Joint signal detection and channel estimation in rank-deficient MIMO systems

L'évolution de la prospère famille des standards 802.11 a encouragé le développement des technologies appliquées aux réseaux locaux sans fil (WLANs). Pour faire face à la toujours croissante nécessité de rendre possible les communications à très haut débit, les systèmes à antennes multiples (MIMO) sont une solution viable. Ils ont l'avantage d'accroître le débit de transmission sans avoir recours à plus de puissance ou de largeur de bande. Cependant, l'industrie hésite encore à augmenter le nombre d'antennes des portables et des accésoires sans fil. De plus, à l'intérieur des bâtiments, la déficience de rang de la matrice de canal peut se produire dû à la nature de la dispersion des parcours de propagation, ce phénomène est aussi occasionné à l'extérieur par de longues distances de transmission. Ce projet est motivé par les raisons décrites antérieurement, il se veut un étude sur la viabilité des transcepteurs sans fil à large bande capables de régulariser la déficience de rang du canal sans fil. On vise le développement des techniques capables de séparer M signaux co-canal, même avec une seule antenne et à faire une estimation précise du canal. Les solutions décrites dans ce document cherchent à surmonter les difficultés posées par le medium aux transcepteurs sans fil à large bande. Le résultat de cette étude est un algorithme transcepteur approprié aux systèmes MIMO à rang déficient

Iterative Receiver Techniques for Data-Driven Channel Estimation and Interference Mitigation in Wireless Communications

Wireless mobile communications were initially a way for people to communicate through low data rate voice call connections. As data enabled devices allow users the ability to do much more with their mobile devices, so to will the demand for more reliable and pervasive wireless data. This is being addressed by so-called 4th generation wireless systems based on orthogonal frequency division multiplexing (OFDM) and multiple-input multiple-output (MIMO) antenna systems. Mobile wireless customers are becoming more demanding and expecting to have a great user experience over high speed broadband access at any time and anywhere, both indoor and outdoor. However, these promising improvements cannot be realized without an e±cient design of the receiver. Recently, receivers utilizing iterative detection and decoding have changed the fundamental receiver design paradigm from traditional separated parameter estimation and data detection blocks to an integrated iterative parameter estimator and data detection unit. Motivated by this iterative data driven approach, we develop low complexity iterative receivers with improved sensitivity compared to the conventional receivers, this brings potential benefits for the wireless communication system, such as improving the overall system throughput, increasing the macro cell coverage, and reducing the cost of the equipments in both the base station and mobile terminal. It is a challenge to design receivers that have good performance in a highly dynamic mobile wireless environment. One of the challenges is to minimize overhead reference signal energy (preamble, pilot symbols) without compromising the performance. We investigate this problem, and develop an iterative receiver with enhanced data-driven channel estimation. We discuss practical realizations of the iterative receiver for SISO-OFDM system. We utilize the channel estimation from soft decoded data (the a priori information) through frequency-domain combining and time-domain combining strategies in parallel with limited pilot signals. We analyze the performance and complexity of the iterative receiver, and show that the receiver's sensitivity can be improved even with this low complexity solution. Hence, seamless communications can be achieved with better macro cell coverage and mobility without compromising the overall system performance. Another challenge is that a massive amount of interference caused by MIMO transmission (spatial multiplexing MIMO) reduces the performance of the channel estimation, and further degrades data detection performance. We extend the iterative channel estimation from SISO systems to MIMO systems, and work with linear detection methods to perform joint interference mitigation and channel estimation. We further show the robustness of the iterative receivers in both indoor and outdoor environment compared to the conventional receiver approach. Finally, we develop low complexity iterative spatial multiplexed MIMO receivers for nonlinear methods based on two known techniques, that is, the Sphere Decoder (SD) method and the Markov Chain Monte Carlo (MCMC) method. These methods have superior performance, however, they typically demand a substantial increase in computational complexity, which is not favorable in practical realizations. We investigate and show for the first time how to utilize the a priori information in these methods to achieve performance enhancement while simultaneously substantially reducing the computational complexity. In our modified sphere decoder method, we introduce a new accumulated a priori metric in the tree node enumeration process. We show how we can improve the performance by obtaining the reliable tree node candidate from the joint Maximum Likelihood (ML) metric and an approximated a priori metric. We also show how we can improve the convergence speed of the sphere decoder (i.e., reduce the com- plexity) by selecting the node with the highest a priori probability as the starting node in the enumeration process. In our modified MCMC method, the a priori information is utilized for the firrst time to qualify the reliably decoded bits from the entire signal space. Two new robust MCMC methods are developed to deal with the unreliable bits by using the reliably decoded bit information to cancel the interference that they generate. We show through complexity analysis and performance comparison that these new techniques have improved performance compared to the conventional approaches, and further complexity reduction can be obtained with the assistance of the a priori information. Therefore, the complexity and performance tradeoff of these nonlinear methods can be optimized for practical realizations

