Vehicular communications in future sixth-generation (6G) networks are expected to leverage integrated sensing and communications (ISACs) and mobile edge computing (MEC) techniques. However, the rapid proliferation of vehicle user equipment (V-UE) and the diversity of ISAC-aided and MEC-empowered vehicular communication and computation services demand a more intelligent and efficient resource allocation framework for the next-generation vehicular networks. To address this issue, we propose a comprehensive ISAC-aided vehicle-to-everything (V2X) MEC framework, where the V-UEs can offload their tasks to the edge server collocated at the roadside unit (RSU). We aim to minimize the long-term average total service delay of all the V-UEs by jointly optimizing the offloading decisions of all the V-UEs, the computation resource allocation at the ISAC-aided RSU, the transmission power, and the allocation of resource blocks for all the V-UEs, where the total service delay of a V-UE includes the task processing delay and the transmission delay if the V-UE offloads its task to the RSU. To solve the formulated mixed integer nonlinear programming problem, we design a multiagent deep deterministic policy gradient (MADDPG)-based offloading optimization and resource allocation algorithm (MADDPG-O2RA2). Simulation results demonstrate that our proposed algorithm outperforms the benchmarks in terms of convergence and the long-term average delay among all the V-UEs.

The emergence of 6th generation (6G) mobile networks brings new challenges in supporting high-mobility communications, particularly in addressing the issue of channel aging. While existing channel prediction methods offer improved accuracy at the expense of increased computational complexity, limiting their practical application in mobile networks. To address these challenges, we present LinFormer, an innovative channel prediction framework based on a scalable, all-linear, encoder-only Transformer model. Our approach, inspired by natural language processing (NLP) models such as BERT, adapts an encoder-only architecture specifically for channel prediction tasks. We propose replacing the computationally intensive attention mechanism commonly used in Transformers with a time-aware multi-layer perceptron (TMLP), significantly reducing computational demands. The inherent time awareness of TMLP module makes it particularly suitable for channel prediction tasks. We enhance LinFormer’s training process by employing a weighted mean squared error loss (WMSELoss) function and data augmentation techniques, leveraging larger, readily available communication datasets. Our approach achieves a substantial reduction in computational complexity while maintaining high prediction accuracy, making it more suitable for deployment in cost-effective base stations (BS). Comprehensive experiments using both simulated and measured data demonstrate that LinFormer outperforms existing methods across various mobility scenarios, offering a promising solution for future wireless communication systems.

Localization has become a prominent use case for 6G mobile networks, and the integrated localization and communication (ILAC) system represents an inevitable trend. To design and manage such ILAC systems effectively and efficiently, quantification of its performance bound, i.e., an analytical model that reveals the trade-off between communication and localization performance, is a crucial yet unresolved task. To address this, we proposed an analytical framework for an ILAC system that achieves communication and localization. Specifically, we derived a closed-form expression of the capacity loss versus localization Cramer-Rao lower bound (CRB) loss through time-domain and frequency-domain resource allocation. Through simulations, we validated our analytical model and observed that frequencydomain resource allocation is preferable in scenarios with fewer antennas at the next generation nodeB (gNB) and a larger distance between user equipment (UE) and gNB. Conversely, time-domain resource allocation is preferable in scenarios with more antennas and a smaller distance between UE and the gNB

Wireless positioning technologies hold significant value for applications in autonomous driving, extended reality (XR), unmanned aerial vehicles (UAVs), and more. With the advancement of artificial intelligence (AI), leveraging AI to enhance positioning accuracy and robustness has emerged as a field full of potential. Driven by the requirements and functionalities defined in the 3rd Generation Partnership Project (3GPP) standards, AI/machine learning (ML)-based cellular positioning is becoming a key technology to overcome the limitations of traditional methods. This paper presents a comprehensive survey of AI-driven cellular positioning. We begin by reviewing the fundamentals of wireless positioning and AI models, analyzing their respective challenges and synergies. We provide a comprehensive review of the evolution of 3GPP positioning standards, with a focus on the integration of AI/ML in current and upcoming standard releases. Guided by the 3GPP-defined taxonomy, we categorize and summarize state-of-the-art (SOTA) research into two major classes: AI/ML-assisted positioning and direct AI/ML-based positioning. The former includes line-of-sight (LOS)/non-line-of-sight (NLOS) detection, time of arrival (TOA)/time difference of arrival (TDOA) estimation, and angle prediction; the latter encompasses fingerprinting, knowledge-assisted learning, and channel charting. Furthermore, we review representative public datasets and conduct performance evaluations of AI-based positioning algorithms using these datasets. Finally, we conclude by summarizing the challenges and opportunities of AI-driven wireless positioning.

Deploying the 5 G new radio network on the unlicensed mmWave band (NR-U) is a promising solution to meet the constantly increasing traffic demand of the cellular network. However, the coexistence issue on the unlicensed mmWave spectrum between NR-U and Wireless Gigabit (WiGig) networks becomes a significant concern. The coexistence schemes between LTE and Wi-Fi networks cannot be readily applied to support the coexistence between NR-U and WiGig networks due to the directional beamforming and propagation characteristic of the mmWave band. The directional transmission complicates the coexistence problem but opens up possibilities for increased system capacity via concurrent multi-user MIMO (MU-MIMO) transmission at the expense of interference management. To this end, we consider an NR-U and WiGig coexisting scenario and aim to maximize the total NR-U throughput on the unlicensed spectrum via resource allocation and inter-beam interference mitigation. The optimization problem is formulated into an integer nonlinear programming (INLP) problem and is further transformed into a many-to-one matching game for tractability. We propose a matching-based concurrent algorithm (MBCA) to solve it and the inter-beam interference is mitigated via the forbidden pairs (FPs). In comparison to the exhaustive search-based algorithm (ESA), simulation results show that FP effectively mitigates inter-beam interference and achieves near-optimal performance. The proposed algorithm also outperforms the state-of-art algorithms in existing research.

Orthogonal time frequency space (OTFS) modulation is expected to address the performance degradation of orthogonal frequency division multiplexing (OFDM) modulated signals, particularly due to issues like Doppler shifts in mobile communication environments. In this article, we propose a lightweight deep learning-based framework for end-to-end joint channel estimation and data detection (JCEDD) in an OTFS communication system. To fully exploit the characteristics of OTFS modulation, we introduce a data padding preprocessing method and a slicing data augmentation technique. Furthermore, the performance of the proposed deep learning-based framework could be enhanced dramatically with only a small overhead compared to the superimposed pilot scheme. Ablation experiments demonstrate that the proposed data padding preprocessing method and the slicing data augmentation technique significantly improve the performance of the deep learning-based framework. Simulation results show that the proposed framework outperforms existing algorithms in terms of JCEDD performance, while maintaining a relatively low level of computational complexity.