristics of signal combinations with different frequencies. Moreover, recognizing that the periodic rectangular wave of the secondary transmission would reshape the Doppler profile of the equivalent channel in a highly structured manner, we propose an off-grid structured-sparse Bayesian learning-based CSI estimator. With the obtained equivalent CSI, we propose a low-complexity symbol-wise detection algorithm for detecting primary information, leveraging the pilot guards and interference cancellation technique. Finally, numerical results validate the effectiveness and superiority of the proposed estimator and detector.

This article considers a novel intelligent reflecting surface (IRS)-assisted cell-free symbiotic radio (SR) system, where an IRS is employed to facilitate the transmission from all the base stations (BSs) to the primary receivers (PRs), i.e., the primary transmission, and simultaneously, to send messages to the Internet of Things (IoT) devices, i.e., the secondary transmission. We aim to minimize the bit error rate (BER) of the IRS symbols by jointly optimizing the transmit beamformers at all the BSs and the IRS phase shifts. To solve this nonconvex problem, we propose a plenty-based algorithm by using the multidimensional complex quadratic transform (MCQT) and the consensus alternative direction method of multipliers (ADMMs) methods, and obtain a high-performance solution. Besides, the convergence of the proposed algorithm is analyzed. Simulation results demonstrate the effectiveness of the proposed algorithm and show that the proposed algorithm outperforms two baselines under different setups.

Intelligent reflecting surface (IRS) is a newly emerged technology that can increase the energy and spectral efficiency of wireless communication systems. This paper considers an IRS-aided multi-user multiple-input single-output (MISO) communication system, and presents a detailed analysis and optimization framework for the weighted sum-rate (WSR) of the downlink transmission over Rician fading channels. Unlike most of the prior works where the active beamformer at the base station (BS) and passive beamformer at the IRS are jointly designed based on the instantaneous channel state information (CSI), this paper proposes a low-complexity transmission protocol where the IRS passive beamforming and BS power allocation coefficient vector are optimized in the large timescale based on the statistical CSI, and the BS transmit beamforming is designed in the small timescale based on only the instantaneous CSI of the effective BS-user channels. Therefore, the channel training overhead in each channel coherence interval under our proposed protocol is independent of the number of IRS reflecting elements, which is in sharp contrast to most of the prior works. By considering maximum-ratio transmit beamforming at the BS, we derive a lower bound of the ergodic WSR in closed-form. Then, we propose an efficient algorithm to jointly optimize the IRS passive beamforming and BS power allocation coefficient vector for maximizing the ergodic WSR lower bound. Numerical results validate the tightness of our derived WSR bound and show that the proposed scheme outperforms various existing schemes in terms of complexity or capacity performance.

Decision response and feedback in gambling are interrelated. Different decisions lead to different ranges of feedback, which in turn influences subsequent decisions. However, the mechanism underlying the continuous decision-feedback process is still left unveiled. To fulfill this gap, we applied the hidden Markov model (HMM) to the gambling electroencephalogram (EEG) data to characterize the dynamics of this process. Furthermore, we explored the differences between distinct decision responses (i.e. choose large or small bets) or distinct feedback (i.e. win or loss outcomes) in corresponding phases. We demonstrated that the processing stages in decision-feedback process including strategy adjustment and visual information processing can be characterized by distinct brain networks. Moreover, time-varying networks showed, after decision response, large bet recruited more resources from right frontal and right center cortices while small bet was more related to the activation of the left frontal lobe. Concerning feedback, networks of win feedback showed a strong right frontal and right center pattern, while an information flow originating from the left frontal lobe to the middle frontal lobe was observed in loss feedback. Taken together, these findings shed light on general principles of natural decision-feedback and may contribute to the design of biologically inspired, participant-independent decision-feedback systems.

