Finite-Alphabet MMSE Equalization for All-Digital Massive MU-MIMO mmWave Communication

We propose finite-alphabet equalization, a new paradigm that restricts the entries of the spatial equalization matrix to low-resolution numbers, enabling high-throughput, low-power, and low-cost hardware equalizers. To minimize the performance loss of this paradigm, we introduce FAME, short for finite-alphabet minimum mean-square error (MMSE) equalization, which is able to significantly outperform a naive quantization of the linear MMSE matrix. We develop efficient algorithms to approximately solve the NP-hard FAME problem and showcase that near-optimal performance can be achieved with equalization coefficients quantized to only 1-3 bits for massive multi-user multiple-input multiple-output (MU-MIMO) millimeter-wave (mmWave) systems. We provide very-large scale integration (VLSI) results that demonstrate a reduction in equalization power and area by at least a factor of 3.9x and 5.8x, respectively.Comment: Appeared in the IEEE Journal on Selected Areas in Communication

Técnicas de quantização para sistemas de comunicação híbridos na banda de ondas milimétricas com um número elevado de antenas

Since the appearance of mobile communications, the users of this technology have been growing exponentially every day. The escalating mobile traffic growth it has been imposed by the proliferation of smartphones and tablets. The increasing and more intensive use of wireless communications may lead to a future breaking point, where the traditional systems will fail to support the required capability, spectral and energy efficiency. On the other hand, to cover all this current need to have more and more data it is necessary to provide a new range of data rates around the gigabits per second. Today, almost all mobile communications systems use spectrum in the range of 300MHz – 3GHz. It is needed to start looking to the range of 3GHz – 300GHz spectrum for mobile broadband applications. Millimeter waves are one way to alleviate the spectrum gridlock at lower frequencies. MIMO based systems has been researched for the last 20 years and are now part of the current standards. However, to achieve more gains, a grander view of the MIMO concept envisions the use of a large scale of antennas at each base stations, a concept referred as massive MIMO. The symbiotic combination of these technologies and other ones will lead to the development of a new generation system known as the 5G. The knowledge of the channel state information at the transmitter is very important in real massive MIMO millimeter wave systems. In this dissertation a limited feedback strategy for a hybrid massive MIMO OFDM system is proposed, where only a part of the parameters associated to the link channel are quantized and fed back. The limited feedback strategy employs a uniform-based quantization for channel amplitudes, angle of departure and angle of arrival in time domain. After being fed back, this information is used to reconstruct the overall channel in frequency domain and the transmit antenna array, which are then used to compute the hybrid analog-digital precoders. Numerical results show that the proposed quantization strategy achieve a performance close to the one obtained with perfect full channel, with a low overhead and complexityDesde o aparecimento das comunicações móveis, os utilizadores desta tecnologia têm vindo a crescer exponencialmente todos os dias. A escalada do crescimento do tráfego móvel foi imposta, principalmente, pela proliferação de smartphones e tablets. O uso crescente e intensivo das comunicações sem fios pode levar no futuro a um ponto de rutura, onde os sistemas tradicionais não suportam a capacidade requerida, a eficiência espectral e eficiência enérgica. Por outro lado, para cobrir toda esta necessidade atual de ter mais e mais dados, é necessário fornecer taxas de transmissão mais elevadas, em torno dos gigabits por segundo. Hoje, quase todos os sistemas de comunicações móveis usam espectro na faixa de 300 MHz - 3GHz. É necessário começar a procurar a gama de espectro 3GHz - 300 GHz para aplicações de banda larga móvel. Aqui vamos apresentar as ondas milimétricas, sendo esta uma maneira de aliviar espectro em frequências mais baixas. Os sistemas baseados em MIMO foram alvo de pesquisa nos últimos 20 anos e agora fazem parte dos padrões atuais. No entanto, para obter mais ganhos, uma visão mais ampla do conceito MIMO prevê o uso de uma grande quantidade de antenas em cada estação base, um conceito referido como massive MIMO. A combinação simbiótica destas tecnologias levará ao desenvolvimento de um novo sistema de geração denominado 5G. O desenvolvimento de técnicas de conhecimento da informação do canal no transmissor é muito importante em sistemas massive MIMO millimeter wave reais. Nesta dissertação é proposta e avaliada uma estratégia de envio de informação de canal para o transmissor para sistemas massive MIMO OFDM híbrido, onde apenas uma parte dos parâmetros associados ao canal são quantificados e transmitidos para o transmissor. A estratégia de feedback proposta é baseada numa quantização uniforme das amplitudes de canal, ângulos de partida e de chegada, no domínio do tempo. Depois de serem enviadas, essas informações são usadas para reconstruir o canal geral no domínio da frequência e a matriz da antena de transmissão, que são então usadas para obter os precoders híbridos analógico-digitais. Os resultados numéricos mostram que a estratégia de quantificação proposta atinge um desempenho próximo ao obtido caso se conhecesse o canal perfeito no transmissor, com um baixo overhead e complexidadeMestrado em Engenharia Eletrónica e Telecomunicaçõe

