Conference PaperPDF Available
Universal Waveforms Processor
Martin Danneberg, Ahmad Nimr, Nicola Michailow, Shahab Ehsanfar, Maximilian Matth´
Ana-Belen Martinez, Dan Zhangand Gerhard Fettweis
Vodafone Chair Mobile Communication Systems, Technische Universit¨
at Dresden, Germany
National Instruments Corp., Austin, TX, USA
{first name.last name},
Abstract—This paper presents a flexible physical layer (PHY)
implementation based on the National Instruments (NI) USRP-
RIO software-defined radio (SDR) platform. The implementation
allows to reconfigure important parameters of the physical layer
during run-time to create a multitude of modern waveforms.
In addition, a first performance evaluation of the transceiver is
given. The source code of the field programmable gate array
(FPGA) design is freely available as open source.
The demand to achieve higher data rates for the enhanced
Mobile BroadBand (eMBB) scenario and novel fifth genera-
tion (5G) use cases like ultra-reliable low latency communi-
cation (URLLC) and Massive Machine-type Communications
(mMTC) drive researchers and engineers to consider new
concepts and technologies for future wireless communication
systems. The goal is to identify promising candidate technolo-
gies among a vast number of new ideas and to decide, which
are suitable for implementation in future products. Fig. 1 gives
a simplified overview of the evaluation process.
New ideas and concepts typically first undergo extensive
software simulations, which allow to make first predictions
on the expected performance. After downselection of the
candidates, selected aspects of the envisioned system can be
implemented on a hardware-accellerated platform, e.g., SDR,
in order to learn about real-time behavior and over-the-air per-
formance with real RF components. Technologies that prove
promising in this stage can be further evaluated in testbeds,
where the focus shifts towards the interworking of different
technology building blocks and the realization of complete
end-to-end applications. New concepts and technologies that
have been proven to work in practice will ultimately find their
way into new standards and lastly, industry will adopt them
in future products.
In this paper we present an FPGA-based, real-time imple-
mentation of a modulator/demodulator prototype for multicar-
rier waveforms [1], which provides a large number of degrees
of freedom to the experimenter and offers the flexibility
required for the practical evaluation of 5G concepts. Specific
example applications include experiments with flexible nu-
merology, which is a key differentiator of 5G New Radio (NR)
as compared to fourth generation (4G) long term evolution
(LTE). In addition, characteristics of diverse waveforms can
New ideas
and concepts
Predict expected performance
based on suitable models and
reasonable assumptions.
Find reasonable simplifactions, al-
gorithm partitioning and estimate
implementation complexity.
Demonstrate the interworking of dif-
ferent components under real-world
conditions and in real-time.
Decide which technologies should be in-
cluded in future products.
Fig. 1: Simplified process of downselection
be analyzed. The presented platform is a building block for
testbeds that will in the future assist the design of 5G radio
interfaces and network architectures. The following sections
give a detailed description of the available features and how
they are implemented. First, in Section II, we represent a
unified modem model. In Section III, we detail the transceiver
design. The evaluation of the design is given in Section IV.
Finally, we conclude the paper and highlight the future work
in Section V.
In general multicarrier modulation, a stream of data symbols
is split into time-frequency sub streams dk,m,i, where kis the
index in the frequency domain known as subcarrier, mthe
index in time domain known as subsymbol and irepresents
the block index. Each stream is modulated with transmitter
pulse shapes gt
k,m[n]. The discrete transmitted signal can be
written as
where Nsis the symbol spacing, Kon and Mon are the sets
of active subcarriers and subsymbols, respectively, and xt
978-1-5386-1478-5/18/$31.00 c
2018 IEEE
is the i-th multicarrier symbol, which is given by
In common modulation techniques, e.g. [2], [3], gt
k,m[n]has a
finite length Ntand can be generated using a prototype pulse
shape g[n]through circular shift in time and frequency such
k,m[n]=g[nmK n0N]ej2πk
n=0,··· ,N
where •Ndenotes the modulo Noperation. There, Kis
the number of subcarriers, Mthe number of subsymbols
N=MK Nt. Let Nobe the overhead that represents a
cyclic prefix (CP) and/or cyclic suffix (CS), where Nt=
N+No. The type of overhead is defined by the time shift
n0. For instance, when n0=Nowe get pure CP. Based on
that, we can define a core block xi[n]of length Nsamples
such that
The relation between the core block and the actual transmitted
block is given by
Two specific cases evolve depending on Nsand Nt
NsNt: the transmitted blocks xt
i[n]do not overlap
and the total transmitted signal xt[n]can be generated by
transmitting xt
i[n]independently. Here, NP=NsNt
represents a guard interval between sequential blocks.
