An adaptive detector implementation for MIMO-OFDM downlink

Cognitive radio (CR) systems require flexible and adaptive implementations of signal processing algorithms. An adaptive symbol detector is needed in the baseband receiver chain to achieve the desired flexibility of a CR system. This paper presents a novel design of an adaptive detector as an application-specific instruction-set processor (ASIP). The ASIP template is based on transport triggered architecture (TTA). The processor architecture is designed in such a manner that it can be programmed to support different suboptimal multiple-input multiple-output (MIMO) detection algorithms in a single TTA processor. The linear minimum mean-square error (LMMSE) and three variants of the selective spanning for fast enumeration (SSFE) detection algorithms are considered. The detection algorithm can be switched between the LMMSE and SSFE according to the bit error rate (BER) performance requirement in the TTA processor. The design can be scaled for different antenna configurations and different modulations. Some of the algorithm architecture co-optimization techniques used here are also presented. Unlike most other detector ASIPs, high level language is used to program the processor to meet the time-to-market requirements. The adaptive detector delivers 4.88 - 49.48 Mbps throughput at a clock frequency of 200 MHz on 90 nm technology

Adaptive and Iterative Multi-Branch MMSE Decision Feedback Detection Algorithms for MIMO Systems

In this work, decision feedback (DF) detection algorithms based on multiple processing branches for multi-input multi-output (MIMO) spatial multiplexing systems are proposed. The proposed detector employs multiple cancellation branches with receive filters that are obtained from a common matrix inverse and achieves a performance close to the maximum likelihood detector (MLD). Constrained minimum mean-squared error (MMSE) receive filters designed with constraints on the shape and magnitude of the feedback filters for the multi-branch MMSE DF (MB-MMSE-DF) receivers are presented. An adaptive implementation of the proposed MB-MMSE-DF detector is developed along with a recursive least squares-type algorithm for estimating the parameters of the receive filters when the channel is time-varying. A soft-output version of the MB-MMSE-DF detector is also proposed as a component of an iterative detection and decoding receiver structure. A computational complexity analysis shows that the MB-MMSE-DF detector does not require a significant additional complexity over the conventional MMSE-DF detector, whereas a diversity analysis discusses the diversity order achieved by the MB-MMSE-DF detector. Simulation results show that the MB-MMSE-DF detector achieves a performance superior to existing suboptimal detectors and close to the MLD, while requiring significantly lower complexity.Comment: 10 figures, 3 tables; IEEE Transactions on Wireless Communications, 201

On Investigations of Machine Learning and Deep Learning Techniques for MIMO Detection

This paper reviews in detail the various types of multiple input multiple output (MIMO) detector algorithms. The current MIMO detectors are not suitable for massive MIMO (mMIMO) scenarios where there are a large number of antennas. Their performance degrades with the increase in number of antennas in the MIMO system. For combatting the issues, machine learning (ML) and deep learning (DL) based detection algorithms are being researched and developed. An extensive survey of these detectors is provided in this paper, alongwith their advantages and challenges. The issues discussed have to be resolved before using them for final deployment

Design and Implementation of Efficient Algorithms for Wireless MIMO Communication Systems

