Multi-stage Antenna Selection for Adaptive Beamforming in MIMO Arrays

Increasing the number of transmit and receive elements in multiple-input-multiple-output (MIMO) antenna arrays imposes a substantial increase in hardware and computational costs. We mitigate this problem by employing a reconfigurable MIMO array where large transmit and receive arrays are multiplexed in a smaller set of k baseband signals. We consider four stages for the MIMO array configuration and propose four different selection strategies to offer dimensionality reduction in post-processing and achieve hardware cost reduction in digital signal processing (DSP) and radio-frequency (RF) stages. We define the problem as a determinant maximization and develop a unified formulation to decouple the joint problem and select antennas/elements in various stages in one integrated problem. We then analyze the performance of the proposed selection approaches and prove that, in terms of the output SINR, a joint transmit-receive selection method performs best followed by matched-filter, hybrid and factored selection methods. The theoretical results are validated numerically, demonstrating that all methods allow an excellent trade-off between performance and cost.Comment: Submitted for publicatio

Energy-Efficient System Design for Future Wireless Communications

The exponential growth of wireless data traffic has caused a significant increase in the power consumption of wireless communications systems due to the higher complexity of the transceiver structures required to establish the communication links. For this reason, in this Thesis we propose and characterize technologies for improving the energy efficiency of multiple-antenna wireless communications. This Thesis firstly focuses on energy-efficient transmission schemes and commences by introducing a scheme for alleviating the power loss experienced by the Tomlinson-Harashima precoder, by aligning the interference of a number of users with the symbols to transmit. Subsequently, a strategy for improving the performance of space shift keying transmission via symbol pre-scaling is presented. This scheme re-formulates complex optimization problems via semidefinite relaxation to yield problem formulations that can be efficiently solved. In a similar line, this Thesis designs a signal detection scheme based on compressive sensing to improve the energy efficiency of spatial modulation systems in multiple access channels. The proposed technique relies on exploiting the particular structure and sparsity that spatial modulation systems inherently possess to enhance performance. This Thesis also presents research carried out with the aim of reducing the hardware complexity and associated power consumption of large scale multiple-antenna base stations. In this context, the employment of incomplete channel state information is proposed to achieve the above-mentioned objective in correlated communication channels. The candidate’s work developed in Bell Labs is also presented, where the feasibility of simplified hardware architectures for massive antenna systems is assessed with real channel measurements. Moreover, a strategy for reducing the hardware complexity of antenna selection schemes by simplifying the design of the switching procedure is also analyzed. Overall, extensive theoretical and simulation results support the improved energy efficiency and complexity of the proposed schemes, towards green wireless communications systems

