Sparse Channel Estimation for MIMO-OFDM Systems using Compressed Sensing (original) (raw)
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A hybrid compressed sensing algorithm for sparse channel estimation in MIMO OFDM systems
… , Speech and Signal Processing (ICASSP), 2011 …, 2011
Due to multipath delay spread and relatively high sampling rate in OFDM systems, the channel estimation is formulated as a sparse recovery problem, where a hybrid compressed sensing algorithm as subspace orthogonal matching pursuit (SOMP) is proposed. SOMP first identifies the channel sparsity and then iteratively refines the sparse recovery result, which essentially combines the advantages of orthogonal matching pursuit (OMP) and subspace pursuit (SP). Since SOMP still belongs to greedy algorithms, its computational complexity is in the same order as OMP. With frequency orthogonal random pilot placement, the technique is also extend to MIMO OFDM systems. Simulation results based on 3GPP spatial channel model (SCM) demonstrate that SOMP performs better than OMP, SP and interpolated least square (LS) in terms of normalized mean square error (NMSE).
Channel Estimation in OFDM System using Compressive Sensing Framework: A Review
HELIX, 2020
Paper addresses Channel Estimation by means of Compressive Sensing (CS). Here system under consideration is OFDM. Paper reviews the emerging techniques of pilot design and allocation schemes. With the said pilot allocation techniques performance improvement in Mean square Error (MSE) and Bit Error Rate (BER) is achieved. Spectral efficiency can be considerably improved with channel estimation exploiting compressive sensing. Prominent algorithms are designed to attain better estimation accuracy even with reduction in pilot subcarrier figure. Comparatively simple CS structures with least computation complexity also reviewed in the projected paper.
International Journal of Advancements in Computing Technology, 2013
As an effective spectrum utilization technology, non-contiguous orthogonal frequency division multiplexing (NC-OFDM) can be used in the environment of discrete spectrum. Compared with the traditional algorithm, channel estimation algorithm based on compressive sensing can get better performance with fewer pilots and improve the effectiveness of the system spectrum. In NC-OFDM systems, channel estimation algorithm based on OMP (orthogonal matching pursuit) has been proposed, but it is the first time that SAMP (sparsity adaptive matching pursuit) algorithm is applied to channel estimation for NC-OFDM systems. Moreover, for the reconstruction time-consuming of SAMP algorithm is too large, MAMP (modified adaptive matching pursuit) algorithm as an improved SAMP algorithm is introduced. And it can be seen that the computing speed and reconstruction accuracy has been improved.
Sparse Multipath Channel Estimation Using Compressive Sampling Matching Pursuit Algorithm
Broadband wireless channel is a time dispersive channel and become strongly frequency-selective. However, in most cases, the channel is composed of a few dominant taps and a large part of approximate zero taps or zero. To exploit the sparsity of multi-path channel (MPC), two methods have been proposed. They are, namely, greedy algorithm and convex program. Greedy algorithm is easy to be implemented but not stable; on the other hand, the convex program method is stable but difficult to be implemented as practical channel estimation problems. In this paper, we introduce a novel channel estimation strategy using compressive sampling matching pursuit (CoSaMP) algorithm which was proposed in [1]. This algorithm will combine the greedy algorithm with the convex program method. The effectiveness of the proposed algorithm will be confirmed through comparisons with the existing methods.
Time-domain synchronous orthogonal frequency division multiplexing (TDS-OFDM) outperforms the classical cyclic prefix OFDM (CP-OFDM) in higher spectral efficiency and faster synchronization. However, it has the difficulty to support high-order modulations like 256QAM and suffers from performance loss especially under severely fading channels. To solve this problem, a channel estimation method for OFDM system is proposed under the framework of compressive sensing (CS) in this paper. Firstly, by exploiting the signal structure, the auxiliary channel information is obtained. Secondly, we propose the auxiliary information based compressive sampling matching pursuit (A-CoSaMP) algorithm to utilize a very few frequency-domain pilots embedded in the OFDM block for the exact channel impulse response estimation. Simulation results demonstrate that the CS-based OFDM outperforms the conventional dual pseudo noise padded OFDM and CS-based TDS-OFDM schemes in both static and mobile environments.