Orthogonal frequency division multiplexing (OFDM) and
Generalized frequency division multiplexing (GFDM)
belong to this case.
t:xt[n]can be still produced based on xt
however, additional overlapping of length No=NtNs
must be considered. In this, the last Nosamples of the
current xt[n]are added to the first Nosamples of the
next xt
i[n]. An example of this case is Filtered Multitone
(FMT) [4] with overlapping factor M. In which, Mon
contains one subsymbols and the remaining M1are
non-active. The overlapping by No=K(M1) com-
pensates this overhead. Another example is the weighted-
overlap-and-add (WOLA) processing in [5], where the
ramp-down of the i1-th windowed block overlaps with
ramp-up of the i-th block.
As a result, by the configuration of the prototype pulse shape
gin addition to the parameters, Ns,Nt,n0,K,M,Kon and
Mon we can produce most of the state of the art waveforms.
As illustrated in Fig 3, the process of waveform generation
can be divided into two main stages; the first is computing
the core block xi[n]according to (4). Let us collect the data
symbols dk,m,i for the ith transmit block in a sequence di
using the relation di[mK +k]=dk,m,i. Then, as shown in
[6], the core block generation at the transmitter side can be
written as
xi[mK +k]=
with D[mK+k]=K·IDFT{di[mK+]}[k].
Similarly, at the receiver side, a linear receiver using the
receive filter γ[n]is applied to the core block xi[n]by
dk,m,i =
In analogy to the transmitter, we can reformulate this to
Xi[mK +k]=
di[mK +k]=
KXi[mK +k](10)
=DFT{Xi[mK +]}[k].(11)
Apparently, the modulator and demodulator have the opera-
tions (6), (9) in common, which we denote as the core filtering
operation. In addition, at the transmitter an inverse fast Fourier
transform (IFFT) is employed before the core filtering whereas
at the receiver side, the according fast Fourier transform (FFT)
is applied after the core filtering. Hence, the same filtering and
FFT1implementation can be reused at both transmitter and
receiver side.
The subsequent stage at the transmitter corresponds to block
multiplexing where the CP/CS overhead is added to generate
i[n]as done in (5). At this stage, further signal processing
items, such as windowing or filtering, can be performed
afterwards to support waveforms like F-OFDM [7] or WOLA-
OFDM [5]. Then, the transmitted signal is generated according
to (1) considering overlapping or zero padding. Finally, the
discrete block is converted to an analogue signal using an
digital-to-analogue converter (DAC) with sampling rate Fs.
The subcarrier spacing can be derived as Δf=Fs
Kand the
bandwidth can be controlled via Kon. At the receiver side, a
similar block is employed to perform receiver-side windowing
to suppress collection of spectral leakage from adjacent signals
and CP/CS removal.
The block diagram in Figure 2 depicts the components
which have been integrated in the overall demonstrator. Except
for the blue marked blocks supplied by NI, all other blocks
are implemented highly flexible to support the various require-
ments of 5G wireless networks. The focus of this paper lies
on the PHY aspects of a 5G transceiver, since these signal
processing algorithms have to be implemented in FPGA to
1Switching between FFT and IFFT does usually incur only a neglible
support the throughput and latency requirements. In particular,
we focus on describing the core modem implementation.
medium access control (MAC) and higher layers will be
implemented using software components running on standard
PC hardware as those are not as time critical to require
specialized hardware accelerator support. The design follows
a handeshaking protocol, where each unit is indicating the
previous unit if it can accept data. This way samples cannot
get lost during the processing and a slower radio-frequency
(RF) sampling rate slows down the signal processing.
