PAPR Constrained Power Allocation for Iterative Frequency Domain Multiuser SIMO Detector

Peak to average power ratio (PAPR) constrained power allocation in single carrier multiuser (MU) single-input multiple-output (SIMO) systems with iterative frequency domain (FD) soft cancelation (SC) minimum mean squared error (MMSE) equalization is considered in this paper. To obtain full benefit of the iterative receiver, its convergence properties need to be taken into account also at the transmitter side. In this paper, we extend the existing results on the area of convergence constrained power allocation (CCPA) to consider the instantaneous PAPR at the transmit antenna of each user. In other words, we will introduce a constraint that PAPR cannot exceed a predetermined threshold. By adding the aforementioned constraint into the CCPA optimization framework, the power efficiency of a power amplifier (PA) can be significantly enhanced by enabling it to operate on its linear operation range. Hence, PAPR constraint is especially beneficial for power limited cell-edge users. In this paper, we will derive the instantaneous PAPR constraint as a function of transmit power allocation. Furthermore, successive convex approximation is derived for the PAPR constrained problem. Numerical results show that the proposed method can achieve the objectives described above.Comment: Presented in IEEE International Conference on Communications (ICC) 201

New adaptive transmission schemes for MC-CDMA systems.

by Yin-Man Lee.Thesis (M.Phil.)--Chinese University of Hong Kong, 1999.Includes bibliographical references (leaves 82-[87]).Abstracts in English and Chinese.Chapter 1 --- Introduction --- p.1Chapter 1.1 --- Overview of MC-CDMA --- p.1Chapter 1.2 --- System Model --- p.3Chapter 1.3 --- Receiver Optimization --- p.7Chapter 1.4 --- Transmitter Optimization --- p.9Chapter 1.5 --- Nonlinearly Constrained Optimization --- p.10Chapter 1.6 --- Outline of Thesis --- p.11Chapter 2 --- Centralized Transmitter Optimization for MC-CDMA Systems --- p.13Chapter 2.1 --- Introduction --- p.13Chapter 2.2 --- Problem Development --- p.15Chapter 2.3 --- Lagrangian Optimization Approaches --- p.16Chapter 2.3.1 --- Penalty Function Method --- p.17Chapter 2.3.2 --- Barrier Function Method --- p.19Chapter 2.3.3 --- Powell's Method and Augmented Lagrangian Method --- p.21Chapter 2.4 --- Optimal FDMA System --- p.23Chapter 2.5 --- Modified Centralized Optimization Schemes --- p.25Chapter 2.6 --- Performance --- p.27Chapter 2.6.1 --- Typical Behavior --- p.27Chapter 2.6.2 --- Average Performance --- p.32Chapter 2.7 --- Summary --- p.38Chapter 3 --- Decentralized Transmitter Optimization for MC-CDMA Sys- tems --- p.39Chapter 3.1 --- Introduction --- p.39Chapter 3.2 --- System Model --- p.41Chapter 3.3 --- Optimization --- p.42Chapter 3.3.1 --- Receiver Optimization --- p.43Chapter 3.3.2 --- Single-user Transmitter Optimization --- p.44Chapter 3.3.3 --- Decentralized Transmission Scheme --- p.45Chapter 3.3.4 --- Multirate Transmission with Decentralized Transmission Scheme --- p.47Chapter 3.4 --- Performance --- p.48Chapter 3.5 --- Summary --- p.57Chapter 4 --- Performance Evaluation of Various Adaptive Transmission Schemes --- p.59Chapter 4.1 --- Introduction --- p.59Chapter 4.2 --- Comparison of Different Adaptive Transmission Schemes --- p.61Chapter 4.3 --- Adaptive Transmission Schemes with K > M --- p.64Chapter 4.4 --- Modified Adaptive Transmission Scheme with Graceful Degrada- tion in the SNR --- p.68Chapter 4.5 --- Summary --- p.71Chapter 5 --- Conclusions and Future Work --- p.73Chapter 5.1 --- Conclusions --- p.73Chapter 5.2 --- Future Work --- p.75A The Hungarian Method for Optimal Frequency Assignment --- p.76Bibliography --- p.8

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

Energy Efficient Reduced Complexity Multi-Service, Multi-Channel Scheduling Techniques

The need for energy efficient communications is essential in current and next-generation wireless communications systems. A large component of energy expenditure in mobile devices is in the mobile radio interface. Proper scheduling and resource allocation techniques that exploit instantaneous and long-term average knowledge of the channel, queue state and quality of service parameters can be used to improve the energy efficiency of communication. This thesis focuses on exploiting queue and channel state information as well as quality of service parameters in order to design energy efficient scheduling techniques. The proposed designs are for multi-stream, multi-channel systems and in general have high computational complexity. The large contributions of this thesis are in both the design of optimal/near-optimal scheduling/resource allocation schemes for these systems as well as proposing complexity reduction methods in their design. Methods are proposed for both a MIMO downlink system as well as an LTE uplink system. The effect of power efficiency on quality of service parameters is well studied as well as complexity/efficiency comparisons between optimal/near optimal allocation

