Minimizing Energy Consumption in MU-MIMO via Antenna Muting by Neural Networks with Asymmetric Loss

Transmit antenna muting (TAM) in multiple-user multiple-input multiple-output (MU-MIMO) networks allows reducing the power consumption of the base station (BS) by properly utilizing only a subset of antennas in the BS. In this paper, we consider the downlink transmission of an MU-MIMO network where TAM is formulated to minimize the number of active antennas in the BS while guaranteeing the per-user throughput requirements. To address the computational complexity of the combinatorial optimization problem, we propose an algorithm called neural antenna muting (NAM) with an asymmetric custom loss function. NAM is a classification neural network trained in a supervised manner. The classification error in this scheme leads to either sub-optimal energy consumption or lower quality of service (QoS) for the communication link. We control the classification error probability distribution by designing an asymmetric loss function such that the erroneous classification outputs are more likely to result in fulfilling the QoS requirements. Furthermore, we present three heuristic algorithms and compare them with the NAM. Using a 3GPP compliant system-level simulator, we show that NAM achieves $\sim73\%$ energy saving compared to the full antenna configuration in the BS with $\sim95\%$ reliability in achieving the user throughput requirements while being around $1000\times$ and $24\times$ less computationally intensive than the greedy heuristic algorithm and the fixed column antenna muting algorithm, respectively.Comment: Submitted to IEEE Transactions on Vehicular Technolog

