Design and optimization of joint iterative detection and decoding receiver for uplink polar coded SCMA system

SCMA and polar coding are possible candidates for 5G systems. In this paper, we firstly propose the joint iterative detection and decoding (JIDD) receiver for the uplink polar coded sparse code multiple access (PC-SCMA) system. Then, the EXIT chart is used to investigate the performance of the JIDD receiver. Additionally, we optimize the system design and polar code construction based on the EXIT chart analysis. The proposed receiver integrates the factor graph of SCMA detector and polar soft-output decoder into a joint factor graph, which enables the exchange of messages between SCMA detector and polar decoder iteratively. Simulation results demonstrate that the JIDD receiver has better BER performance and lower complexity than the separate scheme. Specifically, when polar code length N=256 and code rate R=1/2 , JIDD outperforms the separate scheme 4.8 and 6 dB over AWGN channel and Rayleigh fading channel, respectively. It also shows that, under 150% system loading, the JIDD receiver only has 0.3 dB performance loss compared to the single user uplink PC-SCMA over AWGN channel and 0.6 dB performance loss over Rayleigh fading channel

Mapping Design for 2M -Ary Bit-Interleaved Polar Coded Modulation

This paper proposes a mapping design for bit-interleaved polar coded modulation (BIPCM) systems with belief propagation (BP) decoding. We first introduce a two-layer bipartite graph to represent BIPCM, where a new mapping graph linking polar graph to modulator is added to the conventional factor graph. Then, a mapping design is proposed and the design paradigm is to separate sub-channels with lower reliability to different stopping trees of polar codes, aiming to make sure that each stopping tree receives reliable extrinsic information from demodulator. The proposed mapping algorithm is employed for BIPCM with traditional polar codes over 16-quadrature amplitude modulation (QAM) and 256-QAM. Numerical results show that our scheme can improve the error-correcting performance compared to the conventional scheme with a random mapping. Furthermore, to meet code-length requirement of different modulation orders, we propose an efficient method to construct flexible-length polar code (FLPC) by coupling several short length polar codes with a repeat-accumulate (RA) code. Also, the proposed FLPC is employed in the BIPCM system, with the designed mapping algorithm, simulation result also reveals that the block error rate performance of proposed BIPCM scheme with BP decoding outperforms the one with successive cancellation decoding by providing a gain up to 1 dB

Cellular, Wide-Area, and Non-Terrestrial IoT: A Survey on 5G Advances and the Road Towards 6G

The next wave of wireless technologies is proliferating in connecting things among themselves as well as to humans. In the era of the Internet of things (IoT), billions of sensors, machines, vehicles, drones, and robots will be connected, making the world around us smarter. The IoT will encompass devices that must wirelessly communicate a diverse set of data gathered from the environment for myriad new applications. The ultimate goal is to extract insights from this data and develop solutions that improve quality of life and generate new revenue. Providing large-scale, long-lasting, reliable, and near real-time connectivity is the major challenge in enabling a smart connected world. This paper provides a comprehensive survey on existing and emerging communication solutions for serving IoT applications in the context of cellular, wide-area, as well as non-terrestrial networks. Specifically, wireless technology enhancements for providing IoT access in fifth-generation (5G) and beyond cellular networks, and communication networks over the unlicensed spectrum are presented. Aligned with the main key performance indicators of 5G and beyond 5G networks, we investigate solutions and standards that enable energy efficiency, reliability, low latency, and scalability (connection density) of current and future IoT networks. The solutions include grant-free access and channel coding for short-packet communications, non-orthogonal multiple access, and on-device intelligence. Further, a vision of new paradigm shifts in communication networks in the 2030s is provided, and the integration of the associated new technologies like artificial intelligence, non-terrestrial networks, and new spectra is elaborated. Finally, future research directions toward beyond 5G IoT networks are pointed out.Comment: Submitted for review to IEEE CS&

Evaluation of Channel Coding Methods for Next Generation Mobile Communication Standards

La codificación de canales es crucial para los sistemas de comunicación móvil, y los sistemas de comunicación inalámbrica 5G han decidido utilizar los códigos LDPC como esquema de codificación para sus canales de datos y los códigos Polares como esquema de codificación para sus canales de control. Este estudio se centra en los fundamentos de los códigos LDPC y los códigos Polares, especialmente los nuevos códigos polares, explicando en detalle sus características de polarización y las técnicas de decodificación recursiva. También se estudia las especificaciones de diseño relacionadas con estos dos esquemas de codificación de canales en 5G. Mediante simulaciones, se compara el rendimiento del nuevo esquema de codificación de canales inalámbricos 5G con el de los códigos Turbo a diferentes longitudes de bloque y tasas de código, y se extraen conclusiones relevantes para demostrar la aplicabilidad del esquema de codificación de canales 5G NR.Channel coding is essential for mobile communication systems, and the 5G wireless standardization committees decided to use LDPC codes as the coding scheme of its data channel and Polar codes as the coding scheme of its control channel. This study focuses on the fundamentals of LDPC codes and Polar codes, especially the emerging Polar codes, with detailed explanations of their polarization characteristics and recursive decoding techniques. It is also focused on the design specification related to these two channel coding schemes in 5G. The performance of the 5G New Radio channel coding scheme is compared with that of LTE Turbo codes at different block lengths and code rates through simulations, and relevant conclusions are drawn to demonstrate the suitability of the 5G NR channel coding scheme.Grado en Ingeniería en Sistemas de Telecomunicació

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

Avaliação da probabilidade de erro de bit e da eficiência espectral de sistemas celulares MC-CDMA que utilizam detecção multiusuário

