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Advanced Communication Networks (ACN)5 ECTS
(englische Bezeichnung: Advanced Communication Networks)

Modulverantwortliche/r: Laura Cottatellucci
Lehrende: Laura Cottatellucci

Startsemester: SS 2021Dauer: 1 SemesterTurnus: jährlich (SS)
Präsenzzeit: 60 Std.Eigenstudium: 90 Std.Sprache: Englisch


    • Advanced Communication Networks
      (Vorlesung, 3,5 SWS, Laura Cottatellucci, Mi, 12:15 - 13:45, 01.021; Do, 14:15 - 15:45, 01.021)
    • Tutorial for Advanced Communication Networks
      (Übung, 0,5 SWS, Walid Ghanem)

Empfohlene Voraussetzungen:

Es wird empfohlen, folgende Module zu absolvieren, bevor dieses Modul belegt wird:

Digital Communications (WS 2020/2021)


Telecommunications have become ubiquitous in daily life and wireless networks play a fundamental role thanks to their capability to support mobility. In a wireless communication, the concept of link does not exist. Users radiate energy and communicate through the superposition of each other’s transmissions which creates interference. Compared to wireline networks this scenario is extremely challenging but also offers unpredictable opportunities in the development of new technologies (massive MIMO, cognitive radio, etc.) and exploitation of new features, e.g., opportunistic communications and multiuser diversity. The exponentially increasing request of higher and higher throughput is satisfied densifying users and access points per unit area and allowing more and more interference while adopting advanced techniques and innovative resource allocation to mitigate the detrimental effects of interference.

Objective of this course is to introduce the student to advanced techniques for coordinated medium access control and radio resource management in cellular systems. Power allocation, rate adaptation and scheduling will be discussed both in centralized and distributed settings. Some mathematical methods play a fundamental role in resource allocation, namely, classical Perron-Frobenius theory for nonnegative matrices, convex and nonconvex constrained optimization, distributed optimization and game theory. The course introduces the student to such methods and exemplifies their application to various resource allocation problems. Additionally, the course addresses relevant aspects of resource allocation in wireless networks such as fairness and cross-layer design.

Technical Content

  • Properties and challenges of the wireless medium.

  • Basic concepts of communication networks: the layered architecture.

  • Evolution of wireless cellular network architectures: From Global System for Mobile to Advanced-Long Term Evolution.

  • Multiple Access Schemes: CSMA variants, TDMA, FDMA, CDMA, OFDMA, SC-FDMA, SDMA.

  • Uplink-downlink duality.

  • Opportunistic scheduling and multiuser diversity.

  • Advanced concepts: small cells and heterogeneous networks, relaying and cooperation, network coding, cognitive radio networks.

  • Basics of resource allocation: power allocation, rate adaptation, and scheduling.

  • Classical resource allocation techniques: Centralized and distributed power control based on the Perron-Frobenius theorem.

  • Fundamentals of convex constrained optimization and application to resource allocation.

  • Resource allocation and fairness.

  • Fundamentals of nonconvex optimization and relaxation techniques.

  • Applications of nonconvex optimization to resource allocation.

  • Fundamentals of distributed optimization and applications to resource allocation.

  • Fundamental concepts of game theory.

  • Resource contention via game theoretical methods.

Lernziele und Kompetenzen:

The student

  • Describes and/or recognizes wireless channel models.

  • Criticizes the limits of a layered architecture in wireless systems.

  • Defends the use of cross-layer design in wireless network.

  • Appraises and compares the distribution of functionalities in network entities for different architectures.

  • Argue on the pros and contras of different multiple access schemes according to various criteria (e.g. spectral efficiency, power efficiency, robustness to interference).

  • Compares and contrasts micro-diversity and various macro-diversity schemes.

  • Computes the total rate of SDMA with various receivers.

  • Relates the multiple access in uplink to broadcasting in downlink and justifies the concept of uplink-downlink duality.

  • Uses uplink-downlink duality to design a precoder and allocate power.

  • Contrasts multiple access in uplink and broadcasting in downlink in terms of channel state acquisition both for TDD and FDD transmission.

  • Uses multiuser diversity for opportunistic scheduling.

  • Compares multiuser diversity for users having identical and different channel statistics.

  • Contrasts opportunistic scheduling in terms of channel state acquisition and feedback both for uplink and downlink and for both FDD and TDD transmission schemes.

  • Appraises the impact of multiple antennas on opportunistic scheduling.

  • Analyses different settings with interference in small cells and designs countermeasures.

  • Categorizes relaying schemes in LTE.

  • Analyses performance of relaying schemes.

  • Argues on possible improvements of relaying schemes via network coding and physical layer network coding.

  • Uses the Perron-Frobenious theorem to allocate power in a centralized manner.

  • Judges the feasibility of a power control problems and formulates alternative approaches in case of unfeasibility.

  • Uses the Perron-Frobenious theorem to design a distributed power control scheme.

  • Judges the convergences of distributed power control based on the Perron-Frobenius theorem and appraises the robustness of asynchronous power control.

  • Applies techniques of convex optimization to discriminate convex problems and determine necessary and/or sufficient conditions for global optimality.

  • Judges the applicability of KKT conditions and duality.

  • Uses KKT conditions to solve convex optimization problems.

  • Uses duality to solve convex optimization problems.

  • Applies convex optimization to resource allocation in wireless communications.

  • Compares different definitions of fairness and applies them to rate allocation.

  • Appraises the effect of channel knowledge at the transmitter on different fairness criteria.

  • Applies KKT conditions for opportunistic user scheduling.

  • Describes a proportional fair algorithm for opportunistic scheduling.

  • Applies relaxation to nonconvex quadratic constrained quadratic programming.

  • Formulates resource allocation problems as constrained optimization programming.

  • Contrasts various distributed optimization methods.

  • Applies the concept of best response to determine Nash equilibria.

  • Argues about existence and uniqueness of Nash equilibria.

  • Assesses if a given game is a potential game and solves it.

  • Defends the concept of Pareto optimality in resource allocation.

  • Contrasts the concepts of pure and mixed strategies in game theory.

  • Uses coupled constrained concave game to allocate powers in heterogeneous networks.

Verwendbarkeit des Moduls / Einpassung in den Musterstudienplan:
Das Modul ist im Kontext der folgenden Studienfächer/Vertiefungsrichtungen verwendbar:

  1. Advanced Signal Processing & Communications Engineering (Master of Science)
    (Po-Vers. 2020w | TechFak | Advanced Signal Processing & Communications Engineering (Master of Science) | Gesamtkonto | Technical Mandatory Electives | Advanced Communication Networks)


Advanced Communication Networks (Prüfungsnummer: 151664)

(englischer Titel: Advanced Communication Networks)

Prüfungsleistung, mündliche Prüfung, Dauer (in Minuten): 30, benotet, 5 ECTS
Anteil an der Berechnung der Modulnote: 100.0 %
weitere Erläuterungen:
Possibly carried out as a 30-minute digital remote exam with ZOOM.
Evtl. digitale Fernprüfung von 30 Minuten Dauer mittels ZOOM.
Prüfungssprache: Englisch

Erstablegung: SS 2021, 1. Wdh.: WS 2021/2022
1. Prüfer: Laura Cottatellucci

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