Ultra Dense Networks Deployment for beyond 2020 Technologies

A new communication paradigm is foreseen for beyond 2020 society, due to the emergence of new broadband services and the Internet of Things era. The set of requirements imposed by these new applications is large and diverse, aiming to provide a ubiquitous broadband connectivity. Research community has been working in the last decade towards the definition of the 5G mobile wireless networks that will provide the proper mechanisms to reach these challenging requirements. In this framework, three key research directions have been identified for the improvement of capacity in 5G: the increase of the spectral efficiency by means of, for example, the use of massive MIMO technology, the use of larger amounts of spectrum by utilizing the millimeter wave band, and the network densification by deploying more base stations per unit area. This dissertation addresses densification as the main enabler for the broadband and massive connectivity required in future 5G networks. To this aim, this Thesis focuses on the study of the UDN. In particular, a set of technology enablers that can lead UDN to achieve their maximum efficiency and performance are investigated, namely, the use of higher frequency bands for the benefit of larger bandwidths, the use of massive MIMO with distributed antenna systems, and the use of distributed radio resource management techniques for the inter-cell interference coordination. Firstly, this Thesis analyzes whether there exists a fundamental performance limit related with densification in cellular networks. To this end, the UDN performance is evaluated by means of an analytical model consisting of a 1-dimensional network deployment with equally spaced BS. The inter-BS distance is decreased until reaching the limit of densification when this distance approaches 0. The achievable rates in networks with different inter-BS distances are analyzed for several levels of transmission power availability, and for various types of cooperation among cells. Moreover, UDN performance is studied in conjunction with the use of a massive number of antennas and larger amounts of spectrum. In particular, the performance of hybrid beamforming and precoding MIMO schemes are assessed in both indoor and outdoor scenarios with multiple cells and users, working in the mmW frequency band. On the one hand, beamforming schemes using the full-connected hybrid architecture are analyzed in BS with limited number of RF chains, identifying the strengths and weaknesses of these schemes in a dense-urban scenario. On the other hand, the performance of different indoor deployment strategies using HP in the mmW band is evaluated, focusing on the use of DAS. More specifically, a DHP suitable for DAS is proposed, comparing its performance with that of HP in other indoor deployment strategies. Lastly, the presence of practical limitations and hardware impairments in the use of hybrid architectures is also investigated. Finally, the investigation of UDN is completed with the study of their main limitation, which is the increasing inter-cell interference in the network. In order to tackle this problem, an eICIC scheduling algorithm based on resource partitioning techniques is proposed. Its performance is evaluated and compared to other scheduling algorithms under several degrees of network densification. After the completion of this study, the potential of UDN to reach the capacity requirements of 5G networks is confirmed. Nevertheless, without the use of larger portions of spectrum, a proper interference management and the use of a massive number of antennas, densification could turn into a serious problem for mobile operators. Performance evaluation results show large system capacity gains with the use of massive MIMO techniques in UDN, and even greater when the antennas are distributed. Furthermore, the application of ICIC techniques reveals that, besides the increase in system capacity, it brings significant energy savings to UDNs.A partir del año 2020 se prevé que un nuevo paradigma de comunicación surja en la sociedad, debido a la aparición de nuevos servicios y la era del Internet de las cosas. El conjunto de requisitos impuesto por estas nuevas aplicaciones es muy amplio y diverso, y tiene como principal objetivo proporcionar conectividad de banda ancha y universal. En las últimas décadas, la comunidad científica ha estado trabajando en la definición de la 5G de redes móviles que brindará los mecanismos necesarios para garantizar estos requisitos. En este marco, se han identificado tres mecanismos clave para conseguir el necesario incremento de capacidad de la red: el aumento de la eficiencia espectral a través de, por ejemplo, el uso de tecnologías MIMO masivas, la utilización de mayores porciones del espectro en frecuencia y la densificación de la red mediante el despliegue de más estaciones base por área. Esta Tesis doctoral aborda la densificación como el principal mecanismo que permitirá la conectividad de banda ancha y universal requerida en la 5G, centrándose en el estudio de las Redes Ultra Densas o UDNs. En concreto, se analiza el conjunto de tecnologías habilitantes que pueden llevar a las UDNs a obtener su máxima eficiencia y prestaciones, incluyendo el uso de altas frecuencias para el aprovechamiento de mayores anchos de banda, la utilización de MIMO masivo con sistemas de antenas distribuidas y el uso de técnicas de reparto de recursos distribuidas para la coordinación de interferencias. En primer lugar, se analiza si existe un límite fundamental en la mejora de las prestaciones en relación a la densificación. Con este fin, las prestaciones de las UDNs se evalúan utilizando un modelo analítico de red unidimensional con BSs equiespaciadas, en el que la distancia entre BSs se disminuye hasta alcanzar el límite de densificación cuando ésta se aproxima a 0. Las tasas alcanzables en redes con distintas distancias entre BSs son analizadas, considerando distintos niveles de potencia