With the unprecedented development of smart vehicles and roadside units equipped with wireless connectivity, the transportation system is undergoing revolutionary changes in the past decade or two. Bearing safety and efficiency as the utmost objectives, the vehicular environments are witnessing explosive increase of various sensors onboard vehicles and equipped at transportation infrastructures. On the one hand, these sensors are destined to be wirelessly connected to provide more comprehensive situational awareness for transportation purposes. On the other hand, the abundance of sensor data of the environment can potentially shed light on the channel propagation characteristics that lie at the core of any communications system design. The integrated sensing and communications (ISACs) is henceforth both necessary and natural in vehicular communications networks (VCN). Different from existing ISAC works that target generic environments but are limited to dual-function radar-communications (DFRC), in this article we focus on transportation scenarios and applications but take a wholistic view of ISAC possibilities. First, we argue that, even though many sensors in transportation settings are nonradio-frequency (RF)-based, functional ISAC (fISAC) is feasible and necessary, in both communication-centric (CC) or sensing-centric (SC) modes. To facilitate this, the concept of synesthesia is introduced to ISAC to accommodate “machine senses” in the RF and non-RF formats. We then zoom in to RF-based sensors and propose the so-termed signaling ISAC (sISAC), with either unified-hardware (UH) or separate-hardware (SH) platforms, and delineate the unique issues arising in transportation settings. Several transportation-specific case studies are included to demonstrate these various ISAC regimes. Toward the end, the relationships of these ISAC subcategories are discussed with a roadmap laid out.

The low power wide area network (LPWAN) technologies, which is now embracing a booming era with the development in the Internet of things (IoT), may offer a brand new solution for current smart grid communications due to their excellent features of low power, long range, and high capacity. The mission-critical smart grid communications require secure and reliable connections between the utilities and the devices with high quality of service (QoS). This is difficult to achieve for unlicensed LPWAN technologies due to the crowded license-free band. Narrowband IoT (NB-IoT), as a licensed LPWAN technology, is developed based on the existing LTE specifications and facilities. Thus, it is able to provide cellular-level QoS, and henceforth can be viewed as a promising candidate for smart grid communications. In this paper, we introduce NB-IoT to the smart grid and compare it with the existing representative communication technologies in the context of smart grid communications in terms of data rate, latency, and range, etc. The overall requirements of communications in the smart grid from both quantitative and qualitative perspectives are comprehensively investigated and each of them is carefully examined for NB-IoT. We further explore the representative applications in the smart grid and analyze the corresponding feasibility of NB-IoT. Moreover, the performance of NB-IoT in typical scenarios of the smart grid communication environments, such as urban and rural areas, is carefully evaluated via Monte Carlo simulations.

In the era of sixth-generation (6G) wireless communications, integrated sensing and communications (ISAC) is recognized as a promising solution to upgrade the physical system by endowing wireless communications with sensing capability. Existing ISAC is mainly oriented to static scenarios with radio-frequency (RF) sensors being the primary participants, thus lacking a comprehensive environment feature characterization and facing a severe performance bottleneck in dynamic environments. To date, extensive surveys on ISAC have been conducted but are limited to summarizing RF-based radar sensing. Currently, some research efforts have been devoted to exploring multi-modal sensing-communication integration but still lack a comprehensive review. To fill the gap, we embark on an initial endeavor with the goal of establishing a unified framework of intelligent multi-modal sensing-communication integration by generalizing the concept of ISAC and providing a comprehensive review under this framework. Inspired by the human synesthesia, the so-termed Synesthesia of Machines (SoM) gives the clearest cognition of such an intelligent integration and details its paradigm for the first time. We commence by justifying the necessity and potential of the new paradigm. Subsequently, we offer a rigorous definition of SoM and zoom into the detailed paradigm, which is summarized as three operational modes realizing the integration. 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. Afterward, aiming at giving a comprehensive survey on the current research status of multi-modal sensing-communication integration, we cover the technological review on SoM-enhance-based and SoM-concert-based applications in transceiver design and environment sensing. To corroborate the rationality and superiority of SoM, we also present simulation results related to dual-function waveform and predictive beamforming design tailored for dynamic scenarios. Finally, we propose some open issues and potential directions to inspire future research efforts on SoM.

Spatial modulation (SM) is a newly emerging multiple-input-multiple-output technique that activates only a single antenna for transmission at any time instant and uses the index of the active antenna as an additional information-carrying mechanism. However, by its nature, SM decoding is coherent in that channel state information (CSI) is required at the receiver. In fact, coherent SM decoding can be very complex due to the heavily entangled channel estimation and symbol detection. In this correspondence, a differential SM scheme that completely bypasses any CSI at the transmitter or receiver, while preserving the single active transmit antenna property, is developed. The proposed scheme can be applied to any constant energy constellation such as phase-shift keying (PSK) and to systems with arbitrary numbers of transmit and receive antennas. Simulation results are presented under various system configurations. With the same spectral efficiency, the proposed scheme is capable of paying no more than 3 dB of signal-to-noise ratio penalty compared with coherent SM and outperforming the single-antenna differential PSK and differential space-time coding schemes.

In this paper, we investigate flexible power transfer among electric vehicles (EVs) from a cooperative perspective in an EV system. First, the concept of cooperative EV-to-EV (V2V) charging is introduced, which enables active cooperation via charging/discharging operations between EVs as energy consumers and EVs as energy providers. Then, based on the cooperative V2V charging concept, a flexible energy management protocol with different V2V matching algorithms is proposed, which can help the EVs achieve more flexible and smarter charging/discharging behaviors. In the proposed energy management protocol, we define the utilities of the EVs based on the cost and profit through cooperative V2V charging and employ the bipartite graph to model the charging/discharging cooperation between EVs as energy consumers and EVs as energy providers. Based on the constructed bipartite graph, a max-weight V2V matching algorithm is proposed in order to optimize the network social welfare. Moreover, taking individual rationality into consideration, we further introduce the stable matching concepts and propose two stable V2V matching algorithms, which can yield the EV-consumer-optimal and EV-provider-optimal stable V2V matchings, respectively. Simulation results verify the efficiency of our proposed cooperative V2V charging-based energy management protocol in improving the EV utilities and the network social welfare as well as reducing the energy consumption of the EVs.

The development of machine learning and artificial intelligence algorithms, as well as the progress of unmanned aerial vehicle swarm technology, has significantly enhanced the intelligence and autonomy of unmanned aerial vehicles in search missions, resulting in greater efficiency when searching unknown areas. However, as search scenarios become more complex, the existing unmanned aerial vehicle swarm search method lacks scalability and efficient cooperation. Furthermore, due to the increasing scale of search scenarios, the accuracy and real-time performance of global information are difficult to ensure, necessitating the provision of local information. This paper focuses on the large-scale search scenario and split it to provide both local and global information for running unmanned aerial vehicle swarm search algorithms. Since the search environment is often unknown, dynamic, and complex, it requires adaptive decision-making in a constantly changing environment, which is suitable for modeling as a Markov decision process. Considering the sequential-based scenario, we propose a distributed collaborative search method based on a multi-agent reinforcement learning algorithm, which can operate efficiently in complex and large-scale scenarios. Additionally, the proposed method can utilize a convolutional neural network to process high-dimensional map data with almost no loss of the structure information. Experimental results demonstrate that the proposed method can collaboratively search unknown areas, avoid collisions and repetitions, and find all targets faster compared with the benchmarks.