Spatial Wireless Channel Prediction under Location Uncertainty

Spatial wireless channel prediction is important for future wireless networks, and in particular for proactive resource allocation at different layers of the protocol stack. Various sources of uncertainty must be accounted for during modeling and to provide robust predictions. We investigate two channel prediction frameworks, classical Gaussian processes (cGP) and uncertain Gaussian processes (uGP), and analyze the impact of location uncertainty during learning/training and prediction/testing, for scenarios where measurements uncertainty are dominated by large-scale fading. We observe that cGP generally fails both in terms of learning the channel parameters and in predicting the channel in the presence of location uncertainties.\textcolor{blue}{{} }In contrast, uGP explicitly considers the location uncertainty. Using simulated data, we show that uGP is able to learn and predict the wireless channel

Performance Trade-off Investigation of B-IFDMA

A performance trade-off investigation is carried out between different possible uplink multiple access schemes, that are based on Orthogonal Frequency Division Multiplexing (OFDM), for International Mobile Telecommunication (IMT) Advanced systems. Between the Discrete Fourier Transform (DFT) precoded systems with different subcarrier allocation mappings and systems lacking DFT-precoders, Block Interleaved Frequency Division Multiple Access (B-IFDMA) is shown to provide a good trade-off between the frequency diversity collected, envelope properties achieved, and channel estimation performance compared to the other mapping schemes. The schemes are analyzed in the presence of the different possible modules which include equalizers, modulators, interleavers, and channel codes. In particular, robust codes such as Turbo codes are able to collect the diversity provided by such schemes, and B-IFDMA systems is shown to be able to beat the other systems in bit error rate (BER) performance terms

LAPRA: Location-aware Proactive Resource Allocation

Today’s indoor wireless networks employ reactive resource allocation methods to provide fair and efficient usage of the communication system. However, their reactive nature limits the quality of service (QoS) that can be offered to the user locations within the environment. In large crowded areas (airports, conferences), networks can get congested and users may suffer from poor QoS. To mitigate this, we propose and evaluate a location-aware user-centric proactive resource allocation approach (LAPRA), in which the users are proactive and seek good channel quality by moving to locations where the signal quality is good. As a result, the users and their locations are optimized to improve the overall QoS. We demonstrate that the proposed proactive approach enhances the user QoS and improves network throughput of the system

On the Trade-off Between Accuracy and Delay in Cooperative UWB Navigation

In ultra-wide bandwidth (UWB) cooperative navigation, nodes estimate their position by means of shared information. Such sharing has a direct impact on the position accuracy and medium access control (MAC) delay, which needs to be considered when designing UWB navigation systems. We investigate the interplay between UWB position accuracy and MAC delay for cooperative scenarios. We quantify this relation through fundamental lower bounds on position accuracy and MAC delay for arbitrary finite networks. Results show that the traditional ways to increase accuracy (e.g., increasing the number of anchors or the transmission power) as well as inter-node cooperation may lead to large MAC delays. We evaluate one method to mitigate these delays

On the Trade-Off Between Accuracy and Delay in Cooperative UWB Localization: Performance Bounds and Scaling Laws

Ultra-wide bandwidth (UWB) systems allow for accurate positioning in environments where global navigation satellite systems may fail, especially when complemented with cooperative processing. While cooperative UWB has led to centimeter-level accuracies, the communication overhead is often neglected. We quantify how accuracy and delay trade off in a wide variety of operation conditions. We also derive the asymptotic scaling of accuracy and delay, indicating that, in some conditions, standard cooperation offers the worst possible tradeoff. Both avenues lead to the same conclusion: indiscriminately targeting increased accuracy incurs a significant delay penalty. Simple countermeasures can be taken to reduce this penalty and obtain a meaningful accuracy/delay trade-off

