Multiple-input Multiple-output Decoding Research Articles

A computationally efficient detector for MIMO systems

<p>In this work, a newly designed multiple-input multiple-output (MIMO) detector for implementation on software-defined-radio platforms is proposed and its performance and complexity are studied. In particular, we are interested in proposing and evaluating a MIMO detector that provides the optimal trade-off between the decoding complexity and bit error rate (BER) performance as compared to the state of the art detectors. The proposed MIMO decoding technique appears to find the optimal compromise between competing interests encountered in the implementation of advanced MIMO detectors in practical hardware systems where it i) exhibits deterministic decoding complexity, i.e., deterministic latency, ii) enjoys a good complexity–performance trade-off, i.e., it keeps the complexity considerably lower than that of the maximum likelihood detectors with almost optimal performance, iii) allows fully parameterizable performance to complexity trade-off where the performance (or complexity) of the MIMO detector can be adaptively adjusted without the requirement of changing the implementation, iv) enjoys simple implementation and fully supports parallel processing, and v) allows simple and efficient extension to soft-bit output generation for support of turbo decoding. From the simulation results, the proposed MIMO decoding technique shows a substantially improved complexity–performance trade-off as compared to the state of the art techniques.</p>

Efficient Soft Video MIMO Design to Combine Diversity and Spatial Multiplexing Gain

How to strike a balance between diversity gain and spatial multiplexing gain in a soft video delivery system is an open problem. Due to the power limit, it is especially important to achieve the optimal balance during video transmission in the Internet of Things. In this paper, taking the multisimilarity feature of a soft video system into account, we design an adaptive multiple-input, multiple-output (MIMO) receiver that can utilize nearly optimally either diversity gain or multiplexing gain. At the transmitter, we arrange multisimilar video data according to the space-time coding style and transmit them through multiple antennas. At the receiver, we propose using two decoders, i.e., the multisimilar space-time block coding (Ms-STBC) decoder and the soft MIMO decoder. The decoder to be chosen is determined by the predicted performance gain. We show that the proposed Ms-STBC transmission scheme can be considered a joint source-channel design, which is proposed for a soft video multiantenna delivery system. The relationship between intracodewords similarity and the channel signal-to-noise ratio (SNR) gain is derived. The experimental results demonstrate that the proposed designs can achieve a significant improvement over either individual soft MIMO multiplexing decoding or individual space-time block coding decoding in terms of peak SNR (PSNR) under the condition of a time-varying channel and a wide range of SNR. We can obtain at most 4-dB PSNR gain compared with the soft MIMO system.

ICI Cancellation Based on MIMO Decoding for FTN Non-Orthogonal FDM Systems

In this paper, we investigate multiple-input multiple-output (MIMO) decoding in faster-than-Nyquist non-orthogonal frequency-division multiplexing (FTN-NOFDM) for inter-carrier interference (ICI) cancellation. The MIMO decoding is based on depth-first tree search or breadth-first tree search. The sphere decoder (SD) is a commonly used decoding algorithm based on the depth-first tree search. Maximum-likelihood detection with QR decomposition and M-algorithm (QRM-MLD) is a commonly used decoding algorithm based on the breadth-first tree search. By adding the initial radius to both SD and QRM-MLD, the computational complexity of these can be further decreased without the loss of the ICI cancellation performance. In the experiment, SD, QRM-MLD, and their corresponding simplified versions are used for the ICI cancellation in a 28-Gb/s FTN-NOFDM system with transmission over a 10-km standard single-mode fiber. The 3-dB equivalent bandwidth of this system is only approximately 5.5 GHz. Under serious high-frequency distortion caused by limited bandwidth, the bit error rate performance of the FTN-NOFDM can still reach the forward error correction limit due to the bandwidth compression and effective ICI cancellation. FTN-NOFDM with 20% and 30% bandwidth savings has approximately 1.3- and 1.1-dB improvements in receiver sensitivity compared to FTN-NOFDM with 10% bandwidth savings, respectively. To the best of our knowledge, this paper is the first experimental demonstration of the depth-first tree search and breadth-first tree search for FTN-NOFDM in a bandwidth-limited optical transmission system. The experimental results show that FTN-NOFDM is a promising scheme for bandwidth-limited systems.

