Adaptive LDPC coded high data rate based IR-UWB combined with VMIMO system for vehicular communication links

The automotive industry is developing significantly with the evolution of vehicular networks, intelligent transportation systems, and autonomous cars. These technologies are made achievable by recent advances in software, hardware, and communication systems, along with various applications and standardization efforts. On the other side, many impairments have existed due to these evolving, limiting the process of vehicular networks. So, this paper proposes an implementation of adaptive LDPC-coded, high data rate-based Impulse Radio-Ultra Wide Band (IR-UWB) combined with Virtual Multiple Input Multiple Output (VMIMO) systems for Vehicle-to-Vehicle (V2V) and Vehicle-to-Infrastructure (V2I) wireless communications. Furthermore, they are merged with orthogonal pulse shaping using a modified Hermite pulse. Besides, the paper compares Zero-Forcing (ZF) and Minimum Mean Square Error (MMSE) detection techniques. To enhance and mitigate the existing impairments facing the vehicular networks. The proposed system's performance is evaluated using both techniques, utilizing Bit Error Rate (BER), processing time, and resultant throughput analysis for both channel models. The simulation results include Non-Line of Sight (NLoS) and Line of Sight (LoS) scenarios for both channel models. Moreover, the simulation considers the effect of path loss and noise on the transmitted signal. In addition, the recent LDPC decoding algorithm termed “Modified Implementation Efficient Reliability Ratio Weighted Bit Flipping” (MIERRWBF) is evaluated through all latter channel models. At last, a novel adaptive LDPC decoder is proposed to further enhance the performance of all vehicular communication links under study. Consequently, the paper proposed an integrated adaptive LDPC-coded high data rate communication system to enhance vehicular communications using VMIMO mixed with modified Hermite orthogonal pulse shaping and deploying the MMSE recommended detection technique. © 2023 Elsevier GmbH