Friday, December 16, 2016

CoNext 2016 Session 2 -- Wireless 1

1. EMPoWER Hybrid Networks: Exploiting Multiple Paths over Wireless and ElectRical Mediums 

Authors: S├ębastien Henri (EPFL), Christina Vlachou (EPFL), Julien Herzen (Swisscom), and Patrick Thiran (EPFL)

Besides the  advances in mobile computing the high throughput demands cannot always be satisfied because many technologies co-exist (WiFi, power-line communications -PLC-, cellular) but neither  cooperate nor operate at their full capacity. The authors propose the architecture EMPoWER, a system that exploits simultaneously several potentially interfering mediums. EMPoWER operates at layer 2.5 between the MAC and IP layers, and combines routing (to find multiple concurrent routes) and congestion control (to efficiently balance traffic across the routes), by exploiting the rich diversity offered by the multiple networks.

Network aggregation solutions can improve the throughput but routing must be carefully assigned 
in order to avoid congestion in the intermediate nodes. Thus, the paper studies a global throughput optimization problem with several congesting flows for multi-path scenarios. EMPoWER can be used with any protocol, but this work focuses only on a Hybrid Network of WiFI and PLC. Power line communications (aka PLC) is communication through electrical wires, and actually WiFi and PLC can be aggregated in WiFi relays). 

EMPoWER aims to find the optimal rates, avoid congestion, combine wired and wireless paths and provide a multi path controller. Constraints of the optimization problem are: 1. Airtime demand, 2. Airtime must not exceed 100%, 3. Interference of WiFi and PLC links. The controller for the multi-path routing protocol focuses on the total throughput and optimizes a utility function globally. 

The authors simulated their proposed schema and they compared it against the back pressure schema which has been proven optimal. Thus, two schedulers were used: 1. Optimal Scheduler for getting the optimal Throughput, 2. Scheduler with constraints under EMPoWER.

For the numerical results, 1000 random topologies were simulated. EMPoWER was close to the optimal throughput. EMPoWER includes also an actual testbed implementation at layer 2.5, IEEE 905 compatible. Authors demonstrate experimental results where aggregation improve users' throughput. Finally, a comparison between the hybrid PLC and WiFi schema vs multi-channel WiFi is considered. EMPoWER outperforms in 75% of scenarios and can offer up to 10x improvement.

Q1: Does the experimentation included a calibration  phase for calculating the network capacity of the wireless (WiFi) and the wired (electrical lines) medium? Hoes does the capacity change over time due to environment changes (e.g. electrical devices interference etc)?

A1: There are training symbols/information in the header about the modulation schema of WiFi/PLC in order to calculate the capacity. Of course, interference indeed does change the capacity, but the EMPoWER controller can handle time varying capacity changes.

Q2: Could you provide details for the simulator technology?
A2: It was implemented in Matlab and it’s available online.

2. FlexRAN: A Flexible and Programmable Platform for Software Defined Radio Access networks

Authors: Xenofon Foukas (The University of Edinburgh), Navid Nikaei (Eurecom), Mohamed M. Kasse (The University of Edinburgh), Mahesh K. Marina (The University of Edinburgh), and Kimon Kontovasilis (NCSR Demokritos).

Current 4G technologies haven’t been designed to support Internet of Things (IoT), device-to-device (D2D) and machine-to-machine (M2M), therefore, 5G must include programmable network and radio operations, easily and quickly adaptable to the traffic requirements  (i.e.“softwarization” of each stage). Apart from software radio networking (SDN), next generation cellular networks will include software defined radio access network (SD-RAN), and current literature hasn’t yet demonstrated a concrete platform which handle the idiosyncrasies of RAN control.

This work develops an open source SD-RAN platform for experimentation, which support real time applications,  provides a modular SD-RAN design, and allows programmable network functions and programmable radio layer operations. The master controller is a top level orchestrator of the FlexRAN and provides an API for fetching mobile and network statistics from the various entities of the network (UEs, eNBs, management, statistic entities etc). Moreover, the controller allows priorities to network flows at each different component.

Logical separation of control (network) plane/operations and data plane are supported by FlexRAN. For example, downlink traffic scheduling and uplink traffic scheduling flows can be completed separated  Finally, the Virtual Subsystem Functions are being executed by a re-programable scheduler (i.e. different scheduling per type of traffic). Its hierarchical master-agent design controller architecture  is well suited  for real time RAN control operations while allows reprogrammability and reconfigurability and control delegation following Network Function Virtualization.