Spatial Multiplexing for Photon-Counting MIMO-FSO Communication Systems

In this paper, we consider the problem of multiple-input multiple-output (MIMO) free-space optical communications under the Poisson photon-counting detection model. Aiming for high bit rate objectives, we consider the spatial-multiplexing (SMux) solution with M-ary pulse-position modulation, where we propose appropriate optimal and suboptimal decoders and evaluate their complexities. Such novel decoder designs are needed, since the widely spread Gaussian noise-based MIMO decoders are not suitable for the Poisson model. We also carry out an asymptotic performance analysis. This analysis guides a candidate constellation confinement where transmissions are limited to some selected information vectors of the multidimensional SMux constellation. This confinement meets the objective of compromising the multiplexing gains for the sake of improved error-rates. The analyzed SMux solutions with both the unconfined and confined constellations transmit at higher data rates compared with the existing single-aperture systems, MIMO systems with repetition coding, and MIMO systems based on spatial modulation.

A Comparative Study of PMI/RI Selection Schemes for LTE/LTEA Systems

This paper presents a joint calculation of the precoding matrix indicator (PMI) and rank indicator (RI) in multiple-input multiple-output (MIMO) downlink third generation partnership project (3GPP) universal mobile telecommunications system (UMTS)/long-term evolution (LTE) systems. The standard codebook based precoding is used where the mobile station estimates and sends some measures related to the channel state information to the base station for link adaptation purpose. We study the system performance using link-level simulation under realistic assumptions. Specifically, we use K-best MIMO decoding in the presence of perfect and imperfect estimation of the channel and the noise variance, as well as white and colored noise plus interference defined by the 3GPP LTE specification documents. Similar to some previous results, our method for calculating the PMI/RI is based on maximizing the mutual information between the transmitted and received symbols with respect to the precoding matrix. We evaluate the system performance using two channel measures when computing the mutual information. The first measure is the estimated channel whereas the second one is the estimated channel covariance matrix. Although these two measures have been used in different literature, there have not been conclusive results on which one should be used in different practical scenarios. We assess the system performance using Monte Carlo simulations and compare different test cases, e.g., 3GPP channel models, wideband, and subband PMI/RI calculation, white/color noise plus interference, etc. We then conclude our work with technical recommendations for different scenarios.

Energy-Efficient Hardware Implementation of an LR-Aided K-Best MIMO Decoder for 5G Networks

Energy efficiency is a primary design goal for future green wireless communication technologies. Multiple-input multiple-output (MIMO) schemes have been proposed in the literature to improve the throughput of communication systems, and they are expected to play a prominent role in the upcoming fifth generation (5G) standard. This paper presents a novel, high-efficiency MIMO decoder based on the K-Best algorithm with lattice reduction. We have designed a novel hardware architecture for this decoder, which was implemented using 32 nm standard CMOS technology. Our results show that the proposed decoder can achieve on average a four-fold reduction in the power costs compared to recently published designs for 5G networks. The throughput of the design is 506 Mbits/s, which is comparable to existing designs.

Reduced Complexity Tree Search Algorithms for MIMO Decoding

Maximum Likelihood Decoding (MLD) is computationally complex technique for decoding received information in multiple input multiple output (MIMO) systems. Tree search algorithms such as sphere decoding (SD) and QR decomposition with M survivals (QRD-M) are used to reduce the complexity keeping the performance near ML. This paper presents two techniques for reducing the computational complexities of the tree search algorithms further. The first technique is based on selecting the initial radius for sphere decoding. The main contribution of this paper is that the greedy best first search is used to compute initial radius, instead of Babai estimate. The second contribution is, QRD-M algorithm is modified to prune the nodes in the current layer based on maximum metric of child nodes of smallest surviving node. The performance of the proposed techniques is tested for different MIMO systems in terms of bit error rates (BER) and average number of nodes visited. The proposed schemes have improved computational complexity with no degradation of performance.

