A DETAILED ANALYSIS AND OPTIMIZATION OF THE MODIFIED POLAR DECODING RNTI RECOVERY METHOD TO TRACK USER ACTIVITY IN 5G NETWORKS

In this thesis, we analyze and optimize the modified polar decoding and syndrome matching radio network temporary identifier (RNTI) recovery method to de-anonymize the physical downlink control channel (PDCCH) in 5G networks. We present the impact on RNTI recovery of payload length, codeword length, signal-to-noise ratio (SNR) and the Hamming and longest common substring (LCS) recovery methods. Further, we consider the full set of RNTIs and downlink control information (DCI) fields that can be examined for user activity data and propose methods to track user activity within radio networks from the recovered data. Finally, we optimize the RNTI recovery method for different attacker scenarios to demonstrate how an attacker can recover RNTIs, track UEs, and aggregate data about the UE usage patterns and/or metadata about the user.DOD Space, Chantilly, VA 20151Lieutenant Commander, United States NavyApproved for public release. Distribution is unlimited

희소인지를 이용한 전송기술 연구

학위논문 (박사)-- 서울대학교 대학원 : 공과대학 전기·정보공학부, 2019. 2. 심병효.The new wave of the technology revolution, named the fifth wireless systems, is changing our daily life dramatically. These days, unprecedented services and applications such as driverless vehicles and drone-based deliveries, smart cities and factories, remote medical diagnosis and surgery, and artificial intelligence-based personalized assistants are emerging. Communication mechanisms associated with these new applications and services are way different from traditional communications in terms of latency, energy efficiency, reliability, flexibility, and connection density. Since the current radio access mechanism cannot support these diverse services and applications, a new approach to deal with these relentless changes should be introduced. This compressed sensing (CS) paradigm is very attractive alternative to the conventional information processing operations including sampling, sensing, compression, estimation, and detection. To apply the CS techniques to wireless communication systems, there are a number of things to know and also several issues to be considered. In the last decade, CS techniques have spread rapidly in many applications such as medical imaging, machine learning, radar detection, seismology, computer science, statistics, and many others. Also, various wireless communication applications exploiting the sparsity of a target signal have been studied. Notable examples include channel estimation, interference cancellation, angle estimation, spectrum sensing, and symbol detection. The distinct feature of this work, in contrast to the conventional approaches exploiting naturally acquired sparsity, is to exploit intentionally designed sparsity to improve the quality of the communication systems. In the first part of the dissertation, we study the mapping data information into the sparse signal in downlink systems. We propose an approach, called sparse vector coding (SVC), suited for the short packet transmission. In SVC, since the data information is mapped to the position of sparse vector, whole data packet can be decoded by idenitifying nonzero positions of the sparse vector. From our simulations, we show that the packet error rate of SVC outperforms the conventional channel coding schemes at the URLLC regime. Moreover, we discuss the SVC transmission for the massive MTC access by overlapping multiple