Signal Processing and Learning for Next Generation Multiple Access in 6G

Wireless communication systems to date primarily rely on the orthogonality of resources to facilitate the design and implementation, from user access to data transmission. Emerging applications and scenarios in the sixth generation (6G) wireless systems will require massive connectivity and transmission of a deluge of data, which calls for more flexibility in the design concept that goes beyond orthogonality. Furthermore, recent advances in signal processing and learning have attracted considerable attention, as they provide promising approaches to various complex and previously intractable problems of signal processing in many fields. This article provides an overview of research efforts to date in the field of signal processing and learning for next-generation multiple access, with an emphasis on massive random access and non-orthogonal multiple access. The promising interplay with new technologies and the challenges in learning-based NGMA are discussed

Spectrum Sharing, Latency, and Security in 5G Networks with Application to IoT and Smart Grid

The surge of mobile devices, such as smartphones, and tables, demands additional capacity. On the other hand, Internet-of-Things (IoT) and smart grid, which connects numerous sensors, devices, and machines require ubiquitous connectivity and data security. Additionally, some use cases, such as automated manufacturing process, automated transportation, and smart grid, require latency as low as 1 ms, and reliability as high as 99.99\%. To enhance throughput and support massive connectivity, sharing of the unlicensed spectrum (3.5 GHz, 5GHz, and mmWave) is a potential solution. On the other hand, to address the latency, drastic changes in the network architecture is required. The fifth generation (5G) cellular networks will embrace the spectrum sharing and network architecture modifications to address the throughput enhancement, massive connectivity, and low latency. To utilize the unlicensed spectrum, we propose a fixed duty cycle based coexistence of LTE and WiFi, in which the duty cycle of LTE transmission can be adjusted based on the amount of data. In the second approach, a multi-arm bandit learning based coexistence of LTE and WiFi has been developed. The duty cycle of transmission and downlink power are adapted through the exploration and exploitation. This approach improves the aggregated capacity by 33\%, along with cell edge and energy efficiency enhancement. We also investigate the performance of LTE and ZigBee coexistence using smart grid as a scenario. In case of low latency, we summarize the existing works into three domains in the context of 5G networks: core, radio and caching networks. Along with this, fundamental constraints for achieving low latency are identified followed by a general overview of exemplary 5G networks. Besides that, a loop-free, low latency and local-decision based routing protocol is derived in the context of smart grid. This approach ensures low latency and reliable data communication for stationary devices. To address data security in wireless communication, we introduce a geo-location based data encryption, along with node authentication by k-nearest neighbor algorithm. In the second approach, node authentication by the support vector machine, along with public-private key management, is proposed. Both approaches ensure data security without increasing the packet overhead compared to the existing approaches

D6.6 Final report on the METIS 5G system concept and technology roadmap

This deliverable presents the METIS 5G system concept which was developed to fulfil the requirements of the beyond-2020 connected information society and to extend today’s wireless communication systems to include new usage scenarios. The METIS 5G system concept consists of three generic 5G services and four main enablers. The three generic 5G services are Extreme Mobile BroadBand (xMBB), Massive Machine- Type Communications (mMTC), and Ultra-reliable Machine-Type Communication (uMTC). The four main enablers are Lean System Control Plane (LSCP), Dynamic RAN, Localized Contents and Traffic Flows, and Spectrum Toolbox. An overview of the METIS 5G architecture is given, as well as spectrum requirements and considerations. System-level evaluation of the METIS 5G system concept has been conducted, and we conclude that the METIS technical objectives are met. A technology roadmap outlining further 5G development, including a timeline and recommended future work is given.Popovski, P.; Mange, G.; Gozalvez -Serrano, D.; Rosowski, T.; Zimmermann, G.; Agyapong, P.; Fallgren, M.... (2014). D6.6 Final report on the METIS 5G system concept and technology roadmap. http://hdl.handle.net/10251/7676

Analysis and Design of Non-Orthogonal Multiple Access (NOMA) Techniques for Next Generation Wireless Communication Systems

The current surge in wireless connectivity, anticipated to amplify significantly in future wireless technologies, brings a new wave of users. Given the impracticality of an endlessly expanding bandwidth, there’s a pressing need for communication techniques that efficiently serve this burgeoning user base with limited resources. Multiple Access (MA) techniques, notably Orthogonal Multiple Access (OMA), have long addressed bandwidth constraints. However, with escalating user numbers, OMA’s orthogonality becomes limiting for emerging wireless technologies. Non-Orthogonal Multiple Access (NOMA), employing superposition coding, serves more users within the same bandwidth as OMA by allocating different power levels to users whose signals can then be detected using the gap between them, thus offering superior spectral efficiency and massive connectivity. This thesis examines the integration of NOMA techniques with cooperative relaying, EXtrinsic Information Transfer (EXIT) chart analysis, and deep learning for enhancing 6G and beyond communication systems. The adopted methodology aims to optimize the systems’ performance, spanning from bit-error rate (BER) versus signal to noise ratio (SNR) to overall system efficiency and data rates. The primary focus of this thesis is the investigation of the integration of NOMA with cooperative relaying, EXIT chart analysis, and deep learning techniques. In the cooperative relaying context, NOMA notably improved diversity gains, thereby proving the superiority of combining NOMA with cooperative relaying over just NOMA. With EXIT chart analysis, NOMA achieved low BER at mid-range SNR as well as achieved optimal user fairness in the power allocation stage. Additionally, employing a trained neural network enhanced signal detection for NOMA in the deep learning scenario, thereby producing a simpler signal detection for NOMA which addresses NOMAs’ complex receiver problem

