Real time on air experiment is performed on Software-defined Radio (SDR) for radio virtualization using orthogonal frequency division multiplexing and filter bank multi carrier to support diverse mobile service requirements for user equipment.

1. Received: 11 August 2020 Revised: 21 September 2020 Accepted: 24 September 2020
DOI: 10.1002/ett.4154
R E S E A R C H A R T I C L E
Air-interface virtualization using filter bank multicarrier
and orthogonal frequency division
multiplexing configurations
Malik Muhammad Saad1,2 Farrukh Aziz Bhatti1 Adnan Zafar1
Sobia Jangsher1 Dongkyun Kim2 Moazam Maqsood1
1
WiSP Lab, Electrical Engineering
Department, Institute of Space
Technology, Islamabad, Pakistan
2
School of Computer Science and
Engineering, Kyungpook National
University, Daegu, Korea
Correspondence
Farrukh Aziz Bhatti, WiSP Lab, Electrical
Engineering Department, Institute of
Space Technology, Islamabad 44000,
Pakistan.
Email: farrukh.aziz@ist.edu.pk
Dongkyun Kim, School of Computer
Science and Engineering, Kyungpook
National University, Daegu 41566,
Republic of Korea.
Email: dongkyun@knu.ac.kr
Funding information
Higher Education Commission, Pakistan,
Grant/Award Number: HEC NRPU
20-4339/R&D/HEC/14/235; SW Human
Resource Development Program for
Supporting Smart Life, Grant/Award
Number: Ministry of Education, School of
Computer Science and Engineering,
Kyungpook National University, Korea
(21A20131600005)
Abstract
Mobile networks have to cater for diverse services that have distinct require-
ments in terms of bandwidth, latency, and so on. Network virtualization is a
key technology that efficiently utilizes the network resources to meet these ser-
vice requirements. In this work, we investigate radio virtualization for creating
fine-grained network slices using orthogonal frequency division multiplexing
(OFDM) and filter bank multicarrier (FBMC) combinations; the two are indi-
vidually referred to as virtual radios (VRs). Using universal software radio
peripherals (USRPs), we experimentally analyze multiple configurations of VRs
including OFDM-OFDM, OFDM-FBMC, and FBMC-FBMC. An extensive per-
formance comparison is done on the basis of error rate, spectral efficiency,
interference power, and computational complexity of the VR configurations
that are confined within an operational bandwidth. An increase in transmit
power of the VRs is theoretically expected to decrease the error rate, but there
is also an increase in adjacent channel interference. Therefore, after a certain
decrease in error rate with increase in transmit power, an inflection point is
reached after which the error rate starts to increase. Of the three combinations,
FBMC-FBMC gives the lowest error rate (at the highest transmit power), that
is, 10% and 18% lower than OFDM-FBMC and OFDM-OFDM, respectively.
However, FBMC-FBMC also has the highest complexity. We conclude that this
air-interface virtualization framework allows the network to use the waveform
configuration that is best suited to a particular set of services, while considering
the pros and cons of the individual waveforms.
1 INTRODUCTION
Mobile data traffic is growing at a rapid pace in multiple dimensions, that is, volume, average speed, and diversity. The
diversity aspect is triggered by a wide variety of devices, services, and applications that are emerging due to technolog-
ical developments in different areas, such as, self-driving cars, smart metering, remote surgeries, HD video streaming,
and so on. Each of these wireless use cases has distinct service requirements in terms of bandwidth, latency, and mobil-
ity management, and so on. To efficiently support these diverse requirements mobile network needs to create logical
Trans Emerging Tel Tech. 2020;e4154. wileyonlinelibrary.com/journal/ett © 2020 John Wiley & Sons, Ltd. 1 of 17
https://doi.org/10.1002/ett.4154

