Intelligent Reflecting Surface Aided Vehicular Communications

Abstract

We investigate the use of an intelligent reflecting surface (IRS) in a millimeter-wave (mmWave) vehicular communication network. An intelligent reflecting surface consists of passive elements, which can reflect the incoming signals with adjustable phase shifts. By properly tuning the phase shifts we can improve link performance. This is known as phase optimization or passive beamforming. We consider the problem of rate maximization in the uplink, which utilizes an IRS. However, using an IRS brings more challenges in terms of channel estimation. We propose two schemes to reduce the channel estimation overhead associated with utilizing an IRS. One method uses the grouping of reflecting elements and the other one performs passive beamforming based on the position of the device. Numerical results show IRS can bring significant improvements to existing communication. Furthermore, to get a practical insight into vehicular communications aided by an IRS, we use a commercial ray-tracing tool to evaluate the performance.

Reconfigurable Intelligent Surfaces for Wireless Communications: Overview of Hardware Designs, Channel Models, and Estimation Techniques

“…Remarkable developments in the field of RIS-empowered communications has been witnessed in the past 3 years, and the underlying RIS technology has become mature enough for both academia and industry to explore its promising future use-cases. Particularly, within the context of RIS wireless systems, researchers have focused on joint active and passive beamforming problems [9], [28], [29], channel modeling under different conditions [30]- [34] and performance analyses [35]- [37], physical layer security [38]- [40], nonorthogonal multiple access schemes (NOMA) [41]- [43], noncoherent modulation [44], [45], vehicular networks [46], [47], hybrid passive and sensing designs [48]- [50], active and joint reflective-transmissive designs [51], [52], deep learning solutions [53]- [57], reflection modulation schemes [58]- [60], real-word experiments with prototyping [31], [61], [62], along with many other interesting application areas.…”

Beamforming Design in Vehicular Communication Systems With Multiple Reconfigurable Intelligent Surfaces: A Deep Learning Approach

“…Moreover, p x ∈ C Mt ×1 denotes the receive beamforming vector for the x-th beamforming codeword designed by using Maximal ratio combining (MRC) [40] at the BS. Further, the signals are combined from the RISs, following which the BS selects the optimal reflection beamforming vector for each RIS according to…”

Reconfigurable intelligent surfaces for wireless communications: Overview of hardware designs, channel models, and estimation techniques

“…Remarkable developments in the field of RISempowered communications have been witnessed in the past 3 years, and the underlying RIS technology has become mature enough for both academia and industry to explore its promising future use-cases. Particularly, within the context of RIS wireless systems, researchers have focused on joint active and passive beamforming problems [9,12,28] , channel modeling under different conditions [29−33] and performance analyses [34−36] , physical layer security [37−39] , non-orthogonal multiple access schemes (NOMA) [40−42] , non-coherent modulation [43,44] , vehicular networks [45,46] , hybrid passive and sensing designs [47−49] , active and joint reflective-transmissive designs [50,51] , deep learning solutions [52−56] , reflection modulation schemes [57−59] , real-word experiments with prototyping [30,60,61] , along with many other interesting application areas.…”