A Scalable VLSI Architecture for Soft-Input Soft-Output Depth-First Sphere Decoding

Multiple-input multiple-output (MIMO) wireless transmission imposes huge challenges on the design of efficient hardware architectures for iterative receivers. A major challenge is soft-input soft-output (SISO) MIMO demapping, often approached by sphere decoding (SD). In this paper, we introduce the - to our best knowledge - first VLSI architecture for SISO SD applying a single tree-search approach. Compared with a soft-output-only base architecture similar to the one proposed by Studer et al. in IEEE J-SAC 2008, the architectural modifications for soft input still allow a one-node-per-cycle execution. For a 4x4 16-QAM system, the area increases by 57% and the operating frequency degrades by 34% only.Comment: Accepted for IEEE Transactions on Circuits and Systems II Express Briefs, May 2010. This draft from April 2010 will not be updated any more. Please refer to IEEE Xplore for the final version. *) The final publication will appear with the modified title "A Scalable VLSI Architecture for Soft-Input Soft-Output Single Tree-Search Sphere Decoding

Successive interference cancellation aided sphere decoder for multi-input multi-output systems

In this paper, sphere decoding algorithms are proposed for both hard detection and soft processing in multi-input multi-output (MIMO) systems. Both algorithms are based on the complex tree structure to reduce the complexity of searching the unique minimum Euclidean distance and multiple Euclidean distances, and obtain the corresponding transmit symbol vectors. The novel complex hard sphere decoder for MIMO detection is presented first, and then the soft processing of a novel sphere decoding algorithm for list generation is discussed. The performance and complexity of the proposed techniques are demonstrated via simulations in terms of bit error rate (BER), the number of nodes accessed and floating-point operations (FLOPS)

Low complexity MIMO detection algorithms and implementations

University of Minnesota Ph.D. dissertation. December 2014. Major: Electrical Engineering. Advisor: Gerald E. Sobelman. 1 computer file (PDF); ix, 111 pages.MIMO techniques use multiple antennas at both the transmitter and receiver sides to achieve diversity gain, multiplexing gain, or both. One of the key challenges in exploiting the potential of MIMO systems is to design high-throughput, low-complexity detection algorithms while achieving near-optimal performance. In this thesis, we design and optimize algorithms for MIMO detection and investigate the associated performance and FPGA implementation aspects.First, we study and optimize a detection algorithm developed by Shabany and Gulak for a K-Best based high throughput and low energy hard output MIMO detection and expand it to the complex domain. The new method uses simple lookup tables, and it is fully scalable for a wide range of K-values and constellation sizes. This technique reduces the computational complexity, without sacrificing performance and the complexity scales only sub-linearly with the constellation size. Second, we apply the bidirectional technique to trellis search and propose a high performance soft output bidirectional path preserving trellis search (PPTS) detector for MIMO systems. The comparative error analysis between single direction and bidirectional PPTS detectors is given. We demonstrate that the bidirectional PPTS detector can minimize the detection error. Next, we design a novel bidirectional processing algorithm for soft-output MIMO systems. It combines features from several types of fixed complexity tree search procedures. The proposed approach achieves a higher performance than previously proposed algorithms and has a comparable computational cost. Moreover, its parallel nature and fixed throughput characteristics make it attractive for very large scale integration (VLSI) implementation.Following that, we present a novel low-complexity hard output MIMO detection algorithm for LTE and WiFi applications. We provide a well-defined tradeoff between computational complexity and performance. The proposed algorithm uses a much smaller number of Euclidean distance (ED) calculations while attaining only a 0.5dB loss compared to maximum likelihood detection (MLD). A 3x3 MIMO system with a 16QAM detector architecture is designed, and the latency and hardware costs are estimated.Finally, we present a stochastic computing implementation of trigonometric and hyperbolic functions which can be used for QR decomposition and other wireless communications and signal processing applications

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

VLSI Implementation of a Soft-Output Signal Detector for Multi-Mode Adaptive MIMO Systems

This paper presents a multimode soft-output multiple-input multiple-output (MIMO) signal detector that is efficient in hardware cost and energy consumption. The detector is capable of dealing with spatial-multiplexing (SM),break space-division-multiple-access (SDMA), and spatial-diversity (SD) signals of 4 ✕ 4 antenna and 64-QAM modulation. Implementation-friendly algorithms, which reuse most of the mathematical operations in these three MIMO modes, are proposed to provide accurate soft detection information, i.e., log-likelihood ratio, with much reduced complexity. A unified reconfigurable VLSI architecture has been developed to eliminate the implementation of multiple detector modules. In addition, several block level technologies, such as parallel metric update and fast bit-flipping, are adopted to enable a more efficient design. To evaluate the proposed techniques, we implemented the triple-mode MIMO detector in a 65-nm CMOS technology. The core area is 0.25 mm2 with 83.7 K gates. The maximum detecting throughput is 1 Gb/s at 167-MHz clock frequency and 1.2-V supply, which archives the data rate envisioned by the emerging long-term evolution advanced standard. Under frequency-selective channels, the detector consumes 59.3-, 10.5-, and 169.6-pJ energy per bit detection in SM, SD, and SDMA modes, respectively

A Review to Massive MIMO Detection Algorithms: Theory and Implementation

Multiple-input multiple-output (MIMO) systems entered most major standards in the past decades, including IEEE 802.11n (Wi-Fi) and long-term evolution (LTE). Moreover, MIMO techniques will be used for 5G by increasing the number of antennas at the base station end. MIMO systems enable spatial multiplexing, which has the potential of increasing the capacity of the communication channel linearly with the minimum of the number of antennas installed at both sides without sacrificing any additional bandwidth or power. To handle the space-division multiplexing (SDM), receivers have to implement new algorithms to exploit the spatial information in order to distinguish the transmitted data streams. This chapter provides an overview of the most well-known and promising MIMO detectors, as well as some unusual-yet-interesting ones. We focus on the description of the different paradigms to highlight the different approaches that have been studied. For each paradigm, we describe the mathematical framework and give the underlying philosophy. When hardware implementations are available in the literature, we provide the results reported and give the according references

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