disponible en la red y varios grados de cooperación entre celdas. Además, el comportamiento de las UDNs se estudia junto al uso masivo de antenas y la utilización de anchos de banda mayores. Más concretamente, las prestaciones de ciertas técnicas híbridas MIMO de precodificación y beamforming se examinan en la banda milimétrica. Por una parte, se analizan esquemas de beamforming en BSs con arquitectura híbrida en función de la disponibilidad de cadenas de radiofrecuencia en escenarios exteriores. Por otra parte, se evalúan las prestaciones de ciertos esquemas de precodificación híbrida en escenarios interiores, utilizando distintos despliegues y centrando la atención en los sistemas de antenas distribuidos o DAS. Además, se propone un algoritmo de precodificación híbrida específico para DAS, y se evalúan y comparan sus prestaciones con las de otros algoritmos de precodificación utilizados. Por último, se investiga el impacto en las prestaciones de ciertas limitaciones prácticas y deficiencias introducidas por el uso de dispositivos no ideales. Finalmente, el estudio de las UDNs se completa con el análisis de su principal limitación, el nivel creciente de interferencia en la red. Para ello, se propone un algoritmo de control de interferencias basado en la partición de recursos. Sus prestaciones son evaluadas y comparadas con las de otras técnicas de asignación de recursos. Tras este estudio, se puede afirmar que las UDNs tienen gran potencial para la consecución de los requisitos de la 5G. Sin embargo, sin el uso conjunto de mayores porciones del espectro, adecuadas técnicas de control de la interferencia y el uso masivo de antenas, las UDNs pueden convertirse en serios obstáculos para los operadores móviles. Los resultados de la evaluación de prestaciones de estas tecnologías confirman el gran aumento de la capacidad de las redes mediante el uso masivo de antenas y la introducción de mecanismos de IA partir de l'any 2020 es preveu un nou paradigma de comunicació en la societat, degut a l'aparició de nous serveis i la era de la Internet de les coses. El conjunt de requeriments imposat per aquestes noves aplicacions és ampli i divers, i té com a principal objectiu proporcionar connectivitat universal i de banda ampla. En les últimes dècades, la comunitat científica ha estat treballant en la definició de la 5G, que proveirà els mecanismes necessaris per a garantir aquests exigents requeriments. En aquest marc, s'han identificat tres mecanismes claus per a aconseguir l'increment necessari en la capacitat: l'augment de l'eficiència espectral a través de, per exemple, l'ús de tecnologies MIMO massives, la utilització de majors porcions de l'espectre i la densificació mitjançant el desplegament de més estacions base per àrea. Aquesta Tesi aborda la densificació com a principal mecanisme que permetrà la connectivitat de banda ampla i universal requerida en la 5G, centrant-se en l' estudi de les xarxes ultra denses (UDNs). Concretament, el conjunt de tecnologies que poden dur a les UDNs a la seua màxima eficiència i prestacions és analitzat, incloent l'ús d'altes freqüències per a l'aprofitament de majors amplàries de banda, la utilització de MIMO massiu amb sistemes d'antenes distribuïdes i l'ús de tècniques distribuïdes de repartiment de recursos per a la coordinació de la interferència. En primer lloc, aquesta Tesi analitza si existeix un límit fonamental en les prestacions en relació a la densificació. Per això, les prestacions de les UDNs s'avaluen utilitzant un model analític unidimensional amb estacions base equidistants, en les quals la distància entre estacions base es redueix fins assolir el límit de densificació quan aquesta distància s'aproxima a 0. Les taxes assolibles en xarxes amb diferents distàncies entre estacions base s'analitzen considerant diferents nivells de potència i varis graus de cooperació entre cel·les. A més, el comportament de les UDNs s'estudia conjuntament amb l'ús massiu d'antenes i la utilització de majors amplàries de banda. Més concretament, les prestacions de certes tècniques híbrides MIMO de precodificació i beamforming s'examinen en la banda mil·limètrica. D'una banda, els esquemes de beamforming aplicats a estacions base amb arquitectures híbrides és analitzat amb disponibilitat limitada de cadenes de radiofreqüència a un escenari urbà dens. D'altra banda, s'avaluen les prestacions de certs esquemes de precodificació híbrida en escenaris d'interior, utilitzant diferents estratègies de desplegament i centrant l'atenció en els sistemes d' antenes distribuïdes (DAS). A més, es proposa un algoritme de precodificació híbrida distribuïda per a DAS, i s'avaluen i comparen les seues prestacions amb les de altres algoritmes. Per últim, s'investiga l'impacte de les limitacions pràctiques i altres deficiències introduïdes per l'ús de dispositius no ideals en les prestacions de tots els esquemes anteriors. Finalment, l' estudi de les UDNs es completa amb l'anàlisi de la seua principal limitació, el nivell creixent d'interferència entre cel·les. Per tractar aquest problema, es proposa un algoritme de control d'interferències basat en la partició de recursos. Les prestacions de l'algoritme proposat s'avaluen i comparen amb les d'altres tècniques d'assignació de recursos. Una vegada completat aquest estudi, es pot afirmar que les UDNs tenen un gran potencial per aconseguir els ambiciosos requeriments plantejats per a la 5G. Tanmateix, sense l'ús conjunt de majors amplàries de banda, apropiades tècniques de control de la interferència i l'ús massiu d'antenes, les UDNs poden convertir-se en seriosos obstacles per als operadors mòbils. Els resultats de l'avaluació de prestacions d' aquestes tecnologies confirmen el gran augment de la capacitat de les xarxes obtingut mitjançant l'ús massiu d'antenes i la introducciGiménez Colás, S. (2017). Ultra Dense Networks Deployment for beyond 2020 Technologies [Tesis doctoral no publicada]. Universitat Politècnica de València. https://doi.org/10.4995/Thesis/10251/86204TESI

