Superposed IM-OFDM (S-IM-OFDM): An Enhanced OFDM for Integrated Sensing and Communications

Integrated sensing and communications (ISAC) is a critical enabler for emerging 6G applications, and at its core lies in the dual-functional waveform design. While orthogonal frequency division multiplexing (OFDM) has been a popular basic waveform, its primitive version falls short in sensing due to the inherent unregulated auto-correlation properties. Furthermore, the sensitivity to Doppler shift hinders its broader applications in dynamic scenarios. To address these issues, we propose a superposed index-modulated OFDM (S-IM-OFDM). The proposed scheme improves the sensing performance without excess power consumption by translating the energy efficiency of IM-OFDM onto sensing-oriented signals over OFDM. Also, it maintains excellent communication performance in time-varying channels by leveraging the sensed parameters to compensate for Doppler. Compared to conventional OFDM, the proposed S-IM-OFDM waveform exhibits better sensing capabilities and wider applicability in dynamic scenarios. Both theoretical analyses and simulations corroborate its dual benefits.

Beam Pattern Modulation Embedded mmWave Hybrid Transceiver Design Towards ISAC

Integrated Sensing and Communication (ISAC) emerges as a promising technology for B5G/6G, particularly in the millimeter-wave (mmWave) band. However, the widespread adoption of hybrid architecture in mmWave systems compromises multiplexing gain due to limited radio-frequency chains, resulting in mediocre performance when embedding sensing functionality. To avoid sacrificing the spectrum efficiency in hybrid structures while addressing performance bottlenecks in its extension to ISAC, we present an optimized beam pattern modulation-embedded ISAC (BPM-ISAC). BPM-ISAC applies index modulation over beamspace by selectively activating communication beams, aiming to minimize sensing beampattern mean squared error (MSE) under communication MSE constraints through dedicated hybrid transceiver design. Optimization involves the analog part through a min-MSE-based beam selection algorithm, followed by the digital part using an alternating optimization algorithm. Convergence and asymptotic pairwise error probability (APEP) analyses accompany numerical simulations, validating its overall enhanced ISAC performance over existing alternatives.

Integrated Sensing and Communications towards Proactive Beamforming in mmWave V2I via Multi-Modal Feature Fusion (MMFF)

The future of vehicular communication networks relies on mmWave massive multi-input-multi-output antenna arrays for intensive data transfer and massive vehicle access. However, reliable vehicle-to-infrastructure links require narrow beam alignment, which traditionally involves excessive signaling overhead. To address this issue, we propose a novel proactive beamforming scheme that integrates multi-modal sensing and communications via Multi-Modal Feature Fusion Network (MMFF-Net), which is composed of multiple neural network components with distinct functions. Unlike existing methods that rely solely on communication processing, our approach obtains comprehensive environmental features to improve beam alignment accuracy. We verify our scheme on the ViWi dataset, which we enriched with realistic vehicle drifting behavior. Our proposed MMFF-Net achieves more accurate and stable angle prediction, which in turn increases the achievable rates and reduces the communication system outage probability. Even in complex dynamic scenarios, robust prediction results can be guaranteed, demonstrating the feasibility and practicality of the proposed proactive beamforming approach.

Intelligent Multi-Modal Sensing-Communication Integration: Synesthesia of Machines

In the era of sixth-generation (6G) wireless communications, integrated sensing and communications (ISAC) is recognized as a promising solution to upgrading the physical system by endowing wireless communications with sensing capability. Existing ISAC is mainly oriented to static scenarios with radio-frequency sensors being the primary participants, thus lacking a comprehensive environment feature characterization and facing a severe performance bottleneck in dynamic environments. In light of this, we generalize the concept of ISAC by mimicking human synesthesia to support intelligent multi-modal sensing-communication integration. The so-termed Synesthesia of Machines (SoM) is not only oriented to generic scenarios, but also particularly suitable for solving challenges arising from dynamic scenarios. We commence by justifying the necessity and potentials of SoM. Subsequently, we offer the definition of SoM and zoom into the specific operating modes, followed by discussions of the state-of-the-art, corresponding objectives, and challenges. To facilitate SoM research, we overview the prerequisite of SoM research, that is, mixed multi-modal (MMM) datasets, and introduce our work. Built upon the MMM datasets, we introduce the mapping relationships between multi-modal sensing and communications, and discuss how channel modeling can be customized to support the exploration of such relationships. Afterwards, we delve into the current research state and implementing challenges of SoM-enhance-based and SoM-concert-based applications. We first overview the SoM-enhance-based communication system designs and present simulation results related to dual-function waveform and predictive beamforming design. Afterwards, we review the recent advances of SoM-concert for single-agent and multi-agent environment sensing. Finally, we propose some open issues and potential directions.

Scalable Bayesian Meta-Learning through Generalized Implicit Gradients

Meta-learning owns unique effectiveness and swiftness in tackling emerging tasks with limited data. Its broad applicability is revealed by viewing it as a bi-level optimization problem. The resultant algorithmic viewpoint however, faces scalability issues when the inner-level optimization relies on gradient-based iterations. Implicit differentiation has been considered to alleviate this challenge, but it is restricted to an isotropic Gaussian prior, and only favors deterministic meta-learning approaches. This work markedly mitigates the scalability bottleneck by cross-fertilizing the benefits of implicit differentiation to probabilistic Bayesian meta-learning. The novel implicit Bayesian meta-learning (iBaML) method not only broadens the scope of learnable priors, but also quantifies the associated uncertainty. Furthermore, the ultimate complexity is well controlled regardless of the inner-level optimization trajectory. Analytical error bounds are established to demonstrate the precision and efficiency of the generalized implicit gradient over the explicit one. Extensive numerical tests are also carried out to empirically validate the performance of the proposed method.