1,259 research outputs found
Trellis-Based Equalization for Sparse ISI Channels Revisited
Sparse intersymbol-interference (ISI) channels are encountered in a variety
of high-data-rate communication systems. Such channels have a large channel
memory length, but only a small number of significant channel coefficients. In
this paper, trellis-based equalization of sparse ISI channels is revisited. Due
to the large channel memory length, the complexity of maximum-likelihood
detection, e.g., by means of the Viterbi algorithm (VA), is normally
prohibitive. In the first part of the paper, a unified framework based on
factor graphs is presented for complexity reduction without loss of optimality.
In this new context, two known reduced-complexity algorithms for sparse ISI
channels are recapitulated: The multi-trellis VA (M-VA) and the
parallel-trellis VA (P-VA). It is shown that the M-VA, although claimed, does
not lead to a reduced computational complexity. The P-VA, on the other hand,
leads to a significant complexity reduction, but can only be applied for a
certain class of sparse channels. In the second part of the paper, a unified
approach is investigated to tackle general sparse channels: It is shown that
the use of a linear filter at the receiver renders the application of standard
reduced-state trellis-based equalizer algorithms feasible, without significant
loss of optimality. Numerical results verify the efficiency of the proposed
receiver structure.Comment: To be presented at the 2005 IEEE Int. Symp. Inform. Theory (ISIT
2005), September 4-9, 2005, Adelaide, Australi
Semi-blind sparse channel estimation with constant modulus symbols
We propose two methods for the estimation of sparse communication channels. In the first method, we consider the problem of channel estimation based on training symbols, and formulate it as an optimization problem. In this formulation, we combine the objective of fidelity to the received data with a non-quadratic constraint reflecting the prior information about the sparsity of the channel. This approach leads to accurate channel estimates with much shorter training sequences than conventional methods. The second method we propose is aimed at taking advantage of any available training-based data, as well as any "blind" data based on unknown, constant modulus symbols. We propose a semi-blind optimization framework making use of these two types of data, and enforcing the sparsity of the channel, as well as the constant modulus property of the symbols. This approach improves upon the channel estimates based only on training sequences, and also produces accurate estimates for the unknown symbols
Sparsity Enhanced Decision Feedback Equalization
For single-carrier systems with frequency domain equalization, decision
feedback equalization (DFE) performs better than linear equalization and has
much lower computational complexity than sequence maximum likelihood detection.
The main challenge in DFE is the feedback symbol selection rule. In this paper,
we give a theoretical framework for a simple, sparsity based thresholding
algorithm. We feed back multiple symbols in each iteration, so the algorithm
converges fast and has a low computational cost. We show how the initial
solution can be obtained via convex relaxation instead of linear equalization,
and illustrate the impact that the choice of the initial solution has on the
bit error rate performance of our algorithm. The algorithm is applicable in
several existing wireless communication systems (SC-FDMA, MC-CDMA, MIMO-OFDM).
Numerical results illustrate significant performance improvement in terms of
bit error rate compared to the MMSE solution
Sparse Signal Processing Concepts for Efficient 5G System Design
As it becomes increasingly apparent that 4G will not be able to meet the
emerging demands of future mobile communication systems, the question what
could make up a 5G system, what are the crucial challenges and what are the key
drivers is part of intensive, ongoing discussions. Partly due to the advent of
compressive sensing, methods that can optimally exploit sparsity in signals
have received tremendous attention in recent years. In this paper we will
describe a variety of scenarios in which signal sparsity arises naturally in 5G
wireless systems. Signal sparsity and the associated rich collection of tools
and algorithms will thus be a viable source for innovation in 5G wireless
system design. We will discribe applications of this sparse signal processing
paradigm in MIMO random access, cloud radio access networks, compressive
channel-source network coding, and embedded security. We will also emphasize
important open problem that may arise in 5G system design, for which sparsity
will potentially play a key role in their solution.Comment: 18 pages, 5 figures, accepted for publication in IEEE Acces
Soft-Decision-Driven Sparse Channel Estimation and Turbo Equalization for MIMO Underwater Acoustic Communications
Multi-input multi-output (MIMO) detection based on turbo principle has been shown to provide a great enhancement in the throughput and reliability of underwater acoustic (UWA) communication systems. Benefits of the iterative detection in MIMO systems, however, can be obtained only when a high quality channel estimation is ensured. In this paper, we develop a new soft-decision-driven sparse channel estimation and turbo equalization scheme in the triply selective MIMO UWA. First, the Homotopy recursive least square dichotomous coordinate descent (Homotopy RLS-DCD) adaptive algorithm, recently proposed for sparse single-input single-output system identification, is extended to adaptively estimate rapid time-varying MIMO sparse channels. Next, the more reliable a posteriori soft-decision symbols, instead of the hard decision symbols or the a priori soft-decision symbols, at the equalizer output, are not only feedback to the Homotopy RLS-DCD-based channel estimator but also to the minimum mean-square-error (MMSE) equalizer. As the turbo iterations progress, the accuracy of channel estimation and the quality of the MMSE equalizer are improved gradually, leading to the enhancement in the turbo equalization performance. This also allows the reduction in pilot overhead. The proposed receiver has been tested by using the data collected from the SHLake2013 experiment. The performance of the receiver is evaluated for various modulation schemes, channel estimators, and MIMO sizes. Experimental results demonstrate that the proposed a posteriori soft-decision-driven sparse channel estimation based on the Homotopy RLS-DCD algorithm and turbo equalization offer considerable improvement in system performance over other turbo equalization schemes
Sparse Filter Design Under a Quadratic Constraint: Low-Complexity Algorithms
This paper considers three problems in sparse filter design, the first involving a weighted least-squares constraint on the frequency response, the second a constraint on mean squared error in estimation, and the third a constraint on signal-to-noise ratio in detection. The three problems are unified under a single framework based on sparsity maximization under a quadratic performance constraint. Efficient and exact solutions are developed for specific cases in which the matrix in the quadratic constraint is diagonal, block-diagonal, banded, or has low condition number. For the more difficult general case, a low-complexity algorithm based on backward greedy selection is described with emphasis on its efficient implementation. Examples in wireless channel equalization and minimum-variance distortionless-response beamforming show that the backward selection algorithm yields optimally sparse designs in many instances while also highlighting the benefits of sparse design.Texas Instruments Leadership University Consortium Progra
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