Carrier Diversity Incorporation to Low-Complexity Near-ML Detection for Multicarrier Systems over V2V Radio Channel.

Area of Science:

• Electrical Engineering
• Wireless Communications
• Signal Processing

Background:

• Inter-carrier interference (ICI) poses a significant challenge in vehicle-to-vehicle (V2V) orthogonal frequency division multiplexing (OFDM) systems, complicating data detection.
• Existing ICI mitigation techniques, such as linear iterative receivers, demand extensive iterations, while non-linear methods fail to leverage channel frequency diversity.

Purpose of the Study:

• To propose a novel transmission and reception scheme for low-complexity data detection in doubly selective, highly time-varying V2V channels.
• To enhance data detection performance by effectively mitigating inter-carrier interference.

Main Methods:

• The proposed technique combines discrete Fourier transform (DFT) spreading with non-linear detection.
• This approach aims to harness the channel's frequency diversity for improved data detection accuracy.
• The scheme is designed for low computational complexity in highly dynamic V2V environments.

Main Results:

• The developed scheme achieves performance comparable to the optimal maximum likelihood (ML) detector.
• Compared to iterative linear minimum mean square error (LMMSE) detection, the proposed system demonstrates superior bit error rate (BER) performance.
• Significant computational cost reductions were observed: a third reduction with 48 subcarriers and a two-order magnitude reduction with 512 subcarriers in an OFDM system.

Conclusions:

• The proposed DFT-spreading coupled with non-linear detection offers an effective solution for ICI mitigation in V2V OFDM systems.
• This method provides a favorable trade-off between performance and computational complexity, outperforming traditional iterative receivers.
• The technique successfully exploits channel frequency diversity, leading to near-optimal data detection in challenging V2V communication scenarios.