Low Earth orbit (LEO) satellite constellations are a key enabler for non-terrestrial networks (NTNs) in sixth-generation (6G) systems. However, achieving high reliability over NTN links for mission-critical applications remains challenging, particularly for high-mobility terminals. Existing transmission diversity techniques based on selection combining or maximal ratio combining (MRC) provide no guaranteed recovery under simultaneous channel outages. Moreover, conventional retransmission schemes in the NTN are incompatible with strict latency constraints in 6G. This paper introduces a diversity transform that integrates forward error correction (FEC) coding with transmission diversity to improve the reliability of orthogonal time frequency space (OTFS)-modulated LEO NTN downlinks. It guarantees block-erasure recovery up to a prescribed number of subchannels, with residual errors corrected by the outer FEC, thereby eliminating retransmissions. The transform is inserted between the FEC encoder and the modulator, enabling compatibility with various coding schemes and channel models. In this system, a data word is first partitioned into subwords, each independently FEC encoded. A diversity mapper then distributes these encoded subwords across the available subchannels using a binary transform matrix derived from a Reed–Solomon code. This structure guarantees block-erasure recovery whenever a sufficient number of subchannels survive, with residual errors corrected by the outer FEC. An ephemeris-driven reliability prediction framework is developed, combining orbital mechanics, atmospheric models, and a finite-blocklength error probability analysis derived for OTFS transmission over transparent satellite relays. The framework enables proactive outage prediction without channel state information feedback. Simulation results show that the proposed diversity transform provides lower block error rate and reduced latency compared to standalone FEC and retransmission schemes, especially under blockage and high-Doppler conditions characteristic of LEO NTN links.

Industry 4.0, driven by enhanced connectivity by wireless technologies such as 5G and Wi-Fi 6, fosters flexible industrial scenarios for high-yield production and services. Private 5G networks and 802.11ax networks in unlicensed spectrum offer very unique opportunities, however existing techniques limit the flexibility needed to serve diverse industrial use cases. In order to address a subset of these challenges, this paper offers a solution for time-sensitive application use cases. A new technique is proposed to enable data-driven operations through Machine Learning for technologies sharing unlicensed bands. This enables proportionate spectrum sharing informed by data to improve critical applications performance metrics. The results presented reveal improved performance to serve critical industrial operations, without degrading spectrum utilization.

Millimeter wave (mmWave) has been recognized as one of key technologies for 5G and beyond networks due to its potential to enhance channel bandwidth and network capacity. The use of mmWave for various applications including vehicular communications has been extensively discussed. However, applying mmWave to vehicular communications faces challenges of high mobility nodes and narrow coverage along the mmWave beams. Due to high mobility in dense networks, overlapping beams can cause strong interference which leads to performance degradation. As a remedy, beam switching capability in mmWave can be utilized. Then, frequent beam switching and cell change become inevitable to manage interference, which increase computational and signalling complexity. In order to deal with the complexity in interference control, we develop a new strategy called Multi-Agent Context Learning (MACOL), which utilizes Contextual Bandit to manage interference while allocating mmWave beams to serve vehicles in the network. Our approach demonstrates that by leveraging knowledge of neighbouring beam status, the machine learning agent can identify and avoid potential interfering transmissions to other ongoing transmissions. Furthermore, we show that even under heavy traffic loads, our proposed MACOL strategy is able to maintain low interference levels at around 10%.

Due to its low latency and high data rates support, mmWave communication has been an important player for vehicular communication. However, this carries some disadvantages such as lower transmission distances and inability to transmit through obstacles. This work presents a Contextual Multi-Armed Bandit Algorithm based beam selection to improve connection stability in next generation communications for vehicular networks. The algorithm, through machine learning (ML), learns about the mobility contexts of the vehicles (location and route) and helps the base station make decisions on which of its beam sectors will provide connection to a vehicle. In addition, the proposed algorithm also smartly extends, via relay vehicles, beam coverage to outage vehicles which are either in NLOS condition due to blockages or not served any available beam. Through a set of experiments on the city map, the effectiveness of the algorithm is demonstrated, and the best possible solution is presented.

