The Ultimate Buyer's Guide for Purchasing Open Source RFSOC Algorithm Verification Evaluation Board

In today’s rapidly developing wireless communication technology, USRP SDR (Software Defined Radio) has become a powerful tool for engineers, researchers and enthusiasts to explore the field of wireless communication, thanks to its high flexibility and scalability. This article will provide you with a detailed and practical beginner’s guide to help you master USRP SDR.

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1. Basic Concepts of USRP SDR

USRP (Universal Software Radio Peripheral) is the core hardware component in the Software Defined Radio (SDR) system. The functionality of traditional radio equipment is determined by fixed hardware circuits and is difficult to change once manufactured. SDR breaks this tightly coupled hardware-software model by implementing as many radio functions as possible in software, greatly enhancing the versatility and flexibility of the equipment.

As the hardware carrier of SDR, USRP can receive and transmit signals in different frequency bands. By combining with different software, it can perform various tasks from simple FM radio reception to complex 5G communication signal processing.

Taking common communication scenarios as an example, traditional radios can only receive broadcast signals in specific frequency bands with single functionality. In contrast, systems built based on USRP SDR can not only receive broadcasts in different frequency bands, but also analyze, decode, and even simulate signal transmission, meeting diverse needs such as scientific research experiments and signal monitoring.

2. Hardware Components of USRP-LW SDR

The USRP product line is diverse, with common models including USRP-LW N210, USRP-LW B210, and USRP-LW X310, each suitable for different application scenarios. Its hardware mainly consists of RF front-end, digital signal processing module, and interface components.

RF Front-End:

The RF front-end is responsible for operations such as filtering, amplification, and frequency conversion of RF signals. When receiving signals, it converts the received high-frequency signals into intermediate frequency or baseband signals suitable for processing by the digital signal processing module. When transmitting signals, it upconverts the baseband signals output by the digital signal processing module to the specified RF frequency band and performs power amplification.

For example, the RF front-end of USRP-LW B210 supports a frequency range of 70 MHz to 6 GHz, meeting the application requirements of most common wireless communication frequency bands. In practical applications, the performance of the RF front-end directly affects the sensitivity of received signals and the power and quality of transmitted signals. In environments with weak signals, it is necessary to reasonably adjust the gain of the RF front-end to obtain clear signals.

Digital Signal Processing Module:

The digital signal processing module performs signal sampling, quantization, and preliminary processing. It converts the analog signals output by the RF front-end into digital signals and carries out a series of digital signal processing operations, such as filtering and modulation/demodulation.

Taking USRP-LW N210 as an example, its digital signal processing module uses Xilinx FPGA, which can implement complex digital signal processing algorithms and support high-speed data processing. In actual projects, by programming the FPGA, the digital signal processing flow can be customized according to specific needs to achieve unique signal processing functions.

Interface Section:

The interface section (such as Ethernet port, USB port, etc.) is used to connect with computers to achieve data transmission and control command interaction. Different interface types vary in data transmission speed and stability.

For example, USRP-LW X310 connects to computers through Gigabit Ethernet interfaces, enabling high-speed data transmission and is suitable for processing large amounts of signal data. USRP B210 supports both USB 3.0 and Ethernet interfaces, allowing users to choose according to actual needs. When selecting USRP equipment, parameters such as operating frequency band, bandwidth, sampling rate, and data transmission requirements should be considered.

3. Software Installation and Configuration

USRP SDR requires corresponding software to function. GNU Radio is its commonly used open-source software platform, and UHD (USRP Hardware Driver), as the driver software for USRP devices, is also essential. Below are detailed installation and configuration steps as well as solutions to common problems:

System Environment Preparation:

GNU Radio and UHD have certain requirements for the system environment. It is recommended to use Linux systems (such as Ubuntu 20.04) for development, as they have better compatibility with open-source software. Before installation, ensure the system is updated to the latest version and install necessary dependency libraries such as build-essential, libboost-all-dev, and libusb-1.0-0-dev.

UHD Installation:

Download the UHD source code package from the official Ettus Research website, decompress it, enter the directory, and execute the compilation and installation commands in sequence. After installation, use the uhd_find_devices command to check whether the USRP device is correctly recognized. If the device is not recognized, it may be due to incorrect driver installation or device connection issues. Check the device connection cables, reinstall the UHD driver, or update the device firmware.

GNU Radio Installation:

GNU Radio can be installed in multiple ways, either through a package manager or by compiling from source code. It is recommended to compile from source code to obtain the latest features and better compatibility. Download the source code from the GNU Radio official website, decompress it, enter the directory, and execute the corresponding installation commands. During installation, you may encounter missing dependency libraries. Install the corresponding dependency libraries according to the error prompts. After installation, open GNU Radio Companion (GRC), a graphical programming environment where you can quickly build signal processing flows by dragging and dropping modules and connecting ports.

