A robust machine learning method for cell-load approximation in wireless networks

We propose a learning algorithm for cell-load approximation in wireless networks. The proposed algorithm is robust in the sense that it is designed to cope with the uncertainty arising from a small number of training samples. This scenario is highly relevant in wireless networks where training has to be performed on short time scales because of a fast time-varying communication environment. The first part of this work studies the set of feasible rates and shows that this set is compact. We then prove that the mapping relating a feasible rate vector to the unique fixed point of the non-linear cell-load mapping is monotone and uniformly continuous. Utilizing these properties, we apply an approximation framework that achieves the best worst-case performance. Furthermore, the approximation preserves the monotonicity and continuity properties. Simulations show that the proposed method exhibits better robustness and accuracy for small training sets in comparison with standard approximation techniques for multivariate data.Comment: Shorter version accepted at ICASSP 201

Detection for 5G-NOMA: An Online Adaptive Machine Learning Approach

Non-orthogonal multiple access (NOMA) has emerged as a promising radio access technique for enabling the performance enhancements promised by the fifth-generation (5G) networks in terms of connectivity, low latency, and high spectrum efficiency. In the NOMA uplink, successive interference cancellation (SIC) based detection with device clustering has been suggested. In the case of multiple receive antennas, SIC can be combined with the minimum mean-squared error (MMSE) beamforming. However, there exists a tradeoff between the NOMA cluster size and the incurred SIC error. Larger clusters lead to larger errors but they are desirable from the spectrum efficiency and connectivity point of view. We propose a novel online learning based detection for the NOMA uplink. In particular, we design an online adaptive filter in the sum space of linear and Gaussian reproducing kernel Hilbert spaces (RKHSs). Such a sum space design is robust against variations of a dynamic wireless network that can deteriorate the performance of a purely nonlinear adaptive filter. We demonstrate by simulations that the proposed method outperforms the MMSE-SIC based detection for large cluster sizes.Comment: Accepted at ICC 201

Robustes Lernen in drahtlosen Netzwerken : Wirksamkeit von Modellen und Vorkenntnisse beim Lernen aus kleinen Datensätzen

