40 research outputs found
ns3-gym: Extending OpenAI Gym for Networking Research
OpenAI Gym is a toolkit for reinforcement learning (RL) research. It includes
a large number of well-known problems that expose a common interface allowing
to directly compare the performance results of different RL algorithms. Since
many years, the ns-3 network simulation tool is the de-facto standard for
academic and industry research into networking protocols and communications
technology. Numerous scientific papers were written reporting results obtained
using ns-3, and hundreds of models and modules were written and contributed to
the ns-3 code base. Today as a major trend in network research we see the use
of machine learning tools like RL. What is missing is the integration of a RL
framework like OpenAI Gym into the network simulator ns-3. This paper presents
the ns3-gym framework. First, we discuss design decisions that went into the
software. Second, two illustrative examples implemented using ns3-gym are
presented. Our software package is provided to the community as open source
under a GPL license and hence can be easily extended
LtFi: Cross-technology Communication for RRM between LTE-U and IEEE 802.11
Cross-technology communication (CTC) was proposed in recent literature as a
way to exploit the opportunities of collaboration between heterogeneous
wireless technologies. This paper presents LtFi, a system which enables to
set-up a CTC between nodes of co-located LTE-U and WiFi networks. LtFi follows
a two-step approach: using the air-interface LTE-U BSs are broadcasting
connection and identification data to adjacent WiFi nodes, which is used to
create a bi-directional control channel over the wired Internet. This way LtFi
enables the development of advanced cross-technology interference and radio
resource management schemes between heterogeneous WiFi and LTE-U networks.
LtFi is of low complexity and fully compliant with LTE-U technology and works
on WiFi side with COTS hardware. It was prototypically implemented and
evaluated. Experimental results reveal that LtFi is able to reliably decoded
the data transmitted over the LtFi air-interface in a crowded wireless
environment at even very low LTE-U receive power levels of -92dBm. Moreover,
results from system-level simulations show that LtFi is able to accurately
estimate the set of interfering LTE-U BSs in a typical LTE-U multi-cell
environment
Towards MAC/Anycast Diversity in IEEE 802.11n MIMO Networks
Opportunistic Routing (OR) is a novel routing technique for wireless mesh
networks that exploits the broadcast nature of the wireless medium. OR combines
frames from multiple receivers and therefore creates a form of Spatial
Diversity, called MAC Diversity. The gain from OR is especially high in
networks where the majority of links has a high packet loss probability. The
updated IEEE 802.11n standard improves the physical layer with the ability to
use multiple transmit and receive antennas, i.e. Multiple-Input and
Multiple-Output (MIMO), and therefore already offers spatial diversity on the
physical layer, i.e. called Physical Diversity, which improves the reliability
of a wireless link by reducing its error rate. In this paper we quantify the
gain from MAC diversity as utilized by OR in the presence of PHY diversity as
provided by a MIMO system like 802.11n. We experimented with an IEEE 802.11n
indoor testbed and analyzed the nature of packet losses. Our experiment results
show negligible MAC diversity gains for both interference-prone 2.4 GHz and
interference-free 5 GHz channels when using 802.11n. This is different to the
observations made with single antenna systems based on 802.11b/g, as well as in
initial studies with 802.11n
XZero: On Practical Cross-Technology Interference-Nulling for LTE-U/WiFi Coexistence
LTE-U/WiFi coexistence can be significantly improved by placing so-called
coexistence gaps in space through cross-technology interference-nulling (CTIN)
from LTE-U BS towards WiFi nodes. Such coordinated co-existence scheme
requires, for the exchange of control messages, a cross-technology control
channel (CTC) between LTE-U and WiFi networks which was presented recently.
However, it is unclear how a practical CTIN operates in the absence of channel
state information which is needed for CTIN but cannot be obtained from the CTC.
We present XZero, the first practical CTIN system that is able to quickly find
the suitable precoding configuration used for interference nulling without
having to search the whole space of angular orientations. XZero performs a
tree-based search to find the direction for the null beam(s) by exploiting the
feedback received from the WiFi AP on the tested null directions. We have
implemented a prototype of XZero using SDR platform for LTE-U and commodity
hardware for WiFi and evaluated its performance in a large indoor testbed.
