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Relay-Aided Communication in Large Interference Limited Wireless Networks

Papsdorf, Daniel (2020)
Relay-Aided Communication in Large Interference Limited Wireless Networks.
Technische Universität Darmstadt
doi: 10.25534/tuprints-00011624
Dissertation, Erstveröffentlichung

Kurzbeschreibung (Abstract)

In recent years, the number of active wireless devices increases exponentially and it is, therefore, to expect that the interference increases as well. Interference between communication links is the major performance limiting factor in today's communication networks. Hence, the handling of the overall interference in a network is one major challenge in wireless communication networks of the future. If the interference signals are weak in comparison to the useful signal, they can be simply treated as noise. If the interference signals are strong in comparison to the useful signal, they can be reliably decoded and subtracted from the received signal at the receivers. However, in multiuser communication networks, the interference and the useful signal are often of comparable signal strength. The conventional approach to handle these interference signals is to orthogonalize the useful signal and the interference signals using, e.g., time division multiple access (TDMA) or frequency division multiple access (FDMA). In the past few years, instead of orthogonalization, interference alignment (IA) has been developed as an efficient technique to handle interference signals, especially in the high signal to noise ratio (SNR) region. The basic idea of IA is to align multiple interference signals in a particular subspace of reduced dimension at each receiver. The objective is to minimize the signal dimensions occupied by interference at each receiver. In order to perform IA, the receive space is divided into two disjoint subspaces, the useful signal subspace and the interference signal subspace. Each transmitting node designs its transmit filters in such a way that at each receiving node, all interference signals are within the interference subspace and only the useful signal is in the useful subspace.

In this thesis, the focus is on large interference limited wireless communication networks. In contrast to the conventional use of relays, for extending the coverage, in this thesis, the relays are used to manipulate the effective end-to-end channel between the transmitters and receivers to perform IA in the network. Since the relays are used to assist the process of IA and not interested in the data streams transmitted by the nodes, amplify-and-forward relays are sufficient to support the process of IA. Therefore, the main focus of this thesis is on amplify-and-forward relays. Throughout this thesis, it is assumed that all nodes and relays are multi-antenna half-duplex devices. When considering large networks, the assumption that all nodes are connected to all relays does not hold due to physical propagation phenomena, e.g., high path loss and shadowing. In such large networks, the distances between different nodes may differ a lot, leading to links of considerably different signal strengths, where sufficiently weak links may be neglected. Hence, large networks are in general partially connected. In this thesis, three important interference-limited relay aided wireless network topologies are investigated, the partially connected relay aided multi-pair pair-wise communication network, the fully connected multi-group multi-way relaying network and the partially connected multi-group multi-way relaying network. For each of these topologies, new algorithms to perform IA are developed in this thesis.

First, a large partially connected relay aided pair-wise communication network is considered. The concept of an appropriate partitioning of a partially connected network into subnetworks which are themselves fully connected is introduced. Each of these subnetworks contains a single relay and all nodes being connected to this relay. Some nodes or even communication pairs may be connected to multiple relays. The bidirectional pair-wise communication between the nodes takes place via the intermediate relays, using the two-way relaying protocol. Only relays which are connected to both nodes of a communication pair can serve this pair. Hence, it is assumed that all communication pairs in the entire network are served by at least one relay. The most challenging part of such a partially connected network is the handling of nodes which are connected to multiple relays. Hence, techniques called simultaneous signal alignment (SSA) and simultaneous channel alignment (SCA), are proposed to perform signal alignment (SA) and channel alignment (CA) with multiple relays simultaneously. SA means that all nodes transmit to the relay in such a way that the signals of each communicating pair are pair-wise aligned at the relay. For CA, which is dual to SA, the receive filter of each node is designed such that the effective channels between the relay and both nodes of a communicating pair span the same subspace. A closed-form solution to perform IA in this network topology is obtained and the properness conditions for SSA and SCA are derived. It is shown that local channel state information (CSI) is sufficient to perform IA in partially connected networks, whereas in fully connected relay aided networks, global CSI is required in general. Through simulations, it is shown that the proposed closed-form solution achieves more degrees of freedom (DoF) than the reference algorithms and has better sum-rate performance, especially in the high SNR-region. Especially in large wireless networks, it may happen that not both nodes of a communication pair are connected to the same relays. If a single node of a communication pair is in addition connected to a relay which, therefore, cannot assist the communication, this node receives only interference and no useful signal from this relay. Such a node suffers from inter-subnetwork interference, due to the connection by an inter-subnetwork link to the additional relay. Hence, in this thesis, a closed form algorithm which minimizes the inter-subnetwork interference power in the whole partially connected network is proposed and the properness conditions are derived. The condition under which an interference free-communication can be achieved by the proposed inter-subnetwork interference power minimization algorithm is derived. Further, it is shown that the proposed inter-subnetwork interference power minimization algorithm achieves a higher sum rate in comparison to the considered reference algorithm.