2. 2 of 17 SAAD et al.
network partitions a.k.a. network slices, both in the core network (CN) domain and in the radio access network (RAN)
domain. This is particularly challenging in RAN domain which is typically the most resource-demanding part of the
mobile network.1
RAN has to deal with issues such as resource management and interference management while using
different air-interfaces, programmable platforms and virtualization techniques to match service requirements of differ-
ent devices/machines or applications. Wireless network virtualization is a promising approach to provide this flexibility
and it relies on multiple VRs that are implemented in software and share the same radio frequency (RF) front end.2
1.1 Related work
Wireless network virtualization is an umbrella term that encompasses wireless link virtualization, radio virtualization,
and air-interface virtualization.3,4
In this article, we use the later two interchangeably. In the literature, radio virtual-
ization has been extensively studied.5-8
In Reference 5, the authors have proposed the implementation mechanism of a
radio virtual machine. The article compares different programming descriptors that are used to define different hard-
ware/processing blocks. The concept has been validated by implementing different physical layer blocks defined in IEEE
802.11a protocol on a Lua virtual machine (VM). In Reference 9, the authors have evaluated the performance of a par-
ticular software-defined radio (SDR) platform for implementing a practical VR. The authors claim that virtualization of
SDR platform brings certain overheads that depend on multiple factors. The overheads associated with the VM can only
be minimized if the VR descriptor is optimized according to the architecture of the implementation platform.
Spectrum slicing in primary user (PU) and secondary user (SU) scenarios has been demonstrated in Reference 10.
A 20 MHz spectrum was divided into four 5 MHz channels for the combined transmission of IEEE 802.15.4 and IEEE
802.11g protocols. It was shown that for equal power allocated to both schemes and with no filtering applied, the adjacent
channel interference caused by the secondary user goes to a maximum level of −20 dB. Authors in Reference 11 have
proposed the use of SDR virtualization for the implementation of combined EHF (extremely high frequency)/FSO (free
space optics) links for high throughput satellites. However, instead of using virtualization for the purpose of resource
slicing, it was merely proposed for the sake of hardware reconfigurability, where an associated software-defined network
(SDN) can select from several available smart gateways for signal routing.
In Reference 12, a framework for operator-to-waveform 5G RAN slicing is proposed. This framework considers mobile
network operator’s (MNO) selection of base stations, maximum number of users, and waveform-level scheduling of
resource blocks. However, it presents system-level simulations and does not consider virtualization of physical layer. In
Reference 13 dynamic network slicing strategies are considered for mixed traffics in virtualized RAN. Deep reinforcement
learning-based autonomous slicing algorithm is proposed that balances user’s quality of service (QoS) requirements and
resource utilization. However, the performance is evaluated based on system-level simulations and while only consider-
ing OFDM-based RAN. In Reference 14 programmable data plane is introduced to fulfill the requirements imposed by
the network slices for different vertical applications. Considering the communication parameters such as delay, packet
loss, and jitter in data path, the network is sliced in a way to cope with different applications simultaneously. Allocation
of network resources of virtual networks (VN) to substrate network (SN) requires computational cost. In Reference 15
self-adaptive mechanism based on reinforcement learning is proposed for the allocation of network resources to provide
seamless connectivity to end users, considering the cost of allocating resources. In Reference 16 network slicing is con-
sidered for the heterogeneous networks for mobile users, which not only offloads the traffic from 3GPP to Wi-Fi but also
instantiate the non-3GPP slice to satisfy the user demands. In Reference 17, certain design and implementation aspects
of a software-defined RAN experimental testbed are presented with slicing capability. It focuses on provisioning of RAN
slices and operation of radio resource management (RRM) functionality. This framework is based on 3GPP release 15 and
therefore only utilize LTE as physical layer. In Reference 18, end-to-end (E2E) network slices are applied for flash crowd
and emergency scenarios. It presents a virtual resource manager (VRM) that performs efficient slice placement and pro-
visioning and also maps cloud hardware resources (CPU and memory) onto the needed wireless network resource. It
utilizes two transport protocols, that is, UDP and TCP, for different types of service provisioning. However, it also utilizes
only LTE and does not consider radio virtualization.
In Reference 19, the design and implementation of an SDR hypervisor to support coexistence of multiple simultane-
ous VRs following different physical layer schemes is presented. The implemented scheme provides sufficient isolation
between coexisting VRs so they remain unaffected while changing their physical layer parameters, such as center fre-
quency, channel bandwidth, baseband processing scheme, and so on. However, they considered fixed transmission gain
for VRs, therefore, did not confront the issue of adjacent channel interference between the VRs. The proposed scheme

3. SAAD et al. 3 of 17
multiplexed two services, that is, LTE and narrow band-Internet of Things (NB-IoT), and it was shown that error-free
coexistence of both services was possible at the expense of large number of FFT/IFFT bins. In Reference 20 a wireless
hypervisor is proposed that multiplexes the signals of various OFDM-based radio access technologies (RATs) where the
number of subcarriers of RATs are integer multiples of one another. This work also proposed to abstract an RF front end
into a number of configurable virtual RF front ends (similar to Reference 19), thus allowing different RATs to share the
same RF front-end. It focused on achieving spectral efficiency by considering 12 virtual RATs; however, it did not con-
sider FBMC or the impact on the spectral efficiency of virtual RATs when the transmit power is increased. In Reference 21
eXtensible Virtualization Layer (XVL) is presented for enabling RAN as a Service (RANaaS). This XVL is added at the top
of radio hypervisor layer to address resource management functionality and in Reference 8, the authors have investigated
radio hypervisors as enablers for RANaaS.
In Reference 22, OFDM-based radio virtualization has been implemented using SDRs. The main issue with OFDM
is spectral leakage that creates adjacent channel interference for the neighboring channels that deteriorates the overall
performance. FBMC is a promising candidate for spectrally efficient transmissions.23
However, it has not been considered
for radio virtualization so far.
1.2 Motivation
Network slicing enables a mobile network operator to create multiple virtual (or logical) networks on top of a common
physical network infrastructure. The logical networks are thus able to do service differentiation by utilizing network slices
(NS) that are tailored to meet specific requirements of each service, for example, HD video streaming requires higher
bandwidth but can afford higher latency. Similarly, connected cars traveling at high speed may require low data rate
communication with ultra-low latency and IoT-based applications may require very low bandwidth with tolerance for
very high latency. Slices can also provide virtual infrastructure for mobile virtual network operators (MVNOs) and can
help improve their services as well. An operator can create NS to offer services on top of its existing network, also referred
to as NS as a Service (NSaaS). For example, a security agency operating its own network for surveillance applications
while utilizing exclusive spectrum might be underutilizing its network resources. Thus it can monetize its underutilized
network resources by offering NSaaS tailored to the client’s requirements.
Network slicing of an E2E network comprises of CN slicing and RAN slicing.24
Both of these network segments can be
independently abstracted, sliced, and isolated and they can also be combined for realizing E2E network slices. Therefore,
NSaaS is a combination of CN as a Service (CNaaS) and RAN as a Service (RANaaS). Significant research work has been
done on CNaaS side,7
but relatively lesser work has been done on the RANaaS side.8
Radio virtualization can abstract
the base station RF front-end into several virtual RF front-ends using software radio.6
This can obviate the requirement
for an exclusive RF-front end for each radio link; moreover, VRs can also facilitate RANaaS. However, there are issues
related to adjacent channel interference that need to be addressed—which we discuss in this article.
The different QoS requirements of various services as discussed above mainly depend on throughput (bits/second)
and latency. The throughput in turn depends on the bandwidth, access time, and the received power level. Therefore,
for creating a network slice, a network has to manipulate these four factors, that is, latency, bandwidth, access time, and
received power (at the intended receiver). Latency in the E2E network is contributed by RAN, CN, and the backhaul
between RAN and CN. Typically mobile edge computing and caching is employed to reduce latency; however, this is not
the focus of this article.25
Access time is the duration for which the subcarriers are allocated to a user, thus greater time
allocation results in higher throughput. Bandwidth and receive power lie within the domain of RAN. In the context of real-
izing radio virtualization in cloud-RAN (C-RAN), the choice of waveform has a direct impact on bandwidth and transmit
power allocation to a VR. Increasing transmit power also increases the adjacent channel interference for other VRs.
FBMC scheme has some obvious advantages for VRs. Compared with OFDM, FBMC offers higher throughput because
of no Cyclic Prefix (CP); however, FBMC also introduces long filter tails which may reduce the spectral efficiency signifi-
cantly when the block size is small.26
Due to this reason, FBMC is better suited for higher bandwidth services that utilize
larger block sizes. FBMC also has a major advantage of low sideband power levels that significantly reduces adjacent
channel interference. Thus, guard band requirement is also relaxed which results in bandwidth saving. The bandwidth
saved can in turn be allocated to more subcarriers resulting in enhancement of the overall service bandwidth. Radio vir-
tualization can assist in fine-grained service differentiation by efficiently utilizing the waveform that is better suited for
a set of services. Using radio virtualization the same RF frontend can be utilized to instantiate multiple waveforms each
suited to a specific service.