SVC-based packets into the same resources. Using the spare vector overlapping and multiuser CS decoding scheme, SVC-based transmission provides robustness against the co-channel interference and also provide comparable performance than other non-orthogonal multiple access (NOMA) schemes. By using the fact that SVC only identifies the support of sparse vector, we extend the SVC transmission without pilot transmission, called pilot-less SVC. Instead of using the support, we further exploit the magnitude of sparse vector for delivering additional information. This scheme is referred to as enhanced SVC. The key idea behind the proposed E-SVC transmission scheme is to transform the small information into a sparse vector and map the side-information into a magnitude of the sparse vector. Metaphorically, E-SVC can be thought as a standing a few poles to the empty table. As long as the number of poles is small enough and the measurements contains enough information to find out the marked cell positions, accurate recovery of E-SVC packet can be guaranteed. In the second part of this dissertation, we turn our attention to make sparsification of the non-sparse signal, especially for the pilot transmission and channel estimation. Unlike the conventional scheme where the pilot signal is transmitted without modification, the pilot signals are sent after the beamforming in the proposed technique. This work is motivated by the observation that the pilot overhead must scale linearly with the number of taps in CIR vector and the number of transmit antennas so that the conventional pilot transmission is not an appropriate option for the IoT devices. Primary goal of the proposed scheme is to minimize the nonzero entries of a time-domain channel vector by the help of multiple antennas at the basestation. To do so, we apply the time-domain sparse precoding, where each precoded channel propagates via fewer tap than the original channel vector. The received channel vector of beamformed pilots can be jointly estimated by the sparse recovery algorithm.5세대 무선통신 시스템의 새로운 기술 혁신은 무인 차량 및 항공기, 스마트 도시 및 공장, 원격 의료 진단 및 수술, 인공 지능 기반 맟춤형 지원과 같은 전례 없는 서비스 및 응용프로그램으로 부상하고 있다. 이러한 새로운 애플리케이션 및 서비스와 관련된 통신 방식은 대기 시간, 에너지 효율성, 신뢰성, 유연성 및 연결 밀도 측면에서 기존 통신과 매우 다르다. 현재의 무선 액세스 방식을 비롯한 종래의 접근법은 이러한 요구 사항을 만족할 수 없기 때문에 최근에 sparse processing과 같은 새로운 접근 방법이 연구되고 있다. 이 새로운 접근 방법은 표본 추출, 감지, 압축, 평가 및 탐지를 포함한 기존의 정보 처리에 대한 효율적인 대체기술로 활용되고 있다. 지난 10년 동안 compressed sensing (CS)기법은 의료영상, 기계학습, 탐지, 컴퓨터 과학, 통계 및 기타 여러 분야에서 빠르게 확산되었다. 또한, 신호의 희소성(sparsity)를 이용하는 CS 기법은 다양한 무선 통신이 연구되었다. 주목할만한 예로는 채널 추정, 간섭 제거, 각도 추정, 및 스펙트럼 감지가 있으며 현재까지 연구는 주어진 신호가 가지고 있는 본래의 희소성에 주목하였으나 본 논문에서는 기존의 접근 방법과 달리 인위적으로 설계된 희소성을 이용하여 통신 시스템의 성능을 향상시키는 방법을 제안한다. 우선 본 논문은 다운링크 전송에서 희소 신호 매핑을 통한 데이터 전송 방법을 제안하며 짧은 패킷 (short packet) 전송에 적합한 CS 접근법을 활용하는 기술을 제안한다. 제안하는 기술인 희소벡터코딩 (sparse vector coding, SVC)은 데이터 정보가 인공적인 희소벡터의 nonzero element의 위치에 매핑하여 전송된 데이터 패킷은 희소벡터의 0이 아닌 위치를 식별함으로 원신호 복원이 가능하다. 분석과 시뮬레이션을 통해 제안하는 SVC 기법의 패킷 오류률은 ultra-reliable and low latency communications (URLLC) 서비스를 지원을 위해 사용되는 채널코딩방식보다 우수한 성능을 보여준다. 또한, 본 논문은 SVC기술을 다음의 세가지 영역으로 확장하였다. 첫째로, 여러 개의 SVC 기반 패킷을 동일한 자원에 겹치게 전송함으로 상향링크에서 대규모 전송을 지원하는 방법을 제안한다. 중첩된 희소벡터를 다중사용자 CS 디코딩 방식을 사용하여 채널 간섭에 강인한 성능을 제공하고 비직교 다중 접속 (NOMA) 방식과 유사한 성능을 제공한다. 둘째로, SVC 기술이 희소 벡터의 support만을 식별한다는 사실을 이용하여 파일럿 전송이 필요없는 pilotless-SVC 전송 방법을 제안한다. 채널 정보가 없는 경우에도 희소 벡터의 support의 크기는 채널의 크기에 비례하기 때문에 pilot없이 복원이 가능하다. 셋째로, 희소벡터의 support의 크기에 추가 정보를 전송함으로 복원 성능을 향상 시키는 enhanced SVC (E-SVC)를 제안한다. 