Novel Aspects of Interference Alignment in Wireless Communications

Interference alignment (IA) is a promising joint-transmission technology that essentially enables the maximum achievable degrees-of-freedom (DoF) in K-user interference channels. Fundamentally, wireless networks are interference-limited since the spectral efficiency of each user in the network is degraded with the increase of users. IA breaks through this barrier, that is caused by the traditional interference management techniques, and promises large gains in spectral efficiency and DoF, notably in interference limited environments. This dissertation concentrates on overcoming the challenges as well as exploiting the opportunities of IA in K-user multiple-input multiple-output (MIMO) interference channels. In particular, we consider IA in K-user MIMO interference channels in three novel aspects. In the first aspect, we develop a new IA solution by designing transmit precoding and interference suppression matrices through a novel iterative algorithm based on Min-Maxing strategy. Min-Maxing IA optimization problem is formulated such that each receiver maximizes the power of the desired signal, whereas it preserves the minimum leakage interference as a constraint. This optimization problem is solved by relaxing it into a standard semidefinite programming form, and additionally its convergence is proved. Furthermore, we propose a simplified Min-Maxing IA algorithm for rank-deficient interference channels to achieve the targeted performance with less complexity. Our numerical results show that Min-Maxing IA algorithm proffers significant sum-rate improvement in K-user MIMO interference channels compared to the existing algorithms in the literature at high signal-to-noise ratio (SNR) regime. Moreover, the simplified algorithm matches the optimal performance in the systems of rank-deficient channels. In the second aspect, we deal with the practical challenges of IA under realistic channels, where IA is highly affected by the spatial correlation. Data sum-rate and symbol error-rate of IA are dramatically degraded in real-world scenarios since the correlation between channels decreases the SNR of the received signal after alignment. For this reason, an acceptable sum-rate of IA in MIMO orthogonal frequency-division-multiplexing (MIMO-OFDM) interference channels was obtained in the literature by modifying the locations of network nodes and the separation between the antennas within each node in order to minimize the correlation between channels. In this regard, we apply transmit antenna selection to MIMO-OFDM IA systems either through bulk or per-subcarrier selection aiming at improving the sum-rate and/or error-rate performance under real-world channel circumstances while keeping the minimum spatial antenna separation of half-wavelengths. A constrained per-subcarrier antenna selection is performed to avoid subcarrier imbalance across the antennas of each user that is caused by per-subcarrier selection. Furthermore, we propose a sub-optimal antenna selection algorithm to reduce the computational complexity of the exhaustive search. An experimental testbed of MIMO-OFDM IA with antenna selection in indoor wireless network scenarios is implemented to collect measured channels. The performance of antenna selection in MIMO IA systems is evaluated using measured and deterministic channels, where antenna selection achieves considerable improvements in sum-rate and error-rate under real-world channels. Third aspect of this work is exploiting the opportunity of IA in resource management problem in OFDM based MIMO cognitive radio systems that coexist with primary systems. We propose to perform IA based resource allocation to improve the spectral efficiency of cognitive systems without affecting the quality of service (QoS) of the primary system. IA plays a vital role in the proposed algorithm enabling the secondary users (SUs) to cooperate and share the available spectrum aiming at increasing the DoF of the cognitive system. Nevertheless, the number of SUs that can share a given subcarrier is restricted to the IA feasibility conditions, where this limitation is considered in problem formulation. As the optimal solution for resource allocation problem is mixed-integer, we propose a two-phases efficient sub-optimal algorithm to handle this problem. In the first phase, frequency-clustering with throughput fairness consideration among SUs is performed to tackle the IA feasibility conditions, where each subcarrier is assigned to a feasible number of SUs. In the second phase, the power is allocated among subcarriers and SUs without violating the interference constraint to the primary system. Simulation results show that IA with frequency-clustering achieves a significant sum-rate increase compared to cognitive radio systems with orthogonal multiple access transmission techniques. The considered aspects with the corresponding achievements bring IA to have a powerful role in the future wireless communication systems. The contributions lead to significant improvements in the spectral efficiency of IA