En la última década, uno de los avances tecnológicos más importantes que han hecho culminar la nueva generación de banda ancha inalámbrica es la comunicación mediante sistemas de múltiples entradas y múltiples salidas (MIMO). Las tecnologías MIMO han sido adoptadas por muchos estándares inalámbricos tales como LTE, WiMAS y WLAN. Esto se debe principalmente a su capacidad de aumentar la máxima velocidad de transmisión , junto con la fiabilidad alcanzada y la cobertura de las comunicaciones inalámbricas actuales sin la necesidad de ancho de banda extra ni de potencia de transmisión adicional. Sin embargo, las ventajas proporcionadas por los sistemas MIMO se producen a expensas de un aumento sustancial del coste de implementación de múltiples antenas y de la complejidad del receptor, la cual tiene un gran impacto sobre el consumo de energía. Por esta razón, el diseño de receptores de baja complejidad es un tema importante que se abordará a lo largo de esta tesis. En primer lugar, se investiga el uso de técnicas de preprocesado de la matriz de canal MIMO bien para disminuir el coste computacional de decodificadores óptimos o bien para mejorar las prestaciones de detectores subóptimos lineales, SIC o de búsqueda en árbol. Se presenta una descripción detallada de dos técnicas de preprocesado ampliamente utilizadas: el método de Lenstra, Lenstra, Lovasz (LLL) para lattice reduction (LR) y el algorimo VBLAST ZF-DFE. Tanto la complejidad como las prestaciones de ambos métodos se han evaluado y comparado entre sí. Además, se propone una implementación de bajo coste del algoritmo VBLAST ZF-DFE, la cual se incluye en la evaluación. En segundo lugar, se ha desarrollado un detector MIMO basado en búsqueda en árbol de baja complejidad, denominado detector K-Best de amplitud variable (VB K-Best). La idea principal de este método es aprovechar el impacto del número de condición de la matriz de canal sobre la detección de datos con el fin de disminuir la complejidad de los sistemasRoger Varea, S. (2012). Design and Implementation of Efficient Algorithms for Wireless MIMO Communication Systems [Tesis doctoral no publicada]. Universitat Politècnica de València. https://doi.org/10.4995/Thesis/10251/16562Palanci

Soft MIMO Detection on Graphics Processing Units and Performance Study of Iterative MIMO Decoding

In this thesis we have presented an implementation of soft Multi Input Multi Output (MIMO) detection, single tree search algorithm on Graphics Processing Units (GPUs). We have compared its performance on different GPUs and a Central Processing Unit (CPU). We have also done a performance study of iterative decoding algorithms. We have shown that by increasing the number of outer iterations error rate performance can be further improved. GPUs are specialized devices specially designed to accelerate graphics processing. They are massively parallel devices which can run thousands of threads simultaneously. Because of their tremendous processing power there is an increasing interest in using them for scientific and general purpose computations. Hence companies like Nvidia, Advanced Micro Devices (AMD) etc. have started their support for General Purpose GPU (GPGPU) applications. Nvidia came up with Compute Unified Device Architecture (CUDA) to program its GPUs. Efforts are made to come up with a standard language for parallel computing that can be used across platforms. OpenCL is the first such language which is supported by all major GPU and CPU vendors. MIMO detector has a high computational complexity. We have implemented a soft MIMO detector on GPUs and studied its throughput and latency performance. We have shown that a GPU can give throughput of up to 4Mbps for a soft detection algorithm which is more than sufficient for most general purpose tasks like voice communication etc. Compare to CPU a throughput increase of ~7x is achieved. We also compared the performances of two GPUs one with low computational power and one with high computational power. These comparisons show effect of thread serialization on algorithms with the lower end GPU's execution time curve shows a slope of 1/2. To further improve error rate performance iterative decoding techniques are employed where a feedback path is employed between detector and decoder. With an eye towards GPU implementation we have explored these algorithms. Better error rate performance however, comes at a price of higher power dissipation and more latency. By simulations we have shown that one can predict based on the Signal to Noise Ratio (SNR) values how many iterations need to be done before getting an acceptable Bit Error Rate (BER) and Frame Error Rate (FER) performance. Iterative decoding technique shows that a SNR gain of ~1:5dB is achieved when number of outer iterations is increased from zero. To reduce the complexity one can adjust number of possible candidates the algorithm can generate. We showed that where a candidate list of 128 is not sufficient for acceptable error rate performance for a 4x4 MIMO system using 16-QAM modulation scheme, performances are comparable with the list size of 512 and 1024 respectively

Application of integer quadratic programming in detection of high-dimensional wireless systems