Evaluation results reveal on average a reduction by 15.7 dB in
interference-to-noise ratio at the nulled WiFi nodes when using a ULA with four
antennas. Moreover, XZero has a sub-second reconfiguration delay which is up to
10x smaller as compared to naive exhaustive linear search.Comment: 9 page
The Future is Unlicensed: Coexistence in the Unlicensed Spectrum for 5G
5G has to fulfill the requirements of ultra-dense, scalable, and customizable
networks such as IoT while increasing spectrum and energy efficiency. Given the
diversity of envisaged applications and scenarios, one crucial property for 5G
New Radio (NR) is flexibility: flexible UL/DL allocation, bandwidths, or
scalable transmission time interval, and most importantly operation at
different frequency bands. In particular, 5G should exploit the spectral
opportunities in the unlicensed spectrum for expanding network capacity when
and where needed. However, unlicensed bands pose the challenge of "coexisting
networks", which mostly lack the means of communication for negotiation and
coordination. This deficiency is further exacerbated by the heterogeneity,
massive connectivity, and ubiquity of IoT systems and applications. Therefore,
5G needs to provide mechanisms to coexist and even converge in the unlicensed
bands. In that regard, WiFi, as the most prominent wireless technology in the
unlicensed bands, is both a key enabler for boosting 5G capacity and competitor
of 5G cellular networks for the shared unlicensed spectrum. In this work, we
describe spectrum sharing in 5G and present key coexistence solutions, mostly
in the context of WiFi. We also highlight the role of machine learning which is
envisaged to be critical for reaching coexistence and convergence goals by
providing the necessary intelligence and adaptation mechanisms.Comment: 7 pages, 4 figure
Coexistence Gaps in Space: Cross-Technology Interference-Nulling for Improving LTE-U/WiFi Coexistence
To avoid the foreseeable spectrum crunch, LTE operators have started to
explore the option to directly use 5 GHz unlicensed spectrum band being used by
IEEE 802.11 (WiFi). However, as LTE is not designed with shared spectrum access
in mind, there is a major issue of coexistence with WiFi networks. Current
coexistence schemes to be deployed at the LTE-U BS create coexistence gaps only
in one domain (e.g., time, frequency, or space) and can provide only
incremental gains due to the lack of coordination among the coexisting WiFi and
LTE-U networks. Therefore, we propose a coordinated coexistence scheme which
relies on cooperation between neighboring LTE-U and WiFi networks. Our proposal
suggests that LTE-U BSs equipped with multiple antennas can create coexistence
gaps in space domain in addition to the time domain gaps by means of
cross-technology interference nulling towards WiFi nodes in the interference
range. In return, LTE-U can increase its own airtime utilization while trading
off slightly its antenna diversity. The cooperation offers benefits to both
LTE-U and WiFi in terms of improved throughput and decreased channel access
delay. More specifically, system-level simulations reveal a throughput gain up
to 221% for LTE-U network and 44% for WiFi network depending on the setting,
e.g., the distance between the two cell, number of LTE antennas, and WiFi users
in the LTE-U BS neighborhood. Our proposal provides significant benefits
especially for moderate separation distances between LTE-U/WiFi cells where
interference from a neighboring network might be severe due to the hidden
network problem.Comment: 11 page
Deep Learning for Cross-Technology Communication Design
Recently, it was shown that a communication system could be represented as a
deep learning (DL) autoencoder. Inspired by this idea, we target the problem of
OFDM-based wireless cross-technology communication (CTC) where both
in-technology and CTC transmissions take place simultaneously. We propose
DeepCTC, a DL-based autoencoder approach allowing us to exploit DL for joint
optimization of transmitter and receivers for both in-technology as well as CTC
communication in an end-to-end manner. Different from classical CTC designs, we
can easily weight in-technology against CTC communication. Moreover, CTC
broadcasts can be efficiently realized even in the presence of heterogeneous
CTC receivers with diverse OFDM technologies. Our numerical analysis confirms
the feasibility of DeepCTC as both in-technology and CTC messages can be
decoded with sufficient low block error rate.Comment: 6 pages, 8 figure
On the Frequency-Selective Scheduling Gain in SDMA-OFDMA Systems
