An Opportunistic Routing Protocol Based on Position Information for Beam Alignment in Millimeter Wave Vehicular Communications

Millimeter wave (mmWave) communication is one viable solution to support Gbps sensor data sharing in vehicular networks. The use of large antenna arrays at mmWave and high mobility in vehicular communication make it challenging to design fast beam alignment solutions. In this paper, we propose a novel framework that learns the channel angle-of-departure (AoD) statistics at a base station (BS) and uses this information to efficiently acquire channel measurements. Our framework integrates online learning for compressive sensing (CS) codebook learning and the optimized codebook is used for CS-based beam alignment. We formulate a CS matrix optimization problem based on the AoD statistics available at the BS. Furthermore, based on the CS channel measurements, we develop techniques to update and learn such channel AoD statistics at the BS. We use the upper confidence bound (UCB) algorithm to learn the AoD statistics and the CS matrix. Numerical results show that the CS matrix in the proposed framework provides faster beam alignment than standard CS matrix designs. Simulation results indicate that the proposed beam training technique can reduce overhead by 80% compared to exhaustive beam search, and 70% compared to standard CS solutions that do not exploit any AoD statistics.

Two major factors that limit the throughput in multi-hop wireless networks are the unreliability of wireless transmissions and co-channel interference. One promising technique that combats lossy wireless transmissions is opportunistic routing (OR). OR involves multiple forwarding candidates to relay packets by taking advantage of the broadcast nature and spacial diversity of the wireless medium. Furthermore, recent advances in multi-radio multi-channel transmission technology allows more concurrent transmissions in the network, and shows the potential of substantially improving the system capacity. However, the performance of OR in multi-radio multi-channel systems is still unknown, and the methodology of studying the performance of traditional routing (TR) can not be directly applied to OR. In this paper, we present our research on computing an end-to-end throughput bound of OR in multi-radio multi-channel systems. We formulate the capacity of OR as a linear programming (LP) problem which jointly solves the radio-channel assignment and transmission scheduling. Leveraging our analytical model, we gain the following insights into OR: 1) OR can achieve better performance than TR under different radio/channel configurations, however, in particular scenarios, TR is more preferable than OR; 2) OR can achieve comparable or even better performance than TR by using less radio resource; 3) for OR, the throughput gained from increasing the number of potential forwarding candidates becomes marginal.

Opportunistic routing (OR) involves multiple forwarding candidates to relay packets by taking advantage of the broadcast nature and multi-user diversity of the wireless medium. Compared with Traditional Routing (TR), OR can evidently improve the end to end throughput of Wireless Mesh Networks (WMNs). At present, there are many achievements concerning OR in the single radio wireless network. However, the study of OR in multi-radio wireless network stays the beginning stage. In this paper, we focus on OR in multi-radio multi-channel WMNs. Directly using the existing channel assignment leads to decreasing the number of candidate forwarder and the performance of OR. According to OR with one-to-more transmission mode, the Candidate Forwarder Set based Channel Assignment (CFSCA) for OR is proposed. Firstly, according to the candidate forwarder selected by routing, we bound the sender node and its candidate forwarders as a Candidate Forwarder Set(CFS). Then we calculate the interference among all the CFSs. Then the channel assignment for OR is described as a minimum CFS interference optimization problem, which must obey the constraints of the channel number of CFS and the number of radio interfaces. The evaluation results show that CFSCA improves 25.2%, 10%, 19% of the aggregative throughput than random, uniform and tradition channel assignment, respectively.Keywordswireless mesh networksmulti-radio and multi-channelopportunistic routingchannel assignment