Control PC
Host controller
To GFDM block
To UDP packet
D/A + RF
RF + A/D
Listen before Talk, Sensing
Fig. 2: Block diagram of the transceiver.
A. Baseband modem
The baseband signal processing is conducted by the resource
mapper, modulator, demodulator and resource demapper as
depicted in Figure 3. A first implementation of the modulator
has been presented in [8]. Before the modulator, the incoming
data don
ihas to be mapped to the two-dimensional time-
frequency resource grid. Further, control channel or additional
training sequences should be multiplexed into the data stream
dias well. To accomplish this, the resource mapper as depicted
in Figure 3 has several input sources. Whenever a new data
block is going to be created the multiplexing pattern is read-out
of the memory to control the switch at the output of this block.
This mechanism allows to support any user-defined resource
grid which can be adapted during run-time to the respective
standard as also pre-defined training-sequences can be read
out of a memory.
As mentioned, the modulation and demodulation operation
can be separated into two main functional blocks. The first
block is the IFFT to convert the data into time domain and
the second block applies the actual modulation process. In this
context it is called core-modem. The demodulation operation
consists of the same processing blocks in a reversed order.
First the demodulation has to be applied on the received data
via the core-modem block and afterwards it is transformed
back into frequency domain by a FFT. The modulation and
demodulation process inside the core-modem are performed
with the same architecture, the only difference are the used
filter coefficients.
The first task of the core-modem block is to split the
incoming inverse discrete Fourier transform (IDFT) output
stream δinto Msub-symbols with Ksamples each, e.g. δ0,
δ1. In the following, each individual sub-symbol has to be
repeated Mtimes, such that the filter gwith Nsamples can
be applied. Therefore, the Ksamples of each sub-symbol are
stored inside an independent subsymbol data memory bank.
These Mparallel banks are read out sample by sample in
parallel. The filter gis stored in Mparallel subsymbol filter
banks, too. Each of the filter memory bank contains Ksamples
of the filter which are representing a sub-symbol as similar to
the data. However, the filter has to be multiplied with the data
in a circular shifted way. This is implemented by connecting
a different filter bank to the multiplier for each data memory
bank. For instance, first the Ksamples of the first sub-symbol
δ0are multiplied with g0. Afterwards the same Ksamples
are multiplied with g1until they have been multiplied with
all filter coefficients. In the case of the second sub-symbol,
the Ksamples stored inside the memory are multiplied first
with g1and then with g2. The last step of the process is to
accumulate the contributions from all Mparallel branches to
get the transmit signal.
B. Post modem processing
After the modulator the CP and CS, a additional time-
window and a preamble have to be added. The general frame
format of the implemented transceiver is based on a preamble
which is used for synchronization and channel estimation and
one or several data blocks concatenated to each other. Figure 4
depictes the general frame format. CP and CS can be applied
on both preamble and data block. NP,CP defines the length
of the CP for the preamble, NCP for the data block, NP,CS
defines the length of the CS for the preamble and NCS for
the data block. It is assumed that the window is symmetric,
thus the length of one half is given by NP,W and NW.NP
denotes the length of the preamble. A detailed overview which
frame structure and PHY parameters are suitable for different
scenarios is given in [9].
CP GFDM block CS
PreambleCP CS
Fig. 4: Considered frame structure with one preamble and one
data block.
Different to the data, the complete preamble is precalculated
on the host computer. Therefore, it will be written to a memory
during the configuration phase of the transceiver. The CP, CS
for the data is added via three first in, first out buffers (FIFOs)
in Figure 3. A control logic handles which data is stored inside
which FIFO and in which order the content is read-out. In the
end, all FIFOs can be seen as variable delays to shift the data
samples into the right output order.
The windowing unit follows, where only the rising half is
stored inside a memory. An integrated counter in the control
logic counts up until NWis reached to trigger the memory for
the appropriate samples. During the main data block the unit
Control chann el
Resource map
BRAM ...
BRAM ...
CP/CS + WindowingPulse Shaping Unit
Resource Mapper Block multiplexing
xn N
Fig. 3: Block diagram of the transmitter excluding encoder, symbol mapper and the RF logic.
is disabled. Finally, the same counter is decreased to create
the falling part.