Progressive edge-growth algorithm for low-density MIMO codes

In low-density parity-check (LDPC) coded multiple-input multiple-output (MIMO) communication systems, probabilistic information are exchanged between an LDPC decoder and a MIMO detector. The MIMO detector has to calculate probabilistic values for each bit which can be very complex. In [1], the authors presented a class of linear block codes named low-density MIMO codes (LDMC) which can reduce the complexity of MIMO detector. However, this code only supports the outer-iterations between the MIMO detector and decoder, but does not support the inner-iterations inside the LDPC decoder. In this paper, a new approach to construct LDMC codes is introduced. The new LDMC codes can be encoded efficiently at the transmitter side and support both of the inner-iterations and outer-iterations at the receiver side. Furthermore they can achieve the design rates and perform very well over MIMO channels.

Improved QRD-QLD Algorithm for Low Complexity MIMO Decoding

In this paper we present a decoding algorithm for multiple-input multiple-output (MIMO) communications. The algorithm is based on the QRD-QLD algorithm (see, e.g., Radosavljevic et al. 2012), proposes an improved path metric computation, and achieves a low probability of error which is close to the single tree search sphere decoder, at low implementation complexity. The proposed improved path metric computation increases the reliability of combining the results of the partial searches into sequences representing the maximum likelihood (ML) and the counter ML hypotheses in soft input MIMO decoding. The excellent performance of the improved algorithm are demonstrated via numerical simulations.

Algorithm and hardware design of a 2D sorter-based K-best MIMO decoder

In the field of multiple input multiple output (MIMO) decoder, K-best has been well investigated because it guarantees an SNR-independent fixed-throughput with a performance close to the optimal maximum likelihood detection (MLD). However, the complexity of its expansion and sorting tasks is significantly affected by the constellation size W. In this paper, we propose an algorithm and hardware design of a 2D sorter-based K-best MIMO decoder whose complexity is negligibly affected by W. The main novelties of the algorithm are the following: (1) Direct expansion and parent node grouping ideas are proposed for reducing the expansion task’s complexity. (2) Two-dimensional (2D) sorter is proposed for simplifying the sorting task. The hardware design of the decoder supports up to 256-QAM modulation, which aims to apply into 4 × 4 MIMO 802.11n and 11ac systems. The paper shows that the proposed decoder outperforms the Bell Labs layered space-time (BLAST) minimum mean square error (MMSE) and lattice-reduction aided (LRA) MMSE, and is close to the full K-best in terms of bit error rate (BER) performance. The hardware design of the decoder is synthesized in application specific integrated circuit (ASIC) and compared with the previous works. As a result, it achieves the highest throughput (up to 2.7 Gbps), consumes the least power (56 mW), obtains the best hardware efficiency (15.2 Mbps/Kgate), and has the shortest latency (0.07 µs).

A Parameterized Programmable MIMO Decoding Architecture With a Scalable Instruction Set and Compiler

We present a novel multiple-input multiple-output (MIMO) decoder accelerator and its associated integrated design environment. The accelerator architecture allows tradeoffs in decoding algorithm, antenna configuration, modulation scheme, and bandwidth at run-time via user programming. The accelerator delivers an improvement over a general purpose digital signal processor (DSP) reaching three orders of magnitude for matrix processing and linear MIMO decoding. The hardware architecture is user-configurable through ten independently set parameters. The parameterization allows independent control over the size and structure of the processing core as well as the structure, size, and access scheme of data memory. We provide a custom high level script and a scalable machine level instruction set and compiler. The elements of hardware configuration and programmability are combined in a user-friendly design flow that takes the MIMO decoder designer from simulation to hardware with dedicated-hardware-like performance in no time.