Secondly, a fully connected multi-group multi-way relaying networks is considered. In such a network, multiple nodes form a group and each node wants to share its message with all other nodes in its group via an intermediate relay. The group-wise communication between the nodes inside a group takes place via the intermediate relay, using a transmission strategy considering several multiple access (MAC) phases and several multicast (MC) phases, in general. In this thesis, a multicast IA algorithm to handle the interference in such a network is proposed. The idea of the proposed algorithm is that in each of the MC phases, a multiple input multiple output (MIMO) interference multicast channel is created by separating the antennas of the relay into as many clusters as groups in the network. Each of these clusters serves a specific group of nodes and transmits in such a way that the signals transmitted from different clusters are aligned at the receiving nodes of the non-intended multicast groups. It is shown that the minimum required number of antennas at the relay is independent of the number of nodes per group, which is an important property since the number of antennas available at the relay is limited in general. Furthermore, the properness conditions for the proposed multicast IA algorithm are derived. It is shown that the proposed multicast algorithm outperforms a reference algorithm for a broad range of SNR values, while still requiring less antennas at the relay.

Finally, a large partially connected multi-group multi-way relay network is considered. In contrast to the fully connected multi-group multi-way relaying network, multiple relays are considered in this partially connected network. Such a partially connected network can be partitioned into subnetworks that are themselves fully connected. Hence, such a partially connected network consists of multiple subnetworks, where each of these contains a single relay and all groups of nodes which are connected to this relay. Each group of nodes may be connected to one or multiple relays. This means that not all groups of nodes are connected to all relays in the network. However, any group is connected to at least one relay which serves this group of nodes. The group-wise exchange of data between the nodes inside a group is performed via the multi-way relaying protocol. The most challenging part of such a partially connected network is the handling of the nodes inside groups which are connected to multiple relays. To overcome this challenge, new techniques called simultaneous group signal alignment (SGSA) and simultaneous group channel alignment (SGCA) are introduced to perform SA and CA in partially connected multi-group multi-way relaying networks. A closed-form IA solution for this network topology is obtained and the properness conditions for the solvability of SGSA and SGCA are derived. It is shown that the proposed IA algorithm outperforms the reference algorithm in terms of sum rate and DoF.

Typ des Eintrags: Dissertation
Erschienen: 2020
Autor(en): Papsdorf, Daniel
Art des Eintrags: Erstveröffentlichung
Titel: Relay-Aided Communication in Large Interference Limited Wireless Networks
Sprache: Englisch
Referenten: Klein, Prof. Dr. Anja ; Weber, Prof. Dr. Tobias
Publikationsjahr: 2020
Ort: Darmstadt
Datum der mündlichen Prüfung: 26 März 2020
DOI: 10.25534/tuprints-00011624
URL / URN: https://tuprints.ulb.tu-darmstadt.de/11624
Kurzbeschreibung (Abstract):