Multidimensional Graph Neural Networks for Wireless Communications

Graph neural networks (GNNs) have been shown promising in improving the efficiency of learning communication policies by leveraging their permutation properties. Nonetheless, existing works design GNNs only for specific wireless policies, lacking a systematical approach for modeling graph and selecting structure. Based on the observation that the mismatched permutation property from the policies and the information loss during the update of hidden representations have large impact on the learning performance and efficiency, in this paper we propose a unified framework to learn permutable wireless policies with multidimensional GNNs. To avoid the information loss, the GNNs update the hidden representations of hyper-edges. To exploit all possible permutations of a policy, we provide a method to identify vertices in a graph. We also investigate the permutability of wireless channels that affects the sample efficiency, and show how to trade off the training, inference, and designing complexities of GNNs. We take precoding in different systems as examples to demonstrate how to apply the framework. Simulation results show that the proposed GNNs can achieve close performance to numerical algorithms, and require much fewer training samples and trainable parameters to achieve the same learning performance as the commonly used convolutional neural networks

Angle-of-Arrival Measurement Techniques for Enhanced Positioning in Beyond 5G Systems

The new generation of mobile communication systems introduces new methods and technologies that may enhance positioning accuracy in some scenarios when the GNSS system cannot meet the requirements, such as indoor positioning and outdoor autonomous driving. The 3GPP standard and for the first time included the angle measurement as new positioning methods in 5G. The Angle of Arrival (AoA) is the angle measurement method on the uplink direction that can enjoy the new capabilities in 5G systems to enhance the positioning downs to centimeters.The new generation of mobile communication systems introduces new methods and technologies that may enhance positioning accuracy in some scenarios when the GNSS system cannot meet the requirements, such as indoor positioning and outdoor autonomous driving. The 3GPP standard and for the first time included the angle measurement as new positioning methods in 5G. The Angle of Arrival (AoA) is the angle measurement method on the uplink direction that can enjoy the new capabilities in 5G systems to enhance the positioning downs to centimeters. Multiple Signal Classification Method (MUSIC) is a high-accuracy super-resolution algorithm for AoA estimation. The MUSIC method for estimating AoA has many shortcomings that make it unsuitable for a wide variety of scenarios. Correlated multipath signals substantially reduce estimation accuracy. Additionally, this method is a searching algorithm that requires a significant amount of time to resolve AoA. In this thesis, a CASCADE algorithm was proposed to overcome MUSIC's constraints by estimating a coarse range of AoA using a rapid AoA algorithm and then passing that range to the second stage represented by MUSIC to estimate AoA correctly. Multipath signals were eliminated by modifying the proposed CASCADE to detect only the line of sight (LOS), which is the essential path for angular localization. Additionally, the thesis compares many AoA algorithms in the context of 5G systems. A sounding reference signal (SRS) in the mm-wave band was generated according to the 3GPP standards and utilized as the input to those algorithms. A simulation was conducted throughout this thesis by evaluating six AoA algorithms: Bartlet Beamforming, MVDR, MUSIC, ESPRIT, FFT, and the proposed CASCADE method. The results showed that the proposed algorithm achieves the best performance when using less than 64 array antenna elements. On the other hand, FFT alone can provide high accuracy when using an ultra massive antenna system (e.g., 256,512,1024). Additionally, the findings observed the effect of key parameters on the performance of AoA algorithms, such as low SNR, a small number of snapshots (samples), and the effect of multipath signals