Channel estimation in massive MIMO systems

Last years were characterized by a great demand for high data throughput, good quality and spectral efficiency in wireless communication systems. Consequently, a revolution in cellular networks has been set in motion towards to 5G. Massive multiple-input multiple-output (MIMO) is one of the new concepts in 5G and the idea is to scale up the known MIMO systems in unprecedented proportions, by deploying hundreds of antennas at base stations. Although, perfect channel knowledge is crucial in these systems for user and data stream separation in order to cancel interference. The most common way to estimate the channel is based on pilots. However, problems such as interference and pilot contamination (PC) can arise due to the multiplicity of channels in the wireless link. Therefore, it is crucial to define techniques for channel estimation that together with pilot contamination mitigation allow best system performance and at same time low complexity. This work introduces a low-complexity channel estimation technique based on Zadoff-Chu training sequences. In addition, different approaches were studied towards pilot contamination mitigation and low complexity schemes, with resort to iterative channel estimation methods, semi-blind subspace tracking techniques and matrix inversion substitutes. System performance simulations were performed for the several proposed techniques in order to identify the best tradeoff between complexity, spectral efficiency and system performance

Secured Audio Signal Transmission in 5G Compatible mmWave Massive MIMO FBMC System with Implementation of Audio-to-image Transformation Aided Encryption Scheme

In this paper, we have made comprehensive study for the performance evaluation of mmWave massive MIMO FBMC wireless communication system. The 165F2;56 large MIMO antenna configured simulated system under investigation incorporates three modern channel coding (Turbo, LDPC and (3, 2) SPC, higher order digital modulation (256-QAM)) and various signal detection (Q-Less QR, Lattice Reduction(LR) based Zero-forcing(ZF), Lattice Reduction (LR) based ZF-SIC and Complex-valued LLL(CLLL) algorithm implemented ZF-SIC) schemes. An audio to image conversion aided chaos-based physical layer security scheme has also been implemented in such study. On considering transmission of encrypted audio signal in a hostile fading channel, it is noticeable from MATLAB based simulation study that the LDPC Channel encoded system is very much robust and effective in retrieving color image under utilization of Lattice Reduction(LR) based ZF-SIC signal detection and 16- QAM digital modulation techniques

Towards versatile access networks (Chapter 3)

Compared to its previous generations, the 5th generation (5G) cellular network features an additional type of densification, i.e., a large number of active antennas per access point (AP) can be deployed. This technique is known as massive multipleinput multiple-output (mMIMO) [1]. Meanwhile, multiple-input multiple-output (MIMO) evolution, e.g., in channel state information (CSI) enhancement, and also on the study of a larger number of orthogonal demodulation reference signal (DMRS) ports for MU-MIMO, was one of the Release 18 of 3rd generation partnership project (3GPP Rel-18) work item. This release (3GPP Rel-18) package approval, in the fourth quarter of 2021, marked the start of the 5G Advanced evolution in 3GPP. The other items in 3GPP Rel-18 are to study and add functionality in the areas of network energy savings, coverage, mobility support, multicast broadcast services, and positionin

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

Low-latency Networking: Where Latency Lurks and How to Tame It

While the current generation of mobile and fixed communication networks has been standardized for mobile broadband services, the next generation is driven by the vision of the Internet of Things and mission critical communication services requiring latency in the order of milliseconds or sub-milliseconds. However, these new stringent requirements have a large technical impact on the design of all layers of the communication protocol stack. The cross layer interactions are complex due to the multiple design principles and technologies that contribute to the layers' design and fundamental performance limitations. We will be able to develop low-latency networks only if we address the problem of these complex interactions from the new point of view of sub-milliseconds latency. In this article, we propose a holistic analysis and classification of the main design principles and enabling technologies that will make it possible to deploy low-latency wireless communication networks. We argue that these design principles and enabling technologies must be carefully orchestrated to meet the stringent requirements and to manage the inherent trade-offs between low latency and traditional performance metrics. We also review currently ongoing standardization activities in prominent standards associations, and discuss open problems for future research

Implementation of Deep-Learning-Based CSI Feedback Reporting on 5G NR-Compliant Link-Level Simulator

Advances in machine learning have widened the range of its applications in many fields. In particular, deep learning has attracted much interest for its ability to provide solutions where the derivation of a rigorous mathematical model of the problem is troublesome. Our interest was drawn to the application of deep learning for channel state information feedback reporting, a crucial problem in frequency division duplexing (FDD) 5G networks, where knowledge of the channel characteristics is fundamental to exploiting the full potential of multiple-input multiple-output (MIMO) systems. We designed a framework adopting a 5G New Radio convolutional neural network, called NR-CsiNet, with the aim of compressing the channel matrix experienced by the user at the receiver side and then reconstructing it at the transmitter side. In contrast to similar solutions, our framework is based on a 5G New Radio fully compliant simulator, thus implementing a channel generator based on the latest 3GPP 3-D channel model. Moreover, realistic 5G scenarios are considered by including multi-receiving antenna schemes and noisy downlink channel estimation. Simulations were carried out to analyze and compare the performance with current feedback reporting schemes, showing promising results for this approach from the point of view of the block error rate and throughput of the 5G data channel