In recent years, the number of active wireless devices increases exponentially and it is, therefore, to expect that the interference increases as well. Interference between communication links is the major performance limiting factor in today's communication networks. Hence, the handling of the overall interference in a network is one major challenge in wireless communication networks of the future. If the interference signals are weak in comparison to the useful signal, they can be simply treated as noise. If the interference signals are strong in comparison to the useful signal, they can be reliably decoded and subtracted from the received signal at the receivers. However, in multiuser communication networks, the interference and the useful signal are often of comparable signal strength. The conventional approach to handle these interference signals is to orthogonalize the useful signal and the interference signals using, e.g., time division multiple access (TDMA) or frequency division multiple access (FDMA). In the past few years, instead of orthogonalization, interference alignment (IA) has been developed as an efficient technique to handle interference signals, especially in the high signal to noise ratio (SNR) region. The basic idea of IA is to align multiple interference signals in a particular subspace of reduced dimension at each receiver. The objective is to minimize the signal dimensions occupied by interference at each receiver. In order to perform IA, the receive space is divided into two disjoint subspaces, the useful signal subspace and the interference signal subspace. Each transmitting node designs its transmit filters in such a way that at each receiving node, all interference signals are within the interference subspace and only the useful signal is in the useful subspace.

In this thesis, the focus is on large interference limited wireless communication networks. In contrast to the conventional use of relays, for extending the coverage, in this thesis, the relays are used to manipulate the effective end-to-end channel between the transmitters and receivers to perform IA in the network. Since the relays are used to assist the process of IA and not interested in the data streams transmitted by the nodes, amplify-and-forward relays are sufficient to support the process of IA. Therefore, the main focus of this thesis is on amplify-and-forward relays. Throughout this thesis, it is assumed that all nodes and relays are multi-antenna half-duplex devices. When considering large networks, the assumption that all nodes are connected to all relays does not hold due to physical propagation phenomena, e.g., high path loss and shadowing. In such large networks, the distances between different nodes may differ a lot, leading to links of considerably different signal strengths, where sufficiently weak links may be neglected. Hence, large networks are in general partially connected. In this thesis, three important interference-limited relay aided wireless network topologies are investigated, the partially connected relay aided multi-pair pair-wise communication network, the fully connected multi-group multi-way relaying network and the partially connected multi-group multi-way relaying network. For each of these topologies, new algorithms to perform IA are developed in this thesis.

First, a large partially connected relay aided pair-wise communication network is considered. The concept of an appropriate partitioning of a partially connected network into subnetworks which are themselves fully connected is introduced. Each of these subnetworks contains a single relay and all nodes being connected to this relay. Some nodes or even communication pairs may be connected to multiple relays. The bidirectional pair-wise communication between the nodes takes place via the intermediate relays, using the two-way relaying protocol. Only relays which are connected to both nodes of a communication pair can serve this pair. Hence, it is assumed that all communication pairs in the entire network are served by at least one relay. The most challenging part of such a partially connected network is the handling of nodes which are connected to multiple relays. Hence, techniques called simultaneous signal alignment (SSA) and simultaneous channel alignment (SCA), are proposed to perform signal alignment (SA) and channel alignment (CA) with multiple relays simultaneously. SA means that all nodes transmit to the relay in such a way that the signals of each communicating pair are pair-wise aligned at the relay. For CA, which is dual to SA, the receive filter of each node is designed such that the effective channels between the relay and both nodes of a communicating pair span the same subspace. A closed-form solution to perform IA in this network topology is obtained and the properness conditions for SSA and SCA are derived. It is shown that local channel state information (CSI) is sufficient to perform IA in partially connected networks, whereas in fully connected relay aided networks, global CSI is required in general. Through simulations, it is shown that the proposed closed-form solution achieves more degrees of freedom (DoF) than the reference algorithms and has better sum-rate performance, especially in the high SNR-region. Especially in large wireless networks, it may happen that not both nodes of a communication pair are connected to the same relays. If a single node of a communication pair is in addition connected to a relay which, therefore, cannot assist the communication, this node receives only interference and no useful signal from this relay. Such a node suffers from inter-subnetwork interference, due to the connection by an inter-subnetwork link to the additional relay. Hence, in this thesis, a closed form algorithm which minimizes the inter-subnetwork interference power in the whole partially connected network is proposed and the properness conditions are derived. The condition under which an interference free-communication can be achieved by the proposed inter-subnetwork interference power minimization algorithm is derived. Further, it is shown that the proposed inter-subnetwork interference power minimization algorithm achieves a higher sum rate in comparison to the considered reference algorithm.