Whenever the controller has finished reading the first data
block of a frame into the data-FIFO, the preamble insertion
unit is triggered to push the preamble samples to the DAC.
C. Inner Receiver: Synchronization and Channel Estimation
Despite focusing on the core modem in this paper, a
complete transceiver chain requires adequate synchronization
and channel estimation blocks. For the present evaluation,
we employ a preamble-based synchronization and channel
estimation. The synchronization algorithm is derived from the
Schmidl-Cox algorithm [10] and in detail described in [11].
For the channel estimation, a frequency domain least-squares
estimator with subsequent linear interpolation is implemented
on the real-time platform. In particular, the channel estimation
unit already provides the inverse channel coefficients for the
subsequent frequency domain channel equalizer. Hence, the
equalizer will only employ multiplications instead of divisions
in the signal’s frequency domain, which can reduce overall
latency when multiple payload blocks share a common pream-
D. Additional Units
Currently, a convolutional code with code-rate 1
2and a
QPSK mapper is included as these were available processing
units in LabVIEW. Additionally, a channel sensing function
is implemented facilitating the second RF chain of the SDR
platform. It consists of a power spectral density (PSD) calcula-
tion unit followed by a filter. This allows to scan the spectrum
in real-time and support listen-before-talk access schemes via
filtering the received energy in a given frequency band.
E. Flexible Configuration
Using the described blocks, a multitude of waveforms for
wireless communications systems can be emulated. A subset
of existing possibilities is shown in Table I. There, for different
waveforms, the signal processing blocks are parametrized
differently or even bypassed, such that in total the desired
waveform is generated. For example, multicarrier waveforms
such as OFDM or filter bank multicarrier (FBMC) employ
the FFT operation at the modem to obtain a frequency-division
multiplexing structure. On the other hand, the definition of the
resource map strongly depends on the actual frame structure
and no general statement can be made. Here, the flexibil-
ity of the resource mapper to support different waveforms
shall be emphasized. Finally, the pulse shaping unit cannot
only be used for classical filtering, but it can also serve as
the spreading operator for direct-sequence spread spectrum
(DSSS) signalling. Further, if the signal processing of one
frame is finished the configuration can be switched within 20
clock cycles. This can be achieved using the presented memory
structures as chaches for several configurations. In this case
the other configurations are stored using a address offset inside
the memory, such that by adding a constant offset a different
part with a different configuration is accessed during the signal
The full-duplex capable FPGA implementation of the
transceiver fits into one NI USRP-RIO 2953R SDR platform.
In this section the latency and the performance on an additive
white Gaussian noise (AWGN) channel is evaluated.
The transmit latency of the processing in FPGA clock cycles
can be expressed as
LTX =(N+8)
Encoder +(7+N)Resource Mapper
+LFFT(K)+(N+ 23)Modulator
+(N+NCP +NCS )CP&CS + (10)Windowing +N.
The latency is shown based on the input of the first data sample
at the encoder towards the last sample given to the RF control
logic. The index of each bracket is indicating the latencies
introduced by specific subblocks. It is assumed, that the RF
bandwidth is high enough to not stall the transmitter. Further,
all subcarriers are allocated with payload. The latency of the
XILINX FFT core is stated as LFFT(K)[16] and for the DFT
Waveform Resource Mapper FFT Pulse shaping CP/CS Windowing Overlapping Preamble
OFDM Frame-dependent ON OFF ON OFF OFF Frame-
WiFi Header/Payload ON OFF ON OFF OFF ON
Single-Carrier Frame-dependent OFF OFF ON OFF OFF Frame-
5G NR [7] Pilot/Control ON OFF ON ON ON OFF
GFDM [12] Frame-dependent ON GFDM pulse ON ON OFF Frame-
FBMC [13] Pilot/Control ON FBMC pulse OFF ON ON OFF
[14] Frame-dependent OFF Spreading function OFF OFF OFF Frame-
[15] Frame-dependent ON Chirp function ON OFF OFF Frame-
TABLE I: Configuration for waveform generation
core as LDFT(N)[17]. The receiver latency behaves as
LRX =(5×396)Synchronization +(11+2×LDFT (N))Equalizer
+LFFT(K)+(N+ 23)Demodulator
Demapper +2×((49)Decoder +N).