based wireless systems and the reliability of IA under real-world channels.Interference Alignment (IA) ist eine vielversprechende kooperative Übertragungstechnik, die die meisten Freiheitsgrade (engl. degrees-of-freedom, DoF) in Bezug auf Zeit, Frequenz und Ort in einem Mehrnutzer Überlagerungskanal bietet. Im Grunde sind Funksysteme Interferenz begrenzt, da die Spektraleffizienz jedes einzelnen Nutzers mit zunehmender Nutzerzahl sinkt. IA durchbricht die Schranke, die herkömmliches Interferenzmanagement errichtet und verspricht große Steigerungen der Spektraleffizienz und der Freiheitsgrade, besonders in Interferenzbegrenzter Umgebung. Die vorliegende Dissertation betrachtet bisher noch unerforschte Möglichkeiten von IA in Mehrnutzerszenarien für Mehrantennen- (MIMO) Kanäle sowie deren Anwendung in einem kognitiven Kommunikationssystem. Als erstes werden mit Hilfe eines effizienten iterativen Algorithmus, basierend auf der Min-Maxing Strategie, senderseitige Vorkodierungs- und Interferenzunterdrückungs Matrizen entwickelt. Das Min-Maxing Optimierungsproblem ist dadurch beschreiben, dass jeder Empfänger seine gewünschte Signalleistung maximiert, während das Minimum der Leck-Interferenz als Randbedingung beibehalten wird. Zur Lösung des Problems wird es in eine semidefinite Form überführt, zusätzlich wird deren Konvergenz nachgewiesen. Des Weiteren wird ein vereinfachter Algorithmus für nicht vollrangige Kanalmatrizen vorgeschlagen, um die Rechenkomplexität zu verringern. Wie numerische Ergebnisse belegen, bedeutet die Min-Maxing Strategie eine wesentliche Verbesserung des Systemdurchsatzes gegenüber den bisher in der Literatur beschriebenen Algorithmen für Mehrnutzer MIMO Szenarien im hohen Signal-Rausch-Verhältnis (engl. signal-to-noise ratio, SNR). Mehr noch, der vereinfachte Algorithmus zeigt das optimale Verhalten in einem System mit nicht vollrangigen Kanalmatrizen. Als zweites werden die IA Herausforderungen an Hand von realistischen/realen Kanälen in der Praxis untersucht. Hierbei wird das System stark durch räumliche Korrelation beeinträchtigt. Der Datendurchsatz sinkt und die Symbolfehlerrate steigt dramatisch unter diesen Bedingungen, da korrelierte Kanäle den SNR des empfangenen Signals nach dem Alignment verschlechtern. Aus diesem Grund wurde in der Literatur für IA in MIMO-OFDM Überlagerungskanälen sowohl die Position der einzelnen Netzwerkknoten als auch die Trennung zwischen den Antennen eines Knotens variiert, um so die Korrelierung der verschiedenen Kanäle zu minimieren. Das vorgeschlagene MIMO-OFDM IA System wählt unter mehreren Sendeantennen, entweder pro Unterträger oder für das komplette Signal, um so die Symbolfehlerrate und/oder die gesamt Datenrate zu verbessern, während die räumliche Trennung der Antennen auf die halbe Wellenlänge beschränkt bleiben soll. Bei der Auswahl pro Unterträger ist darauf zu achten, dass die Antennen gleichmäßig ausgelastet werden. Um die Rechenkomplexität für die vollständige Durchsuchung gering zu halten, wird ein suboptimaler Auswahlalgorithmus verwendet. Mit Hilfe einer Innenraummessanordnung werden reale Kanaldaten für die Simulationen gewonnen. Die Evaluierung des MIMO IA Systems mit Antennenauswahl für deterministische und gemessene Kanäle hat eine Verbesserung bei der Daten- und Fehlerrate unter realen Bedingungen ergeben. Als drittes beschäftigt sich die vorliegende Arbeit mit den Möglichkeiten, die sich durch MIMO IA Systeme für das Ressourcenmanagementproblem bei kognitiven Funksystemen ergeben. In kognitiven Funksystemen müssen MIMO IA Systeme mit primären koexistieren. Es wird eine IA basierte Ressourcenzuteilung vorgeschlagen, um so die spektrale Effizienz des kognitiven Systems zu erhöhen ohne die Qualität (QoS) des primären Systems zu beeinträchtigen. Der vorgeschlagenen IA Algorithmus sorgt dafür, dass die Zweitnutzer (engl. secondary user, SU) untereinander kooperieren und sich das zur Verfügung stehende Spektrum teilen, um so die DoF des kognitiven Systems zu erhöhen. Die Anzahl der SUs, die sich eine Unterträgerfrequenz teilen, ist durch die IA Randbedingungen begrenzt. Die Suche nach der optimalen Ressourcenverteilung stellt ein gemischt-ganzzahliges Problem dar, zu dessen Lösung ein effizienter zweistufiger suboptimaler Algorithmus vorgeschlagen wird. Im ersten Schritt wird durch Frequenzzusammenlegung (Clusterbildung), unter Berücksichtigung einer fairen Durchsatzverteilung unter den SUs, die IA Anforderung erfüllt. Dazu wird jede Unterträgerfrequenz einer praktikablen Anzahl an SUs zugeteilt. Im zweiten Schritt wird die Sendeleistung für die einzelnen Unterträgerfrequenzen und SUs so festgelegt, dass die Interferenzbedingungen des Primärsystems nicht verletzt werden. Die Simulationsergebnisse für IA mit Frequenzzusammenlegung zeigen eine wesentliche Verbesserung der Datenrate verglichen mit kognitiven Systemen, die auf orthogonalen Mehrfachzugriffsverfahren beruhen. Die in dieser Arbeit betrachteten Punkte und erzielten Lösungen führen zu einer wesentlichen Steigerung der spektralen Effizienz von IA Systemen und zeigen deren Zuverlässigkeit unter realen Bedingungen