Orthogonal Frequency Division Multiple Access (OFDMA) is a multi-user version
of the Orthogonal Frequency Division Multiplexing (OFDM) transmission
technique, which divides a wideband channel into a number of orthogonal
narrowband subchannels, called subcarriers. An OFDMA system takes advantage of
both frequency diversity (FD) gain and frequency-selective scheduling (FSS)
gain. A FD gain is achieved by allocating a user the subcarriers distributed
over the entire frequency band whereas a FSS gain is achieved by allocating a
user adjacent subcarriers located within a subband of a small bandwidth having
the most favorable channel conditions among other subbands in the entire
frequency band. Multi-User Multiple Input Multiple Output (MU-MIMO) is a
promising technology to increase spectral efficiency. A well-known MU-MIMO mode
is Space-Division Multiple Access (SDMA) which can be used in the downlink
direction to allow a group of spatially separable users to share the same
time/frequency resources. In this paper, we study the gain from FSS in
SDMA-OFDMA systems using the example of WiMAX. Therefore, a complete SDMA-OFDMA
MAC scheduling solution supporting both FD and FSS is proposed. The proposed
solution is analyzed in a typical urban macro-cell scenario by means of
system-level packet-based simulations, with detailed MAC and physical layer
abstractions. By explicitly simulating the MAC layer overhead (MAP) which is
required to signal every packed data burst in the OFDMA frame we can present
the overall performance to be expected at the MAC layer. Our results show that
in general the gain from FSS when applying SDMA is low. However, under specific
conditions, small number of BS antennas or large channel bandwidth, a
significant gain can be achieved from FSS.Comment: 7 pages, 8 figure
NxWLAN: Neighborhood eXtensible WLAN
The increased usage of IEEE 802.11 Wireless LAN (WLAN) in residential
environments by unexperienced users leads to dense, unplanned and chaotic
residential WLAN deployments. Often WLAN Access Points (APs) are deployed
unprofitable in terms of radio coverage and interference conditions. In many
cases the usage of the neighbor's AP would be beneficial as it would provide
better radio coverage in some parts of the residential user's apartment.
Moreover, the network performance can be dramatically improved by balancing the
network load over spatially co-located APs.
We address this problem by presenting Neighborhood extensible WLAN (NxWLAN)
which enables the secure extension of user's home WLANs through usage of
neighboring APs in residential environments with zero configuration efforts and
without revealing WPA2 encryption keys to untrusted neighbor APs. NxWLAN makes
use of virtualization techniques utilizing neighboring AP by deploying
on-demand a Wireless Termination Point (WTP) on the neighboring AP and by
tunneling encrypted 802.11 traffic to the Virtual Access Point (VAP) residing
on the home AP. This allows the client devices to always authenticate against
the home AP using the WPA2-PSK passphrase already stored in the device without
any additional registration process.
We implemented NxWLAN prototypically using off-the-shelf hardware and open
source software. As the OpenFlow is not suited for forwarding native 802.11
frames, we built software switch using P4 language. The performance evaluation
in a small 802.11 indoor testbed showed the feasibility of our approach. NxWLAN
is provided to the community as open source.Comment: Technical report, Telecommunication Networks Group, Technische
Universitaet Berli
ResFi: A Secure Framework for Self Organized Radio Resource Management in Residential WiFi Networks
In dense deployments of residential WiFi networks individual users suffer
performance degradation due to both contention and interference. While Radio
Resource Management (RRM) is known to mitigate this effects its application in
residential WiFi networks being by nature unplanned and individually managed
creates a big challenge. We propose ResFi - a framework supporting creation of
RRM functionality in legacy deployments. The radio interfaces are used for
efficient discovery of adjacent APs and as a side-channel to establish a secure
communication among the individual Access Point Management Applications within
a neighborhood over the wired Internet backbone. We have implemented a
prototype of ResFi and studied its performance in our testbed. As a showcase we
have implemented various RRM applications among others a distributed channel
assignment algorithm using ResFi. ResFi is provided to the community as open
source