Here it is assumed that the input samples are arriving with
40MHz. Further, the syncronization unit operates with a
40MHz clock rate, which is a five times longer processing
time than other FPGA modules. The decoder runs with a
100MHz clock and therefore needs twice the time. Any other
signal processing block reaches the 200MHz clock rate. In
addition, it is assumed that the channel has been estimated
already and the channel coefficients are stored inside the
equalizer unit, e.g. calculated based on the preamble of a
previous data frame. Fig. 5 and Fig. 6 show the achieved
latencies depending on the number of samples for one data
frame. In both figures the minimum latency is depicted in
case a single-carrier waveform is created and the maximum
latency for the GFDM case where all processing units are
needed. The processing time of the FFTs impacts the overall
processing latency. In the graphs this is visible as a step
where the total amount of samples Nis the same, however
the number of subcarriers Kand thus the FFT latency is
increasing. Both transmit and receive latency are in all cases
below 100μs such that the transceiver leaves enough time for
higher network layers to fulfil the targeted latencies defined
by 5G communications. The performance of the implemented
transceiver is firstly evaluated in the laboratory using a cable
setup between USRP transmit port and recieve port to emulate
a AWGN channel. The noise can be varied to simulate different
signal to noise ratio (SNR) levels using a second NI USRP-
RIO 2953R. All components are connected via cables such that
the channel is defined mainly by the generated noise. A two-
way RF combiner realizes the summation between noise and
Fig. 5: The transmit latency depicted over Nat 200MHz clock
rate. The results for the first 200 samples are shown in the
upper corner for better readability.
the transmitted data. The configuration used by the transceiver
are represented in Table II. Fig. 7 shows the achieved result
Filter Raised-cosine with roll-off factor 0
(M,K, CP, CS ) (9,64,32,16)
Mon {1,··· ,8}
Kon {2,··· ,28}∪{37,··· ,62}
Frame length 80 Bytes
TABLE II: Evaluation parameters
for different SNRs, where the SNR is estimated based on
the transmitted and received preamble in AWGN channel.
The simulation is using a complex floating point reference,
while the FPGA implementation uses complex fixed-point
with 32 bits precision. The performance is consistent with
Fig. 6: The receive latency depicted over Nat 200MHz clock
rate. The results for the first 200 samples are shown in the
upper corner for better readability.
 
  
 
  
Fig. 7: Measurement results using the presented setup.
the simulations with a difference of up to 1 dB due to the
reduced precision of the FPGA signal processing. Moreover,
the presented architecture achieves up to 9.6Mbit/s throughput
under the given configuration and conditions.
In this paper a flexible FPGA transceiver design for modern
waveforms is presented. It allows flexible reconfigurations
such as changing the number of subcarriers and subsymbols,
the length of the CP and CS. In addition, arbitrary pulse
shaping filters or recieve filters, windowing functions and
preambles are supported as well. With the proposed transceiver
architecture, a multitude of waveforms can be generated by
simply reconfiguring the device without the need to provide a
specialized implementation for each waveform. Hence, PHY-
level real-time evaluations using different waveforms becomes
feasible and aids academia and industry understanding the
benefits and limitations of diverse waveforms. Further, the pre-
sented transceiver design allows to change its waveform con-
figuration quickly and therefore supports research on mixed
numerology applications. [18]
This work has been supported by National Instruments with
hardware, software and technical support. This project has
received funding from the European Union’s Horizon 2020
research and innovation programme under grant agreement No
732174 (ORCA) and grant agreement No 688116 ”eWINE”.
This work has received support from Prof. Dr. Mendes and
his team at INATEL, Brazil.
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IEEE Softwarization.
... Particularly, this allows to use software frameworks such as LabVIEW Application Frameworks or OpenAirInterface in the 26/28GHz frequency range using standard SDRs and further enabling the development of mmWave beam steering algorithms in a simpler and accessible manner. As an example of how this setup can be used, we have combined our flexible real-time physical layer (PHY) based on the generalized frequency division multiplexing (GFDM) [6] with the mmWave antennas [5]. This implementation includes a real-time beam tracking algorithm based on a basic medium access control (MAC) mechanism. ...
