Evolution of NOMA Toward Next Generation Multiple Access (NGMA) for 6G

Due to the explosive growth in the number of wireless devices and diverse wireless services, such as virtual/augmented reality and Internet-of-Everything, next generation wireless networks face unprecedented challenges caused by heterogeneous data traffic, massive connectivity, and ultra-high bandwidth efficiency and ultra-low latency requirements. To address these challenges, advanced multiple access schemes are expected to be developed, namely next generation multiple access (NGMA), which are capable of supporting massive numbers of users in a more resource- and complexity-efficient manner than existing multiple access schemes. As the research on NGMA is in a very early stage, in this paper, we explore the evolution of NGMA with a particular focus on non-orthogonal multiple access (NOMA), i.e., the transition from NOMA to NGMA. In particular, we first review the fundamental capacity limits of NOMA, elaborate on the new requirements for NGMA, and discuss several possible candidate techniques. Moreover, given the high compatibility and flexibility of NOMA, we provide an overview of current research efforts on multi-antenna techniques for NOMA, promising future application scenarios of NOMA, and the interplay between NOMA and other emerging physical layer techniques. Furthermore, we discuss advanced mathematical tools for facilitating the design of NOMA communication systems, including conventional optimization approaches and new machine learning techniques. Next, we propose a unified framework for NGMA based on multiple antennas and NOMA, where both downlink and uplink transmissions are considered, thus setting the foundation for this emerging research area. Finally, several practical implementation challenges for NGMA are highlighted as motivation for future work.Comment: 34 pages, 10 figures, a survey paper accepted by the IEEE JSAC special issue on Next Generation Multiple Acces

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

Técnicas de transmissão e recepção para sistemas MIMO heterogéneos na banda das ondas milimétricas

Mestrado em Engenharia Eletrónica e TelecomunicaçõesCom o crescimento dos dispositivos de comunicações móveis e de serviços de banda larga, os requisitos do sistema tornam-se cada vez mais exigentes. O LTE-Advanced apresenta um melhoramento progressivo relativamente ao seu antecessor LTE, introduzindo redes heterogéneas, que têm vindo provar constituir uma solução sólida para melhorar tanto a capacidade, como a cobertura da rede. Quanto à implementação do 5G, será necessário um salto disruptivo na tecnologia, que permita novas possibilidades, tal como a de conectar pessoas e coisas. Para tornar isso possível, é necessário investigar e testar novas tecnologias. MIMO massivo e comunicações em ondas milimétricas são algumas das tecnologias que têm vindo a demonstrar resultados com potencial, tais como o aumento da capacidade e da eficiência espectral. No entanto, devido às características da propagação de ondas milimétricas, a existência de cenários com redes heterogéneas ultradensas é uma possibilidade. Ao se considerar cenários ultradensos com um número massivo de utilizadores, o sistema fica limitado devido à interferência, mesmo operando na banda das ondas milimétricas. Como tal, é de extrema importância o desenvolvimento de técnicas que mitiguem essa interferência. Nesta dissertação, propõe-se uma arquitetura de baixa complexidade para um transmissor e um recetor a operarem no sentido ascendente, numa rede heterogénea ultradensa. Nesta arquitetura são aplicadas tecnologias como MIMO massivo, ondas milimétricas e técnicas de beamforming, com o intuito de mitigar a interferência entre células. Usando a probabilidade de erro de bit como métrica de performance, os resultados mostram que a arquitetura proposta consegue remover a interferência eficientemente, alcançando resultados próximos de uma arquitetura completamente digital.With the constant increase of mobile communication devices and broadband services, the system requirements are getting more demanding. Long Term Evolution (LTE) Advanced comes as a progressive enhancement to its predecessor LTE, introducing heterogeneous networks (HetNets), which have proven to be great solutions to improve both capacity and coverage. As for 5G, it takes more of a disruptive step, enabling new possibilities, such as connecting people and things. To enable such a step, new technologies and techniques need to be researched and tested. Massive Multiple-Input Multiple-Output (MIMO) and millimeter wave (mmWave) communications are two of such technologies, as they show promising results such as increased capacity and spectral efficiency. However, due to the mmWave propagation constraints, the existence of ultra-dense HetNet scenarios may be a possibility. When considering ultra-dense scenarios with a massive number of users, the system becomes interference-limited, even using mmWave band. As such, the design of interference mitigation techniques that deal with both inter and intra-tier interference are of the utmost importance. In this dissertation, a low complexity analog-digital hybrid architecture for both the transmitter and receiver in the uplink scenario is proposed. It is designed for an ultra-dense heterogeneous system and employing massive MIMO, mmWave and beamforming techniques in order to mitigate both intra- and inter-tier interference. Considering the Bit Error Rate (BER) as the performance metric, the results show that the proposed architecture efficiently removes both inter- and intra-tier interferences, achieving a result close to its fully digital counterpart

