Robust massive MIMO Equilization for mmWave systems with low resolution ADCs

Leveraging the available millimeter wave spectrum will be important for 5G. In this work, we investigate the performance of digital beamforming with low resolution ADCs based on link level simulations including channel estimation, MIMO equalization and channel decoding. We consider the recently agreed 3GPP NR type 1 OFDM reference signals. The comparison shows sequential DCD outperforms MMSE-based MIMO equalization both in terms of detection performance and complexity. We also show that the DCD based algorithm is more robust to channel estimation errors. In contrast to the common believe we also show that the complexity of MMSE equalization for a massive MIMO system is not dominated by the matrix inversion but by the computation of the Gram matrix.Comment: submitted to WCNC 2018 Workshop

DeepTx: Deep Learning Beamforming with Channel Prediction

Machine learning algorithms have recently been considered for many tasks in the field of wireless communications. Previously, we have proposed the use of a deep fully convolutional neural network (CNN) for receiver processing and shown it to provide considerable performance gains. In this study, we focus on machine learning algorithms for the transmitter. In particular, we consider beamforming and propose a CNN which, for a given uplink channel estimate as input, outputs downlink channel information to be used for beamforming. The CNN is trained in a supervised manner considering both uplink and downlink transmissions with a loss function that is based on UE receiver performance. The main task of the neural network is to predict the channel evolution between uplink and downlink slots, but it can also learn to handle inefficiencies and errors in the whole chain, including the actual beamforming phase. The provided numerical experiments demonstrate the improved beamforming performance.Comment: 27 pages, this work has been submitted to the IEEE for possible publication; v2: Fixed typo in author name, v3: a revisio

Uplink data measurement and analysis for 5G eCPRI radio unit

Abstract. The new 5G mobile network generation aims to enhance the performance of the cellular network in almost every possible aspect, offering higher data rates, lower latencies, and massive number of network connections. Arguably the most important change from LTE are the new RU-BBU split options for 5G promoted by 3GPP and other organizations. Another big conceptual shift introduced with 5G is the open RAN concept, pushed forward by organizations such as the O-RAN alliance. O-RAN aims to standardize the interfaces between different RAN elements in a way that promotes vendor interoperability and lowers the entry barrier for new equipment suppliers. Moreover, the 7-2x split option standardized by O-RAN has risen as the most important option within the different low layer split options. As the fronthaul interface, O-RAN has selected the packet-based eCPRI protocol, which has been designed to be more flexible and dynamic in terms of transport network and data-rates compared to its predecessor CPRI. Due to being a new interface, tools to analyse data from this interface are lacking. In this thesis, a new, Python-based data analysis tool for UL eCPRI data was created for data quality validation purposes from any O-RAN 7-2x functional split based 5G eCPRI radio unit. The main goal for this was to provide concrete KPIs from captured data, including timing offset, signal power level and error vector magnitude. The tool produces visual and text-based outputs that can be used in both manual and automated testing. The tool has enhanced eCPRI UL datapath testing in radio unit integration teams by providing actual quality metrics and enabling test automation.Uplink datamittaukset ja -analyysi 5G eCPRI radiolla. Tiivistelmä. Uusi 5G mobiiliverkkogeneraatio tuo mukanaan parannuksia lähes kaikkiin mobiiliverkon ominaisuuksiin, tarjoten nopeamman datasiirron, pienemmät viiveet ja valtavat laiteverkostot. Luultavasti tärkein muutos LTE teknologiasta ovat 3GPP:n ja muiden organisaatioiden ehdottamat uudet radion ja systeemimoduulin väliset funktionaaliset jakovaihtoehdot. Toinen huomattava muutos 5G:ssä on O-RAN:in ajama avoimen RAN:in konsepti, jonka tarkoituksena on standardisoida verkkolaitteiden väliset rajapinnat niin, että RAN voidaan rakentaa eri valmistajien laitteista, laskien uusien laitevalmistajien kynnystä astua verkkolaitemarkkinoille. O-RAN:n standardisoima 7-2x funktionaalinen jako on noussut tärkeimmäksi alemman tason jakovaihtoehdoista. Fronthaul rajapinnan protokollaksi O-RAN on valinnut pakettitiedonsiirtoon perustuvan eCPRI:n, joka on suunniteltu dynaamisemmaksi ja joustavammaksi datanopeuksien ja lähetysverkon suhteen kuin edeltävä CPRI protokolla. Uutena protokollana, eCPRI rajapinnalle soveltuvia data-analyysityökaluja ei ole juurikaan saatavilla. Tässä työssä luotiin uusi pythonpohjainen data-analyysityökalu UL suunnan eCPRI datalle, jotta datan laatu voidaan määrittää millä tahansa O-RAN 7-2x funktionaaliseen jakoon perustuvalla 5G eCPRI radiolla. Työkalun päätarkoitus on analysoida ja kuvata datan laatua laskemalla datan ajoitusoffsettia, tehotasoa, sekä EVM:ää. Työkalu tuottaa tulokset visuaalisena ja tekstipohjaisena, jotta analyysia voidaan tehdä niin manuaalisessa kuin automaattisessa testauksessa. Työkalun käyttöönotto on tehostanut UL suunnan dataputken testausta radio-integrointitiimeissä, tarjoten datan laatua kuvaavaa metriikkaa sekä mahdollistaen testauksen automatisoinnin

A Theoretical and Experimental Investigation on the Measurement of the Electromagnetic Field Level Radiated by 5G Base Stations

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Massive MIMO Systems for 5G Communications

Massive MIMO will improve the performance of future 5G systems in terms of data rate and spectral efficiency, while accommodating a large number of users. Furthermore, it allows for 3D beamforming in order to provide more degrees of freedom and increase the number of high-throughput users. Massive MIMO is expected to provide more advantages compared to other solutions in terms of energy and spectral efficiency. This will be achieved by focusing the radiation towards the direction of the intended users, thus implementing simultaneous transmission to many users while keeping interference low. It can boost the capacity compared to a conventional antenna solution, resulting in a spectral efficiency up to 50 times greater than that provided by actual 4G technology. However, to take full advantage of this technology and to overcome the challenges of implementation in a real environment, a complicated radio system is required. The purpose of this work is to present the MIMO technology evolution and challenges in a simple introductory way and investigate potential system enhancements