In this article, a low-profile, compact (1.36 λ0 × 1.24 λ0 × 0.04 λ0), microstrip, annular leaky wave antenna, with beam scanning capacity, (−44° to +45°) has been proposed in the frequency band of 5.5–6.5 GHz. The leaky wave structure consists of four annular microstrip rings arranged in a circular manner. The 2D cylindrical leaky wave propagates outward radially from the source along the interfaces of this structure. The periodic repetition of the rings in a circular pattern results in a compact structure compared to the linear repetition in leaky wave structures, adding to the novelty of the design. The dimensions of the ring are so chosen as to excite the dominant TM mode of the structure and thereby generate directive radiation patterns with beam scanning property. The operating frequency band covers the sub-6 GHz band, which is suitable for automated vehicular communications. Most of the antenna designs earlier reported for vehicle-to-vehicle communications produced omnidirectional patterns. On the contrary to this, the structure presented in this work, prevents unnecessary dissemination of signals by producing a directional pattern that gives wider angular coverage through beam scanning. A symmetric beam scan is obtained in the frequency band of interest, with a measured gain of 1.2 dB and a gain variation between 0.6 dB and 2.62 dB.

Introduction: Routing protocols in Wireless Sensor Networks (WSN) are of major concern because of the important factors like energy efficiency and reliability. Opportunistic Routing (OR) is the simplest and reliable routing technique for ad-hoc networks and WSN. The OR protocol guarantees data delivery in the network. As WSN need energy-efficient routing, therefore, energy- efficient OR protocols gained popularity in the last three years. Objectives: Opportunistic routing improves the performance of the network by utilizing the broadcasting abilities of wireless channels. In this paper, a new energy-efficient and reliable routing protocol has been proposed that is based on the opportunistic routing technique. Methods: The proposed protocol is based on the energy-efficient routing metric. The proposed OR protocol prioritizes among the candidate forwarders on the basis of energy-efficient routing metric and this will lead to the achievement of improved network lifetime. Results: The end-to-end delay is reduced because of the selection of candidate forwarders from the neighbor list of each node. The proposed protocol uses selective acknowledgment mechanisms. The simulation results of the proposed protocol depict the improved performance as compared to other recent OR protocols. Conclusion: The proposed protocol can directly be used with existing WSN applications. Further improvements can be made by utilizing more parameters like distance and the quality of transmission.

In order to meet the high data rate requirements of emerging roadway use cases, mmWave vehicular communications will be needed. This work studies the ability of vehicles to communicate with a Roadside Unit (RSU) placed at an intersection. Practical mmWave radios utilize a codebook, a discrete set of analog beams, that is periodically searched during runtime to find the optimal beam to use for each receiver. This search creates overhead as the wireless channel is not used for communication while this beam search is happening. This work focuses on reducing the overhead of beam training by optimizing the site-specific codebook design of a RSU. Owing to the sparsity of the mmWave channel and the user distribution for vehicles, it is found that 85% of beams can be removed from the codebook with zero-impact. By carefully selecting the usage of wide beams the codebook size can be further reduced to just 64 beams while still providing omni-directional coverage for an intersection. Other research thrusts have focused on attempting to augment or remove beam training entirely; however, this necessitates a change to the PHY layer. Codebook optimization achieves approximately 80% of the communications performance that would be achieved if beam training overhead could be completely removed while only requiring a radio configuration update. Thus, this work finds that today’s commercial mmWave radios are sufficient for deployments in RSUs. To validate the proposed codebook optimization algorithm, a detailed mmWave ray tracing framework that encompasses 3D environmental information and material properties of reflectors is developed.

Advanced vehicle-to-everything (V2X) communication is expected to require very large data rates, ultra high reliability, and low latency. This motivates the design of antennas able to operate in the mmWave spectrum with sufficient bandwidth and high gain. In this paper, candidate antennas are investigated and Dielectric Resonator Antennas (DRAs) are found to be an effective solution. An aperture-coupled circular DRA made from Rogers 6010 substrate is designed and simulated. The simulated peak gain and impedance bandwidth are 6.4 dBi and 2.51 GHz, respectively. The designed antenna shows potential for use in mmWave vehicular communication.

