Advanced 5G wireless infrastructure should support any-to-any connectivity between densely arranged smart objects that form the emerging paradigm known as the Internet of Everything (IoE). While traditional wireless networks enable communication between devices using a single technology, 5G networks will need to support seamless connectivity between heterogeneous wireless objects, and consequently enable the proliferation of IoE networks. To tackle the complexity and versatility of the future IoE networks, 5G has to guarantee optimal usage of both spectrum and energy resources and further support technology-agnostic connectivity between objects. This can be realized by combining intelligent network control with adaptive software-defined air interfaces. In order to achieve this, current radio technology paradigms like Cloud RAN and Software Defined Radio (SDR) utilize centralized baseband signal processing mainly performed in software. With traditional SDR platforms, composed of separate radio and host commodity computer units, computationally-intensive signal processing algorithms and high-throughput connectivity between processing units are hard to realize. In addition, significant power consumption and large form factor may preclude any real-life deployment of such systems. On the other hand, modern hybrid FPGA technology tightly couples a FPGA fabric with hard core CPU on a single chip. This provides opportunities for implementing air interfaces based on hardware/software co-processing, resulting in increased processing throughput, reduced form factor and power consumption, while at the same time preserving flexibility. This paper examines how hybrid FPGAs can be combined with novel ideas such as RF Network-on-Chip (RFNoC) and partial reconfiguration, to form a flexible and compact platform for implementing low-power adaptive air interfaces. The proposed platform merges software and hardware processing units of SDR systems on a single chip. Therefore, it can provide interfaces for on-the-fly composition and reconfiguration of software and hardware radio modules. The resulting system enables the abstraction of air interfaces, where each access technology is composed of a structured sequence of modular radio processing units.
2. Overview
Common SDR approach
Propposed approach
Hardware accelerated SDR
Use case example
3. Common SDR approach
Intensive signal processing is done in host PC
No real time processing
Significant power and space consumption (no portability)
FPGA is seriously underutilized!
USRP
Host PC
?
13. Hardware accelerated SDR platform on top of Zynq
Xilinx Zynq
Dual Core ARM Cortex A9
GNU Radio
FPGA Accelerated
Block
Linux Kernel Device Driver
ARM to FPGA Interface
FPGA Accelerator
FPGA Fabric
ARM
TFlow
GnuRadio with HW
acceleration capabilities
GReasy
ARM - FPGA
shared memory
separate control and data
plane interfaces
14. Hardware accelerated SDR platform on top of Zynq
This concept enables
Offload of GnuRadio blocks
to FPGA
Frees up processor to
perform other tasks
15. Hardware accelerated SDR platform on top of Zynq
This concept enables
Offload of GnuRadio blocks
to FPGA
Real time reconfigurability
Frees up processor to
perform other tasks
Configuration
Port or ICAP
Configuration
Port Full
Bit File
Partial
Bit Files
FunctionA1
FunctionB1
FunctionC1FunctionC2
FunctionB2
FunctionA2FunctionA3
16. Hardware accelerated SDR platform on top of Zynq
This concept enables
Offload of GnuRadio blocks
to FPGA
Whole SDR system should
fit on one board
Real time reconfigurability
Frees processor to perform
other tasks
17. Example Scenario
Different applications –
different wireless standards
Our platform should support
various existing and future
emerging wireless technologies
at same time
IoT-CUBE HUB IoT-CUBE HUB
Internet
Repository
of SDR
library and
HW ACC
802.11g
device 802.11ac
device
802.15.4
device
BLE
device
802.11ah
xyz
device
Download SDR packages from
cloud Wireless as a Service
18. First step
Locally reconfiguring FPGA part of
platform
Changing 802.15.4 Tx with 802.11g
Tx
Bitstream for 802.11g is stored
locally on SD memory card
Sniffing simultaneously 802.11g and
802.15.4 packets to detect
reconfiguration
IoT-CUBE HUB
SD memory
card
802.11g
device
802.15.4
device
Intensive signal processing is done in host PC, on processor,
- We know that CPUs have sequential-general propose nature, because of that there
is no guarantee that certain processing task could be completed on time (or better to say there is no real time processing)
- As a result limited number of radios can be supported with traditional SDR platforms
- Also this approach implies the usage of host PC, which consumes significant power and space and precludes
deployment of such a system in real life
In same time USRP with FPGA as main processing unit is used just as interface for simple down/up conversion to/from baseband towards IF and RF
- FPGA which is typically good candidate for dedicated parallel processing is in practice underutilized
This is block scheme typical OFDM transmitter
Which contains several units in baseband processing chain
Realization of such system in GnuRadio would assume that all baseband processing is done on HOST PC
While USRP with FPGA as main processing unit is used just for simple up conversion of signal from baseband to (IF or RF) or vice versa
But there are 2 algorithms or processing blocks in transmitter chain
1. which are computationally intensive compared to others: IFFT and pulse shaping filtering
General proposed processor is not dedicated for parallel and real time processing
- Implementation of those algorithms on general proposed processor can imply that they will be bottlenecks of system
1. In same time the FPGA is underutilized at all, while those algorithms could be implemented on it
1. Obviously some things are done wrong in common
Such a platform could be formed on top of Zynq SoC as it contains ARM (as an CPU) processing unit and
FPGA on a single chip.
There are several tools that should be run on ARM to form SW part of platform
- TFlow is tool used for bitstream construction, which places and routes parameterized pre-compiled modules into a FPGA bitstream, and does so in a few seconds time (at least based on
paper) – similar like software-only flow
- Standard GnuRadio library is extended with HW accelerated blocks
- Those two SW components are forming SDR framework which is called Greasy, originally developing is started computational laboratory at Virginia Institute of Technology.
3. Between ARM and FPGA there 4 high throughput interfaces which are used as data plane interface for forwarding samples
4. Memory mapped interfaces are used as control plane interfaces for control of FPGA accelerators from GnuRadio
1. What this approach certainly enables is offloading of computationally intensive GnuRadio blocks to FPGA
2. This frees up processor to perform other tasks
1. With addition of Partial Reconfiguration this approach could enable even real time configurability (less then 10ms)
2. Partial Reconfiguration is the ability to dynamically modify blocks of logic by downloading partial bit files while the remaining logic continues to operate without interruption.
1. Such a system should be able to fit on one small PCB board which would enable usage of SDR in real life scenarios
Here we will show example usage of such a platform in real life scenario. In future there will be more
and more devices which will use different wireless technologies for establishing connectivity
Also every few years there is new wireless standard emerging
In order to support connectivity of devices that are using various technologies there is need
for development of some sort of upgradable IoT gateway (we can call it IoT-CUBE HUB)
SW part of IoT-CUBE HUB should have ability to download new SDR packages from cloud and to reprogram both HW and SW radio parts on SoC.
We can see this concept as Wireless as a Service)
As a first step we plan to illustrate the switching between two technologies - ZigBee and Wifi Tx
While bitstream for reconfiguration would be loaded on SD memory card, instead of downloading from remote repository