Interference Alignment (IA) and Coordinated Multi-Point (CoMP) with IEEE802.11ac feedback compression: testbed results

We have implemented interference alignment (IA) and joint transmission coordinated multipoint (CoMP) on a wireless testbed using the feedback compression scheme of the new 802.11ac standard. The performance as a function of the frequency domain granularity is assessed. Realistic throughput gains are obtained by probing each spatial modulation stream with ten different coding and modulation schemes. The gain of IA and CoMP over TDMA MIMO is found to be 26% and 71%, respectively under stationary conditions. In our dense indoor office deployment, the frequency domain granularity of the feedback can be reduced down to every 8th subcarrier (2.5MHz), without sacrificing performance.Comment: To appear in ICASSP 201

On Enabling Concurrent Communications in Wireless Networks

Today innumerable devices use the wireless spectrum for communication, including cell-phones, WiFi devices, military radios, public safety radios, satellite phones etc. This crowding is limiting the experience of each device either through interference or by waiting fortheir turn to communicate. So, how do we allow a limited spectral resource to reliably scale to many more devices? This is possible through concurrent communication where multiple links share the spectrum and communicate simultaneously using multi-antenna techniques. One promising technique is Interference Alignment (IA), that has been shown to be Degrees-of-Freedom optimal under some conditions. Still, IA requires accurate channel knowledge to be effective and its ability to achieve high throughput under time varying wireless conditions is yet unproven. We make progress towards understanding these limitations and provide viable solutions.We study an IA system under different models of the time varying channel and derive expressions for the achieved rate over time and the system throughput. Using these, we can arrive at the optimal duration of the data phase that maximizes throughput. We proposetwo strategies that help to counter the effects of a time varying channel. First, data aided receiver beam-tracking along with link adaptation provides a sizable improvement in the received signal to interference and noise ratio. Second, updating the transmit beams during data transmission using short feedback pilots improves alignment at the receivers. In faster varying channels, we get a more stable achieved rate whereas in slower varying channels, we see additional throughput gains. The conclusion from this work is that an IA system must be trained more frequently than the channel coherence time to ensure high throughput and beam adaptation during the data phase gives significant robustness to the system.Lastly, we present an IA based medium access control (MAC) protocol that outperforms traditional protocols. Our concurrent carrier sense multiple access (CSMA) protocol based on beam-nulling is compatible with CSMA and increases the sum throughput by 2 to 3x.We also show that IA outperforms optimal time division multiple access under time varying conditions. Hence a well-designed IA system can enable reliable concurrent communications in a wireless network

Measurement Based Evaluation of Interference Alignment on the Vienna MIMO Testbed

Abweichender Titel laut Übersetzung der Verfasserin/des VerfassersZsfassung in dt. SpracheModerne drahtlose Multiuser-Netzwerke werden oft durch unerwünschte Interferenz gestört, welche die Datenübertragung über die jeweiligen Funkverbindungen verschlechtert. Einige Methoden zur Steigerung der Datenrate in solchen Systemen wurden kürzlich untersucht. -Interference Alignment- sticht dabei als eines der vielversprechendsten Verfahren hervor, da es in der Theorie die maximale Datenrate erreicht, wenn die richtigen Umstände gegeben sind. Mit dem Fortschritt der theoretischen Forschung werden praktische Implementierungen relevant, damit die Theorie bestätigt und mögliche hardwarebedingte Limitationen entdeckt werden. -Interference Alignment- verwendet lineare Sende- und Empfangsfilter. Der Signalraum am jeweiligen Empfänger wird dabei durch die Sendefilter in zwei Unterräume unterteilt, einen Unterraum für das erwünschte Signal und einen Unterraum, in dem alle Interferenz-Signale überlappen. Die gesammelte Interferenz wird dann mittels Empfangsfilter eliminiert und nur das erwünschte Signal bleibt bestehen. Das Verfahren ist nur möglich, wenn alle Benutzer des Netzwerks kooperieren. Diese Arbeit beginnt mit einer theoretischen Abhandlung von -Interference Alignment-. Dabei wird zuerst das relevante System-Modell eingeführt, anschließend werden Voraussetzungen und Filterberechnung besprochen. Als nächstes wird das -Vienna MIMO testbed- charakterisiert, auf welchem -Interference Alignment- im Zuge dieser Arbeit implementiert wurde. Es besteht aus zwei Outdoor-Sendeanlagen auf Häuserdächern, einer Indoor-Sendeanlage und einer Indoor-Empfangsanlage. Die Funkkanäle sind dabei quasi-statisch. Hardware, Software und die benutzten Signale werden beschrieben. Performance-Maße werden eingeführt, die zur Bewertung der -Interference Alignment- Qualität dienen. Anschließend werden Messergebnisse präsentiert. Die Machbarkeit von -Interference Alignment- wird gezeigt, und das Verhalten der Performance-Maße wird untersucht, einmal für variables Signal-Rausch-Verhältnis und einmal für variables Signal-Interferenz-Verhältnis. Beeinträchtigungen durch die verwendete Hardware werden aufgezeigt.Most modern wireless multi-user networks suffer from undesired interference that impairs the data transmission over the individual radio links. In order to maximize the data throughput in such systems, several interference mitigation schemes have been investigated recently. Interference alignment stands out as one of the most promising ones, able to attain the maximum data throughput over interference disturbed links in theory, given the right conditions. As the theoretical research progresses, practical implementations have to be considered in order to confirm the theory and discover limitations introduced by hardware. Interference alignment utilizes linear filtering at each transmitter and receiver of the network. The transmit filters thereby partition the signal space at the receiver into two subspaces, a desired signal subspace containing the signal from the desired transmitter and an interference subspace accumulating all the interfering signals. The aligned interference is then forced to zero by the receive filter and only the desired signal is retained. For this to be accomplished, cooperation of all users in the network is required. This work first deals with the theoretical foundations of interference alignment by introducing the relevant system model and discussing feasibility and filter computation. It then advances to the characterization of the Vienna MIMO testbed on which interference alignment was implemented throughout this work. The testbed employs two outdoor transmitters on rooftops, one indoor transmitter and one indoor receiver. The radio channels in the considered setup are quasi-static. Hardware, software and the used signals are described. Performance measures for evaluation are introduced. Finally, measurement results are presented. The feasibility of interference alignment is shown, and the performance measures are evaluated over variable signal to noise ratio and variable signal to interference ratio. The results are discussed, and impairments introduced by hardware are highlighted.6