Energy-driven techniques for massive machine-type communications

In the last few years, a lot of effort has been put into the development of the fifth generation of cellular networks (5G). Given the vast heterogeneity of devices coexisting in these networks, new approaches have been sought to meet all requirements (e.g., data rate, coverage, delay, etc.). Within that framework, massive machine-type communications (mMTC) emerge as a promising candidate to enable many Internet of Things applications. mMTC define a type of systems where large sets of simple and battery-constrained devices transmit short data packets simultaneously. Unlike other 5G use cases, in mMTC, a low cost and power consumption are extensively pursued. Due to these specifications, typical humantype communications (HTC) solutions fail in providing a good service. In this dissertation, we focus on the design of energy-driven techniques for extending the lifetime of mMTC terminals. Both uplink (UL) and downlink (DL) stages are addressed, with special attention to the traffic models and spatial distribution of the devices. More specifically, we analyze a setup where groups of randomly deployed sensors send their (possibly correlated) observations to a collector node using different multiple access schemes. Depending on their activity, information might be transmitted either on a regular or sporadic basis. In that sense, we explore resource allocation, data compression, and device selection strategies to reduce the energy consumption in the UL. To further improve the system performance, we also study medium access control protocols and interference management techniques that take into account the large connectivity in these networks. On the contrary, in the DL, we concentrate on the support of wireless powered networks through different types of energy supply mechanisms, for which proper transmission schemes are derived. Additionally, for a better representation of current 5G deployments, the presence of HTC terminals is also included. Finally, to evaluate our proposals, we present several numerical simulations following standard guidelines. In line with that, we also compare our approaches with state-of-the-art solutions. Overall, results show that the power consumption in the UL can be reduced with still good performance and that the battery lifetimes can be improved thanks to the DL strategies.En els últims anys, s'han dedicat molts esforços al desenvolupament de la cinquena generació de telefonia mòbil (5G). Donada la gran heterogeneïtat de dispositius coexistint en aquestes xarxes, s'han buscat nous mètodes per satisfer tots els requisits (velocitat de dades, cobertura, retard, etc.). En aquest marc, les massive machine-type communications (mMTC) sorgeixen com a candidates prometedores per fer possible moltes aplicacions del Internet of Things. Les mMTC defineixen un tipus de sistemes en els quals grans conjunts de dispositius senzills i amb poca bateria, transmeten simultàniament paquets de dades curts. A diferència d'altres casos d'ús del 5G, en mMTC es persegueix un cost i un consum d'energia baixos. A causa d'aquestes especificacions, les solucions típiques de les human-type communications (HTC) no aconsegueixen proporcionar un bon servei. En aquesta tesi, ens centrem en el disseny de tècniques basades en l'energia per allargar la vida útil dels terminals mMTC. S'aborden tant les etapes del uplink (UL) com les del downlink (DL), amb especial atenció als models de trànsit i a la distribució espacial dels dispositius. Més concretament, analitzem un escenari en el qual grups de sensors desplegats aleatòriament, envien les seves observacions (possiblement correlades) a un node col·lector utilitzant diferents esquemes d'accés múltiple. Depenent de la seva activitat, la informació es pot transmetre de manera regular o esporàdica. En aquest sentit, explorem estratègies d'assignació de recursos, compressió de dades, i selecció de dispositius per reduir el consum d'energia en el UL. Per millorar encara més el rendiment del sistema, també estudiem protocols de control d'accés al medi i tècniques de gestió d'interferències que tinguin en compte la gran connectivitat d'aquestes xarxes. Per contra, en el DL, ens centrem en el suport de les wireless powered networks mitjançant diferents mecanismes de subministrament d'energia, per als quals es deriven esquemes de transmissió adequats. A més, per una millor representació dels desplegaments 5G actuals, també s'inclou la presència de terminals HTC. Finalment, per avaluar les nostres propostes, presentem diverses simulacions numèriques seguint pautes estandarditzades. En aquesta línia, també comparem els nostres enfocaments amb les solucions de l'estat de l'art. En general, els resultats mostren que el consum d'energia en el UL pot reduir-se amb un bon rendiment i que la durada de la bateria pot millorar-se gràcies a les estratègies del DL.En los últimos años, se han dedicado muchos esfuerzos al desarrollo de la quinta generación de telefonía móvil (5G). Dada la gran heterogeneidad de dispositivos coexistiendo en estas redes, se han buscado nuevos métodos para satisfacer todos los requisitos (velocidad de datos, cobertura, retardo, etc.). En este marco, las massive machine-type communications (mMTC) surgen como candidatas prometedoras para hacer posible muchas aplicaciones del Internet of Things. Las mMTC definen un tipo de sistemas en los cuales grandes conjuntos de dispositivos sencillos y con poca batería, transmiten simultáneamente paquetes de datos cortos. A diferencia de otros casos de uso del 5G, en mMTC se persigue un coste y un consumo de energía bajos. A causa de estas especificaciones, las soluciones típicas de las human-type communications (HTC) no consiguen proporcionar un buen servicio. En esta tesis, nos centramos en el diseño de técnicas basadas en la energía para alargar la vida ´útil de los terminales mMTC. Se abordan tanto las etapas del uplink (UL) como las del downlink (DL), con especial atención a los modelos de tráfico y a la distribución espacial de los dispositivos. Más concretamente, analizamos un escenario en el cual grupos de sensores desplegados aleatoriamente, envían sus observaciones (posiblemente correladas) a un nodo colector utilizando diferentes esquemas de acceso múltiple. Dependiendo de su actividad, la información se puede transmitir de manera regular o esporádica. En este sentido, exploramos estrategias de asignación de recursos, compresión de datos, y selección de dispositivos para reducir el consumo de energía en el UL. Para mejorar todavía más el rendimiento del sistema, también estudiamos protocolos de control de acceso al medio y técnicas de gestión de interferencias que tengan en cuenta la gran conectividad de estas redes. Por el contrario, en el DL, nos centramos en el soporte de las wireless powered networks mediante diferentes mecanismos de suministro de energía, para los cuales se derivan esquemas de transmisión adecuados. Además, para una mejor representación de los despliegues 5G actuales, también se incluye la presencia de terminales HTC. Finalmente, para evaluar nuestras propuestas, presentamos varias simulaciones numéricas siguiendo pautas estandarizadas. En esta línea, también comparamos nuestros enfoques con las soluciones del estado del arte. En general, los resultados muestran que el consumo de energía en el UL puede reducirse con un buen rendimiento y que la duración de la batería puede mejorarse gracias a las estrategias del DLPostprint (published version

