Long-term measurements of spectrum occupancy characteristics

Abstract

This paper describes the results from a series of long-term observations of spectrum occupancy in the range 300 MHz-4.9 GHz made at a single location. Over 6 months of data has been gathered from a measurement system that is designed to operate continuously, allowing a full picture of spectrum occupancy to be built up. The large amount of data captured permits analysis to identify the portions of spectrum that are occupied non-continuously (i.e. with a duty cycle of less than 100%); these channels might be most suitable for use by a cognitive radio (CR) system that is able to identify temporary spectrum holes and then exploit them. The aim of performing a comprehensive analysis of channel occupancy and its variability is to improve the potential for smart spectrum access by CR devices, by informing their choices concerning the particular portions of spectrum to scan, and how frequently. Results allow measurement channels to be categorised according to their duty cycle; spectrum with a duty cycle between 10% and 90% is considered to be particularly suitable and is found in the regions below 500 MHz and between 1 GHz and 1.2 GHz. A predictable pattern of time occupancy is discovered, caused by variable activity in the portions of spectrum allocated to cellular systems. The characteristics of the occupancy duty cycle according to the time of day is also investigated; some frequencies are shown to exhibit considerable variation of occupancy depending on the hour of observation. A subset of channels is selected for more detailed investigation including short-term variations in channel occupancy.

A Survey of Measurement-Based Spectrum Occupancy Modeling for Cognitive Radios

“…Power: [11] − [17], [20], [25], [27] Occupancy: [12] − [15], [17] − [19], [21] − [24], [26], [28], [29] Duty cycle: [12] − [19], [28] CDF, PDF…”

“…Also, in some cases, the noise is not Gaussian [28]. Finally, instead of using the calculated noise floor, the detection threshold in (1) is often set a few dBs above the noise floor such that the probability of false alarm satisfies a certain value to take into account strong noise samples that are comparable to signals.…”

“…This will allow the extraction of long-term statistics from the measurements. Indeed, reference [28] revealed the possibility of using the difference between weekdays and weekends to explore spectrum opportunities and determined a set of "suitable" channels above 1 GHz, while reference [29] examined the seasonal, weekly and daily trends in different frequency bands.…”

“…This may be important, as wireless relaying also happens locally between peers that may be difficult to be captured by settings used in other measurement campaigns. In [28] and [29], long-term measurement campaigns were conducted. While most previous works take measurements for a few days, [28] took measurements for six months while [29] took measurements for three years.…”

“…In [28] and [29], long-term measurement campaigns were conducted. While most previous works take measurements for a few days, [28] took measurements for six months while [29] took measurements for three years. This will allow the extraction of long-term statistics from the measurements.…”