Secondly, a fully connected multi-group multi-way relaying networks is considered. In such a network, multiple nodes form a group and each node wants to share its message with all other nodes in its group via an intermediate relay. The group-wise communication between the nodes inside a group takes place via the intermediate relay, using a transmission strategy considering several multiple access (MAC) phases and several multicast (MC) phases, in general. In this thesis, a multicast IA algorithm to handle the interference in such a network is proposed. The idea of the proposed algorithm is that in each of the MC phases, a multiple input multiple output (MIMO) interference multicast channel is created by separating the antennas of the relay into as many clusters as groups in the network. Each of these clusters serves a specific group of nodes and transmits in such a way that the signals transmitted from different clusters are aligned at the receiving nodes of the non-intended multicast groups. It is shown that the minimum required number of antennas at the relay is independent of the number of nodes per group, which is an important property since the number of antennas available at the relay is limited in general. Furthermore, the properness conditions for the proposed multicast IA algorithm are derived. It is shown that the proposed multicast algorithm outperforms a reference algorithm for a broad range of SNR values, while still requiring less antennas at the relay.

Finally, a large partially connected multi-group multi-way relay network is considered. In contrast to the fully connected multi-group multi-way relaying network, multiple relays are considered in this partially connected network. Such a partially connected network can be partitioned into subnetworks that are themselves fully connected. Hence, such a partially connected network consists of multiple subnetworks, where each of these contains a single relay and all groups of nodes which are connected to this relay. Each group of nodes may be connected to one or multiple relays. This means that not all groups of nodes are connected to all relays in the network. However, any group is connected to at least one relay which serves this group of nodes. The group-wise exchange of data between the nodes inside a group is performed via the multi-way relaying protocol. The most challenging part of such a partially connected network is the handling of the nodes inside groups which are connected to multiple relays. To overcome this challenge, new techniques called simultaneous group signal alignment (SGSA) and simultaneous group channel alignment (SGCA) are introduced to perform SA and CA in partially connected multi-group multi-way relaying networks. A closed-form IA solution for this network topology is obtained and the properness conditions for the solvability of SGSA and SGCA are derived. It is shown that the proposed IA algorithm outperforms the reference algorithm in terms of sum rate and DoF.

Alternatives oder übersetztes Abstract:
Alternatives AbstractSprache

In den letzten Jahren ist die Anzahl der drahtlosen Geräte exponentiell gestiegen und es ist daher zu erwarten, dass auch die Interferenz steigt. Die Interferenz zwischen Kommunikationsverbindungen ist der maßgebliche limitierende Faktor in heutigen Kommunikationsnetzen und der Umgang mit dieser daher eine der größten Herausforderungen in drahtlosen Kommunikationsnetzen der Zukunft. Wenn die Interferenzsignale im Vergleich zum Nutzsignal schwach sind, so können diese einfach als Rauschen betrachtet werden. Sind die Interferenzsignale im Vergleich zum Nutzsignal stark, so können diese zuverlässig dekodiert und an den Empfängern vom Empfangssignal subtrahiert werden. In Mehrbenutzer-Kommunikationsnetzen sind die Interferenzsignale und das Nutzsignal jedoch oft von vergleichbarer Signalstärke. Der herkömmliche Ansatz zur Verarbeitung dieser Interferenzsignale besteht darin, das Nutzsignal und die Interferenzsignale zu orthogonalisieren, z.B. durch Zeitmultiplexverfahren (Time Division Multiple Access, TDMA) oder Frequenzmultiplexverfahren (Frequency Division Multiple Access, FDMA). Anstelle der Orthogonalisierung wurde in den letzten Jahren Interference Alignment (IA) als effiziente Technik zur Verarbeitung von Interferenzsignalen entwickelt, insbesondere im Bereich eines hohen Signal-zu-Rausch-Verhältnisses (SNR). Die Grundidee von IA besteht darin, an jedem Empfänger mehrere Interferenzsignale in einen bestimmten Teilraum mit reduzierter Dimension zu bündeln. Ziel ist es, die durch Interferenzsignale belegten Signaldimensionen an jedem Empfänger zu minimieren. Um IA durchzuführen, wird der Empfangsraum in zwei Unterräume aufgeteilt, den Nutzsignal-Unterraum und den Interferenzsignal-Unterraum. Jeder Sendeknoten gestaltet seine Sendefilter so, dass sich an jedem Empfangsknoten alle Interferenzsignale innerhalb des Interferenzsignal-Unterraums befinden und sich lediglich das Nutzsignal im interferenzfreien Nutzsignal-Unterraum befindet.

