Abstract
A wide range of new ultra reliable low latency communication (URLLC) applications in next generation (NG) wireless systems demand real-time radio frequency (RF) data analytics of channel utilization (CU) that can help in making proactive resource allocation decisions. However, such real-time RF data analytics require processing of tens of millions of in-phase and quadrature (IQ) samples per second and sending huge quantities of samples to a resource allocating entity is not practical. We present design and implementation of an RF data analytics system which utilizes field-programmable gate arrays (FPGAs) at the network edge to process real-time streaming IQ samples from RF transceiver. FPGAs process millions of samples per second and output low-overhead descriptive statistics of wireless CU, such as mean CU values, maximum CU values, and entire histograms to obtain probability distribution of CU values, to a resource controller server where a quantile estimation based technique is used to detect congestion in CU in real-time. The FPGA-based modules are implemented on Xilinx's Zynq-7000 devices mounted with RF transceivers. We evaluate the performance of the implemented analytics system using extensive measurements, testing, and statistical analyses that are performed in both laboratory and over-the-air environments.
Highlights
Generation (NG) wireless networks are being designed to deliver very high data rates for enhanced mobile broadband and to provide support for a wide range of new ultra reliable low latency communication (URLLC) applications [1]
The main purpose of our work is to present design and implementation of a radio frequency (RF) data analytics system that can process in-phase and quadrature (IQ) samples to obtain in real-time wireless channel utilization (CU) descriptive statistics
We present a proactive CU congestion control method which can utilize real-time CU statistics collected via implemented RF analytic (RA) devices to determine if an access points (APs) needs more resources than allocated to it
Summary
Generation (NG) wireless networks are being designed to deliver very high data rates for enhanced mobile broadband and to provide support for a wide range of new ultra reliable low latency communication (URLLC) applications [1]. The creation of generations of wireless networks that can support a variety of new applications and technologies will require transformation in a variety of ways One such way is to use cloud technology based resource controllers that use dedicated radio frequency (RF) monitoring modules to collect/analyze data for better resource provisioning decisions [3], [4]. The main purpose of our work is to present design and implementation of a RF data analytics system that can process in-phase and quadrature (IQ) samples to obtain in real-time wireless channel utilization (CU) descriptive statistics. These statistics can help in making proactive resource allocation decisions at cloud technology based resource controllers.
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