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Innovations in Wideband RF Signal Processing

Key Takeaways

  • Wideband (WB) RF signal processing is handled by the systems supporting analog to digital conversion, digital to analog conversion, filtering, and signal synthesis.

  • Compressed sampling and analog-to-information converters are the two innovative approaches introduced in aerospace communication engineering. 

  • According to the Shannon theorem, “to reconstruct a one-dimensional signal from a set of samples, the sampling rate must be equal to or greater than twice the highest frequency in the signal”. 

RF signals are used for wireless communication

RF communication networks

Wireless communication systems, warfare electronics, and RF instrumentation are a few sectors that use Wideband (WB) RF signal processing to provide industry-standard performance. Engineers demand high input bandwidth, dynamic range, low noise, high-speed data transfer, acquisition, and conversion in RF signal application systems. 

The WB RF signal processing is handled by the systems supporting analog to digital conversion, digital to analog conversion, filtering, and signal synthesis. The main goal of RF signal processing is to enable Giga Samples Per Second (GSPS) data transfer rate, zero loss, good quality, and high-performance. As industries continue to demand uninterrupted communication and seamless connectivity, wideband RF signal processing systems continue to grow in importance.

Signal Conversion in WB RF Signal Processing

Advancements in signal conversion technology have restructured conventional RF communication systems. The advantage of modern RF converters is that the count of hardware components in the circuits is reduced, and high resolution, quality, and reconfigurability features are achieved. RF converters satisfy the size, weight, and power (SWaP) considerations and help to open unlimited possibilities in the data acquisition system. 

In the next section, we discuss the application of WB RF signal conversions in radars and aerospace wireless  communication systems. 

Direct WB RF Analog to Digital Conversion in Radars

In earlier days, the analog to digital conversion in radars had intermediate steps such as analog frequency down conversions and other signal processing operations. Currently, conventional radar receivers are replaced by direct WB analog to digital converters. The direct WB RF converters are robust and so advanced that they could skip the stages of mixers and local oscillators. Direct WB RF analog to digital conversion is inexpensive, and its performance is excellent. 

In direct WB RF analog to digital conversion, the desired performance is achieved with reduced hardware, size, weight, and power consumption. This type of direct conversion possesses high frequency as well as dynamic range operation. It samples the high-frequency analog signals using a comparatively low-performance digitizer. The perfect sampling downconversion with reduced hardware, SWaP considerations, and cost make direct WB RF signal processing popular. Since the mixers are missing in direct WB RF signal conversion, you don’t have to worry too much about harmonics and non-linearities in this direct signal conversion. Direct WB RF signal processing assures reliability, sustainability, cost-effectiveness, and wide frequency bandwidth in RF signal applications in radars. 

WB RF Signal Acquisition

Space station

RF signal measurements can be from space stations situated in satellites.

Wideband Spectrum Sensing (WSS) is an efficient method of RF signal characterization in the aerospace wireless communication field. RF signal measurements can be from ground stations or space stations situated in satellites. Compressed sampling and analog-to-information converters are the two innovative approaches introduced in aerospace communication engineering. 

The traditional sampling method is called the ‘sample-and-then-compress’ technique. In this traditional sampling scheme, the signal or data acquisition is carried out following the Shannon theorem.  According to the Shannon theorem, “to reconstruct a one-dimensional signal from a set of samples, the sampling rate must be equal to or greater than twice the highest frequency in the signal”.  Once the signal is acquired by following the Shannon theorem, part of it is discarded for the sake of compression. A lossy compression algorithm is usually utilized for reducing the size of the acquired signal.  

Compressed Sampling Technology

Compressed sampling technology is based on the direct sampling-of-the-information concept. Compressed sampling technology is employed in the acquisition of the analog RF signals. The signals are acquired directly at the information rate. Compressed sampling is focused on improving the linearity and operational bandwidth. The major distinguishing factor of compressed sampling from classical sample-and-then compress methods is the reduction in the number of acquired samples. A linear transformation of the RF signals in the analog domain is used for reducing the acquired signal samples. 

Analog-to-Information Converters

The analog-to-information converter implements the compressed sampling concept and acquires information from the RF signals. The analog-to-information converter aims at expanding the operating region by following SWaP considerations. Even though aerospace  wireless applications utilize the WB RF spectrum, only a small portion of the spectrum carries useful signals. Due to this small fraction of spectrum utilization, the signal acquisition in WB always faces signal sparsity in the frequency domain. The analog-to-information converters are useful in overcoming these limitations in aerospace applications and are employed along with the compressed sampling technique. 

The signal acquisition and conversion that comes with WB RF signal processing schemes are critical in achieving the desired performance. To achieve high-quality RF wireless communication and instrumentation infrastructure, signal conversion and acquisition technologies are optimized to efficiently capture the WB signals with low distortion, minimum loss, and high data rate.