... The presented signal processing blocks are implemented on the field programmable gate array (FPGA) of the SDR to allow the PHY layer to operate in real-time. The coarsely described processing blocks are based on the GFDM waveform framework [7], which can create a multitude of waveforms as mentioned in [6]. An overview for all signal processing blocks is given in Fig. 1. ...
... Fig. 2: Considered frame structure with one preamble and one data block.[6] ...
... Application Framework [12] are supported. The open source implementation of the flexible generalized frequency division multiplexing (GFDM) transceiver [13] as well as the WilDCAT Project [14] are available for studying the characteristics of diverse waveforms. Since the experiment control server is virtual, control frameworks like OMF can also be used. ...
... A transceiver is needed, where flexible precoding, filtering, and windowing can be configured depending on the requirements of the different use cases. In this regard, GFDM multicarrier framework [15] provides a ready solution. The GFDM framework is able to process most of the conventional waveforms. ...
... In its early proposal, generalized frequency division multiplexing (GFDM) [1] has been suggested as an alternative to orthogonal frequency division multiplexing (OFDM). Recently, GFDM has been extended to a multicarrier framework, that is able to process most of the state of the art waveforms and allows the design of new waveforms [2]. The welldefined structure of GFDM enables a feasible real-time modem implementation on hardware. ...
Full-text available
The conventional receiver designs of generalized frequency division multiplexing (GFDM) consider a large scale multiple-input multiple-output (MIMO) system with a block circular matrix of combined channel and modulation. Exploiting this structure, several approaches have been proposed for low complexity joint linear minimum mean squared error (LMMSE) receiver. However, the joint design is complicated and inappropriate for hardware implementation. In this paper, we define the concept of GFDM-based linear receivers, which first performs channel equalization (CEq) and afterwards the equalized signal is processed with GFDM demodulator. We show that the optimal joint LMMSE receiver is equivalent to a GFDM-based one, that applies LMMSE-CEq and zero-forcing demodulation. For orthogonal modulation, the optimal LMMSE receiver has an implementation-friendly structure. For the non-orthogonal case, we propose two practical designs that approach the performance of the joint LMMSE. Finally, we analytically prove that GFDM-based receivers achieve equal signal-to-interference-plus-noise ratio per subsymbols within the same subcarrier.
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Flexible numerology of the physical layer has been introduced in the latest release of 5G new radio (NR) and the baseline waveform generation is chosen to be cyclic-prefix based orthogonal frequency division multiplexing (CP-OFDM). Thanks to the narrow subcarrier spacing and low complexity one tap equalization (EQ) of OFDM, it suits well to time-dispersive channels. For the upcoming 5G and beyond use-case scenarios, it is foreseen that the users might experience high mobility conditions. While the frame structure of the 5G NR is designed for long coherence times, the synchronization and channel estimation (CE) procedures are not fully and reliably covered for diverse applications. The research on alternative multi-carrier waveforms has brought up valuable results in terms of spectral efficiency, applications coexistence and flexibility. Nevertheless, the receiver design becomes more challenging for multiple-input multiple-output (MIMO) non-orthogonal multi-carriers because the receiver must deal with multiple dimensions of interference. This thesis aims to deliver accurate pilot-aided estimations of the wireless channel for coherent detection. Considering a MIMO non-orthogonal multi-carrier, e.g. generalized frequency division multiplexing (GFDM), we initially derive the classical and Bayesian estimators for rich multi-path fading channels, where we theoretically assess the choice of pilot design. Moreover, the well time- and frequency-localization of the pilots in non-orthogonal multi-carriers allows to reuse their energy from cyclic-prefix (CP). Taking advantage of this feature, we derive an iterative approach for joint CE and EQ of MIMO systems. Furthermore, exploiting the block-circularity of GFDM, we comprehensively analyze the complexity aspects, and propose a solution for low complexity implementation. Assuming very high mobility use-cases where the channel varies within the symbol duration, further considerations, particularly the channel coherence time must be taken into account. A promising candidate that is fully independent of the multi-carrier choice is unique word (UW) transmission, where the CP of random nature is replaced by a deterministic sequence. This feature, allows per-block synchronization and channel estimation for robust transmission over extremely doubly-dispersive channels. In this thesis, we propose a novel approach to extend the UW-based physical layer design to MIMO systems and we provide an in-depth study of their out-of-band emission, synchronization, CE and EQ procedures. Via theoretical derivations and simulation results, and comparisons with respect to the state-of-the-art CP-OFDM systems, we show that the proposed UW-based frame design facilitates robust transmission over extremely doubly-dispersive channels.
