Vehicular ad hoc networks (VANETs) need to support the timely end-to-end transmissions of safety and non-safety messages. Medium access control (MAC) protocols can ensure fair and efficient sharing of channel resources among multiple vehicles for VANETs, which can provide timely packet transmissions and significantly improve road safety. In this paper, we review the standards of some countries for VANETs. Then, we divide the MAC protocols proposed for VANETs into single-channel MAC protocols and multi-channel MAC protocols according to the number of physical occupied spectrum resources. Both are further discussed based on their hierarchical structures, i.e., distributed and centralized structures. General design and optimization mechanisms of these commonly used MAC protocols for VANETs are reviewed. From the viewpoint of 7 aspects, we compare the advantages and disadvantages of these typical MAC protocols. Finally, we discuss the open issues to improve the MAC performance and future work on MAC design for VANETs.

A high-scanning rate leaky-wave antenna (LWA) working at Ka -band with continuous backward-to-forward beam-scanning capability, good open stopband (OSB) suppression and wide scanning angle is exhibited. The proposed LWA is made by etching an array of cascaded high-Q resonators and open discontinuities on the substrate integrated waveguide (SIW). The resonator is designed by combining two symmetrically unbalance split ring resonators (USRRs) on the top surface of the SIW. The LWA's working mechanics were thoroughly investigated through band structure theory. An in-depth interpretation of the LWA's characteristics is provided by studying the surface current distribution and the equivalent circuit model of the resonator structure. The LWA's OSB is effectively alleviated by properly selecting the length of the radiation slot. Yet, the antenna scanning rate is directly improved by the oscillation quality of the resonators (i.e., USRRs), which is realized by tuning the split gap of the USRR. In the experiments, a prototype of the proposed LWA was fabricated and measured. The simulated and measured data show good agreement in terms of the antenna's scanning range and radiation gain. The measured results indicates that a continuous beam scanning from −56° to +48° is rendered over the frequency band from 32 to 36 GHz, which implies a spectral scanning rate of 8.84° per percentage. Finally, it is shown that the average gain can reach up to 9.1 dBi.

Complex dynamic services and heterogeneous network environments make the asymmetrical control a curial issue to handle on the Internet. With the advent of the Internet of Things (IoT) and the fifth generation (5G), the emerging network applications lead to the explosive growth of mobile traffic while bringing forward more challenging service requirements to future radio access networks. Therefore, how to effectively allocate limited heterogeneous network resources to improve content delivery for massive application services to ensure network quality of service (QoS) becomes particularly urgent in heterogeneous network environments. To cope with the explosive mobile traffic caused by emerging Internet services, this paper designs an intelligent optimization strategy based on deep reinforcement learning (DRL) for resource allocation in heterogeneous cloud-edge-end collaboration environments. Meanwhile, the asymmetrical control problem caused by complex dynamic services and heterogeneous network environments is discussed and overcome by distributed cooperation among cloud-edge-end nodes in the system. Specifically, the multi-layer heterogeneous resource allocation problem is formulated as a maximal traffic offloading model, where content caching and request aggregation mechanisms are utilized. A novel DRL policy is proposed to improve content distribution by making cache replacement and task scheduling for arriving content requests in accordance with the information about users’ history requests, in-network cache capacity, available link bandwidth and topology structure. The performance of our proposed solution and its similar counterparts are analyzed in different network conditions.

The phaseless-data inverse scattering problems (PD-ISPs) are non-trivial due to their serious nonlinearity and ill-posed nature. The conventional methods for PD-ISPs can be categorized into single-step methods and two-step methods, where the first ones directly reconstruct the image and the second ones retrieve the phase and amplitude of scattered fields, and then reconstruct the image as the traditional FD-ISPs. However, the single-step methods correspond to solve higher nonlinear optimization problems and the two-step methods highly depend on the quality of the retrieved phase and amplitude information. To solve the above issues, we propose a two-step method with a cascaded complex U-net (CCU-net) model to solve the PD-ISPs in the complex domain. The CCU-net consists of two parts, that is, Phase Retrieval Net (PRNet) and Image Reconstruction Net (IRNet), where the PRNet recovers the phase and amplitude of the scattered field from the measured modulus of the total field and the IRNet takes the recovered scattered field as input to reconstruct the image. These two parts can be independently controlled and achieve joint optimization. Thanks to the strong nonlinear representation ability of complex neural networks, the physical relationship between the scattered fields and the images can be reserved and constructed. Several representative tests including both the synthetic and experimental examples verify that the proposed CCU-net in the complex domain has good robustness, generalization ability, and strong inversion capability when tackling high contrast scatterers and can also fulfill one-step implementation in real time.

This paper addresses self-interference (SI) cancellation for millimeter-wave (mmWave) based full-duplex backhaul systems, which involves not only the near-end cross talk due to antenna leakage, but also the far-end cross talk due to reflections from obstacles around the transmission path. The SI channel is with a long response, particularly for mmWave systems where the bandwidth is large. In order to manage the SI comprehensively, we put forward an adaptive multi-segment frequency-domain cancellation (MSFDC) scheme based on the frequency-domain block least-mean-square algorithm, which can cover the long SI channel with low complexity. In addition, we introduce an output branch on the traditional adaptive training loop and perform tap selection, so that the cancellation error decreases while the convergence and tracking abilities are maintained. To demonstrate the effectiveness of the proposed tap-selection based MSFDC, comprehensive evaluations are carried out in an E-band full-duplex backhaul scenario. It is shown that the number of multiplication operations can be greatly reduced by dividing the SI channel into multiple segments. Meanwhile, in a wide range of cases, the proposed scheme can cancel the SI to the degree below the noise floor, and significantly outperforms the traditional method without tap selection.

Internet of Drones (IoD) has become crucial due to the surge of drone-based applications. In a dynamic wireless network, drones often share data with multiple users, which is vulnerable to various security threats, e.g., eavesdropping, privacy leakage, and impersonation. Ensuring secure sharing of drone operation data is a critical issue. Existing schemes employ blockchain to ensure accountability in untrusted IoD contexts. However, drones perform excessive operations, e.g., consensus and homomorphic encryption, leading to impractical drone computation overhead. Furthermore, these existing schemes perform point-to-multipoint (P2MP) data sharing by using identity-based broadcast encryption (IBBE) as it has stateless receiver property, i.e., drones can directly establish sharing groups based on the receiver identities. However, these IBBE-based schemes need to preset the sharing group capacity, reducing sharing efficiency and flexibility in dynamic IoD networks. Also, identities in sharing groups probably result in receiver privacy breaches. In this paper, we propose a blockchain-assisted data-sharing scheme with accountability and privacy-preserving for IoD networks. We utilize on-chain immutable smart contracts and short signatures to construct a lightweight accountability mechanism that can verify and punish the above misbehavior. We develop an efficient privacy-preserving IBBE algorithm for P2MP sharing of drone operation data that enables dynamic group sharing. We prove that our algorithm is secure and receiver anonymity against chosen ciphertext attacks. The experiment results show that our scheme outperforms existing schemes in computational and communication overhead. In real-world logistics scenarios, the wireless data rate of 50 Mbps can satisfy the communication requirements for ciphertext lengths up to 19.5 KB. For a sharing group of 60 receivers, drone encryption costs only about 101 ms. The decryption cost remains constant and takes about 3.1 ms.