Mobility management in multi-RAT multiI-band heterogeneous networks

Support for user mobility is the raison d'etre of mobile cellular networks. However, mounting pressure for more capacity is leading to adaption of multi-band multi-RAT ultra-dense network design, particularly with the increased use of mmWave based small cells. While such design for emerging cellular networks is expected to offer manyfold more capacity, it gives rise to a new set of challenges in user mobility management. Among others, frequent handovers (HO) and thus higher impact of poor mobility management on quality of user experience (QoE) as well as link capacity, lack of an intelligent solution to manage dual connectivity (of user with both 4G and 5G cells) activation/deactivation, and mmWave cell discovery are the most critical challenges. In this dissertation, I propose and evaluate a set of solutions to address the aforementioned challenges. The beginning outcome of our investigations into the aforementioned problems is the first ever taxonomy of mobility related 3GPP defined network parameters and Key Performance Indicators (KPIs) followed by a tutorial on 3GPP-based 5G mobility management procedures. The first major contribution of the thesis here is a novel framework to characterize the relationship between the 28 critical mobility-related network parameters and 8 most vital KPIs. A critical hurdle in addressing all mobility related challenges in emerging networks is the complexity of modeling realistic mobility and HO process. Mathematical models are not suitable here as they cannot capture the dynamics as well as the myriad parameters and KPIs involved. Existing simulators also mostly either omit or overly abstract the HO and user mobility, chiefly because the problems caused by poor HO management had relatively less impact on overall performance in legacy networks as they were not multi-RAT multi-band and therefore incurred much smaller number of HOs compared to emerging networks. The second key contribution of this dissertation is development of a first of its kind system level simulator, called SyntheticNET that can help the research community in overcoming the hurdle of realistic mobility and HO process modeling. SyntheticNET is the very first python-based simulator that fully conforms to 3GPP Release 15 5G standard. Compared to the existing simulators, SyntheticNET includes a modular structure, flexible propagation modeling, adaptive numerology, realistic mobility patterns, and detailed HO evaluation criteria. SyntheticNET’s python-based platform allows the effective application of Artificial Intelligence (AI) to various network functionalities. Another key challenge in emerging multi-RAT technologies is the lack of an intelligent solution to manage dual connectivity with 4G as well 5G cell needed by a user to access 5G infrastructure. The 3rd contribution of this thesis is a solution to address this challenge. I present a QoE-aware E-UTRAN New Radio-Dual Connectivity (EN-DC) activation scheme where AI is leveraged to develop a model that can accurately predict radio link failure (RLF) and voice muting using the low-level measurements collected from a real network. The insights from the AI based RLF and mute prediction models are then leveraged to configure sets of 3GPP parameters to maximize EN-DC activation while keeping the QoE-affecting RLF and mute anomalies to minimum. The last contribution of this dissertation is a novel solution to address mmWave cell discovery problem. This problem stems from the highly directional nature of mmWave transmission. The proposed mmWave cell discovery scheme builds upon a joint search method where mmWave cells exploit an overlay coverage layer from macro cells sharing the UE location to the mmWave cell. The proposed scheme is made more practical by investigating and developing solutions for the data sparsity issue in model training. Ability to work with sparse data makes the proposed scheme feasible in realistic scenarios where user density is often not high enough to provide coverage reports from each bin of the coverage area. Simulation results show that the proposed scheme, efficiently activates EN-DC to a nearby mmWave 5G cell and thus substantially reduces the mmWave cell discovery failures compared to the state of the art cell discovery methods

Multiantenna Downlink Interference Management for Next Generation Mobile Networks

Department of Electrical EngineeringIn downlink multi-input single-output (MISO) networks, achieving optimal sum-rate with limited channel state information (CSI) is still a challenge even with a single user per cell. In this dissertation, three cooperative downlink multicell MISO beamforming schemes are proposed with highly limited information exchange among the base stations (BSs) to maximize the sum-rate. In the proposed schemes, each BS can design its beamforming vector with only local CSI based on limited information exchange on CSI. Unlike previous studies, the proposed beamforming designs are non-iterative and do not require any vector or matrix feedback but require only quantized scalar information. In the first work, the beamforming vector at each BS is designed to minimize the sum of its weighted generating-interference (WGI) with local CSI and the aid of information exchange between the BSs. The generating-interference weight coefficients are designed in pursuit of increasing the sum-rate. Simulation results show that the proposed scheme outperforms the existing scheme in the mid to high signal-to-noise ratio (SNR) regime even with much reduced amount of information exchange via backhaul. In the second work, the proposed beamforming design is based on the combination of the maximization of weighted signal-to-leakage-plus-noise ratio (WSLNR) and WGI. The weights in WSLNR and WGI are designed via choosing a proper set of users who shall be interference-free, which has never been endeavored in the literature. Though there have been extensive studies on downlink multicell beamforming, the proposed scheme closely achieves the optimal sum-rate bound in almost all SNR regime based on non-iterative optimization with lower amount of information exchange than existing schemes, which is justified by numerical simulations. In addition, the proposed scheme achieves a better trade-off between the amount of the information exchange and the sum-rate than existing schemes. In the third work, a beamforming vector design based on a deep neural network (DNN) is proposed for multicell multi-input single-output channels with scalar information exchange and local CSI. The beamforming vectors are designed making zero generating-interference to the selected interference-free users (IFUs). The set of IFUs is chosen from the DNN based on supervised learning where the inputs can be obtained with only local CSI and limited scalar information exchange. Simulation results show that the DNN is well-trained in estimating the unknown CSI from the inputs with only local CSI in multicell networks.clos