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Advanced industrial applications for human-machine interaction such as augmented reality support for maintenance works or mobile control panels for operating production facility set high demands on underlying wireless connectivity so- lution. Based on 802.11 standard, this paper proposes a concept of a new system, which is capable of those requirements. For increasing reliability, an agile triple-band (2.4 GHz, 5 GHz and 60 GHz) communication system can be used. In order to deal with latency and deterministic channel access, PHY and MAC techniques such as new waveforms or hybrid MAC schemes are investigated. Integration of precise localization introduces new possibilities for safety-critical applications.
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Future wireless communication systems are demanding a more flexible physical layer. GFDM is a block filtered multicarrier modulation scheme proposed to add multiple degrees of freedom and to cover other waveforms in a single framework. In this paper, GFDM modulation and demodulation is presented as a frequency-domain circular convolution, allowing for a reduction of the implementation complexity when MF, ZF and MMSE filters are employed as linear demodulators. The frequency-domain circular convolution shows that the DFT used in the GFDM signal generation can be seen as a precoding operation. This new point-of-view opens the possibility to use other unitary transforms, further increasing the GFDM flexibility and covering a wider set of applications. The following three precoding transforms are considered in this paper to illustrate the benefits of precoded GFDM: (i) Walsh Hadamard Transform; (ii) CAZAC transform and; (iii) Discrete Hartley Transform. The PAPR and symbol error rate of these three unitary transform combined with GFDM are analyzed as well.
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Chirp waveform plays a significant role in radar and communication systems for its ability of pulse compression and spread spectrum. This paper presents a principle of orthogonally multiplexing a bank of linear chirp waveforms within the same bandwidth. The amplitude and phase of the chirps are modulated for information communication. As Fourier trans-form is the basis for orthogonal frequency division multiplexing (OFDM), Fresnel transform underlies the proposed orthogonal chirp division multiplexing (OCDM). Digital implementa-tion of the OCDM system using discrete Fresnel transform is proposed. Based on the con-volution theorem of the Fresnel transform, the transmission of the OCDM signal is analyzed under the linear time-invariant or quasi-static channel with additive noise, which can gener-alize typical linear transmission channels. Based on the eigen-decomposition of Fresnel transform, efficient digital signal processing algorithm is proposed for compensating chan-nel dispersion by linear single- tap equalizers. The implementation details of the OCDM system is discussed with emphasis on its compatibility to the OFDM system. Finally, simula-tion are provided to validate the feasibility of the proposed OCDM under wireless channels. It is shown that the OCDM system is able to utilize the multipath diversity and outperforms the OFDM system under the multipath fading channels.
Conference Paper
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Innovative 5G applications will challenge future cellular systems with new requirements. The OFDM based 4G standard will not be able to address all of them. Generalized frequency division multiplexing is a flexible multicarrier wave-form with additional degrees of freedom. This paper presents a strategy towards a flexible FPGA implementation of GFDM, which is reconfigurable at run-time.
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In the vision towards future radio systems, where access to information and sharing of data is to be available anywhere and anytime to anyone for anything, a wide variety of applications and services are therefore envisioned. This naturally calls for a more flexible system to support. Moreover, the demand for drastically increased data traffic, as well as the fact of spectrum scarcity, would eventually force future spectrum access to a more dynamic fashion. For addressing the challenges, a powerful and flexible physical layer technology must be prepared, which naturally brings us to the question whether the legacy of the OFDM system can still fit in this context. In fact, during the past years, extensive research effort has been made in this area and several enhanced alternatives have been reported in the literature. Nevertheless, up to date, all of the proposed schemes have advantages and disadvantages. In this paper, we give a detailed analysis on these well-known schemes from different aspects and point out their open issues. Then, we propose a new scheme that aims to maximally overcome the identified drawbacks of its predecessors while still trying to keep their advantages. Simulation results illustrate the improvement achieved by our proposal.
