Decoding by Sampling: A Randomized Lattice Algorithm for Bounded Distance Decoding

Despite its reduced complexity, lattice reduction-aided decoding exhibits a widening gap to maximum-likelihood (ML) performance as the dimension increases. To improve its performance, this paper presents randomized lattice decoding based on Klein's sampling technique, which is a randomized version of Babai's nearest plane algorithm (i.e., successive interference cancelation (SIC)). To find the closest lattice point, Klein's algorithm is used to sample some lattice points and the closest among those samples is chosen. Lattice reduction increases the probability of finding the closest lattice point, and only needs to be run once during pre-processing. Further, the sampling can operate very efficiently in parallel. The technical contribution of this paper is two-fold: we analyze and optimize the decoding radius of sampling decoding resulting in better error performance than Klein's original algorithm, and propose a very efficient implementation of random rounding. Of particular interest is that a fixed gain in the decoding radius compared to Babai's decoding can be achieved at polynomial complexity. The proposed decoder is useful for moderate dimensions where sphere decoding becomes computationally intensive, while lattice reduction-aided decoding starts to suffer considerable loss. Simulation results demonstrate near-ML performance is achieved by a moderate number of samples, even if the dimension is as high as 32

Superposition Signal Input Decoding for Lattice Reduction-Aided MIMO Receivers

This paper proposes a novel approach to low complexity soft input decoding for lattice reduction-aided MIMO receivers. The proposed approach feeds a soft input decoder with soft signals made from hard decision signals generated by using a lattice reduction-aided linear detector. The soft signal is a weighted-sum of some candidate vectors that are near by the hard decision signal coming out from the lattice reduction-aided linear detector. This paper proposes a technique to adjust the weight adapt to the channel for the higher transmission performance. Furthermore, we propose to introduce a coefficient that is used for the weights in order to enhance the transmission performance. The transmission performance is evaluated in a 4×4 MIMO channel. When a linear MMSE filter or a serial interference canceller is used as the linear detector, the proposed technique achieves about 1.0dB better transmission performance at the BER of 10-5 than the decoder fed with the hard decision signals. In addition, the low computational complexity of the proposed technique is quantitatively evaluated

Wireless receiver designs: from information theory to VLSI implementation

Receiver design, especially equalizer design, in communications is a major concern in both academia and industry. It is a problem with both theoretical challenges and severe implementation hurdles. While much research has been focused on reducing complexity for optimal or near-optimal schemes, it is still common practice in industry to use simple techniques (such as linear equalization) that are generally significantly inferior. Although digital signal processing (DSP) technologies have been applied to wireless communications to enhance the throughput, the users' demands for more data and higher rate have revealed new challenges. For example, to collect the diversity and combat fading channels, in addition to the transmitter designs that enable the diversity, we also require the receiver to be able to collect the prepared diversity. Most wireless transmissions can be modeled as a linear block transmission system. Given a linear block transmission model assumption, maximum likelihood equalizers (MLEs) or near-ML decoders have been adopted at the receiver to collect diversity which is an important metric for performance, but these decoders exhibit high complexity. To reduce the decoding complexity, low-complexity equalizers, such as linear equalizers (LEs) and decision feedback equalizers (DFEs) are often adopted. These methods, however, may not utilize the diversity enabled by the transmitter and as a result have degraded performance compared to MLEs. In this dissertation, we will present efficient receiver designs that achieve low bit-error-rate (BER), high mutual information, and low decoding complexity. Our approach is to first investigate the error performance and mutual information of existing low-complexity equalizers to reveal the fundamental condition to achieve full diversity with LEs. We show that the fundamental condition for LEs to collect the same (outage) diversity as MLE is that the channels need to be constrained within a certain distance from orthogonality. The orthogonality deficiency (od) is adopted to quantify the distance of channels to orthogonality while other existing metrics are also introduced and compared. To meet the fundamental condition and achieve full diversity, a hybrid equalizer framework is proposed. The performance-complexity trade-off of hybrid equalizers is quantified by deriving the distribution of od. Another approach is to apply lattice reduction (LR) techniques to improve the ``quality' of channel matrices. We present two widely adopted LR methods in wireless communications, the Lenstra-Lenstra-Lovasz (LLL) algorithm [51] and Seysen's algorithm (SA), by providing detailed descriptions and pseudo codes. The properties of output matrices of the LLL algorithm and SA are also quantified. Furthermore, other LR algorithms are also briefly introduced. After introducing LR algorithms, we show how to adopt them into the wireless communication decoding process by presenting LR-aided hard-output detectors and LR-aided soft-output detectors for coded systems, respectively. We also analyze the performance of proposed efficient receivers from the perspective of diversity, mutual information, and complexity. We prove that LR techniques help to restore the diversity of low-complexity equalizers without increasing the complexity significantly. When it comes to practical systems and simulation tool, e.g., MATLAB, only finite bits are adopted to represent numbers. Therefore, we revisit the diversity analysis for finite-bit represented systems. We illustrate that the diversity of MLE for systems with finite-bit representation is determined by the number of non-vanishing eigenvalues. It is also shown that although theoretically LR-aided detectors collect the same diversity as MLE in the real/complex field, it may show different diversity orders when finite-bit representation exists. Finally, the VLSI implementation of the complex LLL algorithms is provided to verify the practicality of our proposed designs.Ph.D.Committee Chair: Ma, Xiaoli; Committee Member: Anderson, David; Committee Member: Barry, John; Committee Member: Chen, Xu-Yan; Committee Member: Kornegay, Kevi