4. 4 of 17 SAAD et al.
RRH
Fiber
RRH
Virtual BBU Pool
S1/X2 RRH
Backhaul
Transport
Control
Synch
Backhaul
Transport
Control
Synch
Backhaul
Transport
Control
Synch
(A) C-RAN without VR.
Backhaul
Transport
Control
Synch
VirtualRadio
XVL
Backhaul
Transport
Control
Synch
VirtualRadio
XVL
Backhaul
Transport
Control
Synch
VirtualRadio
XVL
RRH
Fiber
RRH
Virtual BBU Pool with
Radio Virtualization
S1/X2 RRH
(B) C-RAN with VR.
F I G U R E 1 Base station
architecture in C-RAN
Figure 1 shows the mobile base station architecture in a C-RAN. The radio functionalities are performed by remote
radio head (RRH) positioned at the base station, whereas the baseband unit (BBU) performs the signal processing (physi-
cal layer (PHY)/medium access control layer (MAC)) on a cloud-based BBU pool. The fiber link provides the much needed
low latency and high bandwidth connection between the RRH and BBU. The S1/X2 interfaces connect to other base sta-
tions and to the core network. Figure 1A shows base station connecting to two devices using dedicated radio links. On the
other hand, Figure 1B shows the base station using radio virtualization for connecting with two devices on a single link.
The virtualization function is executed in BBU and an eXtensible Virtualization Layer (XVL)19
is used for implementing
RANaaS on top of VRs. In this scenario, the interference between the VRs needs to be managed effectively.
1.3 Contributions
Contrary to the previous work, we consider two different well-known air-interfaces, that is, OFDM and FBMC and inves-
tigate radio virtualization by focusing on issues that arise in radio domain. Especially when VRs are instantiated on a
base station that typically operates on high transmit power levels, due consideration is required to achieve spectral effi-
ciency. The combination of OFDM and FBMC gives greater flexibility in serving users with varying service requirements
in a spectrally efficient way. The main contributions of this article are as follows.
1. Virtualization layer is devised on USRPs using two custom created blocks in GNU radio. For FBMC VR, a sample
collector block is created (Algorithm 1) that buffers the samples needed before the transmission, to avoid under-runs
in the USRP. Receiver frame synchronization and FBMC frame extractor (Algorithm 3) is implemented using a

5. SAAD et al. 5 of 17
custom signal processing block written in C++. This will further assist other researchers in experimenting with these
waveforms.
2. We present a case for the use of VR configurations, that is, FBMC-FBMC, OFDM-OFDM, and FBMC-OFDM, for
fine-grained service differentiation in RANaaS. Virtualization layer is implemented in Python within GNU radio.
3. An extensive performance comparison of these VR combinations is carried out while varying the transmit pow-
ers of VRs and analyzing its subsequent effect on the adjacent channel interference that arises between them. We
analyze their performance on the basis of error rate, spectral efficiency, interference power, and computational com-
plexity. This gives valuable insights on the use of different waveform configurations on VRs particularly for their
implementation on base stations that typically have higher transmit power levels.
The rest of the article is structured as follows. The system model is presented in Section 2. Section 3describes the exper-
imental setup. Section 4 presents the experimental results on air-interface virtualization and finally Section 5 concludes
the article.
2 SYSTEM MODEL
This section briefly describes the transmission model for OFDM and FBMC that are used for radio virtualization. By
utilizing these concepts, signal processing blocks are implemented in GNU radio, a detailed explanation for which is
provided in experimental setup section. However, we do not propose modifications to either OFDM or FBMC that are
already mature technologies, nor do we present any theoretical analysis.
2.1 Orthogonal frequency division multiplexing
In OFDM, the baseband discrete time signal can be written as
x[i] =
1
√
N
N−1
∑
n=0
∞
∑
m=−∞
sm,ng[i − mN]ej 2𝜋
N
ni
=
1
√
N
N−1
∑
n=0
∞
∑
m=−∞
sm,n 𝜁m,n, (1)
where 𝜁m,n = g[i − mN]ej 2𝜋
N
ni
is the synthesis basis function, n denotes the subcarrier index, the data transmitted on the
nth subcarrier of the mth OFDM symbol and is expressed as sm, n which is a complex symbol from a M-QAM (quadrature
amplitude modulation) constellation, M is the modulation order, N represent the total number of subcarriers in an OFDM
symbol, 1
√
N
is the power normalization factor, and g is the rectangular window function with its time domain coefficients
defined as
g[i] =
⎧
⎪
⎨
⎪
⎩
1
√
T
if |i| ≤ T
2
0 if |i| > T
2
, (2)
where T = 1
Δf
= NTs is the OFDM symbol duration, Ts is the sampling interval, and Δf is the subcarrier spacing. We can
see that x[i] is the output of an N-point inverse discrete Fourier transform (IDFT) of sm, n. IDFT can be performed using
fast Fourier transform which is a computationally efficient way. To eliminate the inter symbol interference (ISI) and inter
carrier interference (ICI), a CP of length Lcp is added to the OFDM symbol whose length is equal or greater than the
channel delay spread. Although the use of CP ensures ISI- and ICI-free transmission; however, the signal to noise ratio
(SNR) is reduced by a factor 𝛼 = N
N+Lcp
. Adding a CP with OFDM symbol before transmission will transform x with length
Lx to xcp with length Lx + Lcp and is expressed as
xcp = x[Lx − Lcp], … , x[Lx − 1], x[0], … , x[Lx − 1]. (3)