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Cellular systems of the fourth generation (4G) have been optimized to provide high data rates and reliable coverage to mobile users. Cellular systems of the next generation will face more diverse application requirements: the demand for higher data rates exceeds 4G capabilities; battery-driven communication sensors need ultra-low power consumption; control applications require very short response times. We envision a unified physical layer waveform, referred to as Generalized Frequency Division Multiplexing (GFDM), to address these requirements. In this paper we analyze main characteristics of the proposed waveform and highlight relevant features. After introducing the principles of GFDM, this paper contributes to the following areas: (i) means for engineering the waveform’s spectral properties, (ii) analytical analysis of symbol error performance over different channel models, (iii) concepts for MIMO-GFDM to achieve diversity, (iv) preamble-based synchronization that preserves the excellent spectral properties of the waveform, (v) bit error rate performance for channel coded GFDM transmission using iterative receivers, (vi) relevant application scenarios and suitable GFDM parameterizations, (vii) GFDM proof-of-concept and implementation aspects of the prototype using hardware platforms available today. In summary, the flexible nature of GFDM makes this waveform a suitable candidate for future 5G networks.
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Generalized frequency division multiplexing (GFDM) is a block filtered multicarrier modulation scheme recently proposed for future wireless communication systems. It generalizes the concept of orthogonal frequency division multiplexing (OFDM), featuring multiple circularly pulse-shaped subsymbols per subcarrier. This paper presents an algorithm for GFDM synchronization and investigates the use of a preamble that consists of two identical parts combined with a windowing process in order to satisfy low out of band radiation requirements. The performance of time and frequency estimation, with and without windowing, is evaluated in terms of the statistical properties of residual offsets and the impact on symbol error rate over frequency-selective channels. A flexible metric that quantifies the penalty of misalignments is derived. The results show that this approach performs practically as state-of-the-art OFDM schemes known in the literature, while it additionally can reduce the sidelobes of the spectrum emission.
Generalized frequency division multiplexing (GFDM) is a non-orthogonal multi carrier scheme that provides flexible pulse shaping. This is attractive for various applications like machine-to-machine communications or cognitive radio. The additional flexibility however is traded for self-created interference that degrades BER performance. In this paper a linear system description for GFDM is presented and three receiver methods are derived. An iterative interference cancellation technique allows to further improve the performance.
We propose code hopping-direct sequence spread spectrum (CH-DSSS) with binary phase shift-keying (BPSK) modulation to compensate for intersymbol interference (ISI) in an ultra-wideband system. The central idea is that code hopping (CH) affects ISI amplitudes that are produced by both the cross- and the auto-correlation properties of spreading codes for codewords; not just by the latter as occurs without CH. We also propose a low complexity CH pattern search algorithm to find good CH patterns. To evaluate the performance of the CH-DSSS system, a bit error rate (BER) expression is derived for a Rake receiver by applying the Beaulieu series method. Computational results show that significant gains can be obtained by CH for both the average BER and the outage probability (e.g. a 6 dB gain in outage probability for a non-line of sight channel).
A filter-bank modulation technique called filtered multitone (FMT) and its application to data transmission for very high-speed digital subscriber line technology are described. The proposed scheme leads to significantly lower spectral overlapping between adjacent subchannels than for known multicarrier techniques such as discrete multitone (DMT) or discrete wavelet multitone. FMT modulation mitigates interference due to echo and near-end crosstalk signals, and increases the system throughput and reach. Signal equalization in an FMT receiver is accomplished in the form of per-subchannel symbol-spaced or fractionally spaced linear or decision-feedback equalization. The problem of channel coding for this type of modulation is also addressed, and an approach that allows combined removal of intersymbol-interference via precoding and trellis coding is described. Furthermore, practical design aspects regarding filter-bank realization, initial transceiver training, adaptive equalization, and timing recovery are discussed. Finally, simulation results of the performance achieved by FMT modulation for very high-speed digital subscriber line systems, where upstream and downstream signals are separated by frequency-division duplexing, are presented and compared with DMT modulation