Congestion Control for Massive Machine-Type Communications: Distributed and Learning-Based Approaches

The Internet of things (IoT) is going to shape the future of wireless communications by allowing seamless connections among wide range of everyday objects. Machine-to-machine (M2M) communication is known to be the enabling technology for the development of IoT. With M2M, the devices are allowed to interact and exchange data without or with little human intervention. Recently, M2M communication, also referred to as machine-type communication (MTC), has received increased attention due to its potential to support diverse applications including eHealth, industrial automation, intelligent transportation systems, and smart grids. M2M communication is known to have specific features and requirements that differ from that of the traditional human-to-human (H2H) communication. As specified by the Third Generation Partnership Project (3GPP), MTC devices are inexpensive, low power, and mostly low mobility devices. Furthermore, MTC devices are usually characterized by infrequent, small amount of data, and mainly uplink traffic. Most importantly, the number of MTC devices is expected to highly surpass that of H2H devices. Smart cities are an example of such a mass-scale deployment. These features impose various challenges related to efficient energy management, enhanced coverage and diverse quality of service (QoS) provisioning, among others. The diverse applications of M2M are going to lead to exponential growth in M2M traffic. Associating with M2M deployment, a massive number of devices are expected to access the wireless network concurrently. Hence, a network congestion is likely to occur. Cellular networks have been recognized as excellent candidates for M2M support. Indeed, cellular networks are mature, well-established networks with ubiquitous coverage and reliability which allows cost-effective deployment of M2M communications. However, cellular networks were originally designed for human-centric services with high-cost devices and ever-increasing rate requirements. Additionally, the conventional random access (RA) mechanism used in Long Term Evolution-Advanced (LTE-A) networks lacks the capability of handling such an enormous number of access attempts expected from massive MTC. Particularly, this RA technique acts as a performance bottleneck due to the frequent collisions that lead to excessive delay and resource wastage. Also, the lengthy handshaking process of the conventional RA technique results in highly expensive signaling, specifically for M2M devices with small payloads. Therefore, designing an efficient medium access schemes is critical for the survival of M2M networks. In this thesis, we study the uplink access of M2M devices with a focus on overload control and congestion handling. In this regard, we mainly provide two different access techniques keeping in mind the distinct features and requirements of MTC including massive connectivity, latency reduction, and energy management. In fact, full information gathering is known to be impractical for such massive networks of tremendous number of devices. Hence, we assure to preserve the low complexity, and limited information exchange among different network entities by introducing distributed techniques. Furthermore, machine learning is also employed to enhance the performance with no or limited information exchange at the decision maker. The proposed techniques are assessed via extensive simulations as well as rigorous analytical frameworks. First, we propose an efficient distributed overload control algorithm for M2M with massive access, referred to as M2M-OSA. The proposed algorithm can efficiently allocate the available network resources to massive number of devices within relatively small, and bounded contention time and with reduced overhead. By resolving collisions, the proposed algorithm is capable of achieving full resources utilization along with reduced average access delay and energy saving. For Beta-distributed traffic, we provide analytical evaluation for the performance of the proposed algorithm in terms of the access delay, total service time, energy consumption, and blocking probability. This performance assessment accounted for various scenarios including slightly, and seriously congested cases, in addition to finite and infinite retransmission limits for the devices. Moreover, we provide a discussion of the non-ideal situations that could be encountered in real-life deployment of the proposed algorithm supported by possible solutions. For further energy saving, we introduced a modified version of M2M-OSA with traffic regulation mechanism. In the second part of the thesis, we adopt a promising alternative for the conventional random access mechanism, namely fast uplink grant. Fast uplink grant was first proposed by the 3GPP for latency reduction where it allows the base station (BS) to directly schedule the MTC devices (MTDs) without receiving any scheduling requests. In our work, to handle the major challenges associated to fast uplink grant namely, active set prediction and optimal scheduling, both non-orthogonal multiple access (NOMA) and learning techniques are utilized. Particularly, we propose a two-stage NOMA-based fast uplink grant scheme that first employs multi-armed bandit (MAB) learning to schedule the fast grant devices with no prior information about their QoS requirements or channel conditions at the BS. Afterwards, NOMA facilitates the grant sharing where pairing is done in a distributed manner to reduce signaling overhead. In the proposed scheme, NOMA plays a major role in decoupling the two major challenges of fast grant schemes by permitting pairing with only active MTDs. Consequently, the wastage of the resources due to traffic prediction errors can be significantly reduced. We devise an abstraction model for the source traffic predictor needed for fast grant such that the prediction error can be evaluated. Accordingly, the performance of the proposed scheme is analyzed in terms of average resource wastage, and outage probability. The simulation results show the effectiveness of the proposed method in saving the scarce resources while verifying the analysis accuracy. In addition, the ability of the proposed scheme to pick quality MTDs with strict latency is depicted