제안된 E-SVC 전송 방식의 핵심 아디디어는 짧은 패킷을 전송되는 정보를 희소 벡터로 변환하고 정보 복원을 보조하는 추가 정보를 희소 벡터의 크기 (magnitude)로 매핑하는 것이다. 마지막으로, SVC 기술을 파일럿 전송에 활용하는 방법을 제안한다. 특히, 채널 추정을 위해 채널 임펄스 응답의 신호를 희소화하는 프리코딩 기법을 제안한다. 파일럿 신호을 프로코딩 없이 전송되는 기존의 방식과 달리, 제안된 기술에서는 파일럿 신호를 빔포밍하여 전송한다. 제안된 기법은 기지국에서 다중 안테나를 활용하여 채널 응답의 0이 아닌 요소를 최소화하는 시간 영역 희소 프리코딩을 적용하였다. 이를 통해 더 적확한 채널 추정을 가능하며 더 적은 파일럿 오버헤드로 채널 추정이 가능하다.Abstract i Contents iv List of Tables viii List of Figures ix 1 INTRODUCTION 1 1.1 Background . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1 1.1.1 Three Key Services in 5G systems . . . . . . . . . . . . . . . 2 1.1.2 Sparse Processing in Wireless Communications . . . . . . . . 4 1.2 Contributions and Organization . . . . . . . . . . . . . . . . . . . . . 7 1.3 Notation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 10 2 Sparse Vector Coding for Downlink Ultra-reliable and Low Latency Communications 12 2.1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 13 2.2 URLLC Service Requirements . . . . . . . . . . . . . . . . . . . . . 15 2.2.1 Latency . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 15 2.2.2 Ultra-High Reliability . . . . . . . . . . . . . . . . . . . . . 17 2.2.3 Coexistence . . . . . . . . . . . . . . . . . . . . . . . . . . . 18 2.3 URLLC Physical Layer in 5G NR . . . . . . . . . . . . . . . . . . . 18 2.3.1 Packet Structure . . . . . . . . . . . . . . . . . . . . . . . . 19 2.3.2 Frame Structure and Latency-sensitive Scheduling Schemes . 20 2.3.3 Solutions to the Coexistence Problem . . . . . . . . . . . . . 22 2.4 Short-sized Packet in LTE-Advanced Downlink . . . . . . . . . . . . 24 2.5 Sparse Vector Coding . . . . . . . . . . . . . . . . . . . . . . . . . . 25 2.5.1 SVC Encoding and Transmission . . . . . . . . . . . . . . . 25 2.5.2 SVC Decoding . . . . . . . . . . . . . . . . . . . . . . . . . 30 2.5.3 Identification of False Alarm . . . . . . . . . . . . . . . . . . 33 2.6 SVC Performance Analysis . . . . . . . . . . . . . . . . . . . . . . . 36 2.7 Implementation Issues . . . . . . . . . . . . . . . . . . . . . . . . . 48 2.7.1 Codebook Design . . . . . . . . . . . . . . . . . . . . . . . . 48 2.7.2 High-order Modulation . . . . . . . . . . . . . . . . . . . . . 49 2.7.3 Diversity Transmission . . . . . . . . . . . . . . . . . . . . . 50 2.7.4 SVC without Pilot . . . . . . . . . . . . . . . . . . . . . . . 50 2.7.5 Threshold to Prevent False Alarm Event . . . . . . . . . . . . 51 2.8 Simulations and Discussions . . . . . . . . . . . . . . . . . . . . . . 52 2.8.1 Simulation Setup . . . . . . . . . . . . . . . . . . . . . . . . 52 2.8.2 Simulation Results . . . . . . . . . . . . . . . . . . . . . . . 53 2.9 Summary . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 56 3 Sparse Vector Coding for Uplink Massive Machine-type Communications 59 3.1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 60 3.2 Uplink NOMA transmission for mMTC . . . . . . . . . . . . . . . . 61 3.3 Sparse Vector Coding based NOMA for mMTC . . . . . . . . . . . . 63 3.3.1 System Model . . . . . . . . . . . . . . . . . . . . . . . . . 63 3.3.2 Joint Multiuser Decoding . . . . . . . . . . . . . . . . . . . . 66 3.4 Simulations and Discussions . . . . . . . . . . . . . . . . . . . . . . 68 3.4.1 Simulation Setup . . . . . . . . . . . . . . . . . . . . . . . . 68 3.4.2 Simulation Results . . . . . . . . . . . . . . . . . . . . . . . 69 3.5 Summary . