Tensor Network alternating linear scheme for MIMO Volterra system identification

This article introduces two Tensor Network-based iterative algorithms for the identification of high-order discrete-time nonlinear multiple-input multiple-output (MIMO) Volterra systems. The system identification problem is rewritten in terms of a Volterra tensor, which is never explicitly constructed, thus avoiding the curse of dimensionality. It is shown how each iteration of the two identification algorithms involves solving a linear system of low computational complexity. The proposed algorithms are guaranteed to monotonically converge and numerical stability is ensured through the use of orthogonal matrix factorizations. The performance and accuracy of the two identification algorithms are illustrated by numerical experiments, where accurate degree-10 MIMO Volterra models are identified in about 1 second in Matlab on a standard desktop pc

Implementation of a Combined OFDM-Demodulation and WCDMA-Equalization Module

For a dual-mode baseband receiver for the OFDMWireless LAN andWCDMA standards, integration of the demodulation and equalization tasks on a dedicated hardware module has been investigated. For OFDM demodulation, an FFT algorithm based on cascaded twiddle factor decomposition has been selected. This type of algorithm combines high spatial and temporal regularity in the FFT data-flow graphs with a minimal number of computations. A frequency-domain algorithm based on a circulant channel approximation has been selected for WCDMA equalization. It has good performance, low hardware complexity and a low number of computations. Its main advantage is the reuse of the FFT kernel, which contributes to the integration of both tasks. The demodulation and equalization module has been described at the register transfer level with the in-house developed Arx language. The core of the module is a pipelined radix-23 butterfly combined with a complex multiplier and complex divider. The module has an area of 0.447 mm2 in 0.18 ¿m technology and a power consumption of 10.6 mW. The proposed module compares favorably with solutions reported in literature

Spatial modulation schemes and modem architectures for millimeter wave radio systems

The rapid growth of wireless industry opens the door to several use cases such as internet of things and device-to-device communications, which require boosting the reliability and the spectral efficiency of the wireless access network, while reducing the energy consumption at the terminals. The vast spectrum available in millimeter-wave (mmWave) frequency band is one of the most promising candidates to achieve high-speed communications. However, the propagation of the radio signals at high carrier frequencies suffers from severe path-loss which reduces the coverage area. Fortunately, the small wavelengths of the mmWave signals allow packing a large number of antennas not only at the base station (BS) but also at the user terminal (UT). These massive antenna arrays can be exploited to attain high beamforming and combining gains and overcome the path-loss associated with the mmWave propagation. In conventional (fully digital) multiple-input-multiple-output (MIMO) transceivers, each antenna is connected to a specific radio-frequency (RF) chain and high resolution analog-to-digital-converter. Unfortunately, these devices are expensive and power hungry especially at mmWave frequency band and when operating in large bandwidths. Having this in mind, several MIMO transceiver architectures have been proposed with the purpose of reducing the hardware cost and the energy consumption. Fully connected hybrid analog and digital precoding schemes were proposed in with the aim of replacing some of the conventional RF chains by energy efficient analog devices. These fully connected mapping requires many analog devices that leads to non-negligible energy consumption. Partially connected hybrid architectures have been proposed to improve the energy efficiency of the fully connected transceivers by reducing the number of analog devices. Simplifying the transceiver’s architecture to reduce the power consumption results in a degradation of the attained spectral efficiency. In this PhD dissertation, we propose novel modulation schemes and massive MIMO transceiver design to combat the challenges at the mmWave cellular systems. The structure of the doctoral manuscript can be expressed as In Chapter 1, we introduce the transceiver design challenges at mmWave cellular communications. Then, we illustrate several state of the art architectures and highlight their limitations. After that, we propose scheme that attains high-energy efficiency and spectrum efficiency. In chapter 2, first, we mathematically describe the state of the art of the SM and highlight the main challenges with these schemes when applied at mmWave frequency band. In order to combat these challenges (for example, high cost and high power consumption), we propose novel SM schemes specifically designed for mmWave massive MIMO systems. After that, we explain how these schemes can be exploited in attaining energy efficient UT architecture. Finally, we present the channel model, systems assumptions and the transceiver devices power consumption models. In chapter 3, we consider single user SM system. First, we propose downlink (DL) receive SM (RSM) scheme where the UT can be implemented with single or multiple radio-frequency chains and the BS can be fully digital or hybrid architecture. Moreover, we consider different precoders at the BS and propose low complexity and efficient antenna selection schemes for narrowband and wideband transmissions. After that, we propose joint uplink-downlink SM scheme where we consider RSM in the DL and transmit SM (TSM) in the UL based on energy efficient hybrid UT architecture. In chapter 4, we extend the SM system to the multi-user case. Specifically, we develop joint multi-user power allocation, user selection and antenna selection algorithms for the broadcast and the multiple access channels. Chapter 5 is presented for concluding the thesis and proposing future research directions.Considerando los altos requerimientos de los servicios de nueva generación, las infraestructuras de red actual se han visto obligadas a evolucionar en la forma de manejar los diferentes recursos de red y computación. Con este fin, nuevas tecnologías han surgido para soportar las funcionalidades necesarias para esta evolución, significando también un gran cambio de paradigma en el diseño de arquitecturas para la futura implementación de redes.En este sentido, este documento de tesis doctoral presenta un análisis sobre estas tecnologías, enfocado en el caso de redes inter/intra Data Centre. Por consiguiente, la introducción de tecnologías basadas en redes ópticas ha sido estudiada, con el fin de identificar problemas actuales que puedan llegar a ser solucionados mediante el diseño y aplicación de nuevas técnicas, asimismo como a través del desarrollo o la extensión de los componentes de arquitectura de red.Con este propósito, se han definido una serie de propuestas relacionadas con aspectos cruciales, así como el control de dispositivos ópticos por SDN para habilitar el manejo de redes híbridas, la necesidad de definir un mecanismo de descubrimiento de topologías ópticas capaz de exponer información precisa, y el analizar las brechas existentes para la definición de una arquitectura común en fin de soportar las comunicaciones 5G.Para validar estas propuestas, se han presentado una serie de validaciones experimentales por medio de escenarios de prueba específicos, demostrando los avances en control, orquestación, virtualización y manejo de recursos con el fin de optimizar su utilización. Los resultados expuestos, además de corroborar la correcta operación de los métodos y componentes propuestos, abre el camino hacia nuevas formas de adaptar los actuales despliegues de red respecto a los desafíos definidos en el inicio de una nueva era de las telecomunicaciones.Postprint (published version

