an optimized gfdm software implementation for future cloud ... · heads (rrhs). thus, we present...

An optimized GFDM software implementation for future Cloud-RAN and fieldtests

Johannes Demel [email protected] Bockelmann [email protected] Dekorsy [email protected]

Department of Communications Engineering, University of Bremen,Bremen, Germany

Andrej Rode [email protected] Koslowski [email protected] K. Jondral [email protected]

Communications Engineering Lab, Karlsruhe Institute of Technology,Karlsruhe, Germany

Abstract5th Generation (5G) and Industry 4.0 (I4.0)cellular systems have many different use-caseswhich impose a diverse set of requirements ona candidate Multi-Carrier System (MCS). Theserequirements demand high flexibility and recon-figurability from any candidate waveform. In thispaper we present gr-gfdm a Software-DefinedRadio (SDR) implementation of the waveformcandidate Generalized Frequency Division Mul-tiplexing (GFDM) in GNU Radio, discuss the de-tails of our implementation and show how theSDR flexibility can be employed for use in sim-ulations and field trials. With a careful designthe same implementation can be used in testing,simulation, field trials and interfaces to other pro-gramming languages are easily added. Further-more we facilitate an over-the-air transmissionwith our implementation in order to demonstrateits capabilities, where we could achieve samplerates of up to 25MS/s.

1. IntroductionCurrent implementations of waveform candidates are fo-cused on investigations of their performance in an aca-demic setup or they are implemented in hardware withField Programmable Gate Arrays (FPGAs) (Danneberget al., 2015). This results in rather high performance lossesin the software implementation or missing flexibility inthe hardware implementation. For further analysis of new

Proceedings of the 7 th GNU Radio Conference, Copyright 2017by the author(s).

waveforms a performant SDR implementation which canbe used in both, simulation and field trials, is desirablesince it retains its flexibility without suffering much per-formance losses. Additionally, an SDR implementation iscrucial for Cloud Radio Access Network (Cloud RAN) inorder to move the computing away from the Remote RadioHeads (RRHs). Thus, we present the GFDM sofware im-plementation gr-gfdm that offers the required performanceand flexibility (Rode, 2017).

1.1. Waveform

For upcoming 5G and I4.0 applications new MCS candi-dates are proposed. These waveforms try to overcome Or-thogonal Frequency Division Multiplexing (OFDM) short-comings, such as high Out-Of-Band (OOB) emissionswhile retaining its advantages.

One of the goals of the upcoming 5G standard is to pro-vide a common communication standard for all types ofmodern communication. This includes data driven appli-cations for end users where high data rates are requiredbut also Machine-type-Communication (MTC) for indus-trial applications where latency and reliability are crucial.Each type of communication imposes its own requirementson the physical layer, but the waveform does not have tomeet requirements for all communication types at the sametime. This is an opportunity to introduce a flexible wave-form implemented in software which can be reconfiguredon the fly for different types of communication.

For I4.0 closed loop control the waveform can be param-eterized to meet low latency and robustness requirementsand for high data throughput the parameters can be changedto achieve maximal spectrum efficiency and throughput.For the envisioned cooperative environment new wave-

An optimized GFDM software implementation for future Cloud-RAN and field tests

forms require low OOB emissions in order to coexist ina heterogeneous environment.

Current 4th Generation (4G) systems rely on OFDM whichis a simple and effective MCS. However, several shortcom-ings with regards to OFDM were identified (Schaich &Wild, 2014). As the spectrum becomes more and morecrowded while bandwidth demands increase, the coexis-tence properties of new waveforms need to be improvedtoo. Aside of poor OOB emission properties, strict syn-chronization requirements and poor spectral efficiency arefurther OFDM shortcomings.

Several MCS candidate waveforms exist which offer differ-ent approaches to overcome OFDM shortcomings (Sahinet al., 2014). Filter-Bank Multi-Carrier (FBMC) minimizesOOB emissions by filtering each subcarrier but introduceslarge filter delays (Schaich & Wild, 2014). Universal Fil-terbank Multi-Carrier (UFMC) groups multiple subcarriersand then filters each group jointly in order to decrease filterdelays, though it still only considers timeslots individually(Vakilian et al., 2013).