This work studies robust learning in dynamic wireless environments. Modern wireless data networks are complex and modeling their behavior accurately is difficult. As a result, machine learning and artificial intelligence have been making significant inroads into wireless networks. State-of-the-art machine learning algorithms (e.g., neural networks) assume a stationary learning environment and they generally require large training sets. However, modern radio access networks (RANs) are dynamic, and by the time a large training set is collected the environment may have changed so much as to render the learning useless. Therefore, in dynamic networks learning frameworks must work with small training sets. Assuming that each training sample is informative, the lack of a large training set results in uncertainty about the underlying phenomenon/function to be learned. In light of these facts, we study "hybrid" learning approaches in which the above-mentioned uncertainty is combated by the inclusion of model based prior knowledge in the proposed learning frameworks. In Chapter 2 we study cell-load approximation in RANs using a small sample set and a robust learning framework armed with model based prior knowledge. To this end, we study the nonlinear load-coupling model and prove some salient properties of cell-load as a function of user rates. We show how this prior knowledge can be used to decrease the uncertainty resulting from a small training set. In Chapter 3 we study robust multiuser detection in dynamic wireless networks in which users transmit sporadically. Though it is known that the optimal multiuser detector is nonlinear, learning this detector using conventional methods requires a large number of training samples. Additionally, all nonlinear detectors are sensitive to small changes in the environment. In modern wireless applications, such as machine-type communications, users transmit sporadically and as a result performance of nonlinear detectors may deteriorate. To address this issue, we propose a novel online learning framework that combines the expressive power of a nonlinear filter with the robustness of a linear filter. The proposed "sum filter" is designed in a reproducing kernel Hilbert space (RKHS) constructed by taking the direct sum of an RKHS associated with a linear kernel and an RKHS associated with a nonlinear kernel. We derive the nonlinear kernel from the multiuser detection model by exploiting the connection between the optimal nonlinear filter and certain RKHSs. Working in RKHSs and, in general, Hilbert spaces allows for low-complexity projection based algorithms which are well-known for their robustness to noise and their numerical stability. In Chapter 4 we use the celebrated projection onto convex sets (POCS) technique to learn probability density functions (pdfs) in a Hilbert space. Here again, we combine a small training sample set with prior knowledge based on general properties of pdfs. We then show how to apply our learning framework to distributed multiuser detection in a cloud RAN network.Diese Arbeit untersucht robustes Lernen in dynamischen Umgebungen der drahtlosen Datenkommunikation. Moderne drahtlose Datennetze sind komplex und die genaue Modellierung ihres Verhaltens ist schwierig. Infolgedessen haben maschinelles Lernen und künstliche Intelligenz einen bedeutenden Einzug in drahtlose Netzwerke gehalten. Moderne Lernalgorithmen (z.B. neuronale Netze) setzen aber eine stationäre Lernumgebung voraus und erfordern in der Regel große Trainingssätze. Funkzugangsnetze (RANs) sind jedoch dynamisch, und wenn ein großer Trainingssatz gesammelt wurde, kann sich die Umgebung so stark verändert haben, dass das gelernte Modell unbrauchbar wird. Daher muss in dynamischen Netzwerken mit kleinen Trainingssätzen gearbeitet werden. Unter der Annahme, dass jeder Trainingsdatenpunkt informativ ist, führt das Fehlen eines großen Trainingssatzes zu Unsicherheit über das zu lernende Phänomen/Funktion. Vor diesem Hintergrund untersucht diese Arbeit ''hybride'' Lernansätze, bei denen die oben erwähnte Unsicherheit durch die Einbeziehung von modellbasiertem Vorwissen reduziert wird. In Kapitel 2 wird die Approximation der Zellenlast in RANs unter Verwendung eines kleinen Trainingssatzes und einer robusten Lernmethode untersucht, das mit modellbasiertem Vorwissen ausgestattet ist. Zu diesem Zweck wird das nichtlineare Lastkopplungsmodell untersucht und einige hervorstechende Eigenschaften der Funkzellenlast als Funktion der Benutzerraten bewiesen. Es wird gezeigt wie dieses Vorwissen genutzt werden kann, um die aus einem kleinen Trainingssatz resultierende Unsicherheit zu verringern. In Kapitel 3 wird die robuste Mehrbenutzer-Demodulation in dynamischen drahtlosen Netzwerken, in denen Benutzer sporadisch senden, untersucht. Obwohl bekannt ist, dass der optimale Mehrbenutzer-Demodulator nichtlinear ist, erfordert das Erlernen dieses Detektors mit herkömmlichen Methoden einen großen Trainingssatz. Darüber hinaus sind alle nichtlinearen Detektoren empfindlich in Bezug auf kleine Änderungen in ihrer Umgebung. In modernen drahtlosen Anwendungen, wie z.B. in der maschinellen Kommunikation, senden die Benutzer sporadisch und als Folge davon kann sich die Leistung der nichtlinearen Detektors verschlechtern. Um dieses Problem zu lösen, wird eine neuartige Online-Lernmethode vorgeschlagen, das die Ausdruckskraft eines nichtlinearen Filters mit der Robustheit eines linearen Filters kombiniert. Der vorgeschlagene ''Summenfunktion'' ist in einem reproduzierenden Hilbertraum (RKHS) konstruiert, der aus der direkten Summe eines RKHS, der mit einem linearen Kernel assoziiert ist, und eines RKHS, der mit einem nichtlinearen Kernel assoziiert ist, besteht. Der nichtlineare Kernel wird aus dem Mehrbenutzermodell abgeleitet, indem eine Zusammenhang zwischen dem optimalen nichtlinearen Filter und gewissen RKHSs ausgenutzt wird. RKHSs und im Allgemeinen Hilberträumen ermöglichen die Konstruktion von auf Projektion basierenden Algorithmen mit geringer Komplexität, die für ihre Robustheit gegenüber Rauschen und ihre numerische Stabilität bekannt sind. In Kapitel 4 wird die berühmte Projektion auf konvexe Mengen (POCS) Methode verwendet, um Wahrscheinlichkeitsdichtefunktionen in einem Hilbertraum zu lernen. Auch hier wird ein kleiner Trainingssatz mit modellbasierten Vorwissen kombiniert, welches auf den allgemeinen Eigenschaften von Wahrscheinlichkeitsdichtefunktionen basiert. Anschließend wird gezeigt, wie diese Lernmethode auf die verteilte Mehrbenutzer-Demodulation in einem Cloud-RAN angewendet werden kann

GPU-accelerated partially linear multiuser detection for 5G and beyond URLLC systems

In this feasibility study, we have implemented a recently proposed partially linear multiuser detection algorithm in reproducing kernel Hilbert spaces (RKHSs) on a GPU-accelerated platform. Partially linear multiuser detection, which combines the robustness of linear detection with the power of nonlinear methods, has been proposed for a massive connectivity scenario with the non-orthogonal multiple access (NOMA). This is a promising approach, but detecting payloads within a received orthogonal frequency division multiplexing (OFDM) radio frame requires the execution of a large number of inner product operations, which are the main computational burden of the algorithm. Although inner-product operations consist of simple kernel evaluations, their vast number poses a challenge in ultra-low latency (ULL) applications, because the time needed for computing the inner products might exceed the sub-millisecond latency requirement. To address this problem, this study demonstrates the acceleration of the inner-product operations through massive parallelization. The result is a GPU-accelerated real-time OFDM receiver that enables sub-millisecond latency detection to meet the requirements of 5th generation (5G) and beyond ultra-reliable and low latency communications (URLLC) systems. Moreover, the parallelization and acceleration techniques explored and demonstrated in this study can be extended to many other signal processing algorithms in Hilbert spaces, such as those based on projection onto convex sets (POCS) and adaptive projected subgradient method (APSM) algorithms. Experimental results and comparisons with the state-of-art confirm the effectiveness of our techniques.Comment: submitted to IEEEAcces