High-dimensional wireless systems have recently generated a great deal of interest due to their ability to accommodate increasing demands for high transmission data rates with high communication reliability. Examples of such large-scale systems include single-input, single-output symbol spread OFDM system, large-scale single-user multi-input multi-output (MIMO) OFDM systems, and large-scale multiuser MIMO systems. In these systems, the number of symbols required to be jointly detected at the receiver is relatively large. The challenge with the practical realization of these systems is to design a detection scheme that provides high communication reliability with reasonable computational complexity, even as the number of simultaneously transmitted independent communication signals becomes very large.^ Most of the optimal or near-optimal detection techniques that have been proposed in the literature of relatively low-dimensional wireless systems, such as MIMO systems in which number of antennas is less than 10, become problematic for high-dimensional detection problems. That is, their performance degrades or the computational complexity becomes prohibitive, especially when higher-order QAM constellations are employed.^ In the first part of this thesis, we propose a near-optimal detection technique which offers a flexible trade-off between complexity and performance. The proposed technique formulates the detection problem in terms of Integer Quadratic Programming (IQP), which is then solved through a controlled Branch and Bound (BB) search tree algorithm. In addition to providing good performance, an important feature of this approach is that its computational complexity remains roughly the same even as we increase the constellation order from 4-QAM to 256-QAM. The performance of the proposed algorithm is investigated for both symbol spread OFDM systems and large-scale MIMO systems with both frequency selective and at fading channels.^ The second part of this work focuses on a reduced complexity version of IQP referred to as relaxed quadratic programming (QP). In particular, QP is used to reformulate two widely used detection schemes for MIMO OFDM: (1) Successive Interference Cancellation (SIC) and (2) Iterative Detecting and Decoding (IDD). First, SIC-based algorithms are derived via a QP formulation in contrast to using a linear MMSE detector at each stage. The resulting QP-SIC algorithms offer lower computational complexity than the SIC schemes that employ linear MMSE at each stage, especially when the dimension of the received signal vector is high. Three versions of QP-SIC are proposed based on various trade-offs between complexity and receiver performance; each of the three QP-SIC algorithms outperforms existing SIC techniques. Second, IDD-based algorithms are developed using a QP detector. We show how the soft information, in terms of the Log Likelihood Ratio (LLR), can be extracted from the QP detector. Further, the procedure for incorporating the a-priori information that is passed from the channel decoder to the QP detector is developed. Simulation results are presented demonstrating that the use of QP in IDD offers improved performance at the cost of a reasonable increase in complexity compared to linear detectors

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

Spectrum Optimisation in Wireless Communication Systems: Technology Evaluation, System Design and Practical Implementation

Two key technology enablers for next generation networks are examined in this thesis, namely Cognitive Radio (CR) and Spectrally Efficient Frequency Division Multiplexing (SEFDM). The first part proposes the use of traffic prediction in CR systems to improve the Quality of Service (QoS) for CR users. A framework is presented which allows CR users to capture a frequency slot in an idle licensed channel occupied by primary users. This is achieved by using CR to sense and select target spectrum bands combined with traffic prediction to determine the optimum channel-sensing order. The latter part of this thesis considers the design, practical implementation and performance evaluation of SEFDM. The key challenge that arises in SEFDM is the self-created interference which complicates the design of receiver architectures. Previous work has focused on the development of sophisticated detection algorithms, however, these suffer from an impractical computational complexity. Consequently, the aim of this work is two-fold; first, to reduce the complexity of existing algorithms to make them better-suited for application in the real world; second, to develop hardware prototypes to assess the feasibility of employing SEFDM in practical systems. The impact of oversampling and fixed-point effects on the performance of SEFDM is initially determined, followed by the design and implementation of linear detection techniques using Field Programmable Gate Arrays (FPGAs). The performance of these FPGA based linear receivers is evaluated in terms of throughput, resource utilisation and Bit Error Rate (BER). Finally, variants of the Sphere Decoding (SD) algorithm are investigated to ameliorate the error performance of SEFDM systems with targeted reduction in complexity. The Fixed SD (FSD) algorithm is implemented on a Digital Signal Processor (DSP) to measure its computational complexity. Modified sorting and decomposition strategies are then applied to this FSD algorithm offering trade-offs between execution speed and BER