Design and Realization of Fully-digital Microwave and Mm-wave Multi-beam Arrays with FPGA/RF-SOC Signal Processing

There has been a constant increase in data-traffic and device-connections in mobile wireless communications, which led the fifth generation (5G) implementations to exploit mm-wave bands at 24/28 GHz. The next-generation wireless access point (6G and beyond) will need to adopt large-scale transceiver arrays with a combination of multi-input-multi-output (MIMO) theory and fully digital multi-beam beamforming. The resulting high gain array factors will overcome the high path losses at mm-wave bands, and the simultaneous multi-beams will exploit the multi-directional channels due to multi-path effects and improve the signal-to-noise ratio. Such access points will be based on electronic systems which heavily depend on the integration of RF electronics with digital signal processing performed in Field programmable gate arrays (FPGA)/ RF-system-on-chip (SoC). This dissertation is directed towards the investigation and realization of fully-digital phased arrays that can produce wideband simultaneous multi-beams with FPGA or RF-SoC digital back-ends. The first proposed approach is a spatial bandpass (SBP) IIR filter-based beamformer, and is based on the concepts of space-time network resonance. A 2.4 GHz, 16-element array receiver, has been built for real-time experimental verification of this approach. The second and third approaches are respectively based on Discrete Fourier Transform (DFT) theory, and a lens plus focal planar array theory. Lens based approach is essentially an analog model of DFT. These two approaches are verified for a 28 GHz 800 MHz mm-wave implementation with RF-SoC as the digital back-end. It has been shown that for all proposed multibeam beamformer implementations, the measured beams are well aligned with those of the simulated. The proposed approaches differ in terms of their architectures, hardware complexity and costs, which will be discussed as this dissertation opens up. This dissertation also presents an application of multi-beam approaches for RF directional sensing applications to explore white spaces within the spatio-temporal spectral regions. A real-time directional sensing system is proposed to capture the white spaces within the 2.4 GHz Wi-Fi band. Further, this dissertation investigates the effect of electro-magnetic (EM) mutual coupling in antenna arrays on the real-time performance of fully-digital transceivers. Different algorithms are proposed to uncouple the mutual coupling in digital domain. The first one is based on finding the MC transfer function from the measured S-parameters of the antenna array and employing it in a Frost FIR filter in the beamforming backend. The second proposed method uses fast algorithms to realize the inverse of mutual coupling matrix via tridiagonal Toeplitz matrices having sparse factors. A 5.8 GHz 32-element array and 1-7 GHz 7-element tightly coupled dipole array (TCDA) have been employed to demonstrate the proof-of-concept of these algorithms

SYMBOL LEVEL PRECODING TECHNIQUES FOR HARDWARE AND POWER EFFICIENT WIRELESS TRANSCEIVERS

Large-scale antennas are crucial for next generation wireless communication systems as they improve spectral efficiency, reliability and coverage compared to the traditional ones that are employing antenna arrays of few elements. However, the large number of antenna elements leads to a big increase in power consumption of conventional fully digital transceivers due to the one Radio Frequency (RF) chain / per antenna element requirement. The RF chains include a number of different components among which are the Digital-to-Analog Converters (DACs)/Analog-to-Digital Converters (ADCs) that their power consumption increases exponential with the resolution they support. Motivated by this, in this thesis, a number of different architectures are proposed with the view to reduce the power consumption and the hardware complexity of the transceiver. In order to optimize the transmission of data through them, corresponding symbol level precoding (SLP) techniques were developed for the proposed architectures. SLP is a technique that mitigates multi-user interference (MUI) by designing the transmitted signals using the Channel State Information and the information-bearing symbols. The cases of both frequency flat and frequency selective channels were considered. First, three different power efficient transmitter designs for transmission over frequency flat channels and their respective SLP schemes are considered. The considered systems tackle the high hardware complexity and power consumption of existing SLP techniques by reducing or completely eliminating fully digital RF chains. The precoding design is formulated as a constrained least squares problem and efficient algorithmic solutions are developed via the Coordinate Descent method. Next, the case of frequency selective channels is considered. To this end, Constant Envelope precoding in a Multiple Input Multiple Output Orthogonal Frequency Division Multiplexing system (CE MIMO-OFDM) is considered. In CE MIMO-OFDM the transmitted signals for each antenna are designed to have constant amplitude regardless of the channel realization and the information symbols that must be conveyed to the users. This facilitates the use of power-efficient components, such as phase shifters and non-linear power amplifiers. The precoding problem is firstly formulated as a least-squares problem with a unit-modulus constraint and solved using an algorithm based on the coordinate descent (CCD) optimization framework and then, after reformulating the problem into an unconstrained non-linear least squares problem, a more computationally efficient solution using the Gauss-Newton algorithm is presented. Then, CE MIMO-OFDM is considered for a system with low resolution DACs. The precoding design problem is formulated as a mixed discrete- continuous least-squares optimization one which is NP-hard. An efficient low complexity solution is developed based also on the CCD optimization framework. Finally, a precoding scheme is presented for OFDM transmission in MIMO systems based on one-bit DACs and ADCs at the transmitter’s and the receiver’s end, respectively, as a way to reduce the total power consumption. The objective of the precoding design is to mitigate the effects of one-bit quantization and the problem is formulated and then is split into two NP hard least squares optimization problems. Algorithmic solutions are developed for the solution of the latter problems, based on the CCD framework