OFDM passive radar employing compressive processing in MIMO configurations

A key advantage of passive radar is that it provides a means of performing position detection and tracking without the need for transmission of energy pulses. In this respect, passive radar systems utilising (receiving) orthogonal frequency division multiplexing (OFDM) communications signals from transmitters using OFDM standards such as long term evolution (LTE), WiMax or WiFi, are considered. Receiving a stronger reference signal for the matched filtering, detecting a lower target signature is one of the challenges in the passive radar. Impinging at the receiver, the OFDM waveforms supply two-dimensional virtual uniform rectangul ararray with the first and second dimensions refer to time delays and Doppler frequencies respectively. A subspace method, multiple signals classification (MUSIC) algorithm, demonstrated the signal extraction using multiple time samples. Apply normal measurements, this problem requires high computational resources regarding the number of OFDM subcarriers. For sub-Nyquist sampling, compressive sensing (CS) becomes attractive. A single snap shot measurement can be applied with Basis Pursuit (BP), whereas l1-singular value decomposition (l1-SVD) is applied for the multiple snapshots. Employing multiple transmitters, the diversity in the detection process can be achieved. While a passive means of attaining three-dimensional large-set measurements is provided by co-located receivers, there is a significant computational burden in terms of the on-line analysis of such data sets. In this thesis, the passive radar problem is presented as a mathematically sparse problem and interesting solutions, BP and l1-SVD as well as Bayesian compressive sensing, fast-Besselk, are considered. To increase the possibility of target signal detection, beamforming in the compressive domain is also introduced with the application of conve xoptimization and subspace orthogonality. An interference study is also another problem when reconstructing the target signal. The networks of passive radars are employed using stochastic geometry in order to understand the characteristics of interference, and the effect of signal to interference plus noise ratio (SINR). The results demonstrate the outstanding performance of l1-SVD over MUSIC when employing multiple snapshots. The single snapshot problem along with fast-BesselK multiple-input multiple-output configuration can be solved using fast-BesselK and this allows the compressive beamforming for detection capability