This article addresses the issue of symbol detection in ambient backscatter communication systems with the multiantenna reader. Focusing on the ON-OFF keying modulation, the optimal detector minimizing the bit error rate (BER) is devised based on the maximum a posteriori principle. We then analyze the exact closed-form BER expression for the optimal detector. Moreover, simple approximate BER expressions are derived in certain asymptotic regimes. Furthermore, a simple energy detector is analyzed as a benchmark scheme, and the asymptotic BER is devised in a closed form. The findings of this article suggest that implementing multiple antennas at the reader is an effective means to enhance the BER performance and extend the tag-reader communication range. Also, the optimal detector always outperforms the energy detector. In particular, the optimal detector can avoid the error floor phenomenon, which is inevitable for the energy detector in the high SNR regime.

Ambient backscatter communication is a newly emerged ultra-low-power technology for the internet of things network. In this paper, we study the symbol detection of multi-antenna ambient backscatter communication system. In particular, the maximum-eigenvalue detector is derived from general likelihood ratio test, and the approximative BER expressions are characterized. The analytical results show that the BER of the proposed detector decreases with sampling rate N, signal-to-noise ratio γ and number of receiving antenna M, however, settles in high γ or M regime. In addition, we find the proposed detector eliminates the knowledge of noise power, which is of uncertainty and difficult to estimate. Then the simulation results validate the correctness of the theoretical analysis, and show that the proposed detector outperforms the existing energy detector in terms of BER performance.

The ambient backscatter communication is a newly rising paradigm for the Internet-of-Things networks, which enables the connection of low-cost devices. This paper proposes a novel MFSK modulation for the Tag of ambient backscatter communications systems, and the corresponding detectors are designed depending on the capability of the Reader. In the case the Reader is not capable of removing the direct interference from the ambient source, a maximum likelihood detector is proposed. In another case, leveraging on the frequency shift feature of the MFSK modulation, the Reader can remove the direct interference. Then, a simple energy detector is proposed, and the closed-form expressions for the symbol error rate (SER) and outage probability of the system are derived. The findings of this paper suggest that the proposed MFSK modulation outperforms the popular ON-OFF keying modulation, and the impact of modulation order on the SER performance depends heavily on the operating bit signal to noise ratio. Moreover, it is shown that it is desirable to place the Tag close to the Reader in terms of minimizing the outage probability.

This paper deals with symbol detection in ambient backscatter communication systems with multi-antenna reader. Unlike most of the existing works which assume deterministic ambient radio frequency (RF) signals, we consider another important scenario with complex Gaussian RF signals. Focusing on the on-off keying modulation, the optimal detector minimizing the bit error rate (BER) is devised based on the maximum a posteriori principle, and an exact closed-form expression for the BER is derived. In addition, a simple energy detector is proposed to serve as a performance benchmark. Simulation results show that, implementing multiple antennas at the reader is an effective means to enhance the BER performance. Also, the proposed optimal detector always outperforms the energy detector. Furthermore, unlike the energy detector, whose BER settles in the high signal to noise ratio regime, no error floor exists for the proposed optimal detector.

Ambient backscatter communication is a newly emerged paradigm, which utilizes the ambient radio frequency signal as the carrier to reduce the system battery requirement, and is regarded as a promising solution for enabling large-scale deployment of future Internet of Things networks. The key issue of ambient backscatter communication systems is how to perform reliable detection. In this paper, we propose novel encoding methods at the information tag and devise the corresponding symbol detection methods at the reader. In particular, Manchester coding and differential Manchester coding are adopted at the information tag, and the corresponding semi-coherent Manchester (SeCoMC) and non-coherent Manchester (NoCoMC) detectors are developed. In addition, analytical bit-error-rate (BER) expressions are characterized for both detectors assuming either complex Gaussian or unknown deterministic ambient signal. Simulation results show that the BER performance of unknown deterministic ambient signal is better, and the SeCoMC detector outperforms the NoCoMC detector. Finally, compared with the prior detectors for ambient backscatter communications, the proposed detectors have the advantages of achieving superior BER performance with lower communication delay.