Der Schwerpunkt dieser Arbeit liegt auf großen, interferenzbegrenzten, drahtlosen Kommunikationsnetzen. Im Gegensatz zum herkömmlichen Gebrauch von Relais zur Erhöhung der Reichweite werden in dieser Arbeit die Relais dazu verwendet, den effektiven Ende-zu-Ende Kanal zwischen den Sendern und Empfängern zu beeinflussen, um IA im Netzwerk durchzuführen. Da die Relais lediglich zur Unterstützung des IA Prozesses verwendet werden, sind die von den Knoten übertragenen Datenströme für die Relais irrelevant. Somit reichen Verstärkungs- und Weiterleitungsrelais aus, um den IA Prozess zu unterstützen. Der Hauptfokus dieser Arbeit liegt daher auf Verstärkungs- und Weiterleitungsrelais. Es gilt die Annahme, dass alle Knoten und Relais Mehrantennengeräte sind, welche im Halbduplex-Modus arbeiten. Werden große Netzwerke betrachtet, so kann aufgrund physikalischer Ausbreitungsphänomene, wie z.B. hoher Pfadverlust und Abschattung, nicht angenommen werden, dass alle Knoten mit allen Relais verbunden sind. In solch großen Netzwerken können die Abstände zwischen den einzelnen Knoten stark variieren, was zu Verbindungen mit deutlich unterschiedlichen Signalstärken führt. Ausreichend schwache Verbindungen können hierbei vernachlässigt werden. Somit sind große Netzwerke im Allgemeinen nur teilweise verbunden. In dieser Arbeit werden folgende drei wichtige, interferenzlimitierte, relaisgestützte, drahtlose Netzwerktopologien untersucht: das teilweise verbundene, relaisgestützte Mehrpaar-Paarweise-Kommunikationsnetzwerk, das vollständig verbundene Mehrgruppen-Mehrwege-Relaisnetzwerk und das teilweise verbundene Mehrgruppen-Mehrwege-Relaisnetzwerk. Für jede dieser Topologien werden in dieser Arbeit neue Algorithmen zur Durchführung von IA entwickelt.