Configuring USRP in GRC:

Add USRP Source or USRP Sink modules in GRC and correctly configure parameters such as device IP (if connected via Ethernet), sampling rate, center frequency, and gain. Note that the sampling rate should be set according to actual needs and device performance. An excessively high sampling rate may cause excessive data transmission pressure or even packet loss. The center frequency should be set to the frequency band of the target signal. Gain adjustment should be optimized based on signal strength to avoid signal overload or low signal-to-noise ratio.

4. Practical Operations: Typical Applications Based on Different User Backgrounds

Radio Enthusiasts

For radio enthusiasts, USRP is an excellent tool to explore the world of wireless communication. Taking FM radio reception as an example, add the USRP Source module in GRC, set parameters such as center frequency (e.g., 88-108 MHz for local FM broadcast), sampling rate, and gain. Then add the FM Demodulator module to demodulate the received signal, and add the Audio Sink module to output the demodulated audio signal to the speaker. After connecting all modules, run the program to listen to FM broadcasts.

In addition, enthusiasts can try building simple wireless data transmission systems, such as using USRP to achieve low-speed data transmission based on ASK (Amplitude Shift Keying) or FSK (Frequency Shift Keying) modulation. By writing simple code, text messages can be transmitted between two USRP devices, experiencing the fun of wireless communication.

Researchers

Researchers use USRP to conduct cutting-edge wireless communication technology research. In 5G communication research, researchers use high-performance devices such as USRP-LW X310 to build 5G signal testing platforms. Configure the USRP Source module in GRC, set the center frequency to the 5G frequency band, adjust appropriate sampling rates and gains to generate 5G signals. Then add various digital signal processing modules to study and verify key technologies such as modulation/demodulation and channel coding of 5G signals.

In the field of cognitive radio research, researchers use USRP to monitor spectrum usage in real-time. By analyzing received signals, they detect idle spectrum resources and attempt to communicate on these idle frequency bands, achieving efficient spectrum utilization.

Enterprises

Enterprises widely use USRP in product development and testing processes. In wireless communication equipment manufacturing enterprises, USRP is used for prototype development and testing of new products. For example, when developing a new wireless sensor network device, engineers use USRP to build test environments, simulate different wireless channel conditions, and test and optimize the communication performance of the device.

In the field of intelligent transportation, enterprises can use USRP to develop and test V2X communication devices for the Internet of Vehicles. By using USRP devices to simulate communication signals between vehicles and infrastructure, and between vehicles, they can test the stability, reliability, and data transmission rates of communication, ensuring that the Internet of Vehicles devices meet practical application requirements.

Universities

USRP plays an important role in teaching and research activities at universities. In communication principle courses, teachers use USRP for experimental teaching, allowing students to intuitively understand the working principles of communication systems. For example, by building a simple AM modulation and demodulation experimental system, students can observe the generation of modulated signals and the demodulation process, deepening their understanding of theoretical knowledge.

In terms of research projects, university research teams use USRP to conduct innovative research in the field of wireless communication, such as new antenna technology research and wireless energy transmission research. Team members program the FPGA of USRP to implement custom signal processing algorithms and experimentally verify research results. If there is no audio output after running the program, it may be due to insufficient signal strength, incorrect audio output device settings, or unreasonable demodulation parameter settings. This can be troubleshooted by adjusting the gain of the USRP Source, checking the parameter configuration of the Audio Sink module, and optimizing the parameters of the FM Demodulator module.

5. USRP Buying Guide: Choosing the Ideal Model That Fits Your Needs

Faced with a wide variety of USRP models, how to accurately identify the one that best meets your needs is a key challenge for beginners. When choosing, you can consider the following key dimensions:

Frequency Band Requirements

Different application scenarios have specific requirements for the operating frequency band. For example, for FM broadcast-related research or experiments, the USRP device needs to cover the FM broadcast frequency band (88-108 MHz). If it involves 5G communication research, the frequency band range needs to extend to the corresponding 5G frequency band. For example, the RF front-end of USRP-LW B210 supports a frequency range of 70 MHz to 6 GHz, meeting most applications of common wireless communication frequency bands, suitable for users with broad frequency band requirements and diverse research fields. For professional applications in certain specific frequency bands, such as satellite communication research, models with more precise and targeted frequency band coverage may be required.

