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EN
We simultaneously live in two worlds—our internal world of thoughts, values, de-sires, and experiences; and our external world in which we are born, and perform ac-tions. Modernity, despite successfully developing our external world, has failed to de-velop our internal world. This has resulted in our lower nature being unleashed and a crisis of morals and values taking over society. This paper, drawing from the “science of consciousness” as detailed in the Indian tradition, looks at the nature of modernity and how we can address the challenges being posed by it, by training our consciousness to higher levels.
EN
This work examines the efficacy of deep learning (DL) based non-orthogonal multiple access (NOMA) receivers in vehicular communications (VC). Analytical formulations for the outage probability (OP), symbol error rate (SER), and ergodic sum rate for the researched vehicle networks are established Rusing i.i.d. Nakagami-m fading links. Standard receivers, such as least square (LS) and minimum mean square error (MMSE), are outperformed by the stacked long-short term memory (S-LSTM) based DL-NOMA receiver. Under real time propagation circumstances, including the cyclic prefix (CP) and clipping distortion, the simulation curves compare the performance of MMSE and LS receivers with that of the DL-NOMA receiver. According to numerical statistics, NOMA outperforms conventional orthogonal multiple access (OMA) by roughly 20% and has a high sum rate when considering i.i.d. fading links.
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