Distributed cooperation among the forwarding candidates is the main challenge of opportunistic routing design for wireless multi-hop networks. Timer-based coordination is an efficient way to solve this problem. However this method leads to unnecessary duplicate transmission problem which will degrade the system performance. In the scenario where nodes are nonadjacent, the duplicate transmission problem gets even worse. To avoid the useless retransmission, this paper proposes a pure network layer scheme called Dual Priority Cooperative Opportunistic Routing Scheme (DPCOR). DPCOR exploits the global network information to complete the network layer confirmation. The nodes with lower global priority receive a duplicate packet will not respond again. Furthermore, it combines the local link quality variation and adjacent information to classify the forwarding candidates into three levels with local priority. As the packets are broadcasted at different time according to the local priority, the scheme can get more time diversity, which can avoid collision and improve performance effectively. We test DPCOR by using the simulation in various network scenarios. The results show that DPCOR achieve significant performance improvement with less transmission cost compared to the traditional routing and typical opportunistic routing.

Wireless channels have a broadcast nature based on which opportunistic routing protocols work. In opportunistic routing protocols, packets are forwarded by the intermediate nodes that hear their transmissions, which are called candidate forwarders. In most of these algorithms, the forwarder list is pre-determined. However, the energy-efficient selection of the forwarder list is a research topic that is not considered well. Energy Efficient OPportunistic Routing algorithm (EEOPR) is presented in this paper in which the forwarders are determined on the packets’ fly. EEOPR is a flexible method that performs the routing process locally, and the candidate forwarders are selected during the routing and for each packet. The process of the candidate nodes’ selection and their packet forwarding are managed by the Genetic algorithm according to the nodes’ remaining energy and their regions. Simulation results show that network performance is improved in EEOPR compared to ROMER and CORP-M in terms of throughput, the number of duplicate packets, and the network nodes’ residual energy.

Beam alignment is a process for receive and transmit antenna arrays to find the correct beamforming directions. It is typically based on beam scanning and peak-energy searching, which lead to time-consuming beam training process in communication protocols, such as 802.11ad for vehicular networks. In this correspondence, we propose a fast beam alignment method for analog arrays, based on directly estimating the angle-of-arrival (AoA) of the incoming signals. We propose simple and highly efficient AoA estimators, by approximating the power of the array response as a Gaussian function. One estimator is based on the power ratio and can coherently combine multiple measurements scanned at arbitrary intervals and with different beam widths. The other two are based on an innovative idea of Gaussian curve fitting with weighted least square techniques, and one of them can even work without knowing the beam width. Simulation results validate the effectiveness of the proposed scheme.

In Wireless Sensor Networks (WSNs), multiple-objective cluster head (CH) depending on energy-aware routing seeks to maximize data aggregation while ensuring security and energy efficiency. To balance energy consumption along with network durability, this strategy entails choosing CHs based on a count of factors, such as energy levels, node distance, and data security. Longer processing times and more computing overhead might result from the difficulty of maximizing numerous objectives. Furthermore, predefined models and heuristics may not always work well in dynamic WSNs due to factors like variable node densities and energy levels. To overcome these issues, in this paper Enhanced Data Security in WSNs through Multi-Objective Energy-Aware Routing Utilizing Interpretable Generalized Additive Neural Network Approach (OEWSN-MA-IGANN) is proposed. Thus, data aggregation basis Interpretable Generalized Additive Neural Network (IGANN) is employed to choose CH utilizing a multiple-objective fitness function. Energy, latency, output, distance between nodes, capacity, collision, traffic rate, and cluster density are among the variables taken into consideration by this fitness function. Then, a Bitterling Fish Optimization (BFO) is proposed to optimize the weight parameters of IGANN. The proposed OEWSN-MA-IGANN approach is implemented in Python and evaluated with some metrics, like packet delivery ratio (PDR), network lifetime, computational time, delay, scalability, network load, network size, and overhead. The simulation outcomes depict that the proposed OEWSN-MA-IGANN approach reaches 23.12%, 24.14%, and 21.08% higher network lifetime, 21.12%, 22.11%, and 21.06% higher PDR are analyzed with existing methods.