6. 6 of 17 SAAD et al.
The signal after passing through the channel is received by the receiver as y and is given as
y[i] = xcp[i] ∗ h[i] =
Lcp
∑
k=0
h[k]x[i − k]Lx
= xcp[i] ⊗ h[i]. (4)
Note that xcp[i − n] = x[i − k]Lx
for 0 ≤ i ≤ Lx − 1, where [.]Lx
indicates a modulo-Lx operation and ⊗ represents cyclic
convolution. We can see that cyclic prefix transforms the linear convolution of the transmitted signal with the chan-
nel impulse response into circular/cyclic convolution. This circular/cyclic convolution will result in a circulant channel
matrix which is diagonalized by the FFT in the receiver. This diagonalization guarantee flat fading in each subchannel
and single tap equalization is enough to overcome the channel effects.
Assuming a noiseless and distortion-free channel, the estimated symbol ̂sm,n at the receiver output will be same as
the transmitted symbol sm, n if the internal product of the received signal y and the analysis basis function 𝜁m,n = g[i −
mN]e−j 2𝜋
N
ni
constitutes an orthonormal function, that is,
⟨ ∞
∑
i=−∞
g[i − mN]g[i − m′
N]ej 2𝜋
N
(n−n′)(i−
Lx−1
2
⟩
= 𝛿n,n′ 𝛿m,m′ , (5)
where ⟨u, v⟩ is the internal product between u and v and 𝛿m,m′ represents the Kronecker delta.
2.2 Filter bank multicarrier
Contrary to OFDM, each subcarrier in an FBMC system is modulated with a real-valued symbol to satisfy the orthogonal-
ity requirement. To maintain the same data rate as OFDM system without CP, the FBMC system transmit a real symbol
every half symbol duration, that is, T
2
, resulting in so called FBMC/OQAM system.27
This is performed at the transmitter
side where each complex data symbol sm, n, is separated into real/in-phase sI
m,n and imaginary/quadrature phase sQ
m,n com-
ponents. If T represents complex OFDM symbol duration with no CP, then 𝜏0 = T
2
represents the symbol duration of the
real FBMC/OQAM symbol. However, the subcarrier spacing v0 in FBMC/OQAM is same as OFDM, that is, v0 = Δf. Thus
for FBMC/OQAM system we have 𝜏0v0 = 1
2
which means that the density of the subcarriers in time frequency plan is twice
greater in FBMC/OQAM than the conventional OFDM where TΔf = 1. The information carried by one complex-valued
OFDM symbol with duration T is now carried by two real-valued symbols in FBMC/OQAM system each with duration
T
2
. Hence, the spectral efficiency of FBMC/OQAM is same as that of conventional OFDM with no CP.
The discrete time baseband transmitted signal in FBMC/OQAM can be written as
x[i] =
N−1
∑
n=0
∞
∑
m=−∞
am,ng[i − mN∕2]ej 2𝜋
N
n(i−D∕2)
ej𝜙m,n
=
N−1
∑
n=0
∞
∑
m=−∞
am,n 𝜁m,n[i], (6)
where 𝜁m,n[i] = g[i − mN∕2]ej 2𝜋
N
n(i−D∕2)
ej𝜙m,n is the synthesis basis function and am,n ∈ {sI
m,n, sQ
m,n} is a real-valued symbol
which is either the real or imaginary part of the input QAM symbol sm, n. While g[i] represent the well localized prototype
filter. The term D/2 is the delay term that depends on the length of the prototype filter, that is, D = KN − 1. The phase term
𝜙m,n ensures that the phase shift of ±𝜋∕2 is between adjacent pulse amplitude modulation (PAM) symbols along the time
frequency axis and is given as 𝜙m,n = 𝜋
2
(m + n).
Assuming a noiseless and distortion-free channel, the estimated symbol ̂am,n at the receiver output will be same as
the transmitted symbol am, n if the real internal product between the received signal y and the analysis basis function
𝜁m,n[i] = g[i − mN∕2]e−j 2𝜋
N
n(i−D∕2)
ej𝜙m,n constitutes an orthonormal function, that is,
⟨ ∞
∑
i=−∞
g
[
i −
mN
2
]
g
[
i −
m′
N
2
]
ej 2𝜋
N
(n−n′)(i−
Lx−1
2 ej(𝜙m′,n′ −𝜙m,n)
⟩
ℜ
= 𝛿n,n′ 𝛿m,m′ , (7)