GFDM goes beyond symbol-based modulation by modu-lating entire frames (Michailow et al., 2014). Generally,GFDM is a highly flexible non-orthogonal waveform. Cir-cular filters retain the option to use a Cyclic Prefix (CP) anda Cyclic Suffix (CS) for whole frames. Furthermore, thesefilters avoid large delays and can minimize OOB emissions.The parametrization can then be adjusted for either low la-tency requirements or high data throughput and spectraleffiency.

1.2. Software Defined Radio

Straight forward MCS implementations often suffer fromvery heavy computational demands which makes theirimplementation on General Purpose Processor (GPP) in-tractable for SDR applications. Efficient software imple-mentations for simulations and field tests are crucial to ver-ify the performance of these new MCS. This enables pa-rameterization of the waveform during run-time and bay beperformed in a computation center in the Cloud RAN. Thisfurther stresses the need for an optimized software imple-mentation of the used waveform.

In (Danneberg et al., 2015) a GFDM implementation inLabView targeting the Universal Software Radio Periph-eral (USRP) X310 is presented. All relevant parts of thesignal processing chain are implemented for this specifichardware and run on an FPGA. This contradicts the targetto use the same codebase for simulations and field trials aswell as its portability. An SDR implementation for simula-tions and field tests requires more portability and flexibilityin order to adapt the communication system for its currentpurpose.

ResourceMapper

GFDMModulator

Cyclic PrefixInsertion

PreambleInsertion

d x

Figure 1. Transmitter processing steps

1.3. Main Contribution

The main contribution of this paper is a presentation ofthe open source SDR GFDM implementation gr-gfdm inGNU Radio (Rode, 2017). The implementation aims tofill the gap between slow software simulation implementa-tions and inflexible hardware implementations. We discussthe theoretical background as well as the implementationwith its design concepts. The paper is concluded with apresentation of over-the-air transmission capabilites of theimplementation.

2. GFDM system descriptionThis Section introduces the concepts of Generalized Fre-quency Division Multiplexing (GFDM). It is split into atransmitter and receiver subsections, depicted in Fig. 1 and2, which detail the specifics of the system. A stream ofcomplex symbols d ∈ C goes into the transmitter and astream of estimates for the transmitted symbols d is pro-duced by the receiver. In contrast to OFDM, GFDM fo-cuses on whole frames instead of individual timeslots in or-der to optimize its transmission properties. This approachintroduces more flexibility and thus, GFDM can be bettermatched to individual use-cases. Also, latencies may beminimized by designing a system accordingly.

2.1. Transmitter

The transmitter portion of a GFDM system is composed ofthree stages depicted in Fig. 1. In this paper we do not con-sider channel coding but we expect it to be part of a com-plete system. Multiple complex symbols d are grouped intoa frame and assigned to their point on the time-frequencyplane, or lattice (Sahin et al., 2014). The modulator trans-forms this frame into a complex baseband signal vector. ACP is added in order to obtain the cyclic channel propertiesat the receiver and thus enable simple one-tap Frequency-Domain Equalization (FDE).

MCS waveforms modulate symbols such that each symbolin a frame with M timeslots and K subcarriers is locatedat its unique point on the lattice. Thus, a maximum of N =KM points may be transmitted. The lattice for a GFDMframe is represented by the matrix D ∈ CM×K where eachelement dm,k corresponds to a symbol in the mth timesloton the kth subcarrier (Michailow et al., 2012). Often, somesubcarriers are not used for transmission but only Kon ≤ Kare used.


In case Kon < K, subcarriers which are unused correspondto columns in D which are filled with zeros and conse-quently the occupied bandwidth is reduced. Thus, the re-source mapper groups MKon complex symbols togetherwith M(K − Kon) zeros into D. Stacking D’s columnsdk according to

d =[dT0 dT1 . . . dTk . . . dTK−2 dTK−1

]T(1)

returns a symbol vector d ∈ CN×1 containing all symbolsof a GFDM frame.