Hybrid error recovery protocol for video streaming in vehicle ad hoc networks

Multi-hop Wireless Mesh Networks (WMNs) is a promising new technique for communication with routing protocol designs being critical to the effective and efficient of these WMNs. A common approach for routing traffic in these networks is to select a minimal distance from the source to destination as in wireless networks. Opportunistic Routing (OR) makes use of the broadcasting ability of wireless network and is especially very helpful for WMN because all nodes are static. Our proposed scheme of Multicast Opportunistic Routing (MOR) in WMNs is based on the broadcast transmissions and Learning Automata (LA) to expand the potential candidate nodes that can aid in the process of retransmission of the data. The receivers are required to be in sync with one another in order to avoid duplicated broadcasting of data which is generally achieved by formulating the forwarding candidates according to some LA based metric. The most helpful aspect of this protocol is that it intelligently learns from the past experience and improves its performance. The results obtained via this approach of MOR show that the proposed scheme outperforms some existing schemes and is an improved and more effective version of opportunistic routing in a mesh network.

Vehicular communication is undergoing a paradigm shift with the emergence of intelligent transportation systems that rely on real-time, adaptive communication among vehicles (V2V), infrastructure (V2I), and other entities (V2X). Traditional protocol stacks like DSRC and C-V2X, while effective in static conditions, often fail under dynamic, high-density environments due to fixed scheduling and protocol rigidity. In this study, we propose NeuroV2X-Net, a novel AI-driven hybrid framework that integrates spatial-temporal deep learning, reinforcement learning–based protocol adaptation, and edge-intelligent deployment to enable robust next-generation V2X communication. The framework employs 1D/2D CNNs and Transformer encoders for spatio-temporal feature extraction, fuses V2V and V2I information via an attention-based mechanism, and leverages reinforcement learning to dynamically select optimal communication protocols based on latency, packet delivery, and network load. Evaluated across simulated urban and highway settings using CARLA and NS-3, NeuroV2X-Net achieved an average accuracy of 93.3% in communication decision-making tasks, packet delivery ratio (PDR) of 96.7%, and reduced latency to 18.3 ms, significantly outperforming existing approaches like DeepV2X, CNN-GRU, and static DSRC-based models. The model’s deployment on edge devices using quantization and federated learning further confirms its real-time applicability with energy-efficient performance. NeuroV2X-Net not only enhances vehicular communication reliability but also paves the way for context-aware, scalable, and secure traffic management systems. This study demonstrates the model’s efficacy in diverse V2X scenarios and lays the foundation for future enhancements incorporating multimodal sensor data, 6G connectivity, and autonomous navigation intelligence.

Performance of vehicle-to-vehicle (V2V) communications depends highly on the employed scheduling approach. While centralized network schedulers offer high V2V communication reliability, their operation is conventionally restricted to areas with full cellular network coverage. In contrast, in out-of-cellular-coverage areas, comparatively inefficient distributed radio resource management is used. To exploit the benefits of the centralized approach for enhancing the reliability of V2V communications on roads lacking cellular coverage, we propose VRLS (Vehicular Reinforcement Learning Scheduler), a centralized scheduler that proactively assigns resources for out-of-coverage V2V communications \textit{before} vehicles leave the cellular network coverage. By training in simulated vehicular environments, VRLS can learn a scheduling policy that is robust and adaptable to environmental changes, thus eliminating the need for targeted (re-)training in complex real-life environments. We evaluate the performance of VRLS under varying mobility, network load, wireless channel, and resource configurations. VRLS outperforms the state-of-the-art distributed scheduling algorithm in zones without cellular network coverage by reducing the packet error rate by half in highly loaded conditions and achieving near-maximum reliability in low-load scenarios.