Real-time spectrum analyzer Gianfranco Miele, Ph.D www.eng.docente.unicas.it/gianfranco_miele g.miele@unicas.it
The evolution of RF signals Nowadays we can assist to the increasingly widespread success of cellular technology and wireless data networks. This has enabled manufacturers to embed relatively simple RF devices into all sorts of commodity products.
The evolution of RF signals As RF signals have become ubiquitous, problems with interference between the devices that generate them arise. Mobile phones that operate in licensed spectrum must be designed in order not to transmit RF power into adjacent frequency channels and cause interference. Devices that operate in unlicensed frequency bands must be designed to function properly in the presence of interfering signals, and are legally required to transmit in short bursts at low power levels.
Measurement challenges Considering the dynamic and complex nature of modern RF signals the following measurement tasks must be carefully accomplished: discovery of rare, short duration events or transient signals; discovering weak signals masked by stronger ones or noise; monitoring spectrum usage; characterizing time-variant modulation schemes; Analyzing burst, spread-spectrum and frequency hopping signals.
Real-time spectrum analyzer architecture From Fundamentals of real-time spectrum analysis Tektronix Inc.
Seamless capture Frame 1 Frame 2 Frame 3 Frame 4 FFT 1 FFT 2 FFT 3
Limits of traditional swept-tuned spectrum analyzers
Limits of traditional vector signal analyzers Frame 1 Frame 2 Frame 4 FFT 1 FFT 2
RF and IF section From Fundamentals of real-time spectrum analysis Tektronix Inc.
RF and IF section Capture bandwidth From Fundamentals of real-time spectrum analysis Tektronix Inc.
Frequency/time trade-off Wider capture bandwidth Narrower capture bandwidth Wide span Frequency domain effects Sample rate increases RBW increases Freq. resolution decreases Time domain effects Time resolution increases Frame length decreases Max record length decreases Narrow Span Frequency domain effects Sample rate decreases RBW decreases Freq resolution increases Time domain effects Time resolution decreases Frame length increases Max record length increases
Digital signal processing block diagram From Fundamentals of real-time spectrum analysis Tektronix Inc.
Trigger From Fundamentals of real-time spectrum analysis Tektronix Inc.
Frequency mask trigger Frequency mask area Trigger event Define a frequency mask which can be used to trigger on specific events in the frequency domain Reliably detect and capture elusive RF signals that a level trigger cannot see in a crowded spectral environment
Trigger From Fundamentals of real-time spectrum analysis Tektronix Inc.
Spectrogram The spectrogram shows how an RF signal changes over time in the frequency domain Frequency is the horizontal axis, time is the vertical axis, and power is represented by the color of the trace
Spectrogram burst signal
Overlap FFT No overlap Frame 1 Frame 2 Frame 3 Frame 4
Overlap FFT Overlap Frame 1 Frame 2 Frame 3 Overlap samples Frame 4 Frame 5
Overlap FFT Top spectrogram shows no overlap, Frame Resolution = 40us 3.48 msec 768 FFT points overlap (FFT interval 256 points) Frame resolution = 10us 870 usec 960 FFT points overlap (FFT interval 64 points) Frame resolution = 2.5us 217 usec