Millimeter-wave (mmWave) radar, with its high resolution, sensitivity to micro-vibrations, and adaptability to various environmental conditions, holds immense potential across multi-modal fusion sensing. Although there exist review papers on mmWave radar, there is a noticeable lack of comprehensive reviews focusing on its multi-modal fusion sensing capabilities. Addressing this gap, our review offers an extensive exploration of mmWave radar multi-modal fusion sensing, emphasizing its integration with other modalities. This review discusses the complex realm of millimeter-wave radar multi-modal fusion sensing, detailing its importance, hardware and software aspects, principles, and current potential and applications. It delves into data characteristics and datasets associated with mmWave radar, focusing on Doppler, point cloud, and multi-modal data formats. The review highlights how these data types enhance multi-modal fusion sensing and discusses methodologies, including signal processing and learning algorithms. Three categories of multi-modal fusion methodologies are proposed to optimally manage and interpret fused data. Various practical applications of mmWave radar multi-modal fusion sensing are illustrated, underlining the unique capabilities it provides when integrated with other sensors. The review concludes by identifying potential future research avenues, underscoring the immense potential of this field for further exploration and advancement.

Unmanned aerial vehicle (UAV)-assisted mobile edge computing (MEC) has largely extended the border and capacity of artificial intelligence of things (AIoT) by providing a key element for enabling flexible distributed data inputs, computing capacity, and high mobility. To enhance data privacy for AIoT applications, federated learning (FL) is becoming a potential solution to perform training tasks locally on distributed IoT devices. However, with the limited onboard resources and battery capacity of each UAV node, optimization is required to achieve a large-scale and high-precision FL scheme. In this work, an optimized multi-UAV-assisted FL framework is designed, where regular IoT devices are in charge of performing training tasks, and multiple UAVs are leveraged to execute local and global aggregation tasks. An online resource allocation (ORA) algorithm is proposed to minimize the training latency by jointly deciding the selection decisions of clients and a global aggregation server. By leveraging the Lyapunov optimization technique, virtual energy queues are studied to depict the energy deficit. With the help of the actor-critic learning framework, a deep reinforcement learning (DRL) scheme is designed to improve per-round training performance. A deep neural network (DNN)-based actor module is designed to derive client selection decisions, and a critic module is proposed through a conventional optimization method to evaluate the obtained selection decisions. Moreover, a greedy scheme is developed to find the optimal global aggregation server. Finally, extensive simulation results demonstrate that the proposed ORA algorithm can achieve optimal training latency and energy consumption under various system settings.

Human sensing and understanding is a key requirement for many intelligent systems, such as smart monitoring, human-computer interaction, and activity analysis, etc. In this paper, we present mmParse, the first human parsing design for dynamic point cloud from commercial millimeter-wave radar devices. mmParse proposes an end-to-end neural network design that addresses the inherent challenges in parsing mmWave point cloud (e.g., sparsity and specular reflection). First, we design a novel multi-task learning approach, in which an auxiliary task can guide the network to understand human structural features. Secondly, we introduce a multi-task feature fusion method that incorporates both intra-task and inter-task attention to aggregate spatio-temporal features of the subject from a global view. Through extensive experiments in both indoor and outdoor environments, we demonstrate that our proposed system is able to achieve ~ 92% accuracy and ~ 84% IoU accuracy. We also show that the predicted semantic labels can increase the performance of two downstream tasks (pose estimation and action recognition) by ~ 18% and ~ 6% respectively.

In the era of 6G, with compelling visions of intelligent transportation systems and digital twins, remote surveillance is poised to become a ubiquitous practice. Substantial data volume and frequent updates present challenges in wireless networks. To address these challenges, we propose a novel agent-driven generative semantic communication (A-GSC) framework based on reinforcement learning. In contrast to the existing research on semantic communication (SemCom), which mainly focuses on either semantic extraction or semantic sampling, we seamlessly integrate both by jointly considering the intrinsic attributes of source information and the contextual information regarding the task. Notably, the introduction of generative artificial intelligence (GAI) enables the independent design of semantic encoders and decoders. In this work, we develop an agent-assisted semantic encoder with cross-modality capability, which can track the semantic changes, channel condition, to perform adaptive semantic extraction and sampling. Accordingly, we design a semantic decoder with both predictive and generative capabilities, consisting of two tailored modules. Moreover, the effectiveness of the designed models has been verified using the UA-DETRAC dataset, demonstrating the performance gains of the overall A-GSC framework in both energy saving and reconstruction accuracy.

The state-of-the-art datacenter load balancing designs commonly optimize bisection bandwidth with homogeneous switching granularity. Their performances surprisingly degrade under mixed traffic containing both short and long flows. Specifically, the short flows suffer from long-tailed delay, while the throughputs of long flows also degrade dramatically due to low link utilization and packet reordering. To solve these problems, we design a traffic-aware load balancing (TLB) scheme to adaptively adjust the switching granularity of long flows according to the load strength of short ones. Under the heavy load of short flows, the long flows use large switching granularity to help short ones obtain more opportunities in choosing short queues to complete quickly. On the contrary, the long flows reroute flexibly with small switching granularity to achieve high throughput. Furthermore, under extremely bursty scenario, we utilize the packet slicing scheme for long flows to release bandwidth for short ones. The experimental results of NS2 simulation and testbed implementation show that TLB significantly reduces the average flow completion time of short flows by 16%-67% over the state-of-the-art load balancers and achieves the high throughput for long flows. Moreover, for extreme bursty case, at the acceptable throughput degradation of long flows, TLB with packet slicing reduces the deadline missing ratio of bursty short flows by up to 80%.