Efficient DSP and Circuit Architectures for Massive MIMO: State-of-the-Art and Future Directions

Massive MIMO is a compelling wireless access concept that relies on the use of an excess number of base-station antennas, relative to the number of active terminals. This technology is a main component of 5G New Radio (NR) and addresses all important requirements of future wireless standards: a great capacity increase, the support of many simultaneous users, and improvement in energy efficiency. Massive MIMO requires the simultaneous processing of signals from many antenna chains, and computational operations on large matrices. The complexity of the digital processing has been viewed as a fundamental obstacle to the feasibility of Massive MIMO in the past. Recent advances on system-algorithm-hardware co-design have led to extremely energy-efficient implementations. These exploit opportunities in deeply-scaled silicon technologies and perform partly distributed processing to cope with the bottlenecks encountered in the interconnection of many signals. For example, prototype ASIC implementations have demonstrated zero-forcing precoding in real time at a 55 mW power consumption (20 MHz bandwidth, 128 antennas, multiplexing of 8 terminals). Coarse and even error-prone digital processing in the antenna paths permits a reduction of consumption with a factor of 2 to 5. This article summarizes the fundamental technical contributions to efficient digital signal processing for Massive MIMO. The opportunities and constraints on operating on low-complexity RF and analog hardware chains are clarified. It illustrates how terminals can benefit from improved energy efficiency. The status of technology and real-life prototypes discussed. Open challenges and directions for future research are suggested.Comment: submitted to IEEE transactions on signal processin

A distributed accelerated gradient algorithm for distributed model predictive control of a hydro power valley

A distributed model predictive control (DMPC) approach based on distributed optimization is applied to the power reference tracking problem of a hydro power valley (HPV) system. The applied optimization algorithm is based on accelerated gradient methods and achieves a convergence rate of O(1/k^2), where k is the iteration number. Major challenges in the control of the HPV include a nonlinear and large-scale model, nonsmoothness in the power-production functions, and a globally coupled cost function that prevents distributed schemes to be applied directly. We propose a linearization and approximation approach that accommodates the proposed the DMPC framework and provides very similar performance compared to a centralized solution in simulations. The provided numerical studies also suggest that for the sparsely interconnected system at hand, the distributed algorithm we propose is faster than a centralized state-of-the-art solver such as CPLEX

Stochastic Optimization for Deep CCA via Nonlinear Orthogonal Iterations

Deep CCA is a recently proposed deep neural network extension to the traditional canonical correlation analysis (CCA), and has been successful for multi-view representation learning in several domains. However, stochastic optimization of the deep CCA objective is not straightforward, because it does not decouple over training examples. Previous optimizers for deep CCA are either batch-based algorithms or stochastic optimization using large minibatches, which can have high memory consumption. In this paper, we tackle the problem of stochastic optimization for deep CCA with small minibatches, based on an iterative solution to the CCA objective, and show that we can achieve as good performance as previous optimizers and thus alleviate the memory requirement.Comment: in 2015 Annual Allerton Conference on Communication, Control and Computin