Bandwidth and Sampling Rate Considerations

Bandwidth determines the device’s ability to process signals per unit time, and the sampling rate affects the accuracy of digitized signals. High bandwidth and high sampling rates can handle more complex and rapidly changing signals, but they also place higher demands on device performance and data transmission. For high-speed digital communication system development, such as 5G signal simulation and testing, the device needs to have a large bandwidth (e.g., USRP-LW X310 supports up to 160 MHz bandwidth) and high sampling rate (its ADC sampling rate can reach up to 200 MS/s). On the contrary, if only simple low-rate signal monitoring is performed, lower bandwidth and sampling rate can meet the requirements, such as some models in the USRP-LW B series, which can achieve real-time bandwidth of up to 56 MHz, meeting some conventional monitoring tasks at a relatively low cost.

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Data Transmission Requirements

The type and speed of the data transmission interface are crucial. If large amounts of signal data need to be processed, such as long-term, high-resolution spectrum monitoring, USRP-LW X310 can achieve high-speed data transmission through Gigabit Ethernet or even 10 Gigabit Ethernet interfaces connected to computers, ensuring fast and stable data transmission. If the application scenario involves relatively small amounts of data and does not require high transmission rates, the USB 3.0 interface of USRP-LW B210 can meet the requirements. The USB interface is easy to connect, suitable for users who require device portability and simple connectivity, such as field testing scenarios.

Budget Constraints

After years of reverse engineering, work ongoing, https://github.com/openxc7

  Open source FPGA toolchain for AMD/Xilinx Series 7 chips, including Kintex-7. Supports Kintex7 (including 325/420/480t), Artix7, Spartan7 and Zynq7

https://f4pga.org & https://news.ycombinator.com/item?id= (umbrella project)

  Open source toolchain for the development of FPGAs of multiple vendors. Currently, it targets the Xilinx 7-Series, Lattice iCE40, Lattice ECP5 FPGAs, QuickLogic EOS S3 and is gradually being expanded to provide a comprehensive end-to-end FPGA synthesis flow.

https://www.bunniestudios.com/blog//litex-vs-vivado-firs...

> There’s already IP cores for DRAM, PCI express, ethernet, video, a softcore CPU (your choice of or1k or lm32) and more.. LiteX produces a design that uses about 20% of an XC7A50 FPGA with a runtime of about 10 minutes, whereas Vivado produces a design that consumes 85% of the same FPGA with a runtime of about 30-45 minutes.

https://news.ycombinator.com/item?id=#

> you can.. get [FPGA] parts for significant discounts in 1-off quantities through legit Chinese distributors like LCSC. For example, a XC7A35T-2FGG484I is 90$ on Digikey and 20$ at LCSC. I think a personalized deal for that part would be cheaper than 20$ though...

You're conflating open source IP cores such as LiteX with open source FPGA tooling to try to make the latter look better by using the former. Everyone knows that vendor IP is pretty terrible if you don't use it in the very narrow window in which it's validated and tested. That's why big defense contractors all use either the Northwest Logic or Rambus PCI-e cores on everything prior to Versal.

But at the same time, those cores are big and powerful, and optimize horribly because the customers who actually use them need all of those features. Those customers aren't really concerned with area but rather with meeting performance requirements. Using the Xilinx provided QDMA core, I've been able to achieve line rate performance on PCI-e 4.0 x16 for large DMA transactions with a setup time of about 3 total days of work. I'd like to see an open source solution that could even do that with just ACKing raw TLPs because I haven't found one yet.

As for pricing, AMD/Xilinx and Altera don't want you as a customer. They want to sign $10M+/yr accounts or accounts which push the envelope of what's possible in terms of frequency (HFT). And they price their products accordingly for the public. If you actually end up as a direct customer, the prices are significantly cheaper to the point where those cheaper Chinese vendors don't make sense to use.

Some contributors to the open hardware community (https://fossi-foundation.org/events/archive) can be followed on social media. See videos from FOSSI conferences and comments in these HN threads:

, "FPGA Dev Boards for $150 or Less", 80 comments, https://news.ycombinator.com/item?id=

, "FPGA dev board that's cheap, simple and supported by OSS toolchain", 70 comments, https://news.ycombinator.com/item?id=

Not an FPGA, but if you already have a recent Ryzen device, the AMD NPU might be worth a look, with Xilinx lineage and current AI/LLM market frenzy, https://news.ycombinator.com/item?id=

> The Versal AI Engine is the NPU. And the Ryzen CPUs NPU is almost exactly a Versal AI Engine IP block to the point that in the Linux kernel they share the same driver (amdxdna) and the reference material the kernel docs link to for the Ryzen NPUs is the Versal SoC's AI Engine architecture reference manual.

