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Semantic communication (SemCom) has recently been considered a promising solution to guarantee high resource utilization and transmission reliability for future wireless networks. Nevertheless, the unique demand for background knowledge matching makes it challenging to achieve efficient wireless resource management for multiple users in SemCom-enabled networks (SC-Nets). To this end, this article investigates SemCom from a networking perspective, where two fundamental problems of user association (UA) and bandwidth allocation (BA) are systematically addressed in the SC-Net. First, considering varying knowledge matching states between mobile users and associated base stations, we identify two general SC-Net scenarios, namely perfect knowledge matching-based SC-Net and imperfect knowledge matching-based SC-Net. Afterward, for each SC-Net scenario, we describe its distinctive semantic channel model from the semantic information theory perspective, whereby a concept of bit-rate-to-message-rate transformation is developed along with a new semantics-level metric, namely system throughput in message (STM), to measure the overall network performance. In this way, we then formulate a joint STM-maximization problem of UA and BA for each SC-Net scenario, followed by a corresponding optimal solution proposed. Numerical results in both scenarios demonstrate significant superiority and reliability of our solutions in the STM performance compared with two benchmarks.

The 57–71-GHz millimeter-wave (mmWave) industrial, scientific, and medical (ISM) band holds significant potential for enhancing the performance of next-generation industrial wireless applications. This article first presents the design and analysis of a compact and high-performance eight-element series-fed frequency beam-scanning array designed to cover the entire 21.87% fractional bandwidth (FBW) of the 57–71-GHz ISM band. Using this as a subarray, a hybrid parallel–series feed topology is designed to construct a 64-element ( $8\times8$ ) planar array with high-gain directional beams. The planar array provides a peak measured gain of 20.12 dBi at 64 GHz and maintains a flat gain of over 19.23 dBi throughout the band, with a 1-dB gain bandwidth of 13 GHz. Its narrow directional beams provide an average half-power beamwidth (HPBW) of 9.7° and 11.78° in the elevation and azimuth planes, respectively, facilitating point-to-point mmWave connectivity and high-resolution beam scanning. The inherent phase variation of the series-fed topology is employed to produce a beam-scanning range of 40° within the 57–71-GHz band, with a scan loss of less than 1 dB. The proposed array is a low-cost and reproducible solution for seamless integration with V-band mmWave equipment, as elucidated through practical demonstration frameworks using the mmWave power sensor and EK1HMC6350 evaluation board. The proposed array is well-suited for emerging industrial wireless sensing and imaging applications, and mmWave frequency scanning radars. Its versatility extends to various 60-GHz protocols, such as IEEE 802.11ay, IEEE 802.11ad, IEEE 802.15.3c/d, and WirelessHD, and other customized industrial protocols such as WirelessHP.

Driven by the development of blockchain infrastructures and the promotion of Web 3, more than 4000 Decentralized Autonomous Organizations (DAOs) have been developed as online organizations jointly owned and managed by their members who work for the same interests. Voting mechanisms as the democratic administration of DAOs without the involvement of central authority, are crucial to both the development of the DAO community and the protection of individual interests. This paper is one of the first analyses of the critical role of voting mechanisms in DAOs’ operation. In the absence of systematic studies of voting mechanisms in DAOs, we propose five tiers of decentralization in DAO voting which marks the critical difference between DAO voting and conventional voting. We also define four dimensions to comprehensively evaluate the performance of DAO voting mechanisms, which identify the demands and characteristics of DAO voting and put forward clear design guidelines for voting mechanisms in DAOs. Finally, we take seven typical voting mechanisms as examples and analyze their performance in our proposed evaluation schemes.

Reconfigurable intelligent surface (RIS) has been identified as a promising disruptive innovation to realize a faster, safer and more efficient communication system. In this paper, we study the broad beamwidth design of RIS. A problem is formulated to achieve broadbeam with maximum and equal power gain within a pre-defined angular region given constraints of the unit modulus weights of RIS. Since the formulated problem is non-convex, where the optimal solution cannot be analytically obtained, we propose the difference-of-convex-based semi-definite programming (DC-SDP) algorithm. In addition, as important guidance of signal coverage for arbitrary angular regions, we mathematically derive the relationship between the angular range of the spatial sector and the maximum average received power. The upper bounds of the average received power with different RIS configurations are also obtained, where uniform rectangular array (URA) and uniform linear array (ULA) are considered. Simulation results demonstrate the effectiveness of our derivations and verify that our proposed DC-SDP algorithm is applicable in practical applications and outperforms other baseline methods. Overall, this work can be viewed as a foundation for the practical implementation of RIS on coverage enhancement and can also be seen as an initial step towards achieving channel estimation.

