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