Terahertz Communications and Sensing for 6G and Beyond: A Comprehensive View

The next-generation wireless technologies, commonly referred to as the sixth generation (6G), are envisioned to support extreme communications capacity and in particular disruption in the network sensing capabilities. The terahertz (THz) band is one potential enabler for those due to the enormous unused frequency bands and the high spatial resolution enabled by both short wavelengths and bandwidths. Different from earlier surveys, this paper presents a comprehensive treatment and technology survey on THz communications and sensing in terms of the advantages, applications, propagation characterization, channel modeling, measurement campaigns, antennas, transceiver devices, beamforming, networking, the integration of communications and sensing, and experimental testbeds. Starting from the motivation and use cases, we survey the development and historical perspective of THz communications and sensing with the anticipated 6G requirements. We explore the radio propagation, channel modeling, and measurements for THz band. The transceiver requirements, architectures, technological challenges, and approaches together with means to compensate for the high propagation losses by appropriate antenna and beamforming solutions. We survey also several system technologies required by or beneficial for THz systems. The synergistic design of sensing and communications is explored with depth. Practical trials, demonstrations, and experiments are also summarized. The paper gives a holistic view of the current state of the art and highlights the issues and challenges that are open for further research towards 6G.Comment: 55 pages, 10 figures, 8 tables, submitted to IEEE Communications Surveys & Tutorial

Numerical Simulation of Fracture at Asphalt Mastic Materials

International audienceIn this paper, numerical simulations have been conducted to investigate how damage initiates and propagates at mastic materials. Mastic is known as the matrix component of asphalt concrete. The 2D specimen digital model has been created by using a layer of mastic material which is inserted between two coarse aggregate. Cohesive elements have been inserted into mastic to simulate crack initiation and propagation. The effects of loading and stiffness modulus will be investigated. Many important conclusions will be given

Interference Alignment with Frequency-Clustering for Efficient Resource Allocation in Cognitive Radio Networks

International audienceIn this paper, we investigate the resource management problem in orthogonal frequency division multiplexing (OFDM) based multiple-input multiple-output (MIMO) cognitive radio (CR) systems. We propose to perform resource allocation based on interference alignment (IA) in order to improve the spectral efficiency of CR systems without affecting the quality of service of the primary system. IA plays a role in the proposed algorithm to enable the secondary users (SUs) to cooperate and share the available spectrum, which leads to a considerable increase in the spectral efficiency of CR systems. However, IA based spectrum sharing is restricted to a certain number of SUs per subcarrier in order to satisfy the IA feasibility conditions. Accordingly, the resource allocation problem is formulated as a mixed-integer optimization problem which is considered as an N P-hard problem. To reduce the computational complexity of the problem, a two-phases efficient sub-optimal algorithm is proposed. In the first phase, frequency-clustering is performed in order to satisfy the IA feasibility conditions, where each subcarrier is assigned to a feasible number of SUs. Whenever possible, frequency-clustering stage considers the fairness among the SUs. In the second stage, the available power is allocated among the subcarriers and SUs without violating the constraints that limit the maximum interference induced to the primary system. Simulation results show that IA with frequency-clustering achieves a significant sum rate increase compared to CR systems with orthogonal multiple access transmission techniques