7. SAAD et al. 7 of 17
where ⟨u, v⟩ℜ is the real internal product between u and v and 𝛿m,m′ represent the Kronecker delta. Note that since, the
orthogonality condition in FBMC/OQAM is restricted to the real field only;23
we therefore require real inner product in
case of FBMC/OQAM system.
3 EXPERIMENTAL SETUP
The experimental setup comprises of three SDRs, which includes two USRPs model B210 and a USRP model N210. The
N210 acts as a transmitter on which VRs are implemented, while the other two B210s act as receivers, one for each radio,
respectively. The complete experimental setup is shown in Figure 2. The USRP Txr on the right side is the N210 while
the two shown in the middle are B210s, labeled Rxr 1 and Rxr 2. The daughter board used with N210 is CBX which has
a frequency range from 1.2 to 6 GHz and supports a bandwidth of 40 MHz. B210 has a frequency range of 70 MHz to 6
GHz and supports a bandwidth of 56 MHz.28
Each of the USRP connects to a host computer (Ubuntu 16.04 LTS) with an
ethernet cable (N210) or a USB 3.0 (B210) interface that provides a high data rate link for the downconverted IQ (In-phase
and Quadrature) samples.
GNU radio software is used to control USRPs and implement baseband processing functions such as VR instantiation
at the transmitter, waveform generation, and reception. GNU radio is an open source software toolkit which provides
flexibility for designing functional blocks in Python and C++.29
Signal processing blocks for GNU radio are written in C++
and Python to avoid samples overhead in the USRP, to achieve FBMC receiver frame synchronization and to implement
the virtualization layer. Here USRP Txr is used as an RF frontend for the VRs, that is, two VRs instantiated on the same
USRP serve two separate receivers, Rxr 1 and Rxr 2. The individual VRs are allocated the same bandwidth within the
operational bandwidth of the USRP and they experience adjacent channel interference as the transmit power of either
VRs is increased. The subsections below describes the implementation of the individual radios and their virtualization.
3.1 Virtual Radio 1: OFDM
In our experimental setup VR 1 is implemented using OFDM as the physical layer waveform. OFDM is a very widely used
technology and has well-known advantages including simple way of subcarrier generation using IFFT/FFT.
The block diagram of OFDM implementation in GNU radio is shown in Figure 3. It starts with the generation of
QAM symbols using binary data stream. The IFFT block is then used to generate time domain samples from a block of 64
symbols in which 52 are data symbols and four pilot symbols are inserted after every 13 data symbols. These pilot symbols
are used for channel estimation at the receiver. The remaining eight symbols are used for zero padding to mitigate the
F I G U R E 2 Experimental setup showing the USRPs
connected with host computers
F I G U R E 3 OFDM implementation in GNU radio

8. 8 of 17 SAAD et al.
Waveform OFDM FBMC
FFT length 64 64
Data symbols 52 52
Zero padding 8 12
Overlapping factor — 4
Pilot symbols 4 —
Cyclic prefix ratio 1/16 —
Baseband modulation 4-QAM OQAM
TA B L E 1 Waveform parameters
effect of ISI introduced at the receiver as the signal is received from multiple paths. The symbol window is extended by
means of guard interval introduced using cyclic prefix and zero padding. The CP is inserted with a ratio of 1/16, that is, it
duplicates the last four samples of OFDM symbol and place those at the beginning of the symbol. The CP guards against
ISI, but also reduces the efficiency of the system, this drawback is addressed by FBMC. The main OFDM parameters are
tabulated in Table 1.
At the receiver side, the CP is first removed and then the signal is passed through an FFT block to convert time domain
samples into frequency domain symbols. A demodulator is then used to extract information bits as shown in Figure 3.
The output data from the demodulator is sent to a handler via call back function in Python. This function runs in sepa-
rate threads to ensure a new entry each time in the queue of the demodulated data. This function checks the correctness
of data by checking the cyclic redundancy code (CRC). In the receiver, the demodulator performs frame synchroniza-
tion and demapping of symbols into bits. Due to the good autocorrelation characteristic of pseudorandom noise (PN)
sequence, it is inserted in OFDM frame. In the receiver, these symbols assist in frame synchronization and detect the start-
ing point of the frame. Frame (or time) synchronization is done before the FFT module. Frame synchronization block is
implemented in python and it consists of two parts. The first part finds the starting point of each frame while utilizing the
known symbols inserted at the start of each frame. The second part computes the frequency offset of the frame compared
with the carrier frequency, a.k.a. coarse frequency synchronization. The synchronized signal is then passed through FFT
module to transform it into frequency domain. Post-FFT frequency synchronization, a.k.a. fine synchronization, helps to
remove the error left by coarse synchronization. The subcarrier frequency offset is corrected by finding similarity between
the received pilot subcarriers and shifted version of known subcarriers. After that demapping is performed to extract
the bits.
3.2 Virtual Radio 2: FBMC
VR 2 is implemented using FBMC as the physical layer waveform. Contrary to OFDM, each subcarrier in a FBMC sys-
tem is modulated with a real-valued symbol to satisfy the orthogonality requirement. To maintain the same data rate as
OFDM system without CP, the FBMC system transmit a real symbol every half symbol duration, resulting in the so called
FBMC/orthogonal quadrature amplitude modulation (OQAM) system. FBMC system also utilizes a specially designed
prototype filter which is well localized both in time and frequency that enables it to have higher spectral efficiency and
better spectral containment compared with conventional OFDM system.30
The block diagram of FBMC-based GNU radio system is shown in Figure 4. The block diagram starts with the receiving
of data bits from the socket PDU. The FBMC payload generator block is implemented on top of MAC and PHY encoder
block that receives data from the user datagram protocol (UDP) socket and performs fragmentation. It then splits the
byte packets to sizes that fit into the FBMC frames. After this, MAC encoder receives the byte packets and adds a MAC
header and CRC and sends it to the physical encoder as shown in Figure 4. The PHY encoder receives MAC-Header +
Byte data + CRC and applies channel coding with code rate 1/2. Next, FBMC carrier allocator transforms the byte data
into modulated symbols. The 64 subcarriers are divided into logical channels. The logical channel block, designed in GNU
radio, takes the first number as the number of subcarriers in the logical channel, whereas the later numbers represent the
indices of the subcarriers. Next it is passed to OQAM block which separates the complex symbols into real and imaginary
parts and then IFFT operation is performed to convert it into time domain samples. The time domain signal is then
passed through a filter bank to perform per subcarrier filtering as shown in Figure 4. The prototype filter used in the filter