Performance improvement of SS-WDM passive optical networks using semiconductor optical amplifiers: Modeling and experiment

Les sources incohérentes sont proposées comme alternatives aux lasers stabilisés en longueur d'onde pour réduire le coût des réseaux optiques passifs utilisant le multiplexage par longueur d'onde découpée dans le spectre (SS-WDM PONs). À cause de leur nature incohérente, ces sources génèrent au récepteur un large bruit d'intensité. Ce bruit limite l'efficacité spectrale et/ou le taux binaire pouvant être achevé. Cette thèse étudie l'utilisation des amplificateurs optique à semi-conducteur SOAs pour nettoyer le bruit d'intensité. De plus, lors de cette thèse, nous explorons les outils numériques et expérimentaux qui nous permettent d'analyser les performances des SOAs dans le cadre de systèmes de communication multi-canaux, incluant le SS-WDM. Nous présentons des modèles mathématiques pour le bruit d'intensité, ce bruit étant celui qui limite les performances des systèmes de communication utilisant des sources incohérentes. Nous discutons les dynamiques complexes des SOAs et présentons les équations qui gouvernent l'évolution des porteurs de charges dans ces amplificateurs. Nous identifions et soulignons l'effet des paramètres les plus importants, qui affectent le processus ainsi que la dynamique de nettoyage du bruit d'intensité. Nous passons en revue, les différentes techniques de nettoyage de bruit avec les SOAs, qui ont démontré les meilleurs résultats connus. De plus, nous effectuons une revue de littérature poussée pour ce qui a attrait au problème de post-filtrage lorsque le SOA est placé au transmetteur, avant la modulation. Notre première contribution pendant ce travail de recherche est de démontrer, en utilisant l'intermodulation de gain d'un SOA au récepteur pour convertir le signal incohérent en laser cohérent, une amélioration significative du taux d'erreur binaire BER. Cette méthode est spectralement efficace, d'autant plus qu'elle ne souffre point la limitation occasionnée par le post-filtrage au récepteur. En contre partie elle nécessite un ample budget de puissance qui doit assurer une saturation suffisante de l'amplificateur au récepteur. Une source laser est aussi nécessaire au récepteur. Ceci est un inconvénient, même si une telle source n'ait pas besoin d'une quelconque stabilisation. Pour contourner le problème causé par le post-filtrage quand le SOA est au transmetteur, nous proposons un nouveau récepteur pour les systèmes de communication WDM, qui permet de mieux garder le nettoyage de bruit, et ce malgré le filtrage optique au récepteur. La nouvelle méthode consiste en un détecteur balancé utilisé au récepteur: d'un bord, tous les canaux sont détectés sans distinction. De l'autre, le signal désiré est bloqué pendant que tous les autres canaux sont détectés. Avec cette méthode, il est facile de saturer l'amplificateur pour une meilleure suppression de bruit tout en évitant en grande partie la dégradation causé par le post-filtrage. Nous avons expérimentalement démontré un système WDM dense de 8 x 10 Gbps avec une source incohérente et un SOA en saturation. Une autre contribution originale de ce travail est le développement d'un outil de simulation pour les SOAs qui est numériquement plus efficace et léger que les modèles connus à ce jour. Nous avons donc développé un modèle théorique simple, pouvant être implémenté par diagramme block, dans le but de simuler les performances des hens de communications WDM. Notre modèle démontre une bonne concordance avec les résultats expérimentaux ainsi qu'une diminution de temps de calcul de l'ordre de 20 fois. Finalement, lors de la dernière partie de ces travaux, nous nous sommes occupés de mesurer, de façon précise, le temps de recouvrement du gain dans un SOA. Le temps de recouvrement des porteurs dans un SOA est un des paramètres les plus importants qui sont à l'origine du phénomène de nettoyage de bruit et qui régissent le comportement ainsi que les dynamiques de l'amplificateur. Nous avons étudié en particulier, la dépendance de ce temps de recouvrement r de la longueur d'onde. Pour le SOA utilisé lors de notre étude expérimentale, nous avons démontré que r dépendait de la longueur d'onde de façon similaire au spectre de gain. Ces mesures ont été possibles grâce au développement d'un nouveau dispositif de mesure pompe/sonde, qui permettait de mesurer le recouvrement du gain pour une pompe et une sonde à la même longueur d'onde et ayant le même état de polarisation