We investigate multichannel relay assisted non-orthogonal multiple access (NOMA) in slotted ALOHA systems, where each user randomly accesses one of different channel slots and different transmit power for uplink transmissions over two-hop links, to and from the relay. By using multi-agent reinforcement learning, we propose greedy and non-greedy random access methods so that each user can learn its best strategies of random access over multiple relay slots. Random collisions and fading over the relay slots are both considered. The behaviors of relay-aided NOMA-ALOHA strategies are evaluated with the simulation. It is shown that the greedy method outperforms the non-greedy method in terms of average success rate. For deployment of relay, the greedy method benefits in improving transmission reliability under the symmetric relay channels (between the two-hop links) compared to asymmetric channels. Thus, it is interpreted that the proposed greedy method is more promising to the NOMA-ALOHA systems under a symmetric multichannel relay.

Due to ever-increasing data and resource-hungry applications, the need for new spectrum by mobile networks keeps increasing. Unlicensed spectrum is still expected to play a crucial part in meeting the capacity demand for future mobile networks. But if this will be a reality, fair coexistence attained via practical and efficient channel access procedures would be necessary. In designing such channel access schemes, awareness of the number of nodes contending for the channel resource will be required. This paper investigates a node number estimation approach using channel idle time and analysed via machine learning (ML) techniques. When multiple nodes access the same unlicensed channel, varying idle times can be associated with a statistical distribution. In this paper, a statistical distribution of the idle-time slots over the channel is used to characterise and analyse the channel contention based on the number of nodes. Three ML model-based approaches are evaluated and the results confirm the proposed solution’s viability but also reveal the best-performing ML technique of the three, for the task of node number estimations.

We are on the cusp of a new era of connected autonomous vehicles with unprecedented user experiences, tremendously improved road safety and air quality, highly diverse transportation environments and use cases, and a plethora of advanced applications. Realizing this grand vision requires a significantly enhanced vehicle-to-everything (V2X) communication network that should be extremely intelligent and capable of concurrently supporting hyperfast, ultrareliable, and low-latency massive information exchange. It is anticipated that the sixth-generation (6G) communication systems will fulfill these requirements of the next-generation V2X. In this article, we outline a series of key enabling technologies from a range of domains, such as new materials, algorithms, and system architectures. Aiming for truly intelligent transportation systems, we envision that machine learning (ML) will play an instrumental role in advanced vehicular communication and networking. To this end, we provide an overview of the recent advances of ML in 6G vehicular networks. To stimulate future research in this area, we discuss the strength, open challenges, maturity, and enhancing areas of these technologies.

Vehicular networks, an enabling technology for Intelligent Transportation System (ITS), smart cities, and autonomous driving, can deliver numerous on-board data services, e.g., road-safety, easy navigation, traffic efficiency, comfort driving, infotainment, etc. Providing satisfactory Quality of Service (QoS) in vehicular networks, however, is a challenging task due to a number of limiting factors such as erroneous and congested wireless channels (due to high mobility or uncoordinated channel-access), increasingly fragmented and congested spectrum, hardware imperfections, and anticipated growth of vehicular communication devices. Therefore, it will be critical to allocate and utilize the available wireless network resources in an ultra-efficient manner. In this paper, we present a comprehensive survey on resource allocation schemes for the two dominant vehicular network technologies, e.g. Dedicated Short Range Communications (DSRC) and cellular based vehicular networks. We discuss the challenges and opportunities for resource allocations in modern vehicular networks and outline a number of promising future research directions.

In the 5G network, dense deployment and millimetre wave (mmWave) are some of the key approaches to boost network capacity. Dense deployment of mmWave small cells using narrow directional beams will escalate the cell and beam related handovers for high mobility of vehicles, which may in turn limits the performance gain promised by 5G-mmWave based vehicle-to-infrastructure (V2I) communication. One of the research issues in mmWave handover is to minimise the handover needs by identifying long lasting connections. In this paper, we first develop an analytical model to derive the vehicle sojourn time within a beam coverage. When multiple connections offered by nearby all mmWave small cells are available when upon a handover event, we further derive the longest sojourn time among all potential connections which represents the theoretical upper-bound limit of the sojourn time performance. We then design a Fuzzy Logic (FL) based distributed beam-centric handover decision algorithm to maximise vehicle sojourn time. Simulation experiments are conducted to validate our analytical model and show the performance advantage of our proposed FL-based solution when compared with commonly used approach of connecting to the strongest connection.