Opportunistic communications in large uncoordinated networks

(English) The increase of wireless devices offering high data rate services limits the coexistence of wireless systems sharing the same resources in a given geographical area because of inter-system interference. Therefore, interference management plays a key role in permitting the coexistence of several heterogeneous communication services. However, classical interference management strategies require lateral information giving rise to the need for inter-system coordination and cooperation, which is not always practical. Opportunistic communications offer a potential solution to the problem of inter-system interference management. The basic principle of opportunistic communications is to efficiently and robustly exploit the resources available in a wireless network and adapt the transmitted signals to the state of the network to avoid inter-system interference. Therefore, opportunistic communications depend on inferring the available network resources that can be safely exploited without inducing interference in coexisting communication nodes. Once the available network resources are identified, the most prominent opportunistic communication techniques consist in designing scenario-adapted precoding/decoding strategies to exploit the so-called null space. Despite this, classical solutions in the literature suffer from two main drawbacks: the lack of robustness to detection errors and the need for intra-system cooperation. This thesis focuses on the design of a null space-based opportunistic communication scheme that addresses the drawbacks exhibited by existing methodologies under the assumption that opportunistic nodes do not cooperate. For this purpose, a generalized detection error model independent of the null-space identification mechanism is introduced that allows the design of solutions that exhibit minimal inter-system interference in the worst case. These solutions respond to a maximum signal-to-interference ratio (SIR) criterion, which is optimal under non-cooperative conditions. The proposed methodology allows the design of a family of orthonormal waveforms that perform a spreading of the modulated symbols within the detected null space, which is key to minimizing the induced interference density. The proposed solutions are invariant within the inferred null space, allowing the removal of the feedback link without giving up coherent waveform detection. In the absence of coordination, the waveform design relies solely on locally sensed network state information, inducing a mismatch between the null spaces identified by the transmitter and receiver that may worsen system performance. Although the proposed solution is robust to this mismatch, the design of enhanced receivers using active subspace detection schemes is also studied. When the total number of network resources increases arbitrarily, the proposed solutions tend to be linear combinations of complex exponentials, providing an interpretation in the frequency domain. This asymptotic behavior allows us to adapt the proposed solution to frequency-selective channels by means of a cyclic prefix and to study an efficient modulation similar to the time division multiplexing scheme but using circulant waveforms. Finally, the impact of the use of multiple antennas in opportunistic null space-based communications is studied. The performed analysis reveals that, in any case, the structure of the antenna clusters affects the opportunistic communication, since the proposed waveform mimics the behavior of a single-antenna transmitter. On the other hand, the number of sensors employed translates into an improvement in terms of SIR.(Català) El creixement incremental dels dispositius sense fils que requereixen serveis d'alta velocitat de dades limita la coexistència de sistemes sense fils que comparteixen els mateixos recursos en una àrea geogràfica donada a causa de la interferència entre sistemes. Conseqüentment, la gestió d'interferència juga un paper fonamental per a facilitar la coexistència de diversos serveis de comunicació heterogenis. No obstant això, les estratègies clàssiques de gestió d'interferència requereixen informació lateral originant la necessitat de coordinació i cooperació entre sistemes, que no sempre és pràctica. Les comunicacions oportunistes ofereixen una solució potencial al problema de la gestió de les interferències entre sistemes. El principi bàsic de les comunicacions oportunistes és explotar de manera eficient i robusta els recursos disponibles en una xarxa sense fils i adaptar els senyals transmesos a l'estat de la xarxa per evitar interferències entre sistemes. Per tant, les comunicacions oportunistes depenen de la inferència dels recursos de xarxa disponibles que poden ser explotats de manera segura sense induir interferència en els nodes de comunicació coexistents. Una vegada que s'han identificat els recursos de xarxa disponibles, les tècniques de comunicació oportunistes més prominents consisteixen en el disseny d'estratègies de precodificació/descodificació adaptades a l'escenari per explotar l'anomenat espai nul. Malgrat això, les solucions clàssiques en la literatura sofreixen dos inconvenients principals: la falta de robustesa als errors de detecció i la necessitat de cooperació intra-sistema. Aquesta tesi tracta el disseny d'un esquema de comunicació oportunista basat en l'espai nul que afronta els inconvenients exposats per les metodologies existents assumint que els nodes oportunistes no cooperen. Per a aquest propòsit, s'introdueix un model generalitzat d'error de detecció independent del mecanisme d'identificació de l'espai nul que permet el disseny de solucions que exhibeixen interferències mínimes entre sistemes en el cas pitjor. Aquestes solucions responen a un criteri de