FlexRAN master controller has been implemented in C++ and the FlexRAN agent is written in C. The whole system has implemented over the OpenAirInterface (OAI) LTE platform. The experimentation with the system showed no difference in service quality compared to preexisting controller Vanilla  of OAI.

Experimental results show that even in the worst case configuration (i.e. running real time schedulers on top of master controller which constantly sends statistics per ms) FlexRAN could respond to the resulted load without any degradation of QoS. For 50 UEs (worst case scenario) stats reporting inside FlexRAN could be up to 100 mbps (intra-traffic communications between the controller and the Network Functions  is a crucial performance factor).

Q: How would be FlexRAN be able to handle adaptive video streaming?
A: FlexRAN is able to create a new virtual operation per a new policy (i.e.  a new requested bitrate), The Fair scheduling policy per node dynamically adjusts the bitrate so the user will experience a smoothly transition in his bitrate.

3. Mudra: User-friendly Fine-grained Gesture Recognition Using WiFi Signals.

Authors: Ouyang Zhang (Ohio State University) and Kannan Srinivasan (Ohio State University)

This work presents Mudra, a framework for fine grained recognition of hands’ gestures. The authors leverage pervasive WiFI signals  in order to detect extremely fine, subtle finger gestures which can be used to human-to-machine interaction or other type of control. This framework can enable over the air interaction (i.e. resolve screen limitation) and allow communication in contact-forbidden scenarios  (e.g. infectious medical task).Prior work’s limitations include the need of large scale space for the deployment of large antenna array methods and that is limited to coarser hands’ gestures.

Mudra uses a two-antenna receiver to detect and recognize finger gesture. It uses the signals received from one antenna to cancel the signal from the other. This
“cancellation” is extremely sensitive to and enables the detection of small variation in channel due to finger movements. Since Mudra decodes gestures with existing WiFi transmissions, Mudra enables gesture recognition without sacrificing WiFi transmission opportunities. Besides, Mudra is user-friendly with no need of user training. Mudra’s prototype implementation was done on a NI-based SDR platform and used COTS WiFi adapter. Experimental evaluation showed that the system can achieve 96% accuracy.

Q: Did the experimentation include other movements in the environment apart from the fingers’ gesture?

A: No, future work will investigate how other body’s movement could affect the system.

4. Passive Communication with Ambient Light

Authors:  Qing Wang (Delft University of Technology), Marco Zuniga (Delft University of Technology), and Domenico Giustiniano (IMDEA Networks Institute)

This work studies, implements and evaluates a Visible Light Communications system. The transmitters are LEDS (i.e. bits 0/1 are represented by a specific light wavelength), but the information is modulated by the environment, i.e. via reflection. The receiver of this communication schema is a photodiode or a camera (i.e. a device which is capable detecting light changes).

The key concept and the motivation around this short range communication schema using visible light spectrum, is the sustainability due to the pervasive nature of the light. Communication via visible ambient light offers several advantages such as high data rate, localization via the light illumination etc. However, ambient light cannot be easily controlled and there are many challenges in the design of this system.

The system consists of: (a) An emitter (any light source) (b) Surface with reflective material and (c) a receiver which is a light sensitive optical device such as a photodiode. The surface modulates and reflects the incoming light depending on the data source to be transmitted.

The channel capacity (i.e. the throughput) depends on several parameters such as the inter-symbol interference, the symbol wavelength etc. Thus, the throughput is time variant and moreover decreases exponential with the distance.

There are several system design challenges such as defining the reflection properties  and the dimensions of the modulating surface, the channel distortion from the moving vehicle  (i.e. time varying speed), packet collisions due to multi-path etc. The modulation/coding scheme uses high reflection coefficient material for bit 1 and vice versa for bit 0. Most importantly, after receiving the symbol the receiver can perform the decoding based on the received signal strength of reflected light. The system evaluation included outdoor evaluation with mobile object at 18 km/h .

Q1: How is your system compared to a barcode? 
A1: For decoding a barcode, a “camera” is being utilized as a receiver, the light source is constant (i.e. the transmitter is the barcode picture). The concept is different and it is a static scenario. The communication system in this paper considers a moving vehicle as the transmitter and the receiver.

Q2: How do you envision the generalization of the system? 

A2: Future directions include: More controllable as possible via “re-programmable” surface properties, embedding mobility, considering cloudy vs sunlight conditions etc.