GFDM modulation is a linear operation which can be de-noted in matrix notation

x = Ad (2)

where x is the transmit vector and A ∈ CN×N is a modula-tion matrix containing the filter coefficients for one symbolin each column (Michailow et al., 2014). This modulationmatrix A can be written as

A =[g0,0 g0,1 . . . g1,0 . . . gK−1,M−1

](3)

where the columns represent filters derived from a proto-type filter g ∈ CN×1. The n-th element of the derivedfilter for the k-th subcarrier in the m-th timeslot is obtainedby modulating and circularly shifting the prototype filter

gk,m[n] = g[(n−mK) mod N ] · ej2πn kK . (4)

From (2) it can be observed that GFDM is a linear, frame-based multicarrier modulation scheme. The desired spec-tral properties can be matched by means of the chosen pro-totype filter g, e.g. Root-Raised-Cosine (RRC) or Gaussianfilters. The chosen prototype filter may constitute orthog-onal or non-orthogonal modulation and thus controls howmuch self-interference is present in a specific GFDM sys-tem. Appropriate prototype filter design may be performedwith the aid of ambiguity functions (Matthias Wolteringet al., 2015; Du, 2008). In general, non-orthogonal modu-lation must be assumed and self-interference must be con-sidered.

Due to the cyclic filter shift in (4), a GFDM frame is cyclic.Thus, a CP can be employed to obtain a cyclic channel atthe receiver and one-tap FDE is feasible. In contrast toOFDM, only one CP per frame is necessary which can beexploited to shorten frames and thus in turn reduce latency.

Matrix multiplication is an expensive operation, especiallywhen N tends to be large. Frequency domain modulationand thus demodulation promises to drastically reduce com-plexity (Gaspar et al., 2013). Therefore, (2) can be rewrit-ten to

x = F−1N

K−1∑k=0

P(k)N×MLGML×MLRML×MFMdk (5)

where dk denotes the complex symbols modulated ontoone subcarrier. First, these symbols are transformed to fre-quency domain with an M -point Fourier transform FM .Next, upsampling in frequency domain is performed bymeans of a repetition matrix RML×M , where L ≤ K is theoverlap factor. GML×ML is a diagonal filter matrix withthe ML prototype filter taps on its diagonal. P (k)

N×ML per-forms subcarrier modulation by shifting the samples into avector of size N at the corresponding position of the kthsubcarrier. For K = L, (5) is an alternative representationof (2). If the prototype filter is chosen such that its OOBleakage decays outside its subcarrier bandwidth L can be-come smaller than K. In case RRC filters are used, onlyadjacent subcarriers overlap. Typically L = 2 in this caseand it becomes clear that L controls the modulators com-putational complexity.

From (5), it becomes apparent that M and K should bothbe a power of two in order to exploit the efficient Cooley-Tukey Fast Fourier Transform (FFT) algorithm. However,M is chosen such that it is an odd number because GFDMsystems exhibit bad performance if both M and K are evennumbers because Zero-Forcing (ZF) receivers do not exist(Matthe et al., 2014).

The next transmitter stage performs CP insertion. This ispossible on a per frame basis because of the chosen cyclicshift in filter taps. Also, a CS may be inserted. Havingonly one CP and CS, of size NCP and NCS respectively,per frame improves spectral efficiency while still enablingsimple one-tap FDE at the receiver.

Apart from CP insertion, frame windowing is performed inthis stage in order to reduce OOB emissions (Michailowet al., 2014). Here frame windowing is applied to wholeframes similar to (IEEE, 2012) where it is employed on aper-symbol basis. In accordance with (IEEE, 2012), usu-ally a Raised-Cosine (RC) filter is applied. For the framewindow Nw samples at the beginning and end of eachframe are considered.

Before a GFDM frame is transmitted, a preamble isprepended as depicted in Fig. 1. This preamble is usedfor synchronization in the receiver but may also be used foran initial channel estimate.

2.2. Synchronization

A GFDM receiver must first locate a received frame yin the received sample stream before it can be demodu-lated. In (Demel et al., 2015), it has been shown that framesynchronization is a computationally expensive operation.Thus, we consider multiple synchronization stages in orderto bound complexity.

