<div>Given the increasing importance of mobile data access, extending broadband wireless access have become a global grand challenge. Wireless sensor networks (WSNs) and millimeter wave (mmWave) systems have been introduced to resolve these issues which motivate us to have further investigation. In this paper, the first two work assuming a quantized-and-forward WSN. We first develop a rate adaptive integer forcing source coding (RAIF) scheme to enhance the system throughput by assigning optimal quantization rate to each sensor optimally. Then, we are interested in developing an supervised online technique for solving classification problems. In order to enhance the classification performance, we developed this technique by jointly training the decision function that determines/estimates class label, quantizers across all sensors, and reliability of sensors such that M' most reliable sensors are enabled. Finally, we develop an idea to provide a folded low-resolution ADC array architecture that can utilize any of the widely published centralized folded ADC (FADC) implementation by placing the centralized FADC branches at different antenna elements in a millimeter wave (mmWave) system. With adding a simple analog shift and modulo operations prior to the sign quantizer, we show that the multiple low-resolution ADCs across the array elements can be properly designed such that they can be combined into an effective high-resolution ADC with excellent performance characteristics.</div>