9. SAAD et al. 9 of 17
F I G U R E 4 FBMC implementation in GNU radio
bank has an overlapping factor that determines the length of the prototype-filter in time domain. With 64 subcarriers
and an overlapping-factor of 4, the prototype-filter has a length of 4 × 64 = 256 samples. Before the signal is digitally up
converted, the samples are collected in buffer frame called sample collector. This sample collector block is written in C++.
Algorithm 11 is the pseudocode of the sample collector block. It buffers the samples needed before the transmission
that arrives from modulation chain. This block has a message port as input and an stream port as output. Therefore the
scheduler permanently polls its work function. Airtime defines the sleep periods between two calls of work. This way the
CPU does not run at 100%. If the samples overlap, that is, airtime is less than the duration required to transmit the FBMC
samples, then it would drop the frames. This avoids the underruns in the USRP. The dropped samples are transmitted in
the next go to avoid overheads in the USRP for an even transmission. The samples to be transmitted are determined by the
buffer space. In our experiment we select buffer space = sensor_pre_push*sensor_chunk_len. Where sensor_pre_push=2
and sensor_chunk_len=500k. The FBMC parameters are tabulated in Table 1.
On the receiver side, the received signal is first passed through the analysis filter bank (AFB) and then it is passed
through a cross correlation block as shown in Figure 4. Algorithm 2 is the pseudocode for the polyphase network (PPN)
receive filter applied on the subcarriers. PPN filter used in this experiment is implemented by the PHYDAS project.30
It is an efficient implementation for pulse shaping per subcarrier and each of the subcarriers after the FFT gets shaped
with isotropic orthogonal transfer algorithm (IOTA) filter. Hence we transformed the rectangular pulse shape into far
smoother pulse shape.
The correlation block performs cross correlation with preambles for frame synchronization. It not only detects the
frame, that is, symbol timing offset but it also measures the carrier frequency offset. The training symbols inserted as
preambles are used to estimate frequency offset compared with carrier frequency. Correlation block when detects the
frame, this information is passed on to the FBMC frame extractor block. Algorithm 31
is the pseudocode for FBMC frame
extractor.
FBMC frame extractor checks the input items for new frames. It iterates over the logical channels and buffers the
received frames. Then it collects the received frames and copies it into the frame structure. Finally, after collecting the
samples, it deletes the buffer to free space. The samples from the FBMC frame extractor are then converted into frequency
domain symbols using FFT and after this the post OQAM block takes the real and imaginary samples and returns the
estimated complex symbols as shown in Figure 4.
3.3 Implementation of virtual radios
In a general multicarrier transmission system, IFFT block serves as a multicarrier modulator and the FFT block
serves as a multicarrier demodulator. For the implementation of VRs, virtualization layer is implemented. The VRs
1
This source code is available at GitHub (https://github.com/maliksaad84/Virtualization-of-radio.git) released by us.

10. 10 of 17 SAAD et al.
are implemented using IQ samples that are transformed into the frequency domain using FFT module. These sam-
ples are collected individually with respect to the bandwidth of the radio interfaces. Collected samples are then placed
in the same buffer for each VR based on the overall available system bandwidth. Frequency spacing is set between
the two VRs before mapping these on to a single composite signal. Algorithm 42 is the pseudocode for the mapping
of VRs.
Algorithm 1. FBMC sample collector
Check if new frame is not too late
if airtime < chunk::low_time then
Dropping frame;
else
Frames Send ++;
if new_samples_needed > sensor_pre_push*sensor_chunk_len then
Too many new samples needed;
else
Check if new chunks have to be appended
if new_samples_needed > 0 then
int new_chunks_needed = (new_samples_needed + chunk::chunk_len - 1) /
chunk::chunk_len;
for <int i=0; i<new_chunks_needed; i++> do
sensor_chunk_vec.push_back(new chunk());
end
else
Return;
end
end
end
Algorithm 2. Polyphase network receive filter
veclen ← veclength;
filterlen ← filterlength;
sizet alig = volk_get_alignment();
filter = (gr_complex*) volk_malloc(filterlen*veclen*sizeof(gr_complex), alig);
gr_complex *buffer = (gr_complex*) volk_malloc(filterlen*veclen*sizeof(gr_complex),
alig);
for <int i=0; i<noutput_items; i++> do
for <int j=0; j<filterlen; j++> do
volk_32fc_x2_multiply_32fc(&buffer[j*veclen], &in[(i+2*j)*veclen], &fil-
ter[j*veclen], veclen);
end
for <int k=1; k<filterleng; k++> do
volk_32f_x2_add_32f((float*) buffer, (const float*) buffer, (const float*)
&buffer[k*veclen], 2*veclen);
end
memcpy(&out[i*veclen], buffer, veclen*sizeof(gr_complex));
end
volk_free(buffer);
return noutput_items;
2
The source code is publically available at GitHub (https://github.com/maiconkist/gr-hydra.git).

11. SAAD et al. 11 of 17
Algorithm 3. FBMC frame extractor
Step 1: Check input items for new frames
for <int i=0; i<noutput_items; i++>
if in0[i*veclen] == 0 then
continue;
for <int j=0; j<log_chann; j++> do
int tau = in0[i*veclen + j*3 + 1];
if tau ≠ veclen then
frames_received ++;
else
Return;
end
end
else
Return;
end
Step 2: Collect frame samples from input items
for <int i=0; i<frame_vec.size(); i++> do
if frame_vec[i]->data_cnt > 0 then
continue;buffer = frame::frame_len - frame_vec[i]->data_cnt;
else
buffer = frame::frame_len;
end
end
memcpy(&frame_vec[i]->data[frame_vec[i]
->data_cnt], &in1[rel_idx], buffer*sizeof(gr_complex)); frame_vec[i]->data_cnt += buffer;
Step 3:Delete frames
for <int i ≤ frame_vec.size()-1; i ≤ 0; i– > do
if frame_vec[i]->data_cnt == frame::frame_len then
delete frame_vec[i];
frame_vec.erase(frame_vec.begin() + i);
else
Return;
end
end
Algorithm 4. VR mapping
BW ← Bandwidth available;
FC ← Center Frequency of Radio;
VR_FC ← Center Frequency of Virtual∕Radio;
Offset = (VR_FC − VRbw∕2.0) − (FC − BW∕2.0);
int temp = BW ∕ fftmlen;
int sc = offset ∕ temp;
sizet fftn = VRbw∕temp;
if sc ≤ 0 || sc ≥ fftmlen then
Cannot allocate subcarriers for VR ;
Return
else
For<; sc < fftmlen; sc++>
if Already allocated then
Return
else
themap.pushback(sc);
themap.size() == fftn;
break;
end
end

