Empirical Evaluation Of Component-Level Optimization Models For Communication Interfaces From A Pragmatic Perspective.

An efficient mmWave communication interface requires optimized selection of channel-noise aware transmitter components, noise-reduction amplifiers, low-BER (Bit Error Rate) receiver-components and efficient modulation techniques. A wide variety of such optimization models are proposed by researchers, and each of them very in terms of their internal & external operating characteristics. For instance, channel modelling techniques focus on optimizing transmitter components, while amplifier & de-amplifier design models focus on modulation & demodulation component optimizations. Due to such a wide variation is component designs, it becomes ambiguous for designers to identify optimal models for the context-specific communication interface designs. To reduce this ambiguity, a detailed survey of modern-day mmWave communication interfaces is discussed in this text. This discussion includes analysis of these models in terms of their functional nuances, contextual advantages, application-specific limitations, and deployment-specific future scopes. It was observed that deep learning models & bioinspired optimization techniques outperform others in terms of communication efficiency under real-time use cases. Based on this discussion, readers will be able to observe similar recommendations and identify optimal models for their mmWave communication radio designs. This discussion is further extended via a comparative evaluation of these models in terms of their computational complexity, processing delay, efficiency of optimization, BER levels, and scalability levels. Using this comparison, readers will be able to evaluate these models, and use them for their performance-specific use cases. To further contemplate this discussion, these parameters were combined to calculate a novel Communication Interface Rank Metric (CIRM), which will assist readers to identify micro-level communication interfaces that have higher overall efficiency levels even under different noise and channel conditions. [ABSTRACT FROM AUTHOR]