Energy-Efficient and Robust Hybrid Analog-Digital Precoding for Massive MIMO Systems

The fifth-generation (5G) and future cellular networks are expected to facilitate wireless communication among tens of billions of devices with enormously high data rate and ultra-high reliability. At the same time, these networks are required to embrace green technology by significantly improving the energy efficiency of wireless communication to reduce their carbon footprint. The massive multiple-input multiple-output (MIMO) systems, in which the base stations are equipped with hundreds of antenna elements, can provide immensely high data rates and support a large number of users by employing the precoding at the base stations. However, the conventional precoding techniques - which require a dedicated radio-frequency chain for each antenna element - become prohibitively expensive for massive MIMO systems. To address this shortcoming, the hybrid analog-digital precoding architecture is proposed, which requires fewer radio-frequency chains than the antenna elements. The reduced hardware costs in this novel architecture, however, comes at the expense of reduced degrees of freedom for the precoding, which deteriorates the energy efficiency of the network. In this thesis, we consider the design of energy-efficient hybrid precoding techniques in multiuser downlink massive MIMO systems. These systems are fundamentally interference limited. To mitigate the interference, we adopt two interference management strategies while designing the hybrid precoding schemes. They are, namely, interference suppression-based hybrid precoding, and interference exploitation-based hybrid precoding. The former approach results in a lower computational complexity - as the resulting precoders remain the same as long as the channel is unchanged when compared to the latter approach. On the other hand, the interference exploitation-based hybrid precoding is more energy efficient due to judicious use of transmit symbol information, as compared to the interference suppression-based hybrid precoding. In the hybrid analog-digital precoding, analog precoders are implemented in analog radio-frequency domain using a large number of phase shifters, which are relatively inexpensive. These phase shifters, however, typically suffer from artifacts; their actual values differ from their nominal values. These imperfect phase shifters can lead to symbol estimation errors at the users, which may not be tolerable in many applications of future cellular networks. To establish a high-reliable communication under the plight of imperfect phase shifters in the hybrid precoding architecture, in this thesis, we propose an energy-efficient, robust hybrid precoding technique. The designed scheme guarantees 100% robustness against the considered hardware artifacts. Moreover, the thesis demonstrates that the proposed technique can save up to 12% transmit power when compared to a conventional method. Another critically important requirement of the future cellular networks - apart from ultra-high reliability and energy efficiency - is ultra-low latency. Some envisioned extreme real-time applications of 5G, such as autonomous driving and remote surgery, demand an end-to-end latency smaller than one millisecond. To fulfill such a stringent demand, we devise an efficient implementation scheme for the proposed robust hybrid precoding technique to reduce the required computational time. The devised scheme exploits special structures present in the algorithm to reduce the computational complexity and can compute the precoders in a distributed manner on a parallel hardware architecture. The results show that the proposed implementation scheme can reduce the average computation time of the algorithm by 35% when compared to a state-of-the-art method. Finally, we consider the hybrid precoding in heterogeneous networks, where the cell edge users typically experience severe interference. We propose a coordinated hybrid precoding technique based on the interference exploitation approach. The numerical results reveal that the proposed coordinated hybrid precoding results in a significant transmit power savings when compared to the uncoordinated hybrid precoding