Parallel Channel over Wide Optical Spectrum: Mining and Matching-Based Optimization

Abstract

A question that hinges on the wireless communication is the application of big data technology to wireless communication in the physical layer or media access layer. We address the matching-based communication system optimization over parallel channels in wide optical spectrum. It is found that the dimension can be reduced significantly under certain line-of-sight optical wireless communication scenarios. Based on the dimension reduction, we design the quantization points in the reduced dimensional space, and store the optimal scheduling policy corresponding to each quantization point obtained in an offline manner.We first investigate the covariance of the achievable rates of the parallel channels. Then we formulate system optimization framework via matching the link gains of parallel channels to the quantization points, and obtain the sufficient conditions such that the utility loss of employing the quantization-based solution can be sufficiently small under sufficiently small quantization distortion in the reduced dimensional space. Such sufficient conditions can be satisfied for certain common optimization examples. We also address the matching-based communication system design for the matching under noisy channel estimation.