Future wireless communication networks are expected to extensively utilise unmanned aerial vehicles (UAVs) and reconfigurable intelligent surfaces (RISs) to improve performance, extend coverage, and enhance energy efficiency. In this letter, the fundamental limits of the system performance of a multi-hop RIS-assisted UAV communication system are analysed and characterised. We derive accurate closed-form approximations for the statistical distributions of the RIS-assisted channels which are then exploited to derive an analytical expression for the signal-to-noise ratio (SNR), outage probability, and bit error rate (BER). Moreover, we derive outage probability expressions in the high SNR regime. Our analysis demonstrates that strategically placing RIS with an adequate number of elements can effectively enhance the performance of UAV communications in different fading scenarios. RIS dynamically adapts to changing channels and user positions, enhancing performance as the UAV navigates diverse geographical areas, when ideal Line of Sight (LoS) conditions may not be consistently attainable due to the UAV’s altitude and dynamic mobility.

In this paper, we establish a task-oriented cross-system design framework to minimize the required packet rate for timely and accurate modeling of a real-world robotic arm in the Metaverse, where sensing, communication, prediction, control, and rendering are considered. To optimize a scheduling policy and prediction horizons, we design a Constraint Proximal Policy Optimization (C-PPO) algorithm by integrating domain knowledge from relevant systems into the advanced reinforcement learning algorithm, Proximal Policy Optimization (PPO). Specifically, the Jacobian matrix for analyzing the motion of the robotic arm is included in the state of the C-PPO algorithm, and the Conditional Value-at-Risk (CVaR) of the state-value function characterizing the long-term modeling error is adopted in the constraint. Besides, the policy is represented by a two-branch neural network determining the scheduling policy and the prediction horizons, respectively. To evaluate our algorithm, we build a prototype including a real-world robotic arm and its digital model in the Metaverse. The experimental results indicate that domain knowledge helps to reduce the convergence time and the required packet rate by up to 50%, and the cross-system design framework outperforms a baseline framework in terms of the required packet rate and the tail distribution of the modeling error.

Symbiotic communication (SC), exploiting the analogy of biological ecosystem to establish communication device ecosystems, can enable cooperative service/resource exchanges across heterogeneous devices thus realizing the complementarity among different communication resources. However, considering unstable wireless links, high network dynamics, and complex electromagnetic interference in such an ecosystem, it is difficult to perform service/resource exchanges without securing a trusted environment. Moreover, multi-dimensional service/resource exchange demanded by massive symbiotic devices (SDs) in the ecosystems exposes additional challenges for exchange decision-making. To deal with the above difficulties, in this article, we propose a blockchain-empowered intelligent coevolution for symbiotic devices (BIO-SD). Specifically, to guarantee the trustworthiness of service/resource exchange and resist malicious attacks, a direct acyclic graph (DAG)-based blockchain architecture is applied to the BIO-SD scheme. Furthermore, a modified multi-agent deep deterministic policy gradient (MADDPG) approach is adopted to make service/resource exchange decisions under this trusted environment. The simulation results show that the proposed BIO-SD scheme outperforms some conventional solutions in terms of transmission rate and transmission latency under both non-attack and malicious attack scenarios.

The detection of head movement plays a crucial role in human–computer interaction systems. These systems depend on control signals to operate a range of assistive and augmented technologies, including wheelchairs for Quadriplegics, as well as virtual/augmented reality and assistive driving. Driver drowsiness detection and alert systems aided by head movement detection can prevent major accidents and save lives. Wearable devices, such as MagTrack consist of magnetic tags and magnetic eyeglasses clips and are intrusive. Vision-based systems suffer from ambient lighting, line of sight, and privacy issues. Contactless sensing has become an essential part of next-generation sensing and detection technologies. Wi-Fi and radar provide contactless sensing, however, in assistive driving they need to be inside enclosures or dashboards, which for all practical purposes in this article have been considered as through walls. In this study, we propose a contactless system to detect human head movement with and without walls. We used ultra-wideband (UWB) radar and Wi-Fi signals, leveraging machine and deep learning (DL) techniques. Our study analyzes the six common head gestures: right, left, up, and down movements. Time-frequency multiresolution analysis based on wavelet scalograms is used to obtain features from channel state information values, along with spectrograms from radar signals for head movement detection. Feature fusion of both radar and Wi-Fi signals is performed with state-of-the-art DL models. A high classification accuracy of 83.33% and 91.8% is achieved overall with the fusion of VGG16 and InceptionV3 model features trained on radar and Wi-Fi time–frequency maps with and without the walls, respectively.