The receiver processing chain, depicted in Fig. 2, is splitinto two stages. In the initial energy-based synchronization


Energy-basedSync

Preamble-basedSync

Cyclic PrefixRemoval

GFDMDemodulator

ResourceDemapper

y

d

Figure 2. Receiver processing steps

stage, a coarse frame start is detected by detecting a risein energy. The energy-based synchronization stage maybe skipped if coarse synchronization is already achieved.Afterwards a high precision synchronization stage followswhich also facilitates tracking. This preamble-based syn-chronization stage only searches in a window around thedetected coarse frame start for the known preamble.

This fine synchronization is performed with an improvedSchmidl&Cox algorithm (Awoyesila et al., 2008) which isadopted for GFDM (Gaspar et al., 2014). The synchroniza-tion algorithm requires the preamble to consist of two iden-tical parts which are transmitted consecutively. In (Gasparet al., 2014) a short GFDM frame with M = 2 timeslots isproposed with identical pseudo-random symbols in the firstand second timeslot. The algorithm first performs a fixed-lag autocorrelation of length subcarriers K which yieldsa frame timing estimation with moderate accuracy. Fur-thermore, the fixed-lag autocorrelation is used to estimatea Carrier-Frequency-Offset (CFO).

In a window around this moderately accurate timing esti-mation a crosscorrelation with the received samples and theknown preamble is performed. In this window, element-wise multiplication of the fixed-lag autocorrelation andcrosscorrelation values results in a high precision timingsynchronization. Eventually, synchronization yields a re-ceived frame vector including its CP and CS.

2.3. Receiver

A GFDM receiver must perform equalization and demod-ulate a received frame. Since GFDM is a non-orthogonalmodulation scheme, Interference-Cancellation (IC) mightbe performed in order to remove self-interference.

Considering Fig. 2 after the synchronization stages,the modulated received frame y = Hx + n is ob-tained by removing the CP and CS with H being acyclic block fading channel matrix and n being Addi-tive White Gaussian Noise (AWGN). A ZF receiver withGZF = A−1 would remove all self-interference, intro-duced by the non-orthogonal waveform, at the expenseof noise-enhancement. In order to maximize Signal-to-Noise-Ratio (SNR) a Matched-Filter (MF) receiver withGMF = AH is used but does not remove self-interference.

Unlike OFDM, GFDM enables to control self-interferenceby means of filter design. In case of RRC filters, only adja-cent subcarriers need to be considered.

Similar to (5), demodulation is performed in frequency do-main with GML×ML = GMF. First, the frame y is par-tially demodulated per subcarrier k

y0RX,k = (RML×M )

TGML×ML

(P

(k)N×ML

)THH

F FN y

(6)where the diagonalized frequency domain channel matrixHF = FH is employed for one-tap channel equalization.y0

RX,k suffers from interference from adjacent subcarriers.This interference is combated with J IC iterations wherej ∈ {0, . . . , J − 1} (Gaspar et al., 2013). In each iterationj

djk = q{F−1M yjRX,k} (7)

is performed to obtain hard symbol decisions. Then, theinterference to adjacent subcarriers

yjI,k = GIFM (dj(k+1) mod K + dj(k−1) mod K) (8)

is calculated where GI accounts for the weighted interfer-ence derived from the used filters. Eventually, the next ICiteration j + 1 is performed with updated receive vectors

yj+1RX,k = y0

RX,k − yjI,k. (9)

This process from (7) onwards is repeated J-times in or-der to minimize self-interference. After J iterations thedemodulator returns interference-reduced soft decisionsdk = F−1

M yJ−1RX,k for all subcarriers k.

3. Software Defined RadioSDR is a term coined by Joseph Mitola (Mitola, 1992). Theconcept describes how formerly fixed hardware for signalprocessing is moved to the software domain. GNU Radioprovides a powerful framework for SDR implementations(GNU Radio, 2017). It enables myriads of new applicationsand use-cases and offers unprecedented flexibility.

