Journal ArticleDOI
A unified framework for tree search decoding: rediscovering the sequential decoder
TLDR
The excellent performance-complexity tradeoff achieved by the proposed MMSE-DFE Fano decoder is established via simulation results and analytical arguments in several multiple-input multiple-output (MIMO) and intersymbol interference (ISI) scenarios.Abstract:
We consider receiver design for coded transmission over linear Gaussian channels. We restrict ourselves to the class of lattice codes and formulate the joint detection and decoding problem as a closest lattice point search (CLPS). Here, a tree search framework for solving the CLPS is adopted. In our framework, the CLPS algorithm is decomposed into the preprocessing and tree search stages. The role of the preprocessing stage is to expose the tree structure in a form matched to the search stage. We argue that the forward and feedback (matrix) filters of the minimum mean-square error decision feedback equalizer (MMSE-DFE) are instrumental for solving the joint detection and decoding problem in a single search stage. It is further shown that MMSE-DFE filtering allows for solving underdetermined linear systems and using lattice reduction methods to diminish complexity, at the expense of a marginal performance loss. For the search stage, we present a generic method, based on the branch and bound (BB) algorithm, and show that it encompasses all existing sphere decoders as special cases. The proposed generic algorithm further allows for an interesting classification of tree search decoders, sheds more light on the structural properties of all known sphere decoders, and inspires the design of more efficient decoders. In particular, an efficient decoding algorithm that resembles the well-known Fano sequential decoder is identified. The excellent performance-complexity tradeoff achieved by the proposed MMSE-DFE Fano decoder is established via simulation results and analytical arguments in several multiple-input multiple-output (MIMO) and intersymbol interference (ISI) scenarios.read more
Citations
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Journal ArticleDOI
Multiuser MIMO Achievable Rates With Downlink Training and Channel State Feedback
TL;DR: It is shown that very significant downlink throughput is achievable with simple and efficient channel state feedback, provided that the feedback link is properly designed.
Journal ArticleDOI
Fifty Years of MIMO Detection: The Road to Large-Scale MIMOs
Shaoshi Yang,Lajos Hanzo +1 more
TL;DR: In this article, the authors provide a recital on the historic heritages and novel challenges facing massive/large-scale multiple-input multiple-output (LS-MIMO) systems from a detection perspective.
Journal ArticleDOI
Fixing the Complexity of the Sphere Decoder for MIMO Detection
L.G. Barbero,John Thompson +1 more
TL;DR: A new detection algorithm for uncoded multiple input-multiple output (MIMO) systems based on the complex version of the sphere decoder (SD) is presented, overcoming the two main problems of the SD from an implementation point of view: its variable complexity and its sequential nature.
Journal ArticleDOI
Asymptotic Performance of Linear Receivers in MIMO Fading Channels
TL;DR: In this article, the authors investigated the error probability for fixed high-signal-to-noise-ratio (SNR) regime in terms of the diversity-multiplexing tradeoff (DMT).
Journal Article
Lattice Reduction
TL;DR: This article provides some background on point lattices and gives a tutorial-style introduction to the theoretical and practical aspects of lattice reduction, and describes the most important lattice Reduction algorithms.
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A Course in Computational Algebraic Number Theory
TL;DR: The first seven chapters guide readers to the heart of current research in computational algebraic number theory, including recent algorithms for computing class groups and units, as well as elliptic curve computations, while the last three chapters survey factoring and primality testing methods.
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Achieving near-capacity on a multiple-antenna channel
TL;DR: This work provides a simple method to iteratively detect and decode any linear space-time mapping combined with any channel code that can be decoded using so-called "soft" inputs and outputs and shows that excellent performance at very high data rates can be attained with either.