Collaborative modulation multiple access for single hop and multihop networks

While the bandwidth available for wireless networks is limited, the world has seen an unprecedented growth in the number of mobile subscribers and an ever increasing demand for high data rates. Therefore efficient utilisation of bandwidth to maximise link spectral efficiency and number of users that can be served simultaneously are primary goals in the design of wireless systems. To achieve these goals, in this thesis, a new non-orthogonal uplink multiple access scheme which combines the functionalities of adaptive modulation and multiple access called collaborative modulation multiple access (CMMA) is proposed. CMMA enables multiple users to access the network simultaneously and share the same bandwidth even when only a single receive antenna is available and in the presence of high channel correlation. Instead of competing for resources, users in CMMA share resources collaboratively by employing unique modulation sets (UMS) that differ in phase, power, and/or mapping structure. These UMS are designed to insure that the received signal formed from the superposition of all users’ signals belongs to a composite QAM constellation (CC) with a rate equal to the sum rate of all users. The CC and its constituent UMSs are designed centrally at the BS to remove ambiguity, maximize the minimum Euclidian distance (dmin) of the CC and insure a minimum BER performance is maintained. Users collaboratively precode their transmitted signal by performing truncated channel inversion and phase rotation using channel state information (CSI ) obtained from a periodic common pilot to insure that their combined signal at the BS belongs to the CC known at the BS which in turn performs a simple joint maximum likelihood detection without the need for CSI. The coherent addition of users’ power enables CMMA to achieve high link spectral efficiency at any time without extra power or bandwidth but on the expense of graceful degradation in BER performance. To improve the BER performance of CMMA while preserving its precoding and detection structure and without the need for pilot-aided channel estimation, a new selective diversity combining scheme called SC-CMMA is proposed. SC-CMMA optimises the overall group performance providing fairness and diversity gain for various users with different transmit powers and channel conditions by selecting a single antenna out of a group of L available antennas that minimises the total transmit power required for precoding at any one time. A detailed study of capacity and BER performance of CMMA and SC-CMMA is carried out under different level of channel correlations which shows that both offer high capacity gain and resilience to channel correlation. SC-CMMA capacity even increase with high channel correlation between users’ channels. CMMA provides a practical solution for implementing the multiple access adder channel (MAAC) in fading environments hence a hybrid approach combining both collaborative coding and modulation referred to as H-CMMA is investigated. H-CMMA divides users into a number of subgroups where users within a subgroup are assigned the same modulation set and different multiple access codes. H-CMMA adjusts the dmin of the received CC by varying the number of subgroups which in turn varies the number of unique constellation points for the same number of users and average total power. Therefore H-CMMA can accommodate many users with different rates while flexibly managing the complexity, rate and BER performance depending on the SNR. Next a new scheme combining CMMA with opportunistic scheduling using only partial CSI at the receiver called CMMA-OS is proposed to combine both the power gain of CMMA and the multiuser diversity gain that arises from users’ channel independence. To avoid the complexity and excessive feedback associated with the dynamic update of the CC, the BS takes into account the independence of users’ channels in the design of the CC and its constituent UMSs but both remain unchanged thereafter. However UMS are no longer associated with users, instead channel gain’s probability density function is divided into regions with identical probability and each UMS is associated with a specific region. This will simplify scheduling as users can initially chose their UMS based on their CSI and the BS will only need to resolve any collision when the channels of two or more users are located at the same region. Finally a high rate cooperative communication scheme, called cooperative modulation (CM) is proposed for cooperative multiuser systems. CM combines the reliability of the cooperative diversity with the high spectral efficiency and multiple access capabilities of CMMA. CM maintains low feedback and high spectral efficiency by restricting relaying to a single route with the best overall channel. Two possible variations of CM are proposed depending on whether CSI available only at the users or just at the BS and the selected relay. The first is referred to Precode, Amplify, and Forward (PAF) while the second one is called Decode, Remap, and Forward (DMF). A new route selection algorithm for DMF based on maximising dmin of random CC is also proposed using a novel fast low-complexity multi-stage sphere based algorithm to calculate the dmin at the relay of random CC that is used for both relay selection and detection

Recent Advances in Wireless Communications and Networks

This book focuses on the current hottest issues from the lowest layers to the upper layers of wireless communication networks and provides "real-time" research progress on these issues. The authors have made every effort to systematically organize the information on these topics to make it easily accessible to readers of any level. This book also maintains the balance between current research results and their theoretical support. In this book, a variety of novel techniques in wireless communications and networks are investigated. The authors attempt to present these topics in detail. Insightful and reader-friendly descriptions are presented to nourish readers of any level, from practicing and knowledgeable communication engineers to beginning or professional researchers. All interested readers can easily find noteworthy materials in much greater detail than in previous publications and in the references cited in these chapters