Research and development may be accelerated with soft-ware development techniques, e.g. rapid prototyping, in-troduced with the SDR concept (Otterbach et al., 2013).Field tests and simulations which share a common codebase drastically improve technology verification and enabledeeper investigation of the system of interest. Also, soft-ware development techniques help to improve code quality.Preferably, the switch from simulation to field test is per-formed by only switching out drivers as opposed to mov-ing to a different implementation. A SDR implementationoffers the advantage to combine these features and it canreveal feasibility in real world scenarios in regard to di-verse requirements formulated by future 5G and I4.0 ap-plications. Wireless Networks in-the-Loop (WiNeLo) is an


implementation that enables this easy switch from simula-tions to field tests.

Additionally, future Cloud RANs will benefit from a soft-ware implementation (Rost et al., 2015) which will enablemore efficient use of available hardware. This could enablemoving the signal processing away from the Radio AccessNetwork (RAN) to centralized and virtualized computingresources. This also helps to capture challenges arisingduring research and development. Thus, development ofnew waveforms can be accelerated. Additional abstrac-tion layers could also motivate borrowing techniques likeheterogeneous computing from other industries with highcomputing requirements.

In this paper we focus on SDRs which are implemented insoftware and target GPP hardware but consider radio hard-ware constraints. Moreover, programming hardware logicis excluded from SDR because it contradicts with the targetto virtualize signal processing for Cloud RAN.

Cooperative development of new waveforms is fosteredwith open-source tools, frameworks and waveforms. Thisis underlined for example in (Bloessl et al., 2013). Whilemultiple implementations for WiFi exist, open-source im-plementations offer the possibility to investigate the pro-posed algorithms and collaboratively extend the basic sys-tem. This stands in contrast to proprietary software whichrequires extended efforts in terms of collaboration. Again,the presented approach to the mentioned WiFi implementa-tion emphasizes the importance of a software developmentcycle approach to waveform development.

4. ImplementationIn this Section we introduce the current implementation ofgr-gfdm. The project was started in 2015 and heavily op-timized since then. An analysis of expected implementa-tion latencies for the underlying algorithms can be found in(Demel et al., 2017).

The implementation follows the division into logical seg-ments of operation described in Sec. 2. Most of gr-gfdmis implemented in C++ for optimized code where functionkernels separate optimized GFDM C++ functions from theGNU Radio interface as shown in Fig. 3. Apart from ab-stracting implementation details from the GNU Radio in-terface they enable reuse of the same implementation forother signal processing frameworks such as Python withNumPy and SciPy. Also, Python is used for implementa-tion validation and initialization during start-up. All blocksin the transmitter and receiver can be parameterized withtotal number of subcarriers and timeslots. Most blocks aredesigned to be flexible and thus can be customized withmore parameters depending on the blocks role.

Pythoncode

C/C++kernels

GNU Radiointerface

Cythoninterface

fast,multi-threaded,HW interface

easy-to-use,accessible

(e.g. Python,Matlab)

Figure 3. function kernel design concept

Using GNU Radio with the function kernels abstractionprovides necessary flexibility to prototype and verify codein GNU Radio and Python in a timely manner. Addition-ally, the implementation is easily extendable with inter-faces for other programming languages.

Licensing GNU Radio and Vector-Optimized Library ofKernels (VOLK) are freely available under the terms of theGPLv3 and Fastest Fourier Transform in The West (FFTW)is freely available under the terms of the GPLv2. The usageof libraries available under the terms of a GPL license hasa huge advantage above propietary libraries. Performanceimpact of used algorithms can be examined and improvedby modifying the source code. Reproducability of the im-plementation is ensured by checking used libraries for sideeffects.

4.1. Libraries and frameworks

This Subsection introduces the most important libraries andframeworks which are used in gr-gfdm (Rode, 2017).

GNU Radio A modular, multi-threaded framework forrapid-prototyping of SDR applications (GNU Radio,2017). It offers a variety of basic tools for signal pro-cessing, visualization and infrastructure in order to developnew waveforms. A developer may focus on the actual al-gorithms at hand while GNU Radio provides a frameworkto deal with software design implications. This is particu-larly interesting when dealing with multi-threading becauseGNU Radio manages threads and data and allows writingthread-safe applications by default. GNU Radio providesa C++ API for developing performance critical parts ofthe algorithm. To speed up development prototyping GNURadio also provides a Python API and the resulting flowgraph representing the signal flow between signal process-ing blocks is generated using Python.