Optimal Linear Precoding Strategies for Wideband Non-Cooperative Systems based on Game Theory-Part I: Nash Equilibria

In this two-parts paper we propose a decentralized strategy, based on a game-theoretic formulation, to find out the optimal precoding/multiplexing matrices for a multipoint-to-multipoint communication system composed of a set of wideband links sharing the same physical resources, i.e., time and bandwidth. We assume, as optimality criterion, the achievement of a Nash equilibrium and consider two alternative optimization problems: 1) the competitive maximization of mutual information on each link, given constraints on the transmit power and on the spectral mask imposed by the radio spectrum regulatory bodies; and 2) the competitive maximization of the transmission rate, using finite order constellations, under the same constraints as above, plus a constraint on the average error probability. In Part I of the paper, we start by showing that the solution set of both noncooperative games is always nonempty and contains only pure strategies. Then, we prove that the optimal precoding/multiplexing scheme for both games leads to a channel diagonalizing structure, so that both matrix-valued problems can be recast in a simpler unified vector power control game, with no performance penalty. Thus, we study this simpler game and derive sufficient conditions ensuring the uniqueness of the Nash equilibrium. Interestingly, although derived under stronger constraints, incorporating for example spectral mask constraints, our uniqueness conditions have broader validity than previously known conditions. Finally, we assess the goodness of the proposed decentralized strategy by comparing its performance with the performance of a Pareto-optimal centralized scheme. To reach the Nash equilibria of the game, in Part II, we propose alternative distributed algorithms, along with their convergence conditions.Comment: Paper submitted to IEEE Transactions on Signal Processing, September 22, 2005. Revised March 14, 2007. Accepted June 5, 2007. To be published on IEEE Transactions on Signal Processing, 2007. To appear on IEEE Transactions on Signal Processing, 200

Information Technology

The new millennium has been labeled as the century of the personal communications revolution or more specifically, the digital wireless communications revolution. The introduction of new multimedia services has created higher loads on available radio resources. These services can be presented in different levels of quality of service. Namely, the task of the radio resource manager is to provide these levels. Radio resources are scarce and need to be shared by many users. The sharing has to be carried out in an efficient way avoiding as much as possible any waste of resources. The main contribution focus of this work is on radio resource management in opportunistic systems. In opportunistic communications dynamic rate and power allocation may be performed over the dimensions of time, frequency and space in a wireless system. In this work a number of these allocation schemes are proposed. A downlink scheduler is introduced in this work that controls the activity of the users. The scheduler is a simple integral controller that controls the activity of users, increasing or decreasing it depending on the degree of proximity to a requested quality of service level. The scheduler is designed to be a best effort scheduler; that is, in the event the requested quality of service (QoS) cannot be attained, users are always guaranteed the basic QoS level provided by a proportional fair scheduler. In a proportional fair scheduler, the user with the best rate quality factor is selected. The rate quality here is the instantaneous achievable rate divided by the average throughput Uplink scheduling is more challenging than its downlink counterpart due to signalling restrictions and additional constraints on resource allocations. For instance, in long term evolution systems, single carrier FDMA is to be utilized which requires the frequency domain resource allocation to be done in such a way that a user could only be allocated subsequent bands. We suggest for the uplink a scheduler that follows a heuristic approach in its decision. The scheduler is mainly based on the gradient algorithm that maximizes the gradient of a certain utility. The utility could be a function of any QoS. In addition, an optimal uplink scheduler for the same system is presented. This optimal scheduler is valid in theory only, nevertheless, it provides a considerable benchmark for evaluation of performance for the heuristic scheduler as well as other algorithms of the same system. A study is also made for the feedback information in a multi-carrier system. In a multi-carrier system, reporting the channel state information (CSI) of every subcarrier will result in huge overhead and consequent waste in bandwidth. In this work the subcarriers are grouped into subbands which are in turn grouped into blocks and a study is made to find the minimum amount of information for the adaptive modulation and coding (AMC) of the blocks. The thesis also deals with admission control and proposes an opportunistic admission controller. The controller gradually integrates a new user requesting admission into the system. The system is probed to examine the effect of the new user on existing connections. The user is finally fully admitted if by the end of the probing, the quality of service (QoS) of existing connections did not drop below a certain threshold. It is imperative to mention that the research work of this thesis is mainly focused on non-real time applications.fi=Opinnäytetyö kokotekstinä PDF-muodossa.|en=Thesis fulltext in PDF format.|sv=Lärdomsprov tillgängligt som fulltext i PDF-format