ASIC Implementation of Soft-Input Soft-Output MIMO Detection Using MMSE Parallel Interference Cancellation

Multiple-input multiple-output (MIMO) technology is the key to meet the demands for data rate and link reliability of modern wireless communication systems, such as IEEE 802.11n or 3GPP-LTE. The full potential of MIMO systems can, however, only be achieved by means iterative MIMO decoding relying on soft-input soft-output (SISO) data detection. In this paper, we describe the first ASIC implementation of a SISO detector for iterative MIMO decoding. To this end, we propose a low-complexity minimum mean-squared error (MMSE) based parallel interference cancellation algorithm, develop a suitable VLSI architecture, and present a corresponding four-stream 1.5 mm <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup> detector chip in 90 nm CMOS technology. The fabricated ASIC includes all necessary preprocessing circuitry and exceeds the 600 Mb/s peak data-rate of IEEE 802.11n. A comparison with state-of-the-art MIMO-detector implementations demonstrates the performance benefits of our ASIC prototype in practical system-scenarios.

Energy-Constrained Link Adaptation for MIMO OFDM Wireless Communication Systems

We present a link adaptation strategy for multiple-input multiple-output (MIMO) orthogonal frequency division multiplexing (OFDM) based wireless communications. Our objective is to choose the optimal mode that will maximize energy efficiency or data throughput subject to a given quality of service (QoS) constraint. We formulate the link adaptation problem as a convex optimization problem and expand the set of parameters under the control of the link adaptation protocol to include: number of spatial streams, number of transmit/receive antennas, use of spatial multiplexing or space time block coding (STBC), constellation size, bandwidth, transmit power and choice of maximum likelihood (ML) or zero-forcing (ZF) for MIMO decoding. Additionally, we increase the fidelity of the energy consumption modeling relative to the prior art. The resulting solution allows us to easily and quickly search the space of possible system parameters to deliver on the QoS with minimal energy consumption. Moreover, it provides us insight into where crossovers occur in the choice of the radio parameters. Application of the results to a generic MIMO-OFDM radio shows that the proposed strategy can provide an order of magnitude improvement in energy efficiency or data throughput relative to a static strategy.

Augmented Lattice Reduction for MIMO Decoding

Lattice reduction algorithms, such as the Lenstra-Lenstra-Lovasz (LLL) algorithm, have been proposed as preprocessing tools in order to enhance the performance of suboptimal receivers in multiple-input multiple-output (MIMO) communications. A different approach, introduced by Kim and Park, allows to combine right preprocessing and detection in a single step by performing lattice reduction on an v{augmented channel matrix}. In this paper we propose an improvement of the augmented matrix approach which guarantees a better performance. We prove that our method attains the maximum receive diversity order of the channel. Simulation results evidence that it significantly outperforms LLL reduction followed by successive interference cancellation (SIC) while requiring a moderate increase in complexity. A theoretical bound on the complexity is also derived.

An improved Markov Chain Monte Carlo method for MIMO iterative detection and decoding

Recently, a new soft-in soft-out detection algorithm based on the Markov Chain Monte Carlo (MCMC) simulation technique for Multiple-Input Multiple-Output (MIMO) systems is proposed, which is shown to perform significantly better than their sphere decoding counterparts with relatively low complexity. However, the MCMC simulator is likely to get trapped in a fixed state when the channel SNR is high, thus lots of repetitive samples are observed and the accuracy of A Posteriori Probability (APP) estimation deteriorates. To solve this problem, an improved version of MCMC simulator, named forced-dispersed MCMC algorithm is proposed. Based on the a posteriori variance of each bit, the Gibbs sampler is monitored. Once the trapped state is detected, the sample is dispersed intentionally according to the a posteriori variance. Extensive simulation shows that, compared with the existing solution, the proposed algorithm enables the markov chain to travel more states, which ensures a near-optimal performance.