Hybrid Beamforming Design for Millimeter Wave Massive MIMO Communications

Wireless connectivity is a key driver for the digital transformation that is changing how people communicate, do business, consume entertainment and search for information. As the world advances into the fifth generation (5G) and beyond-5G (B5G) of wireless mobile technology, new services and use cases are emerging every day, bringing the demand to expand the broadband capability of mobile networks and to provide ubiquitous access and specific capabilities for any device or application. To fulfill these demands, 5G and B5G systems will rely on innovative technologies, such as the ultradensification, the mmWave, and the massive MIMO. To bring together these technologies, 5G and B5G systems will employ hybrid analog-digital beamforming, which separates the signal processing into the baseband (digital) and the radio-frequency (analog) domains. Unlike conventional beamforming, where every antenna is connected to an RF chain, and the signal is entirely processed in the digital domain, hybrid beamforming uses fewer RF chains than the total number of antennas, resulting in a less expensive and less energy-consuming design. The analog beamforming is usually implemented using switching networks or phase-shifting networks, which impose severe hardware constraints making the hybrid beamforming design very challenging. This thesis addresses the hybrid analog-digital beamforming design and is organized into three parts. In the first part, two adaptive algorithms for solving the switching-network-based hybrid beamforming design problem, also known as the joint antenna selection and beamforming (JASB) problem, are proposed. The adaptive algorithms are based on the minimum mean square error (MMSE) and minimum-variance distortionless response (MVDR) criteria and employ an alternating optimization strategy, in which the beamforming and the antenna selection are designed iteratively. The proposed algorithms can attain high levels of SINR while strictly complying with the hardware limitations. Moreover, the proposed algorithms have very low computational complexity and can track channel variations, making them suitable for non-stationary environments. Numerical simulations have validated the effectiveness of the algorithms in different operation scenarios. The second part addresses the phase-shifting-network-based hybrid beamforming design for narrowband mmWave massive MIMO systems. A novel joint hybrid precoder and combiner design is proposed. The analog precoder and combiner design is formulated as constrained low-rank channel decomposition, which can simultaneously harvest the array gain provided by the massive MIMO system and suppress intra-user and inter-user interferences. The constrained low-rank channel decomposition is solved as a series of successive rank-1 channel decomposition, using the projected block coordinate descent method. The digital precoder and combiner are obtained from the optimal SVD-based solution for the single-user case and the regularized channel diagonalization method for the multi-user case. Simulation results have demonstrated that the proposed design can consistently attain near-optimal performance and provided important insights into the method's convergence and its performance under practical phase-shifter quantization constraints. Finally, the phase-shifting-network-based hybrid beamforming design for frequency-selective mmWave massive MIMO-OFDM systems is considered in the third part. The hybrid beamforming design for MIMO-OFDM systems is significantly more challenging than for narrowband MIMO systems since, in these systems, the analog precoder and combiner are shared among all subcarriers and must be jointly optimized. Thus, by leveraging the OFDM systems' multidimensional structure, the analog precoder and combiner design is formulated as constrained low-rank Tucker2 tensor decomposition and solved by a successive rank-(1,1) tensor decomposition using the projected alternate least square (ALS) method. The digital precoder and combiner are obtained on a per-subcarrier basis using the techniques presented in the second part. Numerical simulations have confirmed the design effectiveness, demonstrating its ability to consistently attain near-optimal performance and outperform other existing design in nearly all scenarios. They also provided insights into the convergence of the proposed method and its performance under practical phase-shifter quantization constraints and highlighted the differences between this design and that for the narrowband massive MIMO systems

Computational optimization methods for modeling the effect of muscle forces on bone strength adaptation

An improved understanding of the mechanical influences that alter the strength of a bone can aid in the refinement of the wide array of currently available techniques to counteract the losses of bone strength that occur due to age or disuse both on Earth and in Space. To address this need, computational modeling methods to quantitatively analyze and compare the effects of mechanical factors on the strength of a targeted bone within a multibone, multimuscle system are developed and implemented in this work. Through a more detailed representation of the system in which the bone acts and the creation of a model that does not require experimentally based parameters, the developed techniques eliminate many of the difficulties that have often hindered these musculoskeletal phenomena from being studied with the tools and methods readily employed in the investigation of their inert mechanical counterparts. The computational techniques developed couple the determination of the muscle forces acting within the system studied, the stresses they induce within the bones of the system, and the ensuing adaptations of the shape of one of these bones, altering its strength. This is accomplished through the use of gradient based optimization methods, finite element methods, and gradientless optimization methods, respectively. The developed gradientless optimization methods in this work progress the bone shape design toward one with a more uniform state of stress through the relative effects of measures of the local stress state, the global stress state, and the variation of the local stress state over the region being optimized. Quantitative measures of the progression towards a uniformity of the stress state during the optimization process are defined so that relative changes can be directly compared between the various mechanical factors studied. Similarly, methods are developed to independently assess the ability of the conditions studied to induce bone shape alterations that improve the strength of the bone under a standard set of loading conditions. The implementation of the model in a parametric study of methods to improve the resistance of the tibia bone to stress fractures demonstrates its ability to evaluate the effects of various loading conditions, with forces and stresses studied ranging three orders of magnitude. From this investigation, loading modes are identified that improve the bone\u27s strength in the fracture prone region by up to 20%. The developed computational modeling techniques eliminate the difficulties inherent in the experimental investigation of mechanically based alterations to bone strength and provide a means for the improved understanding and, ultimately, better control of these adaptive phenomena

1-D broadside-radiating leaky-wave antenna based on a numerically synthesized impedance surface

A newly-developed deterministic numerical technique for the automated design of metasurface antennas is applied here for the first time to the design of a 1-D printed Leaky-Wave Antenna (LWA) for broadside radiation. The surface impedance synthesis process does not require any a priori knowledge on the impedance pattern, and starts from a mask constraint on the desired far-field and practical bounds on the unit cell impedance values. The designed reactance surface for broadside radiation exhibits a non conventional patterning; this highlights the merit of using an automated design process for a design well known to be challenging for analytical methods. The antenna is physically implemented with an array of metal strips with varying gap widths and simulation results show very good agreement with the predicted performance

