| 1. |
- Astély, David, et al.
(författare)
-
Spatio-Temporal Interference Rejection Combining
- 2005
-
Ingår i: Smart Antennas - State-of-the-Art. - : Hindawi Publishing Corporation. - 9775945097 ; , s. 5-26
-
Bokkapitel (övrigt vetenskapligt/konstnärligt)
|
|
| 2. |
|
|
| 3. |
|
|
| 4. |
|
|
| 5. |
|
|
| 6. |
- Bengtsson, Mats, 1967-, et al.
(författare)
-
Optimal Downlink BeamformingUsing Semidefinite Optimization
- 1999
-
Ingår i: Proc. of 37th Annual Allerton Conference on Communication,Control, and Computing. ; , s. 987-996
-
Konferensbidrag (övrigt vetenskapligt/konstnärligt)abstract
- When using antenna arrays at the base station of a cellular system, one critical aspectis the transmission strategy. An optimal choice of beamformers for simultaneous transmissionto several co-channel users must be solved jointly for all users and base stations in anarea. We formulate an optimal transmit strategy and show how the solution can be calculatedefficiently using interior point methods for semidefinite optimization. The algorithmminimizes the total transmitted power under certain constraints to guarantee a specificquality of service. The method provides large flexibility in the choice of constraints andcan be extended to be robust to channel perturbations.
|
|
| 7. |
- Bergman, Svante, 1979-, et al.
(författare)
-
Spatial multiplexing over Rician fading channels : Linear precoding transmission strategies
- 2005
-
Ingår i: Nordic Conference on Radio Science and Communications (RVK).
-
Konferensbidrag (övrigt vetenskapligt/konstnärligt)abstract
- Using information about the channel at the transmittercan improve the performance of multi-input multi-output(MIMO) communication systems. When perfect channelstate information (CSI) is available, data can be multiplexed and optimized over independent spatial channels.However, when the available channel information is imperfect or partial, crosstalk between the spatial channels isinevitable complicating the design of the transmitted data.This paper extends our earlier work on practical bit andpower loading algorithmsthat enable the use of partial CSIfor spatial multiplexing, to the case of correlated Ricianfading CSI models. Furthermore, the bit and power loading algorithm is updated for improved BER performanceas well as lower computational complexity.
|
|
| 8. |
- Björnson, Emil, 1983-, et al.
(författare)
-
Pilot-based Bayesian Channel Norm Estimation in Rayleigh Fading Multi-antenna Systems
- 2008
-
Ingår i: Proceedings of the twentieth Nordic Conference on Radio Science and Communications, Växjö, Sweden.
-
Konferensbidrag (övrigt vetenskapligt/konstnärligt)abstract
- Pilot-based estimation of the squared Euclidean norm of the channel vector of a Rayleigh fading system is considered. Unlike most previous work in the area of estimation of multiple antenna channels, we consider Bayesian estimation where the long-term channel statistics are known a priori. Closed-form expressions of the minimum mean square error (MMSE) estimator and its mean squared error (MSE) are derived for the cases of either an unweighted or a weighted unitary pilot matrix. The problem of finding the optimal pilot weighting, in the sense of minimizing the average MSE, is solved and a simple algorithm is proposed to achieve this power allocation numerically. The numerical evaluation shows that an optimal weighting can significantly improve the estimation quality in spatially correlated environments.
|
|
| 9. |
- Devarakota, Pandu R, et al.
(författare)
-
Confidence estimation in classification decision : A method for detecting unseen patterns
- 2007
-
Ingår i: Proceedings of the Sixth International Conference on Advances in Pattern Recognition. - SINGAPORE : WORLD SCIENTIFIC PUBL CO PTE LTD. ; , s. 290-294
-
Konferensbidrag (övrigt vetenskapligt/konstnärligt)abstract
- The classification task for a real world application shall include a confidence estimation to handle unseen patterns i.e., patterns which were not considered during the learning stage of a classifier. This is important especially for safety critical applications where the goal is to assign these situations as "unknown" before they can lead to a false classification. Several methods were proposed in the past which were based on choosing a threshold on the estimated class membership probability. In this paper we extend the use of Gaussian mixture model (GMM)to estimate the uncertainty of the estimated class membership probability in terms of confidence interval around the estimated class membership probability. This uncertainty measure takes into account the number of training patterns available in the local neighborhood of a test pattern. Accordingly, the lower bound of the confidence interval or the number of training samples around a test pattern, can be used to detect the unseen patterns. Experimental results on a real-world application are discussed.
|
|
| 10. |
|
|