VOLK A library of math functions on vectors which aretypically used in signal processing (VOLK, 2017).


Figure 4. Transmitter in GNU Radio

It makes use of Single-Instruction-Multiple-Data (SIMD)extensions which are present in many modern GPP hard-ware architectures such as Streaming SIMD Extensions(SSE), Advanced Vector Extensions (AVX) or NEON. Itabstracts individual implementations for specific hardwareand provides a canonical interface to all of them. VOLKenables developing a platform independent software whichenables increased performance through usage of SIMDwithout being limited to a single hardware architecture.

FFTW One of the fastest known software implementa-tions for Fourier transforms (Frigo & Johnson, 2005). It isa de facto standard for many software projects, both com-mercial and open-source.

4.2. Transmitter

This subsection introduces the transmitter signal process-ing blocks. It is assumed that data is provided in form ofcomplex symbols. The GFDM transmitter first maps thesesymbols to a frame lattice, then performs GFDM modula-tion and finally adds a CP and or CS together with window-ing. This is split into three steps illustrated in Fig. 4. Fi-nally, a preamble is inserted before each modulated frame.

The Resource Mapper takes in blocks of MKon sam-ples and maps them onto a block with MK samples. Theoutput vector contains the samples grouped subcarrier-wisefor modulation. Unused subcarriers and unused latticepoints are filled with 0 + j0. Unused subcarriers can bedetermined by setting the number of active subcarriers anda subcarrier map during initialization of the block. The sub-carrier map specifies the used subcarriers in each frame.

The Modulator expects vectors with MK sampleswhich are then modulated. This block consumes the mosttransmitter resources and was optimized using the VOLKand FFTW libraries. The algorithms are implemented ac-cording to (5). To make sure the implementation worksas expected tests compare the implemented algorithm with(2). These tests may be run after each compilation to en-sure conformity. Customization of this block is allowed byspecifying the overlap between subcarriers and the taps ofthe prototype subcarrier FIR filter in the frequency domain.With this implementation different subcarrier filters can beswitched out very easily. This allows easy testing and sim-ulation of the waveform.

Figure 5. Energy-based synchronization

The CP block adds a CP and CS and performs a window-ing operation. It expects input vectors of size N = MKand puts out vectors of size N +NCP +NCS . Afterwardsframes are ready to be transmitted over-the-air. Parameteri-zation of this block is allowed by specifying the CP and CSlength. The window ramp length Nramp and window tapscan be specified as well.

4.3. Synchronization

Synchronization is typically a computationally heavy op-eration as shown in (Demel et al., 2015). We decided tomatch synchronization specifically to our system. Sincewe expect to work in a high SNR region, we start off with acoarse energy-detection-based synchronization stage. Finesynchronization is then performed with a simplified algo-rithm introduced in Section 2.2.

The energy-based synchronization stage is comprised ofstandard GNU Radio blocks as shown in Fig. 5. A ris-ing edge over a certain threshold in the sample energy con-tent is detected within a window. This synchronizationstage has low complexity and provides a rough frame de-tection. The number of samples which are expected in aframe can be extracted and passed on to the next synchro-nization stage based on a detected peak.

The preamble is expected to be located within a windowNpw at the beginning of a frame. This narrows the searchregion for the preamble to a fixed window. In order to meeta high throughput, this synchronization stage is optimizedwith VOLK and FFTW. The fine synchronization stageemits a vector containing exactly one synchronized frame.Furthermore, CFO compensation as well as Automatic-Gain-Control (AGC) may be performed with only a smalloverhead.

4.4. Demodulation

GFDM is generally a non-orthogonal waveform and thusself-interference must be expected. For demodulation asimple demodulator without IC, ignoring self-interference,and an advanced demodulator with IC are implemented.The implemented receiver flowgraph is shown in Fig. 6.The demodulation algorithms are implemented accordingto (6) which uses the optimized operation in the frequencydomain. Unit tests ensure the implementation works as ex-pected.


