Wei Feng’s research while affiliated with Tsinghua University and other places

Quick response to disasters is crucial for saving lives and reducing loss. This requires low-latency uploading of situation information to the remote command center. Since terrestrial infrastructures are often damaged in disaster areas, non-terrestrial networks (NTNs) are preferable to provide network coverage, and mobile edge computing (MEC) could be integrated to improve the latency performance. Nevertheless, the communications and computing in MEC-enabled NTNs are strongly coupled, which complicates the system design. In this paper, an edge information hub (EIH) that incorporates communication, computing and storage capabilities is proposed to synergize communication and computing and enable systematic design. We first address the joint data scheduling and resource orchestration problem to minimize the latency for uploading sensing data. The problem is solved using an optimal resource orchestration algorithm. On that basis, we propose the principles for resource configuration of the EIH considering payload constraints on size, weight and energy supply. Simulation results demonstrate the superiority of our proposed scheme in reducing the overall upload latency, thus enabling quick emergency rescue.

Ambient backscatter communication enables low-cost low-rate wireless interconnections for Internet of Things (IoT) applications. In this work, new signal detectors for different cases of ambient backscatter communications are derived. Specifically, both coherent and partially coherent detectors are obtained for Gaussian ambient signals and phase shift keying (PSK) ambient signals. Maximum likelihood detection method and improved energy detection method (including energy detection and magnitude detection as special cases) are adopted. Numerical results show that the energy detection method has the best performance when the ambient signals are Gaussian, while the magnitude detection method has the best performance when the ambient signals are PSK modulated. Both are comparable to the optimum maximum likelihood detection. Numerical results also show that the improved energy detection method is very flexible and that detectors for PSK ambient signals are slightly better than those for Gaussian ambient signals.

To cover remote areas where terrestrial cellular networks may not be available, non-terrestrial infrastructures such as satellites and unmanned aerial vehicles (UAVs) can be utilized in the upcoming sixth-generation (6G) era. Considering the spectrum scarcity problem, satellites and UAVs need to share the spectrum to save costs, leading to a cognitive satellite-UAV network. Due to the openness of both satellite links and UAV links, communication security has become a major concern in cognitive satellite-UAV networks. In this paper, we safeguard a cognitive satellite-UAV network from a physical layer security (PLS) perspective. Using only the slowly-varying large-scale channel state information (CSI), we jointly allocate the transmission power and subchannels to maximize the secrecy sum rate of UAV users. The optimization problem is a mixed integer nonlinear programming (MINLP) problem with coupling constraints. We propose a heuristic algorithm which relaxes the coupling constraints by the penalty method and obtains a sub-optimal low-complexity solution by utilizing random matrix theory, the max-min optimization tool, and the bipartite graph matching algorithm. The simulation results corroborate the superiority of our proposed scheme.

Jitter effects caused by airflow and vibrations could be harmful to UAV communications, e.g., it may cause mismatch between the transmit beam and actual user direction. However, jitter introduces additional randomness to the channel, which may have merit in sparse channels that are relatively static and deep faded. For this consideration, we pick the commonly used air-to-sea two ray propagation model and investigate the impact of UAV jitter on the channel fading statistics. Assuming a Gaussian random model for the jitter angle, we derive analytical expressions for the jitter-beneficial probability (the probability that UAV jitter increases the receive power), and the average receive power under jitter, respectively. Accordingly, the jitter-beneficial conditions are analyzed and verified via simulations.

With the rapid development of marine activities, there has been an increasing use of Internet-of-Things (IoT) devices for maritime applications. This leads to a growing demand for high-speed and ultra-reliable maritime communications. Current maritime communication networks (MCNs) mainly rely on satellites and on-shore base stations (BSs). The former generally provides limited transmission rate while the latter lacks wide-area coverage capability. As a result, the development of current MCN lags far behind the terrestrial fifth-generation (5G) network.