Orientador: Celso de AlmeidaTese (doutorado) - Universidade Estadual de Campinas, Faculdade de Engenharia Elétrica e de ComputaçãoResumo: Uma técnica que combina múltiplo acesso por divisão de código (CDMA) e multiplexação por divisão de frequências ortogonais (OFDM) foi proposta como uma opção para futuros padrões de comunicações móveis. Esta técnica é conhecida como múltiplo acesso por divisão de código com multiportadoras (MC-CDMA), a qual além de herdar as vantagens das técnicas CDMA e OFDM, também possui uma inerente diversidade em frequência. Apesar de ser uma técnica de múltiplo acesso, MC-CDMA foi tipicamente estudada usando detectores de um único usuário no receptor. Além disso, alguns trabalhos que têm estudado seu desempenho com detectores multi-usuário usam apenas simulações. Ademais, cenários de uma célula são tipicamente considerados, embora sistemas móveis operem em ambientes celulares. Esta dissertação visa complementar parcialmente as pesquisas prévias sobre MC-CDMA. Em geral, este trabalho aborda o desempenho do enlace reverso de sistemas MC-CDMA em termos da taxa de erro de bit (BER) e da eficiência espectral celular. Para isto, um sistema celular que usa os esquemas de reuso de frequências fracionário (FFR) e suave (SFR) é suposto. Entrelaçamento no domínio da frequência é usado no transmissor dos equipamentos dos usuários e, detecção multiusuário e um arranjo de antenas são considerados nos receptores das estações radio base. O transmissor dos equipamentos dos usuários também realiza controle de potência perfeito. Além disso, ruído aditivo Gaussiano branco, perda de percurso e desvanecimento lento e seletivo que segue a distribuição de Rayleigh são considerados no modelo do canal. As contribuições desta dissertação são resumidas a seguir. Expressões fechadas são obtidas para avaliar a BER média de um sistema celular que usa os detectores multiusuário: zero-forcing (ZF), minimum mean square error (MMSE) e maximum likelihood detector (MU-MLD). Adicionalmente, a técnica signal space diversity (SSD) é usada no sistema celular MC-CDMA. Para isto, o MU-MLD precisa ser empregado no receptor da estação radio base. Uma expressão precisa para avaliar a BER média neste cenário é também derivada. Ademais, uma análise assintótica das expressões da BER é feita para se obter mais informações sobre a ordem da diversidade e o comportamento do sistema no regime de alta relação sinal-ruído mais interferência. A complexidade computacional dos detectores multi-usuário também é obtida em termos do número de operações complexas realizadas durante o processo de detecção. Em particular, o MU-MLD é implementado através de um algoritmo de decodificação esférica (SD), a fim de reduzir sua complexidade. Algumas técnicas são fornecidas para reduzir ainda mais a complexidade da SD. Finalmente, uma expressão para avaliar a eficiência espectral celular média do sistema MC-CDMA nos cenários FFR e SFR é obtida. Esta análise é baseada em um algoritmo que calcula os raios de cobertura da célula para cada modulação usada no sistema, assumindo que modulação adaptativa é empregada. Para todos os cenários, modulações BPSK e M-QAM são consideradas. Simulações de Monte Carlo corroboram a precisão da análise matemática apresentadaAbstract: A hybrid technique combining code division multiple access (CDMA) and orthogonal frequency division multiplexing (OFDM) has been proposed as an option for future mobile communication standards. This technique is known as multicarrier code division multiple access (MC-CDMA), which, besides inheriting the advantages of CDMA and OFDM techniques, also possesses an inherent frequency diversity. Despite being a multiple access technique, MC-CDMA has been typically studied employing single-user detectors in the receiver. Moreover, some works that have studied their performance with multiuser detectors have done so far using only simulations. Furthermore, single cell scenarios are typically considered although mobile systems operate in cellular environments. This dissertation aims to partially complement previous research on MC-CDMA. In general, this work addresses the uplink performance of MC-CDMA systems in terms of the bit error rate (BER) and the cellular spectral efficiency. For this, a cellular system employing fractional frequency reuse (FFR) and soft frequency reuse (SFR) schemes is assumed. Frequency domain interleaving is performed in the transmitter into the user equipments and, multiuser detection and an antenna array are considered in the receivers at the base stations. The transmitter into the user equipments also performs perfect power control. Furthermore, additive white Gaussian noise, path-loss and slow frequency-selective Rayleigh fading are considered in the channel model. The contributions of this dissertation are summarized in the following. Closed-form expressions are derived to evaluate the mean BER of MC-CDMA cellular systems using the multiuser detectors: zero-forcing (ZF), minimum mean square error (MMSE) and maximum likelihood detector (MU-MLD). In addition, signal space diversity (SSD) is used in the MC-CDMA cellular system. For this, MU-MLD must be employed in the receiver at the base station. An accurate expression to evaluate the mean BER in this scenario is also derived. Moreover, an asymptotic analysis of the BER expressions is performed to obtain further insights of the diversity order and system behavior at the high signal-to-noise-plus-interference ratio regime. The computational complexity of the multiuser detectors is also obtained in terms of the number of complex operations performed during the detection process. In particular, MU-MLD is implemented via a sphere decoder (SD) algorithm in order to reduce its complexity. Some techniques are provided in order to further reduce the SD complexity. Finally, an expression to evaluate the mean cellular spectral efficiency of the MC-CDMA system in FFR and SFR scenarios is obtained. This analysis is based on an algorithm that calculates the cell coverage radius for each modulation used in the system, assuming that adaptive modulation is employed. For all analyzed scenarios, BPSK and M-QAM modulations are considered. Monte Carlo simulations corroborate the accuracy of the presented mathematical analysisDoutoradoTelecomunicações e TelemáticaDoutor em Engenharia ElétricaCAPE