NXP Accelerates 5G Designs with RapidRF

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As Open RAN (O-RAN) continues to be deployed across the world, NXP enables faster 5G O-RAN deployments by creating enhanced reference designs. This includes an RF approach into customer designs with our RapidRF Smart LDMOS front-end solutions known as the 'SL Series'.

NXP’s 2nd generation RapidRF front-end reference board has been designed to save board space, reduce overall complexity and provide a complete ready-to-use circuit for TDD cellular applications. Like previous generations, this 2nd generation RapidRF implements fully matched amplifiers 50 ohms in and 50 ohms out. This allows for a complete line-up with no tuning required for ease of use.

RapidRF reference circuit showing complete layout of NXP’s PA, pre-driver, Rx LNA with T/R switch and a circulator.

The 2nd generation RapidRF now also includes a PA module with integrated autobias control that is factory preset for the optimal bias point, or it can be adjusted by the programmable serial interface to adjust for each application. The integrated autobias control has a novel closed-looped feedback circuit that adjusts the LDMOS gate voltage to maintain a consistent quiescent bias current for continuous temperature tracking. The integrated precise temperature sensor can be accessed via the serial interface for closely monitoring the PA’s operating temperature. With NXP’s full TX/RX line-up already implemented within the board, it streamlines 5G mMIMO radio designs because of its smaller, light weight assembly. The series is ideally suited for massive MIMO radio units (64T64R), outdoor small cells and as drivers for high-power macro base stations.

Block diagram of the RapidRF Smart LDMOS front-end design

No Compromise on Performance

The RapidRF design offers an overall compact footprint that still delivers on performance. Its integrated Doherty power amplifier is in a 10mm x 8mm package that delivers 40% efficiency at rated power. The design also includes a linear pre-driver, Rx LNA with T/R switch and a circulator.

An up close image of the RapidRF line-up showing the module and additional components.

The RapidRF front-end designs are ideal for 5G radio units requiring 2.5 to 8 Watts (34-39 dBm) average transmit power at the antenna. Versions for multiple bands use a common PCB layout, simplifying both design and manufacturing for faster time-to-market.

Want more information? Access our website.

The new RapidRF boards are available for order through our website. You can find our extensive set of reference circuits that cover 3.4 – 4.0 GHz.

Orderable Board Part Number Frequency Band / Power Board Description Available
RAPIDRF-36SL039 3400-3800 MHz,
8 W Avg. at 8 dB OBO 		64T64R mMIMO radio unit for B42 and B48 bands
A3M36SL039: Multi-Chip Module with integrated bias control
BTS6201U: Tx pre-driver
BTS7203U: Rx analog front-end IC with LNA/Tx switch 		Now
RAPIDRF-39SL039 3700-4000 MHz,
8 W Avg. at 8 dB OBO 		64T64R mMIMO radio unit for C-band
A3M39SL039: Multi-Chip Module with integrated bias control
BTS6201U: Tx pre-driver
BTS7203U: Rx analog front-end IC with LNA/Tx switch

Dive deeper into NXP's RapidRF 'SL' front-end designs.

Tags: Communication Infrastructure

Author

Megan Faust

Megan Faust

Tactical Marketer Radio Power Solutions, NXP Semiconductors

Megan is the Tactical Marketer for NXP's Radio Power Solutions Marketing Team at NXP Semiconductors. Megan Faust has worked with NXP in collaborating with both teams in RF Cellular Infrastructure and Multi-Market. NXP RP applications include SiGe, LDMOS and GaN RF solution designs for mobile and communication infrastructure as well as consumer, industrial and aerospace/defense. The NXP Radio Power marketing team is focused on making RF easier to use, to enable customers and address new market segments. Megan is responsible for the logistics, operations and distribution support for RF devices and evaluation boards. While also enabling innovation and marketing development for NXP Radio Power. Megan has earned a Bachelor of Arts degree in Business and is certified in Applied Business Data Analytics from Arizona State University.