Zunächst wird ein großes, teilweise verbundenes, relaisgestütztes Kommunikationsnetzwerk betrachtet, in welchem paarweise kommuniziert wird. Es wird ein Konzept zur geeigneten Aufteilung dieses Netzwerks in Teilnetzwerke vorgestellt, die wiederum vollständig verbunden sind. Jedes dieser Teilnetzwerke enthält ein einzelnes Relais und alle Knoten, die mit diesem Relais verbunden sind. Einige Knoten oder sogar Kommunikationspaare können mit mehreren Relais verbunden sein. Die bidirektionale, paarweise Kommunikation zwischen den Knoten erfolgt über die Relais, unter Verwendung des Zweiwege-Relaisprotokolls. Da nur Relais, welche mit beiden Knoten eines Kommunikationspaares verbunden sind, dieses Paar bedienen können, wird angenommen, dass alle Kommunikationspaare im gesamten Netzwerk von mindestens einem Relais bedient werden. Der Umgang mit Knoten, die mit mehreren Relais verbunden sind, stellt in einem solch teilweise verbundenen Netzwerk die größte Herausforderung dar. Daher werden die als Simultaneous Signal Alignment (SSA) und Simultaneous Channel Alignment (SCA) bezeichneten Verfahren vorgeschlagen, um Signal Alignment (SA) und Channel Alignment (CA) mit mehreren Relais gleichzeitig durchzuführen zu können. Bei SA senden alle Knoten derart an das Relais, dass die Signale jedes Kommunikationspaares am Relais paarweise ausgerichtet werden. Für CA, dem dualen Problem zu SA, wird das Empfangsfilter jedes Knotens so entworfen, dass die effektiven Kanäle zwischen den Relais und beiden Knoten eines Kommunikationspaares den gleichen Teilraum aufspannen. In dieser Arbeit wird eine Lösung in geschlossener Form zur Durchführung von IA in dieser Netzwerktopologie erzielt und die Voraussetzungen für SSA und SCA werden hergeleitet. Es wird gezeigt, dass lokale Kanalzustandsinformation (Channel State Information, CSI) ausreicht, um IA in teilweise verbundenen Netzwerken durchzuführen, während in vollständig verbundenen, relaisgestützten Netzwerken im Allgemeinen globale CSI erforderlich ist. Durch Simulationen wird veranschaulicht, dass die vorgeschlagene geschlossene Lösung mehr Freiheitsgrade (DoF) erreicht als die Referenzalgorithmen sowie eine höhere Summenrate erzielt, insbesondere im hohen SNR-Bereich. Vor allem in großen, drahtlosen Netzwerken kann es vorkommen, dass nicht beide Knoten eines Kommunikationspaares mit den gleichen Relais verbunden sind. Wenn ein einzelner Knoten eines Kommunikationspaares eine zusätzliche Verbindung zu einem Relais aufweist, kann dieses Relais die Kommunikation nicht unterstützen. Der betreffende Knoten empfängt demzufolge nur Interferenzen und kein Nutzsignal von diesem Relais; er unterliegt somit Inter-Subnetzwerkinterferenz. In dieser Arbeit wird ein Algorithmus in geschlossener Form vorgeschlagen, der die Inter-Subnetzwerkinterferenzleistung im gesamten, teilweise verbundenen Netzwerk minimiert und dessen Voraussetzungen werden hergeleitet. Es werden Voraussetzungen formuliert, unter denen mit dem Algorithmus sogar eine interferenzfreie Kommunikation erreicht werden kann. Des Weiteren wird gezeigt, dass der vorgeschlagene Algorithmus zur Minimierung der Inter-Subnetzwerkinterferenz im Vergleich zum betrachteten Referenzalgorithmus eine höhere Summenrate erreicht.

Anschließend wird ein vollständig verbundenes Mehrgruppen-Mehrwege-Relaisnetzwerk betrachtet. In einem solchen Netzwerk bilden mehrere Knoten eine Gruppe und jeder Knoten möchte seine Nachricht über das Relais mit allen anderen Knoten seiner Gruppe teilen. Die gruppenweise Kommunikation zwischen den Knoten innerhalb einer Gruppe erfolgt über das Relais unter Verwendung einer Übertragungsstrategie, die im Allgemeinen mehrere Multiple Access (MAC) Phasen und mehrere Multicast (MC) Phasen verwendet. In dieser Arbeit wird ein Multicast IA Algorithmus zur Verarbeitung der in einem solchen Netzwerk auftretenden Interferenz vorgeschlagen. Die Idee beruht darauf, dass in jeder der MC-Phasen ein Multiple-Input-Multiple-Output (MIMO)-Interferenz-Multicast-Kanal erzeugt wird. Dazu werden die Antennen des Relais in so viele Cluster aufgeteilt, wie Gruppen im Netzwerk vorhanden sind. Jedes dieser Cluster bedient eine bestimmte Gruppe von Knoten und sendet derartig, dass die von verschiedenen Clustern übertragenen Signale an den Empfangsknoten der nicht beabsichtigten Multicast-Gruppen gleich ausgerichtet sind. Es wird gezeigt, dass die mindestens erforderliche Anzahl von Antennen am Relais unabhängig von der Anzahl der Knoten pro Gruppe ist. Dies ist eine wichtige Eigenschaft, da die Anzahl der am Relais verfügbaren Antennen im Allgemeinen begrenzt ist. Darüber hinaus werden die Voraussetzungen für den vorgeschlagenen Multicast IA-Algorithmus hergeleitet. Es wird gezeigt, dass der vorgeschlagene Multicast-Algorithmus den Referenzalgorithmus für einen weiten SNR-Bereich übertrifft, während er gleichzeitig weniger Antennen am Relais benötigt.

