Performance Evaluation of Multi-Antenna Receivers for Vehicular Communications in Live LTE Networks

Abstract

Cellular Vehicle to Everything (C-V2X) communications with its safety and infotainment services will require a high performance receivers to cope with challenging throughput, latency and reliability requirements. With increasing levels of interference due to cell densification and introduction of the roadside units, single antenna receivers may not be able to provide the required quality of service. In this work we experimentally study the performance of multi antenna receivers based on more than 150 km of data recorded during experiments using a customized software defined radio testbed. The performance of sixteen antennas Maximum Ratio Combiner (MRC) is compared with the receive beamforming technique for the live cellular signals in the 1.8 GHz band. This study is followed by an analysis of the impact of interference and measurement environment on the receiver's performance. The results show that receive beamforming can outperform MRC in low-interfered scenarios with high Line of Sight (LoS) probability, like highways or rural areas, while ensuring comparable performance even in dense urban scenarios where LoS communication cannot be guaranteed.

A USRP-Based Multi-Antenna Testbed for Reception of Multi-Site Cellular Signals

“…First, for validating the performance of multi-antenna receivers: beamforming and MRC are studied in [29]. The study focuses on the downlink interference observed by a vehicle and its impact on beamforming and MRC performance.…”

Enhancing Vehicular Link Performance Using Directional Antennas at the Terminal

“…The driven roads comprise a total of more than 150 km and the speed of the vehicle varied from 0 km/h in urbanized areas up to 100 km/h in the highway. Readers can refer to [8] for further details on the driven routes. The LTE connectivity was provided by a Danish network operator whose BSs locations were known.…”

“…Alternative solutions applied from the vehicle side become attractive for car vendors due to their possible implementation without standardization. In [8], authors have shown the potential DL Signal to Interference and Noise Ratio (SINR) gains of using receive beamforming on the vehicle side. However, receive beamforming requires expensive and synchronized hardware equipment in addition to high computational complexity of AoA estimation.…”

Neural Network Equalisation and Symbol Detection for 802.11p V2V Communication at 5.9GHz

“…Another proposal introduced in [5] attempts to predict Doppler changes to allow for easier compensation of the channel effects, the results outperform a benchmark least squares estimation however only simulation results are presented thus its ability to perform under real world constraints are unknown. Further proposals include the use of multiple input multiple output (MIMO) systems [6], cooperative communication [7], and integration into existing long term evolution (LTE) and future 5G systems [8]. There have also been several proposals to use machine learning in the vehicular network, such as in [9] where they investigate the use of artificial neural networks (ANNs) to increase throughput in a vehicular ad-hoc network (VANET) by optimising the medium access control (MAC) layer of the 802.11p protocol.…”