Figure 6. RX flowgraph in GNU Radio

The IC implementation is particularly necessary if higherorder modulation is desired for the transmission and thusself-interference degrades performance more significantly.Using properties of the self interference, it is possible tosignificantly improve the quality of received symbols.

The demodulator may be provided with an initial chan-nel estimate for equalization. This is especially necessaryfor field trials and simulations with channels other thanAWGN, otherwise IC may degrade the performance of thesystem. A preamble based channel estimation algorithm isimplemented in order to provide an initial channel estimate.

Figure 7. Hardware setup with Laptop, USRPs and signal gener-ator

5. Field TrialA major advantage of GNU Radio is its ability to serve asboth, a simulation environment as well as a SDR. In thissection we present the results of our efforts to use the im-plementation for over-the-air transmissions.

The hardware setup for this field trial is shown in Fig. 7and summarized in Tab. 1. It consists of a laptop for trans-mit and receive signal processing in GNU Radio, USRPsas hardware frontends and a signal generator for frequencysynchronization between the USRPs via their 10MHz ref-erence clock input. The GFDM system is parameterizedas described in Tab. 2. The subcarrier filters have Root-Raised-Cosine shape, while the windowing function uses a

Software Ubuntu 16.04GNU Radio 3.7.11

Hardware Intel Core i7-6700HQ with 16Gb RAMFrontend 2×USRP2 with RFX2400 daughterboards

HP 33120A signal generator

Table 1. Software and hardware for field trial

Timeslots M 9Subcarriers K 64

Active subcarriers Kon 52Cyclic Prefix length NCP 32Cyclic Suffix length NCS 16

Window ramp length NCP 16Number of IC iterations J 2

Table 2. GFDM system parameters

Raised-Cosine. Packets are transmitted every 1ms with asample rate of 3.125MS/s. This results in a packet lengthof 256 µs including a preamble. The corresponding receiverflowgraph is shown in Fig. 6.

The receiver chain is tapped during multiple stages in orderto monitor the current state of the flowgraph with a GUI asshown in Fig. 8. A receiver symbol constellation showsthe demodulated symbols, Quadrature Phase Shift Keying(QPSK) in this case. The channel estimate with its real andimaginary component is shown right next to it. Further-more, the synchronization is facilitated in multiple stageswith the corresponding output shown in the ”Synchroniza-tion” part of the GUI. An energy-based burst detection isshown first and afterwards the time-synchronized bursts.The GNU Radio stream tag feature is used to indicate thestart of a burst as detected by the receiver.

The presented field trial runs without underflows and over-flows. Its resource requirements are measured with htopand indicate a load of approximately 90%, thus only oneCentral Processing Unit (CPU) core is occupied. Higher


Figure 8. RX flowgraph GUI, showing a constellation, channel estimate and time samples for different synchronization stages

bandwidth may only affect the synchronization portion ofthe flowgraph. Thus, even at a sampling rate of 25MS/sthe load increases to approximately 250% while USRPsource and sink consume more than 50% load.

6. ConclusionIn this paper we presented gr-gfdm, a GFDM implementa-tion in GNU Radio. We made heavy use of GNU Radio’sfeatures in order to simplify the implementation efforts. Itincludes VOLK usage for massive speed-ups. This is, tothe best of our knowledge, the first open source GFDM im-plementation in GNU Radio. The gr-gfdm can be used forsimulations as well as field trials and its capabilities weredemonstrated in a field trial. We showed the overall imple-mentation is ready to be used for field trials and may run inreal-time even at a sampling rate of 25MS/s. We believeopen source SDR implementations of new waveforms con-tribute to faster exploration of their capabilities. Thus theyfoster the adoption of new multicarrier waveforms becauseinterested parties are free to use and contribute to the code.

AcknowledgmentThis work was partly funded by the German ministry ofeducation and research (BMBF) under grant 16KIS0263K(HiFlecs).

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an optimized gfdm software implementation for future cloud ... · heads (rrhs). thus, we present...

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