12. 12 of 17 SAAD et al.
Initially, this block gets the available bandwidth of the system before mapping of VRs. The required bandwidth for
the interface is fed to the virtualization layer. To map each radio interface, center frequency (FC) is also fed to the block
for carrier spacing. Offset is decided based on the FC of the VR. This is the shift from the FC of the USRP at which
the multiplexed virtualized stream is transmitted. Subcarriers are allocated to each VR based on N points, where N is a
function of bandwidth of VR. In Figure 5 N1 and N2 shows the FFT points for VR 1 and VR 2, respectively. The frequency
components of each VR are converted into time domain samples using IFFT operation and then transmitted as a single
composite RF signal. Figure 5 shows the overview of the two VRs being virtualized into a single stream for transmission
as a single composite signal. In this figure, Radio 1 and Radio 2 represent two different logical networks implemented
using FBMC and OFDM waveforms. The IQ samples of each radio are passed through virtualization layer where VRs are
mapped. The two logical networks share the same physical network infrastructure as represented by a single transmitter
USRP. All the parameters for virtualization of radios are tabulated in Table 2.
F I G U R E 5 Virtualization of OFDM and FBMC radios on a single stream
Parameter Value
Bandwidth of system 12.5 MHz
Sampling rate 12.5 MSps
Bandwidth of Radio 1 5 MHz
Bandwidth of Radio 2 5 MHz
Center freq. of Radio 1 1.896 GHz
Center freq. of Radio 2 1.904 GHz
Carrier spacing 2 MHz
Air-interface for Radio 1 OFDM
Air-interface for Radio 2 FBMC
Sampling rate for Radio 1 5 MSps
Sampling rate for Radio 2 5 MSps
FFT Length 512
TA B L E 2 System parameters

13. SAAD et al. 13 of 17
4 RESULTS
In this section, we present the results of our experiments on radio virtualization. Two VRs, VR 1 and VR 2, are imple-
mented on USRPs as shown in Figure 2. They are centered at frequencies 1.896 and 1.904 GHz, respectively. All the
measurements with USRPs are performed while these are placed 10 m apart in the WiSP Lab (IST). The total operational
bandwidth of the transmitted signal is 12.5 MHz that accommodates the two VRs of 5 MHz bandwidth each. The transmit-
ter USRP gain is varied from 0 to 33 dB. The experiments were performed for the following three cases for virtualization;
(a) Case I: OFDM-OFDM (OFDM is implemented on both VRs), (b) Case II: FBMC-FBMC (FBMC is implemented on
both VRs), and (c) Case III: OFDM-FBMC (OFDM is implemented on VR1 and FBMC on VR2). The performance of these
cases is analyzed using error rate, spectral efficiency, interference power, and computational complexity. In addition to
these, we have also implemented OFDM and FBMC schemes as a reference (without virtualization), to quantitatively
assess the performance degradation experienced due to virtualization. For our results, we have implemented two VRs but
it can be extended to more number of VRs which further increase its use in different applications.
Figure 6A-C shows the observed spectrum for OFDM-OFDM, FBMC-FBMC, and OFDM-FBMC VR transmissions,
respectively. We observe an overlap in the sidelobes spectrum of both VRs. The sidelobes for Case II (FBMC-FBMC)
are approximately 10 dB more suppressed compared with Case I (OFDM-OFDM), whereas intermediate suppression is
observed in Case III (OFDM-FBMC).
Figure 7 shows the bit error rate (BER) of the three VR cases as well as for the individual OFDM and FBMC signals. The
error rate is measured by transmitting known bit sequence and comparing it with the received bit sequence and computing
the percentage of erroneous bits. The receiver gain is kept constant at 25 dB for all cases. Transmit gain varies between
0 and 33 dB for which the corresponding signal power varies between −10 and 23 dBm. The error rate is relatively high
as the forward error correction is not performed. Since we are presenting relative performance comparison of different
VR configurations; therefore, it does not make a difference. The initial error rate for all the cases is 45% when the gain is
11 dB. As the transmit gain is increased the error rate starts to decrease and at 26 dB gain it falls below 10%, as shown in
(A) CaseI: OFDM-OFDM (B) CaseII: FBMC-FBMC
(C) CaseIII: OFDM-FBMC
F I G U R E 6 Observed spectrum of different VR configurations acquired in GNU radio