FuSSI-Net: Fusion of Spatio-temporal Skeletons for Intention Prediction Network

Pedestrian intention recognition is very important to develop robust and safe autonomous driving (AD) and advanced driver assistance systems (ADAS) functionalities for urban driving. In this work, we develop an end-to-end pedestrian intention framework that performs well on day- and night- time scenarios. Our framework relies on objection detection bounding boxes combined with skeletal features of human pose. We study early, late, and combined (early and late) fusion mechanisms to exploit the skeletal features and reduce false positives as well to improve the intention prediction performance. The early fusion mechanism results in AP of 0.89 and precision/recall of 0.79/0.89 for pedestrian intention classification. Furthermore, we propose three new metrics to properly evaluate the pedestrian intention systems. Under these new evaluation metrics for the intention prediction, the proposed end-to-end network offers accurate pedestrian intention up to half a second ahead of the actual risky maneuver.Comment: 5 pages, 6 figures, 5 tables, IEEE Asilomar SS

Wireless Channel Prediction with Location Uncertainty

Spatial wireless channel prediction is important for future wireless networks, andin particular for anticipatory networks to perform proactive resource allocationat different layers of the protocol stack. In this thesis, we study location-awarechannel prediction with uncertainty in location information and understand its utilization to enhance the communication capabilities in wireless networks. Paper A discusses challenges of 5G networks, which include an increase in traffic and number of devices, robustness for mission-critical services, and a reduction in total energy consumption and latency. We then argue how location information can be leveraged in addressing several of the key challenges in 5G with location-aware channel prediction by maintaining a channel database. We use Gaussian processes (GP) from machine learning in developing a framework for location-aware channel prediction. We then give a broad overview of using location-aware channel prediction in addressing the aforementioned challenges across different layers of the protocol stack.In Paper B, we investigate two frameworks, classical Gaussian processes (cGP) and uncertain Gaussian processes (uGP), and analyze the impact of location uncertaintyduring both training and testing. We have demonstrated that, when heterogeneous location uncertainties are present, the cGP framework is unable to (i) learn the underlying channel parameters properly; (ii) predict the expected channel quality metric. By introducing a GP that operates directly on the location distribution, we find uGP, which is able to both learn and predict in the presence of location uncertainties. Paper C studies the tradeoffs in utilizing location information in the robust link scheduling problem (RLSP) at the medium access control layer. We compare two approaches to RLSP, one using channel gain estimates and the other using location information. Our comparison reveals that both approaches yield similarperformances, but with different overhead

Wireless Channel Prediction with Location Uncertainty

Spatial wireless channel prediction is important for future wireless networks, andin particular for anticipatory networks to perform proactive resource allocationat different layers of the protocol stack. In this thesis, we study location-awarechannel prediction with uncertainty in location information and understand its utilization to enhance the communication capabilities in wireless networks. Paper A discusses challenges of 5G networks, which include an increase in traffic and number of devices, robustness for mission-critical services, and a reduction in total energy consumption and latency. We then argue how location information can be leveraged in addressing several of the key challenges in 5G with location-aware channel prediction by maintaining a channel database. We use Gaussian processes (GP) from machine learning in developing a framework for location-aware channel prediction. We then give a broad overview of using location-aware channel prediction in addressing the aforementioned challenges across different layers of the protocol stack.In Paper B, we investigate two frameworks, classical Gaussian processes (cGP) and uncertain Gaussian processes (uGP), and analyze the impact of location uncertaintyduring both training and testing. We have demonstrated that, when heterogeneous location uncertainties are present, the cGP framework is unable to (i) learn the underlying channel parameters properly; (ii) predict the expected channel quality metric. By introducing a GP that operates directly on the location distribution, we find uGP, which is able to both learn and predict in the presence of location uncertainties. Paper C studies the tradeoffs in utilizing location information in the robust link scheduling problem (RLSP) at the medium access control layer. We compare two approaches to RLSP, one using channel gain estimates and the other using location information. Our comparison reveals that both approaches yield similarperformances, but with different overhead