| 1. |
- Girnyk, Maksym A., et al.
(författare)
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Asymptotic Performance Analysis of a K-Hop Amplify-and-Forward Relay MIMO Channel
- 2016
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Ingår i: IEEE Transactions on Information Theory. - : Institute of Electrical and Electronics Engineers (IEEE). - 0018-9448 .- 1557-9654. ; 62:6, s. 3532-3546
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Tidskriftsartikel (refereegranskat)abstract
- This paper studies the asymptotic performance of multi-hop amplify-and-forward relay multiple-antenna communication channels. Each multi-antenna relay terminal in the considered network amplifies the received signal, sent by a source, and retransmits it upstream toward a destination. Achievable ergodic rates of the relay channel with both jointly optimal detection and decoding and practical separate-decoding receiver architectures for arbitrary signaling schemes, along with average bit error rates for various types of detectors are derived in the regime where the number of antennas at each terminal grows large without a bound. To overcome the difficulty of averaging over channel realizations, we apply a large-system analysis based on the replica method from statistical physics. The validity of the large-system analysis is further verified through Monte Carlo simulations of realistic finite-sized systems.
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| 2. |
- Takeuchi, Keigo, et al.
(författare)
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Large-System Analysis of Joint Channel and Data Estimation for MIMO DS-CDMA Systems
- 2012
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Ingår i: IEEE Transactions on Information Theory. - 0018-9448 .- 1557-9654. ; 58:3, s. 1385-1412
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Tidskriftsartikel (refereegranskat)abstract
- This paper presents a large-system analysis of the performance of joint channel estimation, multiuser detection, and per-user decoding (CE-MUDD) for randomly spread multiple-input multiple-output (MIMO) direct-sequence code-division multiple-access (DS-CDMA) systems. A suboptimal receiver based on successive decoding in conjunction with linear minimum mean-squared error (LMMSE) channel estimation is investigated. The replica method, developed in statistical mechanics, is used to evaluate the performance in the large-system limit, where the number of users and the spreading factor tend to infinity while their ratio and the number of transmit and receive antennas are kept constant. Joint CE-MUDD based on LMMSE channel estimation is compared to several methods of one-shot channel estimation, i.e., methods that do not utilize decoded data symbols to refine the initial channel estimates, in terms of spectral efficiency. Joint CE-MUDD is found to significantly reduce the rate loss caused by transmission of pilot signals when compared to the receivers based on one-shot channel estimation. This holds particularly for multiple-antenna systems. As a result, joint CE-MUDD can provide significant performance gains compared to the receivers based on one-shot channel estimation.
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| 3. |
- Takeuchi, Keigo, et al.
(författare)
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On an Achievable Rate of Large Rayleigh Block-Fading MIMO Channels With No CSI
- 2013
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Ingår i: IEEE Transactions on Information Theory. - 0018-9448 .- 1557-9654. ; 59:10, s. 6517-6541
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Tidskriftsartikel (refereegranskat)abstract
- Training-based transmission over Rayleigh block-fading multiple-input multiple-output (MIMO) channels is investigated. As a training method a combination of a pilot-assisted scheme and a biased signaling scheme is considered. The achievable rates of successive decoding (SD) receivers based on the linear minimum mean-squared error (LMMSE) channel estimation are analyzed in the large-system limit, by using the replica method under the assumption of replica symmetry. It is shown that negligible pilot information is best in terms of the achievable rates of the SD receivers in the large-system limit. The obtained analytical formulas of the achievable rates can improve the existing lower bound on the capacity of the MIMO channel with no channel state information (CSI), derived by Hassibi and Hochwald, for all SNRs. The comparison between the obtained bound and a high-SNR approximation of the channel capacity, derived by Zheng and Tse, implies that the high-SNR approximation is unreliable unless quite high SNR is considered. Energy efficiency in the low-SNR regime is also investigated in terms of the power per information bit required for reliable communication. The required minimum power is shown to be achieved at a positive rate for the SD receiver with no CSI, whereas it is achieved in the zero-rate limit for the case of perfect CSI available at the receiver. Moreover, numerical simulations imply that the presented large-system analysis can provide a good approximation for not so large systems. The results in this paper imply that SD schemes can provide a significant performance gain in the low-to-moderate SNR regimes, compared to conventional receivers based on one-shot channel estimation.
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| 4. |
- Vehkaperä, Mikko, et al.
(författare)
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Analysis of Regularized LS Reconstruction and Random Matrix Ensembles in Compressed Sensing
- 2016
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Ingår i: IEEE Transactions on Information Theory. - : IEEE. - 0018-9448 .- 1557-9654. ; 62:4, s. 2100-2124
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Tidskriftsartikel (refereegranskat)abstract
- The performance of regularized least-squares estimation in noisy compressed sensing is analyzed in the limit when the dimensions of the measurement matrix grow large. The sensing matrix is considered to be from a class of random ensembles that encloses as special cases standard Gaussian, row-orthogonal, geometric, and so-called T-orthogonal constructions. Source vectors that have non-uniform sparsity are included in the system model. Regularization based on l(1)-norm and leading to LASSO estimation, or basis pursuit denoising, is given the main emphasis in the analysis. Extensions to l(2)-norm and zero-norm regularization are also briefly discussed. The analysis is carried out using the replica method in conjunction with some novel matrix integration results. Numerical experiments for LASSO are provided to verify the accuracy of the analytical results. The numerical experiments show that for noisy compressed sensing, the standard Gaussian ensemble is a suboptimal choice for the measurement matrix. Orthogonal constructions provide a superior performance in all considered scenarios and are easier to implement in practical applications. It is also discovered that for non-uniform sparsity patterns, the T-orthogonal matrices can further improve the mean square error behavior of the reconstruction when the noise level is not too high. However, as the additive noise becomes more prominent in the system, the simple row-orthogonal measurement matrix appears to be the best choice out of the considered ensembles.
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