• Journal of the Korea Institute of Information and Communication Engineering
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• v.19 no.6
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• pp.1313-1320
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• 2015

In this paper, we propose a user detection technique based on parallel orthogonal matching pursuit (POMP) for uplink multi-user random access networks (RANs) with a number of users and receiver antennas. In general RANs, it is difficult to estimate the number of users simultaneously transmitting packets at the receiver because users with data send the data without grant of BS. In this paper, therefore, we modify the original POMP for the RAN and evaluate its performances through extensive computer simulations. Simulation results show that the proposed POMP can effectively detect activated users more than about 2%~8% compared with the conventional OMP in RANs.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.17 no.8
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• pp.1784-1789
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• 2013

In this paper, parallel orthogonal matching pursuit (POMP) is proposed to supplement the orthogonal matching pursuit (OMP) which has been widely used as a greedy algorithm for sparse signal recovery. The process of POMP is simple but effective: (1) multiple indexes maximally correlated with the observation vector are chosen at the firest iteration, (2) the conventional OMP process is carried out in parallel for each selected index, (3) the index set which yields the minimum residual is selected for reconstructing the original sparse signal. Empirical simulations show that POMP outperforms than the existing sparse signal recovery algorithms in terms of exact recovery ratio (ERR) for sparse pattern and mean-squared error (MSE) between the estimated signal and the original signal.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.18 no.7
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• pp.1557-1564
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• 2014

In this paper, we propose a signal detection technique based on the parallel orthogonal matching pursuit (POMP) is proposed for generalized shift space keying (GSSK) systems, which is a modified version of the orthogonal matching pursuit (OMP) that is widely used as a greedy algorithm for sparse signal recovery. The signal recovery problem in the GSSK systems is similar to that in the compressed sensing (CS). In the proposed POMP technique, multiple indexes which have the maximum correlation between the received signal and the channel matrix are selected at the first iteration, while a single index is selected in the OMP algorithm. Finally, the index yielding the minimum residual between the received signal and the M recovered signals is selected as an estimate of the original transmitted signal. POMP with Quantization (POMP-Q) is also proposed, which combines the POMP technique with the signal quantization at each iteration. The proposed POMP technique induces the computational complexity M times, compared with the OMP, but the performance of the signal recovery significantly outperform the conventional OMP algorithm.

• Proceedings of the Korean Society of Broadcast Engineers Conference
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• 2012.07a
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• pp.473-474
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• 2012

Sparse한 신호 복원 방법으로 underdetermined system에서 ll-minimization을 이용한 compressive sensing의 연구와 함께, ll-minimization비에 간단한 greed 알고리듬도 활발히 연구되고 있다. 이에 본 논문은 greed 알고리듬의 대표적인 orthogonal matching pursuit기법에서 iteration 마다 후보 support를 유지하는 알고리듬을 연구한다. 모의 실험을 통해 OMP의 iteration 단계에서 하나의 support만 선택하는 것보다 후보 support를 유지하는 것이 sparse 신호를 복원하는 경우는 OMP와 비슷한 성능을 보이지만 덜 sparse한 신호복원에서는 더 좋은 성능을 보임을 확인 할 수 있다.

• Journal of the Institute of Electronics Engineers of Korea SP
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• v.49 no.2
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• pp.122-129
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• 2012

As a greedy algorithm reconstructing the sparse signal from underdetermined system, orthogonal matching pursuit (OMP) algorithm has received much attention in recent years. In this paper, we present an extension of OMP for pursuing efficiency of the index selection. Our approach, referred to as generalized OMP (gOMP), is literally a generalization of the OMP in the sense that multiple (N) columns are identified per step. Using the restricted isometry property (RIP), we derive the condition for gOMP to recover the sparse signal exactly. The gOMP guarantees to reconstruct sparse signal when the sensing matrix satisfies the RIP constant ${\delta}_{NK}$ < $\frac{\sqrt{N}}{\sqrt{K}+2\sqrt{N}}$. In addition, we show recovery performance and the reduced number of iteration required to recover the sparse signal.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.17 no.10
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• pp.2252-2258
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• 2013

In this paper, parallel orthogonal matching pursuit (POMP) is proposed to supplement the simultaneous orthogonal matching pursuit (S-OMP) which has been widely used as a greedy algorithm for sparse signal recovery for multiple measurement vector (MMV) problem. The process of POMP is simple but effective: (1) multiple indexes maximally correlated with the observation vector are chosen at the first iteration, (2) the conventional S-OMP process is carried out in parallel for each selected index, (3) the index set which yields the minimum residual is selected for reconstructing the original sparse signal. Empirical simulations show that POMP for MMV outperforms than the conventional S-OMP both in terms of exact recovery ratio (ERR) and mean-squared error (MSE).

• The Journal of Korean Institute of Communications and Information Sciences
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• v.39A no.7
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• pp.424-430
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• 2014

In this paper, we propose a compressed sensing-based signal recovery technique for an uplink multi-user spatial modulation (MU-SM) system. In the MU-SM system, only one antenna among $N_t$ antennas of each user becomes active by nature. Thus, this characteristics is exploited for signal recovery at a base station. We modify the conventional orthogonal matching pursuit (OMP) algorithm which has been widely used for sparse signal recovery in literature for the MU-SM system, which is called MU-OMP. We also propose a parallel OMP algorithm for the MU-SM system, which is called MU-POMP. Specifically, in the proposed algorithms, antenna indices of a specific user who was selected in the previous iteration are excluded in the next iteration of the OMP algorithm. Simulation results show that the proposed algorithms outperform the conventional OMP algorithm in the MU-SM system.