Design and Implementation of a Hardware Module for MIMO Decoding in a 4G Wireless Receiver

Future 4th Generation (4G) wireless multiuser communication systems will have to provide advanced multimedia services to an increasing number of users, making good use of the scarce spectrum resources. Thus, 4G system design should pursue both higher-transmission bit rates and higher spectral efficiencies. To achieve this goal, multiple antenna systems are called to play a crucial role. In this contribution we address the implementation in FPGAs of a multiple-input multiple-output (MIMO) decoder embedded in a prototype of a 4G mobile receiver. This MIMO decoder is part of a multicarrier code-division multiple-access (MC-CDMA) radio system, equipped with multiple antennas at both ends of the link, that is able to handle up to 32 users and provides raw transmission bit-rates up to 125 Mbps. The task of the MIMO decoder is to appropriately combine the signals simultaneously received on all antennas to construct an improved signal, free of interference, from which to estimate the transmitted symbols. A comprehensive explanation of the complete design process is provided, including architectural decisions, floating-point to fixed-point translation, and description of the validation procedure. We also report implementation results using FPGA devices of the Xilinx Virtex-4 family.

Efficient High-Performance Decoding for Overloaded MIMO Antenna Systems

The practical challenge of capacity-achieving forward error-correcting codes (e.g., space-time turbo codes) is overcoming the tremendous complexity associated by their optimal joint maximum-likelihood (ML) decoding. For this reason, iterative soft decoding has been studied to approach the optimal ML decoding performance at affordable complexity. In multiple-input multiple-output (MIMO) channels, a judicious decoding strategy consists of two stages: 1) estimate the soft bits using list version of sphere decoding or its variants, and 2) update the soft bits through iterative soft decoding. A promising MIMO decoder is required to produce reliable soft-bit estimates at the first stage before iterative soft decoding is performed. In this paper, we focus on the overloaded (or fat) MIMO antenna systems where the number of receive antennas is less than the number of signals multiplexed in the spatial domain. In this scenario, the original form of sphere decoding is inherently not applicable and our aim is to generalize sphere decoding geometrically to cope with overloaded detection. The so-called slab-sphere decoding (SSD) proposed guarantees to obtain exact-ML hard detection while reducing complexity greatly. With the list-version of SSD, (his paper proposes an efficient MIMO soft decoder, which can generate reliable soft-bit estimates at affordable complexity as inputs for iterative soft decoding for promising performance. A case study in the IEEE 802.16 settings is carried out for performance evaluation

Impact of the Gaussian Approximation on the Performance of the Probabilistic Data Association MIMO Decoder

The probabilistic data association (PDA) decoder is investigated for use in coded multiple-input multiple-output (MIMO) systems and its strengths and weaknesses are determined. The conventional PDA decoder includes two approximations. The received symbols are assumed to be statistically independent and a Gaussian approximation is applied for the interference and noise term. We provide an analytical formula for the exact probability density function (PDF) of the interference and noise term, which is used to discuss the impact of the Gaussian approximation in the presence of a soft-input soft-output channel decoder. The results obtained resemble those obtained for the well-known PDA multiuser detector in coded CDMA systems for which similar investigations have been done before.

Monte Carlo FEXT cancellers for DSL channels

Crosstalk coupling is a severe impairment to transmission on twisted-pair copper cables such as digital subscriber telephone loops (DSLs). In this paper, we investigate the problem of far-end crosstalk (FEXT) cancellation for DSL. We propose a new soft interference canceller for coordinated self-FEXT cancellation across multiple twisted-pairs. The proposed receiver treats the FEXT-impaired channel as a multiple-input multiple-output (MIMO) channel; it uses the sequential Monte Carlo (SMC) technique to exploit the sequential structure inherent in iterative cancellation techniques for MIMO decoding. On telephone loops with strong FEXT, the proposed algorithm shows 0.5-dB coding gain with respect to previously proposed techniques. Furthermore, being soft-input-soft-output in nature, the proposed SMC receiver can iteratively exchange extrinsic information with a MAP decoder to successively cancel FEXT, leading to a turbo FEXT canceller.