màxima relació de senyal a interferència (SIR), que és òptim en condicions de no cooperació. La metodologia proposada permet dissenyar una família de formes d'ona ortonormals que realitzen un spreading dels símbols modulats dins de l'espai nul detectat, que és clau per minimitzar la densitat d’interferència induïda. Les solucions proposades són invariants dins de l'espai nul inferit, permetent suprimir l'enllaç de retroalimentació i, tot i així, realitzar una detecció coherent de forma d'ona. Sota l’absència de coordinació, el disseny de la forma d'ona es basa únicament en la informació de l'estat de la xarxa detectada localment, induint un desajust entre els espais nuls identificats pel transmissor i receptor que pot empitjorar el rendiment del sistema. Tot i que la solució proposada és robusta a aquest desajust, també s'estudia el disseny de receptors millorats fent ús de tècniques de detecció de subespai actiu. Quan el nombre total de recursos de xarxa augmenta arbitràriament, les solucions proposades tendeixen a ser combinacions lineals d'exponencials complexes, proporcionant una interpretació en el domini freqüencial. Aquest comportament asimptòtic permet adaptar la solució proposada a entorns selectius en freqüència fent ús d'un prefix cíclic i estudiar una modulació eficient derivada de l'esquema de multiplexat per divisió de temps emprant formes d'ona circulant. Finalment, s’estudia l'impacte de l'ús de múltiples antenes en comunicacions oportunistes basades en l'espai nul. L'anàlisi realitzada permet concloure que, en cap cas, l'estructura de les agrupacions d'antenes tenen un impacte sobre la comunicació oportunista, ja que la forma d'ona proposada imita el comportament d'un transmissor mono-antena. D'altra banda, el nombre de sensors emprat es tradueix en una millora en termes de SIR.(Español) El incremento de los dispositivos inalámbricos que ofrecen servicios de alta velocidad de datos limita la coexistencia de sistemas inalámbricos que comparten los mismos recursos en un área geográfica dada a causa de la interferencia inter-sistema. Por tanto, la gestión de interferencia juega un papel fundamental para facilitar la coexistencia de varios servicios de comunicación heterogéneos. Sin embargo, las estrategias clásicas de gestión de interferencia requieren información lateral originando la necesidad de coordinación y cooperación entre sistemas, que no siempre es práctica. Las comunicaciones oportunistas ofrecen una solución potencial al problema de la gestión de las interferencias entre sistemas. El principio básico de las comunicaciones oportunistas es explotar de manera eficiente y robusta los recursos disponibles en una red inalámbricas y adaptar las señales transmitidas al estado de la red para evitar interferencias entre sistemas. Por lo tanto, las comunicaciones oportunistas dependen de la inferencia de los recursos de red disponibles que pueden ser explotados de manera segura sin inducir interferencia en los nodos de comunicación coexistentes. Una vez identificados los recursos disponibles, las técnicas de comunicación oportunistas más prominentes consisten en el diseño de estrategias de precodificación/descodificación adaptadas al escenario para explotar el llamado espacio nulo. A pesar de esto, las soluciones clásicas en la literatura sufren dos inconvenientes principales: la falta de robustez a los errores de detección y la necesidad de cooperación intra-sistema. Esta tesis propone diseñar un esquema de comunicación oportunista basado en el espacio nulo que afronta los inconvenientes expuestos por las metodologías existentes asumiendo que los nodos oportunistas no cooperan. Para este propósito, se introduce un modelo generalizado de error de detección independiente del mecanismo de identificación del espacio nulo que permite el diseño de soluciones que exhiben interferencias mínimas entre sistemas en el caso peor. Estas soluciones responden a un criterio de máxima relación de señal a interferencia (SIR), que es óptimo en condiciones de no cooperación. La metodología propuesta permite diseñar una familia de formas de onda ortonormales que realizan un spreading de los símbolos modulados dentro del espacio nulo detectado, que es clave para minimizar la densidad de interferencia inducida. Las soluciones propuestas son invariantes dentro del espacio nulo inferido, permitiendo suprimir el enlace de retroalimentación sin renunciar a la detección coherente de forma de onda. En ausencia de coordinación, el diseño de la forma de onda se basa únicamente en la información del estado de la red detectada localmente, induciendo un desajuste entre los espacios nulos identificados por el transmisor y receptor que puede empeorar el rendimiento del sistema. A pesar de que la solución propuesta es robusta a este desajuste, también se estudia el diseño de receptores mejorados usando técnicas de detección de subespacio activo. Cuando el número total de recursos de red aumenta arbitrariamente, las soluciones propuestas tienden a ser combinaciones lineales de exponenciales complejas, proporcionando una interpretación en el dominio frecuencial. Este comportamiento asintótico permite adaptar la solución propuesta a canales selectivos en frecuencia mediante un prefijo cíclico y estudiar una modulación eficiente derivada del esquema de multiplexado por división de tiempo empleando formas de onda circulante. Finalmente, se estudia el impacto del uso de múltiples antenas en comunicaciones oportunistas basadas en el espacio nulo. El análisis realizado revela que la estructura de las agrupaciones de antenas no afecta la comunicación oportunista, ya que la forma de onda propuesta imita el comportamiento de un transmisor mono-antena. Por otro lado, el número de sensores empleado se traduce en una mejora en términos de SIR.Postprint (published version