Beam scanning by liquid-crystal biasing in a modified SIW structure

A fixed-frequency beam-scanning 1D antenna based on Liquid Crystals (LCs) is designed for application in 2D scanning with lateral alignment. The 2D array environment imposes full decoupling of adjacent 1D antennas, which often conflicts with the LC requirement of DC biasing: the proposed design accommodates both. The LC medium is placed inside a Substrate Integrated Waveguide (SIW) modified to work as a Groove Gap Waveguide, with radiating slots etched on the upper broad wall, that radiates as a Leaky-Wave Antenna (LWA). This allows effective application of the DC bias voltage needed for tuning the LCs. At the same time, the RF field remains laterally confined, enabling the possibility to lay several antennas in parallel and achieve 2D beam scanning. The design is validated by simulation employing the actual properties of a commercial LC medium

NOVEL USER-CENTRIC ARCHITECTURES FOR FUTURE GENERATION CELLULAR NETWORKS: DESIGN, ANALYSIS AND PERFORMANCE OPTIMIZATION

Ambitious targets for aggregate throughput, energy efficiency (EE) and ubiquitous user experience are propelling the advent of ultra-dense networks. Inter-cell interference and high energy consumption in an ultra-dense network are the prime hindering factors in pursuit of these goals. To address this challenge, we investigate the idea of transforming network design from being base station-centric to user-centric. To this end, we develop mathematical framework and analyze multiple variants of the user-centric networks, with the help of advanced scientific tools such as stochastic geometry, game theory, optimization theory and deep neural networks. We first present a user-centric radio access network (RAN) design and then propose novel base station association mechanisms by forming virtual dedicated cells around users scheduled for downlink. The design question that arises is what should the ideal size of the dedicated regions around scheduled users be? To answer this question, we follow a stochastic geometry based approach to quantify the area spectral efficiency (ASE) and energy efficiency (EE) of a user-centric Cloud RAN architecture. Observing that the two efficiency metrics have conflicting optimal user-centric cell sizes, we propose a game theoretic self-organizing network (GT-SON) framework that can orchestrate the network between ASE and EE focused operational modes in real-time in response to changes in network conditions and the operator's revenue model, to achieve a Pareto optimal solution. The designed model is shown to outperform base-station centric design in terms of both ASE and EE in dense deployment scenarios. Taking this user-centric approach as a baseline, we improve the ASE and EE performance by introducing flexibility in the dimensions of the user-centric regions as a function of data requirement for each device. So instead of optimizing the network-wide ASE or EE, each user device competes for a user-centric region based on its data requirements. This competition is modeled via an evolutionary game and a Vickrey-Clarke-Groves auction. The data requirement based flexibility in the user-centric RAN architecture not only improves the ASE and EE, but also reduces the scheduling wait time per user. Offloading dense user hotspots to low range mmWave cells promises to meet the enhance mobile broadband requirement of 5G and beyond. To investigate how the three key enablers; i.e. user-centric virtual cell design, ultra-dense deployments and mmWave communication; are integrated in a multi-tier Stienen geometry based user-centric architecture. Taking into account the characteristics of mmWave propagation channel such as blockage and fading, we develop a statistical framework for deriving the coverage probability of an arbitrary user equipment scheduled within the proposed architecture. A key advantage observed through this architecture is significant reduction in the scheduling latency as compared to the baseline user-centric model. Furthermore, the interplay between certain system design parameters was found to orchestrate the ASE-EE tradeoff within the proposed network design. We extend this work by framing a stochastic optimization problem over the design parameters for a Pareto optimal ASE-EE tradeoff with random placements of mobile users, macro base stations and mmWave cells within the network. To solve this optimization problem, we follow a deep learning approach to estimate optimal design parameters in real-time complexity. Our results show that if the deep learning model is trained with sufficient data and tuned appropriately, it yields near-optimal performance while eliminating the issue of long processing times needed for system-wide optimization. The contributions of this dissertation have the potential to cause a paradigm shift from the reactive cell-centric network design to an agile user-centric design that enables real-time optimization capabilities, ubiquitous user experience, higher system capacity and improved network-wide energy efficiency