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 71 4 Pilot-less Sparse Vector Coding for Short Packet Transmission 72 4.1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 73 4.2 Pilot-less Sparse Vector Coding Processing . . . . . . . . . . . . . . 75 4.2.1 SVC Processing with Pilot Symbols . . . . . . . . . . . . . . 75 4.2.2 Pilot-less SVC . . . . . . . . . . . . . . . . . . . . . . . . . 76 4.2.3 PL-SVC Decoding in Multiple Basestation Antennas . . . . . 78 4.3 Simulations and Discussions . . . . . . . . . . . . . . . . . . . . . . 80 4.3.1 Simulation Setup . . . . . . . . . . . . . . . . . . . . . . . . 80 4.3.2 Simulation Results . . . . . . . . . . . . . . . . . . . . . . . 81 4.4 Summary . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 82 5 Joint Analog and Quantized Feedback via Sparse Vector Coding 84 5.1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 84 5.2 System Model for Joint Spase Vector Coding . . . . . . . . . . . . . 86 5.3 Sparse Recovery Algorithm and Performance Analysis . . . . . . . . 90 5.4 Applications . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 95 5.4.1 Linear Interpolation of Sensing Information . . . . . . . . . . 96 5.4.2 Linear Combined Feedback . . . . . . . . . . . . . . . . . . 96 5.4.3 One-shot Packet Transmission . . . . . . . . . . . . . . . . . 96 5.5 Simulations . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 97 5.5.1 Assumptions . . . . . . . . . . . . . . . . . . . . . . . . . . 97 5.5.2 Results and Discussions . . . . . . . . . . . . . . . . . . . . 98 5.6 Summary . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 98 6 Sparse Beamforming for Enhanced Mobile Broadband Communications 101 6.1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 102 6.1.1 Increase the number of transmit antennas . . . . . . . . . . . 102 6.1.2 2D active antenna system (AAS) . . . . . . . . . . . . . . . . 103 6.1.3 3D channel environment . . . . . . . . . . . . . . . . . . . . 104 6.1.4 RS transmission for CSI acquisition . . . . . . . . . . . . . . 106 6.2 System Design and Standardization of FD-MIMO Systems . . . . . . 107 6.2.1 Deployment scenarios . . . . . . . . . . . . . . . . . . . . . 108 6.2.2 Antenna configurations . . . . . . . . . . . . . . . . . . . . . 108 6.2.3 TXRU architectures . . . . . . . . . . . . . . . . . . . . . . 109 6.2.4 New CSI-RS transmission strategy . . . . . . . . . . . . . . . 112 6.2.5 CSI feedback mechanisms for FD-MIMO systems . . . . . . 114 6.3 System Model . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 116 6.3.1 Basic System Model . . . . . . . . . . . . . . . . . . . . . . 116 6.3.2 Beamformed Pilot Transmission . . . . . . . . . . . . . . . . 117 6.4 Sparsification of Pilot Beamforming . . . . . . . . . . . . . . . . . . 118 6.4.1 Time-domain System Model without Pilot Beamforming . . . 119 6.4.2 Pilot Beamforming . . . . . . . . . . . . . . . . . . . . . . . 120 6.5 Channel Estimation of Beamformed Pilots . . . . . . . . . . . . . . . 124 6.5.1 Recovery using Multiple Measurement Vector . . . . . . . . . 124 6.5.2 MSE Analysis . . . . . . . . . . . . . . . . . . . . . . . . . 128 6.6 Simulations and Discussion . . . . . . . . . . . . . . . . . . . . . . . 129 6.6.1 Simulation Setup . . . . . . . . . . . . . . . . . . . . . . . . 129 6.6.2 Simulation Results . . . . . . . . . . . . . . . . . . . . . . . 130 6.7 Summary . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 133 7 Conclusion 136 7.1 Summary . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 136 7.2 Future Research Directions . . . . . . . . . . . . . . . . . . . . . . . 139 Abstract (In Korean) 152Docto