Localization is crucial for various applications, this includes resource coordination in small and ultra-small cells, as well as the whole range of Location Based Service (LBS). Multilateration is a localization technique that is based on distance measurements between multiple reference nodes and a target node. This paper introduces a multilateration localization approach that uses Singular Value Decomposition (SVD) for 3D indoor positioning. It also provides a mathematical multilateration formulation which considers the coordinates of the reference nodes and the relative distance between transmitting nodes. In practical deployments, the relative distance can be estimated using RSSI; we apply Kalman filtering to the RSSI measurements aiming to get a more accurate RSSI value. The approach is complemented by using two selection methods which help chosing the best nodes for multilateration computation. The paper concludes with a discussion of the experimental evaluation results obtained.

Large 3D meshes are emerging as the new media for various applications but are still hard to use in mobile applications, due to the limited resources of mobile systems. This paper introduces a large 3D mesh streaming framework which flexibly deals with the limited resources of mobile systems and also provides a user with interactive controls and random accessibilities. To reduce resource usage, our framework presents a uniform mesh partitioning algorithm, in which each partition of a large mesh has the same number of vertices. Our uniform partitioning is based on a k-d tree clustering and extended for out-of-core meshes. A median search with heaps in the main memory is designed for faster external sorting. Large 3D meshes are transformed into a set of partitioned and simplified meshes in the server. For interactive 3D browsing, on mobile devices, our framework presents a mobile 3D viewer which is hierarchically designed with intuitive interfaces. A user can experience rapid 3D searches with 3D previews of simplified meshes. Multi touch inputs can control zooming and the level of detail in meshes automatically. Double tap touches enable a user to randomly select a region of a large mesh, and a set of partitions for the selected region will be streamed and displayed on a mobile client. As a result, our framework enables a user to browse large 3D meshes on a mobile system interactively, while optimizing system resource usage and protecting the original data in the server.

In this paper, we propose a utility-based spectrum aggregation algorithm to enhance the performance of a cognitive radio network considering multiple objectives: (i) maximization of overall throughput, (ii) reduction of channel switching, (iii) reducing the number of sub-channels comprising the aggregate channel, aimed at opportunistic spectrum use by secondary users (SUs). These three objectives are integrated into a weighted sum utility function. The weight associated with each objective can be set differently (typically done manually) depending on the metric to be optimized. In this article however, we propose and evaluate an automatic mechanism for setting weights. The proposed algorithm including the learning module allows for automated (no manual intervention) adaptable setting of objective- function weights depending on the environment changes and its performance is also shown via the simulation results.

Previous mesh compression techniques provide decent properties such as high compression ratio, progressive decoding, and out-of-core processing. However, only a few of them supports the random accessibility in decoding, which enables the details of any specific part to be available without decoding other parts. This paper proposes an effective framework for the random accessibility of mesh compression. The key component of the framework is a wire-net mesh constructed from a chartification of the given mesh. Charts are compressed separately for random access to mesh parts and a wire-net mesh provides an indexing and stitching structure for the compressed charts. Experimental results show that random accessibility can be achieved with competent compression ratio, which is only a little worse than single-rate and comparable to progressive encoding. To demonstrate the merits of the framework, we apply it to process huge meshes in an out-of-core manner, such as out-of-core rendering and out-of-core editing.

We present a progressive encoding technique specifically designed for complex isosurfaces. It achieves better rate distortion performance than all standard mesh coders, and even improves on all previous single rate isosurface coders. Our novel algorithm handles isosurfaces with or without sharp features, and deals gracefully with high topologic and geometric complexity. The inside/outside function of the volume data is progressively transmitted through the use of an adaptive octree, while a local frame based encoding is used for the fine level placement of surface samples. Local patterns in topology and local smoothness in geometry are exploited by context-based arithmetic encoding, allowing us to achieve an average of 6.10 bits per vertex (b/v) at very low distortion. Of this rate only 0.65 b/v are dedicated to connectivity data: this improves by 24% over the best previous single rate isosurface encoder.

We present Angle-Analyzer, a new single-rate compression algorithm for triangle-quad hybrid meshes. Using a carefully-designed geometry-driven mesh traversal and an efficient encoding of intrinsic mesh properties, Angle-Analyzer produces compression ratios 40% better in connectivity and 20% better in geometry than the leading Touma and Gotsman technique for the same level of geometric distortion. The simplicity and performance of this new technique is demonstrated, and we provide extensive comparative tests to contrast our results with the current state-of-the-art techniques. Categories and Subject Descriptors (according to ACM CCS): I.3.3 [Computer Graphics]: Surface mesh compression, connectivity coding, geometry coding.