At one point, cheap ex-miner FPGAs were on eBay, https://hackaday.com//12/10/a-xilinx-zynq-linux-fpga-boa.... The Zynq (Arm + Xilinx FPGA) dev board is around $200, https://www.avnet.com/americas/products/avnet-boards/avnet-b.... There was an M.2 Xilinx FPGA (PicoEVB) that conveniently fit into a laptop for portable development, but it's not sold anymore. PCIe FPGAs are used for DMA security testing, some of those boards are available, https://github.com/ufrisk/pcileech-fpga

You also need to bring time to market, product lifetime, the need for upgrades, fixes and flexibility, risks and R&D cost including skillset and NRE when comparing FPGAs and ASICs. Most, basically all ASICs start out as FPGAs, either in labs or in real products.

Another aspect where FPGAs are interesting alternatives are security. Open up a fairly competent HSM and you will find FPGAs. FPGAs, esp ones that can be locked to a bitstream - for example anti-fuse or Flash based FPGAs from Microchip are used in high security systems. The machines can be built in a less secure setting, and the injection, provisioning of a machine can be done in a high security setting.

Dynamically reconfigurable systems was a very interesting idea. With support for partial reconfiguration, which allowed you to change accelarator cores connected to a CPU platform seemed to bring a lot of promise. Xilinx was an early provider with the C6x family IRRC through company they bought. AMD also provided devices with support for partial reconfiguration. There were also some research devices and startups for this in the early s. I planned to do a PhD around this topic. But tool, language support and the added cost in the devices seemed to have killed this. At least for now.

Today, in for example mobile systems, FPGAs provide the compute power CPUs can't do with the added ability do add new features as the standards evolve, regional market requirements affect the HW. But this is more like FW upgrades.

FPGA vs ASIC is a boring and tired comparison. Yeah obviously. For a fixed configuration an ASIC is basically just an FPGA, but without any of the parts that make an FPGA programmable.

If you don't need programmability, then all that flexibility represents pure waste. But then again, we can make the same argument with ASIC vs CPUs and GPUs. The ASIC always wins, because CPUs and GPUs come with unnecessary flexibility.

The real problem with FPGAs isn't even that they get beaten by ASICs, because you can always come up with a low volume market for them, especially as modern process nodes get more and more expensive to the point where bleeding edge FPGAs are becoming more and more viable. You can now have FPGAs on 7nm with better performance than ASICs with older but more affordable process nodes that fit in your budget.

The real problem is that the vast majority of FPGA manufacturers don't even play the same game as GPUs and CPUs. You can have fast single and double precision floats on a CPU and really really fast single precision floats on GPUs, but on FPGAs? Those are reserved for the elite Versal series (or Intel's equivalent). Every other FPGA manufacturer? Fixed point arithmetic plus bfloat16 if you are lucky.

Now let me tell you. For AI this doesn't really matter. The FPGAs that do AI, focus primarily on supporting a truckload of simultaneous of camera inputs. There is no real competition here. No CPU or GPU will let you connect as many cameras as an FPGA, unless its an SoC specifically built for VR headsets.

Meanwhile for everything else, not having single precision floats is a curse. Porting an algorithm from floating point to fixed point arithmetic is non-trivial and requires extensive engineering effort. You not only need to know how to work with hardware, but also need to understand the algorithm in its entirety and all the numerical consequences that entails. You go from dropping someone's algorithm into your code and having it work from the get go, to needing to understand every single line and having it break anyway.

These problems aren't impossible to fix, but they are guaranteed to go away the very instant you get your hands on floating point arithmetic. This leads to a paradox. FPGAs are extremely flexible, but simultaneously extremely constricting. The appeal is lost.

> Too expensive to scale, so you switch to ASIC ?

I think it's not so much about too expensive, but once you've got the resources it will always be better to switch to an ASIC.

Not a hardware engineer, but it seems obvious to me that any circuitry implemented using an FPGA will be physically bigger with more "wiring" (more resistance, more energy, more heat) than the equivalent ASIC, and accordingly the tolerances will need to be larger so clock speeds will be lower.

Basically, at scale an ASIC will always win out over an FPGA, unless your application is basically "give the user an FPGA" (but this is begging the question—unless your users are hardware engineers this can't be a goal).

If you look at the XC datasheet[1], you'll see there are 12,038 configuration bits, only of those bits actually program the LUTs, and 90% are for routing. For general purpose logic replacement, that offers great flexibility.

If you want to do general purpose computing, it's my strong (and minority) opinion that routing fabrics are a premature optimization. The trend has been in the wrong direction.

If you were to go the other way, and just build a systolic array of look up tables, as I have hypothesized for years with my BitGrid, you could save 90% of the silicon, and still get almost all of the compute. It gets better when you consider the active logic would only be between neighboring cells, thus capacitive power would be much lower, and speeds could be higher.

[1] https://downloads.reactivemicro.com/Electronics/FPGA/Xilinx%...

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