Towards addressing training data scarcity challenge in emerging radio access networks: a survey and framework

The future of cellular networks is contingent on artificial intelligence (AI) based automation, particularly for radio access network (RAN) operation, optimization, and troubleshooting. To achieve such zero-touch automation, a myriad of AI-based solutions are being proposed in literature to leverage AI for modeling and optimizing network behavior to achieve the zero-touch automation goal. However, to work reliably, AI based automation, requires a deluge of training data. Consequently, the success of the proposed AI solutions is limited by a fundamental challenge faced by cellular network research community: scarcity of the training data. In this paper, we present an extensive review of classic and emerging techniques to address this challenge. We first identify the common data types in RAN and their known use-cases. We then present a taxonomized survey of techniques used in literature to address training data scarcity for various data types. This is followed by a framework to address the training data scarcity. The proposed framework builds on available information and combination of techniques including interpolation, domain-knowledge based, generative adversarial neural networks, transfer learning, autoencoders, fewshot learning, simulators and testbeds. Potential new techniques to enrich scarce data in cellular networks are also proposed, such as by matrix completion theory, and domain knowledge-based techniques leveraging different types of network geometries and network parameters. In addition, an overview of state-of-the art simulators and testbeds is also presented to make readers aware of current and emerging platforms to access real data in order to overcome the data scarcity challenge. The extensive survey of training data scarcity addressing techniques combined with proposed framework to select a suitable technique for given type of data, can assist researchers and network operators in choosing the appropriate methods to overcome the data scarcity challenge in leveraging AI to radio access network automation

Quasi-deterministic channel modeling and experimental validation in cooperative and massive MIMO deployment topologies