Interference Alignment Based Resource Management in MIMO Cognitive Radio Systems

Print ISBN: 978-3-8007-3621-8International audienceIn this paper, interference alignment (IA) is utilized to obtain an efficient and fair resource allocation algorithm in MIMO cognitive radio (CR) systems. In the proposed algorithm, IA enables all secondary users to share the available spectrum without affecting the quality-of-service of the primary system. The considered methodology increases the total degreesof-freedom of the CR systems and achieves fairness among CR users. An optimal power allocation based on IA is formulated in order to maximize the total sum-rate while keeping the interference introduced to the primary system lower than the prescribed interference threshold. Furthermore, a sub-optimal power allocation scheme is proposed to overcome the high computational complexity of the optimal scheme. Simulations reveal that IA technique achieves significant sum-rate increase of CR systems compared to frequency division multiple access (FDMA) CR systems. Moreover, the sup-optimal algorithm approaches the optimal sum-rate performance

Power Loading and Spectral Efficiency Comparison of MIMO OFDM/FBMC for Interference Alignment Based Cognitive Radio Systems

International audienceInterference alignment (IA) has been proposed to optimally manage the interference aiming at providing the maximum degrees of freedom of interference channels with multiple dimensions across space, time, or/and frequency for multiuser wireless communications. Therefore, an optimal IA based power loading is proposed in this paper to improve the throughput of the OFDM/FBMC based MIMO cognitive radio (CR) systems. In the proposed algorithm, all secondary users are enabled to share the available spectrum on the base of IA technique without affecting the quality-of-service of the primary system. Furthermore, a sub-optimal power loading algorithm is proposed to achieve the performance of the optimal algorithm with lower computational complexity. Moreover, spectral efficiency comparison between MIMO-OFDM and MIMO-FBMC is presented. Simulation results show that IA based power loading achieves a significant sum-rate increase of CR systems compared to traditional orthogonal multiple access techniques. Additionally, IA based power loading achieves better sum-rate improvement with FBMC than OFDM physical laye

WIRELESS SYNCHRONIZATION AND INTERFERENCE ALIGNMENT WITH LIMITED INTERFERER FOR DISTRIBUTED LARGE SCALE MULTI USER MIMO

Large Scale (Massive) MIMO enhances the advantages of the conventional MIMO in terms of data rate, energy efficiency and reliability. To increase the scalability of conventional massive MIMO, the distributed large scale MIMO is recommended. Synchronization for distributed large scale MIMO is needed due to the lack of common clock source to synchronize the transmitters. Limited Inter-User Connected Interference Alignment for K-User is proposed for Large Scale MU-MIMO precoding scheme. Moreover, we discuss phase rotation factor estimation and compensation to synchronize the distributed large scale MIMO. The Interference Alignment algorithm with antenna selection also employed to show the effect of the synchronization in distributed large scale MIMO performance

Near-Wall Models for Improved Heat Transfer Predictions in Channel Flow Applications

International audienceThis work aims to improve the accuracy of the predicted temperature profile in the near-wall region of turbulent boundary layers. It is well known that practical understanding of turbulent boundary layers is central to heat transfer enhancement in many engineering applications. In one case, an eddy viscosity formulation based on a near-wall turbulent kinetic energy k(+) function and the van Driest mixing length equation are used. In the second case, the turbulent Prandtl number is not assumed constant but rather is modeled by incorporating improved turbulent Prandtl number relations into existing models. The test case is the fully developed plane channel flow, which is considered to be the simplest and most idealized boundary-layer flow. These new formulations show improved agreement against the temperature profiles of direct numerical simulations of turbulent channel flow versus simulated cases using a commercial computational fluid dynamics package

CONSTANT-WEIGHT CODES USING METASYMPLECTIC SPACE F4,1(Q) AND ITS RESIDUE

In this paper we generate few families of non-linear binary constant-weight codes using the metasymplectic spaces F4,1(q) and its residue