Sub-optimal Deep Pipelined Implementation of MIMO Sphere Detector on FPGA

Sphere detector (SD) is an effective signal detection approach for the wireless multiple-input multiple-output (MIMO) system since it can achieve near-optimal performance while reducing significant computational complexity. In this work, we proposed a novel SD architecture that is suitable for implementation on the hardware accelerator. We first perform a statistical analysis to examine the distribution of valid paths in the SD search tree. Using the analysis result, we then proposed an enhanced hybrid SD (EHSD) architecture that achieves quasi-ML performance and high throughput with a reasonable cost in hardware. The fine-grained pipeline designs of 4 × 4 and 8 × 8 MIMO system with 16-QAM modulation delivers throughput of 7.04 Gbps and 14.08 Gbps on the Xilinx Virtex Ultrascale+ FPGA, respectively

Advanced Radio Frequency Antennas for Modern Communication and Medical Systems

The main objective of this book is to present novel radio frequency (RF) antennas for 5G, IOT, and medical applications. The book is divided into four sections that present the main topics of radio frequency antennas. The rapid growth in development of cellular wireless communication systems over the last twenty years has resulted in most of world population owning smartphones, smart watches, I-pads, and other RF communication devices. Efficient compact wideband antennas are crucial in RF communication devices. This book presents information on planar antennas, cavity antennas, Vivaldi antennas, phased arrays, MIMO antennas, beamforming phased array reconfigurable Pabry-Perot cavity antennas, and time modulated linear array

Proceedings. 19. Workshop Computational Intelligence, Dortmund, 2. - 4. Dezember 2009

Dieser Tagungsband enthält die Beiträge des 19. Workshops „Computational Intelligence“ des Fachausschusses 5.14 der VDI/VDE-Gesellschaft für Mess- und Automatisierungstechnik (GMA) und der Fachgruppe „Fuzzy-Systeme und Soft-Computing“ der Gesellschaft für Informatik (GI), der vom 2.-4. Dezember 2009 im Haus Bommerholz bei Dortmund stattfindet

Using MapReduce Streaming for Distributed Life Simulation on the Cloud

Distributed software simulations are indispensable in the study of large-scale life models but often require the use of technically complex lower-level distributed computing frameworks, such as MPI. We propose to overcome the complexity challenge by applying the emerging MapReduce (MR) model to distributed life simulations and by running such simulations on the cloud. Technically, we design optimized MR streaming algorithms for discrete and continuous versions of Conway’s life according to a general MR streaming pattern. We chose life because it is simple enough as a testbed for MR’s applicability to a-life simulations and general enough to make our results applicable to various lattice-based a-life models. We implement and empirically evaluate our algorithms’ performance on Amazon’s Elastic MR cloud. Our experiments demonstrate that a single MR optimization technique called strip partitioning can reduce the execution time of continuous life simulations by 64%. To the best of our knowledge, we are the first to propose and evaluate MR streaming algorithms for lattice-based simulations. Our algorithms can serve as prototypes in the development of novel MR simulation algorithms for large-scale lattice-based a-life models.https://digitalcommons.chapman.edu/scs_books/1014/thumbnail.jp