High-accuracy positioning has gained significant interest for many use-cases across various domains such as industrial internet of things (IIoT), healthcare and entertainment. Radio frequency (RF) measurements are widely utilized for user localization. However, challenging radio conditions such as non-line-of-sight (NLOS) and multipath propagation can deteriorate the positioning accuracy. Machine learning (ML)-based estimators have been proposed to overcome these challenges. RF measurements can be utilized for positioning in multiple ways resulting in time-based, angle-based and fingerprinting-based methods. Different methods, however, impose different implementation requirements to the system, and may perform differently in terms of accuracy for a given setting. In this paper, we use artificial neural networks (ANNs) to realize time-of-arrival (ToA)-based and channel impulse response (CIR) fingerprinting-based positioning. We compare their performance for different indoor environments based on real-world ultra-wideband (UWB) measurements. We first show that using ML techniques helps to improve the estimation accuracy compared to conventional techniques for time-based positioning. When comparing time-based and fingerprinting schemes using ANNs, we show that the favorable method in terms of positioning accuracy is different for different environments, where the accuracy is affected not only by the radio propagation conditions but also on the density and distribution of reference user locations used for fingerprinting.

Wireless high-accuracy positioning has recently attracted growing research interest due to diversified nature of applications such as industrial asset tracking, autonomous driving, process automation, and many more. However, obtaining a highly accurate location information is hampered by challenges due to the radio environment. A major source of error for time-based positioning methods is inaccurate time-of-arrival (ToA) or range estimation. Machine leaning (ML) techniques emerged as potential solutions to mitigate ToA-related errors. However, existing ML-based solutions either employ a set of features representing channel measurements only to a limited extent, or account for only device-specific proprietary methods of ToA estimation. In this paper, we propose a convolutional neural network (CNN) to estimate and mitigate the errors of a variety of ToA estimation methods utilizing channel impulse responses (CIRs). Based on real-world measurements from two independent campaigns, the proposed method yields significant improvements in ranging accuracy (up to 37%) of conventional ToA estimators, often eliminating the need of optimizing the underlying conventional methods.

High-accuracy positioning enables various applications such as industrial asset tracking, autonomous driving and process automation. Accurate location information relies on accurate time-of-arrival (ToA) or time-difference-of-arrival (TDoA) estimation in widely utilized time-based ranging. In this paper, we propose artificial neural network (ANN) based methods either to estimate T(D)oA directly or to mitigate the error of the conventional estimators. Based on real-world channel measurements, we show that the proposed direct ANN estimator outperforms the conventional estimators at least by approximately 37% and 24% in the 90th percentile ranging error, derived from ToA and TDoA estimations, respectively. Additionally, the proposed T(D)oA error-mitigating ANNs outperform the benchmark error mitigation methods with a gain varying between 17–43% in the 90th percentile ranging error, depending on the underlying conventional estimator. The ranging accuracy delivered by the direct estimation and error mitigation methods using ANNs are similar. Furthermore, the ANN estimators yield a more robust performance than the conventional estimators when the carrier frequency of the positioning signal is varied. ANN-based ToA estimation yields a marginally better ranging accuracy than ANN-based TDoA estimation. This advantage comes at the expense of a larger communication latency, while avoiding the need for synchronization among the positioning anchors.

Cellular networks enable high reliability of vehicle-to-vehicle (V2V) communications thanks to centralized, efficient coordination of radio resources. Collision-free transmissions are possible, where base stations could allocate orthogonal resources to the vehicles. However, in case of limited resources in relation to the data traffic load, the resource allocation task becomes a challenge. Current solutions propose heuristic algorithms that focus on resource reuse, often based on the location of the vehicles. Such schedulers are mainly designed assuming ideal network coverage conditions and are prone to performance degradation in case of coverage loss. Further, they typically rely on frequent scheduling updates, which increases the dependency on coverage. In this paper, we propose a reinforcement learning-based approach to scheduling V2V communications. Our solution, called iVRLS, delivers higher reliability than an enhanced version of a state-of-the-art benchmark algorithm in case of intermittent coverage conditions, while requiring less frequent scheduling. Following this approach, we enable a unified scheduler deployment irrespective of coverage, which offers graceful performance behavior across varying coverage conditions, thus making iVRLS a robust alternative to existing schedulers.

We explore a new approach to radio resource allocation for vehicle-to-vehicle (V2V) communications in case of out-of-coverage areas that are delimited by network infrastructure. By collecting and predicting information such as vehicle velocity, density and message traffic, the network infrastructure ensures reliability of the V2V services. We propose reserving required amount of resources for services that cannot be pre-scheduled (e.g., emergency braking, crash notifications, etc.), and scheduling those services that can be pre-scheduled (e.g., platooning). We analyze the resource reservation as a function of target reliability under varying vehicle densities and sizes of out-of-coverage area. For pre-scheduled services, we explore how variations in the vehicle velocities and predictions affect successful transmissions. The results indicate that increase in required reliability does not penalize the system prohibitively. On the other hand, speed prediction errors decrease the transmission success rate considerably, thus calling for a more flexible scheduler design.

The Fifth Generation (5G) of mobile and wireless networks is expected to fulfill the requirements of high variety of new use cases. One of the key challenges will be the varying capacity and coverage demands, non-uniformly distributed over space and time. In the current wireless networks, small cells are deployed at fixed locations via network planning, and, hence, lack the flexibility of reacting to spatially varying service requirements. Dynamic radio topology is an emerging concept towards 5G to address the real- time provisioning of services by means of flexible network deployment. In this regard, one promising system element of flexible network deployment to complement the existing heterogeneous networks (HetNets) is Vehicular Nomadic Nodes (VNNs). A VNN is a low-power access node which has a flexible backhaul, is movable, and activated temporarily to provide additional system capacity and/or coverage on demand. VNNs can be integrated into vehicles especially in urban areas, such as those of car sharing fleets or taxicab services. In this work, system-level performance of VNNs considering several flexible deployment scenarios is evaluated. Experiments are carried out separately in uplink (UL) and downlink (DL), where the performance of VNNs in terms of the user throughputs is compared with that of stationary picocells in a fixed HetNet, taking the macrocell- only deployment as the reference. The results show that flexible network deployment via VNNs is a promising enhancement to the current HetNets.