Das enorme Wachstum des mobilen Datenaufkommens wird zu substantiellen Veränderungen in mobilen Netzwerken führen. Neue drahtlose Funksysteme müssen alle verfügbaren Freiheitsgrade des Übertragungskanals ausnutzen um die Kapazität zu maximieren. Dies beinhaltet die Nutzung größerer Bandbreiten, getrennter Übertragungskanäle, Antennenarrays, Polarisation und Kooperation zwischen Basisstationen. Dafür benötigt die Funkindustrie Kanalmodelle, welche das wirkliche Verhalten des Übertragungskanals in all diesen Fällen abbilden. Viele aktuelle Kanalmodelle unterstützen jedoch nur einen Teil der benötigten Funktionalität und wurden nicht ausreichend durch Messungen in relevanten Ausbreitungsszenarien validiert. Es ist somit unklar, ob die Kapazitätsvorhersagen, welche mit diesen Modellen gemacht werden, realistisch sind. In der vorliegenden Arbeit wird ein neuen Kanalmodell eingeführt, welches korrekte Ergebnisse für zwei wichtige Anwendungsfälle erzeugt: Massive MIMO und Joint-Transmission (JT) Coordinated Multi-Point (CoMP). Dafür wurde das häufig verwendete WINNER Kanalmodell um neue Funktionen erweitert. Dazu zählen 3-D Ausbreitungseffekte, sphärische Wellenausbreitung, räumliche Konsistenz, die zeitliche Entwicklung von Kanälen sowie ein neues Modell für die Polarisation. Das neue Kanalmodell wurde unter dem Akronym "QuaDRiGa" (Quasi Deterministic Radio Channel Generator, dt.: quasideterministischer Funkkanalgenerator) eingeführt. Um das Modell zu validieren wurden Messungen in Dresden und Berlin durchgeführt. Die Messdaten wurden zunächst verwendet um die Modellparameter abzuleiten. Danach wurden die Messkampagnen im Modell nachgestellt um die Reproduzierbarkeit der Ergebnisse nachzuweisen. Essentielle Leistungsindikatoren wie z.B. der Pfadverlust, die Laufzeitstreuung, die Winkelstreuung, der Geometriefaktor, die MIMO Kapazität und die Dirty-Paper-Coding Kapazität wurden für beide Datensätze berechnet. Diese wurden dann miteinander sowie mit Ergebnissen aus dem Rayleigh i.i.d. Modell und dem 3GPP-3D Kanalmodell verglichen. Für die Messungen in Dresden erzeugt das neue Modell nahezu identische Ergebnisse wenn die nachsimulierten Kanäle anstatt der Messdaten für die Bestimmung der Modellparameter verwendet werden. Solch ein direkter Vergleich war bisher nicht möglich, da die vorherigen Modelle keine ausreichend langen Kanalsequenzen erzeugen können. Die Kapazitätsvorhersagen des neuen Modells sind zu über 90% korrekt. Im Vergleich dazu konnte das 3GPP-3D Model nur etwa 80% Genauigkeit aufweisen. Diese Vorhersagen konnten auch für das Messszenario in Berlin gemacht werden, wo mehrere Basisstationen zeitgleich vermessen wurden. Dadurch konnten die gegenseitigen Störungen mit in die Bewertung eingeschlossen werden. Die Ergebnisse bestätigen die generelle Annahme, dass es möglich ist den Ausbreitungskanal sequenziell für einzelne Basisstationen zu vermessen und danach Kapazitätsvorhersagen für ganze Netzwerke mit der Hilfe von Modellen zu machen. Das neue Modell erzeugt Kanalkoeffizienten welche ähnliche Eigenschaften wie Messdaten haben. Somit können neue Algorithmen in Funksystemen schneller bewertet werden, da es nun möglich ist realistische Ergebnisse in einem frühen Entwicklungsstadium zu erhalten.The tremendous growth of mobile data traffic will lead to substantial architectural changes in wireless networks. New wireless systems need to exploit all available degrees of freedom in the wireless channel such as wider bandwidth, multi-carrier operation, large antenna arrays, polarization, and cooperation between base stations, in order to maximize the performance. The wireless industry needs channel models that reproduce the true behavior of the radio channel in all these use cases. However, many state-of-the-art models only support parts of the required functionality and have not been thoroughly validated against measurements in relevant propagations scenarios. It is therefore unclear if the performance predictions made by these models are realistic. This thesis introduces a new geometry-based stochastic channel model that creates accurate results for two important use cases: massive multiple-input multiple-output (MIMO) and joint transmission (JT) coordinated multi-point (CoMP). For this, the popular WINNER channel model was extended to incorporate 3-D propagation, spherical wave propagation, spatial consistency, temporal evolution of channels, and a new model for the polarization. This model was introduced under the acronym ``QuaDRiGa'' - quasi deterministic radio channel generator. To validate the model, measurements were done in downtown Dresden, Germany, and downtown Berlin, Germany. Those were used to derive the model parameters. Then, the measurements were resimulated with the new channel model and benchmarked against the Rayleigh i.i.d. model and the 3GPP-3D channel model. Essential performance indicators such as path gain, shadow fading, delay spread, angular spreads, geometry factor, single-link capacity, and the dirty-paper coding capacity were computed from both the measured and resimulated data. In Dresden, the resimulated channels produce almost identical results as the measured channels. When using the resimulated channels to derive the model parameters, the same results can be obtained as when using the measurement data. Such a direct comparison was not possible with the previous models because they cannot produce sufficiently long sequences of channel data. The performance predictions from the new model are more than 90% accurate whereas only 80% accuracy could be achieved with the 3GPP-3D model. In Berlin, accurate performance predictions could also be made in a multi-cellular environment where the mutual interference between the base stations could be studied. This confirms that it is generally sufficient to use single-link measurements to parameterize channel models that are then used to predict the achievable performance in wireless networks. The new model can generate channel traces with similar characteristics as measured data. This might speed up the evaluation of new algorithms because it is now possible to obtain realistic performance results already in an early stage of development

