Millimeter-wave (mmWave) technology has been extensively studied for indoor short-range communications. In such fixed network applications, the emerging FTTR architecture allows mmWave technology to be well cascaded with in-room optical network terminals, supporting high-speed communication at rates over tens of Gbit/s. In this Fiber-to-the-Room (FTTR)-mmWave system, the severe signal attenuation over distance and high penetration loss through room walls are no longer bottlenecks for practical mmWave deployment. Instead, these properties create high spatial isolation, which prevents mutual interference between data streams and ensures information security. This paper surveys the promising integration of FTTR and mmWave access for next-generation indoor high-speed communications, with a particular focus on the Ultra-Converged Access Network (U-CAN) architecture. It is structured in two main parts: it first traces this new FTTR-mmWave architecture from the perspective of Wi-Fi and mmWave communication evolution, and then focuses specifically on the development of key mmWave chipsets for FTTR-mmWave Wi-Fi applications. This work aims to provide a comprehensive reference for researchers working toward immersive, untethered indoor wireless experiences for users.
The increasing demand for high throughput and low latency in Wi-Fi 7 necessitates a robust receiver design. Traditional receiver architectures, which rely on a cascade of complex, independent signal processing modules, often face performance bottlenecks. Rather than focusing on semantic-level tasks or simplified Additive White Gaussian Noise (AWGN) channels, this paper investigates a bit-level end-to-end receiver for a practical Wi-Fi 7 Multiple-Input Multiple-Output Orthogonal Frequency Division Multiplexing (MIMO-OFDM) physical layer. A lightweight Transformer-based encoder-only architecture is proposed to directly map synchronized OFDM signals to decoded bitstreams, replacing the conventional channel estimation, equalization, and data detection. By leveraging the multi-head self-attention mechanism of the Transformer encoder, our model effectively captures long-range spatial–temporal dependencies across antennas and subcarriers, thus learning to compensate for channel distortions without explicit channel state information. This mechanism eliminates the need for explicit channel estimation, enabling the direct extraction of crucial channel and signal features. Experimental results validate the efficacy of the proposed design, demonstrating the significant potential of deep learning for future wireless receiver architectures.
As Fiber-to-the-Room (FTTR) networks proliferate, multi-device deployments pose significant energy consumption challenges. This paper proposes a Quality of Service (QoS)-aware energy-saving scheme based on a multi-threshold buffer energy saving (MBES) scheme to reduce consumption while ensuring energy QoS. MBES leverages the centralized control of the main fiber unit (MFU) and the wireless-state awareness of subordinate fiber units (SFUs) for synergistic fiber-wireless energy savings. The scheme assigns independent, dynamic buffer thresholds to service queues on SFUs, enabling low-latency reporting for high-priority traffic while accumulating low-priority data to extend sleep cycles. At the MFU, a coordinated scheduling algorithm accounts for Wi-Fi access delay and creates an adaptive closed-loop control by adjusting SFUs’ buffer thresholds based on end-to-end delay feedback. Simulation results show that, while satisfying strict latency requirements, MBES achieves a maximum energy saving of 17.75% compared with the no energy saving (NES) scheme and provides a superior trade-off between latency control and energy efficiency compared with the single-threshold buffer energy saving (SBES) scheme.
Open-set object detectors, as exemplified by Grounding DINO, have attracted significant attention due to their remarkable performance on in-domain datasets like Common Objects in Context (COCO) after only few-shot fine-tuning. However, their generalization capabilities in cross-domain scenarios remain substantially inferior to their in-domain few-shot performance. Prior work on fine-tuning Grounding DINO for cross-domain few-shot object detection has primarily focused on data augmentation, leaving broader systemic optimizations unexplored. To bridge this gap, we propose a comprehensive end-to-end fine-tuning framework specifically designed to optimize Grounding DINO for cross-domain few-shot scenarios. In addition, we propose Mixture-of-Experts (MoE)-Grounding DINO, a novel architecture that integrates the MoE architecture to enhance adaptability in cross-domain settings. Our approach demonstrates a significant 15.4 Mean Average Precision (mAP) improvement over the Grounding DINO baseline on the Roboflow20-VL benchmark, establishing a new state of the art for cross-domain few-shot object detection (CD-FSOD). The source code and models will be made available upon publication.
