Browsing by Author "Javanmardi, Gowhar"
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- 5G network as key-enabler for vehicular platooningPublication . Duarte, Paulo; Soyturk, Mujdat; Robles, Ramiro; Araújo, Marco; Yaman, Berkay; Goes, Adriano; Mendes, Bruno; Javanmardi, Gowhar; Gutiérrez Gaitán, MiguelThe future of goods transportation will rely on increased efficiency, lower risks, and diminished delays through the use of vehicle platoons that benefit from vehicular connectivity using V2X (Vehicle to Everything) applications. This article describes a system that offers the aforementioned vehicular connectivity to platoons, based on AI-enhanced 5G for resource allocation in wireless platoon intra-communications under three scenarios (latency emergency braking, platoon wireless resource management in tunnels, V2X communications interference in a traffic congestion). Demos are described for each of the scenarios, targeting different layers, starting by the PHY (physical) layer where propagation models are implemented, then a simulation-based MAC (medium access control) layer that allows the allocation of resources to the connected User Equipments (UE) and finally a management and orchestration layer capable of monitoring and managing the radio network, offering features such as network slicing management using O-RAN (Open Radio Access Network) standards.
- Joint spectrum and antenna selection diversity for V2V links with ground reflectionsPublication . Robles, Ramiro; Gutiérrez Gaitán, Miguel; Javanmardi, Gowhar; Kurunathan, HarrisonThis paper addresses the study of a fading-rejection algorithm based on joint spectrum and antenna selection in a vehicle-to-vehicle (V2V) multiple antenna system. The central objective of this selective scheme is to provide resilience against the destructive effects of the superposition of line-of-sight (LOS) and ground-reflected signals. The paper also provides an extension to channels that combine such deterministic superposition of multiple paths and reflections with an uncorrelated double scattering component, which shows how the algorithm is also beneficial under more general channel modelling assumptions. A multiple-ray performance model is used to describe the deterministic signal interactions between multiple antennas across contiguous vehicles. The antenna selection component is shown to reject deterministic fading, particularly at short values of the inter-vehicular distance. By contrast, when the spectrum bands are correctly chosen, the spectrum selection component can exhibit gains for a wider range of inter-vehicular distances than its antenna selection counterpart. This indicates that both components of the proposed solution are, in some cases, complementary, and therefore, they should be considered together in V2V multiple antenna design. Derivation of the statistics of the selective scheme considering an additional double scattering stochastic channel component is here proposed. Simulation results from all expressions show important gains for a given range of inter-vehicular distances.
- Orthogonal Space-Time Block Coding for Double Scattering V2V Links with LOS and Ground ReflectionsPublication . Gaitán, Miguel Gutiérrez; Javanmardi, Gowhar; Robles, RamiroThis work presents the performance analysis of space-time block codes (STBCs) for vehicle-to-vehicle (V2V) fast-fading channels in scenarios with modified line-of-sight (LOS). The objective is to investigate how the V2V MIMO (multiple-input multiple-output) system performance is influenced by two important impairments: deterministic ground reflections and an increased Doppler frequency (time-variant channels). STBCs of various coding rates (using an approximation model) are evaluated by assuming antenna elements distributed over the surface of two contiguous vehicles. A multi-ray model is used to study the multiple constructive/destructive interference patterns of the transmitted/received signals by all pairs of Tx–Rx antenna links considering ground reflections. A double scattering model is used to include the effects of stochastic channel components that depend on the Doppler frequency. The results show that STBCs are capable of counteracting fades produced by destructive self-interference components across a range of inter-vehicle distances and for a range of Doppler frequency values. Notably, the effectiveness of STBCs in deep fades is shown to outperform schemes with exclusive receive diversity, despite the interference created by the loss of orthogonality in time-varying channels with a moderate increase of Doppler frequency (mainly due to higher vehicle speeds, higher frequency or shorter time slots). Higher-order STBCs with rate losses are also evaluated using an approximation model, showing interesting gains even for low coding rate performance, particularly when accompanied by a multiple antenna receiver. Overall, these results can shed light on how to exploit transmit diversity in time-varying vehicular channels with modified LOS.
- Wireless Channel Prediction Using Artificial Intelligence with Constrained Data SetsPublication . Javanmardi, Gowhar; Samano-Robles, RamiroThis work deals with the study of artificial intelligence (AI) tools for purposes of vehicular wireless channel prediction. The objective is to test the ability of different types of AI and machine learning (ML) algorithms under different types of implementation constraints. We focus particularly in highly changing scenarios where the channel state information changes relatively fast and therefore the relevant measurements or long-term statistical models are therefore scarce. This means that the training of our models can be potentially inaccurate or incomplete and we need to investigate which AI algorithm behaves better in this challenging condition. In future work we aim to investigate also computation complexity constraints, real-time deadlines, and outdated/distorted or noisy data set samples. We also aim to correlate the main properties of the well-known Jakes' channel model with the effectiveness of the type of prediction and the parameters of the different algorithms being tested. The objective of channel prediction in vehicular networks is to reduce allocation and transmission errors, thereby reducing latency and improving message transmission reliability, which is crucial for future applications such as autonomous vehicles. Results show that even in situations with incomplete data sets, AI can provide good approximate predictions on the channel outcome.