An Iterative Soft Decision Based LR-Aided MIMO Detector

The demand for wireless and high-rate communication system is increasing gradually and multiple-input-multiple-output (MIMO) is one of the feasible solutions to accommodate the growing demand for its spatial multiplexing and diversity gain. However, with high number of antennas, the computational and hardware complexity of MIMO increases exponentially. This accumulating complexity is a paramount problem in MIMO detection system directly leading to large power consumption. Hence, the major focus of this dissertation is algorithmic and hardware development of MIMO decoder with reduced complexity for both real and complex domain, which can be a beneficial solution with power efficiency and high throughput. Both hard and soft domain MIMO detectors are considered. The use of lattice reduction (LR) algorithm and on-demand-child-expansion for the reduction of noise propagation and node calculation respectively are the two of the key features of our developed architecture, presented in this literature. The real domain iterative soft MIMO decoding algorithm, simulated for 4 × 4 MIMO with different modulation scheme, achieves 1.1 to 2.7 dB improvement over Lease Sphere Decoder (LSD) and more than 8x reduction in list size, K as well as complexity of the detector. Next, the iterative real domain K-Best decoder is expanded to the complex domain with new detection scheme. It attains 6.9 to 8.0 dB improvement over real domain K-Best decoder and 1.4 to 2.5 dB better performance over conventional complex decoder for 8 × 8 MIMO with 64 QAM modulation scheme. Besides K, a new adjustable parameter, Rlimit has been introduced in order to append re-configurability trading-off between complexity and performance. After that, a novel low-power hardware architecture of complex decoder is developed for 8 × 8 MIMO and 64 QAM modulation scheme. The total word length of only 16 bits has been adopted limiting the bit error rate (BER) degradation to 0.3 dB with K and Rlimit equal to 4. The proposed VLSI architecture is modeled in Verilog HDL using Xilinx and synthesized using Synopsys Design Vision in 45 nm CMOS technology. According to the synthesize result, it achieves 1090.8 Mbps throughput with power consumption of 580 mW and latency of 0.33 us. The maximum frequency the design proposed is 181.8 MHz. All of the proposed decoders mentioned above are bounded by the fixed K. Hence, an adaptive real domain K-Best decoder is further developed to achieve the similar performance with less K, thereby reducing the computational complexity of the decoder. It does not require accurate SNR measurement to perform the initial estimation of list size, K. Instead, the difference between the first two minimal distances is considered, which inherently eliminates complexity. In summary, a novel iterative K-Best detector for both real and complex domain with efficient VLSI design is proposed in this dissertation. The results from extensive simulation and VHDL with analysis using Synopsys tool are also presented for justification and validation of the proposed works

An Iterative Soft Decision Based LR-Aided MIMO Detector

The demand for wireless and high-rate communication system is increasing gradually and multiple-input-multiple-output (MIMO) is one of the feasible solutions to accommodate the growing demand for its spatial multiplexing and diversity gain. However, with high number of antennas, the computational and hardware complexity of MIMO increases exponentially. This accumulating complexity is a paramount problem in MIMO detection system directly leading to large power consumption. Hence, the major focus of this dissertation is algorithmic and hardware development of MIMO decoder with reduced complexity for both real and complex domain, which can be a beneficial solution with power efficiency and high throughput. Both hard and soft domain MIMO detectors are considered. The use of lattice reduction (LR) algorithm and on-demand-child-expansion for the reduction of noise propagation and node calculation respectively are the two of the key features of our developed architecture, presented in this literature. The real domain iterative soft MIMO decoding algorithm, simulated for 4 × 4 MIMO with different modulation scheme, achieves 1.1 to 2.7 dB improvement over Lease Sphere Decoder (LSD) and more than 8x reduction in list size, K as well as complexity of the detector. Next, the iterative real domain K-Best decoder is expanded to the complex domain with new detection scheme. It attains 6.9 to 8.0 dB improvement over real domain K-Best decoder and 1.4 to 2.5 dB better performance over conventional complex decoder for 8 × 8 MIMO with 64 QAM modulation scheme. Besides K, a new adjustable parameter, Rlimit has been introduced in order to append re-configurability trading-off between complexity and performance. After that, a novel low-power hardware architecture of complex decoder is developed for 8 × 8 MIMO and 64 QAM modulation scheme. The total word length of only 16 bits has been adopted limiting the bit error rate (BER) degradation to 0.3 dB with K and Rlimit equal to 4. The proposed VLSI architecture is modeled in Verilog HDL using Xilinx and synthesized using Synopsys Design Vision in 45 nm CMOS technology. According to the synthesize result, it achieves 1090.8 Mbps throughput with power consumption of 580 mW and latency of 0.33 us. The maximum frequency the design proposed is 181.8 MHz. All of the proposed decoders mentioned above are bounded by the fixed K. Hence, an adaptive real domain K-Best decoder is further developed to achieve the similar performance with less K, thereby reducing the computational complexity of the decoder. It does not require accurate SNR measurement to perform the initial estimation of list size, K. Instead, the difference between the first two minimal distances is considered, which inherently eliminates complexity. In summary, a novel iterative K-Best detector for both real and complex domain with efficient VLSI design is proposed in this dissertation. The results from extensive simulation and VHDL with analysis using Synopsys tool are also presented for justification and validation of the proposed works

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