Stage-Determined Matching Pursuit for Sparse Channel Estimation in OFDM Systems
IEEE Systems Journal, 2018
As a sampling paradigm to recover the sparse or compressible signals from very few incoherent linear measurements, compressed sensing (CS) has spurred much interest in recent years. Since tractable recovery algorithm is a crucial and major issue of CS, the greedy pursuit (GP) algorithms are generally preferred to enable accurate reconstruction of sparse or compressible signals from very few noisy incoherent measurements. While selecting multiple "correct" indices per iteration to improve the running time of orthogonal matching pursuit algorithm, the chosen indices are usually not the optimal one due to the iterative shortsighted decisions. In this paper, since the demand for fast reconstruction algorithms-possibly operating in linear time-is of significant interest, an algorithm namely, stage-determined matching pursuit (SdMP) is proposed. The SdMP algorithm exploits the selection of few indices (below the target signal sparsity level) per iteration, and then combines a backtracking or pruning step in some later iterations-albeit after satisfying a sparsity level conditions to refine the selected set. Using the restricted isometry property, the theoretical analysis of the SdMP algorithm and the sufficient conditions (guarantees) for realizing an improved reconstruction performance are presented. Through numerical simulations, it is shown that SdMP outperforms many GP algorithms that select multiple indices per iteration in terms of reconstruction accuracy and the running speed.
Sparse Channel Estimation using NLMS Algorithm for MIMO-OFDM System
In wireless communications, channel state data (CSI) refers to known channel properties of a communication link. This data describes but a sign propagates from the transmitter to the receiver and represents the combined results of, as an example, scattering, fading, and power decay with distance. Correct channel state data (CSI) is required for coherent detection in multiple-input multiple outputs (MIMO) communication systems practice orthogonal frequency division multiplexing (OFDM) modulation. One flow-complexity and stable adaptive channel estimation (ACE) approaches are that the normalized least means sq. (NLMS) methodology. The skinny NLMS is introduced to estimate the channel. The introduced novelty is introducing skinny penalties to the value perform of NLMS rule. Projected methodology is implemented in MATLAB and conjointly the results will show the performance of the system.
IEEE Access, 2019
In this paper, we deal with channel estimation (CE) for high-mobility orthogonal frequency division multiplexing (OFDM) systems. To make the numerous (unknown) estimation for the high-mobility OFDM systems practicable, the channels are assumed to be time-and frequency-selective−or doubly selective (DS) and approximated by a basis expansion model (BEM). As the DS channel requires the distributed acquisition of multiple correlated signals in the delay-Doppler channel domain, we proceed to estimate jointly sparse BEM coefficient vectors over a DS channel as against numerous channel coefficients. On account of channel time-variation, the resulting channel matrix in the frequency domain exhibits (approximately banded) pseudo-circular structure, which gives rise to a diagonally dominant yet full matrix rather than a diagonal matrix and thus induces inter-channel interference (ICI). On the premise of this observation, we propose a new pilot design scheme that identifies the optimal pilot placement and values for each pilot cluster to combat ICI. Furthermore, to obtain a channel estimator consistent with the jointly sparse delay-Doppler [i.e., two dimensional (2D)] channel model, an algorithm namely, distributed compressed sensing (DCS)-based stage determined matching pursuit (DCS-SdMP), is proposed. Our claims are supported by simulation results, which are obtained considering Jakes' channels with fairly high Doppler spreads, which show the superiority of the proposed schemes over other different methods of CE. INDEX TERMS Channel estimation (CE), doubly selective (DS) channel, orthogonal frequency division multiplexing (OFDM), distributed compressed sensing (DCS), basis expansion model (BEM), pilot design, wireless communication.