Massive connections and diverse quality of service (QoS) requirements pose a major challenge for machine-type communication (MTC) networks. In this paper, to satisfy various QoS requirements of a massive number of MTC devices (MTCD-s), the devices are divided into multiple clusters based on the QoS characteristics. The cluster access control and intra-cluster resource allocation problems are studied to satisfy the double delay requirements in the access and data transmission phases in a cross-layer approach. Specifically, we formulate an access control problem to maximize the access efficiency with constraints on access and transmission delays. An efficient algorithm is proposed to adaptively adjust the access time intervals and back-off factors of the clusters for different numbers of active MTCDs and transmission rates. Given the access parameters, non-orthogonal multiple access is adopted in resource allocation to maximize the system utility function while guaranteeing the delay requirements for each accessed MTCD. An efficient sequential convex programming iterative algorithm is proposed to solve the NP-hard nonconvex problem with two typical utility objectives: total throughput and consumed power. Simulation results show that the proposed scheme can achieve better performance in terms of access efficiency, delay, throughput, and consumed power than other schemes. The impacts of various parameters, including delay and traffic rate, on the performance are disclosed.

The upcoming sixth generation (6G) network is envisioned to be more open and heterogeneous than earlier generations. This challenges conventional security architectures, which typically rely on the construction of a security perimeter at network boundaries. In this article, we propose a software-defined zero trust architecture (ZTA) for 6G networks, which is promising for establishing an elastic and scalable security regime. This architecture achieves secure access control through adaptive collaborations among the involved control domains, and can effectively prevent malicious access behaviors such as distributed denial of service (DDoS) attacks, malware spread, and zero-day exploits. We also introduce key design aspects of this architecture and show the simulation results of a case study, which shows the effectiveness and robustness of ZTA for 6G. Furthermore, we discuss open issues to further promote this new architecture.

Mobile edge computing (MEC) is considered a novel paradigm for computation-intensive and delay-sensitive tasks in fifth generation (5G) networks and beyond. However, its uncertainty, referred to as dynamic and randomness, from the mobile device, wireless channel, and edge network sides, results in high-dimensional, nonconvex, nonlinear, and NP-hard optimization problems. Thanks to the evolved reinforcement learning (RL), upon iteratively interacting with the dynamic and random environment, its trained agent can intelligently obtain the optimal policy in MEC. Furthermore, its evolved versions, such as deep RL (DRL), can achieve higher convergence speed efficiency and learning accuracy based on the parametric approximation for the large-scale state-action space. This paper provides a comprehensive research review on RL-enabled MEC and offers insight for development in this area. More importantly, associated with free mobility, dynamic channels, and distributed services, the MEC challenges that can be solved by different kinds of RL algorithms are identified, followed by how they can be solved by RL solutions in diverse mobile applications. Finally, the open challenges are discussed to provide helpful guidance for future research in RL training and learning MEC.

With the prosperous development of e-commerce, customized products have gradually become an important part of online transactions, opening up a new promising market for traditional e-commerce. However, mainstream transaction networks do not provide efficient support to transactions of customized products, thus failing to meet the personalized needs of customers, and pricing strategies are usually dominated by sellers, leading to loss of customers' interests. To overcome these problems, we propose an efficient truthful transaction protocol for e-commerce networks, called ETTP. First, we design a screening algorithm to achieve on-demand selection of transaction participants in hierarchical network domains before transaction initiation. This screening algorithm is customer-oriented and offers personalized commodities by preventing unqualified sellers from participating in the transaction. Furthermore, we develop the ETTP-Auction algorithm based on double auction to improve the transaction efficiency. ETTP-Auction inherently ensures the participation of buyers in pricing and protects their interests. Finally, we prove theoretically that ETTP-Auction satisfies the four economic properties (individual rationality, incentive compatibility, budget balance, and computational efficiency) and verify the performance of the proposed method through simulations. Our ETTP protocol achieves significantly higher system performance, reduces communication overhead, and features better scalability compared with benchmark methods.