Addressing training data sparsity and interpretability challenges in AI based cellular networks

To meet the diverse and stringent communication requirements for emerging networks use cases, zero-touch arti cial intelligence (AI) based deep automation in cellular networks is envisioned. However, the full potential of AI in cellular networks remains hindered by two key challenges: (i) training data is not as freely available in cellular networks as in other fields where AI has made a profound impact and (ii) current AI models tend to have black box behavior making operators reluctant to entrust the operation of multibillion mission critical networks to a black box AI engine, which allow little insights and discovery of relationships between the configuration and optimization parameters and key performance indicators. This dissertation systematically addresses and proposes solutions to these two key problems faced by emerging networks. A framework towards addressing the training data sparsity challenge in cellular networks is developed, that can assist network operators and researchers in choosing the optimal data enrichment technique for different network scenarios, based on the available information. The framework encompasses classical interpolation techniques, like inverse distance weighted and kriging to more advanced ML-based methods, like transfer learning and generative adversarial networks, several new techniques, such as matrix completion theory and leveraging different types of network geometries, and simulators and testbeds, among others. The proposed framework will lead to more accurate ML models, that rely on sufficient amount of representative training data. Moreover, solutions are proposed to address the data sparsity challenge specifically in Minimization of drive test (MDT) based automation approaches. MDT allows coverage to be estimated at the base station by exploiting measurement reports gathered by the user equipment without the need for drive tests. Thus, MDT is a key enabling feature for data and artificial intelligence driven autonomous operation and optimization in current and emerging cellular networks. However, to date, the utility of MDT feature remains thwarted by issues such as sparsity of user reports and user positioning inaccuracy. For the first time, this dissertation reveals the existence of an optimal bin width for coverage estimation in the presence of inaccurate user positioning, scarcity of user reports and quantization error. The presented framework can enable network operators to configure the bin size for given positioning accuracy and user density that results in the most accurate MDT based coverage estimation. The lack of interpretability in AI-enabled networks is addressed by proposing a first of its kind novel neural network architecture leveraging analytical modeling, domain knowledge, big data and machine learning to turn black box machine learning models into more interpretable models. The proposed approach combines analytical modeling and domain knowledge to custom design machine learning models with the aim of moving towards interpretable machine learning models, that not only require a lesser training time, but can also deal with issues such as sparsity of training data and determination of model hyperparameters. The approach is tested using both simulated data and real data and results show that the proposed approach outperforms existing mathematical models, while also remaining interpretable when compared with black-box ML models. Thus, the proposed approach can be used to derive better mathematical models of complex systems. The findings from this dissertation can help solve the challenges in emerging AI-based cellular networks and thus aid in their design, operation and optimization

Intelligent Circuits and Systems

ICICS-2020 is the third conference initiated by the School of Electronics and Electrical Engineering at Lovely Professional University that explored recent innovations of researchers working for the development of smart and green technologies in the fields of Energy, Electronics, Communications, Computers, and Control. ICICS provides innovators to identify new opportunities for the social and economic benefits of society.　 This conference bridges the gap between academics and R&D institutions, social visionaries, and experts from all strata of society to present their ongoing research activities and foster research relations between them. It provides opportunities for the exchange of new ideas, applications, and experiences in the field of smart technologies and finding global partners for future collaboration. The ICICS-2020 was conducted in two broad categories, Intelligent Circuits & Intelligent Systems and Emerging Technologies in Electrical Engineering