Performance analysis of interference measurement methods for link adaptation in 5G New Radio

5G New Radio (NR) is coming faster than expected with early deployments which take place early 2019. It is more than a new mobile generation that offers higher data rates compared to previous generations, although it’s still the main driver. It will enable many new use cases and deployment scenarios that can be put into three main categories: enhanced mobile broad band (eMBB), ultra-reliable low latency communications (URLLC) and massive machine type communications (mMTC). 5G NR aims to further increase frequency resources utilization and efficiency. Cell edge users usually suffer from high levels of interference known as inter-cell interference. This phenomenon results in lower performance for the cell edge users and inefficient utilization of radio resources. Link adaptation techniques aim to increase cell edge performance by exploiting varying channel conditions and interference level at user equipment (UE). In this thesis channel state information (CSI) is studied as an essential part of link adaptation process. Channel quality indicator (CQI) is the main component of CSI reports from UE that gives recommendations about the next transmission modulation order and code rate. The accuracy of reported CQI depends on the accuracy of channel and interference measurements. In this thesis two different interference measurement methods based on two reference signals are studied: CSI interference measurement (CSI-IM) and non-zero power CSI reference signal (NZP CSI-RS). In this thesis performance with different configurable factors, different channel models and UE speeds are considered. Overall system overhead is also studied to give recommendation about the configuration of lower system overhead. Simulation results has shown that CSI-IM based interference measurement is more efficient compared to NZP CSI-RS method and operates well in different channel scenarios and different UE speed. While NZP CS-RS shows sensitivity to frequency selective channels and in higher user mobility cases. On the other hand, from overall system overhead perspective, CSI-IM based configuration is the best solution

Cooperative Radio Communications for Green Smart Environments

The demand for mobile connectivity is continuously increasing, and by 2020 Mobile and Wireless Communications will serve not only very dense populations of mobile phones and nomadic computers, but also the expected multiplicity of devices and sensors located in machines, vehicles, health systems and city infrastructures. Future Mobile Networks are then faced with many new scenarios and use cases, which will load the networks with different data traffic patterns, in new or shared spectrum bands, creating new specific requirements. This book addresses both the techniques to model, analyse and optimise the radio links and transmission systems in such scenarios, together with the most advanced radio access, resource management and mobile networking technologies. This text summarises the work performed by more than 500 researchers from more than 120 institutions in Europe, America and Asia, from both academia and industries, within the framework of the COST IC1004 Action on "Cooperative Radio Communications for Green and Smart Environments". The book will have appeal to graduates and researchers in the Radio Communications area, and also to engineers working in the Wireless industry. Topics discussed in this book include: • Radio waves propagation phenomena in diverse urban, indoor, vehicular and body environments• Measurements, characterization, and modelling of radio channels beyond 4G networks• Key issues in Vehicle (V2X) communication• Wireless Body Area Networks, including specific Radio Channel Models for WBANs• Energy efficiency and resource management enhancements in Radio Access Networks• Definitions and models for the virtualised and cloud RAN architectures• Advances on feasible indoor localization and tracking techniques• Recent findings and innovations in antenna systems for communications• Physical Layer Network Coding for next generation wireless systems• Methods and techniques for MIMO Over the Air (OTA) testin

Cooperative Radio Communications for Green Smart Environments

The demand for mobile connectivity is continuously increasing, and by 2020 Mobile and Wireless Communications will serve not only very dense populations of mobile phones and nomadic computers, but also the expected multiplicity of devices and sensors located in machines, vehicles, health systems and city infrastructures. Future Mobile Networks are then faced with many new scenarios and use cases, which will load the networks with different data traffic patterns, in new or shared spectrum bands, creating new specific requirements. This book addresses both the techniques to model, analyse and optimise the radio links and transmission systems in such scenarios, together with the most advanced radio access, resource management and mobile networking technologies. This text summarises the work performed by more than 500 researchers from more than 120 institutions in Europe, America and Asia, from both academia and industries, within the framework of the COST IC1004 Action on "Cooperative Radio Communications for Green and Smart Environments". The book will have appeal to graduates and researchers in the Radio Communications area, and also to engineers working in the Wireless industry. Topics discussed in this book include: • Radio waves propagation phenomena in diverse urban, indoor, vehicular and body environments• Measurements, characterization, and modelling of radio channels beyond 4G networks• Key issues in Vehicle (V2X) communication• Wireless Body Area Networks, including specific Radio Channel Models for WBANs• Energy efficiency and resource management enhancements in Radio Access Networks• Definitions and models for the virtualised and cloud RAN architectures• Advances on feasible indoor localization and tracking techniques• Recent findings and innovations in antenna systems for communications• Physical Layer Network Coding for next generation wireless systems• Methods and techniques for MIMO Over the Air (OTA) testin