Abschließend wird ein großes, teilweise verbundenes Mehrgruppen-Mehrwege-Relaisnetzwerk betrachtet. Im Gegensatz zum vollständig verbundenen Mehrgruppen-Mehrwege-Relaisnetzwerk werden in diesem teilweise verbundenen Netzwerk mehrere Relais berücksichtigt. Ein solches Netzwerk kann in Teilnetze unterteilt werden, die selbst vollständig verbunden sind. Jedes dieser Teilnetzwerke enthält dann ein einzelnes Relais und alle Gruppen von Knoten, die mit diesem Relais verbunden sind. Jede Gruppe von Knoten kann mit einem oder mehreren Relais verbunden sein. Dies bedeutet, dass nicht alle Gruppen von Knoten mit allen Relais im Netzwerk verbunden sind. Jede Gruppe ist jedoch mit mindestens einem Relais verbunden, welches diese Gruppe von Knoten bedient. Der gruppenweise Datenaustausch zwischen den Knoten innerhalb einer Gruppe erfolgt über das Multi-Way-Relaying-Protokoll. Der herausforderndste Teil eines solchen teilweise verbundenen Netzwerks ist die Behandlung der Knoten innerhalb von Gruppen, die mit mehreren Relais verbunden sind. Um diese Herausforderung zu bewältigen, werden neue Verfahren vorgestellt, die als Simultaneous Group Signal Alignment (SGSA) und Simultaneous Group Channel Alignment (SGCA) bezeichnet werden, um SA und CA in teilweise verbundenen Mehrgruppen-Mehrweg-Relaisnetzwerken durchzuführen. Für diese Netzwerktopologie wird eine IA Lösung in geschlossener Form erzielt und die Voraussetzungen für die Lösbarkeit von SGSA und SGCA werden hergeleitet. Es wird gezeigt, dass der vorgeschlagene IA-Algorithmus den Referenzalgorithmus in Bezug auf die Summenrate und die DoF übertrifft.

Deutsch
Freie Schlagworte: emergenCITY, emergenCITY_KOM2, emergenCITY_KOM
URN: urn:nbn:de:tuda-tuprints-116242
Sachgruppe der Dewey Dezimalklassifikatin (DDC): 600 Technik, Medizin, angewandte Wissenschaften > 620 Ingenieurwissenschaften und Maschinenbau
Fachbereich(e)/-gebiet(e): 18 Fachbereich Elektrotechnik und Informationstechnik
18 Fachbereich Elektrotechnik und Informationstechnik > Institut für Nachrichtentechnik
18 Fachbereich Elektrotechnik und Informationstechnik > Institut für Nachrichtentechnik > Kommunikationstechnik
Hinterlegungsdatum: 30 Apr 2020 11:02
Letzte Änderung: 22 Apr 2021 13:18
PPN:
Referenten: Klein, Prof. Dr. Anja ; Weber, Prof. Dr. Tobias
Datum der mündlichen Prüfung / Verteidigung / mdl. Prüfung: 26 März 2020
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