14. 14 of 17 SAAD et al.
10 15 20 25 30 35
Gain (dB)
0
5
10
15
20
25
30
35
40
45
50ErrorRate
OFDM-based
FBMC-based
Case I
Case III
Case II
F I G U R E 7 Gain vs error rate
Figure 7. For the three VR cases the error rate decreases with increase in the transmitter gain; however, when the gain
increases beyond 26 dB the error rate starts to increase as well. This happens because of the increasing overlap between
the spectrums of VRs, or the increasing adjacent channel interference. Among the three VR cases, OFDM-OFDM (Case I)
exhibits the highest error rate because of higher sidelobes of the OFDM VRs. The FBMC-FBMC (Case II) exhibits relatively
lowest error rate because of lesser sidelobes of the FBMC VRs. The OFDM-FBMC (Case III) gives an intermediate error
rate. The error rates of the individual FBMC and OFDM signals are comparable with the VR cases at lower gain values;
however, at higher gain values their error rates continue to decrease, as these individual signals do not experience any
adjacent channel interference.
Figure 8 displays the spectral efficiency for all the cases. Spectral efficiency (bits/second/Hertz) is calculated at the
receiver by taking a ratio of the correctly received bits and the bandwidth (5 MHz) for each VR and normalizing it with
the transmission duration (5 seconds). To achieve lower SNR the transmit power is kept low which results in higher error
rate and lower spectral efficiency. An increasing trend in spectral efficiency is observed for all the cases with increase in
gain up till an experimentally observed optimal gain value of 26 dB (inflection point), after which it starts decreasing. This
decrease is caused due to the overlap of the sidelobes of both VRs which in turn results in increase of error rate thereby
decreasing the spectral efficiency. For the case of FBMC-FBMC (Case II), the spectrum is 4% more efficient compared
with OFDM-OFDM (Case I). This is because the sidelobes in FBMC are approximately 10 dB less compared with OFDM
as shown in Figure 6. The performance of OFDM-FBMC (Case III) is better than OFDM-OFDM (Case I) but inferior to
FBMC-FBMC (Case II).
Figure 9 shows the interference power observed as a function of transmitter gain. The transmitter USRP gain is
increased from 9 to 33 dB. The spacing between the two VRs is kept at 2 MHz while the individual VR’s bandwidth is
10 15 20 25 30 35
Gain (dB)
0.02
0.04
0.06
0.08
0.1
0.12
0.14
0.16
SpectralEfficiency
OFDM-based
FBMC-based
Case I
Case III
Case II
F I G U R E 8 Gain vs spectral efficiency

15. SAAD et al. 15 of 17
F I G U R E 9 Gain vs interference power
10 15 20 25 30 35
Gain (dB)
7
8
9
10
11
12
13
14
15
InterferencePower(dBm)
Case I
Case III
Case II
F I G U R E 10 CPU
performance at the transmitter
side (host) PC for two VR
configurations
(A) OFDM-OFDM VR
(B) FBMC-FBMC VR
5 MHz. Another USRP was used to compute the interference power called the Sensing-USRP (S-USRP)3S-USRP is used
to sense the spectrum of transmitted VRs. After this, the raw bit streams are converted to vectors or arrays of data. FFT
is performed on the received raw data to convert it into the frequency domain. The magnitude of the FFT bins is used to
calculate the power of the signal. Initially a single VR was transmitted by the transmitter, with same transmission param-
eters and the received power was computed by S-USRP over 5 MHz bandwidth centered on carrier frequency of VR 1.
Then the VR combination was transmitted and once again the received power was computed in the same way by S-USRP.
This time the adjacent channel interference of the neighboring VR was also present. By subtracting the former power
from the later the interference power was computed for VR 1. Averaging was done while computing these power levels.
The y-axis in Figure 9 shows this interference power for different values of transmitter gain. For Case 1 (OFDM-OFDM)
the interference power is on average 1dB higher than the Case II (FBMC-FBMC). Case III (OFDM-FBMC) lies in between
the other two cases. The interference power is lesser when the transmitter gain is lower and it increases with increasing
gain value, as the adjacent channel interference gets more pronounced. The increasing trend is a bit jagged due to the
experimental measurements.
Although FBMC VR exhibits greater spectral efficiency compared with OFDM, but it also consumes more compu-
tational resources. The computer connected to the transmitter USRP had four central processing unit (CPU) cores and
Figure 10 shows the CPUs performance for OFDM-OFDM and FBMC-FBMC VR configurations. It shows the CPUs uti-
lization over time where the x-axis represents the duration of the trace and y-axis shows the percentage of CPU capacity
utilized. The legend shows the average utilization of each CPU core in each case. The combined average utilization of
3
We used the standard USRP spectrum sensing algorithm Spectrum_Sensing.py (https://github.com/maliksaad84/Virtualization-of-radio/blob/
master/Spectrum_Sensing.py).

16. 16 of 17 SAAD et al.
four CPU cores is 34.8% in case of OFDM-OFDM VR, whereas in case of FBMC-FBMC VR it is 45.3%. Hence, the system
consumes on average 10.5% more CPU cycles while processing the FBMC-FBMC VR.
5 CONCLUSION
Radio virtualization allows creation of multiple VRs on a single physical radio platform, thus resulting in economy of
hardware resources. This may also help future mobile networks to cater for diverse service requirements of users in terms
of bandwidth, latency, and so on. In this work, we experimentally evaluated combinations of OFDM and FBMC-based
VRs that can help realize fine-grained network slices. The performance of three VR combinations (ie, OFDM-OFDM,
OFDM-FBMC, and FBMC-FBMC) was experimentally analyzed on the basis of error rate, spectral efficiency, adjacent
channel interference, and computational complexity. The FBMC-FBMC VR gives the best performance with lowest error
rate and interference but at the cost of higher implementation complexity of FBMC. On the other hand, OFDM-OFDM VR
combination gives the worst performance but has the lowest complexity. The OFDM-FBMC combination exhibits inter-
mediate performance and complexity. This experimental analysis is insightful for VR implementation on base stations
that typically operate on higher transmit powers and can therefore generate higher interference between VRs. Although
OFDM and FBMC are two distinct waveforms with their own pros and cons, the proposed air-interface virtualization can
leverage these waveforms to generate the VR combination on a single radio link as per the requirement of the receivers
for multiservice scenarios. In future, we will extend this to multiple VRs and will also consider latency as performance
metric.
ACKNOWLEDGMENT
This publication has emanated from the research funded by Higher Education Commission (HEC), Pakistan, under grant
No HEC NRPU 20-4339/R&D/HEC/14/235. In addition this study was supported by the BK21 Plus project (SW Human
Resource Development Program for Supporting Smart Life) funded by the Ministry of Education, School of Computer
Science and Engineering, Kyungpook National University, Korea (21A20131600005).
DATA AVAILABILITY STATEMENT
Data sharing not applicable to this article as no datasets were generated or analyzed during the current study.
ORCID
Malik Muhammad Saad https://orcid.org/0000-0003-1721-4681
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