Optimization of Spectrum Management in Massive Array Antenna Systems with MIMO

Fifth generation (5G), is being considered as a revolutionary technology in the telecommunication domain whose the challenges are mainly to achieve signal quality and great ability to work with free spectrum in the millimetre waves. Besides, other important innovations are the introduction of a more current architecture and the use of multiple antennas in transmission and reception. Digital communication using multiple input and multiple output (MIMO) wireless links has recently emerged as one of the most significant technical advances in modern communications. MIMO technology is able to offer a large increase in the capacity of these systems, without requiring a considerable increase in bandwidth or power required for transmission. This dissertation presents an overview of theoretical concepts of MIMO systems. With such a system a spatial diversity gain can be obtained by using space-time codes, which simultaneously exploit the spatial domain and the time domain. SISO, SIMO and MISO systems are differentiated by their channel capacity and their configuration in relation to the number of antennas in the transmitter/receiver. To verify the effectiveness of the MIMO systems a comparison between the capacity of SISO and MIMO systems has been performed using the Shannon’s principles. In the MIMO system some variations in the number of antennas arrays have been considered, and the superiority of transmission gains of the MIMO systems have been demonstrated. Combined with millimetre waves (mmWaves) technology, massive MIMO systems, where the number of antennas in the base station and the number of users are large, is a promising solution. SDR implementations have been performed considering a platform with Matlab code applied to MIMO 2x2 Radio and Universal Software Peripheral Radio (USRP). A detailed study was initially conducted to analyze the architecture of the USRP. Complex structures of MIMO systems can be simplified by using mathematical methods implemented in Matlab for the synchronization of the USRP in the receiver side. SISO transmission and reception techniques have been considered to refine the synchronization (with 16-QAM), thus facilitating the future implementation of the MIMO system. OpenAirInterface has been considered for 4G and 5G implementations of actual mobile radio communication systems. Together with the practical MIMO, this type of solution is the starting point for future hardware building blocks involving massive MIMO systems.A quinta geração (5G) está sendo considerada uma tecnologia revolucionária no setor de telecomunicações, cujos desafios são principalmente a obtenção de qualidade de sinal e grande capacidade de trabalhar com espectro livre nas ondas milimétricas. Além disso, outras inovações importantes são a introdução de uma arquitetura mais atual e o uso de múltiplas antenas em transmissão e recepção. A comunicação digital usando ligaçõe sem fio de múltiplas entradas e múltiplas saídas (MIMO) emergiu recentemente como um dos avanços técnicos mais significativos nas comunicações modernas. A tecnologia MIMO é capaz de oferecer um elevado aumento na capacidade, sem exigir um aumento considerável na largura de banda ou potência transmitida. Esta dissertação apresenta uma visão geral dos conceitos teóricos dos sistemas MIMO. Com esses sistemas, um ganho de diversidade espacial pode ser obtido utilizando códigos espaço-tempo reais. Os sistemas SISO, SIMO e MISO são diferenciados pela capacidade de seus canais e a sua configuração em relação ao número de antenas no emissor/receptor. Para verificar a eficiência dos sistemas MIMO, realizou-se uma comparação entre a capacidade dos sistemas SISO e MIMO utilizado os princípios de Shannon. Nos sistemas MIMO condecideraram-se algumas variações no número de agregados de antenas, e a superioridade dos ganhos de transmissão dos sistemas MIMO foi demonstrada. Combinado com a tecnologia de ondas milimétricas (mmWaves), os sistemas massivos MIMO, onde o número de antenas na estação base e o número de usuários são grandes, são uma solução promissora. As implementações do SDR foram realizadas considerando uma plataforma com código Matlab aplicado aos rádios MIMO 2x2 e Universal Software Peripheral Radio (USRP). Um estudo detalhado foi inicialmente conduzido para analisar a arquitetura da USRP. Estruturas complexas de sistemas MIMO podem ser simplificadas usando métodos matemáticos implementados no Matlab para a sincronização do USRP no lado do receptor. Consideraram-se técnicas de transmissão e recepção SISO para refinar a sincronização (com 16-QAM), facilitando assim a implementação futura do sistema MIMO . Considerou-se o OpenAirInterface para implementações 4G e 5G de sistemas reais de comunicações móveis. Juntamente com o MIMO na pratica, este tipo de solução é o ponto de partida para futuros blocos de construção de hardware envolvendo sistemas MIMO massivos